WO2015066625A1 - Methods to establish and restore normal gut microbiota function of subject in need thereof - Google Patents

Methods to establish and restore normal gut microbiota function of subject in need thereof Download PDF

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WO2015066625A1
WO2015066625A1 PCT/US2014/063711 US2014063711W WO2015066625A1 WO 2015066625 A1 WO2015066625 A1 WO 2015066625A1 US 2014063711 W US2014063711 W US 2014063711W WO 2015066625 A1 WO2015066625 A1 WO 2015066625A1
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subject
age
microbiota
healthy
bacterial
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PCT/US2014/063711
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French (fr)
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Jeffrey I. Gordon
Sathish Subramanian
Ansel HSIAO
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Washington University
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
    • C12Q1/10—Enterobacteria
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/06—Gastro-intestinal diseases
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • the present invention provides a method to define normal maturation of the gut microbiota using a limited number of bacterial taxa found in the gut microbiota. Regressing the relative abundance of age-discriminatory taxa in their gut microbiota against the chronological age of each healthy subject at the time a sample of the gut microbiota was collected produces a regression model that may be used to characterize the maturity of another subject's gut microbiota to provide a measure of gastrointestinal health without having to query the whole microbiota.
  • the present invention also provides composition and methods for preventing and/or treating a disease in a subject in need thereof.
  • the gut microbiota comprises a complex community whose composition is in flux in infants and is generally stable in adults. Disease, illness, and diet, among other factors, have been shown to affect the proportional representation of the bacterial species comprising the gut microbiota. Accordingly, the gut microbiota is viewed as both a diagnostic and therapeutic target.
  • bacterial taxonomic biomarkers that are highly discriminatory for age as a way to characterize the health status of the gut microbiota.
  • the present disclosure encompasses a method to determine the maturity of a subject's gut microbiota, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the
  • the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A and at least one bacterial taxon listed in rows 7 to 24 of Table A.
  • the present disclosure encompasses a method to classify a subject, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was obtained; and (c) classifying the subject as having normal gut maturation when the microbiota age of the subject is substantially similar to the microbiota age of a healthy subject with a similar
  • the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A and at least one bacterial taxon listed in rows 7 to 24 of Table A.
  • the present disclosure encompasses a method to classify a subject, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least 24 bacterial taxa listed in Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as
  • the group comprises the bacterial taxa listed in Table B.
  • the present disclosure encompasses a method to classify a subject, the method comprising calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A; applying the relative abundances of the bacterial taxa from step (a) and chronological age of the subject to a classification model, wherein the classification model is trained on datasets comprising measurements obtained from a plurality of healthy subjects and a plurality of undernourished subjects, and the measurements including (i) relative abundances of the same bacterial taxa in step (a), as determined from a plurality of gut microbiota samples obtained over time for each healthy and undernourished subject, and (ii) chronological age of the subject at the time the gut microbiota sample was obtained; and wherein the classification model assigns the subject to a category.
  • the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A and at least one
  • the present disclosure encompasses a method to classify a subject, the method comprising calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least 24 bacterial taxa listed in Table A; applying the relative abundances of the bacterial taxa from step (a) and chronological age of the subject to a classification model, wherein the classification model is trained on datasets comprising measurements obtained from a plurality of healthy subjects and a plurality of
  • the classification model assigns the subject to a category.
  • the group comprises the bacterial taxa listed in Table B.
  • the present disclosure encompasses a method for identifying an effect of a therapy, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject before and after administration of the therapy, wherein the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota before and after therapy, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; wherein the therapy has an effect when the microbiota age of the subject changes after therapy.
  • the group comprises at least the bacterial taxa
  • the present disclosure encompasses a method for identifying an effect of a therapy, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject before and after administration of the therapy, wherein the group comprises at least 24 bacterial taxa listed in Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota before and after therapy, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; wherein the therapy has an effect when the microbiota age of the subject changes after therapy.
  • the group comprises the bacterial taxa listed in Table B.
  • the present disclosure encompasses method for identifying an effect of a therapy, the method comprising (a) identifying a set of age- discriminatory bacterial taxa within the bacterial taxa comprising the gut microbiota a group of healthy subject's gut microbiota, the method comprising (i) providing, for each healthy subject, a relative abundance for the bacterial taxa comprising the subject's gut microbiota, wherein the relative abundance of the bacterial taxa in the healthy subject's gut microbiota was determined from a plurality of gut microbiota samples obtained at intervals of time; (ii) regressing the relative abundances of the bacterial taxa comprising each healthy subject's gut microbiota against the chronological age of the healthy subject at the time the gut microbiota sample was collected, thereby producing a prediction of a gut microbiota age that is based only on the relative abundance of age- discriminatory bacterial taxa present in the subject's gut microbiota; and (iii)
  • step (a)(iii) calculating a relative abundance for each bacterial taxon in the set of age-discriminatory taxa from step (a)(iii), using a fecal sample obtained from a subject before and after administration of the therapy; and applying the relative abundances from step (b) to the prediction of gut microbiota age from step (a)(iii) to determine the microbiota age of the subject's gut microbiota before and after therapy; wherein the therapy has an effect when the microbiota age of the subject changes after therapy.
  • the present disclosure encompasses a composition comprising at least one of the bacterial taxa listed in Table A.
  • the present disclosure also contemplates a method of preventing or treating acute malnutrition, acute diarrhea, or chronic diarrhea in a subject in need thereof, the method comprising administering to the subject a composition comprising at least one of the bacterial taxa listed in Table A.
  • the present disclosure encompasses a composition comprising a combination of bacterial taxa, the combination comprising of at least two bacterial taxa listed in Table B.
  • the present disclosure also contemplates a method of preventing or treating acute malnutrition, acute diarrhea, or chronic diarrhea in a subject in need thereof, the method comprising administering to the subject a composition comprising a combination of bacterial taxa, wherein the combination comprises at least two bacterial taxa listed in Table B.
  • the present disclosure encompasses a composition comprising a combination of bacterial taxa listed, wherein the combination is selected from combinations listed in Table D.
  • FIG. 1 depicts graphs and a heat map showing bacterial taxonomic biomarkers for defining gut-microbiota maturation in healthy Bangladeshi children during the first 2 years of life.
  • the curve is a smoothed spline fit between microbiota age and chronologic age in the validation sets (C,D), accounting for the observed sigmoidal relationship (see Methods for Examples 1 -9).
  • F-H Heatmap of mean relative abundances of the 24 age-predictive bacterial taxa plotted against the chronologic age of healthy singletons used to train the Random Forests model (F), and correspondingly in the healthy singletons (G), and twins and triplets (H) used to validate the model (hierarchical clustering performed using the Spearman rank correlation distance metric).
  • FIG. 2 depicts graphs and a schematic showing persistent immaturity of the gut microbiota in children with SAM.
  • A Design of the randomized interventional trial.
  • B Microbiota maturity defined during various phases of treatment and follow-up in children with SAM. Relative microbiota maturity in the upper portion of the panel is based on the difference between calculated microbiota age (Random- Forests-based sparse 24-taxon model) and values calculated in healthy children of similar chronologic age, as interpolated over the first 2 years of life using a spline curve. In the lower portion of the panel, maturity is expressed as a microbiota-for-age Z-score (MAZ). Mean values ⁇ s.e.m.
  • FIG. 3 depicts illustrations of the equations used to calculate 'relative microbiota maturity' and 'microbiota-for-age Z-score'.
  • A,B The procedure to calculate both microbiota maturation metrics are shown for a single faecal sample from a focal child (pink circle) relative to microbiota age values calculated in healthy reference controls. These reference values are computed in samples collected from children used to validate the Random-Forests-based sparse 24-taxon model and are shown in (A), as a broken line of the interpolated spline fit and in (B), as median ⁇ s.d. values for each monthly chronologic age bin from months 1 to 24.
  • FIG. 4 depicts graphs showing transient microbiota immaturity and reduction in diversity associated with diarrhoea in healthy twins and triplets.
  • A The transient effect of diarrhoea in healthy children. Seventeen children from 10 families with healthy twins or triplets had a total of 36 diarrhoeal illnesses where faecal samples were collected. Faecal samples collected in the months immediately before and following diarrhoea in these children were examined in an analysis that included multiple environmental factors in the 'healthy twins and triplets' birth cohort.
  • Linear mixed models of these specified environmental factors indicated that 'diarrhoea', 'month following diarrhoea' and 'presence of formula in diet' have significant effects on relative microbiota maturity, while accounting for random effects arising from within-family and within-child dependence in measurements of this maturity metric.
  • the factors 'postnatal age', 'presence or absence of solid foods', 'exclusive breastfeeding', 'enteropathogen detected by microscopy', 'antibiotics' as well as 'other periods relative to diarrhoea' had no significant effect.
  • the numbers of faecal samples (n) are shown in parenthesis. Mean values ⁇ s.e.m. are plotted.
  • FIG. 5 depicts heatmaps and graphs showing gut microbiota variation in families with twins and triplets during the first year of life.
  • A Maternal influence.
  • B An analogous heatmap of the relative abundance of these taxa in their twin or triplet offspring is shown.
  • Three of these 97%-identity OTUs are members of the top 24 age- discriminatory taxa (blue) and belong to the genus Bifidobacterium.
  • C,D Intrapersonal variation in the bacterial component of the maternal microbiota is greater between the first and fourth months after childbirth than variation in fathers.
  • E,F Distances between the faecal microbiota of spouses (each mother-father pair) compared to distances between all unrelated adults (male-female pairs). The microbial signature of co-habitation is only evident 10 months following childbirth.
  • G-J The degree of similarity between mother and infant during the first postpartum month is significantly greater than the similarity between microbiota of fathers and infants (G,H) while the faecal microbiota of co-twins are significantly more similar to one another than to age-matched unrelated children during the first year of life (l,J).
  • Hellinger and unweighted UniFrac distance matrices were permuted 1 ,000 times between the groups tested.
  • P values represent the fraction of times permuted differences between tested groups were greater than real differences between groups. * P ⁇ 0.05, ** P ⁇ 0.01 , *** P ⁇ 0.001 .
  • FIG. 6 graphically depicts anthropometric measures of nutritional status in children with SAM before, during and after both food interventions.
  • Weight-for-height Z-scores (A) height-for-age Z-scores (HAZ) (B) and weight-for- age Z-scores (WAZ) (C).
  • Mean values ⁇ s.e.m. are plotted and referenced to national average anthropometric values for children surveyed between the ages of 6 and 24 months during the 201 1 Bangladeshi Demographic Health Survey (BDHS) 28 .
  • FIG. 7 graphically depicts persistent reduction of diversity in the gut microbiota of children with SAM.
  • Age-adjusted Shannon diversity index for faecal microbiota samples collected from healthy children (n 50), and from children with SAM at various phases of the clinical trial (mean values ⁇ s.e.m. are plotted).
  • FIG. 8 depicts heatmaps of bacterial taxa significantly altered during the acute phase of treatment and nutritional rehabilitation in the microbiota of children with SAM compared to similar-age healthy children.
  • Bacterial taxa (97%- identity OTUs) significantly altered (FDR-corrected P ⁇ 0.05) in children with SAM are shown (see Table 13 for P values and effect size for individual taxa).
  • Three groups of bacterial taxa are shown: those enriched before the food intervention (A); those enriched during the follow-up phase compared to healthy controls (B); and those that are initially depleted but return to healthy levels (C).
  • Members of the top 24 age- discriminatory taxa are highlighted in blue. Note that there were no children represented in the Khichuri-Halwa arm under the age of 12 months during the 'follow-up after 3 months' period.
  • FIG. 9 depicts heatmaps of bacterial taxa altered during long term follow-up in the faecal microbiota of children with SAM compared to similar-age healthy children.
  • A-H Bacterial taxa (97%-identity OTUs) significantly altered (FDR-corrected P ⁇ 0.05) in children with SAM are shown (see Table 13 for P values and effect sizes for individual taxa).
  • A-C Taxa depleted across all phases of SAM relative to healthy.
  • D-H Those depleted during the follow-up phase. Members of the top 24 age- discriminatory taxa are highlighted in blue. Note that there were no children under the age of 12 months represented in the Khichuri-Halwa treatment arm during the 'follow- up after 3 months' period.
  • FIG. 10 graphically depicts the effects of antibiotics on the microbiota of children with SAM. Plots of microbiota and anthropometric parameters in nine children sampled before antibiotics (abx), after oral amoxicillin plus parenteral gentamicin and ampicillin, and at the end of the antibiotic and dietary interventions administered over the course of nutritional rehabilitation in the hospital. All comparisons were made relative to the pre-antibiotic sample using the non-parametric Wilcoxon matched-pairs rank test, in which each child served as his or her own control.
  • Microbiota parameters plotted as mean values ⁇ s.e.m., include (A) relative microbiota maturity, (B) microbiota-for-age Z-score (MAZ), and (C) SDI. WHZ scores are provided in D.
  • E,F The two predominant bacterial family-level taxa showing significant changes following antibiotic treatment.
  • E family Streptococcaceae and (F) family
  • FIG. 11 graphically depicts the relative microbiota maturity and MAZ correlate with WHZ in children with MAM.
  • A-C WHZ are significantly inversely correlated with relative microbiota maturity (A) and MAZ (B) in a cross-sectional analysis of 33 children at 18 months of age who were above and below the
  • Box plots represent the upper and lower quartiles (boxes), the median (middle horizontal line), and measurements that are beyond 1 .5 times the interquartile range (whiskers) and above or below the 75th and 25th percentiles, respectively (points) (Tukey's method, PRISM software v6.0d). Taxa are presented in descending order of their importance to the Random Forests model.
  • D Faecalibacterium prauznitzii 326792
  • E Dorea longicatena 191687
  • F Frerea longicatena
  • Lactobacillus mucosae 15141 (G) Catenibacterium mitsuokai 287510, (H) Dorea formicigenerans 261912, (I) Clostridium sp. 181834, (J) Bifidobacterium sp. 469873, (K) Clostridiales sp. 185951 , (L) Ruminococcaceae sp. 212619. See FIG. 10A,B.
  • FIG. 12 depicts graphs and a schematic showing cross-sectional assessment of microbiota maturity at 18 months of age in Bangladeshi children with and without MAM, plus extension of the Bangladeshi-based model of microbiota maturity to Malawi.
  • A,B Children with MAM (WHZ lower than -2 s.d.; grey) have significantly lower relative microbiota maturity (A) and MAZ (B) compared to healthy individuals (blue).
  • Mean values ⁇ s.e.m. are plotted ** P ⁇ 0.01 (Mann-Whitney U-test). See FIG.
  • FIG. 13 graphically depicts that R. obeum restricts V. cholerae colonization in adult gnotobiotic mice.
  • n 4-6 mice per group.
  • A Days after gavage of second species and
  • B Days after V. cholerae gavage.
  • C C
  • FIG. 14 graphically depicts that R. obeum AI-2 reduces V. cholerae colonization and virulence gene expression.
  • V. cholerae represses the tcp promoter in V. cholerae (triplicate assays; results representative of four independent experiments).
  • B Faecal V. cholerae levels in gnotobiotic mice 8 h after gavage with V. cholerae and an E. coli strain containing either the PBAD-R- obeum luxS plasmid or vector control.
  • C Faecal vqmA transcript abundance in mono- or co- colonized mice.
  • FIG. 15 depicts graphs and illustrations showing the experimental designs for clinical study and gnotobiotic mouse experiments.
  • A Sampling schedule for human cholera study.
  • B Frequency of diarrhoeal episodes over time for a representative participant (patient A).
  • Initial time black circle represents beginning of diarrhoea.
  • the long vertical line marks enrollment into the study. Colours and short vertical lines denote boundaries of study phases defined in (A).
  • C-H Depict the various gnotobiotic mouse experimental designs. The number (n) of animals in each treatment group is shown.
  • FIG. 16 depicts graphs and heatmaps showing the bacterial taxa associated with diarrhoeal and recovery phase.
  • A Proportion of bacterial species-level taxa that were observed in both diarrhoeal and recovery phases, in D-Ph1 to D-Ph4 only, and in R-Ph1 to R-Ph3 only. Mean values ⁇ s.e.m. are plotted. * P ⁇ 0.05,
  • Proportion of study participants having bacterial taxa associated by indicator species analysis with the diarrhoeal or recovery phase The axis shows species associated with each phase, ranked by proportion of subjects harbouring that species. For each species, 'representation in study participants' is the average presence/absence of all 97%-identity OTUs with that species taxonomic assignment. The OTU table was rarefied to 49,000 reads per sample.
  • F-N Bacterial species identified by indicator analysis as indicative of diarrhoea or recovery phases in adult patients with cholera, and species identified by Random Forests analysis as discriminatory for different stages in the maturation of the gut microbiota of healthy Bangladeshi infants/children aged 1- 24 months (denoted by the symbol ⁇ ).
  • the heat map shows mean relative abundances of species across all individuals during D-Ph1 to D-Ph4, with each phase subdivided into four equal time bins. For recovery time points, columns represent the mean relative abundances for each sampling time point during R-Ph1 to R-Ph3.
  • I-K Mean relative abundance values are also presented for these same species in the faecal microbiota of 50 healthy Bangladeshi children sampled from 1 to 2 years of age at monthly intervals. Unsupervised hierarchical clustering used relative abundances of species in the faecal microbiota of the patients with cholera.
  • FIG. 17 depicts heatmaps showing the 97%-identity OTUs observed in both diarrhoeal and recovery phases.
  • FIG. 18 depicts heatmaps and graphs showing the pattern of appearance of age-discriminatory 97%-identity OTUs in the faecal microbiota of patients with cholera mirrors the normal age-dependent pattern in the faecal microbiota of healthy Bangladeshi infants and children.
  • A-C Shows hierarchical clustering of relative abundance values for each of the top 60 most age-discriminatory 97%-identity OTUs in a Random-Forests-based model of normal maturation of the microbiota in healthy Bangladeshi infants/children (importance scores for the age-discriminatory taxa defined by Random Forests analysis are reported in ref.
  • FIG. 19 graphically depicts the pattern of recovery of the gut microbiota in patients with cholera.
  • A,B Mean unweighted (A) and weighted (B)
  • FIG. 20 depicts heatmaps showing the proportional representation of genes encoding enzymes (classified according to Enzyme Commission number identifiers) in faecal microbiomes sampled during the diarrhoeal and recovery phases of cholera.
  • Shotgun sequencing of faecal community DNA was performed (MiSeq 2000 instrument; 2 ⁇ 250bp paired-end reads; 341 ,701 ⁇ 145,681 reads (mean ⁇ s.d. per sample)). Read pairs were assembled (SHERA software package 34 ). Read counts were collapsed based on their assignment to Enzyme Commission (EC) number identifiers. The significance of differences in EC abundances compared with faecal microbiomes in healthy adult Bangladeshi controls was defined using ShotgunFunctionalizeR 39 .
  • Unsupervised hierarchical clustering identifies groups of ECs that characterize the faecal microbiomes of patients with cholera at varying diarrhoeal and recovery phases.
  • A-E The heat map shows the results of EC-based clustering by phase
  • the heat map presents the results of a global clustering of all time-points and study phases. Genes encoding 102 ECs were identified with (1 ) at least 0.1 % average relative abundance across the study and (2) significant differences in their representation relative to healthy microbiomes in at least one comparison (adjusted P ⁇ 0.00001 based on ShotgunFunctionalizeR). In each of the heat maps, z- scores for each EC across all samples are plotted. ECs are grouped by KEGG level 1 assignment and further annotated based on their KEGG Pathway assignments.
  • FIG. 21 depicts a schematic and graph showing that R.
  • obeum encodes a functional AI-2 system, and R. obeum AI-2 production is stimulated by the presence of V. cholerae.
  • A Relative abundances of R. obeum and V.
  • V. cholerae gavage' refers to the second of two daily gavages of 10 9 c.f.u. V. cholerae into animals that had been colonized 14 days earlier with the 14-member community.
  • B Shows AI-2 signalling pathway components represented in the R. obeum genome.
  • FIG. 22 graphically depicts UPLC-MS analysis of faecal bile acid profiles in gnotobiotic mice.
  • Targeted UPLC-MS used methanol extracts of faecal pellets obtained from age- and gender-matched germ-free C57BL/6J mice and gnotobiotic mice colonized for 3 days with R. obeum alone, for 7 days with the 14- member community ('D1 invasion group'), and for 3 days with the 13-member
  • FIG. 23 depicts a phylogenetic tree of luxS genes present in human gut bacterial symbionts and enteropathogens.
  • A-C The tree was constructed from amino-acid sequence alignments using Clustal X. Red type indicates that the
  • homologue is represented in the genomes of members of the 14-member artificial human gut bacterial community.
  • FIG. 24 graphically depicts in vivo tests of the effects of known quorum-sensing components on R. obeum-mediated reductions in V.
  • the present invention provides a method to identify a set of bacterial taxa that define normal maturation of the gut microbiota.
  • the present invention provides methods for preventing and/or treating a disease in a subject in need thereof by administering a composition
  • bacterial taxa comprising one or more of those bacterial taxa.
  • Useful combinations of bacterial taxa are disclosed herein.
  • a subject refers to a mammal, including, but not limited to, a dog, a cat, a rat, a mouse, a hamster, a mouse, a cow, a horse, a goat, a sheep, a pig, a camel, a non-human primate, and a human.
  • a subject is a human.
  • a "healthy subject” is a subject with no known or diagnosed disease. The health of a subject may also be assessed by anthropometric measurements.
  • a "subject in need of treatment” or a “subject in need thereof is a subject in need of prophylaxis against, or treatment for, a disease, preferably a gastrointestinal disease.
  • a subject in need of treatment may be a healthy subject.
  • a healthy subject may have an increased risk of developing a disease relative to the population at large.
  • a subject in need of treatment may have a disease.
  • a subject may have an immature microbiota and, therefore, may be in need of treatment.
  • microbiota immaturity is described in detail in Section l(C).
  • gut microbiota refers to microbes that have colonized and inhabit the gastrointestinal tract of a subject. While various aspects of the present invention are exemplified with bacteria, the invention is applicable to all microbes including, but not limited to, archaea, bacteria, fungi, protists and viruses.
  • a subject's gut microbiota may be naturally acquired or artificially established. Means by which a subject naturally acquires its gut microbiota are well known. Such examples may include, but are not limited to, exposure during birth, environmental exposure, consumption of foods, and coprophagy. Means by which a subject's gut microbiota may be artificially established are also well known.
  • artificially established gut microbial communities can be established in gnotobiotic animals by inoculating an animal with a defined or undefined consortium of microbes.
  • a naturally acquired gut microbiota is comprised of both culturable and unculturable components.
  • An artificially acquired gut microbiota may be similarly comprised of both culturable and unculturable components, or may consist of only culturable components.
  • the phrase "culturable components" refers to the microbes comprising the gut microbiota that may be cultured in vitro using techniques known in the art. Culture collections of gut microbial communities are described in detail in PCT/US2012/028600, incorporated herein in its entirety by reference.
  • normal maturation of the gut microbiota refers to the ordered change in the relative abundances of bacterial taxa in the gut microbiota over time, as determined from a group of healthy subjects.
  • chronological age of a subject refers to the amount of time a subject has lived.
  • Applicants have discovered that the proportional representation of a minimal number of bacterial taxa defines a healthy gut microbiota as it assembles (i.e. matures). The changes in the relative abundance of these bacterial taxa over time are consistent across substantially all healthy subjects within the same age range.
  • the present invention provides a method to accurately characterize the maturity of a subject's gut microbiota using a limited amount of information. This is exemplified in the Examples using a group of subjects between 0 and about 2 years of age. However, without wishing to be bound by theory, the gut microbiota of a human relatively stabilizes after about 2-3 years provided there are no insults. When there is an insult, the gut microbiota regresses to an immature state that can be discriminated using the relative abundances of these same bacterial taxa.
  • the present invention provides a method to define normal maturation of the gut microbiota using a limited number of bacterial taxa found in the gut microbiota.
  • the relative abundances of these limited number of bacterial taxa, referred to herein as "age-discriminatory bacterial taxa", in gut microbiota samples obtained from healthy subjects changes over time in a consistent way across substantially all healthy subjects within the same age range.
  • a method for identifying a group of age-discriminatory bacterial taxa comprises (a) providing, for each healthy subject, a relative abundance for the bacterial taxa comprising the healthy subject's gut microbiota, wherein the relative abundance of the bacterial taxa in the subject's gut microbiome was determined from a plurality of gut microbiota samples obtained at intervals of time, (b) applying a regression analysis to model the relative abundance of the bacterial taxa comprising each healthy subject's gut microbiota against the amount of time the subject has lived (i.e. the chronological age of the healthy subject) at the time the gut microbiota sample was collected, and (c) selecting the minimum number of bacterial taxa needed for the model to predict gut microbiota age.
  • Suitable methods may be sequencing-based or array-based.
  • An exemplary method is detailed in the Examples. Briefly, the bacterial component of a gut microbiota sample is characterized by sequencing a nucleic acid suitable for taxonomic classification and assigning the sequencing reads to operational taxonomic units (OTUs) with > 97% nucleotide sequence identity to a database of annotated and representative sequences.
  • OTUs operational taxonomic units
  • a representative sequence from each OTU can be selected and compared to a reference set. If a match is identified in the reference set, that OTU can be given an identity.
  • Relative abundance of a bacterial taxon may be defined by the number of sequencing reads that can be unambiguously assigned to each taxon after adjusting for genome uniqueness.
  • a suitable nucleic acid used for taxonomic classification is universally distributed among the gut microbial population being queried allowing for the analysis of phylogenetic relationships among distant taxa, and has both a conserved region and at least one region subject to variation.
  • the presence of at least one variable region allows sufficient diversification to provide a tool for classification, while the presence of conserved regions enables the design of suitable primers for amplification (if needed) and/or probes for hybridization for various taxa at different taxonomic levels ranging from individual strains to whole phyla.
  • any suitable nucleic acid known in the art may be used, one skilled in the art will appreciate that selection of a nucleic acid or region of a nucleic acid to amplify may differ by
  • a nucleic acid queried is a small subunit ribosomal RNA gene.
  • V1 , V2, V3, V4, V5, V6, V7, V8 and/or V9 regions of the 16S rRNA gene are suitable, though other suitable regions are known in the art.
  • Guidance for selecting a suitable 16S rRNA region to amplify can be found throughout the art, including Guo F et al. PLOS One 8(10) e76185, 2013; Soergel DAW et al. ISME Journal 6: 1440, 2012; and Hamady M et al. Genome Res. 19:1 141 , 2009, each hereby incorporated by reference in its entirety.
  • gut microbiota sample refers to a biological sample comprising a plurality of heterogeneous nucleic acids produced by a subject's gut microbiota.
  • Fecal samples are commonly used in the art to sample gut microbiota. Methods for obtaining a fecal sample from a subject are known in the art and include, but are not limited to, rectal swab and stool collection. Suitable fecal samples may be freshly obtained or may have been stored under appropriate temperatures and conditions known in the art. Methods for extracting nucleic acids from a fecal sample are also well known in the art.
  • the extracted nucleic acids may or may not be amplified prior to being used as an input for profiling the relative abundances of bacterial taxa, depending upon the type and sensitivity of the downstream method.
  • nucleic acids may be amplified via polymerase chain reaction (PCR). Methods for performing PCR are well known in the art. Selection of nucleic acids or regions of nucleic acids to amplify are discussed above.
  • the nucleic acids comprising the nucleic acid sample may also be fluorescently or chemically labeled, fragmented, or otherwise modified prior to sequencing or hybridization to an array as is routinely performed in the art.
  • Gut microbiota samples may be obtained from a healthy subject at any suitable interval of time, varying from minutes to hours apart, days to weeks apart, or even weeks to months apart. Gut microbiota samples may be obtained multiple times a day, week, month or year.
  • the duration of sampling can also vary. For example, the duration of sampling may be for about a month, about 6 months, about 1 year, about 2 years, about 3 years, about 4 years, about 5 years, about 6 years, about 7 years, about 8 years, about 9 years, about 10 years, about 1 1 years, about 12 years, about 13 years, about 14 years, about 15 years, about 16 years, about 17 years, about 18 years, about 19 years, about 20 years, about 30 years, or more.
  • the number of healthy subjects from which gut microbiota samples are obtained can and will vary. Generally, a suitable number is the number of healthy subjects needed to give the model produced by the regression analysis an acceptable degree of statistical significance. For example, the number of subject may be 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12 or more subjects.
  • Any suitable machine learning algorithm may be used to regress relative abundances of bacterial taxa against the amount of time the healthy subject has lived at the time the gut microbiota sample was collected.
  • the algorithm is able to detect both linear and nonlinear relationships between the bacterial taxa and chronologic age.
  • the Random Forests machine learning algorithm is used. The regression analysis produces a model that is a prediction of the gut microbiota age based on the microbial taxa present in the subject's gut microbiota sample.
  • An "importance score" for the bacterial taxa may be an output of the learning algorithm.
  • a ranked list of all bacterial taxa, in order of "age- discriminatory importance” may be determined by considering those taxa, whose relative abundance values when permuted leads to a meaningful increase in the error.
  • Bacterial taxa ranked higher on the list e.g. 1 , 2, 3 have a larger increase in error than those lower on the list (e.g. 61 , 62, 63).
  • the predictive performance of sets comprising increasing numbers of the top-ranking bacterial taxa may be evaluated, and when minimal improvements in the predictive performance are observed after adding a new member to the set, then a minimal number of bacterial taxa have been identified.
  • An example of a minimal improvement may be when the root mean-squared error of prediction remains below about 4 or aobut 5 months.
  • the present invention provides a group of bacterial taxa that define normal maturation of the gut microbiota (i.e. a group of age- discriminatory taxa).
  • the phrase "normal maturation of the gut microbiota" is defined above.
  • a group of bacterial taxa that define normal maturation of the gut microbiota can be used to characterize the maturity of a subject's gut microbiota.
  • a group of bacterial taxa that define normal maturation of the gut microbiota can be used to determine whether the maturity of a subject's gut microbiota corresponds to what would be predicted by the subject's chronological age.
  • Such information can be used to guide treatment decisions (e.g. an immature state may indicate a need to initiate or continue therapy) or evaluate the effectiveness of a therapy (e.g. no improvement or a worsening would indicate the therapy is not effective).
  • a group of bacterial taxa that define normal maturation of the gut microbiota in may comprise at least 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, or at least 59 of the bacterial taxa listed in Table A.
  • a group of bacterial taxa that define normal maturation of the gut microbiota in may comprise at least 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, or at least 59 of the bacterial taxa listed in Table A.
  • a bacterial isolate can be identified as belonging to a bacterial taxon listed in Table A if the V4 region of the 16S rRNA gene of the bacterial isolate has at least 97% sequence identity to any of SEQ ID NOs: 1 - 60.
  • the V4 region of the 16S rRNA gene of the bacterial isolate can have 97%, 97.5%, 98%, 98.5%, 99%, 99.5%, or 100% sequence identity to any of SEQ ID NOs: 1 - 60.
  • a group of bacterial taxa that define normal maturation of the gut microbiota comprises (a) at least 12 bacterial taxa listed in Table B; and (b) at least 12 bacterial taxa listed in Table C.
  • a group of bacterial taxa that define normal maturation of the gut microbiota comprises (a) at least 16 bacterial taxa listed in Table B; and (b) at least 8 bacterial taxa listed in Table C.
  • a group of bacterial taxa that define normal maturation of the gut microbiota comprises (a) at least 20 bacterial taxa listed in Table B; and (b) at least 4 bacterial taxa listed in Table C.
  • a group of bacterial taxa that define normal maturation of the gut microbiota comprises the bacterial taxa listed in Table B.
  • a group of bacterial taxa that define normal maturation of the gut microbiota consists of the bacterial taxa listed in Table B.
  • a bacterial isolate can be identified as belonging to a bacterial taxon listed in Table B or Table C if the V4 region of the 16S rRNA gene of the bacterial isolate has at least 97% sequence identity to any of SEQ ID NOs: 1 - 24 (Table A), or 25-60 (Table B).
  • the V4 region of the 16S rRNA gene of the bacterial isolate can have 97%, 97.5%, 98%, 98.5%, 99%, 99.5%, or 100% sequence identity to any of SEQ ID NOs: 1 - 60.
  • the present invention provides a method to determine the maturity of a subject's gut microbiota.
  • the method may comprise: (a) calculating a relative abundance for each bacterial taxon in a group bacterial taxa, from a fecal sample obtained from the subject; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; and (c) calculating the maturity of the subject's gut microbiota using the subject's microbiota age.
  • MAZ Microbiota-for-Age Z score
  • the group of bacterial taxa can be a group described above in Section 1(B), which are
  • the group of bacterial taxa can be identified using a method described in Section 1(A), which are incorporated into this Section by reference. Methods for calculating a relative abundance for a bacterial taxon are described in Section 1(A).
  • the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In another preferred
  • the subject is a human that is about 30 years of age or less. In another preferred embodiment, the subject is a human that is about 40 years of age or less. In another preferred embodiment, the subject is a human that is about 50 years of age or less. In another preferred embodiment, the subject is a human that is about 60 years of age or less. In another preferred embodiment, the subject is a human that is about 70 years of age or less. In another preferred embodiment, the subject is a human that is about 80 years of age or less. In another preferred embodiment, the subject is a human that is about 90 years of age or less. In another preferred embodiment, the subject is a human that is about 100 years of age or less. In another preferred embodiment, the subject is a human that is about 1 10 years of age or less. In exemplary embodiments, the group of bacterial taxa is a group listed in Table A or Table B.
  • the present invention provides a method to classify a subject. For example, because the relative abundances of the age- discriminatory taxa change in a consistent way that corresponds to the chronological age of healthy subjects, it is also possible to classify the maturity of a subject's gut microbiota as perturbed if the abundances of the age-discriminatory taxa are not as predicted by the subject's chronological age.
  • a subject may an immature gut microbiota (i.e. the subject's microbiota age is less than predicted by chronological age alone), or a subject may have a gut microbiota that matured faster than normal (i.e. the subject's microbiota age is less than predicted by chronological age alone).
  • a method to classify a subject may comprise: (a) calculating a relative abundance for each bacterial taxon in a group bacterial taxa, from a fecal sample obtained from the subject; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; and (c) classifying the subject as having normal gut maturation when the microbiota age of the subject is substantially similar to the microbiota age of a healthy subject of a similar age.
  • the group of bacterial taxa can be a group described above in Section 1(B), which are incorporated into this Section by reference.
  • the group of bacterial taxa can be identified using a method described in Section 1(A), which are incorporated into this Section by reference. Methods for calculating a relative abundance for a bacterial taxon are described in Section 1(A).
  • the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In another preferred embodiment, the subject is a human that is about 30 years of age or less. In another preferred embodiment, the subject is a human that is about 40 years of age or less.
  • the subject is a human that is about 50 years of age or less. In another preferred embodiment, the subject is a human that is about 60 years of age or less. In another preferred embodiment, the subject is a human that is about 70 years of age or less. In another preferred embodiment, the subject is a human that is about 80 years of age or less. In another preferred embodiment, the subject is a human that is about 90 years of age or less. In another preferred embodiment, the subject is a human that is about 100 years of age or less. In another preferred embodiment, the subject is a human that is about 1 10 years of age or less. In exemplary embodiments, the group of bacterial taxa is a group listed in Table A or Table B.
  • a method to classify a subject may comprise: (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject; (b) using the relative abundances of the bacterial taxa from step (a) and chronologic age of the subject to indicate the health status of the subject's gut microbiota (e.g. healthy or unhealthy; normal maturation of the gut microbiota or perturbed maturation of the gut microbiota) using a classification model; wherein the classification model is trained on datasets comprising measurements obtained from a plurality of healthy subjects and a plurality undernourished subjects.
  • gut microbiota e.g. healthy or unhealthy; normal maturation of the gut microbiota or perturbed maturation of the gut microbiota
  • the datasets may contain measurements from at least 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, or more healthy subjects and at least 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, or more undernourished subjects.
  • the undernourished subjects may be subjects with acute malnutrition, moderate acute malnutrition, or at risk for moderate acute malnutrition, as defined in Section II.
  • the measurements may at least include relative abundances of the same bacterial taxa from step (a) above from samples of the health and undernourished subjects' gut microbiota obtained over time for each subject, and the chronological age of the subject at the time the gut microbiota sample was collected. Additional measurements may optionally include MUAC measurements (as defined in Section II), WHZ measurements ((as defined in Section II), and other anthropometric
  • the group of bacterial taxa can be a group described above in Section l(B), which are incorporated into this Section by reference.
  • the group of bacterial taxa can be identified using a method described in Section l(A), which are incorporated into this Section by reference. Methods for calculating a relative abundance for bacterial taxon are described in Section 1(A).
  • the subject is human that is about 2 years of age or less.
  • the subject is a human that is about 5 years of age or less.
  • the subject is a human that is about 10 years of age or less.
  • the subject is a human that is about 20 years of age or less.
  • the group of bacterial taxa is a group listed in Table A or Table B.
  • the present invention provides a method for identifying an effect of a therapy.
  • a method for identifying an effect of a therapy may comprise (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject before and after administration of the therapy; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota before and after therapy, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the
  • the group of bacterial taxa can be a group described above in Section l(B), which are incorporated into this Section by reference.
  • the group of bacterial taxa can be identified using a method described in Section l(A), which are incorporated into this Section by reference.
  • the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In another preferred embodiment, the subject is a human that is about 30 years of age or less. In another preferred embodiment, the subject is a human that is about 40 years of age or less. In another preferred embodiment, the subject is a human that is about 50 years of age or less. In another preferred embodiment, the subject is a human that is about 60 years of age or less. In another preferred embodiment, the subject is a human that is about 70 years of age or less. In another preferred embodiment,
  • the subject is a human that is about 80 years of age or less. In another preferred embodiment, the subject is a human that is about 90 years of age or less. In another preferred embodiment, the subject is a human that is about 100 years of age or less. In another preferred embodiment, the subject is a human that is about 1 10 years of age or less.
  • the group of bacterial taxa is a group listed in Table A or Table B.
  • a method for identifying an effect of a therapy may comprise (a) identifying a set of age-discriminatory bacterial taxa within the bacterial taxa comprising the gut microbiota a group of healthy subject's gut microbiota, the method comprising (i) providing, for each healthy subject, a relative abundance for the bacterial taxa comprising the subject's gut microbiota, wherein the relative
  • abundance of the bacterial taxa in the healthy subject's gut microbiota was determined from a plurality of gut microbiota samples obtained at intervals of time; (ii) regressing the relative abundances of the bacterial taxa comprising each healthy subject's gut microbiota against the chronological age of the healthy subject at the time the gut microbiota sample was collected, thereby producing a prediction of a gut microbiota age that is based only on the relative abundance of age-discriminatory bacterial taxa present in the subject's gut microbiota; and (iii) selecting the minimum number of bacterial taxa from step (ii) that is needed to produce a prediction that is substantially similar to or better than the prediction of step (ii); calculating a relative abundance for each bacterial taxon in the set of age-discriminatory taxa from step (a)(iii), using a fecal sample obtained from a subject before and after administration of the therapy; and applying the relative abundances from step (b) to the prediction of gut micro
  • the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In another preferred
  • the subject is a human that is about 30 years of age or less. In another preferred embodiment, the subject is a human that is about 40 years of age or less. In another preferred embodiment, the subject is a human that is about 50 years of age or less. In another preferred embodiment, the subject is a human that is about 60 years of age or less. In another preferred embodiment, the subject is a human that is about 70 years of age or less. In another preferred embodiment, the subject is a human that is about 80 years of age or less. In another preferred embodiment, the subject is a human that is about 90 years of age or less. In another preferred embodiment, the subject is a human that is about 100 years of age or less. In another preferred embodiment, the subject is a human that is about 1 10 years of age or less.
  • the present invention provides a method for preventing and/or treating a disease in a subject in need thereof.
  • a disease in a subject in need thereof.
  • certain bacterial taxa associated with normal maturation of the gut microbiota in healthy subjects i.e. age-discriminatory taxa
  • repair of the gut microbiota i.e. recovery-indicative taxa
  • a method for preventing and/or treating a disease in a subject in need thereof may comprise administering to the subject a therapeutically effective amount of one or more age-indicative taxa associated with repair of the gut microbiota.
  • "preventing" a disease refers to reducing the onset of symptoms or complications of a disease, condition or disorder (collectively, a
  • a disease may involve reducing in treated subjects (e.g. a normal subject) (1 ) the incidence, development or formation of disease, (2) the development, duration, and/or severity of symptoms of disease, (3) death rates, or (4) a combination thereof. In each embodiment, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects.
  • preventing a disease may also involve increasing the median infectious disease dose (ID 50 ) by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 100%, or more.
  • Preventing a disease may also involve increasing the (relative) microbiota maturity, increasing bacterial diversity, increasing MAZ score.
  • treating describes the management and care of a patient for the purpose of combating a disease in a subject.
  • Treating a disease may involve reducing in treated subjects (1 ) the infectious burden (e.g. viral, bacterial, patristic load), (2) the duration and/or severity of symptoms of disease, (3) death rates, or (4) a combination thereof.
  • the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects.
  • Treating a disease may also involve increasing the (relative) microbiota maturity, increasing bacterial diversity, increasing MAZ score.
  • therapeutically effective amount means an amount of one or more bacterial taxa of the invention that will elicit a desired biological or medical response of a tissue, system, or subject that is being sought by a researcher or clinician.
  • the biological or medical response is prevention or treatment of a gastrointestinal disease, !n another aspect, the biological or medical response is prevention or treatment of a gastrointestinal disease caused by an enteropathogenic infection.
  • the biological or medical response is treatment or prevention of acute malnutrition.
  • the biological or medical response is treatment or prevention of acute diarrheal disease.
  • the biological or medical response is treatment or prevention of acute diarrheal disease caused by an enteropathogenic infection.
  • the biological or medical response is treatment or prevention of chronic diarrheal disease.
  • bacterial taxa of the invention refers to age-discriminatory bacterial taxa associated with repair of the gut microbiota. Methods for identifying age-discriminatory bacterial taxa are described in detail in Section I.
  • one or more bacterial taxa of the invention are selected from the group listed in Table A.
  • one or more bacterial taxa of the invention are selected from the group listed in Table B.
  • one or more bacterial taxa of the invention are selected from the group listed in Table C.
  • Preferred combinations of bacterial taxa of the invention include, but are not limited to the combinations listed in Table D, or further described in Section III. Preferred combinations of bacterial taxa of the invention may also be identified by testing for a therapeutic effect in an appropriate animal model. Combinations may also be selected by identifying bacterial taxa associated with repair of the gut microbiota that are under- represented in a subject's gut microbiota as compared to a healthy subject. Section III also describes in further detail formulations comprising bacterial taxa of the invention for administration to a subject. Bacterial taxa of the invention are preferably administered orally or rectally. A bacterial taxon administered to a subject is isolated and biologically pure. A bacterial isolate can be identified as belonging to a bacterial taxon listed in
  • V4 region of the 16S rRNA gene of the bacterial isolate has at least 97% sequence identity to any of SEQ ID NOs: 1 - 60.
  • the V4 region of the 16S rRNA gene of the bacterial isolate can have 97%, 97.5%, 98%, 98.5%, 99%, 99.5%, or 100% sequence identity to any of SEQ ID NOs: 1 - 60.
  • the duration of therapy can and will vary, depending at least in part upon the disease and the severity of the disease.
  • one or more bacterial taxa of the invention may be administered until the microbiota age of the subject is similar to the microbiota age of a healthy subject of the same chronological age.
  • gastrointestinal diseases refer to diseases involving the gastrointestinal tract.
  • the disease is malnutrition.
  • the disease is an infection caused by an enteropathogen.
  • enteropathogens include Escherichia coli (e.g. enterotoxigenic E. coli, enteropathogen ic E. coli, enteroinvasive E. coli, enterohemorrhagic E. coli, enteroaggregative E. coli, etc.), Salmonella enterica, Shigella sp. (e.g. S. flexneri, S. sonnei, S. dysenteriae, etc.), Camplobacter sp. (e.g. C. jejuni, C.
  • coli coli, C. upsaliensis, etc.
  • Yersinia sp. e.g. Y. enterocolitica, Y. pseudotuberculosis, etc.
  • Vibrio sp. e.g. Vibrio cholera, Vibrio parahaemolyticus, etc.
  • Entamoeba sp. e.g.
  • Entamoeba histolytica, Entamoeba dispar, etc. Endolimax nana, lodamoeba butschii, Chilomastix mesnili, Blastocystis hominis, Trichomonas hominis, Coccidian-like body, Giardia sp. (e.g. Giardia intestinalis, Giardia lamblia, etc.) Cryptosporidium parvum, Isospora belli, Dientamoeba fragilis, Microsporidia, Strongyloides stercoralis, Angiostrongylus costaricensis, Schistosoma sp. (e.g. S. mansoni, S. japonicum, etc.) Cyclospora cayetanensis, Enterocytozoon sp. (e.g. Enterocytozoon bieneusi,
  • Encephalitozoon sp. e.g. Encephalitozoon intestinalis, Encephalitozoon cuniculi, etc.
  • Ascaris lumbricoides Trichuris tricuria, Ancylostoma duodenale, Necator americanus, Hymenolepsis nana, rotaviruses, human caliciviruses (e.g. noroviruses and sapoviruses), astroviruses, cytolomegaloviruses.
  • the disease is ulcerative colitis, necrotizing enterocolitis, or Crohn's disease.
  • the disease is traveler's diarrhea.
  • the disease is acute malnutrition.
  • the disease is an enteropathogen infection that has as a symptom acute diarrhea.
  • the disease is a Vibrio cholerae infection.
  • Acute malnutrition results from decreased food consumption and/or illness resulting in sudden weight loss. It is associated with greater risk of medical complications and infections, increased risk of death from illness and infections, and micronutrient deficiencies.
  • micronutrient deficiencies associated with acute malnutrition are iron deficiency, iodine deficiency, and vitamin A deficiency.
  • the most common way to assess malnutrition, particularly in humans of about 19 years of age or less, is through anthropometric measurements. It is usually diagnosed in one of three ways: by weighing a subject and measuring the subject's height; by measuring the circumference of the subject's mid-upper arm (MUAC); and/or by checking for oedema in the subject's lower legs or feet.
  • Acute malnutrition is divided into two types: severe acute malnutrition (SAM) and moderate acute malnutrition
  • a subject is classified as having SAM if the subject's weight-for-height Z-scores (WHZ) is below three standard deviations (-3 s.d.) from the median of the World Health Organization (WHO) reference growth standards.
  • WHO World Health Organization
  • a subject with a WHZ between -2 s.d. and -3 s.d. from the median of the WHO reference growth standards is categorized as having MAM. If a subject is between about six months and about five years of age, a MUAC measurement of less than 12.5 cm also indicates that a subject is suffering from moderate acute malnutrition. Finally, the presence of oedema in both feet and lower legs of a subject is a sign of SAM.
  • WHO reference growth standards are available from the WHO. See for example, World Health Organization Department of Nutrition for Health and Development: WHO child growth standards growth velocity based on weight, length and head circumference: methods and development; World Health Organization, 2009, or the current edition.
  • the subject to be treated is a subject with acute malnutrition and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject.
  • Treating acute malnutrition may involve reducing in treated subjects the duration and/or severity of symptoms of acute
  • the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 80%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating acute malnutrition may also involve increasing a suitable anthropometric measurement in a treated subject including, but not limited to, a subject's WHZ or MUAC.
  • the amount of increase may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects or as compared to the treated subject prior to administration of the combination of the invention.
  • a subject's WHZ may improve to less than -3.0 s.d., less than -2.5 s.d., less than -2.0 s.d., less than -1 .5 s.d., less than -1 .0 s.d., or less than -0.5 s.d. from the median of the WHO reference growth standards. Treating acute malnutrition may also result in no worsening (i.e. decrease) in a subject's WHZ or MUAC. Treating acute malnutrition may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof.
  • the duration of treatment can and will vary, in certain embodiments, the duration of therapy is about 1 , 2, 3, or 4 months. In other embodiments, the duration of therapy is about 1 , 2, 3, 4, 5, 6, 7, or 8 weeks. In still other embodiments, the duration of therapy is about 4 to about 12 weeks, about 6 to about 10 weeks, or about 6 to about 8 weeks. Alternatively, a subject may be
  • One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent.
  • additional therapeutic agents include therapeutic foods, probiotics, antibiotics and vaccines.
  • a subject with acute malnutrition is administered a therapeutically effective amount of one or more bacterial taxa of the invention and a therapeutic food.
  • therapeutic food refers to a food designed for specific, usually nutritional, therapeutic purposes as a form of dietary supplement. Therapeutic foods are usually made of a mixture of protein, carbohydrate, lipid and vitamins and minerals, and are usually produced by grinding all ingredients together, mixing them and packaging without water.
  • the term "therapeutic foods” includes ready-to-use-therapeutic foods (RUTFs). Generally speaking, RUTFs are homogenous mixtures of lipid-rich and water-soluble foods. The lipids used in
  • RUTFs are in a viscous liquid form, and the other ingredients (e.g. protein, carbohydrate, vitamins, minerals) are mixed through the lipids.
  • Non-limiting examples of therapeutic foods include F-75, F-100, K-Mix 2, Citadel spread, Plumpy'nut, Medika Mamba, Ensure, Fortisip, Energyzip, TwoCal, BP-100, and eeZee.
  • the subject to be treated is a subject at risk for acute malnutrition and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject.
  • a subject at risk for acute is a subject at risk for acute malnutrition and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject.
  • malnutrition may have limited access to nutritious foods and/or may have frequent exposure to infectious diseases.
  • a subject at risk for acute malnutrition may also be a subject with a WHZ between 0 s.d. and -2 s.d., between -1 s.d and -2 s.d, or between -1 .5 s.d and -2 s.d from the median of the WHO reference growth standards. Treating a subject at risk for acute malnutrition may prevent acute malnutrition in the subject.
  • preventing acute malnutrition may involve reducing in treated subjects (1 ) the incidence, development or formation of acute malnutrition, (2) the development, duration, and/or severity of symptoms of malnutrition, if malnutrition does develop, (3) death rates associated with acute malnutrition, or (4) a combination thereof.
  • the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects.
  • Preventing acute malnutrition may also involve increasing a suitable anthropometric measurement in a treated subject including, but not limited to, a subject's WHZ or MUAC,
  • the amount of increase may each be about 10%, 20%, 30%, 40%, 50%, 80%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects or as compared to the treated subject prior to administration of the combination of the invention.
  • a subject's WHZ may improve to less than -1 .5 s.d., less than -1 .0 s.d., or less than -1 .5 s.d. from the median of the WHO reference growth standards.
  • Preventing acute malnutrition may also result in no worsening (i.e. decrease) in a subject's WHZ or MUAC. Preventing acute malnutrition may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof.
  • the duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 , 2, 3, or 4 months. In other embodiments, the duration of therapy is about 1 , 2, 3, 4, 5, 6, 7, or 8 weeks. In still other embodiments, the duration of therapy is about 4 to about 12 weeks, about 6 to about 10 weeks, or about 6 to about 8 weeks.
  • a subject may be administered one or more bacterial taxa of the invention for as long as the subject remains at risk. Useful combinations are described in further detail below.
  • One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include therapeutic foods, antibiotics and/or
  • acute diarrhea is defined as three or more stools per day of decreased form (e.g. loose and/or water) from the normal, lasting for less than 14 days; persistent diarrhea is defined as three or more stools per day of decreased form from the normal, lasting for more than 14 days but less than 1 month; and chronic diarrhea is defined as three or more stools per day of decreased form from the normal, lasting for a month or more.
  • Associated symptoms of acute diarrhea, persistent diarrhea, and chronic diarrhea may include abdominal cramps or pain, fever, nausea, vomiting, fatigue, urgency, weight loss, and/or malnutrition.
  • Acute diarrhea, persistent diarrhea, and chronic diarrhea are themselves symptoms.
  • Causes of acute diarrhea are well known in the art, and non-limiting examples include a food allergy, antibiotic use, an enteropathogen infection, and radiation therapy.
  • Causes of persistent diarrhea are well known in the art, and non-limiting examples include a food allergy, antibiotic use, an enteropathogen infection, colon resection, colon cancer, ulcerative colitis, necrotizing enterocolitis, Crohn's disease and radiation therapy.
  • Causes of chronic diarrhea are well known in the art, and non-limiting examples include a food allergy, use of a pharmacological agent, an enteropathogen infection, colon resection, colon cancer, ulcerative colitis, necrotizing enterocolitis, Crohn's disease, and radiation therapy.
  • Pharmacological agents known to cause diarrhea are well known in the art and may include, but are not limited to, an antibiotic, an anti-TNF agent, chemotherapy agents, antacids, proton pump inhibitors, (e.g. asomeprazole, esomeprazole,
  • lansoprazole rabeprazole, patoprazole, cimetidine, ranitidine, naiziatidine
  • drugs that suppress the immne system e.g. mycophenolate
  • antidepressants e.g. mycophenolate
  • blood pressure medications e.g., blood pressure medications, digitalis, diuretics, cholesterol-lowering agents, lithium, theophylline, thyroid hormone, and colchicine.
  • the subject to be treated is a subject with acute diarrhea and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject.
  • Treating acute diarrhea may involve reducing in treated subjects the duration and/or severity of symptoms of acute diarrhea (e.g. fever, bloody stools, vomiting), death rates, or a combination thereof.
  • the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects.
  • Treating an acute diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an acute diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form to less than 6, less than 5, less than 4, or less than 3. Treating acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof.
  • the duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, about 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days.
  • the duration of therapy is about 1 , 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 1 1 , 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days.
  • the duration of therapy may be a month or more.
  • Useful combinations are described in further detail in Section III.
  • One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent.
  • additional therapeutic agents include antibiotics, antimotiiity agents (e.g. loperamide), antisecretory agents (e.g.
  • racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea bulk-forming agents (e.g. isphaghula husk, methylcelluiose, stercuiia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy.
  • acute diarrhea is a symptom of a Vibrio cholerae infection.
  • the subject to be treated is a subject with acute diarrhea and a therapeutically effective amount of a bacterial isolate that is a member of the taxon Ruminococcus obeum OTU ID 178122 is administered to the subject, optionally in combination with one or more additional bacterial taxa of the invention.
  • the subject to be treated is a subject with acute diarrhea and a therapeutically effective amount of Ruminococcus obeum ATCC29714 is administered to the subject, optionally in combination with one or more additional bacterial taxa of the invention.
  • Treating acute diarrhea may involve reducing in treated subjects the duration and/or severity of symptoms of acute diarrhea (e.g. fever, bloody stools, vomiting), death rates, or a combination thereof.
  • the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects.
  • Treating an acute diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects.
  • Treating an acute diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form to less than 6, less than 5, less than 4, or less than 3.
  • Treating acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary.
  • the duration of therapy is about 1 to about 4 weeks, about 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1 , 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 1 1 , 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days. Alternatively, the duration of therapy may be a month or more.
  • Useful combinations comprising Ruminococcus obeum ATCC29714 or a bacterial isolate that is a member of the taxon Ruminococcus obeum OTU ID 178122 are described in further detail in Section III.
  • a composition comprising Ruminococcus obeum ATCC29714 or a bacterial isolate that is a member of the taxon Ruminococcus obeum OTU ID 178122 may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent.
  • additional therapeutic agents include antibiotics, antimotility agents (e.g. loperamide), antisecretory agents (e.g. racecadotrii and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g.
  • acute diarrhea is a symptom of a Vibrio cholerae infection.
  • the subject to be treated is a subject at risk for acute diarrhea and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject.
  • a subject at risk for acute diarrhea may be a subject living in a geographic area with limited or no access to clean drinking water, a subject living in a geographic area that is experiencing a disease outbreak, or a subject that is exposed to others that have an acute diarrhea.
  • a subject at risk for acute diarrhea may be a subject that is malnourished. Treating a subject at risk for acute diarrhea may prevent acute diarrhea in the subject.
  • preventing an acute diarrhea may involve reducing in treated subjects (1 ) the incidence, development or formation of the acute diarrhea , (2) the development, duration, and/or severity of symptoms of the acute diarrhea (e.g. fever, bloody stools, vomiting), if the disease does develop, (3) death rates associated with the acute diarrhea, or (4) a combination thereof.
  • the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects.
  • Preventing an acute diarrhea may also involve result in a minimal change in the number of stools per day of decreased form in treated subjects.
  • the change in the number of stools per day of decreased form may be an increase of 3 or less, 2 or less, or 1 or less.
  • Preventing acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof.
  • the duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1 , 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 1 1 , 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days. Alternatively, the duration of therapy may be a month or more. Useful
  • One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be
  • additional therapeutic agents include antibiotics, antimotiiity agents (e.g. loperamide), antisecretory agents (e.g. racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghula husk, methyiceilulose, sterculia, etc.) prebiotics, probiotics, synbiotscs, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy.
  • additional therapeutic agents include antibiotics, antimotiiity agents (e.g. loperamide), antisecretory agents (e.g. racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghula husk
  • the subject to be treated is a subject with acute diarrhea and a therapeutically effective amount of a bacterial isolate that is a member of the taxon Ruminococcus obeum OTU ID 178122 is administered to the subject, optionally in combination with one or more additional bacterial taxa of the invention.
  • the subject to be treated is a subject at risk for acute diarrhea and a therapeutically effective amount of Ruminococcus obeum ATCC29714 is administered to the subject, optionally in combination with one or more additional bacterial taxa of the invention.
  • a subject at risk for acute diarrhea may be a subject living in a geographic area with limited or no access to clean drinking water, a subject living in a geographic area that is experiencing a disease outbreak, or a subject that is exposed to others that have an acute diarrhea.
  • a subject at risk for acute diarrhea may be a subject that is malnourished. Treating a subject at risk for acute diarrhea may prevent acute diarrhea in the subject. For example, preventing an acute diarrhea may involve reducing in treated subjects (1 ) the incidence, development or formation of the acute diarrhea , (2) the development, duration, and/or severity of symptoms of the acute diarrhea (e.g. fever, bloody stools, vomiting), if the disease does develop, (3) death rates associated with the acute diarrhea, or (4) a combination thereof.
  • the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 80%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects.
  • Preventing an acute diarrhea may also involve result in a minimal change in the number of stools per day of decreased form in treated subjects.
  • the change in the number of stools per day of decreased form may be an increase of 3 or less, 2 or less, or 1 or less.
  • Preventing acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary.
  • the duration of therapy is about 1 to about 4 weeks, 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1 , 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 1 1 , 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days.
  • the duration of therapy may be a month or more.
  • Useful combinations comprising Ruminococcus obeum ATCC29714 or a bacterial isolate that is a member of the taxon Ruminococcus obeum OTU ID 178122 are described in further detail in
  • a composition comprising Ruminococcus obeum ATCC29714 or a bacterial isolate that is a member of the taxon Ruminococcus obeum OTU ID 178122 may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent.
  • additional therapeutic agents include antibiotics, antimotility agents (e.g. loperamide), antisecretory agents (e.g.
  • racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea bulk-forming agents (e.g. isphaghuia husk, methylcellulose, sterculia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy.
  • acute diarrhea is a symptom of a Vibrio choierae infection.
  • the subject to be treated is a subject with chronic diarrhea and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject.
  • Treating chronic diarrhea may involve reducing in treated subjects the duration and/or severity of symptoms of acute diarrhea (e.g. fever, bloody stools, vomiting), death rates, or a combination thereof.
  • the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects.
  • Treating an chronic diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an chronic diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form to less than 6, less than 5, less than 4, or less than 3. Treating acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof.
  • the duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, about 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days.
  • the duration of therapy is about 1 , 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 1 1 , 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days.
  • the duration of therapy may be a month or more.
  • Useful combinations are described in further detail in Section III.
  • One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent.
  • additional therapeutic agents include antibiotics, antimotility agents (e.g. loperamide), antisecretory agents (e.g.
  • racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea bulk-forming agents (e.g. isphaghula husk, methylcel!ulose, stercuiia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy.
  • chronic diarrhea is a symptom of a C. difficile infection, Crohn's disease, ulcerative colitis, necrotizing enterocolitis, traveler's diarrhea, colon cancer, chemotherapy, radiation therapy, or use of pharmacological agent, including but not limited to an antibiotics, or an anti-TNF agent.
  • the subject to be treated is a subject at risk for chronic diarrhea and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject.
  • a subject at risk for chronic diarrhea may be a subject living in a geographic area with limited or no access to clean drinking water, a subject living in a geographic area that is experiencing a disease outbreak, or a subject that is exposed to others that have an acute diarrhea.
  • a subject at risk for chronic diarrhea may be a subject that is malnourished. Treating a subject at risk for chronic diarrhea may prevent acute diarrhea in the subject. For example, preventing an chronic diarrhea may involve reducing in treated subjects (1 ) the incidence,
  • the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 80%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects.
  • Preventing an chronic diarrhea may also involve result in a minimal change in the number of stools per day of decreased form in treated subjects.
  • the change in the number of stools per day of decreased form may be an increase of 3 or less, 2 or less, or 1 or less.
  • Preventing chronic diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof.
  • the duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1 , 2, 3, or 4 weeks, in still other embodiments, the duration of therapy is about 10, 1 1 , 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days. Alternatively, the duration of therapy may be a month or more. Useful combinations are described in further detail in Section III.
  • One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent.
  • additional therapeutic agents include antibiotics, antimoti!ity agents (e.g. loperamide), antisecretory agents (e.g. racecado ril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghuia husk, methy!ce!lu!ose, sterculia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g.
  • chronic diarrhea is a symptom of a C. difficile infection, Crohn's disease, ulcerative colitis, necrotizing enterocolitis, traveler's diarrhea, colon cancer, radiation therapy, or use of pharmacological agent, including but not limited to an antibiotics, or an anti-TNF agent.
  • the present invention provides a composition comprising 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23,
  • each bacterial taxon present in the composition is isolated and biologically pure (i.e. >97% sequence identity to the SEQ ID NO. provided).
  • the present invention provides a combination comprising 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, or 24 bacterial taxa selected from the bacterial taxa listed in Table B, wherein each bacterial taxon present in the composition is isolated and biologically pure (i.e. >97% sequence identity to the SEQ ID NO. provided).
  • the present invention provides a combination comprising 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24,
  • the present invention provides a combination identified below in Table D, wherein each bacterial taxon present in the composition is isolated and biologically pure (i.e. >97% sequence identity to the SEQ ID NO. provided).
  • Strain 1 Faecalibacterium prausnitzii OTU ID 326792
  • Strain 2 Ruminococcus sp. 5 1 39BFAA OTU ID 189827
  • Strain 3
  • Lactobacillus ruminis OTU ID 470663 Lactobacillus ruminis OTU ID 470663, Strain 4: Dorea longicatena OTU ID 191687, Strain 5: Bifidobacterium longum OTU ID 72820, Strain 6: Ruminococcus sp. 5 1 39BFAA OTU ID 194745, Strain 7: Lactobacillus mucosae OTU ID 15141 , Strain 8:
  • a bacterial taxon of the invention is formulated to maintain a suitable level of viable cells during the formulation's shelf life and upon administration to a subject.
  • Each bacterial taxon may be present in a wide range of amounts provided that the composition or combination delivers the effect described.
  • the total amount of bacteria per unit dose is dependent, in part, upon the dosage form and excipients.
  • suitable amounts include from about 10 2 to about 10 12 colony forming units (cfu) of each bacterial strain per unit dose.
  • the amount of each bacterial strain is between about 10 5 and about 10 12 cfu per unit dose, between about 10 6 and about 10 12 cfu per unit dose, between about 10 7 and about 10 12 cfu per unit dose, or between about 10 8 and about 10 12 cfu per unit dose. In other embodiments, the amount of each bacterial strain is between about 10 5 and about 10 11 cfu per unit dose, between about 106 and about 10 11 cfu per unit dose, between about 10 7 and about 10 11 cfu per unit dose, or between about 108 and about 10 11 cfu per unit dose.
  • the amount of each bacterial strain is between about 10 5 and about 10 10 cfu per unit dose, between about 106 and about 10 10 cfu per unit dose, between about 10 7 and about 10 10 cfu per unit dose, or between about 10 8 and about 10 10 cfu per unit dose.
  • the amount of each bacterial taxon is between about 10 5 and about 10 9 cfu per unit dose, between about 10 6 and about 10 9 cfu per unit dose, between about 10 7 and about 10 9 cfu per unit dose, or between about 10 8 and about 10 9 cfu per unit dose.
  • a bacterial strain may be provided as a frozen or freeze-dried culture, or as bacterial spores.
  • a bacterial taxon of the invention, or a combination of bacterial taxa of the invention may be formulated into a formulation for oral or rectal administration comprising one or more bacterial taxa of the invention and one more excipients.
  • excipients include binders, diluents, fillers,, disintegrants,
  • Bacterial taxa of the invention may be formulated in unit dosage form as a solid, semisolid, liquid, capsule, or powder. These formulations are a further aspect of the invention. Usually the amount of a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, is between 0.1 -95% by weight of the formulation, or between 1 and 50% by weight of the formulation.
  • a bacterial taxon of the invention may be mixed with a solid, powdered carrier, e.g. lactose, saccharose, sorbitol, mannitol, starch, amylopectin, cellulose derivatives or gelatin, and optionally with lubricants (e.g. magnesium stearate, calcium stearate, sodium steryl fumarate and polyethylene glycol waxes), release-controlling polymers and other excipients.
  • lubricants e.g. magnesium stearate, calcium stearate, sodium steryl fumarate and polyethylene glycol waxes
  • release-controlling polymers e.g. magnesium stearate, calcium stearate, sodium steryl fumarate and polyethylene glycol waxes
  • the enteric coating is chosen among pharmaceutically acceptable enteric-coating materials e.g. beeswax, shellac or anionic film-forming polymers such as cellulose acetate phthalate, hydroxypropyl methylcellulose phthalate, partly methyl esterified methacrylic acid polymers and the like, if preferred in combination with a suitable plasticizer.
  • enteric-coating materials e.g. beeswax, shellac or anionic film-forming polymers such as cellulose acetate phthalate, hydroxypropyl methylcellulose phthalate, partly methyl esterified methacrylic acid polymers and the like, if preferred in combination with a suitable plasticizer.
  • enteric-coating materials e.g. beeswax, shellac or anionic film-forming polymers such as cellulose acetate phthalate, hydroxypropyl methylcellulose phthalate, partly methyl esterified methacrylic acid polymers and the like, if preferred in combination with a suitable
  • the pharmaceutical compositions may be incorporated into a food product or powder for mixing with a liquid, or administered orally after only mixing with a non-foodstuff liquid.
  • Soft gelatine capsules may also be prepared with capsules comprising a combination of the invention and vegetable oil, fat, or other suitable vehicle for soft gelatine capsules.
  • Soft gelatine capsules are preferably enteric coated as described above.
  • Hard gelatine capsules may contain enteric-coated granules of a combination of the invention.
  • Hard gelatine capsules may also contain a combination of the invention in combination with a solid powdered carrier e.g. lactose, saccharose, sorbitol, mannitol, potato starch, corn starch, amylopectin, cellulose derivatives or gelatine; the hard gelatine capsules are preferably enteric coated as described above.
  • Dosage units for rectal administration may be prepared in the form of suppositories which comprise a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, mixed with a non-irritating excipient that is solid at room temperature but liquid at rectal temperature and therefore will melt in the rectum to release the drug.
  • suitable excipients for rectal suppository embodiments include cocoa butter, beeswax, and polyethylene glycols.
  • a dosage unit for rectal administration may be prepared in the form of a gelatine rectal capsule which comprises a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, in a mixture with a vegetable oil, paraffin oil or other suitable vehicle for gelatine rectal capsules.
  • a bacterial taxon of the invention, or a combination of bacterial taxa of the invention may be prepared in the form of a ready-made enema, or in the form of a dry enema formulation to be
  • Liquid preparations for oral administration may be prepared in the form of syrups or suspensions, e.g. solutions or suspensions containing from 0.2% to 20% by weight of a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, and the remainder consisting of sugar or sugar alcohols and a mixture of ethanol, water, glycerol, propylene glycol and polyethylene glycol.
  • such liquid preparations may contain colouring agents, flavouring agents, saccharine and
  • administration may also be prepared in the form of a dry powder to be reconstituted with a suitable solvent prior to use.
  • compositions in accordance with the present disclosure can be varied to achieve a desired result, such as may be
  • the desired dose may be presented in multiple (e.g., two, three, four, five, six, or more) sub-doses administered at appropriate intervals throughout the day.
  • Severe acute malnutrition and moderate acute malnutrition are typically defined by anthropometric measurements: children are classified as having SAM if their weight-for-height Z-scores (WHZ) 3 are below three standard deviations ( ⁇ 3 s.d.) from the median of the World Health Organization (WHO) reference growth standards, whereas those with WHZ between -2 and -3 s.d. are categorized as having MAM. SAM and MAM typically develop between 3 and 24 months after birth 4 .
  • a standardized treatment protocol for SAM and its complications has been developed in Bangladesh 1 . The result has been a reduction in mortality rate, although the extent to which this protocol results in long-term restoration of normal growth and development needs to be ascertained through longitudinal studies 5,6 . There is similar lack of clarity about the long-term efficacy of nutritional interventions for MAM 7,8 .
  • the bacterial component of their faecal microbiota samples was characterized by V4-16S rRNA sequencing (Table 4) and assigning the resulting reads to operational taxonomic units (OTUs) sharing 97% nucleotide sequence identity (see Methods for Examples 1 -9; a 97%-identity OTU is commonly construed as representing a species-level taxon).
  • OTUs operational taxonomic units
  • the relative abundances of 1 ,222 97%-identity OTUs that passed our filtering criterion 15 were regressed against the chronologic age of each child at the time of faecal sample collection using the Random Forests machine learning algorithm 16 .
  • the regression explained 73% of the variance related to chronologic age.
  • FIG. 1 B-D and F-H maturation across different healthy children living in this geographic locale.
  • microbiota maturity (microbiota age of child) - (microbiota age of healthy children of similar chronologic age)
  • microbiota age values for healthy children were interpolated across the first 2 years of life using a spline fit (FIG. 1 B-D).
  • the second metric, microbiota-for-age Z score was calculated as follows:
  • microbiota age - median microbiota age of healthy children of same chronologic age
  • MAZ microbiota-for-age Z-score
  • median and s.d. of microbiota age were computed for each month up to 24 months.
  • the MAZ accounts for the variance of predictions of microbiota age as a function of different host age ranges (when
  • WHZ averaged -4.2 ⁇ 0.7 (mean ⁇ s.d.) (see Tables 9 and 10 for patient metadata and FIG. 2A for study design).
  • infection control was achieved with parenteral administration of ampicillin and gentamicin for 2 and 7 days, respectively, and oral amoxicillin for 5 days (from days 3 to 7 of the antibiotic treatment protocol).
  • Children with SAM were initially stabilized by being fed the milk-based gruel, 'suji', followed by randomization to either an imported peanut-based RUTF intervention or an intervention with locally produced, rice-and-lentil- based therapeutic foods (Khichuri and Halwa; see Methods for Examples 1 -9 and
  • Table 11 for compositions of all foods used during nutritional rehabilitation).
  • children received 150- 250 kcal kg "1 body weight per day of RUTF or Khichuri-Halwa (3-5 g protein kg "1 per day), plus micronutrients including iron.
  • Children were discharged from the hospital after the completion of this second phase; during the 'post-intervention phase', periodic follow-up examinations were performed to monitor health status. Faecal samples were obtained during the acute phase before treatment with Khichuri-Halwa or RUTF, then every 3 days during the nutritional rehabilitation phase, and monthly thereafter during the post-intervention follow-up period.
  • MAZ like relative microbiota maturity, indicated a transient improvement after RUTF intervention that was not durable beyond 4 months.
  • Khichuri- Halwa group relative microbiota maturity and MAZ improved following treatment, but subsequently regressed, exhibiting significant differences relative to healthy children at 2-3 months, and >4 months after cessation of treatment (FIG. 2B and Table 12).
  • SAM affects approximately 4% of children in developing countries. MAM is more prevalent, particularly in South Central Asia, where it affects
  • FIG. 11A,B Comparing children with MAM to those defined as healthy revealed significantly lower relative microbiota maturity, MAZ and differences in the relative abundances of age-discriminatory taxa in the malnourished group (FIG. 11 D-l ⁇ and FIG. 12A,B) These results suggest that microbiota immaturity may be an additional pathophysiological component of moderately malnourished states.
  • Households may consist of more than one biological family: in these cases all individuals in the household share a gas stove and cooking area immediately outside the main room, although food, cooking pots and utensils are used separately by each biological family. All individuals share a common 'bathroom,' a small space containing a latrine, and sometimes the water pump, located next to the main room.
  • the common practice is to wash the perianal area by hand with water contained in a small, special container called bodna in Bangla.
  • Example 3 Fecal microbiota variation within and between family members during the first year of postnatal life.
  • Example 4 Transient reduction of gut microbiota diversity in healthy twins and triplets associated with diarrhea.
  • SDI Shannon Diversity Index
  • Example 5 Persistent reductions in diversity associated with SAM.
  • the RUTF group showed significant improvement in SDI values between 1 -3 months following cessation of treatment, followed by regression to a persistent lower than healthy SDI beyond 3 months.
  • improvement in SDI was only significant at 3-4 months of follow-up.
  • SAM children in both treatment groups exhibited significant reductions in diversity compared to healthy Bangladeshi children at all phases of treatment and recovery, except for 1 -3 months post-RUTF and 3-4 months post- Khichuri-Halwa (FIG. 7; Table 12).
  • Example 6 Two hundred and twenty bacterial taxa that are significantly different in their proportional representation in microbiota of children with SAM compared to healthy at multiple treatment phases across both groups.
  • Faecalibacterium prausnitzii of which two are age-discriminatory taxa (FDR-corrected P ⁇ 0.05).
  • Taxa that were enriched in children diagnosed with SAM compared to healthy children included those belonging to the family Enterobacteriaceae (genera Escherichia and Klebsiella) as well as Enterococcus faecalis (FDR-corrected P ⁇ 0.05).
  • Taxa enriched in the microbiota of children with SAM after the therapeutic food interventions belonged predominantly to the genus Streptococcus, including 97% ID OTUs identified as Streptococcus lutentiensis,
  • Streptococcus species (FDR-corrected P ⁇ 0.01 ; see FIG. 8 and FIG. 9 for a heatmap depiction of all 97% ID OTUs whose representation in the fecal microbiota is
  • Example 7 Assessing the effects of antibiotics on microbiota maturity during the follow-up period in children with SAM.
  • Example 8 Expanding the sparse Random Forests-based model from 24 to 60 taxa.
  • Example 9 Processed 16S rRNA datasets.
  • a second group studied from this singleton cohort consisted of 33 children sampled cross-sectionally at 18 months, including those who were incorporated as healthy reference controls, and those with a WHZ ⁇ -2 who were classified with MAM (Table 15).
  • SNPs polymorphic single-nucleotide polymorphisms
  • the three healthy Bangladeshi groups used for model training and validation had the following WHZ scores: -0.32 6 1 (mean 6 s.d.; 12 singletons randomized to the training set), -0.4460.8 (13 singletons randomized to one of the two validation sets), and-0.466 0.7 (twins and triplets in the other validation set) (Table 3).
  • the average number of diarrhoeal episodes in the singleton training set, the singleton validation set, and the twin and triplet validation set (4, 4.6 and 1 .7, respectively) was comparable to values reported in previous surveys of another cohort of 0-2-year-old Bangladeshi children (4.25 per child per year) 22
  • Nutritional Rehabilitation Unit of ICDDR,B, Dhaka Hospital suffering from SAM (defined as having a WHZ less than -3 s.d. and/or bilateral pedal oedema) were enrolled in a randomized interventional trial to compare an imported peanut-based RUTF,
  • Plumpy'Nut (Nutriset Plumpyfield, India) and locally produced Khichuri-Halwa (clinical trial NCT01331044).
  • children were stabilized by rehydration and feeding 'suji', which contains whole bovine milk powder, rice powder, sugar and soybean oil
  • Khichuri-Halwa or RUTF groups (approximately 100 kcal kg "1 body weight per day, including 1 .5 g protein kg "1 per day). Children were then randomized to the Khichuri-Halwa or RUTF groups. Khichuri consists of rice, lentils, green leafy vegetables and soybean oil; Halwa consists of wheat flour (atta), lentils, molasses and soybean oil. Children randomized to the Khichuri- Halwa treatment arm also received milk suji '100' during their nutritional rehabilitation phase (a form of suji with a higher contribution of calories from milk powder compared to suji provided during the acute phase).
  • RUTF is a ready-to-use paste that does not need to be mixed with water; it consists of peanut paste mixed with dried skimmed milk, vitamins and minerals (energy density, 5.4kcalg "1 ).
  • Khichuri and Halwa are less energy- dense than RUTF (1 .45 kcal g-1 and 2.4 kcal g "1 , respectively, see Table 11 for a list of ingredients for all foods used during nutritional rehabilitation).
  • the primary outcome measurement, rate of weight gain (g kg "1 per day), along with improvement in WHZ after nutritional rehabilitation are reported by child in Table 9.
  • Faecal samples were collected before randomization to the RUTF and Khichuri-Halwa treatment arms, every 3 days during nutritional rehabilitation and once a month during the follow-up period (information associated with each faecal sample is provided in Supplementary Table 1 1 of Subramanian et al, Nature 2014; S10:417-421 , which is hereby incorporated by reference in its entirety).
  • Bacterial V4-16S rRNA data sets were generated by multiplex sequencing of amplicons prepared from 1 ,897 faecal DNA samples (26,580 6 26,312 (mean 6 s.d.) reads per sample, paired-end 162- or 250-nucleotide reads; lllumina MiSeq platform; Table 4). Reads of 250 nucleotides in length were trimmed to 162 nucleotides, then all reads were processed using previously described custom scripts, and overlapped to 253-nucleotide fragments spanning the entire V4 amplicon 15 .
  • Random Forests regression was used to regress relative abundances of OTUs in the time-series profiling of the microbiota of healthy singletons against their chronologic age using default parameters of the R implementation of the algorithm (R package 'randomForest', ntree 5 10,000, using default mtry of p/3 where p is the number of input 97%-identity OTUs (features)) 26 .
  • the Random Forests algorithm due to its non-parametric assumptions, was applied and used to detect both linear and nonlinear relationships between OTUs and chronologic age, thereby identifying taxa that discriminate different periods of postnatal life in healthy children.
  • the rfcv function implemented in the 'randomForest' package was applied over 100 iterations.
  • a sparse model consisting of the top 24 taxa was then trained on the training set of 12 healthy singletons (272 faecal samples). Without any further parameter optimization, this model was validated in other healthy children (13 singletons, 25 twins and triplets) and then applied to samples from children with SAM and MAM.
  • a smoothing spline function was fit between microbiota age and chronologic age of the host (at the time of faecal sample collection) for healthy children in the validation sets to which the sparse model was applied.
  • Enteropathoqen testing Clinical microscopy was performed for all faecal samples collected at monthly intervals from the singleton birth cohort and from healthy twins and triplets, and screened for Entamoeba histolytica, Entamoeba dispar, Escherichia coli, Blastocystis hominis, Trichomonas hominis, Blastocystis hominis, Coccidian-like bodies, Giardia lamblia, Ascaris lumbricoides, Trichuris Tricuria, Ancylostoma duodenale/Necator americanus, Hymenolepsis nana, Endolimax nana, lodamoebabutschlii and Chilomastixmesnili.
  • enteropathogens detected by microscopy on relative microbiota maturity, MAZ and SDI were included in our analysis of multiple environmental factors in FIG. 4 and Table 6.
  • faecal samples collected before nutritional rehabilitation were cultured for Vibrio cholerae, Shigella flexneri, Shigella boydi, Shigella sonnei, Salmonella enterica, Aeromonas hydrophila and Hafnia alvae. See Tables 9 and 17 for results of enteropathogen testing.
  • NIPORT National Institute of Population Research and Training
  • Mitra and Associates Mitra and Associates
  • ICF International International. Bangladesh Demographic and Health Survey 201 1 . Dhaka, Bangladesh and Calverton, Maryland, USA: NIPORT, Mitra and Associates, and ICF International (2013).
  • Example 10 Members of the human gut microbiota involved in recovery from Vibrio choierae infection
  • cholerae dominated the microbiota of the seven patients with cholera during D-Ph1 (mean maximum relative abundance 55.6%), declining markedly within hours after initiation of oral rehydration therapy.
  • the microbiota then became dominated by either an unidentified Streptococcus species (maximum relative abundance 56.2-98.6%) or by Fusobacterium species (19.4-65.1 % in patients B-E).
  • dominance of the community passed from a Campylobacter species (58.6% maximum) to
  • Indicator species analysis 4 (Methods for Example 10-17) was used to identify 260 bacterial species consistently associated with the diarrhoeal or recovery phases across members of the study group, and in a separate analysis for each subject (Table 21 ). The relative abundance of each of the discriminatory species in each faecal sample was compared with the mean weighted phylogenetic (UniFrac-) distance between that microbiota sample and all microbiota samples collected from the reference cohort of healthy Bangladeshi adults. The results revealed 219 species with significant indicator value assignments to diarrhoeal or recovery phases, and relative abundances with statistically significant Spearman's rank correlation values to community UniFrac distance to healthy control microbiota (Table 22 and FIG.
  • V. cholerae directly correlated with increased distance to a healthy microbiota.
  • Streptococcus and Fusobacterium species which bloomed during the early phases of diarrhoea, were also significantly and positively correlated with distance from a healthy adult microbiota.
  • Increases in the relative abundances of species in the genera Bacteroides, Prevotella,Ruminococcus/Blautia, and Faecalibacterium for example, Bacteroides vulgatus, Prevotella copri,R obeum, and Faecalibacterium prausnitzii
  • Faecalibacterium for example, Bacteroides vulgatus, Prevotella copri,R obeum, and Faecalibacterium prausnitzii
  • Random Forests a machine-learning algorithm, to identify a collection of age-discriminatory bacterial taxa that together define different stages in the postnatal assembly/maturation of the gut microbiota in healthy Bangladeshi children living in the same area as the adult patients with cholera-.
  • OTUs 97%-identity OTUs representing 40 different species
  • 31 species were present in adult patients with cholera. Intriguingly, they followed a similar progression of changing representation during diarrhoea to recovery as they do during normal maturation of the healthy infant gut microbiota (FIG. 16F-N). Twenty- seven of the 31 species were significantly associated with recovery from diarrhoea by indicator species analysis (see FIG.
  • V. cholerae population during diarrhoea ( ⁇ 1 invasion' group).
  • a separate group was gavaged with the community alone and then invaded 14 days later with V. cholerae ('D14invasion' group) (FIG. 15C,D).
  • V. cholerae levels remained at a high level in the D1 invasion group over the first week (maximum 46.3% relative abundance), and then declined rapidly to low levels ( ⁇ 1 %).
  • Introduction of V. cholerae into the established 14-member community produced much lower levels of V. cfto/eraeinfection (range of mean abundances measured daily over the 3 days after gavage of the enteropathogen, 1 .2-2.7%; Table 25).
  • V. cholerae ⁇ eve ⁇ s 1 day after gavage were 100-fold higher in the community that lacked R. obeum; these differences were sustained over time (50-fold higher after 7 days; P ⁇ 0.01 , unpaired Mann-Whitney Litest; FIG. 13A,B).
  • RNA-seq microbial RNA sequencing
  • cholerae colonization/virulence 14, 15, 16,17 an intra-species mechanism involving cholera autoinducer-1 , and an inter-species mechanism involving autoinducer-2 18, 19 .
  • Quorum sensing disrupts expression of V. cholerae virulence determinants through a signalling pathway that culminates in production of the LuxR-family regulator HapR 15, 16 .
  • RNA-seq of the faecal meta-transcriptomes of D1 invasion mice colonized with the 14-member artificial community plus V. cholerae, and mice harbouring the 14-member consortium without V. cholerae, revealed that of
  • luxS (RUMOBE02774) increased significantly in response to V. cholerae(P ⁇ 0.05, Mann-Whitney L/-test; FIG. 13C). Moreover, R. obeum luxS transcript levels directly correlated with V. cholerae levels (FIG. 21 D).
  • the R. obeum strain represented in the artificial community contains homologues of IsrABCK that are responsible for import and phosphorylation of AI-2 in Gram-negative bacteria 23 , as well as homologues of two genes, luxR and luxQ, that play a role in AI-2 sensing and downstream signalling in other organisms 24 .
  • Expression of all these R. obeum genes was detected in vivo, consistent with R. obeum having a functional AI-2 signalling system (FIG. 21 B,C).
  • FIG. 21 B,C See Example 17 for results showing that R. obeum AI-2 production is stimulated by V. cholerae in vitro and in co-colonized animals (FIG.
  • V. cholerae mutants were used to determine whether known V. cholerae AI-2 signalling pathways are required for the observed effects of R. obeum on V. cholerae colonization.
  • LuxP is critical for sensing AI-2 in V. cholerae.
  • Co- colonization experiments in gnotobiotic mice revealed that levels of isogenic ⁇ /uxP or wild-type luxP + V. cholerae strains were not significantly different as a function of the presence of R. obeum (FIG. 24), suggesting that R. obeum modulates V.
  • the luxO and hapRgenes encode central regulators linking known V. cholerae quorum- signalling and virulence regulatory pathways. Deletion of luxO typically results in increased hapR expression 15 . However, our RNA-seq analysis had shown that both luxO and hapR are repressed in the presence of R. obeum (six- to
  • VqmA The quorum-sensing transcriptional regulator VqmA was upregulated more than 25-fold when V. cholerae was introduced into mice mono- colonized with R. obeum (FIG. 14C and Table 26).
  • R. obeum When germ-free mice were gavaged with R. obeum and a mixture of AvqmA (A/acZ) 27 and wild-type V. cholerae (lacZ + ) strains, the AvqmA mutant exhibited an early competitive advantage (FIG. 14D), suggesting that R. obeum may be able to affect early colonization of V.
  • VqmA cholerae through VqmA.
  • VqmA is able to bind to and activate the hapR promoter directly 27 . Since RNA-seq showed that hapR activation did not occur in gnotobiotic mice despite high levels of vgm>4expression (FIG. 24B and Table 26), we postulate that the role played by VqmA in R. obeum modulation of Vibrio virulence genes involves an uncharacterized mechanism rather than the known pathway passing through HapR.
  • R. obeum could function to increase median infectious dose (ID 50 ) for V. cholerae in humans and thus help to determine whether exposure to a given dose of this enteropathogen results in diarrhoeal illness.
  • ID 50 median infectious dose
  • the modest effects of R. obeum AI-2 on V. cholerae virulence gene expression in our adult gnotobiotic mouse model may reflect the possibility that we have only identified a small fraction of the microbiota's full repertoire of virulence-suppressing mechanisms.
  • Culture collections generated from the faecal microbiota of Bangladeshi subjects are a logical starting point for 'second- generation' artificial communities containing R.
  • Example 11 Patient Selection.

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Abstract

The present invention provides a method to define normal maturation of the gut microbiota using a limited number of bacterial taxa found in the gut microbiota. Regressing the relative abundance of age-discriminatory taxa in their gut microbiota against the chronological age of each healthy subject at the time a sample of the gut microbiota was collected produces a regression model that may be used to characterize the maturity of another subject's gut microbiota to provide a measure of gastrointestinal health without having to query the whole microbiota. The present invention also provides composition and methods for preventing and/or treating a disease in a subject in need thereof.

Description

METHODS TO ESTABLISH AND RESTORE NORMAL GUT MICROBIOTA
FUNCTION OF SUBJECT IN NEED THEREOF
GOVERNMENTAL RIGHTS
[0001 ] This invention was made with government support under AI043596 awarded by the National Institutes of Health. The government has certain rights in the invention.
CROSS REFERENCE TO RELATED APPLICATIONS
[0002] This application claims the priority of provisional application number 61/898,938, filed November 1 , 2013, which is hereby incorporated by reference in its entirety.
FIELD OF THE INVENTION
[0003] The present invention provides a method to define normal maturation of the gut microbiota using a limited number of bacterial taxa found in the gut microbiota. Regressing the relative abundance of age-discriminatory taxa in their gut microbiota against the chronological age of each healthy subject at the time a sample of the gut microbiota was collected produces a regression model that may be used to characterize the maturity of another subject's gut microbiota to provide a measure of gastrointestinal health without having to query the whole microbiota. The present invention also provides composition and methods for preventing and/or treating a disease in a subject in need thereof.
REFERENCE TO SEQUENCE LISTING
[0004] A paper copy of the sequence listing and a computer readable form of the same sequence listing are appended below and herein incorporated by reference. The information recorded in computer readable form is identical to the written sequence listing, according to 37 C.F.R. 1 .821 (f). BACKGROUND OF THE INVENTION
[0005] Systematic analyses of the gut microbiota in different healthy and unhealthy populations have been undertaken in the scientific community for many years. The gut microbiota comprises a complex community whose composition is in flux in infants and is generally stable in adults. Disease, illness, and diet, among other factors, have been shown to affect the proportional representation of the bacterial species comprising the gut microbiota. Accordingly, the gut microbiota is viewed as both a diagnostic and therapeutic target. There remains a need in the art, therefore, for methods to define microbiota maturity using bacterial taxonomic biomarkers that are highly discriminatory for age as a way to characterize the health status of the gut microbiota.
SUMMARY OF THE INVENTION
[0006] In an aspect, the present disclosure encompasses a method to determine the maturity of a subject's gut microbiota, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the
chronological age of each healthy subject in the group at the time the gut microbiota sample was obtained; and (c) calculating the maturity of the subject's gut microbiota, wherein the calculation for maturity is defined as relative maturity, and relative maturity = (microbiota age of the subject ) - (microbiota age of a healthy subject of a similar chronological age. In certain embodiments, the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A and at least one bacterial taxon listed in rows 7 to 24 of Table A.
[0007] In another aspect, the present disclosure encompasses a method to determine the maturity of a subject's gut microbiota, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least 24 bacterial taxa listed in Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was obtained; and (c) calculating the maturity of the subject's gut microbiota, wherein the calculation for maturity is defined as relative maturity, and relative maturity = (microbiota age of the subject ) - (microbiota age of a healthy subject of a similar chronological age. In certain embodiments, the group comprises the bacterial taxa listed in Table B.
[0008] In another aspect, the present disclosure encompasses a method to classify a subject, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was obtained; and (c) classifying the subject as having normal gut maturation when the microbiota age of the subject is substantially similar to the microbiota age of a healthy subject with a similar
chronological age. In certain embodiments, the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A and at least one bacterial taxon listed in rows 7 to 24 of Table A.
[0009] In another aspect, the present disclosure encompasses a method to classify a subject, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least 24 bacterial taxa listed in Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as
determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was obtained; and (c) classifying the subject as having normal gut maturation when the microbiota age of the subject is substantially similar to the microbiota age of a healthy subject with a similar
chronological age. In certain embodiments, the group comprises the bacterial taxa listed in Table B.
[0010] In another aspect, the present disclosure encompasses a method to classify a subject, the method comprising calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A; applying the relative abundances of the bacterial taxa from step (a) and chronological age of the subject to a classification model, wherein the classification model is trained on datasets comprising measurements obtained from a plurality of healthy subjects and a plurality of undernourished subjects, and the measurements including (i) relative abundances of the same bacterial taxa in step (a), as determined from a plurality of gut microbiota samples obtained over time for each healthy and undernourished subject, and (ii) chronological age of the subject at the time the gut microbiota sample was obtained; and wherein the classification model assigns the subject to a category. In certain embodiments, the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A and at least one bacterial taxon listed in rows 7 to 24 of Table A.
[001 1 ] In another aspect, the present disclosure encompasses a method to classify a subject, the method comprising calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least 24 bacterial taxa listed in Table A; applying the relative abundances of the bacterial taxa from step (a) and chronological age of the subject to a classification model, wherein the classification model is trained on datasets comprising measurements obtained from a plurality of healthy subjects and a plurality of
undernourished subjects, and the measurements including (i) relative abundances of the same bacterial taxa in step (a), as determined from a plurality of gut microbiota samples obtained over time for each healthy and undernourished subject, and (ii) chronological age of the subject at the time the gut microbiota sample was obtained; and wherein the classification model assigns the subject to a category. In certain embodiments, the group comprises the bacterial taxa listed in Table B.
[0012] In another aspect, the present disclosure encompasses a method for identifying an effect of a therapy, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject before and after administration of the therapy, wherein the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota before and after therapy, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; wherein the therapy has an effect when the microbiota age of the subject changes after therapy. In certain embodiments, the group comprises at least the bacterial taxa listed in rows 1 to 6 of Table A and at least one bacterial taxon listed in rows 7 to 24 of Table A.
[0013] In another aspect, the present disclosure encompasses a method for identifying an effect of a therapy, the method comprising (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject before and after administration of the therapy, wherein the group comprises at least 24 bacterial taxa listed in Table A; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota before and after therapy, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; wherein the therapy has an effect when the microbiota age of the subject changes after therapy. In certain embodiments, the group comprises the bacterial taxa listed in Table B.
[0014] In another aspect, the present disclosure encompasses method for identifying an effect of a therapy, the method comprising (a) identifying a set of age- discriminatory bacterial taxa within the bacterial taxa comprising the gut microbiota a group of healthy subject's gut microbiota, the method comprising (i) providing, for each healthy subject, a relative abundance for the bacterial taxa comprising the subject's gut microbiota, wherein the relative abundance of the bacterial taxa in the healthy subject's gut microbiota was determined from a plurality of gut microbiota samples obtained at intervals of time; (ii) regressing the relative abundances of the bacterial taxa comprising each healthy subject's gut microbiota against the chronological age of the healthy subject at the time the gut microbiota sample was collected, thereby producing a prediction of a gut microbiota age that is based only on the relative abundance of age- discriminatory bacterial taxa present in the subject's gut microbiota; and (iii) selecting the minimum number of bacterial taxa from step (ii) that is needed to produce a prediction that is substantially similar to or better than the prediction of step (ii);
calculating a relative abundance for each bacterial taxon in the set of age-discriminatory taxa from step (a)(iii), using a fecal sample obtained from a subject before and after administration of the therapy; and applying the relative abundances from step (b) to the prediction of gut microbiota age from step (a)(iii) to determine the microbiota age of the subject's gut microbiota before and after therapy; wherein the therapy has an effect when the microbiota age of the subject changes after therapy.
[0015] In another aspect, the present disclosure encompasses a composition comprising at least one of the bacterial taxa listed in Table A. The present disclosure also contemplates a method of preventing or treating acute malnutrition, acute diarrhea, or chronic diarrhea in a subject in need thereof, the method comprising administering to the subject a composition comprising at least one of the bacterial taxa listed in Table A.
[0016] In another aspect, the present disclosure encompasses a composition comprising a combination of bacterial taxa, the combination comprising of at least two bacterial taxa listed in Table B. The present disclosure also contemplates a method of preventing or treating acute malnutrition, acute diarrhea, or chronic diarrhea in a subject in need thereof, the method comprising administering to the subject a composition comprising a combination of bacterial taxa, wherein the combination comprises at least two bacterial taxa listed in Table B.
[0017] In another aspect, the present disclosure encompasses a composition comprising a combination of bacterial taxa listed, wherein the combination is selected from combinations listed in Table D. The present disclosure also
contemplates a method of preventing or treating acute malnutrition, acute diarrhea, or chronic diarrhea in a subject in need thereof, the method comprising administering to the subject a composition comprising a combination of bacterial taxa, wherein the combination is selected from combinations listed in Table D.
[0018] Other aspects and iterations of the invention are described more thoroughly below.
REFERENCE TO COLOR FIGURES
[0019] The application file contains at least one photograph executed in color. Copies of this patent application publication with color photographs will be provided by the Office upon request and payment of the necessary fee.
[0020] FIG. 1 depicts graphs and a heat map showing bacterial taxonomic biomarkers for defining gut-microbiota maturation in healthy Bangladeshi children during the first 2 years of life. (A) Twenty-four age-discriminatory bacterial taxa were identified by applying Random Forests regression of their relative abundances in faecal samples against chronologic age in 12 healthy children (n = 272 faecal samples). Shown are 97%-identity OTUs with their deepest level of confident taxonomic annotation (also see Table 5), ranked in descending order of their importance to the accuracy of the model. Importance was determined based on the percentage increase in mean-squared error of microbiota age prediction when the relative abundance values of each taxon were randomly permuted (mean importance ± s.d., n = 100 replicates). (E) The insert shows tenfold cross-validation error as a function of the number of input 97%-identity OTUs used to regress against the chronologic age of children in the training set, in order of variable importance (blue line). (B-D) Microbiota age predictions in a birth cohort of healthy singletons used to train the 24 bacterial taxa model (B, brown, each circle represents an individual faecal sample). The trained model was subsequently applied to two sets of healthy children: 13 singletons set aside for model testing (C, green circles, n = 276 faecal samples) and another birth cohort of 25 twins and triplets (D, blue circles, n = 448 faecal samples). The curve is a smoothed spline fit between microbiota age and chronologic age in the validation sets (C,D), accounting for the observed sigmoidal relationship (see Methods for Examples 1 -9). (F-H) Heatmap of mean relative abundances of the 24 age-predictive bacterial taxa plotted against the chronologic age of healthy singletons used to train the Random Forests model (F), and correspondingly in the healthy singletons (G), and twins and triplets (H) used to validate the model (hierarchical clustering performed using the Spearman rank correlation distance metric).
[0021 ] FIG. 2 depicts graphs and a schematic showing persistent immaturity of the gut microbiota in children with SAM. (A) Design of the randomized interventional trial. (B) Microbiota maturity defined during various phases of treatment and follow-up in children with SAM. Relative microbiota maturity in the upper portion of the panel is based on the difference between calculated microbiota age (Random- Forests-based sparse 24-taxon model) and values calculated in healthy children of similar chronologic age, as interpolated over the first 2 years of life using a spline curve. In the lower portion of the panel, maturity is expressed as a microbiota-for-age Z-score (MAZ). Mean values ± s.e.m. are plotted. The significance of differences between microbiota indices at various stages of the clinical trial is indicated relative to healthy controls (arrows above the bars) and versus samples collected at enrollment for each intervention group (arrows below the bars) (post-hoc Dunnett's multiple comparison procedure of linear mixed models; *P< 0.05, **P< 0.01 , ***P< 0.001 ). Healthy children not used to train the Random Forests model served as healthy controls (n = 38). (C-F), Plot of microbiota age for each child with SAM at enrollment (C), at the conclusion of the food intervention phase (D), and within (E) and beyond (F) 3 months of follow-up. The curve shown in each panel was fit using predictions in healthy children: this curve is the same as that replicated across each plot in FIG. 1 B-D.
[0022] FIG. 3 depicts illustrations of the equations used to calculate 'relative microbiota maturity' and 'microbiota-for-age Z-score'. (A,B) The procedure to calculate both microbiota maturation metrics are shown for a single faecal sample from a focal child (pink circle) relative to microbiota age values calculated in healthy reference controls. These reference values are computed in samples collected from children used to validate the Random-Forests-based sparse 24-taxon model and are shown in (A), as a broken line of the interpolated spline fit and in (B), as median ± s.d. values for each monthly chronologic age bin from months 1 to 24.
[0023] FIG. 4 depicts graphs showing transient microbiota immaturity and reduction in diversity associated with diarrhoea in healthy twins and triplets. (A) The transient effect of diarrhoea in healthy children. Seventeen children from 10 families with healthy twins or triplets had a total of 36 diarrhoeal illnesses where faecal samples were collected. Faecal samples collected in the months immediately before and following diarrhoea in these children were examined in an analysis that included multiple environmental factors in the 'healthy twins and triplets' birth cohort. Linear mixed models of these specified environmental factors indicated that 'diarrhoea', 'month following diarrhoea' and 'presence of formula in diet' have significant effects on relative microbiota maturity, while accounting for random effects arising from within-family and within-child dependence in measurements of this maturity metric. The factors 'postnatal age', 'presence or absence of solid foods', 'exclusive breastfeeding', 'enteropathogen detected by microscopy', 'antibiotics' as well as 'other periods relative to diarrhoea' had no significant effect. The numbers of faecal samples (n) are shown in parenthesis. Mean values ± s.e.m. are plotted. *P<0.05, ***P <0.001 . See Table 6 for the effects of dietary and environmental covariates. (B) Effect of diarrhoea and recovery on age- adjusted Shannon diversity index (SDI). Mean values of effect on SDI ± s.e.m. are plotted. *P<0.05, **P<0.01 . [0024] FIG. 5 depicts heatmaps and graphs showing gut microbiota variation in families with twins and triplets during the first year of life. (A) Maternal influence. Heatmap of the mean relative abundances of 13 bacterial taxa (97%-identity OTUs) found to be statistically significantly enriched in the first month post-partum in the faecal microbiota of mothers (see column labelled 1 ) compared to microbiota sampled between the second and twelfth months post-partum (FDR-corrected P <0.05; ANOVA of linear mixed-effects model with random by-mother intercepts). (B) An analogous heatmap of the relative abundance of these taxa in their twin or triplet offspring is shown. Three of these 97%-identity OTUs are members of the top 24 age- discriminatory taxa (blue) and belong to the genus Bifidobacterium. (C-J), comparisons of maternal, paternal and infant microbiota. Mean values ± s.e.m. of Hellinger and unweighted UniFrac distances between the faecal microbiota of family members sampled over time were computed. Samples obtained at postnatal months 1 , 4, 10 and 12 from twins and triplets, mothers and fathers were analysed (n = 12 fathers; 12 mothers; 25 children). (C,D) Intrapersonal variation in the bacterial component of the maternal microbiota is greater between the first and fourth months after childbirth than variation in fathers. (E,F) Distances between the faecal microbiota of spouses (each mother-father pair) compared to distances between all unrelated adults (male-female pairs). The microbial signature of co-habitation is only evident 10 months following childbirth. (G-J) The degree of similarity between mother and infant during the first postpartum month is significantly greater than the similarity between microbiota of fathers and infants (G,H) while the faecal microbiota of co-twins are significantly more similar to one another than to age-matched unrelated children during the first year of life (l,J). For all distance analyses, Hellinger and unweighted UniFrac distance matrices were permuted 1 ,000 times between the groups tested. P values represent the fraction of times permuted differences between tested groups were greater than real differences between groups. *P<0.05, **P<0.01 , ***P<0.001 .
[0025] FIG. 6 graphically depicts anthropometric measures of nutritional status in children with SAM before, during and after both food interventions. (A-C)
Weight-for-height Z-scores (WHZ) (A) height-for-age Z-scores (HAZ) (B) and weight-for- age Z-scores (WAZ) (C). Mean values ± s.e.m. are plotted and referenced to national average anthropometric values for children surveyed between the ages of 6 and 24 months during the 201 1 Bangladeshi Demographic Health Survey (BDHS)28.
[0026] FIG. 7 graphically depicts persistent reduction of diversity in the gut microbiota of children with SAM. Age-adjusted Shannon diversity index for faecal microbiota samples collected from healthy children (n = 50), and from children with SAM at various phases of the clinical trial (mean values ± s.e.m. are plotted). The
significance of differences between SDI at various stages of the clinical trial is indicated relative to healthy controls (above the bars) and versus the time of enrollment before treatment (below the bars). *P <0.05, **P <0.01 , ***P <0.001 (post-hoc Dunnett's multiple comparison procedure of linear mixed models). See Table 12.
[0027] FIG. 8 depicts heatmaps of bacterial taxa significantly altered during the acute phase of treatment and nutritional rehabilitation in the microbiota of children with SAM compared to similar-age healthy children. Bacterial taxa (97%- identity OTUs) significantly altered (FDR-corrected P < 0.05) in children with SAM are shown (see Table 13 for P values and effect size for individual taxa). Three groups of bacterial taxa are shown: those enriched before the food intervention (A); those enriched during the follow-up phase compared to healthy controls (B); and those that are initially depleted but return to healthy levels (C). Members of the top 24 age- discriminatory taxa are highlighted in blue. Note that there were no children represented in the Khichuri-Halwa arm under the age of 12 months during the 'follow-up after 3 months' period.
[0028] FIG. 9 depicts heatmaps of bacterial taxa altered during long term follow-up in the faecal microbiota of children with SAM compared to similar-age healthy children. (A-H) Bacterial taxa (97%-identity OTUs) significantly altered (FDR-corrected P < 0.05) in children with SAM are shown (see Table 13 for P values and effect sizes for individual taxa). (A-C) Taxa depleted across all phases of SAM relative to healthy. (D-H) Those depleted during the follow-up phase. Members of the top 24 age- discriminatory taxa are highlighted in blue. Note that there were no children under the age of 12 months represented in the Khichuri-Halwa treatment arm during the 'follow- up after 3 months' period.
[0029] FIG. 10 graphically depicts the effects of antibiotics on the microbiota of children with SAM. Plots of microbiota and anthropometric parameters in nine children sampled before antibiotics (abx), after oral amoxicillin plus parenteral gentamicin and ampicillin, and at the end of the antibiotic and dietary interventions administered over the course of nutritional rehabilitation in the hospital. All comparisons were made relative to the pre-antibiotic sample using the non-parametric Wilcoxon matched-pairs rank test, in which each child served as his or her own control. (A-C) Microbiota parameters, plotted as mean values ± s.e.m., include (A) relative microbiota maturity, (B) microbiota-for-age Z-score (MAZ), and (C) SDI. WHZ scores are provided in D. (E,F) The two predominant bacterial family-level taxa showing significant changes following antibiotic treatment. (E) family Streptococcaceae and (F) family
Enterobacteriaceae. ns, not significant; **P < 0.01 .
[0030] FIG. 11 graphically depicts the relative microbiota maturity and MAZ correlate with WHZ in children with MAM. (A-C) WHZ are significantly inversely correlated with relative microbiota maturity (A) and MAZ (B) in a cross-sectional analysis of 33 children at 18 months of age who were above and below the
anthropometric threshold for MAM (Spearman's Rho = 0.62 and 0.63, respectively; ***P < 0.001 ). In contrast, there is no significant correlation between WHZ and microbiota diversity (C). (D-L), Relative abundances of age-discriminatory 97%-identity OTUs that are inputs to the Random Forests model that are significantly different in the faecal microbiota of children with MAM compared to age-matched 18-month-old healthy controls (Mann- Whitney L/-test, P < 0.05). Box plots represent the upper and lower quartiles (boxes), the median (middle horizontal line), and measurements that are beyond 1 .5 times the interquartile range (whiskers) and above or below the 75th and 25th percentiles, respectively (points) (Tukey's method, PRISM software v6.0d). Taxa are presented in descending order of their importance to the Random Forests model. (D) Faecalibacterium prauznitzii 326792, (E) Dorea longicatena 191687, (F)
Lactobacillus mucosae 15141 , (G) Catenibacterium mitsuokai 287510, (H) Dorea formicigenerans 261912, (I) Clostridium sp. 181834, (J) Bifidobacterium sp. 469873, (K) Clostridiales sp. 185951 , (L) Ruminococcaceae sp. 212619. See FIG. 10A,B.
[0031 ] FIG. 12 depicts graphs and a schematic showing cross-sectional assessment of microbiota maturity at 18 months of age in Bangladeshi children with and without MAM, plus extension of the Bangladeshi-based model of microbiota maturity to Malawi. (A,B) Children with MAM (WHZ lower than -2 s.d.; grey) have significantly lower relative microbiota maturity (A) and MAZ (B) compared to healthy individuals (blue). Mean values ± s.e.m. are plotted **P<0.01 (Mann-Whitney U-test). See FIG. 9 for correlations of metrics of microbiota maturation with WHZ and box-plots of age- discriminatory taxa whose relative abundances are significantly different in children with MAM relative to healthy reference controls. (C) Microbiota age predictions resulting from application of the Bangladeshi 24-taxon model to 47 faecal samples (brown circles) obtained from concordant healthy Malawian twins and triplets are plotted versus the chronologic age of the Malawian donor (collection occurred in individuals ranging from 0.4 to 25.1 months old). The results show the Bangladeshi model generalizes to this population, which is also at high risk for malnutrition (each circle represents an individual faecal sample collected during the course of a previous study11). (D)
Spearman rho and significance of rank order correlations between the relative
abundances of age-discriminatory taxa, and the chronologic age of all healthy
Bangladeshi children described in the present study as well as concordant healthy Malawian twins and triplets. *P <0.05.
[0032] FIG. 13 graphically depicts that R. obeum restricts V. cholerae colonization in adult gnotobiotic mice. (Α,Β) V. cholerae levels in the faeces of mice colonized with the indicated human gut bacterial species (n = 4-6 mice per group). (A) Days after gavage of second species and (B) Days after V. cholerae gavage. (C)
Expression of R. obeum luxS AI-2 synthase in the 14-member community 4 days after introduction of 109 c.f.u. of V. cholerae or no pathogen (n = 5 mice per group). Note that D. longicatena levels fall precipitously after V. cholerae invasion (Table 25). Mean values ± s.e.m. are shown. ND, not detected. *P < 0.05, **P < 0.01 (unpaired Mann- Whitney L/-test). [0033] FIG. 14 graphically depicts that R. obeum AI-2 reduces V. cholerae colonization and virulence gene expression. (A) R. obeum AI-2 produced in E.
coli represses the tcp promoter in V. cholerae (triplicate assays; results representative of four independent experiments). (B) Faecal V. cholerae levels in gnotobiotic mice 8 h after gavage with V. cholerae and an E. coli strain containing either the PBAD-R- obeum luxS plasmid or vector control. (C) Faecal vqmA transcript abundance in mono- or co- colonized mice. (D) Competitive index olAvqmA versus wild-type V. cholerae during co- colonization with R. obeum (n = 5 animals per group). Mean values ± s.e.m. are shown. *P < 0.05, **P < 0.01 , ****P < 0.0001 (unpaired two-tailed Student's f-test).
[0034] FIG. 15 depicts graphs and illustrations showing the experimental designs for clinical study and gnotobiotic mouse experiments. (A) Sampling schedule for human cholera study. (B) Frequency of diarrhoeal episodes over time for a representative participant (patient A). Initial time (black circle) represents beginning of diarrhoea. The long vertical line marks enrollment into the study. Colours and short vertical lines denote boundaries of study phases defined in (A). (C-H) Depict the various gnotobiotic mouse experimental designs. The number (n) of animals in each treatment group is shown.
[0035] FIG. 16 depicts graphs and heatmaps showing the bacterial taxa associated with diarrhoeal and recovery phase. (A) Proportion of bacterial species-level taxa that were observed in both diarrhoeal and recovery phases, in D-Ph1 to D-Ph4 only, and in R-Ph1 to R-Ph3 only. Mean values ± s.e.m. are plotted. *P < 0.05,
***P < 0.001 (unpaired Mann-Whitney U-test). (B-D) Phylum-level analysis. Mean values are plotted. (B) Diarrhea only, (C) Recovery only, and (D) Both phases. (E)
Proportion of study participants having bacterial taxa associated by indicator species analysis with the diarrhoeal or recovery phase. The axis shows species associated with each phase, ranked by proportion of subjects harbouring that species. For each species, 'representation in study participants' is the average presence/absence of all 97%-identity OTUs with that species taxonomic assignment. The OTU table was rarefied to 49,000 reads per sample. (F-N) Bacterial species identified by indicator analysis as indicative of diarrhoea or recovery phases in adult patients with cholera, and species identified by Random Forests analysis as discriminatory for different stages in the maturation of the gut microbiota of healthy Bangladeshi infants/children aged 1- 24 months (denoted by the symbol†). (F-H) The heat map shows mean relative abundances of species across all individuals during D-Ph1 to D-Ph4, with each phase subdivided into four equal time bins. For recovery time points, columns represent the mean relative abundances for each sampling time point during R-Ph1 to R-Ph3. (I-K) Mean relative abundance values are also presented for these same species in the faecal microbiota of 50 healthy Bangladeshi children sampled from 1 to 2 years of age at monthly intervals. Unsupervised hierarchical clustering used relative abundances of species in the faecal microbiota of the patients with cholera. (F,G,I,J) The green portion of the tree encompasses species that are more abundant during recovery whereas (H,K) the red portion encompasses species that are more abundant during diarrhoea. (L-N) Indicator scores are presented in the right-hand portion of the panel, with 'score' for a given taxon defined as its indicator value for recovery minus its indicator value for diarrhoea (-1 , highly diarrhoea-associated; +1 , highly recovery-associated).
Spearman's rank correlation coefficients of mean relative abundances of species by sample in the cholera study versus the mean sample-weighted UniFrac distance to healthy adult faecal microbiota are shown at the extreme right together with the statistical significance of correlations after Benjamini-Hochberg false discovery rate correction for multiple hypothesis testing (NS, not significant; *P < 0.05; **P < 0.01 ; ***P < 0.001 ). Higher coefficients indicate increasing divergence from a healthy configuration with higher relative abundance of a given species. Species shown satisfied two or more of the following criteria: (1 ) presence among the list of the top 40 age-discriminatory species in the Random-Forests-based model of gut microbiota maturation in healthy infants and children; (2) indicator value score greater than 0.7; (3) significant correlation (Spearman'sr) between relative abundance in the faecal microbiota of patients with cholera and UniFrac distance to healthy adult faecal microbiota; and (4) inclusion in the artificial 14-member human gut community (species name highlighted in blue). [0036] FIG. 17 depicts heatmaps showing the 97%-identity OTUs observed in both diarrhoeal and recovery phases. The proportion of 97%-identity OTUs with a given species-level taxonomic assignment that were present in both (A-D) diarrhoeal and (E-H) recovery phases is shown for each individual in the study. The number of 97%-identity OTUs with a given species assignment is shown in
parentheses. Species are ordered based on their 'indicator scores' (defined as indicator valuereCovery minus indicator valuedian-hoea)- Age-discriminatory bacterial species incorporated into a Random-Forests-based model for defining relative microbiota maturity and microbiota-for-age z-scores3 in healthy Bangladeshi infants and children are marked with a '+' symbol. The 97%-identity OTUs were derived from data sets generated from all samples from adult patents with cholera; the OTU table was rarefied to 49,000 reads per sample.
[0037] FIG. 18 depicts heatmaps and graphs showing the pattern of appearance of age-discriminatory 97%-identity OTUs in the faecal microbiota of patients with cholera mirrors the normal age-dependent pattern in the faecal microbiota of healthy Bangladeshi infants and children. (A-C) Shows hierarchical clustering of relative abundance values for each of the top 60 most age-discriminatory 97%-identity OTUs in a Random-Forests-based model of normal maturation of the microbiota in healthy Bangladeshi infants/children (importance scores for the age-discriminatory taxa defined by Random Forests analysis are reported in ref. 3; these 60 97%-identity OTUs can be grouped into 40 species-level taxa). (D-l) Presents the mean relative abundances of these OTUs in samples obtained from patients with cholera during (D-F) D-Ph1 to D- Ph4, and (G-l) R-Ph1 to R-Ph3. The 97%-identity OTUs corresponding to species included in the artificial community that was introduced into gnotobiotic mice are highlighted in blue. (J) Relative abundance of R. obeum strains in the faecal microbiota of healthy Bangladeshi children sampled monthly through the first 3 years of life. Mean values ± s.e.m. are plotted.
[0038] FIG. 19 graphically depicts the pattern of recovery of the gut microbiota in patients with cholera. (A,B) Mean unweighted (A) and weighted (B)
UniFrac distances to healthy adult controls at each of the defined phases of diarrhoea and recovery. (C,D) Principal coordinates analysis of UniFrac distances between gut microbiota samples. Location along the principal axis of variation (PC1 ) shows how acute diarrhoeal communities first resemble those of healthy Bangladeshi children sampled during the first 2 years of life, then evolve their phylogenetic configurations during the recovery phase towards those of healthy Bangladeshi adults. PC1 accounts for 34.3% variation for weighted and 17.7% variation for unweighted UniFrac values. (E) Alpha diversity (whole-tree phylogenetic diversity) measurements of faecal microbial communities through all study phases. Mean values ± s.e.m. are plotted. *P < 0.05, **P < 0.01 , ****P < 0.0001 (Kruskal-Wallis analysis of variance followed by multiple comparisons test).
[0039] FIG. 20 depicts heatmaps showing the proportional representation of genes encoding enzymes (classified according to Enzyme Commission number identifiers) in faecal microbiomes sampled during the diarrhoeal and recovery phases of cholera. Shotgun sequencing of faecal community DNA was performed (MiSeq 2000 instrument; 2 χ 250bp paired-end reads; 341 ,701 ± 145,681 reads (mean ± s.d. per sample)). Read pairs were assembled (SHERA software package34). Read counts were collapsed based on their assignment to Enzyme Commission (EC) number identifiers. The significance of differences in EC abundances compared with faecal microbiomes in healthy adult Bangladeshi controls was defined using ShotgunFunctionalizeR39.
Unsupervised hierarchical clustering identifies groups of ECs that characterize the faecal microbiomes of patients with cholera at varying diarrhoeal and recovery phases. (A-E) The heat map shows the results of EC-based clustering by phase
(diarrhoea/recovery). (F-J) The heat map presents the results of a global clustering of all time-points and study phases. Genes encoding 102 ECs were identified with (1 ) at least 0.1 % average relative abundance across the study and (2) significant differences in their representation relative to healthy microbiomes in at least one comparison (adjusted P < 0.00001 based on ShotgunFunctionalizeR). In each of the heat maps, z- scores for each EC across all samples are plotted. ECs are grouped by KEGG level 1 assignment and further annotated based on their KEGG Pathway assignments. Note that the majority of the 46 ECs that were more prominently represented in faecal microbiomes during diarrhoeal phases in study participants are related to carbohydrate metabolism. The faecal microbiomes of patients during recovery are enriched for genes involved in vitamin and cofactor metabolism (Table 24).
[0040] FIG. 21 depicts a schematic and graph showing that R.
obeum encodes a functional AI-2 system, and R. obeum AI-2 production is stimulated by the presence of V. cholerae. (A) Relative abundances of R. obeum and V.
cholerae in the faecal microbiota after introduction of V. cholerae into mice harbouring the artificial 14-member human gut community (D14invasion group, see FIG. 15C,D). 'Days post V. cholerae gavage' refers to the second of two daily gavages of 109 c.f.u. V. cholerae into animals that had been colonized 14 days earlier with the 14-member community. Mean values ± s.e.m. are shown (n = 4 or 5 mice, *P < 0.05, unpaired Student's i-test). (B) Shows AI-2 signalling pathway components represented in the R. obeum genome. (C) Plots changes in expression of these components as defined by microbial RNA-seq of faecal samples obtained (1 ) 4 days after colonization of mice with the 14-member community and (2) 4 days after gavage of mice with the 14-member community together with 109 c.f.u. of V. cholerae (n = 4-6 animals per group; one faecal sample analysed per animal). Mean values ± s.e.m. are shown. *P < 0.05 (Mann- Whitney L/-test). (D) RNA-seq of faecal samples collected at the time points and treatment groups indicated reveals that R. obeum luxS transcription is directly
correlated to V. cholerae abundance in the context of the 14-member community.
**P < 0.01 (F test). (E) R. obeum luxS expression. Mice were colonized first with R. obeum for 7 day. Faecal samples were collected for microbial RNA-seq analysis 1 day before gavage of 109 c.f.u. of a V. cholerae AluxS mutant, and then 2 days post-gavage (d2pg). Mean values for relative R. obeum luxS transcript levels ( ± s.e.m.) are shown (n = 5 or 6 animals per group per experiment, n = 3 independent experiments;
**P < 0.01 unpaired Mann-Whitney L/-test). (F) AI-2 levels in faecal samples, taken 1 day before and 3 days after gavage of the V. cholerae AluxS strain, from the same mice as those analysed in (A). AI-2 levels were measured based on induction of
bioluminescence in V. harveyi BB170 using the same mass of input faecal sample for all assays. Mean values ± s.e.m. are shown; ****p < 0.0001 (unpaired Mann-Whitney U- test). (G) R. obeum produces AI-2 when co-cultured with V. cholerae in vitro. Aliquots of the supernatant from cultures containing R. obeum alone, or R. obeum plus the V.
cholerae AluxS mutant, were assayed for their ability to induce V.
harveyi bioluminescence. Mean values ± s.e.m. are presented (n = 4 independent experiments). LU, light units; RPKM, reads per kilobase per million reads.
****p < 0.0001 (unpaired Mann-Whitney U-test). Note that (1 ) the number of R. obeum c.f.u. present in the samples obtained from mono-cultures of the organism was similar to the number in co-culture, as measured by selective plating, and (2) the V.
cholerae Δ/uxSmutant cultured alone produced levels of AI-2 signal that were not significantly different from that of R. obeum in mono-culture (data not shown).
[0041 ] FIG. 22 graphically depicts UPLC-MS analysis of faecal bile acid profiles in gnotobiotic mice. Targeted UPLC-MS used methanol extracts of faecal pellets obtained from age- and gender-matched germ-free C57BL/6J mice and gnotobiotic mice colonized for 3 days with R. obeum alone, for 7 days with the 14- member community ('D1 invasion group'), and for 3 days with the 13-member
community that lacked R. obeum (n = 4-6 mice per treatment group; one faecal sample analysed per animal). (A) Faecal levels of taurocholic acid. Mean values ± s.e.m. are plotted. *P < 0.05, **P < 0.01 , Mann-Whitney U-test (B) Mean relative abundance of ten bile acid species in faecal samples obtained from the mice shown in (A).
[0042] FIG. 23 depicts a phylogenetic tree of luxS genes present in human gut bacterial symbionts and enteropathogens. (A-C) The tree was constructed from amino-acid sequence alignments using Clustal X. Red type indicates that the
homologue is represented in the genomes of members of the 14-member artificial human gut bacterial community.
[0043] FIG. 24 graphically depicts in vivo tests of the effects of known quorum-sensing components on R. obeum-mediated reductions in V.
cholerae colonization. (A) Competitive index of AluxP versus wild-type C6706 V.
cholerae when colonized with or without R. obeum (n = 4-6 animals per group).
Horizontal bars, mean values. Data from individual animals are shown using the indicated symbols. (B) Transcript abundance (reads per kilobase per million reads) for selected quorum-sensing and virulence gene regulators in V. cholerae. Microbial RNA- seq was performed on faecal samples collected 2 days after mono-colonization of germ- free mice with V. cholerae (circles), or 2 days after V. cholerae was introduced into mice that had been mono-colonized for 7 days with R. obeum (squares) (n = 5 animals per group; NS, not significant (P > 0.05); **P < 0.01 , ***P < 0.001 , ****P < 0.0001 (unpaired two-tailed Student's i-test)).
DETAILED DESCRIPTION
[0044] Applicants have discovered that maturation of the gut microbiota in healthy subjects (i.e. normal maturation of the gut microbiota) can be characterized or defined by a minimal number of bacterial taxa whose relative abundance changes over time. Accordingly, the present invention provides a method to identify a set of bacterial taxa that define normal maturation of the gut microbiota. The set of bacterial taxa can also be used to characterize the maturity of a test subject's gut microbiota. Determining whether the maturation of a test subject's gut microbiota corresponds to the test subject's chronological age may be used to guide treatment decisions (e.g. an immature state may indicate a need to initiate or continue therapy) or evaluate the effectiveness of a therapy (e.g. no improvement or a worsening would indicate the therapy is not effective).
[0045] Applicants have also discovered a set of bacterial taxa that strongly correlate with normal maturation of the gut microbiota in healthy children and recovery from diseases that perturb a normal / healthy configuration of the gut microbiota in adults. Accordingly, the present invention provides methods for preventing and/or treating a disease in a subject in need thereof by administering a composition
comprising one or more of those bacterial taxa. Useful combinations of bacterial taxa are disclosed herein.
[0046] The term "subject," as used herein, refers to a mammal, including, but not limited to, a dog, a cat, a rat, a mouse, a hamster, a mouse, a cow, a horse, a goat, a sheep, a pig, a camel, a non-human primate, and a human. In a preferred embodiment, a subject is a human. [0047] As used herein, a "healthy subject" is a subject with no known or diagnosed disease. The health of a subject may also be assessed by anthropometric measurements. For humans, the World Health Organization (WHO) Department of Nutrition for Health and Development has published a set of reference standards for individuals of about 19 years of age or less (WHO child growth standards growth velocity based on weight, length and head circumference: methods and development; World Health Organization, 2009; or current edition). Similar standards are known in the art for other subjects. The terms "healthy subject" and "normal subject" may be used interchangeably.
[0048] As used herein, a "subject in need of treatment" or a "subject in need thereof is a subject in need of prophylaxis against, or treatment for, a disease, preferably a gastrointestinal disease. In some embodiments, a subject in need of treatment may be a healthy subject. For example, a healthy subject may have an increased risk of developing a disease relative to the population at large. In other embodiments, a subject in need of treatment may have a disease. In certain
embodiments, a subject may have an immature microbiota and, therefore, may be in need of treatment. The phrase "microbiota immaturity" is described in detail in Section l(C).
[0049] As used herein, the term "gut microbiota" refers to microbes that have colonized and inhabit the gastrointestinal tract of a subject. While various aspects of the present invention are exemplified with bacteria, the invention is applicable to all microbes including, but not limited to, archaea, bacteria, fungi, protists and viruses. A subject's gut microbiota may be naturally acquired or artificially established. Means by which a subject naturally acquires its gut microbiota are well known. Such examples may include, but are not limited to, exposure during birth, environmental exposure, consumption of foods, and coprophagy. Means by which a subject's gut microbiota may be artificially established are also well known. For example, artificially established gut microbial communities can be established in gnotobiotic animals by inoculating an animal with a defined or undefined consortium of microbes. Typically, a naturally acquired gut microbiota is comprised of both culturable and unculturable components. An artificially acquired gut microbiota may be similarly comprised of both culturable and unculturable components, or may consist of only culturable components. The phrase "culturable components" refers to the microbes comprising the gut microbiota that may be cultured in vitro using techniques known in the art. Culture collections of gut microbial communities are described in detail in PCT/US2012/028600, incorporated herein in its entirety by reference.
[0050] As used herein, the phrase "normal maturation of the gut microbiota" refers to the ordered change in the relative abundances of bacterial taxa in the gut microbiota over time, as determined from a group of healthy subjects.
[0051 ] As used herein, the phrase "chronological age of a subject" refers to the amount of time a subject has lived.
I. NORMAL MATURATION OF THE GUT MICROBIOTA
[0052] Applicants have discovered that the proportional representation of a minimal number of bacterial taxa defines a healthy gut microbiota as it assembles (i.e. matures). The changes in the relative abundance of these bacterial taxa over time are consistent across substantially all healthy subjects within the same age range.
Accordingly, the present invention provides a method to accurately characterize the maturity of a subject's gut microbiota using a limited amount of information. This is exemplified in the Examples using a group of subjects between 0 and about 2 years of age. However, without wishing to be bound by theory, the gut microbiota of a human relatively stabilizes after about 2-3 years provided there are no insults. When there is an insult, the gut microbiota regresses to an immature state that can be discriminated using the relative abundances of these same bacterial taxa.
A. Methods to define normal maturation of the gut microbiota using a limited number of bacterial taxa
[0053] In an aspect, the present invention provides a method to define normal maturation of the gut microbiota using a limited number of bacterial taxa found in the gut microbiota. The relative abundances of these limited number of bacterial taxa, referred to herein as "age-discriminatory bacterial taxa", in gut microbiota samples obtained from healthy subjects changes over time in a consistent way across substantially all healthy subjects within the same age range. Regressing the relative abundance of age-discriminatory taxa in their gut microbiota against the chronological age of each healthy subject at the time a sample of the gut microbiota was collected produces a regression model that may be used to characterize the maturity of another subject's gut microbiota to provide a measure of gastrointestinal health without having to query the whole microbiota.
[0054] A method for identifying a group of age-discriminatory taxa that define normal maturation of the gut microbiota is described in detail in the Examples. Briefly, a method for identifying a group of age-discriminatory bacterial taxa comprises (a) providing, for each healthy subject, a relative abundance for the bacterial taxa comprising the healthy subject's gut microbiota, wherein the relative abundance of the bacterial taxa in the subject's gut microbiome was determined from a plurality of gut microbiota samples obtained at intervals of time, (b) applying a regression analysis to model the relative abundance of the bacterial taxa comprising each healthy subject's gut microbiota against the amount of time the subject has lived (i.e. the chronological age of the healthy subject) at the time the gut microbiota sample was collected, and (c) selecting the minimum number of bacterial taxa needed for the model to predict gut microbiota age.
[0055] Methods for profiling the relative abundances of bacterial taxa in biological samples, including biological samples of gut microbiota, are well known in the art. Suitable methods may be sequencing-based or array-based. An exemplary method is detailed in the Examples. Briefly, the bacterial component of a gut microbiota sample is characterized by sequencing a nucleic acid suitable for taxonomic classification and assigning the sequencing reads to operational taxonomic units (OTUs) with > 97% nucleotide sequence identity to a database of annotated and representative sequences. An example of such a database is Greengenes version 4feb201 1 ; however any suitable database may be used. After OTUs are defined, a representative sequence from each OTU can be selected and compared to a reference set. If a match is identified in the reference set, that OTU can be given an identity. Relative abundance of a bacterial taxon may be defined by the number of sequencing reads that can be unambiguously assigned to each taxon after adjusting for genome uniqueness.
[0056] Generally speaking, a suitable nucleic acid used for taxonomic classification is universally distributed among the gut microbial population being queried allowing for the analysis of phylogenetic relationships among distant taxa, and has both a conserved region and at least one region subject to variation. The presence of at least one variable region allows sufficient diversification to provide a tool for classification, while the presence of conserved regions enables the design of suitable primers for amplification (if needed) and/or probes for hybridization for various taxa at different taxonomic levels ranging from individual strains to whole phyla. While any suitable nucleic acid known in the art may be used, one skilled in the art will appreciate that selection of a nucleic acid or region of a nucleic acid to amplify may differ by
environment. In some embodiments, a nucleic acid queried is a small subunit ribosomal RNA gene. For bacterial and archaeal populations, at least the V1 , V2, V3, V4, V5, V6, V7, V8 and/or V9 regions of the 16S rRNA gene are suitable, though other suitable regions are known in the art. Guidance for selecting a suitable 16S rRNA region to amplify can be found throughout the art, including Guo F et al. PLOS One 8(10) e76185, 2013; Soergel DAW et al. ISME Journal 6: 1440, 2012; and Hamady M et al. Genome Res. 19:1 141 , 2009, each hereby incorporated by reference in its entirety.
[0057] As used herein, "gut microbiota sample" refers to a biological sample comprising a plurality of heterogeneous nucleic acids produced by a subject's gut microbiota. Fecal samples are commonly used in the art to sample gut microbiota. Methods for obtaining a fecal sample from a subject are known in the art and include, but are not limited to, rectal swab and stool collection. Suitable fecal samples may be freshly obtained or may have been stored under appropriate temperatures and conditions known in the art. Methods for extracting nucleic acids from a fecal sample are also well known in the art. The extracted nucleic acids may or may not be amplified prior to being used as an input for profiling the relative abundances of bacterial taxa, depending upon the type and sensitivity of the downstream method. When amplification is desired, nucleic acids may be amplified via polymerase chain reaction (PCR). Methods for performing PCR are well known in the art. Selection of nucleic acids or regions of nucleic acids to amplify are discussed above. The nucleic acids comprising the nucleic acid sample may also be fluorescently or chemically labeled, fragmented, or otherwise modified prior to sequencing or hybridization to an array as is routinely performed in the art.
[0058] Gut microbiota samples may be obtained from a healthy subject at any suitable interval of time, varying from minutes to hours apart, days to weeks apart, or even weeks to months apart. Gut microbiota samples may be obtained multiple times a day, week, month or year. The duration of sampling can also vary. For example, the duration of sampling may be for about a month, about 6 months, about 1 year, about 2 years, about 3 years, about 4 years, about 5 years, about 6 years, about 7 years, about 8 years, about 9 years, about 10 years, about 1 1 years, about 12 years, about 13 years, about 14 years, about 15 years, about 16 years, about 17 years, about 18 years, about 19 years, about 20 years, about 30 years, or more.
[0059] The number of healthy subjects from which gut microbiota samples are obtained can and will vary. Generally, a suitable number is the number of healthy subjects needed to give the model produced by the regression analysis an acceptable degree of statistical significance. For example, the number of subject may be 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12 or more subjects.
[0060] Any suitable machine learning algorithm may be used to regress relative abundances of bacterial taxa against the amount of time the healthy subject has lived at the time the gut microbiota sample was collected. Preferably, the algorithm is able to detect both linear and nonlinear relationships between the bacterial taxa and chronologic age. In an exemplary embodiment, the Random Forests machine learning algorithm is used. The regression analysis produces a model that is a prediction of the gut microbiota age based on the microbial taxa present in the subject's gut microbiota sample.
[0061 ] Before selecting a minimal number of bacterial taxa required to discriminate different periods of post-natal life, the importance of the bacterial taxa are first determined. An "importance score" for the bacterial taxa may be an output of the learning algorithm. Generally, a ranked list of all bacterial taxa, in order of "age- discriminatory importance" may be determined by considering those taxa, whose relative abundance values when permuted leads to a meaningful increase in the error. Bacterial taxa ranked higher on the list (e.g. 1 , 2, 3) have a larger increase in error than those lower on the list (e.g. 61 , 62, 63). To select a minimal number of bacterial taxa, the predictive performance of sets comprising increasing numbers of the top-ranking bacterial taxa may be evaluated, and when minimal improvements in the predictive performance are observed after adding a new member to the set, then a minimal number of bacterial taxa have been identified. An example of a minimal improvement may be when the root mean-squared error of prediction remains below about 4 or aobut 5 months.
[0062] These steps produce a "sparse model" that is a prediction of the gut microbiota age based only on the relative abundance of age-discriminatory bacterial taxa present in a sample of the subject's gut microbiota. Additional bacterial taxa may be added to the sparse model to improve the model's performance, though the overall contribution of the additional bacterial taxa is usually minimal.
B. Useful groups of bacterial taxa to define normal maturation of the gut microbiota
[0063] In another aspect, the present invention provides a group of bacterial taxa that define normal maturation of the gut microbiota (i.e. a group of age- discriminatory taxa). The phrase "normal maturation of the gut microbiota" is defined above. A group of bacterial taxa that define normal maturation of the gut microbiota can be used to characterize the maturity of a subject's gut microbiota. Stated another way, a group of bacterial taxa that define normal maturation of the gut microbiota can be used to determine whether the maturity of a subject's gut microbiota corresponds to what would be predicted by the subject's chronological age. Such information can be used to guide treatment decisions (e.g. an immature state may indicate a need to initiate or continue therapy) or evaluate the effectiveness of a therapy (e.g. no improvement or a worsening would indicate the therapy is not effective).
[0064] In some embodiments, a group of bacterial taxa that define normal maturation of the gut microbiota in may comprise at least 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, or at least 59 of the bacterial taxa listed in Table A. In preferred embodiments, a group of bacterial taxa that define normal maturation of the gut microbiota in may comprise at least 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, or at least 59 of the bacterial taxa listed in Table A.A bacterial isolate can be identified as belonging to a bacterial taxon listed in Table A if the V4 region of the 16S rRNA gene of the bacterial isolate has at least 97% sequence identity to any of SEQ ID NOs: 1 - 60. For example, the V4 region of the 16S rRNA gene of the bacterial isolate can have 97%, 97.5%, 98%, 98.5%, 99%, 99.5%, or 100% sequence identity to any of SEQ ID NOs: 1 - 60.
Table A.
Figure imgf000028_0001
21 Weissella cibaria OTU ID 148099
22 Bifidobacterium sp. OTU ID 469873
23 Clostridials sp. OTU ID 185951
24 Ruminococcaceae sp. OTU ID 212619
25 Bifidobacterium bifidum OTU ID 469852
26 Eubacterium desmolans OTU ID 170124
27 Faecalibacterium prausnitzii OTU ID 187010
28 Prevotella copri OTU ID 303304
29 Lactobacillus reuteri OTU ID 470527
30 Enterobacteriaceae sp. OTU ID 210269
31 Clostridium glycolicum OTU ID 182202
32 Ruminococcus obeum OTU ID 178122
33 Enterococcus faecalis OTU ID 540230
34 Prevotella copri OTU ID 309068
35 Bifidobacterium sp. OTU ID 24773
36 Enterococcus sp. OTU ID 554755
37 Escherichia coli OTU ID 305760
38 Prevotella sp. OTU ID 268604
39 Faecalibacterium prausnitzii OTU ID 188900
40 Eubacterium hallii OTU ID 174256
41 Bacteroides fragilis OTU ID 130663
42 Streptococcus sp. OTU ID 292424
43 Staphylococcus sp. OTU ID 269541
44 Ruminococcus sp. 5 1 39BFAA OTU ID 191900
45 Dialister sp. OTU ID 48207
46 Collinsella aerofaciens OTU ID 186029
47 Clostridium bartlettii OTU ID 325608
48 Lactobacillus sp. OTU ID 252321
49 Prevotella sp. OTU ID 195574
50 Ruminococcus sp. 5 1 39BFAA OTU ID 365047
51 Lactobacillus ruminis OTU ID 471308
52 Enterobacteriaceae sp. OTU ID 307981
53 Streptococcus sp. OTU ID 15382
54 Clostridium disporicum OTU ID 302844
55 Streptococcus parasanguinis OTU ID 528842
56 Bifidobacterium sp. OTU ID 131391
57 Megamonas sp. OTU ID 259261 58 58 Clostridium sp. OTU ID 248563
59 59 Megasphaera sp. OTU ID 162427
60 60 Lactobacillus sp. OTU ID 545371
The SEQ ID NO can be used to identify the representative 16S rRNA sequence for the OTU ID (OTU ID = 16S rRNA OTU ID corresponding to Greengenes version 4feb2011).
[0065] In other embodiments, a group of bacterial taxa that define normal maturation of the gut microbiota comprises (a) at least 12 bacterial taxa listed in Table B; and (b) at least 12 bacterial taxa listed in Table C. In other embodiments, a group of bacterial taxa that define normal maturation of the gut microbiota comprises (a) at least 16 bacterial taxa listed in Table B; and (b) at least 8 bacterial taxa listed in Table C. In other embodiments, a group of bacterial taxa that define normal maturation of the gut microbiota comprises (a) at least 20 bacterial taxa listed in Table B; and (b) at least 4 bacterial taxa listed in Table C. In other embodiments, a group of bacterial taxa that define normal maturation of the gut microbiota comprises the bacterial taxa listed in Table B. In other embodiments, a group of bacterial taxa that define normal maturation of the gut microbiota consists of the bacterial taxa listed in Table B. A bacterial isolate can be identified as belonging to a bacterial taxon listed in Table B or Table C if the V4 region of the 16S rRNA gene of the bacterial isolate has at least 97% sequence identity to any of SEQ ID NOs: 1 - 24 (Table A), or 25-60 (Table B). For example, the V4 region of the 16S rRNA gene of the bacterial isolate can have 97%, 97.5%, 98%, 98.5%, 99%, 99.5%, or 100% sequence identity to any of SEQ ID NOs: 1 - 60.
Table B.
Figure imgf000030_0001
9 9 Staphylococcus OTU ID 217996
10 10 Ruminococcus sp. 5 1 39BFAA OTU ID 364234
11 1 1 Catenibacterium mitsuokai OTU ID 287510
12 12 Dorea formicigenerans OTU ID 261912
13 13 Ruminococcus torques OTU ID 361809
14 14 Streptococcus thermophilus OTU ID 108747
15 15 Bifidobacterium sp. OTU ID 533785
16 16 Haemophilus parainfluenzae OTU ID 9514
17 17 Streptococcus sp. OTU ID 561636
18 18 Clostridium sp. OTU ID 312461
19 19 Clostridium ramosum OTU ID 470139
20 20 Clostridium sp. OTU ID 181834
21 21 Weissella cibaria OTU ID 148099
22 22 Bifidobacterium sp. OTU ID 469873
23 23 Clostridials sp. OTU ID 185951
24 24 Ruminococcaceae sp. OTU ID 212619
The SEQ ID NO can be used to identify the representative 16S rRNA sequence for the OTU ID (OTU ID = 16S rRNA OTU ID corresponding to Greengenes version 4feb2011).
Table C.
ROW NO. SEQ ID NO. Bacterial Taxon
1 25 Bifidobacterium bifidum OTU ID 469852
2 26 Eubacterium desmolans OTU ID 170124
3 27 caecalibacterium prausnitzii OTU ID 187010
4 28 °revotella copri OTU ID 303304
5 29 .actobacillus reuteri OTU ID 470527
6 30 Enterobacteriaceae sp. OTU ID 210269
7 31 Clostridium glycolicum OTU ID 182202
8 32 Ruminococcus obeum OTU ID 178122
9 33 Enterococcus faecalis OTU ID 540230
10 34 °revotella copri OTU ID 309068
11 35 Bifidobacterium sp. OTU ID 24773
12 36 Enterococcus sp. OTU ID 554755
13 37 Escherichia coli OTU ID 305760
14 38 °revotella sp. OTU ID 268604
15 39 caecalibacterium prausnitzii OTU ID 188900
16 40 Eubacterium hallii OTU ID 174256 17 41 Bacteroides fragilis OTU ID 130663
18 42 Streptococcus sp. OTU ID 292424
19 43 Staphylococcus sp. OTU ID 269541
20 44 Ruminococcus sp. 5 1 39BFAA OTU ID 191900
21 45 Dialister sp. OTU ID 48207
22 46 Collinsella aerofaciens OTU ID 186029
23 47 Clostridium bartlettii OTU ID 325608
24 48 Lactobacillus sp. OTU ID 252321
25 49 Prevotella sp. OTU ID 195574
26 50 Ruminococcus sp. 5 1 39BFAA OTU ID 365047
27 51 Lactobacillus ruminis OTU ID 471308
28 52 Enterobacteriaceae sp. OTU ID 307981
29 53 Streptococcus sp. OTU ID 15382
30 54 Clostridium disporicum OTU ID 302844
31 55 Streptococcus parasanguinis OTU ID 528842
32 56 Bifidobacterium sp. OTU ID 131391
33 57 Megamonas sp. OTU ID 259261
34 58 Clostridium sp. OTU ID 248563
35 59 Megasphaera sp. OTU ID 162427
36 60 Lactobacillus sp. OTU ID 545371
The SEQ ID NO can be used to identify the representative 16S rRNA sequence for the OTU ID (OTU ID = 16S rRNA OTU ID corresponding to Greengenes version 4feb2011).
C. Method for characterizinq the maturity of a subject's qut microbiota
[0066] In another aspect, the present invention provides a method to determine the maturity of a subject's gut microbiota. The method may comprise: (a) calculating a relative abundance for each bacterial taxon in a group bacterial taxa, from a fecal sample obtained from the subject; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; and (c) calculating the maturity of the subject's gut microbiota using the subject's microbiota age. In some embodiments, the calculation for maturity is defined as relative maturity, and relative maturity = (microbiota age of the subject ) - (microbiota age of a healthy subject of a similar chronological age). In other
embodiments, the calculation for maturity is defined as a Microbiota-for-Age Z score (MAZ), wherein MAZ = ((microbiota age of the subject) - (median microbiota age of a healthy subject of a similar chronological age))/(standard deviation of microbiota age of healthy subjects of the similar chronological age). In each embodiment above, the group of bacterial taxa can be a group described above in Section 1(B), which are
incorporated into this Section by reference. Alternatively, the group of bacterial taxa can be identified using a method described in Section 1(A), which are incorporated into this Section by reference. Methods for calculating a relative abundance for a bacterial taxon are described in Section 1(A). In a preferred embodiment, the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In another preferred
embodiment, the subject is a human that is about 30 years of age or less. In another preferred embodiment, the subject is a human that is about 40 years of age or less. In another preferred embodiment, the subject is a human that is about 50 years of age or less. In another preferred embodiment, the subject is a human that is about 60 years of age or less. In another preferred embodiment, the subject is a human that is about 70 years of age or less. In another preferred embodiment, the subject is a human that is about 80 years of age or less. In another preferred embodiment, the subject is a human that is about 90 years of age or less. In another preferred embodiment, the subject is a human that is about 100 years of age or less. In another preferred embodiment, the subject is a human that is about 1 10 years of age or less. In exemplary embodiments, the group of bacterial taxa is a group listed in Table A or Table B.
[0067] In another aspect, the present invention provides a method to classify a subject. For example, because the relative abundances of the age- discriminatory taxa change in a consistent way that corresponds to the chronological age of healthy subjects, it is also possible to classify the maturity of a subject's gut microbiota as perturbed if the abundances of the age-discriminatory taxa are not as predicted by the subject's chronological age. Within the classification of "perturbed maturation, a subject may an immature gut microbiota (i.e. the subject's microbiota age is less than predicted by chronological age alone), or a subject may have a gut microbiota that matured faster than normal (i.e. the subject's microbiota age is less than predicted by chronological age alone).
[0068] A method to classify a subject may comprise: (a) calculating a relative abundance for each bacterial taxon in a group bacterial taxa, from a fecal sample obtained from the subject; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; and (c) classifying the subject as having normal gut maturation when the microbiota age of the subject is substantially similar to the microbiota age of a healthy subject of a similar age. The group of bacterial taxa can be a group described above in Section 1(B), which are incorporated into this Section by reference.
Alternatively, the group of bacterial taxa can be identified using a method described in Section 1(A), which are incorporated into this Section by reference. Methods for calculating a relative abundance for a bacterial taxon are described in Section 1(A). In a preferred embodiment, the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In another preferred embodiment, the subject is a human that is about 30 years of age or less. In another preferred embodiment, the subject is a human that is about 40 years of age or less. In another preferred embodiment, the subject is a human that is about 50 years of age or less. In another preferred embodiment, the subject is a human that is about 60 years of age or less. In another preferred embodiment, the subject is a human that is about 70 years of age or less. In another preferred embodiment, the subject is a human that is about 80 years of age or less. In another preferred embodiment, the subject is a human that is about 90 years of age or less. In another preferred embodiment, the subject is a human that is about 100 years of age or less. In another preferred embodiment, the subject is a human that is about 1 10 years of age or less. In exemplary embodiments, the group of bacterial taxa is a group listed in Table A or Table B.
[0069] Alternatively, a method to classify a subject may comprise: (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject; (b) using the relative abundances of the bacterial taxa from step (a) and chronologic age of the subject to indicate the health status of the subject's gut microbiota (e.g. healthy or unhealthy; normal maturation of the gut microbiota or perturbed maturation of the gut microbiota) using a classification model; wherein the classification model is trained on datasets comprising measurements obtained from a plurality of healthy subjects and a plurality undernourished subjects. For example, the datasets may contain measurements from at least 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, or more healthy subjects and at least 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, or more undernourished subjects. The undernourished subjects may be subjects with acute malnutrition, moderate acute malnutrition, or at risk for moderate acute malnutrition, as defined in Section II. The measurements may at least include relative abundances of the same bacterial taxa from step (a) above from samples of the health and undernourished subjects' gut microbiota obtained over time for each subject, and the chronological age of the subject at the time the gut microbiota sample was collected. Additional measurements may optionally include MUAC measurements (as defined in Section II), WHZ measurements ((as defined in Section II), and other anthropometric
measurements, as well as measurements such as a response to therapy. The group of bacterial taxa can be a group described above in Section l(B), which are incorporated into this Section by reference. Alternatively, the group of bacterial taxa can be identified using a method described in Section l(A), which are incorporated into this Section by reference. Methods for calculating a relative abundance for bacterial taxon are described in Section 1(A). In a preferred embodiment, the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In exemplary embodiments, the group of bacterial taxa is a group listed in Table A or Table B.
D. Method for identifying the effect of a therapy
[0070] In another aspect, the present invention provides a method for identifying an effect of a therapy.
[0071 ] In some embodiments, a method for identifying an effect of a therapy may comprise (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject before and after administration of the therapy; (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota before and after therapy, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the
chronological age of each healthy subject in the group at the time the gut microbiota sample was collected; wherein the therapy has an effect when the microbiota age of the subject changes after therapy. When the effect is positive, the microbiota age of the subject increases. When the effect is negative, the microbiota age of the subject decreases. The group of bacterial taxa can be a group described above in Section l(B), which are incorporated into this Section by reference. Alternatively, the group of bacterial taxa can be identified using a method described in Section l(A), which are incorporated into this Section by reference. Methods for calculating a relative
abundance for a bacterial taxon are described in Section l(A). In a preferred
embodiment, the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In another preferred embodiment, the subject is a human that is about 30 years of age or less. In another preferred embodiment, the subject is a human that is about 40 years of age or less. In another preferred embodiment, the subject is a human that is about 50 years of age or less. In another preferred embodiment, the subject is a human that is about 60 years of age or less. In another preferred embodiment, the subject is a human that is about 70 years of age or less. In another preferred
embodiment, the subject is a human that is about 80 years of age or less. In another preferred embodiment, the subject is a human that is about 90 years of age or less. In another preferred embodiment, the subject is a human that is about 100 years of age or less. In another preferred embodiment, the subject is a human that is about 1 10 years of age or less. In exemplary embodiments, the group of bacterial taxa is a group listed in Table A or Table B.
[0072] In other embodiments, a method for identifying an effect of a therapy may comprise (a) identifying a set of age-discriminatory bacterial taxa within the bacterial taxa comprising the gut microbiota a group of healthy subject's gut microbiota, the method comprising (i) providing, for each healthy subject, a relative abundance for the bacterial taxa comprising the subject's gut microbiota, wherein the relative
abundance of the bacterial taxa in the healthy subject's gut microbiota was determined from a plurality of gut microbiota samples obtained at intervals of time; (ii) regressing the relative abundances of the bacterial taxa comprising each healthy subject's gut microbiota against the chronological age of the healthy subject at the time the gut microbiota sample was collected, thereby producing a prediction of a gut microbiota age that is based only on the relative abundance of age-discriminatory bacterial taxa present in the subject's gut microbiota; and (iii) selecting the minimum number of bacterial taxa from step (ii) that is needed to produce a prediction that is substantially similar to or better than the prediction of step (ii); calculating a relative abundance for each bacterial taxon in the set of age-discriminatory taxa from step (a)(iii), using a fecal sample obtained from a subject before and after administration of the therapy; and applying the relative abundances from step (b) to the prediction of gut microbiota age from step (a)(iii) to determine the microbiota age of the subject's gut microbiota before and after therapy; wherein the therapy has an effect when the microbiota age of the subject changes after therapy. When the effect is positive, then the microbiota age of the subject increases. When the effect is negative, then the microbiota age of the subject decreases. Methods for calculating a relative abundance for a bacterial taxon are described in Section 1(A). In a preferred embodiment, the subject is human that is about 2 years of age or less. In another preferred embodiment, the subject is a human that is about 5 years of age or less. In another preferred embodiment, the subject is a human that is about 10 years of age or less. In another preferred embodiment, the subject is a human that is about 20 years of age or less. In another preferred
embodiment, the subject is a human that is about 30 years of age or less. In another preferred embodiment, the subject is a human that is about 40 years of age or less. In another preferred embodiment, the subject is a human that is about 50 years of age or less. In another preferred embodiment, the subject is a human that is about 60 years of age or less. In another preferred embodiment, the subject is a human that is about 70 years of age or less. In another preferred embodiment, the subject is a human that is about 80 years of age or less. In another preferred embodiment, the subject is a human that is about 90 years of age or less. In another preferred embodiment, the subject is a human that is about 100 years of age or less. In another preferred embodiment, the subject is a human that is about 1 10 years of age or less.
II. METHOD OF PREVENTING AND/OR TREATING A DISEASE
[0073] In another aspect, the present invention provides a method for preventing and/or treating a disease in a subject in need thereof. As described in detail in the Examples, Applicants have discovered that certain bacterial taxa associated with normal maturation of the gut microbiota in healthy subjects (i.e. age-discriminatory taxa) are also associated with repair of the gut microbiota (i.e. recovery-indicative taxa) in subjects whose gut communities have been affected by a variety of insults (e.g.
malnutrition or gastrointestinal disease). Accordingly, a method for preventing and/or treating a disease in a subject in need thereof may comprise administering to the subject a therapeutically effective amount of one or more age-indicative taxa associated with repair of the gut microbiota. [0074] As used herein, "preventing" a disease refers to reducing the onset of symptoms or complications of a disease, condition or disorder (collectively, a
"disease"), or the disease itself. Preventing a disease may involve reducing in treated subjects (e.g. a normal subject) (1 ) the incidence, development or formation of disease, (2) the development, duration, and/or severity of symptoms of disease, (3) death rates, or (4) a combination thereof. In each embodiment, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. For an infectious disease (e.g. enteropathogenic infection), preventing a disease may also involve increasing the median infectious disease dose (ID50) by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 100%, or more. Preventing a disease may also involve increasing the (relative) microbiota maturity, increasing bacterial diversity, increasing MAZ score.
[0075] As used herein, "treating" describes the management and care of a patient for the purpose of combating a disease in a subject. Treating a disease may involve reducing in treated subjects (1 ) the infectious burden (e.g. viral, bacterial, patristic load), (2) the duration and/or severity of symptoms of disease, (3) death rates, or (4) a combination thereof. In each embodiment, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating a disease may also involve increasing the (relative) microbiota maturity, increasing bacterial diversity, increasing MAZ score.
[0076] As used herein, "therapeutically effective amount" means an amount of one or more bacterial taxa of the invention that will elicit a desired biological or medical response of a tissue, system, or subject that is being sought by a researcher or clinician. In one aspect, the biological or medical response is prevention or treatment of a gastrointestinal disease, !n another aspect, the biological or medical response is prevention or treatment of a gastrointestinal disease caused by an enteropathogenic infection. In another aspect, the biological or medical response is treatment or prevention of acute malnutrition. In another aspect, the biological or medical response is treatment or prevention of acute diarrheal disease. In another aspect, the biological or medical response is treatment or prevention of acute diarrheal disease caused by an enteropathogenic infection. In another aspect, the biological or medical response is treatment or prevention of chronic diarrheal disease.
[0077] As used herein, the phrase "bacterial taxa of the invention" refers to age-discriminatory bacterial taxa associated with repair of the gut microbiota. Methods for identifying age-discriminatory bacterial taxa are described in detail in Section I. In some embodiments, one or more bacterial taxa of the invention are selected from the group listed in Table A. In other embodiments, one or more bacterial taxa of the invention are selected from the group listed in Table B. In other embodiments, one or more bacterial taxa of the invention are selected from the group listed in Table C.
Preferred combinations of bacterial taxa of the invention include, but are not limited to the combinations listed in Table D, or further described in Section III. Preferred combinations of bacterial taxa of the invention may also be identified by testing for a therapeutic effect in an appropriate animal model. Combinations may also be selected by identifying bacterial taxa associated with repair of the gut microbiota that are under- represented in a subject's gut microbiota as compared to a healthy subject. Section III also describes in further detail formulations comprising bacterial taxa of the invention for administration to a subject. Bacterial taxa of the invention are preferably administered orally or rectally. A bacterial taxon administered to a subject is isolated and biologically pure. A bacterial isolate can be identified as belonging to a bacterial taxon listed in
Tables A-D if the V4 region of the 16S rRNA gene of the bacterial isolate has at least 97% sequence identity to any of SEQ ID NOs: 1 - 60. For example, the V4 region of the 16S rRNA gene of the bacterial isolate can have 97%, 97.5%, 98%, 98.5%, 99%, 99.5%, or 100% sequence identity to any of SEQ ID NOs: 1 - 60. The duration of therapy can and will vary, depending at least in part upon the disease and the severity of the disease. In subjects with an immature gut microbiota, one or more bacterial taxa of the invention may be administered until the microbiota age of the subject is similar to the microbiota age of a healthy subject of the same chronological age.
[0078] As used herein, "gastrointestinal diseases" refer to diseases involving the gastrointestinal tract. In some embodiments, the disease is malnutrition. In other embodiments, the disease is an infection caused by an enteropathogen. Non- limiting examples of enteropathogens include Escherichia coli (e.g. enterotoxigenic E. coli, enteropathogen ic E. coli, enteroinvasive E. coli, enterohemorrhagic E. coli, enteroaggregative E. coli, etc.), Salmonella enterica, Shigella sp. (e.g. S. flexneri, S. sonnei, S. dysenteriae, etc.), Camplobacter sp. (e.g. C. jejuni, C. coli, C. upsaliensis, etc.), Yersinia sp. (e.g. Y. enterocolitica, Y. pseudotuberculosis, etc.), Vibrio sp. (e.g. Vibrio cholera, Vibrio parahaemolyticus, etc.) Clostridum sp. (C. difficile), Entamoeba sp. (e.g. Entamoeba histolytica, Entamoeba dispar, etc.) Endolimax nana, lodamoeba butschii, Chilomastix mesnili, Blastocystis hominis, Trichomonas hominis, Coccidian-like body, Giardia sp. (e.g. Giardia intestinalis, Giardia lamblia, etc.) Cryptosporidium parvum, Isospora belli, Dientamoeba fragilis, Microsporidia, Strongyloides stercoralis, Angiostrongylus costaricensis, Schistosoma sp. (e.g. S. mansoni, S. japonicum, etc.) Cyclospora cayetanensis, Enterocytozoon sp. (e.g. Enterocytozoon bieneusi,
Enterocytozoon helium, etc.) Encephalitozoon sp. (e.g. Encephalitozoon intestinalis, Encephalitozoon cuniculi, etc.) Ascaris lumbricoides, Trichuris tricuria, Ancylostoma duodenale, Necator americanus, Hymenolepsis nana, rotaviruses, human caliciviruses (e.g. noroviruses and sapoviruses), astroviruses, cytolomegaloviruses. In other embodiments, the disease is ulcerative colitis, necrotizing enterocolitis, or Crohn's disease. In still other embodiments, the disease is traveler's diarrhea. In a preferred embodiment, the disease is acute malnutrition. In another preferred embodiment, the disease is an enteropathogen infection that has as a symptom acute diarrhea. In an exemplary embodiment, the disease is a Vibrio cholerae infection.
[0079] Acute malnutrition results from decreased food consumption and/or illness resulting in sudden weight loss. It is associated with greater risk of medical complications and infections, increased risk of death from illness and infections, and micronutrient deficiencies. Non-limiting examples of micronutrient deficiencies associated with acute malnutrition are iron deficiency, iodine deficiency, and vitamin A deficiency. The most common way to assess malnutrition, particularly in humans of about 19 years of age or less, is through anthropometric measurements. It is usually diagnosed in one of three ways: by weighing a subject and measuring the subject's height; by measuring the circumference of the subject's mid-upper arm (MUAC); and/or by checking for oedema in the subject's lower legs or feet. Acute malnutrition is divided into two types: severe acute malnutrition (SAM) and moderate acute malnutrition
(MAM). A subject is classified as having SAM if the subject's weight-for-height Z-scores (WHZ) is below three standard deviations (-3 s.d.) from the median of the World Health Organization (WHO) reference growth standards. A subject with a WHZ between -2 s.d. and -3 s.d. from the median of the WHO reference growth standards is categorized as having MAM. If a subject is between about six months and about five years of age, a MUAC measurement of less than 12.5 cm also indicates that a subject is suffering from moderate acute malnutrition. Finally, the presence of oedema in both feet and lower legs of a subject is a sign of SAM. WHO reference growth standards are available from the WHO. See for example, World Health Organization Department of Nutrition for Health and Development: WHO child growth standards growth velocity based on weight, length and head circumference: methods and development; World Health Organization, 2009, or the current edition.
[0080] In some embodiments, the subject to be treated is a subject with acute malnutrition and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject. Treating acute malnutrition may involve reducing in treated subjects the duration and/or severity of symptoms of acute
malnutrition, death rates associated with acute malnutrition, or a combination thereof. In each embodiment, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 80%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating acute malnutrition may also involve increasing a suitable anthropometric measurement in a treated subject including, but not limited to, a subject's WHZ or MUAC. For each aspect, the amount of increase may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects or as compared to the treated subject prior to administration of the combination of the invention. In certain embodiments, a subject's WHZ may improve to less than -3.0 s.d., less than -2.5 s.d., less than -2.0 s.d., less than -1 .5 s.d., less than -1 .0 s.d., or less than -0.5 s.d. from the median of the WHO reference growth standards. Treating acute malnutrition may also result in no worsening (i.e. decrease) in a subject's WHZ or MUAC. Treating acute malnutrition may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary, in certain embodiments, the duration of therapy is about 1 , 2, 3, or 4 months. In other embodiments, the duration of therapy is about 1 , 2, 3, 4, 5, 6, 7, or 8 weeks. In still other embodiments, the duration of therapy is about 4 to about 12 weeks, about 6 to about 10 weeks, or about 6 to about 8 weeks. Alternatively, a subject may be
administered one or more bacterial taxa of the invention for as long as acute
malnutrition persists. Useful combinations are described in further detail below. One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include therapeutic foods, probiotics, antibiotics and vaccines. In exemplary embodiments, a subject with acute malnutrition is administered a therapeutically effective amount of one or more bacterial taxa of the invention and a therapeutic food. As used herein, "therapeutic food" refers to a food designed for specific, usually nutritional, therapeutic purposes as a form of dietary supplement. Therapeutic foods are usually made of a mixture of protein, carbohydrate, lipid and vitamins and minerals, and are usually produced by grinding all ingredients together, mixing them and packaging without water. The term "therapeutic foods" includes ready-to-use-therapeutic foods (RUTFs). Generally speaking, RUTFs are homogenous mixtures of lipid-rich and water-soluble foods. The lipids used in
formulating RUTFs are in a viscous liquid form, and the other ingredients (e.g. protein, carbohydrate, vitamins, minerals) are mixed through the lipids. Non-limiting examples of therapeutic foods include F-75, F-100, K-Mix 2, Citadel spread, Plumpy'nut, Medika Mamba, Ensure, Fortisip, Energyzip, TwoCal, BP-100, and eeZee.
[0081 ] In other embodiments, the subject to be treated is a subject at risk for acute malnutrition and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject. A subject at risk for acute
malnutrition may have limited access to nutritious foods and/or may have frequent exposure to infectious diseases. A subject at risk for acute malnutrition may also be a subject with a WHZ between 0 s.d. and -2 s.d., between -1 s.d and -2 s.d, or between -1 .5 s.d and -2 s.d from the median of the WHO reference growth standards. Treating a subject at risk for acute malnutrition may prevent acute malnutrition in the subject. For example, preventing acute malnutrition may involve reducing in treated subjects (1 ) the incidence, development or formation of acute malnutrition, (2) the development, duration, and/or severity of symptoms of malnutrition, if malnutrition does develop, (3) death rates associated with acute malnutrition, or (4) a combination thereof. In each embodiment, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Preventing acute malnutrition may also involve increasing a suitable anthropometric measurement in a treated subject including, but not limited to, a subject's WHZ or MUAC, For each aspect, the amount of increase may each be about 10%, 20%, 30%, 40%, 50%, 80%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects or as compared to the treated subject prior to administration of the combination of the invention. In certain embodiments, a subject's WHZ may improve to less than -1 .5 s.d., less than -1 .0 s.d., or less than -1 .5 s.d. from the median of the WHO reference growth standards. Preventing acute malnutrition may also result in no worsening (i.e. decrease) in a subject's WHZ or MUAC. Preventing acute malnutrition may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 , 2, 3, or 4 months. In other embodiments, the duration of therapy is about 1 , 2, 3, 4, 5, 6, 7, or 8 weeks. In still other embodiments, the duration of therapy is about 4 to about 12 weeks, about 6 to about 10 weeks, or about 6 to about 8 weeks. Alternatively, a subject may be administered one or more bacterial taxa of the invention for as long as the subject remains at risk. Useful combinations are described in further detail below. One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include therapeutic foods, antibiotics and/or vaccines.
[0082] As used herein, acute diarrhea is defined as three or more stools per day of decreased form (e.g. loose and/or water) from the normal, lasting for less than 14 days; persistent diarrhea is defined as three or more stools per day of decreased form from the normal, lasting for more than 14 days but less than 1 month; and chronic diarrhea is defined as three or more stools per day of decreased form from the normal, lasting for a month or more. Associated symptoms of acute diarrhea, persistent diarrhea, and chronic diarrhea may include abdominal cramps or pain, fever, nausea, vomiting, fatigue, urgency, weight loss, and/or malnutrition. Acute diarrhea, persistent diarrhea, and chronic diarrhea are themselves symptoms. Causes of acute diarrhea are well known in the art, and non-limiting examples include a food allergy, antibiotic use, an enteropathogen infection, and radiation therapy. Causes of persistent diarrhea are well known in the art, and non-limiting examples include a food allergy, antibiotic use, an enteropathogen infection, colon resection, colon cancer, ulcerative colitis, necrotizing enterocolitis, Crohn's disease and radiation therapy. Causes of chronic diarrhea are well known in the art, and non-limiting examples include a food allergy, use of a pharmacological agent, an enteropathogen infection, colon resection, colon cancer, ulcerative colitis, necrotizing enterocolitis, Crohn's disease, and radiation therapy. Pharmacological agents known to cause diarrhea are well known in the art and may include, but are not limited to, an antibiotic, an anti-TNF agent, chemotherapy agents, antacids, proton pump inhibitors, (e.g. asomeprazole, esomeprazole,
lansoprazole, rabeprazole, patoprazole, cimetidine, ranitidine, naiziatidine), drugs that suppress the immne system (e.g. mycophenolate), antidepressants, blood pressure medications, digitalis, diuretics, cholesterol-lowering agents, lithium, theophylline, thyroid hormone, and colchicine.
[0083] In some embodiments, the subject to be treated is a subject with acute diarrhea and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject. Treating acute diarrhea may involve reducing in treated subjects the duration and/or severity of symptoms of acute diarrhea (e.g. fever, bloody stools, vomiting), death rates, or a combination thereof. For each aspect, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an acute diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an acute diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form to less than 6, less than 5, less than 4, or less than 3. Treating acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, about 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1 , 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 1 1 , 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days.
Alternatively, the duration of therapy may be a month or more. Useful combinations are described in further detail in Section III. One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include antibiotics, antimotiiity agents (e.g. loperamide), antisecretory agents (e.g.
racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghula husk, methylcelluiose, stercuiia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy. In exemplary embodiment, acute diarrhea is a symptom of a Vibrio cholerae infection.
[0084] In some embodiments, the subject to be treated is a subject with acute diarrhea and a therapeutically effective amount of a bacterial isolate that is a member of the taxon Ruminococcus obeum OTU ID 178122 is administered to the subject, optionally in combination with one or more additional bacterial taxa of the invention. In other embodiments, the subject to be treated is a subject with acute diarrhea and a therapeutically effective amount of Ruminococcus obeum ATCC29714 is administered to the subject, optionally in combination with one or more additional bacterial taxa of the invention. Treating acute diarrhea may involve reducing in treated subjects the duration and/or severity of symptoms of acute diarrhea (e.g. fever, bloody stools, vomiting), death rates, or a combination thereof. For each aspect, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an acute diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an acute diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form to less than 6, less than 5, less than 4, or less than 3. Treating acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, about 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1 , 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 1 1 , 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days. Alternatively, the duration of therapy may be a month or more. Useful combinations comprising Ruminococcus obeum ATCC29714 or a bacterial isolate that is a member of the taxon Ruminococcus obeum OTU ID 178122 are described in further detail in Section III. A composition comprising Ruminococcus obeum ATCC29714 or a bacterial isolate that is a member of the taxon Ruminococcus obeum OTU ID 178122 may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include antibiotics, antimotility agents (e.g. loperamide), antisecretory agents (e.g. racecadotrii and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghuia husk, methyiceilu!ose, sterculia, etc.) prebiotics, probiofics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy. In exemplary embodiment, acute diarrhea is a symptom of a Vibrio cholerae infection.
[0085] In some embodiments, the subject to be treated is a subject at risk for acute diarrhea and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject. A subject at risk for acute diarrhea may be a subject living in a geographic area with limited or no access to clean drinking water, a subject living in a geographic area that is experiencing a disease outbreak, or a subject that is exposed to others that have an acute diarrhea. A subject at risk for acute diarrhea may be a subject that is malnourished. Treating a subject at risk for acute diarrhea may prevent acute diarrhea in the subject. For example, preventing an acute diarrhea may involve reducing in treated subjects (1 ) the incidence, development or formation of the acute diarrhea , (2) the development, duration, and/or severity of symptoms of the acute diarrhea (e.g. fever, bloody stools, vomiting), if the disease does develop, (3) death rates associated with the acute diarrhea, or (4) a combination thereof. For each aspect, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Preventing an acute diarrhea may also involve result in a minimal change in the number of stools per day of decreased form in treated subjects. For example, the change in the number of stools per day of decreased form may be an increase of 3 or less, 2 or less, or 1 or less. Preventing acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1 , 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 1 1 , 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days. Alternatively, the duration of therapy may be a month or more. Useful
combinations are described in further detail in Section III. One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be
administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include antibiotics, antimotiiity agents (e.g. loperamide), antisecretory agents (e.g. racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghula husk, methyiceilulose, sterculia, etc.) prebiotics, probiotics, synbiotscs, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy. In exemplary embodiment, acute diarrhea is a symptom of a Vibrio choierae infection.
[0086] In some embodiments, the subject to be treated is a subject with acute diarrhea and a therapeutically effective amount of a bacterial isolate that is a member of the taxon Ruminococcus obeum OTU ID 178122 is administered to the subject, optionally in combination with one or more additional bacterial taxa of the invention. In some embodiments, the subject to be treated is a subject at risk for acute diarrhea and a therapeutically effective amount of Ruminococcus obeum ATCC29714 is administered to the subject, optionally in combination with one or more additional bacterial taxa of the invention. A subject at risk for acute diarrhea may be a subject living in a geographic area with limited or no access to clean drinking water, a subject living in a geographic area that is experiencing a disease outbreak, or a subject that is exposed to others that have an acute diarrhea. A subject at risk for acute diarrhea may be a subject that is malnourished. Treating a subject at risk for acute diarrhea may prevent acute diarrhea in the subject. For example, preventing an acute diarrhea may involve reducing in treated subjects (1 ) the incidence, development or formation of the acute diarrhea , (2) the development, duration, and/or severity of symptoms of the acute diarrhea (e.g. fever, bloody stools, vomiting), if the disease does develop, (3) death rates associated with the acute diarrhea, or (4) a combination thereof. For each aspect, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 80%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Preventing an acute diarrhea may also involve result in a minimal change in the number of stools per day of decreased form in treated subjects. For example, the change in the number of stools per day of decreased form may be an increase of 3 or less, 2 or less, or 1 or less. Preventing acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1 , 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 1 1 , 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days.
Alternatively, the duration of therapy may be a month or more. Useful combinations comprising Ruminococcus obeum ATCC29714 or a bacterial isolate that is a member of the taxon Ruminococcus obeum OTU ID 178122 are described in further detail in
Section III. A composition comprising Ruminococcus obeum ATCC29714 or a bacterial isolate that is a member of the taxon Ruminococcus obeum OTU ID 178122 may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include antibiotics, antimotility agents (e.g. loperamide), antisecretory agents (e.g.
racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghuia husk, methylcellulose, sterculia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy. In exemplary embodiment, acute diarrhea is a symptom of a Vibrio choierae infection. [0087] In some embodiments, the subject to be treated is a subject with chronic diarrhea and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject. Treating chronic diarrhea may involve reducing in treated subjects the duration and/or severity of symptoms of acute diarrhea (e.g. fever, bloody stools, vomiting), death rates, or a combination thereof. For each aspect, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an chronic diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form by about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Treating an chronic diarrhea may also involve reducing in treated subjects the number of stools per day of decreased form to less than 6, less than 5, less than 4, or less than 3. Treating acute diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, about 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1 , 2, 3, or 4 weeks. In still other embodiments, the duration of therapy is about 10, 1 1 , 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days.
Alternatively, the duration of therapy may be a month or more. Useful combinations are described in further detail in Section III. One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include antibiotics, antimotility agents (e.g. loperamide), antisecretory agents (e.g.
racecadotril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghula husk, methylcel!ulose, stercuiia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy. In exemplary embodiment, chronic diarrhea is a symptom of a C. difficile infection, Crohn's disease, ulcerative colitis, necrotizing enterocolitis, traveler's diarrhea, colon cancer, chemotherapy, radiation therapy, or use of pharmacological agent, including but not limited to an antibiotics, or an anti-TNF agent.
[0088] In some embodiments, the subject to be treated is a subject at risk for chronic diarrhea and a therapeutically effective amount of one or more bacterial taxa of the invention is administered to the subject. A subject at risk for chronic diarrhea may be a subject living in a geographic area with limited or no access to clean drinking water, a subject living in a geographic area that is experiencing a disease outbreak, or a subject that is exposed to others that have an acute diarrhea. A subject at risk for chronic diarrhea may be a subject that is malnourished. Treating a subject at risk for chronic diarrhea may prevent acute diarrhea in the subject. For example, preventing an chronic diarrhea may involve reducing in treated subjects (1 ) the incidence,
development or formation of the acute diarrhea , (2) the development, duration, and/or severity of symptoms of the acute diarrhea (e.g. fever, bloody stools, vomiting), if the disease does develop, (3) death rates associated with the acute diarrhea, or (4) a combination thereof. For each aspect, the amount of reduction may each be about 10%, 20%, 30%, 40%, 50%, 80%, 70%, 80%, 90%, 95% or about 100% in treated subjects, as compared to untreated subjects. Preventing an chronic diarrhea may also involve result in a minimal change in the number of stools per day of decreased form in treated subjects. For example, the change in the number of stools per day of decreased form may be an increase of 3 or less, 2 or less, or 1 or less. Preventing chronic diarrhea may also involve increasing the subject's relative gut microbiota maturity, MAZ score, gut microbiota diversity, or a combination thereof. The duration of treatment can and will vary. In certain embodiments, the duration of therapy is about 1 to about 4 weeks, 1 to about 3 weeks, about 2 to about 3 weeks, about 1 to about 2 weeks, or about 10 to about 14 days. In other embodiments, the duration of therapy is about 1 , 2, 3, or 4 weeks, in still other embodiments, the duration of therapy is about 10, 1 1 , 12, 13, 14, 15, 16, or 17 days. In yet other embodiments, the duration of therapy is about 3, 4, 5, 6, 7, 8, 9, or 10 days. Alternatively, the duration of therapy may be a month or more. Useful combinations are described in further detail in Section III. One or more bacterial taxa of the invention may be formulated for oral or rectal administration, and may be administered alone or with an additional therapeutic agent. Non-limiting examples of additional therapeutic agents include antibiotics, antimoti!ity agents (e.g. loperamide), antisecretory agents (e.g. racecado ril and other agents that reduce the amount of water that is released into the gut during an episode of diarrhea), bulk-forming agents (e.g. isphaghuia husk, methy!ce!lu!ose, sterculia, etc.) prebiotics, probiotics, synbiotics, supplemental zinc therapy, nonsteroidal anti-inflammatory drugs, mucosal protectants and adsorbents (e.g. kaolin-pectin, activated charcoal, bismuth subsalicylate, etc.) and/or rehydration therapy, in exemplary embodiment, chronic diarrhea is a symptom of a C. difficile infection, Crohn's disease, ulcerative colitis, necrotizing enterocolitis, traveler's diarrhea, colon cancer, radiation therapy, or use of pharmacological agent, including but not limited to an antibiotics, or an anti-TNF agent.
III. COMPOSITIONS
[0089] In an aspect, the present invention provides a composition comprising 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23,
24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, 59, or 60 bacterial taxa selected from list provided in Table A, wherein each bacterial taxon present in the composition is isolated and biologically pure (i.e. >97% sequence identity to the SEQ ID NO. provided).
[0090] In another aspect, the present invention provides a combination comprising 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, or 24 bacterial taxa selected from the bacterial taxa listed in Table B, wherein each bacterial taxon present in the composition is isolated and biologically pure (i.e. >97% sequence identity to the SEQ ID NO. provided).
[0091 ] In another aspect, the present invention provides a combination comprising 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24,
25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, or 36 bacterial taxa selected from the bacterial taxa listed in Table C, wherein each bacterial taxon present in the composition is isolated and biologically pure (i.e. >97% sequence identity to the SEQ ID NO. provided). [0092] In another aspect, the present invention provides a combination identified below in Table D, wherein each bacterial taxon present in the composition is isolated and biologically pure (i.e. >97% sequence identity to the SEQ ID NO. provided).
Table D.
BACTERIAL TAXON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
A
B
C
D
E
F
G
H
1
K
L
M
N
o
P
R
S
T
u
V
w
X
Y
z
AA
AB
AC
AD
AE
AF
AG
AH
Al
AJ BACTERIAL TAXON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
AK x x x x x x x x x x x x x x x
AL x x x x x x x x x x x x x x x x
AM x x x x x x x x x x x x x x x x x
AN x x x x x x x x x x x x x x x x x x
AO X X X X X X X X X X X X X X X X X X X
AP X X X X X X X X X X X X X X X X X X X X
AQ X X X X X X X X X X x x x x x x x x x x x
AR X X X X X X X X X X x x x x x x x x x x x x
AS X X X X X X X X X X X X X X X X X X x x x x x
AT x x
AU x x x
AV x x x x
AW x x x x x
AX x x x x x x
AY x x x x x x x
AZ x x x x x x x x
Al x x x x x x x x x
AJ x x x x x x x x x x
AK x x x x x x x x x x x
AL x x x x x x x x x x x x
AM x x x x x x x x x x x x x
AN x x x x x x x x x x x x x x
AO x x x x x x x x x x x x x x x
AP x x x x x x x x x x x x x x x x
AQ x x x x x x x x x x x x x x x x x
AR x x x x x x x x x x x x x x x x x x
AS X X X X X X X X X X X X X X X X X X X
AT x x x x x x x x x x x x x x x x x x x x
AU X X X X X X X X X X X X X X X X X x x x x
AV X X X X X X X X X X X X X X X X X x x x x x
AW X X X X X X X X X X X X X X X X X X x x x x x
AX x x
AY x x x
AZ x x x x
BA x x x x x
BB x x x x x x
BC x x x x x x x
BD x x x x x x x x
BE x x x x x x x x x
BF x x x x x x x x x x
BG x x x x x x x x x x x
BH x x x x x x x x x x x x
Bl x x x x x x x x x x x x x BACTERIAL TAXON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
BJ x x x x x x x x x x x x x x
BK x x x x x x x x x x x x x x x
BL x x x x x x x x x x x x x x x x
BM x x x x x x x x x x x x x x x x x
BN x x x x x x x x x x x x x x x x x x
BO
BP x x x x x x x x x x x x x x x x x x x x
BQ x x x x x x x x x x x
BR x x
BS x x x
BT x x x x
BU x x x x x
BV x x x x x x
BW x x x x x x x
BX x x x x x x x x
BY x x x x x x x x x
BZ x x x x x x x x x x
CA x x x x x x x x x x x
CB x x x x x x x x x x x x
CC x x x x x x x x x x x x x
CD x x x x x x x x x x x x x x
CE x x x x x x x x x x x x x x x
CF x x x x x x x x x x x x x x x x
CG
CH x x x x x x x x x x x x x x x x x x
CI x x x x x x x x x x x x x x x x x x x
CJ x x x x x x x x x x x x x x x x x x x x
CK x x x x x x x x x x x x x x x x x x x x
CL x x x x x x x x x x x x x x x x x x x x x x
CM x x x x x x x x x x x x x x x x x x x x
CN x x
CO x x x
CP x x x x
CQ x x x x x
CR x x x x x x
CS x x x x x x x
CT x x x x x x x x
CU x x x x x x x x x
CV
CW
CX
CY x x x x x x x x x x x x x
CZ x x x x x x x x x x x x x x BACTERIAL TAXON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
DA x x x x x x x x x x x x x x x
DB x x x x x x x x x x x x x x x x
DC x x x x x x x x x x x x x x x x x
DD x x x x x x x x x x x x x x x x x x
DE x x x x x x x x x x x x x x x x x x x
DF x x x x x x x x x x x x x x x x x x x x
DG x x x x x x x x x x x x x x x x x x x
DH x x x x x x x x x x x x x x x x x x x x x x
Dl x x x x x
DJ x x
DK x x x
DL x x x x
DM x x x x x
DN x x x x x x
DO x x
DP x x x x x x x x
DQ x x x x x x x x x
DR x x x x x x x x x x
DS x x x x x x x x x x x
DT x x x x x x x x x x x x
DU x x x x x x x x x x x x x
DV x x x x x x x x x x x x x x
DW x x x x x x x x x x x x x x x
DX x x x x x x x x x x x x x x x x
DY x x x x x x x x x x x x x x x x x
DZ x x x x x x x x x x x x x x x x x x
EA x x x x x x x x x x x x x x x x x x x
EB x x x x x x x x x x x x x x x x x x x x
EC x x x x x x x x x x x x x x x x x x
ED x x x x x x x x x x x x x x x x x x x x x x
EE x x x x x x x
EF x x x
EG x x x x
EH x x x x x
El x x x x x x
EJ x x x x x x x
EK x x x x x x x x
EL x x x x x x x x x
EM x x x x x x x x x x
EN x x x x x x x x x x x
EO x x x x x x x x x x x x
EP x x x x x x x x x x x x x
EQ x x x x x x x x x x x x x x BACTERIAL TAXON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
ER x x x x x x x x x x x x x x x
ES x x x x x x x x x x x x x x x x
ET x x x x x x x x x x x x x x x x x
EU x x x x x x x x x x x x x x x x x x
EV x x x x x x x x x x x x x x x x x x x
EW x x x x x x x x x x x x x x x x x
EX x x x x x x x x x x x x x x x x x x x x x
EY x x x x x x x x x x x x x x x x x x x x x x
EZ x x x x x x x x x x x x x x x x x x x x x x x
FA x x
FB x x x
FC x x x x
FD x x x x x
FE x x x x x x
FF x x x x x x x
FG x x x x x x x x
FH x x x x x x x x x
Fl x x x x x x x x x x
FJ x x x x x x x x x x x
FK x x x x x x x x x x x x
FL x x x x x x x x x x x x x
FM x x x x x x x x x x x x x x
FN x x x x x x x x x x x x x x x
FO x x x x x x x x x x x x x x x x
FP x x x x x x x x x x x x x x x x x
FQ x x x x x x x x x x x x x x x x x x
FR x x x x x x x x x x x x x x x x x x x
FS x x x x x x x x x x x x x x x x x x x x
FT x x x x x x x x x x x x x x x x x x x x x
FU x x x x x x x x x x x x x x x x x x x x x x
FV x x x x x x x x x x x x x x x x x x x x x x x
FW x x
FX x x x
FY x x x x
FZ x x x x x
GA x x x x x x
GB x x x x x x x
GC x x x x x x x x
GD x x x x x x x x x
GE x x x x x x x x x x
GF x x x x x x x x x x x
GG x x x x x x x x x x x x
GH x x x x x x x x x x x x x BACTERIAL TAXON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Gl x x x x x x x x x x x x x x GJ x x x x x x x x x x x x x x x
GK x x x x x x x x x x x x x x x GL x x x x x x x x x x x x x x x x x
GM x x x x x x x x x x x x x x x x x x
GN x x x x x x x x x x x x x x x x x x x
GO
GP x x x x x x x x x x x x x x x x x x x x x
GQ x x x x x x x x x x x x x x x x x x x x x x
GR x x x x x x x x x x x x x x x x x x x x x x x GR x x
GS x x x
GT x x x x
GU x x x x x
GV x x x x x x
GW x x x x x x x
GX x x x x x x x x
GY x x x x x x x x x
GZ x x x x x x x x x x
HA x x x x x x x x x x x
HB x x x x x x x x x x x x
HC x x x x x x x x x x x x x HD x x x x x x x x x x x x x x HE x x x x x x x x x x x x x x x HF x x x x x x x x x x x x x x x x
HG x x x x x x x x x x x x x x x x x HH x x x x x x x x x x x x x x x x x x
HI x x x x x x x x x x x x x x x x x x x
HJ x x x x x x x x x x x x x x x x x x x x
HK x x x x x x x x x x x x x x
HL x x x x x x x x x x x x x x x x x x x x x x
HM x x x x x x x x x x x x x x x x x x x x x x x HN x x
HO HP x x x x
HQ x x x x x
HR x x x x x x
HS x x x x x x x
HT x x x x x x x x
HU x x x x x x x x x
HV x x x x x x x x x x
HW x x x x x x x x x x x
HX x x x x x x x x x x x x BACTERIAL TAXON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
HY x x x x x x x x x x x x x
HZ x x x x x x x x x x x x x x lA x x x x x x x x x x x x x x x
IB x x x x x x x x x x x x x x x x
IC x x x x x x x x x x x x x x x x x
ID x x x x x x x x x x x x x x x x x x lE x x x x x x x x x x x x x x x x x x x
IF x x x x x x x x x x x x x x x x x x x x
IG x x x x x x x x x x x x x x x x x x x x x
IH x x x x x x x x x x x x x x x x x x x x x x
II x x x x x x x x x x x x x x x x x x x x x x x
IJ x x
IK x x x
IL x x x x
IM x x x x x
IN x x x x x x
10 x x x x x x x
IP x x x x x x x x
IQ x x x x x x x x x
IR x x x x x x x x x x
IS x x x x x x x x x x x
IT x x x x x x x x x x x x lU x x x x x x x x x x x x x
IV x x x x x x x x x x x x
IC x x x x x x x x x x x x x x x
ID x x x x x x x x x x x x x x x x lE x x x x x x x x x x x x x x x x x
IF x x x x x x x x x x x x x x x x x x
IG x x x x x x x x x x x x x x x x x x x
IH x x x x x x x x x x x x x x x x x x x x
11 x x x x x x x x x x x x x x x x x x x x x IJ x x x x x x x x x x x x x x x x x x x x x x IK x x x x x x x x x x x x x x x x x x x x x x x IL x x
IM x x x
IN x x x x
lO x x x x x
IP x x x x x x
IQ x x x x x x x
IR x x x x x x x x
IS x x x x x x x x x
IT x x x x x x x x x x
IU x x x x x x x x x x x BACTERIAL TAXON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
IV x x x x x x x x x x x x
IW x x x x x x x x x x x x x
IX x x x x x x x x x x x lY x x x x x x x x x x x x x x x
IZ x x x x x x x x x x x x x x x x
JA x x x x x x x x x x x x x x x x x
JB x x x x x x x x x x x x x x x x x x
JC x x x x x x x x x x x x x x x x x x x
JD x x x x x x x x x x x x x x x x x x x x
JE x x x x x x x x x x x x x x x x x x x x x
JF x x x x x x x x x x x x x x x x x x x x x x
JK x x x x x x x x x x x x x x x x x x x x x x x
JL x x
JM x x x
JN x x x x
JO
JP
JQ x x x x x x x
JR x x x x x x x x
JS x x x x x x x x x
JT x x x x x x x x x x
JU x x x x x x x x x x x
JV x x x x x x x x x x x x
JW x x x x x x x x x x
JX x x x x x x x x x x x x x x
JY x x x x x x x x x x x x x x x
JZ x x x x x x x x x x x x x x x x
KA x x x x x x x x x x x x x x x x x
KB x x x x x x x x x x x x x x x x x x
KC x x x x x x x x x x x x x x x x x x x
KD x x x x x x x x x x x x x x x x x x x x
KE x x x x x x x x x x x x x x x x x x x x x
KF x x x x x x x x x x x x x x x x x x x x x x
KG x x x x x x x x x x x x x x x x x x x x x x x
KH x x
Kl x x x
KJ x x x x
KK x x x x x
KL x x x x x x
KM x x x x x x x
KN x x x x x x x x
KO x x x x x x x x x
KP x x x x x x x x x x BACTERIAL TAXON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
KQ x x x x x x x x x x x
KR x x x x x x x x x x x x
KS x x x x x x x x x x x x x
KT x x x x x x x x x x x x x x
KU x x x x x x x x x x x x x x x
KV x x x x x x x x x x x x x x x x
KW x x x x x x x x x x x x x x x x x
KX x x x x x x x x x x x x x x x x x x
KY x x x x x x x x x x x x x x x x x x x
KZ x x x x x x x x x x x x x x x x x x x x
LA x x x x x x x x x x x x x x x x x x x x x
LB x x x x x x x x x x x x x x x x x x x x x x
LC x x x x x x x x x x x x x x x x x x x x x x x
LD x x
LE x x x
LF x x x x
LG x x x x x
LH x x x x x x
LI x x x x x x x
LJ x x x x x x x x
LK x x x x x x x x x
LL x x x x x x x x x x
LM x x x x x x x x x x x
LN x x x x x x x x x x x x
LO x x x x x x x x x x x x x
LP x x x x x x x x x x x x x x
LQ x x x x x x x x x x x x x x x
LR x x x x x x x x x x x x x x x x
LS x x x x x x x x x x x x x x x x x
LT x x x x x x x x x x x x x x x x x x
LU x x x x x x x x x x x x x x x x x x x
LV x x x x x x x x x x x x x x x x x x x x
LW x x x x x x x x x x x x x x x x x x x x x
LX x x x x x x x x x x x x x x x x x x x x x x
LY x x x x x x x x x x x x x x x x x x x x x x x
LZ x x
MA x x x
MB x x x x
MC x x x x x
MD x x x x x x
ME x x x x x x x
MF x x x x x x x x
MG x x x x x x x x x BACTERIAL TAXON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
MH
Ml
MJ
MK
ML
MM
MN
MO
MP
MQ
MR
MS
MT
MU
MV
MW
MX
MY
MZ
NA
NB
NC
ND
NE
NF
NG
NH
Nl
NJ
NK
NL
NM
NN
NO
NP
NQ
NR
NS
NT
NU
NV
NW
NX BACTERIAL TAXON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
NY
NZ
OA
OB
oc
OD
OE
OF
OG
OH
01
OJ
OK
OL
OM
ON
oo
OP
OQ
OR
OS
OT
ou
ov
ow
ox
OY
oz
PA
PB
PC
PD
PE
PF
PG
PH
PI
PJ
PK
PL
PM
PN
PO BACTERIAL TAXON
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
PP x
PQ x
PR
PS
PT
PU
PV
PW
PX
PY
PZ
QA
QB
QC
QD
QE
QF
QG
Strain 1 : Faecalibacterium prausnitzii OTU ID 326792, Strain 2: Ruminococcus sp. 5 1 39BFAA OTU ID 189827, Strain 3:
Lactobacillus ruminis OTU ID 470663, Strain 4: Dorea longicatena OTU ID 191687, Strain 5: Bifidobacterium longum OTU ID 72820, Strain 6: Ruminococcus sp. 5 1 39BFAA OTU ID 194745, Strain 7: Lactobacillus mucosae OTU ID 15141 , Strain 8:
Bifidobacterium sp. OTU ID 561483, Strain 9: Staphylococcus sp. OTU ID 217996, Strain 10: Ruminococcus sp. 5 1 39BFAA OTU ID 364234, Strain 11 : Catenibacterium mitsuokai OTU ID 287510, Strain 12: Dorea formicigenerans OTU ID 261912, Strain 13: Ruminococcus torques OTU ID 361809, Strain 14: Streptococcus thermophilus OTU ID 108747, Strain 15: Bifidobacterium sp. OTU ID 533785, Strain 16: Haemophilus parainfluenzae OTU ID 9514, Strain 17: Streptococcus sp. OTU ID 561636, Strain 18:
Clostridium sp. OTU ID 312461 , Strain 19: Clostridium ramosum OTU ID 470139, Strain 20: Clostridium sp. OTU ID 181834, Strain 21 : Weissella cibaria OTU ID 148099, Strain 22: Bifidobacterium sp. OTU ID 469873, Strain 23: Clostridiales sp. OTU ID 185951 , Strain 24: Ruminococcaceae sp. OTU ID 212619. Additional information about these bacterial taxa may be found in Table B.
[0093] A bacterial taxon of the invention, or a combination of bacterial taxa of the invention, is formulated to maintain a suitable level of viable cells during the formulation's shelf life and upon administration to a subject. Each bacterial taxon may be present in a wide range of amounts provided that the composition or combination delivers the effect described. The total amount of bacteria per unit dose is dependent, in part, upon the dosage form and excipients. Non-limiting examples of suitable amounts include from about 102 to about 1012 colony forming units (cfu) of each bacterial strain per unit dose. In some embodiments, the amount of each bacterial strain is between about 105 and about 1012 cfu per unit dose, between about 106 and about 1012 cfu per unit dose, between about 107 and about 1012 cfu per unit dose, or between about 108 and about 1012 cfu per unit dose. In other embodiments, the amount of each bacterial strain is between about 105 and about 1011 cfu per unit dose, between about 106 and about 1011 cfu per unit dose, between about 107 and about 1011 cfu per unit dose, or between about 108 and about 1011 cfu per unit dose. In other embodiments, the amount of each bacterial strain is between about 105 and about 1010 cfu per unit dose, between about 106 and about 1010 cfu per unit dose, between about 107 and about 1010 cfu per unit dose, or between about 108 and about 1010 cfu per unit dose. In other
embodiments, the amount of each bacterial taxon is between about 105 and about 109 cfu per unit dose, between about 106 and about 109 cfu per unit dose, between about 107 and about 109 cfu per unit dose, or between about 108 and about 109 cfu per unit dose. Generally, a bacterial strain may be provided as a frozen or freeze-dried culture, or as bacterial spores.
[0094] A bacterial taxon of the invention, or a combination of bacterial taxa of the invention, may be formulated into a formulation for oral or rectal administration comprising one or more bacterial taxa of the invention and one more excipients. Non- limiting examples of excipients include binders, diluents, fillers,, disintegrants,
effervescent disintegration agents, preservatives, antioxidants, flavor-modifying agents, lubricants and giidants, dispersants, coloring agents, pH modifiers, chelating agents, and release-controlling polymers. Bacterial taxa of the invention, or a combination of bacterial taxa of the invention, may be formulated in unit dosage form as a solid, semisolid, liquid, capsule, or powder. These formulations are a further aspect of the invention. Usually the amount of a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, is between 0.1 -95% by weight of the formulation, or between 1 and 50% by weight of the formulation. Methods of formulating compositions are discussed in, for example, Hoover, John E., Remington's Pharmaceutical Sciences, Mack Publishing Co., Easton, Pa. (1975), and Liberman, H. A. and Lachman, L, Eds., Pharmaceutical Dosage Forms, Marcel Decker, New York, N.Y. (1980).
[0095] In the preparation of formulations in dosage units for oral administration, a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, may be mixed with a solid, powdered carrier, e.g. lactose, saccharose, sorbitol, mannitol, starch, amylopectin, cellulose derivatives or gelatin, and optionally with lubricants (e.g. magnesium stearate, calcium stearate, sodium steryl fumarate and polyethylene glycol waxes), release-controlling polymers and other excipients. The mixture is then processed into granules or pressed into tablets. Since the viability of the a bacterial strain may be negatively impacted by acidic media, the above-mentioned granules or tablets may be coated with an enteric coating which protects the
combination of the invention from acid degradation as long as the dosage form remains in the stomach. The enteric coating is chosen among pharmaceutically acceptable enteric-coating materials e.g. beeswax, shellac or anionic film-forming polymers such as cellulose acetate phthalate, hydroxypropyl methylcellulose phthalate, partly methyl esterified methacrylic acid polymers and the like, if preferred in combination with a suitable plasticizer. To this coating various dyes may be added in order to distinguish among tablets or granules with different combinations or with different amounts of the combination present.
[0096] Alternatively, the pharmaceutical compositions may be incorporated into a food product or powder for mixing with a liquid, or administered orally after only mixing with a non-foodstuff liquid.
[0097] Soft gelatine capsules may also be prepared with capsules comprising a combination of the invention and vegetable oil, fat, or other suitable vehicle for soft gelatine capsules. Soft gelatine capsules are preferably enteric coated as described above. Hard gelatine capsules may contain enteric-coated granules of a combination of the invention. Hard gelatine capsules may also contain a combination of the invention in combination with a solid powdered carrier e.g. lactose, saccharose, sorbitol, mannitol, potato starch, corn starch, amylopectin, cellulose derivatives or gelatine; the hard gelatine capsules are preferably enteric coated as described above.
[0098] Dosage units for rectal administration may be prepared in the form of suppositories which comprise a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, mixed with a non-irritating excipient that is solid at room temperature but liquid at rectal temperature and therefore will melt in the rectum to release the drug. Non-limiting examples of suitable excipients for rectal suppository embodiments include cocoa butter, beeswax, and polyethylene glycols. Alternatively, a dosage unit for rectal administration may be prepared in the form of a gelatine rectal capsule which comprises a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, in a mixture with a vegetable oil, paraffin oil or other suitable vehicle for gelatine rectal capsules. In yet another alternative, a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, may be prepared in the form of a ready-made enema, or in the form of a dry enema formulation to be
reconstituted in a suitable solvent just prior to administration.
[0099] Liquid preparations for oral administration may be prepared in the form of syrups or suspensions, e.g. solutions or suspensions containing from 0.2% to 20% by weight of a bacterial taxon of the invention, or a combination of bacterial taxa of the invention, and the remainder consisting of sugar or sugar alcohols and a mixture of ethanol, water, glycerol, propylene glycol and polyethylene glycol. If desired, such liquid preparations may contain colouring agents, flavouring agents, saccharine and
carboxymethyl cellulose and thickening agent. Liquid preparations for oral
administration may also be prepared in the form of a dry powder to be reconstituted with a suitable solvent prior to use.
[0100] The dosing regimen involving compositions in accordance with the present disclosure can be varied to achieve a desired result, such as may be
determined empirically for a given individual subject, or by extrapolation from data obtained from administering a composition of the invention to a clinical or other test population. The desired dose may be presented in multiple (e.g., two, three, four, five, six, or more) sub-doses administered at appropriate intervals throughout the day.
[0101 ] The following examples are included to demonstrate preferred embodiments of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples that follow represent techniques discovered by the inventors to function well in the practice of the invention. Those of skill in the art should, however, in light of the present disclosure, appreciate that changes may be made in the specific embodiments that are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention. Therefore, all matter set forth or shown in the accompanying drawings is to be interpreted as illustrative and not in a limiting sense.
EXAMPLES
[0102] The following examples illustrate various iterations of the invention.
[0103] Severe acute malnutrition and moderate acute malnutrition (MAM) are typically defined by anthropometric measurements: children are classified as having SAM if their weight-for-height Z-scores (WHZ)3 are below three standard deviations (~3 s.d.) from the median of the World Health Organization (WHO) reference growth standards, whereas those with WHZ between -2 and -3 s.d. are categorized as having MAM. SAM and MAM typically develop between 3 and 24 months after birth4. A standardized treatment protocol for SAM and its complications has been developed in Bangladesh1. The result has been a reduction in mortality rate, although the extent to which this protocol results in long-term restoration of normal growth and development needs to be ascertained through longitudinal studies5,6. There is similar lack of clarity about the long-term efficacy of nutritional interventions for MAM7,8.
[0104] Food is a major factor that shapes the proportional representation of organisms present in the gut microbial community (microbiota), and its gene content (microbiome). The microbiota and microbiome in turn have an important role in extracting and metabolizing dietary ingredients9"14. To investigate the hypothesis that healthy postnatal development (maturation) of the gut microbiota is perturbed in malnutrition12, we monitored 50 healthy Bangladeshi children monthly during the first 2 years after birth (25 singletons, 1 1 twin pairs, 1 set of triplets; 996 faecal samples collected monthly; see Methods for Examples 1 -9 and Tables 1 and 2). By identifying bacterial taxa that discriminate the microbiota of healthy children at different chronologic ages, we were able to test our hypothesis by studying 6 to 20-month-old children presenting with SAM, just before, during, and after treatment with two very different types of food intervention, as well as children with MAM. The results provide a different perspective about malnutrition; one involving disruption of a microbial facet of our normal human postnatal development. [0105] To characterize gut microbiota maturation across unrelated healthy Bangladeshi children living in separate households, faecal samples were collected at monthly intervals up to 23.4 ± 0.5 months of age in a training set of 12 children who exhibited consistently healthy anthropometric scores (WHZ, --0.32 ± 0.98 (mean ± s.d.) 22.7 ± 1 .5 faecal samples per child; Table 3a). The bacterial component of their faecal microbiota samples was characterized by V4-16S rRNA sequencing (Table 4) and assigning the resulting reads to operational taxonomic units (OTUs) sharing 97% nucleotide sequence identity (see Methods for Examples 1 -9; a 97%-identity OTU is commonly construed as representing a species-level taxon). The relative abundances of 1 ,222 97%-identity OTUs that passed our filtering criterion15 were regressed against the chronologic age of each child at the time of faecal sample collection using the Random Forests machine learning algorithm16. The regression explained 73% of the variance related to chronologic age. The significance of the fit was established by comparing fitted to null models in which age labels of samples were randomly permuted with respect to their 16S rRNA microbiota profiles (P = 0.0001 , 9,999 permutations). Ranked lists of all bacterial taxa, in order of 'age-discriminatory importance', were determined by considering those taxa, whose relative abundance values when permuted have a larger marginal increase in mean squared error, to be more important (see Methods for Examples 1 -9). Tenfold cross-validation was used to estimate age-discriminatory performance as a function of the number of top-ranking taxa according to their feature importance scores. Minimal improvement in predictive performance was observed when including taxa beyond the top 24 (see Table 5 for the top 60). The 24 most age- discriminatory taxa identified by Random Forests are shown in FIG. 1 A in rank order of their contribution to the predictive accuracy of the model and were selected as inputs to a sparse 24-taxon model.
[0106] To test the extent to which this sparse model could be applied, we applied it, with no further parameter optimization, to additional monthly faecal samples collected from two other healthy groups of children: 13 singletons (WHZ, -0.4± 0.8 (mean± s.d.)) and 25 children from a birth-cohort study of twins and triplets, (WHZ, -0.5 ± 0.7 (mean ± s.d.)), all born and raised in Mirpur, Bangladesh (Table 3b,c). We found that the model could be applied to both groups (r2 = 0.71 and 0.68, respectively), supporting the consistency of the observed taxonomic signature of microbiota
maturation across different healthy children living in this geographic locale (FIG. 1 B-D and F-H).
[0107] Two metrics of microbiota maturation were defined by applying the sparse model to the 13 healthy singletons and 25 members of twin pairs and triplets that had been used for model validation. The first metric, relative microbiota maturity, was calculated as follows:
relative microbiota maturity = (microbiota age of child) - (microbiota age of healthy children of similar chronologic age)
where microbiota age values for healthy children were interpolated across the first 2 years of life using a spline fit (FIG. 1 B-D). The second metric, microbiota-for-age Z score, was calculated as follows:
MAZ=
(microbiota age - median microbiota age of healthy children of same chronologic age)
(s.d. of microbiota age of healthy children of the same chronologic age) where MAZ is the microbiota-for-age Z-score, and median and s.d. of microbiota age were computed for each month up to 24 months. The MAZ accounts for the variance of predictions of microbiota age as a function of different host age ranges (when
considered in discrete monthly bins) (see FIG. 3 for the calculation of each metric, and Example 8 for discussion of how this approach defines immaturity as a specific recognizable state rather than as a lack of maturity).
[0108] To study the influences of genetic and environmental factors on these microbiota maturation indices, we examined their distribution in healthy
Bangladeshi twins and triplets. Monozygotic twins were not significantly more correlated in their maturity profiles compared to dizygotic twins, and within the set of triplets, the two monozygotic siblings were not more correlated than their fraternal sibling
(monozygotic pairs, 0.1 ± 0.5 (Spear-man's Rho ± s.d.); dizygotic pairs, 0.33 ± 0.3; in the case of the triplets, values for the monozygotic pair and fraternal sibling were 0.1 ; and 0.24 ± 0.3, respectively). Maturity was significantly decreased in faecal samples obtained during and 1 month after diarrhoeal episodes (P < 0.001 and P < 0.01 , respectively) but not beyond that period (FIG. 4). There was no discernible effect of recent antibiotic usage (1 week before sampling) on relative microbiota maturity, whereas intake of infant formula was associated with significantly higher maturity values (Table 6). Family membership explained 29% of the total variance in relative microbiota maturity measurements (log-likelihood ratio = 102.1 ,P < 0.0001 ; linear mixed model) (see Example 3, Tables 7 and 8, and FIG. 5 for analyses of faecal microbiota variation in mother-infant dyads and fathers).
[0109] To investigate the effects of SAM on microbiota maturity, 64 children with SAM who had been admitted to the Nutritional Rehabilitation Unit of the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), Dhaka Hospital, were enrolled in a study to investigate the configuration of their faecal microbiota before, during and after treatment with either an imported, internationally used ready-to-use therapeutic food (RUTF; Plumpy'Nut) or a locally produced, lower- cost nutritional food combination (Khichuri-Halwa). Children ranged in age from 6 to 20 months of age at the time of enrollment and were randomly assigned to either of the treatment arms. At enrollment, WHZ averaged -4.2± 0.7 (mean± s.d.) (see Tables 9 and 10 for patient metadata and FIG. 2A for study design). In the initial 'acute phase' of treatment, infection control was achieved with parenteral administration of ampicillin and gentamicin for 2 and 7 days, respectively, and oral amoxicillin for 5 days (from days 3 to 7 of the antibiotic treatment protocol). Children with SAM were initially stabilized by being fed the milk-based gruel, 'suji', followed by randomization to either an imported peanut-based RUTF intervention or an intervention with locally produced, rice-and-lentil- based therapeutic foods (Khichuri and Halwa; see Methods for Examples 1 -9 and
Table 11 for compositions of all foods used during nutritional rehabilitation). During this second 'nutritional rehabilitation phase' (1 .3 ± 0.7 weeks long) children received 150- 250 kcal kg"1 body weight per day of RUTF or Khichuri-Halwa (3-5 g protein kg"1 per day), plus micronutrients including iron. Children were discharged from the hospital after the completion of this second phase; during the 'post-intervention phase', periodic follow-up examinations were performed to monitor health status. Faecal samples were obtained during the acute phase before treatment with Khichuri-Halwa or RUTF, then every 3 days during the nutritional rehabilitation phase, and monthly thereafter during the post-intervention follow-up period.
[01 10] There was no significant difference in the rate of weight gain between the RUTF and Khichuri-Halwa groups (10.9± 4.6 versus 10.4± 5.4 g kg"1 body weight per day (mean± s.d.); Student's i-test, P = 0.7). The mean WHZ at the
completion of nutritional rehabilitation was significantly improved in both treatment groups(-3.1 ± 0.7(mean± s.d.) RUTF, P < 0.001 ; and -2.7± 1 .6 Khichuri-Halwa, P < 0.0001 ), but not significantly different between groups (P = 0.15). During follow-up, WHZ remained significantly lower compared to healthy children (-2.1 ± 1 .2, Khichuri- Halwa; -2.4±0.8 RUTF versus -0.5±1 .1 for healthy, PO.0001 ; FIG. 6A). Children in both treatment arms also remained markedly below normal height and severely underweight throughout the follow-up period (FIG. 6B,C).
[01 1 1 ] The Random Forests model derived from healthy children was used to define relative microbiota maturity for children with SAM at the time of enrollment, during treatment, at the end of either nutritional intervention, and during the months of follow-up. The results revealed that compared to healthy children, children with SAM had significant microbiota immaturity at the time that nutritional rehabilitation was initiated and at cessation of treatment (Dunnett's post-hoc test, P < 0.0001 for both groups; FIG. 2B). Within 1 month of follow-up, both groups had improved significantly. However, improvement in this metric was short-lived for the RUTF and Khichuri-Halwa groups, with regression to significant immaturity relative to healthy children beyond 4 months after treatment was stopped (FIG. 2B and Table 12). MAZ, like relative microbiota maturity, indicated a transient improvement after RUTF intervention that was not durable beyond 4 months. In the Khichuri- Halwa group, relative microbiota maturity and MAZ improved following treatment, but subsequently regressed, exhibiting significant differences relative to healthy children at 2-3 months, and >4 months after cessation of treatment (FIG. 2B and Table 12).
[01 12] Both food interventions had non-durable effects on other microbiota parameters. The reduced bacterial diversity associated with SAM persisted after Khichuri-Halwa and only transiently improved with RUTF (FIG. 7 and Table 12). We identified a total of 220 bacterial taxa that were significantly different in their proportional representation in the faecal microbiota of children with SAM compared to healthy children; 165 of these 220 97%-identity OTUs were significantly diminished in the microbiota of children with SAM during the longer term follow-up period in both treatment groups (FIG. 8 and 9 and Table 13).
[01 13] Although the majority of children in both treatment arms of the SAM study were unable to provide faecal samples before the initiation of antibiotic treatment due to the severity of the illness, a subset of nine children each provided one or two faecal samples (n = 12) before administration of parenteral ampicillin and gentamicin, and oral amoxicillin. Microbiota immaturity was manifest at this early time-point before antibiotics in these nine children (relative microbiota maturity: -5.15 ± 0.9 months versus -0.03± 0.1 for the 38 reference healthy controls; Mann-Whitney, P < 0.0001 ). Sampling these nine children after treatment with parenteral and oral antibiotics but before initiation of RUTF or Khichuri-Halwa (6± 3.6 days after hospital admission) showed that there was no significant effect on microbiota maturity (Wilcoxon matched-pairs rank test, P = 1 ). When pre-antibiotic faecal samples from these nine children were compared to samples collected at the end of all treatment interventions (dietary and antibiotic, 20 ± 9 days after admission), no significant differences in relative microbiota maturity
(Wilcoxon, P = 0.7), MAZ, bacterial diversity (or WHZ) were found (FIG. 10A-D). This is not to say that these interventions were without effects on overall community
composition: opposing changes in the relative abundance of Streptococcaceae and Enterobacteriaceae were readily apparent (FIG. 10E,F; note that the Random Forests model classified both the microbiota of children with SAM sampled before and at the conclusion of all treatment interventions as immature, indicating lack of a generic immature state). Although these findings indicate that the relative microbiota immaturity associated with SAM was not solely attributable to the antibiotics used to treat these children, we could not, in cases where children were unable to provide pre-intervention faecal samples, measure the effects of other antibiotics, consumed singly or in various combinations during the acute infection control and nutritional rehabilitation phases, on their metrics of microbiota maturation (see Example 7 and Table 14 for further evidence indicating antibiotic use in the follow-up period did not correlate with the persistence of microbiota immaturity in children with SAM).
[01 14] SAM affects approximately 4% of children in developing countries. MAM is more prevalent, particularly in South Central Asia, where it affects
approximately 19% (30 million children)7. Epidemiological studies indicate that periods of MAM are associated with progression to SAM, and with stunting which affects >40% of children under the age of five in Bangladesh17. Therefore, we extended our study to children from the singleton cohort at 18 months of age, when all had transitioned to solid foods (n = 10 children with WHZ lower than -2 s.d., the threshold for MAM; 23 children with healthy WHZ; Table 15). The relationship between relative microbiota maturity, MAZ and WHZ was significant (Spearman's Rho = 0.62 and 0.63, P < 0.001 ,
respectively; FIG. 11A,B) Comparing children with MAM to those defined as healthy revealed significantly lower relative microbiota maturity, MAZ and differences in the relative abundances of age-discriminatory taxa in the malnourished group (FIG. 11 D-l¬ and FIG. 12A,B) These results suggest that microbiota immaturity may be an additional pathophysiological component of moderately malnourished states.
[01 15] In conclusion, definition of microbiota maturity using bacterial taxonomic biomarkers that are highly discriminatory for age in healthy children has provided a way to characterize malnourished states, including whether responses to food interventions endure for prolonged periods of time beyond the immediate period of treatment. RUTF and Khichuri- Halwa produced improvements in microbiota maturity indices that were not sustained. Addressing the question of how to achieve durable responses in children with varying degrees of malnutrition may involve extending the period of administration of existing or new types of food interventions7. One testable
hypothesis is that a population's microbiota conditioned for generations on a diet will respond more favourably to nutrient supplementation based on food groups represented in that diet. Next-generation probiotics using gut-derived taxa may also be required in addition to food-based interventions. The functional roles (niches) of the age- discriminatory taxa identified by our Random Forests model need to be clarified since they themselves maybe therapeutic candidates and/or form the basis for low cost field- based diagnostic assessments.
[01 16] Systematic analyses of microbiota maturation in different healthy and malnourished populations living in different locales, representing different lifestyles andculturaltraditions11 ,18, may yield a taxonomy-based model that is generally applicable to many countries and types of diagnostic and therapeutic assessments. Alternatively, these analyses may demonstrate a need for geographic specificity when constructing such models (and diagnostic tests or therapeutic regimens). Two observations are notable in this regard. First, expansion of our sparse model from 24 to 60 taxa yielded similar results regarding the effects of diarrhoea in healthy individuals, MAM and SAM (and its treatment with RUTF and Khichuri-Halwa) on microbiota maturity (see
Example 8). Second, we applied the model that we used for Bangladeshi children to healthy children in another population at high risk for malnutrition. The results show that the model generalizes (i2 = 0.6) to a cohort of 47 Malawian twins and triplets, aged 0.4- 25.1 months, who were concordant for healthy status in a previous study11 (WHZ, - 0.23± 0.97(mean± s.d.); Table 16). Age-discriminatory taxa identified in healthy
Bangladeshi children show similar age-dependent changes in their representation in the microbiota of healthy Malawian children, as assessed by the Spearman rank correlation metric (FIG. 12C,D).
[01 17] The question of whether microbiota immaturity associated with SAM and MAM is maintained during and beyond childhood also underscores the need to determine the physiologic, metabolic and immunologic consequences of this immaturity, and how they might contribute to the associated morbidities and sequelae of malnutrition, including increased risk for diarrhoeal disease, stunting, impaired vaccine responses, and cognitive abnormalities2,19. Our study raises a testable hypothesis: namely, that assessments of microbiota maturation, including in the context of the maternal-infant dyad, will provide a more comprehensive view of normal human development and of developmental disorders, and generate new directions for preventive medicine. Testing this hypothesis will require many additional clinical studies but answers may also arise from analyses of gut microbiota samples that have already been stored from previous studies.
Example 2. Anthropologic assessment
[01 18] The study population resided in the Mirpur slum of Dhaka,
Bangladesh (23.8042°N 90.3667Έ) in a catchment area consisting of 9,250
households. Most of the homes in this community consist of one main room (-220 square feet), composed of concrete floors and tin roofs with bamboo, metal, and in a few cases cement walls. The average number of household members ranges from 4-10 people, and average monthly family income is 4,000-10,000 Bangladeshi Taka (50 to 130 USD). Infants do not wear diapers, nor do they typically wear any clothing on the bottom halves of their bodies. The importance of hand washing before eating or child feeding is widely understood but rarely practiced due to lack of access to clean water. Families prepare food either on the floor or on a ground-level cement slab located at the entrance of the home; this slab typically straddles an open drain containing wastewater running along the street. Since few families have refrigerators, most food is stored on shelves or under the bed. Households may consist of more than one biological family: in these cases all individuals in the household share a gas stove and cooking area immediately outside the main room, although food, cooking pots and utensils are used separately by each biological family. All individuals share a common 'bathroom,' a small space containing a latrine, and sometimes the water pump, located next to the main room. The common practice is to wash the perianal area by hand with water contained in a small, special container called bodna in Bangla.
Example 3. Fecal microbiota variation within and between family members during the first year of postnatal life.
[01 19] There are few reports of time-series studies charting assembly of the gut microbiota in healthy USA infants and even fewer studies in infants from non- Western populations. The results published to date have revealed pronounced intra- as well as interpersonal variation during the first year of life11 ,29"31. In contrast, the gut microbiota of healthy USA adults is quite stable over time, with signatures of within- individual and within-family similarity evident throughout sampling periods15.
[0120] To obtain a view of gut microbiota development in Bangladeshi infants and children as a function of time after birth and family structure, we collected monthly fecal samples from 1 1 twin pairs and 1 set of triplets and their parents. The first fecal sample was obtained from infants at the time of their enrollment (4±3 days of age). Monthly samples were subsequently obtained from each of these 25 infants and from their mothers while samples were collected from their fathers every three months.
Families were followed for a total of 520±159 days (mean±SD). The duration of exclusive breastfeeding was 28±23 days (mean±SD). Diarrhoea occurred for 1 1 ±12 days (2±3 %of the total number of days followed during the study).
[0121 ] Distances (degree of similarity) between all pairs of fecal microbiota samples in this birth cohort were computed using the Hellinger metric, an abundance- based ecological metric, as well as the phylogeny-based unweighted UniFrac metric where distance is calculated based on the degree to which any two communities share branch length on a bacterial tree of life32. In the case of triplets, we performed all possible pairwise comparisons (self-self; all three possible pairwise comparisons among siblings; each sibling against unrelated age-matched individuals; each sibling against their mother or father).
[0122] We had previously noted that genetically unrelated co-habiting adults in the USA have more similar overall bacterial phylogenetic configurations in their fecal microbiota than unrelated adults living separately11. Comparing the difference (distance) of a Bangladeshi mother's microbiota during her first month post-partum to her microbiota three months later revealed a larger shift in overall structure compared to fathers sampled during the same three-month interval [P=0.01 (Hellinger); P=0.04 (unweighted UniFrac); FIG. 5C,D], thus obscuring a microbial manifestation of their cohabitation. This signature of co-habitation emerged 10 months postpartum, at a time when the preceding marked temporal variation of the mother's microbiota had
diminished (P=0.006 for difference between co-habiting spouses at 10 months postpartum versus non-co-habiting adults in the cohort as measured by the abundance based Hellinger metric; P=0.08 using the presence/absence unweighted UniFrac phylogenetic metric; see FIG. 5E,F).
[0123] During the first postnatal month, the bacterial configuration of the fecal microbiota of infants was more similar to mothers compared to fathers (P<0.001 Hellinger metric; P=0.07 with unweighted UniFrac; FIG. 5G,H). Co-twins were more similar to one another than unrelated age-matched twins during the first postpartum year (P<0.001 Kruskall-Wallis; FIG. 5I,J). An analysis of sources of variation in the microbiota of the twins and triplets over the course of the entire study revealed that age alone captured 19% and 37.7% of variance (Hellinger and unweighted UniFrac metrics, respectively) in contrast to dietary factors (presence/absence of 'breast milk', 'formula', 'solid foods') which explained only 2.5% and 3.8%, respectively (see Table 7 for partitioning of variance by metric and metadata; PERMANOVA as implemented with adonis function in R package vegan)33.
[0124] We identified increases in the proportional representation of 97%- identity OTUs in the microbiota of mothers during the perinatal period, including a number of the age-discriminatory taxonomic biomarkers, notably Bifidobacteria in FIG. 5A,B and Table 8. This latter feature is not unique to Bangladesh: a recent study of 80 Finnish mother-infant pairs sampled at 1 and 6 months post-partum demonstrated that if a mother was positive for B. bifidum 1 month following delivery, the likelihood of her child being colonized was significantly higher34).
Example 4. Transient reduction of gut microbiota diversity in healthy twins and triplets associated with diarrhea.
[0125] In addition to changes in the relative proportions of specific bacterial taxa incorporated into our Random Forests model-derived MAZ and relative microbiota maturity metrics, the developing gut microbiota of infants/children is also characterized by an increase in total community bacterial diversity as judged by the Shannon Diversity Index (SDI). SDI is an ecological measure of within-sample (alpha) diversity that incorporates both the concept of total community size as well as the evenness of the abundance of its members. Across the 50 healthy Bangladeshi children sampled, SDI increased linearly with age (0.1 1 units per month of life with an intercept of 1 .6±0.1 units at birth; mixed model; P<0.0001 ). In twins and triplets, diarrhoea (n=36 episodes) was the only significant clinical parameter associated with a reduction in SDI (-0.44±0.1 ; P<0.01 ). This reduction showed a similar time course of recovery as relative microbiota maturity, persisting for one month (-0.35±0.2 SDI; P<0.05) followed by subsequent recovery (FIG. 4; Table 6c).
Example 5. Persistent reductions in diversity associated with SAM.
[0126] As with measurements of microbiota maturity, the RUTF group showed significant improvement in SDI values between 1 -3 months following cessation of treatment, followed by regression to a persistent lower than healthy SDI beyond 3 months. In the case of Khichuri-Halwa, improvement in SDI was only significant at 3-4 months of follow-up. SAM children in both treatment groups exhibited significant reductions in diversity compared to healthy Bangladeshi children at all phases of treatment and recovery, except for 1 -3 months post-RUTF and 3-4 months post- Khichuri-Halwa (FIG. 7; Table 12).
Example 6. Two hundred and twenty bacterial taxa that are significantly different in their proportional representation in microbiota of children with SAM compared to healthy at multiple treatment phases across both groups.
[0127] During the acute phase, prior to nutritional rehabilitation, 1 16 97%- identity OTUs were significantly altered in SAM. The majority were lower in relative abundance compared to healthy children. The four 97% identity OTUs with the largest reductions in abundance during the acute phase included three classified as belonging to the genus Bifidobacterium (B. longum, two unassigned to a species) and
Faecalibacterium prausnitzii, of which two are age-discriminatory taxa (FDR-corrected P<0.05). Taxa that were enriched in children diagnosed with SAM compared to healthy children included those belonging to the family Enterobacteriaceae (genera Escherichia and Klebsiella) as well as Enterococcus faecalis (FDR-corrected P<0.05).
[0128] In children with SAM, taxa that remain depleted throughout the follow-up period included members of the bacterial families Ruminococcaceae, Veillonellaceae and Prevotellaceae. Taxa enriched in the microbiota of children with SAM after the therapeutic food interventions belonged predominantly to the genus Streptococcus, including 97% ID OTUs identified as Streptococcus lutentiensis,
Streptococcus thermophilus (also age-discriminatory) and other as yet unknown
Streptococcus species (FDR-corrected P<0.01 ; see FIG. 8 and FIG. 9 for a heatmap depiction of all 97% ID OTUs whose representation in the fecal microbiota is
significantly altered in SAM relative to healthy before, during and after the nutritional rehabilitation period; also see Table 13).
Example 7. Assessing the effects of antibiotics on microbiota maturity during the follow-up period in children with SAM.
[0129] As noted in Example 1 , we compared antibiotic use during the post-intervention periods for the two treatment arms. The results indicate that (i) the frequency of antibiotic consumption during this period was comparable to that of healthy children in our training and validation sets (P=0.5, one-way ANOVA); (ii) there was no significant difference in antibiotic use between treatment arms (P=1 ; Fisher's exact test; Table 10); (iii) there was no significant association between recent antibiotic intake (defined as occurring seven or fewer days before collection of a fecal sample) and relative microbiota maturity values [difference in maturity values for samples with versus those without recent antibiotic intake: -0.37±0.8 (mean ± SEM) P=0.6 (ANOVA of linear mixed model; n=100 samples for the 22 children in the post RUTF arm); +0.17±0.9; P=0.9 (n=103 samples, 25 children in the Khichuri-Halwa arm)]. Similarly, we found that diarrhoea was not significantly associated with differences in maturity values in either arm during the post-intervention period (Table 14).
Example 8. Expanding the sparse Random Forests-based model from 24 to 60 taxa.
[0130] It is logical to ask the following questions about our approach for defining microbiota maturity. First, are we defining "immaturity" entirely as a lack of maturity, rather than a specific, recognizable state in and of itself. Ours is a 'positive' composition-based classification. For example, the Bifidobacterium longum OTU in FIG. 1 A ranks 5th in terms of its feature importance score in the 24-taxon Random Forests model. In samples from healthy infants less than 6 months old, this OTU is highly represented [relative abundance = 52.7±30% (mean±SD); >1 % in 94% of samples from the training and validation sets). The remaining seven 97%-identity OTUs that comprise the cluster of early age-discriminatory taxa shown in FIG. F-H together represent 6.35±8% of the microbiota and are present at >1 % abundance in 84% of samples.
[0131 ] Second, was there an outlet for samples containing very few or none the 24 taxa selected by the model? For example, were they deemed
unclassifiable, or classified as "other", or were all samples "forced" onto the maturity scale? How were samples with low feature signal having few to no age-discriminant taxa classified? Only one of the 589 fecal samples in the SAM study had undetectable levels of the 24 age-discriminatory taxa. In the SAM cohort, only 10% of fecal samples (60/589) had an aggregate relative abundance of the 24 age-discriminatory taxa that was less than 10%. In healthy children, this was true for 36/960 samples.
[0132] When we expanded our model to include the top 60 age- discriminatory taxa, we found that < 1 % of SAM samples and none of the healthy samples had an aggregate relative abundance of the 60 age-discriminatory taxa that was less than 10%. Note that in expanding the model, we excluded OTUs that were deemed chimeric when using default BLAST thresholds to the Greengenes reference as implemented in QIIME. The performance of the 24 and 60 taxa models were similar. Predictions made by the two models when they were applied to the healthy validation datasets (all 724 samples considered), and when they were applied to the SAM datasets (all 589 samples considered), showed a strong correlation (r2=0.98 and 0.93, respectively). Both yielded similar results for our analysis of (i) the effects of diarrhoea in healthy twins/triplets (microbiota immaturity was transient), (ii) the SAM trial (the effects of RUTF and Khichuri-Halwa produced transient non-durable improvements compared to healthy controls; antibiotics did not have a significant effect on microbiota maturity measurements either during the acute phase or during the post-intervention; note that the top 60 model includes Enterobacteriaceae and Streptococcaceae OTUs that are highly enriched in children with SAM relative to healthy); and (iii) the MAM study (a significant difference was observed between 18 month old healthy controls versus children with MAM) (data not shown).
Example 9. Processed 16S rRNA datasets.
[0133] Processed 16S rRNA datasets are available here,
http://gordonlab.wustl.edu/Subramanian_6_14/Nature_2014_Processed_16S_rRNA_da tasets.html, as a BIOM-formatted OTU table, along with split libraries of data generated from faecal samples and 'Mock' communities, mapping file, and an augmented reference sequence set (Greengenes version 4feb201 1 plus de novo OTUs picked from sequences generated in the present study).
Methods for Examples 1 -9.
[0134] Summary. All subjects lived in Dhaka, Bangladesh (see
Anthropologic Study below and Example 2 for anthropologic assessment of Mirpur, an urban slum in Bangladesh, where most subjects resided). Informed consent was obtained and studies were conducted using protocols approved by the ICDDR,B, Washington University, and University of Virginia institutional review boards (IRBs). Linear mixed models were applied to test hypotheses in repeated measurements of relative abundance of 97%-identity OTUs and maturation metrics in time-series profiling of faecal microbiota20. To account for similarity between observations from repeated sampling of the same individuals and families, we fit random intercepts for each subject in the case of adults and singletons, nested these intercepts within each family in the case of twins and triplets, and included age as a fixed-effect covariate, while testing the significance of associations between the microbiota and specified host and environmental factors. Differences between microbiota maturation metrics in each treatment phase of SAM were compared to values at enrollment in each treatment group, and to healthy children within the same age range (excluding samples from children used to train the Random Forests model), using analysis of variance (ANOVA) of linear mixed models followed by Dunnett's post-hoc comparisons.
[0135] Singleton birth cohort. Full details of the design of this now- complete birth cohort study have been described previously21. Faecal microbiota samples were profiled from 25 children who had consistently healthy anthropometric measures based on quarterly (every 3 months) measurements (Table 1 ). The WHZ threshold used for 'healthy' (on average above -2 s.d.) was based on median weight and height measurements obtained from age- and gender-matched infants and children by the Multi-Centre Growth Reference study of the World Health Organization3. Clinical parameters, including diarrhoeal episodes and antibiotic consumption associated with each of their faecal samples are provided in Supplementary Table 2 of Subramanian et al, Nature 2014; S10:417-421 , which is hereby incorporated by reference in its entirety.
[0136] A second group studied from this singleton cohort consisted of 33 children sampled cross-sectionally at 18 months, including those who were incorporated as healthy reference controls, and those with a WHZ < -2 who were classified with MAM (Table 15).
[0137] Twins and triplets birth cohort. Mothers with multiple pregnancy, identified by routine clinical and sonographic assessment at the Radda Maternal Child Health and Family Planning (MCH-FP) Clinic in Dhaka, were enrolled in a prospective longitudinal study (n = 1 1 mothers with twins, 1 mother with triplets). The zygosity of twin pairs and triplets was determined using plasma DNA and a panel of 96 polymorphic single-nucleotide polymorphisms (SNPs) (Center for Inherited Disease Research, Johns Hopkins University). Four twin pairs were monozygotic, six were dizygotic, and the set of triplets consisted of a monozygotic pair plus one fraternal sibling (Table 1 ; note that one of the 1 1 twin pairs could not be tested for zygosity because plasma samples were not available). Information about samples from healthy twins, triplets and their parents, including clinical parameters associated with each faecal sample, is provided in Table 2 and in Supplementary Table 2 of Subramanian et al, Nature 2014; S10:417-421 , which is hereby incorporated by reference in its entirety.
[0138] The three healthy Bangladeshi groups used for model training and validation had the following WHZ scores: -0.32 6 1 (mean 6 s.d.; 12 singletons randomized to the training set), -0.4460.8 (13 singletons randomized to one of the two validation sets), and-0.466 0.7 (twins and triplets in the other validation set) (Table 3). The average number of diarrhoeal episodes in the singleton training set, the singleton validation set, and the twin and triplet validation set (4, 4.6 and 1 .7, respectively) was comparable to values reported in previous surveys of another cohort of 0-2-year-old Bangladeshi children (4.25 per child per year) 22
[0139] There were no significant differences in the number of diarrheal episodes per year per child and the number of diarrhoeal days per year per child between the singleton training and validation sets (Student's i-test, P 5 0.5). Moreover, across all training and validation sets, neither of these diarrheal parameters correlated with mean age-adjusted Shannon diversity indices (Spearman's Rho, -0.18 and -0.12, P 5 0.22 and 0.4, respectively). The fraction of faecal samples collected from each child where oral antibiotics had been consumed within the prior 7 days was not significantly different between the training and two validation sets (one-way ANOVA, P 5 0.14; see Table 3).
[0140] Severe acute malnutrition study. Sixty-four children in the
Nutritional Rehabilitation Unit of ICDDR,B, Dhaka Hospital suffering from SAM (defined as having a WHZ less than -3 s.d. and/or bilateral pedal oedema) were enrolled in a randomized interventional trial to compare an imported peanut-based RUTF,
Plumpy'Nut (Nutriset Plumpyfield, India) and locally produced Khichuri-Halwa (clinical trial NCT01331044). Initially, children were stabilized by rehydration and feeding 'suji', which contains whole bovine milk powder, rice powder, sugar and soybean oil
(approximately 100 kcal kg"1 body weight per day, including 1 .5 g protein kg"1 per day). Children were then randomized to the Khichuri-Halwa or RUTF groups. Khichuri consists of rice, lentils, green leafy vegetables and soybean oil; Halwa consists of wheat flour (atta), lentils, molasses and soybean oil. Children randomized to the Khichuri- Halwa treatment arm also received milk suji '100' during their nutritional rehabilitation phase (a form of suji with a higher contribution of calories from milk powder compared to suji provided during the acute phase). RUTF is a ready-to-use paste that does not need to be mixed with water; it consists of peanut paste mixed with dried skimmed milk, vitamins and minerals (energy density, 5.4kcalg"1). Khichuri and Halwa are less energy- dense than RUTF (1 .45 kcal g-1 and 2.4 kcal g"1 , respectively, see Table 11 for a list of ingredients for all foods used during nutritional rehabilitation). [0141 ] The primary outcome measurement, rate of weight gain (g kg"1 per day), along with improvement in WHZ after nutritional rehabilitation are reported by child in Table 9. Faecal samples were collected before randomization to the RUTF and Khichuri-Halwa treatment arms, every 3 days during nutritional rehabilitation and once a month during the follow-up period (information associated with each faecal sample is provided in Supplementary Table 1 1 of Subramanian et al, Nature 2014; S10:417-421 , which is hereby incorporated by reference in its entirety).
[0142] Anthropologic study. To obtain additional information about household practices in the Mirpur slum of Dhaka, in-depth semi-structured interviews and observations were conducted over the course of 1 month in nine households (n = 30 individuals). This survey, approved by the Washington University and ICDDR,B IRBs, involved three ICDDR,B field research assistants, and three senior scientific staff in the ICDDR,B Centre for Nutrition and Food Security, plus two anthropologists affiliated with Washington University in St. Louis. Parameters that might affect interpretation of metagenomic analyses of gut microbial-community structure were noted, including information about daily food preparation, food storage, personal hygiene and childcare practices.
[0143] Characterization of the bacterial component of the gut microbiota by V4-16S rRNA sequencing. Faecal samples were frozen at -20 °C within 30 min of their collection and subsequently stored at -80 °C before extraction of DNA. DNA was isolated by bead-beating in phenol and chloroform, purified further (QIAquick column), quantified (Qubit) and subjected to polymerase chain reaction (PCR) using primers directed at variable region 4 (V4) of bacterial 16S rRNA genes. Bacterial V4-16S rRNA data sets were generated by multiplex sequencing of amplicons prepared from 1 ,897 faecal DNA samples (26,580 6 26,312 (mean 6 s.d.) reads per sample, paired-end 162- or 250-nucleotide reads; lllumina MiSeq platform; Table 4). Reads of 250 nucleotides in length were trimmed to 162 nucleotides, then all reads were processed using previously described custom scripts, and overlapped to 253-nucleotide fragments spanning the entire V4 amplicon15. 'Mock' communities, consisting of mixtures of DNAs isolated from 48 sequenced bacterial members of the human gut microbiota combined in one equivalent and two intentionally varied combinations, were included as internal controls in the lllumina MiSeq runs. Data from the mock communities were used for diversity and precision-sensitivity analyses employing methods described previously15,23.
[0144] Reads with > 97% nucleotide sequence identity (97%-identity) across all studies were binned into operational taxonomic units (OTUs) using QIIME (v 1 .5.0), and matched to entries in the Greengenes reference database (version
4feb201 1 )24,25. Reads that did not map to the Greengenes database were clustered de novo with UCLUST at 97%-identity and retained in further analysis. A total of 1 ,222 97%-identity OTUs were found to be present at or above a level of confident detection (0.1 % relative abundance) in at least two faecal samples from all studies. Taxonomy was assigned based on the naive Bayesian RDP classifier version 2.4 using 0.8 as the minimum confidence threshold for assigning a level of taxonomic classification to each 97%-identity OTU.
[0145] Definition of qut-microbiota maturation in healthy children using Random Forests. Random Forests regression was used to regress relative abundances of OTUs in the time-series profiling of the microbiota of healthy singletons against their chronologic age using default parameters of the R implementation of the algorithm (R package 'randomForest', ntree 5 10,000, using default mtry of p/3 where p is the number of input 97%-identity OTUs (features))26. The Random Forests algorithm, due to its non-parametric assumptions, was applied and used to detect both linear and nonlinear relationships between OTUs and chronologic age, thereby identifying taxa that discriminate different periods of postnatal life in healthy children. A rarefied OTU table at 2,000 sequences per sample served as input data. Ranked lists of taxa in order of Random Forests reported 'feature importance' were determined over 100 iterations of the algorithm. To estimate the minimal number of top ranking age-discriminatory taxa required for prediction, the rfcv function implemented in the 'randomForest' package was applied over 100 iterations. A sparse model consisting of the top 24 taxa was then trained on the training set of 12 healthy singletons (272 faecal samples). Without any further parameter optimization, this model was validated in other healthy children (13 singletons, 25 twins and triplets) and then applied to samples from children with SAM and MAM. A smoothing spline function was fit between microbiota age and chronologic age of the host (at the time of faecal sample collection) for healthy children in the validation sets to which the sparse model was applied.
[0146] Alpha diversity comparisons. Estimates of within-sample diversity were made at a rarefaction depth of 2,000 reads per sample. A linear regression was fit between the Shannon diversity index (SDI) and postnatal age in the 50 healthy children using a mixed model (see the additional details regarding statistical methods, below). An estimate of the coefficient for the slope of SDI with age and intercept was extracted, residuals of this regression were defined as a DSDI metric, and associations of this metric with clinical parameters were tested in the cohort of healthy twins and triplets. To test for differences in SDI as a function of health status and chronologic age in malnourished children, we compared the distribution of age-adjusted ASDIs in children with SAM between treatment phases.
[0147] Detection of associations of bacterial taxa with nutritional status and other parameters. Relative abundances of 97%-identity OTUs were used in linear mixed models as response variables to test for associations with clinical metadata as predictors. For each comparison, we restricted our analysis to 97%-identity OTUs and bacterial families whose relative abundance values reached a level of confident detection (0.1 %) in a minimum of 1 % of samples in each comparison. Pseudocounts of 1 were added to 97%-identity OTUs to account for variable depth of sequencing between samples, and relative abundances were arcs in-square-root-transformed to approximate homoscedasticity when applying linear models. P values of associations of factors with the relative abundances of bacterial taxa were computed using ANOVA type III (tests of fixed effects), subjected to Benjamini-Hochberg false discovery rate (FDR) correction.
[0148] Enteropathoqen testing. Clinical microscopy was performed for all faecal samples collected at monthly intervals from the singleton birth cohort and from healthy twins and triplets, and screened for Entamoeba histolytica, Entamoeba dispar, Escherichia coli, Blastocystis hominis, Trichomonas hominis, Blastocystis hominis, Coccidian-like bodies, Giardia lamblia, Ascaris lumbricoides, Trichuris Tricuria, Ancylostoma duodenale/Necator americanus, Hymenolepsis nana, Endolimax nana, lodamoebabutschlii and Chilomastixmesnili. The effects of enteropathogens, detected by microscopy on relative microbiota maturity, MAZ and SDI were included in our analysis of multiple environmental factors in FIG. 4 and Table 6. In cases in which children presented with SAM plus diarrhoea, faecal samples collected before nutritional rehabilitation were cultured for Vibrio cholerae, Shigella flexneri, Shigella boydi, Shigella sonnei, Salmonella enterica, Aeromonas hydrophila and Hafnia alvae. See Tables 9 and 17 for results of enteropathogen testing.
[0149] Additional details regarding statistical methods. Linear mixed models were applied to test for associations of microbiota metrics (relative microbiota maturity, MAZ and SDI) with genetic and environmental factors in twins and triplets. Log-likelihood ratio tests and F tests were used to perform backward elimination of nonsignificant random and fixed effects27. Relative microbiota maturity, MAZ and SDI were defined at different phases of treatment and at defined periods of follow-up (<1 month, 1-2 ,3-4 and >4 months after completion of the RUTF or Khichuri-Halwa nutritional intervention) in children with SAM relative to healthy children. Treatment phase' was specified as a categorical multi-level factor in a univariate mixed model with random by- child intercepts. Dunnett's post-hoc comparison procedure was performed to compare each treatment phase relative to healthy controls and relative to samples collected at enrollment in each food intervention group.
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Example 10. Members of the human gut microbiota involved in recovery from Vibrio choierae infection
[0150] We used an approved protocol for recruiting Bangladeshi adults living in Dhaka Municipal Corporation area for this study. Of the 1 ,153 patients with acute diarrhoea who were screened, seven passed all entry criteria (Methods for Examples 10-17) and were enrolled (Tables 18 and 19). Faecal samples collected at monthly intervals during the first 2 postnatal years from 50 healthy children living in the Mirpur area of Dhaka city, plus samples obtained at approximately 3-month intervals over a 1 -year period from 12 healthy adult males also living Mirpur, allowed us to compare recovery of the microbiota from cholera with the normal process of assembly of the gut community in infants and children, and with unperturbed communities from healthy adult controls.
[0151 ] Using the standard treatment protocol of the International Centre for Diarrhoeal Disease Research, Bangladesh, study participants with acute cholera received a single oral dose of azithromycin and were given oral rehydration therapy for the duration of their hospital stay. Patients were discharged after their first solid stool. We divided the diarrhoeal period (from the first diarrhoeal stool after admission to the first solid stool) into four proportionately equal time bins: diarrhoeal phase 1 (D-Ph1 ) to D-Ph4. Every diarrhoeal stool was collected from every participant. Faecal samples were also collected every day for the first week after discharge (recovery phase 1 , R- Ph1 ), weekly during the next 3 weeks (R-Ph2), and monthly for the next 2 months (R- Ph3). For each individual, we selected a subset of samples from D-Ph1 to D-Ph3
(Methods for Examples 10-17), plus all samples from D-Ph4 to R-Ph3, for analysis of bacterial composition by sequencing PCR amplicons generated from variable region 4 (V4) of the 16S ribosomal RNA (rRNA) gene (FIG. 15A and Supplementary Table 3 of Hsiao et al, Nature 2014; Epub, which is hereby incorporated by reference in its entirety). Reads sharing 97% nucleotide sequence identity were grouped into operational taxonomic units (97%-identity OTUs; Methods for Example 10-17).
[0152] We identified a total of 1 ,733 97%-identity OTUs assigned to 343 different species after filtering and rarefaction (Methods for Example 10-17). V.
cholerae dominated the microbiota of the seven patients with cholera during D-Ph1 (mean maximum relative abundance 55.6%), declining markedly within hours after initiation of oral rehydration therapy. The microbiota then became dominated by either an unidentified Streptococcus species (maximum relative abundance 56.2-98.6%) or by Fusobacterium species (19.4-65.1 % in patients B-E). In patient G, dominance of the community passed from a Campylobacter species (58.6% maximum) to
a Streptococcus species (98.6% maximum) (Table 20 and Supplementary Table 4 of Hsiao et al, Nature 2014; Epub, which is hereby incorporated by reference in its entirety). Of the 343 species, 47.9 ± 6.6% (mean ± s.d.) were observed throughout both the diarrhoeal and recovery phases, suggesting that microbiota composition during the recovery phase may reflect an outgrowth from reservoirs of bacteria retained during disruption by diarrhoea (FIG. 16).
[0153] Indicator species analysis4 (Methods for Example 10-17) was used to identify 260 bacterial species consistently associated with the diarrhoeal or recovery phases across members of the study group, and in a separate analysis for each subject (Table 21 ). The relative abundance of each of the discriminatory species in each faecal sample was compared with the mean weighted phylogenetic (UniFrac-) distance between that microbiota sample and all microbiota samples collected from the reference cohort of healthy Bangladeshi adults. The results revealed 219 species with significant indicator value assignments to diarrhoeal or recovery phases, and relative abundances with statistically significant Spearman's rank correlation values to community UniFrac distance to healthy control microbiota (Table 22 and FIG. 16F-N). Not surprisingly, the abundance of V. cholerae directly correlated with increased distance to a healthy microbiota. Streptococcus and Fusobacterium species, which bloomed during the early phases of diarrhoea, were also significantly and positively correlated with distance from a healthy adult microbiota. Increases in the relative abundances of species in the genera Bacteroides, Prevotella,Ruminococcus/Blautia, and Faecalibacterium (for example, Bacteroides vulgatus, Prevotella copri,R obeum, and Faecalibacterium prausnitzii) were strongly correlated with a shift in community structure towards a healthy adult configuration (FIG. 16F-N and Table 22).
[0154] Previously we used Random Forests, a machine-learning algorithm, to identify a collection of age-discriminatory bacterial taxa that together define different stages in the postnatal assembly/maturation of the gut microbiota in healthy Bangladeshi children living in the same area as the adult patients with cholera-. Of those 60 most age-discriminatory 97%-identity OTUs representing 40 different species, 31 species were present in adult patients with cholera. Intriguingly, they followed a similar progression of changing representation during diarrhoea to recovery as they do during normal maturation of the healthy infant gut microbiota (FIG. 16F-N). Twenty- seven of the 31 species were significantly associated with recovery from diarrhoea by indicator species analysis (see FIG. 17, 18, 19 for OTU-level and community-wide analyses). These 27 species, which serve as indicators and are potential mediators of restoration of the gut microbiota after cholera, guided construction of a gnotobiotic mouse model that examined the molecular mechanisms by which some of these taxa might affect V. cholerae infection and promote restoration. [0155] We assembled an artificial community of 14 sequenced human gut bacterial species (Table 23) that included (1 ) five species that directly correlated with gut microbiota recovery from cholera and with normal maturation of the infant gut microbiota (R. obeum,Ruminococcus torques, F. prausnitzii, Dorea
longicatena, Collinsella aerofaciens), (2) six species significantly associated with recovery from cholera by indicator species analysis (Bacteroides ovatus, Bacteroides vulgatus, Bacteroides caccae, Bacteroides uniformis, Parabacteroides
distasonis, Eubacterium rectale), and (3) three prominent members of the adult human gut microbiota that have known capacity to process dietary and host glycans
(Bacteroides cellulosilyticus, Bacteroides thetaiotaomicron , Clostridium scindens6, 7· 8; as noted in FIG. 20 and Table 24 and Supplementary Table 8 of Hsiao et al, Nature 2014; Epub, which is hereby incorporated by reference in its entirety, shotgun sequencing of diarrhoeal- and recovery-phase human faecal DNA samples revealed that genes encoding enzymes involved in carbohydrate metabolism were the largest category of identified genes specifying known enzymes that changed in relative abundance within the faecal microbiome during the course of cholera). One group of mice was directly inoculated with approximately 109 colony-forming units (c.f.u.) of V. cholerae at the same time they received the 14-member community to simulate the rapidly
expanding V. cholerae population during diarrhoea (Ό1 invasion' group). A separate group was gavaged with the community alone and then invaded 14 days later with V. cholerae ('D14invasion' group) (FIG. 15C,D).
[0156] V. cholerae levels remained at a high level in the D1 invasion group over the first week (maximum 46.3% relative abundance), and then declined rapidly to low levels (<1 %). Introduction of V. cholerae into the established 14-member community produced much lower levels of V. cfto/eraeinfection (range of mean abundances measured daily over the 3 days after gavage of the enteropathogen, 1 .2-2.7%; Table 25). Control experiments demonstrated that V. cholerae was able to colonize at high levels for at least 7 days when it was introduced alone into germ-free recipients (109- 1010 c.f.u. per milligram wet weight of faeces; FIG. 13A,B) Together, these data suggest that a member or members of the artificial human gut microbiota had the ability to restrict V. cholerae colonization.
[0157] Changes in relative abundances of the 14 community members in faecal samples in response to V. cholerae were consistent for most species across the D1 invasion and D14invasion mice (Table 25). We focused on one member, R. obeum, because its relative abundance increased significantly after introduction of V.
cholerae in both the D1 invasion and D14invasion groups (FIG. 21 A and Table 25) and because it is a prominent age-discriminatory taxon in the Random Forests model of gut microbiota maturation in healthy Bangladeshi children3 (FIG. 18J). Mice were mono- colonized with either R. obeum or V. cholerae for 7 days and then the other species was introduced (FIG. 15E,F). WhenR obeum was present, V. cholerae levels declined by 1- 3 logs (FIG. 13A,B). Germ-free mice were also colonized with the defined 14-member community or the same community without R. obeum or 2 weeks, and V. cholerae was then introduced by gavage (FIG. 15G,H). V. cholerae\eve\s 1 day after gavage were 100-fold higher in the community that lacked R. obeum; these differences were sustained over time (50-fold higher after 7 days; P < 0.01 , unpaired Mann-Whitney Litest; FIG. 13A,B).
[0158] Having established that R. obeum restricts V. cholerae colonization, we used microbial RNA sequencing (RNA-seq) of faecal RNAs to determine the effect of R. obeum on expression of known V. cholerae virulence factors in mono- and co- colonized mice. Co-colonization led to reduced expression of tcpA (a primary
colonization factor in humans9, 10), rtxA and hlyA (encode accessory toxins11 , 12), and VC1447-VC1448 (RtxA transporters) (threefold to fivefold changes; P < 0.05 compared with V. cholerae mono-colonized controls, Mann-Whitney L/-test; see Table 26 for other regulated genes that could impact colonization, plus FIG. 22 for an ultra- performance liquid chromatography mass spectrometry (UPLC-MS) analysis of bile acids reported to effect V. cholerae gene regulation13).
[0159] Two quorum-sensing pathways are known to regulate V.
cholerae colonization/virulence14, 15, 16,17: an intra-species mechanism involving cholera autoinducer-1 , and an inter-species mechanism involving autoinducer-218, 19. Quorum sensing disrupts expression of V. cholerae virulence determinants through a signalling pathway that culminates in production of the LuxR-family regulator HapR15, 16.
Repression of quorum sensing in V. cholerae is important for virulence factor expression and infection20, 21■ 22. The luxS gene encodes the S-ribosylhomocysteine lyase responsible for AI-2 synthesis. Homologues of luxS are widely distributed among bacteria18, 19, including 8 of the 14 species in the artificial human gut community (Table 27 and FIG. 23). RNA-seq of the faecal meta-transcriptomes of D1 invasion mice colonized with the 14-member artificial community plus V. cholerae, and mice harbouring the 14-member consortium without V. cholerae, revealed that of
predicted luxS homologues in the community, only expression of R. obeum
luxS (RUMOBE02774) increased significantly in response to V. cholerae(P < 0.05, Mann-Whitney L/-test; FIG. 13C). Moreover, R. obeum luxS transcript levels directly correlated with V. cholerae levels (FIG. 21 D).
[0160] In addition to luxS, the R. obeum strain represented in the artificial community contains homologues of IsrABCK that are responsible for import and phosphorylation of AI-2 in Gram-negative bacteria23, as well as homologues of two genes, luxR and luxQ, that play a role in AI-2 sensing and downstream signalling in other organisms24. Expression of all these R. obeum genes was detected in vivo, consistent with R. obeum having a functional AI-2 signalling system (FIG. 21 B,C). (See Example 17 for results showing that R. obeum AI-2 production is stimulated by V. cholerae in vitro and in co-colonized animals (FIG. 21 E-G), plus (1 ) a genome-wide analysis of the effects of V. cholerae on R. obeum transcription in co-colonized mice (Table 26c) and (2) a community-wide view of the transcriptional responses of the 14- member consortium to V. cholerae (Table 28).)
[0161 ] Quorum sensing downregulates the V. cholerae tcp operon that encodes components of the toxin co-regulated pilus (TCP) biosynthesis pathway required for infection of humans9, 10. To confirm that R. obeum LuxS could signal through AI-2 pathways, we cloned R. obeum and V. cholerae luxS downstream of the arabinose-inducible PBAD promoter in plasmids that were maintained in anEscherichia coli strain unable to produce its own AI-2 (DH5a)25. High tcp expression can be induced in V. cholerae after slow growth in AKI medium without agitation followed by rapid growth under aerobic conditions26. Addition of culture supernatants harvested from the E. coli strains expressing R. obeum or V. cholerae luxS caused a two- to threefold reduction in tcp induction in V. cholerae (P < 0.05, unpaired Student's i-test; replicated in four independent experiments). Supernatants from a control E. coli strain with the plasmid vector lacking luxS had no effect (FIG. 14A). These findings are consistent with our in vivo RNA-seq results and provide direct evidence thatR obeum AI-2 regulates expression of V. cholerae virulence factor.
[0162] Germ-free mice were then colonized with V. cholerae and E.
coli bearing either the PBAD-R- obeum luxS plasmid or the vector control. Mice that received E. coli expressing R. obeum /uxSshowed a significantly lower level of V.
cholerae colonization 8 h after gavage than mice that received E. coli with vector alone (FIG. 14B; there was no statistically significant difference in levels ofE. coli between the two groups (data not shown)). Together, these results establish a direct causal relationship between R. obeum-mediated restriction of V. cholerae colonization and R. obeum AI-2 synthesis.
[0163] Several V. cholerae mutants were used to determine whether known V. cholerae AI-2 signalling pathways are required for the observed effects of R. obeum on V. cholerae colonization. LuxP is critical for sensing AI-2 in V. cholerae. Co- colonization experiments in gnotobiotic mice revealed that levels of isogenic Δ/uxP or wild-type luxP+ V. cholerae strains were not significantly different as a function of the presence of R. obeum (FIG. 24), suggesting that R. obeum modulates V.
cholerae levels through other quorum-sensing regulatory genes.
The luxO and hapRgenes encode central regulators linking known V. cholerae quorum- signalling and virulence regulatory pathways. Deletion of luxO typically results in increased hapR expression15. However, our RNA-seq analysis had shown that both luxO and hapR are repressed in the presence of R. obeum (six- to
sevenfold, P < 0.0001 ; Mann-Whitney L/-test), as are two important downstream activators of virulence repressed by HapR16, encoded by aphA and aphB. These findings provide additional evidence that R. obeum operates to regulate virulence through a novel regulatory pathway.
[0164] The quorum-sensing transcriptional regulator VqmA was upregulated more than 25-fold when V. cholerae was introduced into mice mono- colonized with R. obeum (FIG. 14C and Table 26). When germ-free mice were gavaged with R. obeum and a mixture of AvqmA (A/acZ)27and wild-type V. cholerae (lacZ+) strains, the AvqmA mutant exhibited an early competitive advantage (FIG. 14D), suggesting that R. obeum may be able to affect early colonization of V.
cholerae through VqmA. VqmA is able to bind to and activate the hapR promoter directly27. Since RNA-seq showed that hapR activation did not occur in gnotobiotic mice despite high levels of vgm>4expression (FIG. 24B and Table 26), we postulate that the role played by VqmA in R. obeum modulation of Vibrio virulence genes involves an uncharacterized mechanism rather than the known pathway passing through HapR.
[0165] We have identified a set of bacterial species that strongly correlate with a process in which the perturbed gut bacterial community in adult patients with cholera is restored to a configuration found in healthy Bangladeshi adults. Several of these species are also associated with the normal assembly/maturation of the gut microbiota in Bangladeshi infants and children, raising the possibility that some of these taxa may be useful for 'repair' of the gut microbiota in individuals whose gut
communities have been 'wounded' through a variety of insults, including
enteropathogen infections. Translating these observations to a gnotobiotic mouse model containing an artificial human gut microbiota composed of recovery- and age- indicative taxa established that one of these species, R. obeum, reduces V.
cholerae colonization. As an entrenched member of the gut microbiota in Bangladeshi individuals, R. obeum could function to increase median infectious dose (ID50) for V. cholerae in humans and thus help to determine whether exposure to a given dose of this enteropathogen results in diarrhoeal illness. The modest effects of R. obeum AI-2 on V. cholerae virulence gene expression in our adult gnotobiotic mouse model may reflect the possibility that we have only identified a small fraction of the microbiota's full repertoire of virulence-suppressing mechanisms. Culture collections generated from the faecal microbiota of Bangladeshi subjects are a logical starting point for 'second- generation' artificial communities containing R. obeum isolates that have evolved in this population, and for testing whether the observed effects ofR obeum generalize across many different strains from different populations. Moreover, the strategy described in this report could be used to mine the gut microbiota of Bangladeshi or other populations where diarrhoeal disease is endemic for additional species that use quorum-related and/or other mechanisms to limit colonization by V. cholerae and potentially other enteropathogens.
Example 11. Patient Selection.
[0166] Of the 1 153 patients screened (Table 18), 796 (69%) were disqualified because of immediate prior antibiotic usage. Another 100 were disqualified because their diarrhea began more than 24 h before enrollment, while 132 were excluded due to lack of a permanent address for follow-up. Of the 1 1 adults (all males) who entered the study, four could not be contacted by staff after discharge from the hospital; their samples were not included in our analysis, leaving a total of seven individuals (A-G; see Table 19 for clinical metadata). The seven patients included in the study experienced diarrhea for 8.0±2.6 hours (mean±SD) prior to hospital admission and for 41 .5±16.7 hours during their hospital stay. They had an average of 1 .5±1 .0 diarrheal stools per hour (Table 19).
[0167] For ethical reasons, we could not withhold treatment with azithromycin (or for that matter oral rehydration therapy). Therefore, our study design did not allow us to isolate the nature and effect sizes of various elements of the treatment protocol on the temporal patterns of change in the gut microbiota during the acute and recovery phases of infection, versus those produced by the diarrheal disease per se.
Example 12. Identifying community-wide changes in representation of 97%- identity OTU and species during diarrhea and recovery phases
[0168] Across all seven individuals, 58.6% of 97%-identity OTUs with a species-level taxonomic assignment associated with recovery (see below) were also detected during both diarrhea and recovery phases (FIG. 15, 16). For individuals C and E, where higher time-resolution analysis was performed, 41 .1 % and 29.9% of species- level taxa were identified in both diarrhea and recovery samples respectively, with 6.9% and 1 1 .1 % of the identified species detected only in recovery phase samples (see Supplementary Table 4c of Hsiao et al, Nature 2014; Epub, which is hereby
incorporated by reference in its entirety).
[0169] Phylogenetic diversity (PD) of the cholera fecal microbiota decreased markedly during D-Ph2/D-Ph3, approaching the PD of healthy Bangladeshi children, before rising during R-Ph1 -R-Ph3 in a temporal pattern that paralleled the UniFrac measurements of similarity to reference healthy adult controls (FIG. 19E).
[0170] Indicator species analysis was performed on the set of 236 fecal specimens selected from the diarrheal and recovery phases of subjects A-G. The statistical significance of associations was defined using permutation tests in which permutations were constrained within subjects: a bacterial species was considered significantly associated if it had a FDR-adjusted P<0.05. This approach identified 260 bacterial species consistently associated with either the diarrheal or recovery phases. 219 of these 260 species also had a significant correlation to community UniFrac distance to healthy control microbiota (FDR-adjusted PO.05; Tables 21, 22, FIG. 16F- N). For species with positive correlations, higher relative abundances in a given microbiota state correlated to an increased difference to healthy fecal microbiota.
Interpersonal differences in the distribution of 97%-identity OTUs comprising these species were also evident (FIG. 16E, 17).
[0171 ] Of the 31 age-discriminatory species-level bacterial taxa in the developing gut microbiota of healthy Bangladeshi children, 24 including R. obeum and F. prausnitzii, also had a significant Spearman rank correlation value between their relative abundance in each fecal sample and the mean weighted UniFrac distance between that sample and all healthy adult Bangladeshi microbiota (Tables 21 , 22, see FIG. 16, 17, 18) for 97%-identity OTU analysis). In addition, Spearman rank correlations revealed that (i) 23 of the 27 species had relative abundances that significantly correlated with chronologic age in healthy children and with time following onset of acute diarrhea, and (ii) the direction of change (increase or decrease) was concordant between the two datasets for all 23 species (Table 20).
Example 13. Changes in relative abundance of genes encoding ECs in fecal microbiomes sampled during diarrhea and recovery.
[0172] Shotgun sequencing of DNA prepared from diarrhea and recovery phase human fecal samples, followed by binning reads into assignable KEGG Enzyme Commission numbers (ECs), revealed that genes encoding enzymes involved in carbohydrate metabolism comprised the largest category of ECs that changed in relative abundance within the fecal microbiome during the course of cholera (see FIG. 20 and Table 24 and Supplementary Table 8 of Hsiao et al, Nature 2014; Epub, which is hereby incorporated by reference in its entirety for EC-based abundance analysis, including Spearman rank correlations of EC abundance as a function of time across D- Ph1 through R-Ph3). These results led us to include Bacteroides cellulosilyticus, Bacteroides thetaiotaiotaomicron, and Clostridium scindens in the artificial community of human gut symbionts, even though they did not satisfy the criteria described above as recovery phase-indicative or significantly correlated to normal maturation of the
Bangladeshi infant microbiota.
Example 14. Genome-wide analysis of how V. cholerae influences the R. obeum transcriptome in vivo.
[0173] Mice were first mono-colonized with R. obeum or V. cholerae for 7 days, followed by introduction of the other organism (FIG. 15C,D). RNA-Seq was performed using fecal samples collected from both groups of mice on the day prior to and 2 days after co-colonization (Table 28). An analysis of changes across the entire R. obeum transcriptome 2d after introduction of V. cholerae revealed few (n=7) functionally annotated transcripts with significant differences in expression (P<0.05 after multiple- hypothesis testing in DESeq40; see Table 26c). LuxS serves a dual role: production of the precursor AI-2 molecule [(S)-4,5-dihydroxyl-2,3-pentanedione] and participation in the pathway that re-generates homocysteine for use in the activated methyl cycle; in the absence of luxS, homocysteine is produced via oxaloacetate involving aspartate and glutamate as intermediates ■ Consistent with this, three R. obeum genes encoding ECs involved in glutamate biosynthesis were significantly down-regulated after introduction of V. cholerae.
Example 15. Genome-wide analysis of how R. obeum influences the V. cholerae transcriptome in vivo.
[0174] R. obeum increased the expression of several V. cholerae genes whose functions could impact its colonization, including five that encode products involved in iron acquisition and transport [VC0365 (bacterioferritin), VC0364
(bacterioferritin-associated ferredoxin), VC0608 (iron(lll) transporter), VC0750 (hesB family protein)] plus five genes thought to be involved in cell wall modification (VC0246, VC0247, VC0245, VC0259, VC0249 in Table 26b; modifications in V. cholerae LPS have been reported to be important in colonization of mice17). Cholera toxin gene (ctxA, ctxB) expression was below the limits of reliable detection in each of the V. cholerae treatment groups, consistent with previous reports that it is not required for colonization of adult mice11 "12.
Example 16. Bile acids and regulation of virulence genes.
[0175] V. cholerae senses host signals, including bile acids, in order to coordinately up-regulate expression of colonization factor genes13,43"47 and down- regulate expression of anti-colonization factors such as the mannose-sensitive hemagglutinin pilus48. The gut microbiota could, in principle, affect colonization by modulating levels of host-derived signals that impact V. cholerae, or the microbiota could produce signaling molecules that directly modulate V. cholerae pathogenesis. To explore these possibilities, we used ultra-performance liquid chromatography-mass spectroscopy (UPLC-MS) to characterize the representation of bile acid species in fecal samples collected from mice colonized with the 14-member community and this community minus R. obeum, (n=5-6 animals/group). We detected 10 bile acid species, and expressed each of their levels as a proportion of the aggregate levels of all 10 bile acids. Comparing fecal samples collected from mice after colonization with the 14- and 13-member communities, we found that the presence or absence of R. obeum did not have a statistically significant effect on levels of the primary bile acids taurocholic acid, tauro-beta-muricholic acid, and beta-muricholic acid that together comprise >80% of the measured pool (P>0.05, unpaired Mann-Whitney L/ test). One primary bile acid, alpha muricholic acid, and a secondary bile acid, urosodeoxycholic acid, were affected, but in aggregate they represent minor constituents (<1 1 %) of the measured pool (FIG. 22). While we could not rule out that the effect of R. obeum on these minor bile acid species impacts V. cholerae colonization/virulence, given the observed lack of change in the predominant bile acid species, we turned our attention to classes of microbial factors known to affect virulence (see Example 10).
Example 17. R. obeum AI-2 production is stimulated by V. cholerae in vitro and in co-colonized animals.
[0176] In mice initially mono-colonized with R. obeum for 7d, R. obeum luxS expression increased more than 2-fold 2d following introduction of V. cholerae (P<0.01 , unpaired Mann-Whitney U test; FIG. 21 E). Using the BB170 AI-2 assay24, we measured fecal AI-2 levels from mice mono-colonized with R. obeum or co-colonized with R. obeum and AluxS V. cholerae (MM883)14, and confirmed that AI-2 levels were modestly but significantly higher in the co-colonized group (FIG. 21 F). When these bacteria were co-cultured in vitro under anaerobic conditions, R. obeum AI-2 signal increased significantly [2.8±0.1 -fold (mean±SEM), P<0.01 , unpaired Mann-Whitney; FIG. 21 G]. Furthermore, we induced expression of cloned R. obeum and V. cholerae luxS genes using an arabinose-inducible PBAD promoter in an AI-2-deficient E. coli strain (DH5a)25 and observed that supernatants from these strains were able to induce increased BB170 bioluminescence over vector controls [7.2±1 .1 and 8.8±2.4-fold (meaniSEM), respectively].
Methods for Examples 10-17.
[0177] Human studies. Subject recruitment. Protocols for recruitment, enrollment, and consent, procedures for sampling the faecal microbiota of healthy Bangladeshi adults and children, and the faecal microbiota of adults during and after cholera infection, plus the subsequent de-identification of these samples, were approved by the Human Studies Committees of the International Centre for Diarrhoeal Disease Research, Bangladesh, and Washington University School of Medicine in St. Louis.
[0178] Enrollment into the adult cholera study was based on the following criteria: residency in the Dhaka Municipal Corporation area, a positive stool test for V. cholerae as judged by dark-field microscopy, diarrhoea for no more than 24 h before enrollment, and a permanent address that allowed follow-up faecal sampling after discharge from Dhaka Hospital (International Centre for Diarrhoeal Disease Research, Bangladesh). Non-prescription antibiotic usage is prevalent in Bangladesh28,29. Since a history of previous antibiotic consumption could be a confounder when interpreting the effects of cholera on the gut microbiota, we excluded individuals if they had received antibiotics in the 7 days preceding admission to the hospital. Since this was an observational study with no experimental treatment arm, blinding for study inclusion was not necessary. See Table 18 for the number of individuals screened for inclusion in the study, the number of potential subjects excluded from the study and the reasons for their exclusion, and the number of subjects enrolled who satisfied all criteria for inclusion.
[0179] The healthy adults were fathers in a cohort of healthy twins, triplets, and their parents living in Mirpur that is described in ref. 3. Fathers were sampled every 3 months during the first 2 years of their offspring's postnatal life. Histories of diarrhoea and antibiotic use were not available for these fathers. However, histories of diarrhoea and antibiotic use in their healthy children were known: 46 of the 49 paternal faecal samples used were obtained during periods when none of their children had diarrhoea; 36 of these 49 samples were collected at a time when there had been no antibiotic use by their children in the preceding 7 days.
[0180] DNA extraction from human faecal samples, sequencing, and analysis. All diarrhoeal stools were collected from each participant (one sterilized bowl per sample), frozen immediately at -80 °C, then subjected to the same bead beating and phenol chloroform extraction procedure for DNA purification that was applied to the formed frozen faecal samples collected from these individuals during the recovery phases (and previously to a wide range of samples collected from individuals representing different ages, cultural traditions, geographical locations, and physiological and disease states3, 30).
[0181 ] DNA was isolated from all frozen faecal samples from D-Ph1 to D- Ph4, from the period of frequent sampling during the first week following discharge (recovery phase 1 ; R-Ph1 ), the period of less frequent sampling during weeks 2-3 (R- Ph2), and from weeks 4 to 12 of recovery (R-Ph3) (n = 1 ,053 samples in total). For analyses involving healthy adult and child control groups, samples were excluded from our analysis where antibiotic use or diarrhoea was known to have occurred in the 7 days before sample collection.
[0182] For each participant in the cholera study, we selected one sample with high DNA yield (>2 g) from each 2-hour period during D-Ph1 to D-Ph3. An additional 7 ± 2 samples (mean ± s.d.) that had been collected during the approximately 5-h period before the rate of diarrhoea began to decrease at the beginning of D-Ph3 were included. All faecal samples collected after this time point (that is, from the remainder of D-Ph3 to R-Ph3), were also included in our analysis (n = 19.7 ± 7.4 total samples (mean ± s.d.) per individual in the diarrhoeal phase, and 14 ± 3.3 total samples per individual in the recovery phase). Two patients (C and E) were chosen for additional sequencing of all their diarrhoeal samples (n = 100 and 50, respectively;
see Supplementary Table 3b of Hsiao et al, Nature 2014; Epub, which is hereby incorporated by reference in its entirety).
[0183] The V4 region of bacterial 16S rRNA genes represented in each selected faecal microbiota sample was amplified by PCR using primers containing sample-specific barcode identifiers. Amplicons were purified, pooled, and paired-end sequenced with an lllumina MiSeq instrument (250 nucleotide paired-end reads;
86,315 ± 2,043 (mean ± s.e.m.) assembled reads per sample; see Supplementary Table 3 of Hsiao et al, Nature 2014; Epub, which is hereby incorporated by reference in its entirety). Healthy control samples were analysed using the same sequencing platform and chemistry (n = 293 total samples). [0184] Sequences were assembled, then de-multiplexed and analysed using the QIIME software package31 and custom Perl scripts. For analysis of diarrhoeal and recovery phase samples, rarefaction was performed to 49,000 reads per sample. For analyses including samples from healthy adults and children, samples were rarefied to 7,900 reads per sample. Reads sharing 97% nucleotide sequence identity were grouped into operational taxonomic units (97%-identity OTUs). To ensure that we retained less abundant bacterial taxa in our analysis of the faecal samples of patients with cholera, a 97%-identity OTU was called 'distinct and reliable' if it appeared at 0.1 % relative abundance in at least one faecal sample. Taxonomic assignments of OTUs to species level were made using the Ribosomal Database Project version 2.4
classifier32 and a manually curated Greengenes database33.
[0185] Indicator species analysis4 was used to classify bacterial species as highly associated with either diarrhoeal phases or recovery. This approach is used in studies of macroecosystems to identify species that associate with different
environmental groupings; it assigns for each species an indicator value that is a product of two components: (1 ) the species' specificity, which is the probability that a sample in which the species is found came from a given group; and (2) the species' fidelity, which is the proportion of samples from a given group that contains the species. We
performed indicator species analysis in the set of 236 faecal specimens, selected from the seven patients according to the subsampling scheme described above, to identify bacterial species consistently associated with the diarrhoeal or recovery phases across members of the study group; statistical significance was defined using permutation tests in which permutations were constrained within subjects. We also conducted a separate indicator species analysis for each subject, using each individual's replicate diarrhoeal and recovery phase samples as the groupings.
[0186] For analyses of variation across communities, we used UniFrac5, a metric that measures the overall degree of phylogenetic similarity of any two
communities based on the degree to which they share branch length on a bacterial tree of life; low pairwise UniFrac distance values indicate that communities are more similar to one another. Unifrac distances were calculated using the QIIME software package31. [0187] The gut microbiomes of study participants were characterized by paired-end 2 χ 250 nucleotide shotgun sequencing of faecal DNA using an lllumina MiSeq instrument (mean 216,698 reads per sample; Supplementary Table 3 of Hsiao et al, Nature 2014; Epub, which is hereby incorporated by reference in its entirety). Paired sequences were assembled into single reads using the SHERA software package34, and annotated by mapping to version 58 of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database35 using U BLAST36.
[0188] Gnotobiotic mouse experiments. All experiments involving animals used protocols approved by the Washington University Animal Studies Committee. Germ-free male C57BL/6J mice were maintained in flexible plastic film gnotobiotic isolators and fed an autoclaved, low-fat, plant polysaccharide-rich mouse chow (B&K, catalogue number 7378000, Zeigler Bros) ad libitum. Mice were 5-8 weeks old at time of gavage. The number of mice used in each experiment is reported in the text, relevant figure legends, and summarized in FIG. 15.
[0189] Bacterial strains and plasmids. Table 23 lists the sequenced human gut-derived bacterial strains used to generate the artificial communities and their sources. Since all Bangladeshi faecal samples were devoted to DNA extraction, we were unable to utilize strains that originated from culture collections generated from study participants' faecal biospecimens. Thus, the strains incorporated into the artificial community were from public repositories, represented multiple individuals, and were typically not accompanied by information about donor health status or living conditions.
[0190] A Ptcp-lux reporter strain was constructed by introducing Ptcp- lux (pJZ376) into V. cholerae C6706 via conjugation from SMI OApir. PBAD- IUXS expression vectors were produced by first amplifying the luxS sequences of V. cholerae C6706 and R. obeum ATCC2917 using PCR and the primers described in Table 29. Amplicons were then cloned into pBAD202 (TOPO TA Expression Kit; Life Technologies), and introduced into E. coli DH5a by electroporation.
[0191 ] All cultures of V. cholerae C6706, the isogenic Δ/uxS mutant (MM883), and E. coli strains containing luxS expression vectors were grown aerobically in Luria Broth (LB) medium with appropriate antibiotics (Table 29). All members of the 14-member artificial human gut microbiota, including R. obeum ATCC29174, were propagated anaerobically in MegaMedium37.
[0192] Colonization of gnotobiotic mice. All animal experiments involved administration of known consortia of bacterial species; as such, no blinding to group allocation was performed. The order of administration of microbial species to given groups of recipient mice was intentionally varied, as described in FIG. 15C-H.
[0193] Mono-colonized animals received either 200 μΙ of overnight cultures of R. obeum strain ATCC29174 or V. cholerae strain C6706. All V. cholerae colonization studies in mice used the current pandemic El Tor biotype (strain C6706). Mice receiving the defined 13- or 14-member communities of sequenced human bacterial symbionts were gavaged with 200 μΙ of an equivalent mixture of bacteria assembled from overnight monocultures of each strain (D6oo nm s 0.4 per strain; grown in MegaMedium). In the case of mice that received mixtures of V. cholerae and E. coli strains with R. obeum /uxS-expressing plasmids (or vector controls), the E. coli strains were first grown overnight in LB medium containing 50 g ml-1 kanamycin. Two millilitres of the culture were removed and cell pellets were obtained by centrifugation, washed three times with 2 ml LB medium to remove antibiotics, and re-suspended in 6 ml LB medium containing 0.1 % arabinose. The suspension of E. coli cells was then incubated at 37 °C for 90 min, and mixed with V. choleraeC6706 such that each mouse was gavaged with -50 μΙ and -2.5 μΙ of overnight cultures of each organism, respectively. All gavages involving V. cholerae were preceded by a gavage of 100 μΙ sterile 1 M sodium bicarbonate to neutralize gastric pH. Colonization levels of V. cholerae were determined by serial dilution plating of faecal homogenates on selective medium.
[0194] Competitive index assays were performed with mice gavaged with 50 μΙ aliquots of cultures of mutant and wild-type V. cholerae C6706 strains that had been grown to D6oo nm = 0.3. For experiments involving competitive index calculations as a function of the presence of R. obeum, 100 μΙ of an overnight R. obeum culture was co-inoculated with the mixture of V. cholerae strains. Faecal samples from recipient gnotobiotic mice were subjected to dilution plating and aerobic growth on LB agar with the LacZ substrate Xgal; blue-white screening was used to determine colonization levels of the individual V. cholerae strains.
[0195] Community profiling by shotgun sequencing (COPRO-seq).
Shotgun sequencing of faecal community DNA was used to define the relative abundance of species in the artificial communities; experimental and computational tools for COPRO-seq have been described previously8.
[0196] Microbial RNA-seq analysis of faecal samples collected from mice colonized with the 14-member artificial community with and without V. cholerae. Faecal samples were collected from colonized gnotobiotic mice and immediately snap-frozen in liquid nitrogen. RNA was extracted using bead-beating in phenol/chloroform/isoamyl alcohol followed by further purification using MEGACIear (Life Technologies). Purified RNA was depleted of 16S rRNA, 5S rRNA, and transfer RNA as previously
described8 or by using a RiboZero kit (Epicentre). Complementary DNA (cDNA) libraries were generated and sequenced (50 nucleotide unidirectional reads; lllumina GA-llx, HiSeq 2000 or MiSeq instruments; see Supplementary Table 3 of Hsiao et al, Nature 2014; Epub, which is hereby incorporated by reference in its entirety). Reads were mapped to the genomes of members of the artificial community using Bowtie38.
[0197] To profile transcriptional responses to V. cholerae, all cDNA reads that mapped to the genomes of the 14 consortium members were binned based on enzyme classification level annotations from KEGG. ShotgunFunctionalizeR39 was then used compare the faecal meta-transcriptomes of 'D14invasion' animals sampled 4 days after gavage of the 14-member community to the faecal meta-transcriptomes of
D1 invasion mice sampled 4 days after gavage of the 14-member community plus V. cholerae. A mean twofold or greater difference in expression between the conditions, with an adjusted P value less than 0.0001 (ShotgunFunctionalizeR) was considered significant. This approach of binning to enzyme classifications mitigates issues with low- abundance transcripts being insufficiently profiled owing to limitations in sequencing depth8.
[0198] Owing to the higher sequencing depth achieved for R.
obeum and V. cholerae in mono- and co-colonization experiments, reads were mapped to reference genomes using Bowtie, and changes at the single transcript level were analysed using DESeq40 (Table 27). Transcripts that satisfied the criteria of (1 ) having greater than twofold differential expression after DESeq normalization, (2) an
adjusted P value less than 0.05, and (3) a minimum mean count value more than 10 were retained.
[0199] AI-2 assays. Previously frozen faecal pellets from gnotobiotic mice were re-suspended in AB medium24 by agitation with a rotary bead-beater (25 mg faecal pellet per millilitre of medium). AI-2 assays were performed using the V. harveyi BB170 bioassay strain24, with reported results representative of at least two independent experiments, each with five technical repeats. V. /?an ey/BB170 cultures were grown aerobically overnight in AB medium, and diluted 1 :500 in this medium for use in the AI-2 bioassay24. Luminescence was measured using a BioTek Synergy 2 instrument after 4 h of growth at 30 °C with agitation (300 r.p.m. using a rotatory incubator).
[0200] For in vitro measurements of R. obeum AI-2 production, a 100 μΙ aliquot from an overnight monoculture of the bacterium grown in MegaMedium without glucose was diluted 1 :20 in fresh MegaMedium without glucose. In addition, cells pelleted from 100 μΙ of an overnight culture of V. cholerae AluxS (MM883 (ref. 14)) grown in LB medium were added to R. obeum that had also been diluted 1 :20 in
MegaMedium without glucose. The resulting mono- and co-cultures were incubated anaerobically at 37 °C for 16 h. Cells were pelleted by centrifugation, and supernatants were harvested and then added to V. harveyi BB170 cultures for AI-2 bioassay.
[0201 ] UPLC-MS. Procedures for UPLC-MS of bile acids have been described in ref. 37.
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Table 1. Ϊ /letadata for 50 healthy Bangladeshi children sampled monthly during the first two years of
Child ID Family ID Birth Cohort Gender Zygosity WHZ WAZ HAZ
Bgsng7035 Bgsng7035 Healthy Singleton Birth Cohort Male NA -0.5±0.5 -1.7±0.2 -1.710.2
Bgsng7106 Bgsng7106 Healthy Singleton Birth Cohort Male NA -0.4±0.6 -0.9±0.9 -0.910.9
Bgsng7115 Bgsng7115 Healthy Singleton Birth Cohort Male NA -1.6±0.5 0.2±0.4 0.210.4
Bgsng7128 Bgsng7128 Healthy Singleton Birth Cohort Female NA -1.4±0.7 -1.3±0.6 -1.310.6
Bgsng7150 Bgsng7150 Healthy Singleton Birth Cohort Male NA 0.0±1.2 0.0±0.8 0.010.8
Bgsng7155 Bgsng7155 Healthy Singleton Birth Cohort Female NA -1.5±1.3 -1.0±1.3 -1.011.3
Bgsng7177 Bgsng7177 Healthy Singleton Birth Cohort Female NA 0.9±0.6 -0.9H .0 -0.911.0
Bgsng7192 Bgsng7192 Healthy Singleton Birth Cohort Female NA 0.0±0.8 -1.610.7 -1.610.7
Bgsng7202 Bgsng7202 Healthy Singleton Birth Cohort Female NA -0.4±1.8 0.110.6 0.110.6
Bgsng7204 Bgsng7204 Healthy Singleton Birth Cohort Male NA -0.7±1.7 -0.110.6 -0.110.6
Bgsng8064 Bgsng8064 Healthy Singleton Birth Cohort Male NA 0.9±0.7 1.710.7 1.710.7
Bgsng8169 Bgsng8169 Healthy Singleton Birth Cohort Male NA 0.9±1.5 1.110.5 1.110.5
Bgsng7018 Bgsng7018 Healthy Singleton Birth Cohort Male NA -0.7±0.7 -0.410.9 -0.410.9
Bgsng7052 Bgsng7052 Healthy Singleton Birth Cohort Male NA 0.0±1.1 -0.311.2 -0.311.2
Bgsng7063 Bgsng7063 Healthy Singleton Birth Cohort Male NA -0.4±0.7 0.010.9 0.010.9
Bgsng7071 Bgsng7071 Healthy Singleton Birth Cohort Male NA 0.5±0.8 0.610.7 0.610.7
Bgsng7082 Bgsng7082 Healthy Singleton Birth Cohort Female NA -0.9±1.4 -0.810.7 -0.810.7
Bgsng7090 Bgsng7090 Healthy Singleton Birth Cohort Male NA -0.4±0.5 -0.310.7 -0.310.7
Bgsng7096 Bgsng7096 Healthy Singleton Birth Cohort Female NA -0.5±0.4 -1.110.6 -1.110.6
Bgsng7114 Bgsng7114 Healthy Singleton Birth Cohort Male NA -0.9±0.3 -0.710.2 -0.710.2
Bgsng7131 Bgsng7131 Healthy Singleton Birth Cohort Male NA -0.9±0.5 -1.310.8 -1.310.8
Bgsng7142 Bgsng7142 Healthy Singleton Birth Cohort Male NA -0.7±1.5 -0.811.5 -0.811.5
Bgsng7149 Bgsng7149 Healthy Singleton Birth Cohort Female NA -1.0±0.4 -0.310.4 -0.310.4
Bgsng7173 Bgsng7173 Healthy Singleton Birth Cohort Male NA -0.5±0.5 -1.210.6 -1.210.6
Bgsng7178 Bgsng7178 Healthy Singleton Birth Cohort Female NA 0.6±1.4 -0.910.6 -0.910.6
Bgtwl 1 Bgtwl Healthy Twins & Triplets Female MZ -0.5±0.7 -2.310.5 -3.110.7
Bgtwl .12 Bgtwl Healthy Twins & Triplets Female MZ -1.3±0.8 -3.010.3 -3.410.7
Bgtw2.T1 Bgtw2 Healthy Twins & Triplets Male DZ -1.2±0.8 -3.810.9 -4.7±0.7
Bgtw2.T2 Bgtw2 Healthy Twins & Triplets Female DZ -0.8±1.0 -3.310.9 -4.2±0.4
Bgtw3.T1 Bgtw3 Healthy Twins & Triplets Male MZ -0.6±0.4 -1.410.5 -1.610.3
Bgtw3.T2 Bgtw3 Healthy Twins & Triplets Male MZ -0.6±0.5 -1.810.6 -2.110.6
MZ co-twin in set
Bgtw4.T1 Bgtw4 Healthy Twins & Triplets Female -0.2±0.8 -2.410.6 -3.410.5 of triplets
MZ co-twin in set
Bgtw4.T2 Bgtw4 Healthy Twins & Triplets Female -0.1±0.8 -2.210.9 -3.410.6 of triplets
Fraternal co-twin
Bgtw4.T3 Bgtw4 Healthy Twins & Triplets Female -1.7±0.9 -2.910.7 -2.610.6 in set of triplets
Bgtw5.T1 Bgtw5 Healthy Twins & Triplets Male DZ -0.5±0.8 -3.110.9 -4.3±0.8
Bgtw5.T2 Bgtw5 Healthy Twins & Triplets Female DZ -0.1±0.5 -2.510.4 -4.0±0.5
Bgtw6.T1 Bgtw6 Healthy Twins & Triplets Male DZ -0.2±0.6 -1.511.8 -1.611.7
Bgtw6.T2 Bgtw6 Healthy Twins & Triplets Male DZ -0.6±0.6 -2.511.2 -2.611.7
Bgtw7.T1 Bgtw7 Healthy Twins & Triplets Female DZ 0.5±1.0 -2.310.5 -3.810.4
Bgtw7.T2 Bgtw7 Healthy Twins & Triplets Female DZ 0.5±1.7 -2.810.8 -4.3±0.2
Bgtw8.T1 Bgtw8 Healthy Twins & Triplets Female DZ -0.9±0.7 -1.610.3 -1.410.4
Bgtw8.T2 Bgtw8 Healthy Twins & Triplets Female DZ -1.4±0.8 -2.610.5 -2.310.6
Bgtw9.T1 Bgtw9 Healthy Twins & Triplets Female MZ 0.1 ±1.2 -3.110.5 -4.0±0.7
Bgtw9.T2 Bgtw9 Healthy Twins & Triplets Female MZ 0.8±1.2 -2.610.6 -4.0±0.9
Bgtwl 0.T1 Bgtwl 0 Healthy Twins & Triplets Female MZ -1.5±0.8 -2.710.5 -2.410.4
Bgtwl 0.T2 Bgtwl 0 Healthy Twins & Triplets Female MZ -1.0±1.1 -2.810.3 -2.810.5
Bgtwl 1.T1 Bgtwl 1 Healthy Twins & Triplets Female not tested -0.2±0.3 -2.910.4 -3.710.4
Bgtwl 1.T2 Bgtwl 1 Healthy Twins & Triplets Female not tested -0.7±0.9 -2.610.4 -2.910.3
Bgtwl 2.T1 Bgtwl 2 Healthy Twins & Triplets Male DZ 1.2±1.3 -2.311.1 -4.4±1.2
Bgtwl 2.T2 Bgtwl 2 Healthy Twins & Triplets Female DZ -0.6±2.0 -2.911.2 -3.610.9
Figure imgf000118_0001
Triplets
Validation -
Bgtw6.T1 286 9 35±25 0.2 7.9 1.3 1.7 0.1 Twins &
6 Triplets
Validation -
Bgtw6.T2 286 10 35±11 0.2 9.4 0.0 0.3 0.1 Twins &
6 Triplets
Validation -
Bgtw7.T1 455 18 26±8 0.0 9.0 0.8 0.4 0.2 Twins &
14 Triplets
Validation -
Bgtw7.T2 455 18 26±7 0.4 9.1 3.2 2.0 0.2 Twins &
12 Triplets
Validation -
Bgtw8.T1 364 14 27±8 1.0 6.1 1.0 1.9 0.2 Twins &
15 Triplets
Validation -
Bgtw8.T2 366 13 29±5 1.0 6.1 0.0 0.0 0.5 Twins &
15 Triplets
Validation -
Bgtw9.T1 37 368 12 30±3 2.1 6.9 3.0 1.4 0.3 Twins &
Triplets
Validation -
Bgtw9.T2 368 14 28±7 2.3 6.9 2.0 2.2 0.1 Twins &
8 Triplets
Validation -
Bgtw10.T1 366 17 23±10 1.0 6.9 5.0 9.6 0.1 Twins &
4 Triplets
Validation -
Bgtw10.T2 365 17 23±11 1.1 6.9 4.0 9.3 0.1 Twins &
4 Triplets
Validation -
Bgtw11.T1 336 12 30±5 0.9 3.9 0.0 0.0 0.1 Twins &
1 Triplets
Validation -
Bgtw11.T2 368 13 30±11 1.1 4.0 0.0 0.0 0.0 Twins &
5 Triplets
Validation -
Bgtw12.T1 372 14 28±6 1.0 8.5 1.0 1.6 0.4 Twins &
6 Triplets
Validation -
Bgtw12.T2 372 13 31 ±5 1.0 8.0 1.0 0.5 0.1 Twins &
6 Triplets
NA, not applicable
Figure imgf000119_0001
Bgtw3 Bgtw3.F Father Bgtw3.F.m4 40.3 4 92 10,854 T34
Bgtw3 Bgtw3.F Father Bgtw3.F.m7 40.5 7 179 34,147 T78
Bgtw3 Bgtw3.F Father Bgtw3.F.m10 40.8 10 274 15,316 TCP2 (runs 1 and 2)
Bgtw3 Bgtw3.F Father Bgtw3.F.m13 41 .0 13 367 22,178 T12
Bgtw3 Bgtw3.F Father Bgtw3.F.m16 41 .3 16 458 30,845 TCP2 (runs 1 and 2)
Bgtw4 Bgtw4.F Father Bgtw4.F.m1 23.0 1 5 27,558 T12
Bgtw4 Bgtw4.F Father Bgtw4.F.m4 23.3 4 92 18,137 T34
Bgtw4 Bgtw4.F Father Bgtw4.F.m7 23.5 7 187 18,674 T34
Bgtw4 Bgtw4.F Father Bgtw4.F.m10 23.8 10 272 23,417 T56 (runs 1 and 2)
Bgtw4 Bgtw4.F Father Bgtw4.F.m13 24.0 13 365 23,890 T56 (runs 1 and 2)
Bgtw5 Bgtw5.F Father Bgtw5.F.m1 38.0 1 4 18,085 T12
Bgtw5 Bgtw5.F Father Bgtw5.F.m4 38.3 4 94 25,138 T56 (runs 1 and 2)
Bgtw5 Bgtw5.F Father Bgtw5.F.m7 38.5 7 195 14,529 T34
Bgtw5 Bgtw5.F Father Bgtw5.F.m10 38.8 10 274 15,468 T12
Bgtw5 Bgtw5.F Father Bgtw5.F.m13 39.0 13 366 17,789 T34
Bgtw6 Bgtw6.F Father Bgtw6.F.m1 29.0 1 2 14,873 T34
Bgtw6 Bgtw6.F Father Bgtw6.F.m4 29.3 4 92 19,520 TCP2 (runs 1 and 2)
Bgtw6 Bgtw6.F Father Bgtw6.F.m7 29.5 7 181 16,345 TCP2 (runs 1 and 2)
Bgtw6 Bgtw6.F Father Bgtw6.F.m10 29.8 10 286 9,139 T12
Bgtw7 Bgtw7.F Father Bgtw7.F.m1 38.0 1 14 21 ,791 T34
Bgtw7 Bgtw7.F Father Bgtw7.F.m4 38.3 4 91 12,730 TCP2 (runs 1 and 2)
Bgtw7 Bgtw7.F Father Bgtw7.F.m7 38.5 7 190 13,123 T12
Bgtw8 Bgtw8.F Father Bgtw8.F.m1 40.0 1 18 14,289 TCP2 (runs 1 and 2)
Bgtw8 Bgtw8.F Father Bgtw8.F.m4 40.3 4 92 18,075 TCP2 (runs 1 and 2)
Bgtw8 Bgtw8.F Father Bgtw8.F.m7 40.5 7 185 29,674 TCP2 (runs 1 and 2)
Bgtw8 Bgtw8.F Father Bgtw8.F.m13 41 .0 13 374 29,593 TCP2 (runs 1 and 2)
Bgtw9 Bgtw9.F Father Bgtw9.F.m1 28.0 1 8 16,769 T34
Bgtw9 Bgtw9.F Father Bgtw9.F.m4 28.3 4 93 8,311 T12
Bgtw9 Bgtw9.F Father Bgtw9.F.m7 28.5 7 185 25,31 1 T56 (runs 1 and 2)
Bgtw9 Bgtw9.F Father Bgtw9.F.m10 28.8 10 278 20,809 T56 (runs 1 and 2)
Bgtw9 Bgtw9.F Father Bgtw9.F.m13 29.0 13 367 26,044 T56 (runs 1 and 2)
BgtwIO Bgtwl O.F Father Bgtw10.F.m1 32.0 1 2 20,923 T12
BgtwIO Bgtwl O.F Father Bgtwl O.F.m4 32.3 4 92 14,148 T34
BgtwIO Bgtwl O.F Father Bgtwl O.F.m7 32.5 7 191 14,161 TCP2 (runs 1 and 2)
BgtwIO Bgtwl O.F Father Bgtw10.F.m10 32.8 10 275 25,813 T56 (runs 1 and 2)
BgtwIO Bgtwl O.F Father Bgtw10.F.m13 33.0 13 365 38,175 T56 (runs 1 and 2)
Bgtw11 Bgtwl 1.F Father Bgtwl 1.F.m1 26.0 1 4 23,237 TCP2 (runs 1 and 2)
Bgtw11 Bgtwl 1.F Father Bgtwl 1.F.m4 26.3 4 88 28,777 T56 (runs 1 and 2)
Bgtw12 Bgtwl 2. F Father Bgtwl 2.F.m1 25.0 1 7 23,115 TCP2 (runs 1 and 2)
Bgtw12 Bgtwl 2. F Father Bgtw12.F.m4 25.3 4 94 22,396 T56 (runs 1 and 2)
Bgtwl Bgtwl M Mother BgtwlM.ml 25.0 1 5 12,057 T34
Bgtwl Bgtwl M Mother BgtwlM.m3 25.2 3 64 15,500 T34
Bgtwl Bgtwl M Mother BgtwlM.m4 25.3 4 92 22,699 T34
Bgtwl Bgtwl M Mother BgtwlM.m5 25.3 5 124 14,512 T34
Bgtwl Bgtwl M Mother BgtwlM.m6 25.5 6 167 15,921 T34
Bgtwl Bgtwl M Mother BgtwlM.m7 25.5 7 188 19,137 T12
Bgtwl Bgtwl M Mother BgtwlM.m8 25.6 8 216 37,309 T56 (runs 1 and 2)
Bgtwl Bgtwl M Mother BgtwlM.m9 25.7 9 243 18,428 T34
Bgtwl Bgtwl M Mother BgtwlM.mlO 25.8 10 274 15,102 T34
Bgtwl Bgtwl M Mother BgtwlM.m11 25.8 11 305 13,393 T34
Bgtwl Bgtwl M Mother BgtwlM.m12 25.9 12 338 19,050 T34
Bgtwl Bgtwl M Mother BgtwlM.m13 26.0 13 365 19,374 T12
Bgtwl Bgtwl M Mother BgtwlM.m14 26.1 14 397 19,439 T12
Bgtwl Bgtwl M Mother BgtwlM.m15 26.2 15 426 22,949 TCP2 (runs 1 and 2)
Bgtwl Bgtwl M Mother BgtwlM.m16 26.2 16 456 10,227 T12
Bgtwl Bgtwl M Mother BgtwlM.m17 26.3 17 488 23,442 T12
Bgtwl Bgtwl M Mother BgtwlM.m18 26.4 18 519 18,496 T12
Bgtwl Bgtwl M Mother BgtwlM.m19 26.5 19 551 18,563 T78
Bgtwl Bgtwl M Mother BgtwlM.m20 26.6 20 579 20,085 T78
Bgtwl Bgtwl M Mother BgtwlM.m21 26.7 21 610 18,046 T78
Bgtwl Bgtwl M Mother BgtwlM.m22 26.8 22 639 16,539 T78
Bgtwl Bgtwl M Mother BgtwlM.m23 26.8 23 669 14,823 T78
Bgtwl Bgtwl M Mother BgtwlM.m24 26.9 24 700 15,480 T56 (runs 1 and 2)
Bgtwl Bgtwl M Mother BgtwlM.m25 27.0 25 730 35,842 T56 (runs 1 and 2)
Bgtw2 Bgtw2.M Mother Bgtw2.M.m1 22.0 1 1 14,645 TRX Bgtw2 Bgtw2.M Mother Bgtw2.M.m2 22.1 2 31 20,841 T34
Bgtw2 Bgtw2.M Mother Bgtw2.M.m3 22.2 3 60 15,540 T12
Bgtw2 Bgtw2.M Mother Bgtw2.M.m4 22.3 4 94 12,088 T12
Bgtw2 Bgtw2.M Mother Bgtw2.M.m5 22.3 5 123 17,342 T34
Bgtw2 Bgtw2.M Mother Bgtw2.M.m6 22.4 6 154 12,497 T34
Bgtw2 Bgtw2.M Mother Bgtw2.M.m7 22.5 7 184 33,721 T56 (runs 1 and 2)
Bgtw2 Bgtw2.M Mother Bgtw2.M.m8 22.6 8 214 13,669 T34
Bgtw2 Bgtw2.M Mother Bgtw2.M.m9 22.7 9 240 35,256 T56 (runs 1 and 2)
Bgtw2 Bgtw2.M Mother Bgtw2.M.m10 22.8 10 275 14,594 T34
Bgtw2 Bgtw2.M Mother Bgtw2.M.m11 22.8 11 302 13,499 T34
Bgtw2 Bgtw2.M Mother Bgtw2.M.m12 22.9 12 333 13,956 T34
Bgtw2 Bgtw2.M Mother Bgtw2.M.m13 23.0 13 367 11 ,822 T34
Bgtw2 Bgtw2.M Mother Bgtw2.M.m14 23.1 14 399 14,283 T34
Bgtw2 Bgtw2.M Mother Bgtw2.M.m15 23.2 15 426 21 ,608 T12
Bgtw2 Bgtw2.M Mother Bgtw2.M.m16 23.3 16 458 20,707 T34
Bgtw2 Bgtw2.M Mother Bgtw2.M.m17 23.3 17 491 21 ,304 T56 (runs 1 and 2)
Bgtw2 Bgtw2.M Mother Bgtw2.M.m18 23.4 18 518 18,054 TCP2 (runs 1 and 2)
Bgtw2 Bgtw2.M Mother Bgtw2.M.m19 23.5 19 547 21 ,550 T56 (runs 1 and 2)
Bgtw2 Bgtw2.M Mother Bgtw2.M.m20 23.6 20 581 24,239 T56 (runs 1 and 2)
Bgtw2 Bgtw2.M Mother Bgtw2.M.m21 23.7 21 609 18,456 T78
Bgtw2 Bgtw2.M Mother Bgtw2.M.m22 23.8 22 639 23,139 T56 (runs 1 and 2)
Bgtw2 Bgtw2.M Mother Bgtw2.M.m23 23.8 23 668 18,401 T78
Bgtw2 Bgtw2.M Mother Bgtw2.M.m24 23.9 24 702 22,541 T56 (runs 1 and 2)
Bgtw3 Bgtw3.M Mother Bgtw3.M.m1 30.0 1 8 18,168 T56 (runs 1 and 2)
Bgtw3 Bgtw3.M Mother Bgtw3.M.m2 30.1 2 32 15,001 T34
Bgtw3 Bgtw3.M Mother Bgtw3.M.m3 30.2 3 61 12,832 T34
Bgtw3 Bgtw3.M Mother Bgtw3.M.m4 30.3 4 92 15,985 T34
Bgtw3 Bgtw3.M Mother Bgtw3.M.m5 30.3 5 123 17,652 T34
Bgtw3 Bgtw3.M Mother Bgtw3.M.m6 30.4 6 151 17,953 TCP2 (runs 1 and 2)
Bgtw3 Bgtw3.M Mother Bgtw3.M.m7 30.5 7 178 17,506 T34
Bgtw3 Bgtw3.M Mother Bgtw3.M.m8 30.6 8 213 7,341 T12
Bgtw3 Bgtw3.M Mother Bgtw3.M.m9 30.7 9 240 16,599 T34
Bgtw3 Bgtw3.M Mother Bgtw3.M.m10 30.8 10 274 24,901 T56 (runs 1 and 2)
Bgtw3 Bgtw3.M Mother Bgtw3.M.m11 30.8 11 304 19,920 T34
Bgtw3 Bgtw3.M Mother Bgtw3.M.m12 30.9 12 341 15,278 TCP2 (runs 1 and 2)
Bgtw3 Bgtw3.M Mother Bgtw3.M.m13 31 .0 13 367 30,379 TCP2 (runs 1 and 2)
Bgtw3 Bgtw3.M Mother Bgtw3.M.m14 31 .1 14 400 26,537 TCP2 (runs 1 and 2)
Bgtw3 Bgtw3.M Mother Bgtw3.M.m15 31 .2 15 430 14,244 T12
Bgtw3 Bgtw3.M Mother Bgtw3.M.m16 31 .3 16 459 22,655 T12
Bgtw3 Bgtw3.M Mother Bgtw3.M.m17 31 .3 17 487 32,562 T56 (runs 1 and 2)
Bgtw3 Bgtw3.M Mother Bgtw3.M.m19 31 .5 19 549 29,619 T56 (runs 1 and 2)
Bgtw3 Bgtw3.M Mother Bgtw3.M.m20 31 .6 20 579 15,865 TCP2 (runs 1 and 2)
Bgtw3 Bgtw3.M Mother Bgtw3.M.m21 31 .7 21 609 17,61 1 TCP2 (runs 1 and 2)
Bgtw3 Bgtw3.M Mother Bgtw3.M.m22 31 .7 22 638 15,61 1 TCP2 (runs 1 and 2)
Bgtw4 Bgtw4.M Mother Bgtw4.M.m1 20.0 1 5 20,006 T34
Bgtw4 Bgtw4.M Mother Bgtw4.M.m2 20.1 2 32 16,042 T34
Bgtw4 Bgtw4.M Mother Bgtw4.M.m3 20.2 3 61 15,325 T34
Bgtw4 Bgtw4.M Mother Bgtw4.M.m4 20.3 4 92 22,921 T12
Bgtw4 Bgtw4.M Mother Bgtw4.M.m5 20.3 5 123 13,727 T34
Bgtw4 Bgtw4.M Mother Bgtw4.M.m6 20.4 6 151 25,198 T56 (runs 1 and 2)
Bgtw4 Bgtw4.M Mother Bgtw4.M.m7 20.5 7 181 20,259 T34
Bgtw4 Bgtw4.M Mother Bgtw4.M.m8 20.6 8 21 1 15,493 T34
Bgtw4 Bgtw4.M Mother Bgtw4.M.m9 20.7 9 251 14,538 T34
Bgtw4 Bgtw4.M Mother Bgtw4.M.m10 20.8 10 277 16,851 T34
Bgtw4 Bgtw4.M Mother Bgtw4.M.m11 20.8 11 305 11 ,467 T34
Bgtw4 Bgtw4.M Mother Bgtw4.M.m12 20.9 12 334 32,997 T56 (runs 1 and 2)
Bgtw4 Bgtw4.M Mother Bgtw4.M.m13 21 .0 13 364 20,722 T12
Bgtw4 Bgtw4.M Mother Bgtw4.M.m14 21 .1 14 395 13,616 T12
Bgtw4 Bgtw4.M Mother Bgtw4.M.m15 21 .2 15 425 21 ,943 T12
Bgtw4 Bgtw4.M Mother Bgtw4.M.m16 21 .3 16 458 22,991 T78
Bgtw4 Bgtw4.M Mother Bgtw4.M.m17 21 .3 17 487 17,381 T78
Bgtw4 Bgtw4.M Mother Bgtw4.M.m18 21 .4 18 517 34,752 T56 (runs 1 and 2)
Bgtw4 Bgtw4.M Mother Bgtw4.M.m19 21 .5 19 547 24,521 T56 (runs 1 and 2)
Bgtw4 Bgtw4.M Mother Bgtw4.M.m20 21 .6 20 575 44,125 T56 (runs 1 and 2)
Bgtw5 Bgtw5.M Mother Bgtw5.M.m1 29.0 1 4 9,540 T34 Bgtw5 Bgtw5.M Mother Bgtw5.M.m2 29.1 2 33 16,280 T34
Bgtw5 Bgtw5.M Mother Bgtw5.M.m3 29.2 3 61 23,552 TCP2 (runs 1 and 2)
Bgtw5 Bgtw5.M Mother Bgtw5.M.m4 29.3 4 95 25,903 T12
Bgtw5 Bgtw5.M Mother Bgtw5.M.m5 29.3 5 123 15,880 T34
Bgtw5 Bgtw5.M Mother Bgtw5.M.m6 29.4 6 152 16,214 T34
Bgtw5 Bgtw5.M Mother Bgtw5.M.m7 29.5 7 181 20,984 T56 (runs 1 and 2)
Bgtw5 Bgtw5.M Mother Bgtw5.M.m8 29.6 8 218 13,008 T34
Bgtw5 Bgtw5.M Mother Bgtw5.M.m9 29.7 9 248 21 ,653 T34
Bgtw5 Bgtw5.M Mother Bgtw5.M.m10 29.8 10 277 13,582 T34
Bgtw5 Bgtw5.M Mother Bgtw5.M.m11 29.8 11 310 14,621 T12
Bgtw5 Bgtw5.M Mother Bgtw5.M.m12 29.9 12 341 18,744 T34
Bgtw5 Bgtw5.M Mother Bgtw5.M.m13 30.0 13 365 15,753 T34
Bgtw5 Bgtw5.M Mother Bgtw5.M.m14 30.1 14 396 21 ,882 T56 (runs 1 and 2)
Bgtw5 Bgtw5.M Mother Bgtw5.M.m15 30.2 15 429 17,027 T12
Bgtw5 Bgtw5.M Mother Bgtw5.M.m16 30.3 16 458 14,496 T78
Bgtw5 Bgtw5.M Mother Bgtw5.M.m17 30.4 17 493 14,010 T78
Bgtw5 Bgtw5.M Mother Bgtw5.M.m18 30.4 18 514 18,506 T78
Bgtw5 Bgtw5.M Mother Bgtw5.M.m19 30.5 19 548 19,202 T56 (runs 1 and 2)
Bgtw5 Bgtw5.M Mother Bgtw5.M.m20 30.6 20 578 22,358 T56 (runs 1 and 2)
Bgtw5 Bgtw5.M Mother Bgtw5.M.m21 30.7 21 613 41 ,854 T56 (runs 1 and 2)
Bgtw5 Bgtw5.M Mother Bgtw5.M.m22 30.8 22 640 14,899 T78
Bgtw6 Bgtw6.M Mother Bgtw6.M.m1 18.0 1 6 21 ,134 T12
Bgtw6 Bgtw6.M Mother Bgtw6.M.m2 18.1 2 35 22,561 TCP2 (runs 1 and 2)
Bgtw6 Bgtw6.M Mother Bgtw6.M.m3 18.2 3 63 12,071 TCP2 (runs 1 and 2)
Bgtw6 Bgtw6.M Mother Bgtw6.M.m4 18.3 4 92 15,084 T12
Bgtw6 Bgtw6.M Mother Bgtw6.M.m6 18.4 6 152 8,105 T12
Bgtw6 Bgtw6.M Mother Bgtw6.M.m7 18.5 7 182 24,708 TCP2 (runs 1 and 2)
Bgtw6 Bgtw6.M Mother Bgtw6.M.m8 18.6 8 212 25,775 T56 (runs 1 and 2)
Bgtw6 Bgtw6.M Mother Bgtw6.M.m9 18.7 9 246 31 ,301 TCP2 (runs 1 and 2)
Bgtw6 Bgtw6.M Mother Bgtw6.M.m10 18.8 10 286 22,264 TCP2 (runs 1 and 2)
Bgtw7 Bgtw7.M Mother Bgtw7.M.m1 32.0 1 12 12,620 T34
Bgtw7 Bgtw7.M Mother Bgtw7.M.m2 32.1 2 32 12,895 T34
Bgtw7 Bgtw7.M Mother Bgtw7.M.m3 32.2 3 61 14,622 T34
Bgtw7 Bgtw7.M Mother Bgtw7.M.m4 32.2 4 90 15,904 T12
Bgtw7 Bgtw7.M Mother Bgtw7.M.m5 32.3 5 125 13,728 T34
Bgtw7 Bgtw7.M Mother Bgtw7.M.m6 32.5 6 167 25,899 TCP2 (runs 1 and 2)
Bgtw7 Bgtw7.M Mother Bgtw7.M.m7 32.5 7 189 16,171 TCP2 (runs 1 and 2)
Bgtw7 Bgtw7.M Mother Bgtw7.M.m8 32.6 8 214 21 ,054 TCP2 (runs 1 and 2)
Bgtw7 Bgtw7.M Mother Bgtw7.M.m9 32.7 9 244 16,054 TCP2 (runs 1 and 2)
Bgtw7 Bgtw7.M Mother Bgtw7.M.m10 32.8 10 275 16,972 T78
Bgtw7 Bgtw7.M Mother Bgtw7.M.m11 32.8 11 307 18,622 T78
Bgtw7 Bgtw7.M Mother Bgtw7.M.m12 32.9 12 336 17,114 T56 (runs 1 and 2)
Bgtw7 Bgtw7.M Mother Bgtw7.M.m13 33.0 13 364 24,908 TCP2 (runs 1 and 2)
Bgtw7 Bgtw7.M Mother Bgtw7.M.m14 33.1 14 397 25,642 T56 (runs 1 and 2)
Bgtw7 Bgtw7.M Mother Bgtw7.M.m15 33.2 15 427 34,912 T56 (runs 1 and 2)
Bgtw7 Bgtw7.M Mother Bgtw7.M.m16 33.2 16 455 26,763 T56 (runs 1 and 2)
Bgtw8 Bgtw8.M Mother Bgtw8.M.m2 35.1 2 35 14,743 T34
Bgtw8 Bgtw8.M Mother Bgtw8.M.m3 35.2 3 64 12,586 T34
Bgtw8 Bgtw8.M Mother Bgtw8.M.m4 35.2 4 90 15,905 T12
Bgtw8 Bgtw8.M Mother Bgtw8.M.m5 35.3 5 122 22,094 T56 (runs 1 and 2)
Bgtw8 Bgtw8.M Mother Bgtw8.M.m6 35.4 6 157 11 ,955 T12
Bgtw8 Bgtw8.M Mother Bgtw8.M.m7 35.5 7 185 12,986 T12
Bgtw8 Bgtw8.M Mother Bgtw8.M.m8 35.6 8 212 24,345 T56 (runs 1 and 2)
Bgtw8 Bgtw8.M Mother Bgtw8.M.m9 35.7 9 247 24,921 T56 (runs 1 and 2)
Bgtw8 Bgtw8.M Mother Bgtw8.M.m11 35.8 11 305 18,724 T56 (runs 1 and 2)
Bgtw8 Bgtw8.M Mother Bgtw8.M.m12 35.9 12 333 22,064 T56 (runs 1 and 2)
Bgtw8 Bgtw8.M Mother Bgtw8.M.m13 36.0 13 364 22,114 T78
Bgtw9 Bgtw9.M Mother Bgtw9.M.m2 20.1 2 38 21 ,305 TCP2 (runs 1 and 2)
Bgtw9 Bgtw9.M Mother Bgtw9.M.m3 20.2 3 65 11 ,004 T12
Bgtw9 Bgtw9.M Mother Bgtw9.M.m4 20.3 4 93 17,898 T12
Bgtw9 Bgtw9.M Mother Bgtw9.M.m5 20.3 5 120 12,883 T12
Bgtw9 Bgtw9.M Mother Bgtw9.M.m7 20.5 7 185 22,936 TCP2 (runs 1 and 2)
Bgtw9 Bgtw9.M Mother Bgtw9.M.m8 20.6 8 218 18,803 T78
Bgtw9 Bgtw9.M Mother Bgtw9.M.m10 20.7 10 273 16,732 TCP2 (runs 1 and 2)
Bgtw9 Bgtw9.M Mother Bgtw9.M.m12 20.9 12 336 29,995 T56 (runs 1 and 2) Bgtw9 Bgtw9.M Mother Bgtw9.M.m13 21.0 13 368 19,398 T56 (runs 1 and 2)
BgtwIO BgtwIO. M Mother BgtwIO. M.m1 30.0 1 3 22,263 TCP2 (runs 1 and 2)
BgtwIO BgtwIO. M Mother BgtwIO. M.m2 30.1 2 33 18,615 T12
BgtwIO BgtwIO. M Mother BgtwIO. M.m3 30.2 3 60 22,139 T12
BgtwIO BgtwIO. M Mother BgtwIO. M.m4 30.3 4 93 19,762 TRX
BgtwIO BgtwIO. M Mother BgtwIO. M.m5 30.3 5 123 15,019 T78
BgtwIO BgtwIO. M Mother BgtwIO. M.m6 30.4 6 154 24,658 T56 (runs 1 and 2)
BgtwIO BgtwIO. M Mother BgtwIO. M.m7 30.5 7 184 18,035 T78
BgtwIO BgtwIO. M Mother BgtwIO. M.m8 30.6 8 212 19,391 T56 (runs 1 and 2)
BgtwIO BgtwIO. M Mother BgtwIO. M.m9 30.7 9 245 38,351 T56 (runs 1 and 2)
BgtwIO BgtwIO. M Mother Bgtw10.M.m10 30.7 10 271 26,347 T56 (runs 1 and 2)
BgtwIO BgtwIO. M Mother Bgtw10.M.m11 30.8 11 303 31 ,076 T56 (runs 1 and 2)
BgtwIO BgtwIO. M Mother Bgtw10.M.m12 30.9 12 332 27,492 T56 (runs 1 and 2)
BgtwIO BgtwIO. M Mother Bgtw10.M.m13 31.0 13 365 13,612 T78
Bgtw11 Bgtw11.M Mother Bgtw11.M.m1 21.0 1 3 26,161 T56 (runs 1 and 2)
Bgtw11 Bgtw11.M Mother Bgtw11.M.m2 21.1 2 27 33,396 T56 (runs 1 and 2)
Bgtw11 Bgtw11.M Mother Bgtw11.M.m3 21.2 3 60 38,371 T56 (runs 1 and 2)
Bgtw11 Bgtw11.M Mother Bgtw11.M.m4 21.2 4 88 18,784 T78
Bgtw11 Bgtw11.M Mother Bgtw11.M.m5 21.3 5 120 31 ,702 T56 (runs 1 and 2)
Bgtw11 Bgtw11.M Mother Bgtw11.M.m6 21.4 6 151 27,234 TCP2 (runs 1 and 2)
Bgtw11 Bgtw11.M Mother Bgtw11.M.m7 21.5 7 192 18,199 TCP2 (runs 1 and 2)
Bgtw11 Bgtw11.M Mother Bgtw11.M.m8 21.6 8 216 41 ,854 T56 (runs 1 and 2)
Bgtw11 Bgtw11.M Mother Bgtw11.M.m9 21.7 9 243 19,865 T56 (runs 1 and 2)
Bgtw11 Bgtw11.M Mother Bgtw11.M.m10 21.8 10 279 16,839 T78
Bgtw11 Bgtw11.M Mother Bgtw11.M.m11 21.8 11 306 20,396 T78
Bgtw11 Bgtw11.M Mother Bgtw11.M.m12 21.9 12 334 17,489 T78
Bgtw12 Bgtw12.M Mother Bgtw12.M.m1 22.0 1 7 18,105 T56 (runs 1 and 2)
Bgtw12 Bgtw12.M Mother Bgtw12.M.m2 22.1 2 31 38,175 T56 (runs 1 and 2)
Bgtw12 Bgtw12.M Mother Bgtw12.M.m3 22.2 3 60 16,379 T78
Bgtw12 Bgtw12.M Mother Bgtw12.M.m4 22.2 4 91 40,446 T56 (runs 1 and 2)
Bgtw12 Bgtw12.M Mother Bgtw12.M.m5 22.3 5 125 12,674 T78
Bgtw12 Bgtw12.M Mother Bgtw12.M.m6 22.4 6 158 41 ,746 T56 (runs 1 and 2)
Bgtw12 Bgtw12.M Mother Bgtw12.M.m7 22.5 7 187 21 ,437 T56 (runs 1 and 2)
Bgtw12 Bgtw12.M Mother Bgtw12.M.m8 22.6 8 216 36,976 T56 (runs 1 and 2)
Bgtw12 Bgtw12.M Mother Bgtw12.M.m9 22.7 9 245 15,573 T78
Bgtw12 Bgtw12.M Mother Bgtw12.M.m10 22.8 10 280 16,713 T78
Bgtw12 Bgtw12.M Mother Bgtw12.M.m11 22.9 11 314 16,498 T78
Bgtw12 Bgtw12.M Mother Bgtw12.M.m12 22.9 12 341 15,881 TRX
Bgtw12 Bgtw12.M Mother Bgtw12.M.m13 23.0 13 372 17,612 TRX
Table 3. Characteristics of children in training and validation sets used for Random
Forests age-discriminatory model
Characteristics Training
Weight-for-Height Z score -0.32 ± 1
Male / Female 7 / 5
Number of fecal samples collected per child 22.7 ± 1.5
Age at first fecal sample collection (days) 6 ± 1
Age at last fecal sample collection (days) 712 ± 15
Mean sampling interval (days) 33 ± 3.0
Months of exclusive breastfeeding 3.2 ± 2.3
Age of first introduction of solid food (months) 6.3 ± 2.3
Number of diarrhoeal episodes per year 4.0 ± 1.9
% Days with diarrhoea during sampling period 3.9 ± 1.7
Fraction of samples collected where antibiotics had been consumed within prior 7 days 0.2 ± 0.1 (b) Validation
Characteristics Validation
Weight-for-Height Z score -0.44 ± 0.8
Male / Female 9 / 4
Number of fecal samples collected per child 21.2 ± 2.2
Age at first fecal sample collection (days) 7 ± 4
Age at last fecal sample collection (days) 709 ± 9
Mean sampling interval (days) 35.1 ± 4.3 Months of exclusive breastfeeding 3.6 ± 2.3
Age of first introduction of solid food (months) 6.4 ± 6.4
Number of diarrhoeal episodes per year 4.6 ± 2.4
% Days with diarrhoea during sampling period 4.3 ± 2.7
Fraction of samples collected where antibiotics had been consumed within prior 7 days 0.2 ± 0.2
(c) Validation - Twins & Triplets
Characteristics Validation - Twins & Triplets
Weight-for-Height Z score -0.46 ± 0.7 Male / Female 7 / 18
Number of fecal samples collected per child 17.9 ± 5.3
Age at first fecal sample collection (days) 13 ± 12
Age at last fecal sample collection (days) 497 ± 147
Mean sampling interval (days) 29 ± 3.1
Months of exclusive breastfeeding 0.7 ± 2.3
Age of first introduction of solid food (months) 7.0 ± 1.9
Number of diarrhoeal episodes per year 1.7 ± 1.3
% Days with diarrhoea during sampling period 2.2 ± 2.6
Fraction of samples collected where antibiotics had been consumed within prior 7 days 0.1 ± 0.1
Mean values ± SD are shown
Table 4. Bacterial V4-16S rRNA sequencing statistics
V4-16S rF ΪΝΑ reads per high
Study Subjects M jmber of fe sal Total number of sample (mean + SD) samples quality reads
50 Healthy Children (Singletons, Twins and Triplets) 25,288 13,990 996 25,187,256
Mothers & Fathers 20,233 7,120 243 4,916,630 Severe Acute Malnutrition Randomized Clinical Trial 21 ,545 15,731 589 12,689,785 Moderate Acute Malnutrition Cross-Sectional Analysis 22,964 8,713 22 505,207 Concordant Healthy Malawian Twins and Triplets 151 ,578 65,521 47 7,124,158
1897 50,423,036
Figure imgf000124_0001
TGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACT
GACGCTGAGGTTCGAAAGCGTGGGTAGCAAACAGG
TACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGG
GAGCGTAGACGGCACGGCAAGCCAGATGTGAAAGCCCGGGGC
3.27 Firmicutes;Clostridia;Clostrid
TCAACCCCGGGACTGCATTTGGAACTGCTGAGCTAGAGTGTCG
191687 ± iales;Lachnospiraceae;Dore
GAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAT
0.23 a;Dorea_longicatena
ATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACGATG
ACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGGG
Actinobacteria;1760;Bifidoba CTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTT
2.86
± cteriales;Bifidobacteriaceae; AACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGT
72820
Bifidobacterium; Bifidobacteri AGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGAT 0.14
umjongum ATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTT
ACTGACGCTGAGGAGCGAAAGCGTGGGGAGCGAACAGG
TACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGG
Firmicutes;Clostridia;Clostrid GAGCGTAGACGGTGTGGCAAGTCTGATGTGAAAGGCATGGGCT
2.43
± iales;Ruminococcaceae;Ru CAACCCGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGA
194745
minococcus;Ruminococcus GGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATAT 0.19
sp_5_1_39BFAA TAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAAC
TGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTGGCAAGCGTTATCCGGATTTATTGGGCGTAAAGCG
Firmicutes;Bacilli;Lactobacill AGCGCAGGCGGTTTGATAAGTCTGATGTGAAAGCCTTTGGCTTA
1.98
ales;Lactobacillaceae;Lacto ACCAAAGAAGTGCATCGGAAACTGTCAGACTTGAGTGCAGAAGA
15141 ±
bacillus;Lactobacillus_muco GGACAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATAT 0.12
sae GGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGCAACTG
ACGCTGAGGCTCGAAAGCATGGGTAGCGAACAGG
TACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGGG
CTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTT
1.59 Actinobacteria;1760;Bifidoba
± AACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGT
561483 cteriales;Bifidobacteriaceae;
AGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGAT
0.10 Bifidobacterium
ATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTT
ACTGACGCTGAGGAGCGGAAGCGTGGGGAGCGAACAGG
TACGTAGGTGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGCG
CGCGTAGGCGGTTTTTTAAGTCTGATGTGAAAGCCCACGGCTCA
0.99 Firmicutes;Bacilli;Bacillales;
± ACCGTGGAGGGTCATTGGAAACTGGAAAACTTGAGTGCAGAAG
217996 Staphylococcaceae;Staphyl
AGGAAAGTGGAATTCCATGTGTAGCGGTGAGATGCGCAGAGAT
0.06 ococcus
ATGGAGGAACACCAGTGGCGAAGGCGACTTTCTGGTCTGTAACT
GACGCTGATGTGCGAAAGCGTGGGGATCAAACAGG
TACGTAGGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGG
Firmicutes;Clostridia;Clostrid AGCGTAGACGGTGTGGCAAGTCTGATGTGAAAGGCATGGGCTC
0.95
iales;Ruminococcaceae;Ru AACCTGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGAG
364234 ±
minococcus;Ruminococcus GGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATT 0.12
sp_5_1_39BFAA AGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACT
GACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTGGCGAGCGTTATCCGGAATCATTGGGCGTAAAGA
Firmicutes;Erysipelotrichi;Er GGGAGCAGGCGGCCGCAAGGGTCTGTGGTGAAAGACCGAAGC
0.92
ysipelotrichales; Erysipelotric TAAACTTCGGTAAGCCATGGAAACCGGGCGGCTAGAGTGCGGA
287510 ±
haceae ; Cateni bacteri urn ; Cat AGAGGATCGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATA 0.08
enibacterium_mitsuokai TATGGAGGAACACCAGTGGCGAAGGCGACGGTCTGGGCCGCAA
CTGACGCTCATTCCCGAAAGCGTGGGGAGCAAATAGG
TACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGG
GAGCGTAGACGGCTGTGCAAGTCTGAAGTGAAAGGCATGGGCT
0.88 Firmicutes;Clostridia;Clostrid
CAACCTGTGGACTGCTTTGGAAACTGTGCAGCTAGAGTGTCGGA
261912 ± iales;Lachnospiraceae;Dore
GAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATAT
0.10 a;Dorea_formicigenerans
TAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATGAC
TGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTATGGTGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGG
Firmicutes;Clostridia;Clostrid AGCGTAGACGGAGTGGCAAGTCTGATGTGAAAACCCGGGGCTC
0.61
± iales;Ruminococcaceae;Ru AACCCCGGGACTGCATTGGAAACTGTCAATCTAGAGTACCGGAG
361809
minococcus;Ruminococcus_ AGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATT 0.1 1
torques AGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACT
GACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTCCCGAGCGTTGTCCGGATTTATTGGGCGTAAAGCG
Firmicutes;Bacilli;Lactobacill AGCGCAGGCGGTTTGATAAGTCTGAAGTTAAAGGCTGTGGCTCA
0.52
ales ; Streptococcaceae ; Stre ACCATAGTTCGCTTTGGAAACTGTCAAACTTGAGTGCAGAAGGG
108747 ±
ptococcus ; Streptococcus_th GAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATG 0.05
ermophilus GAGGAACACCGGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGA
CGCTGAGGCTCGAAAGCGTGGGGAGCGAACAGG TACGTAGGGCGCAAGCGTTATCCGGATTTATTGGGCGTAAAGG
GCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCT
0.51 Actinobacteria;1760;Bifidoba
TAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGG
533785 ± cteriales;Bifidobacteriaceae;
TAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGA
0.05 Bifidobacterium
TATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGT
TACTGACGCTGAGGAGCGAAAGCGTGGGGAGCGAACAGG
TACGGAGGGTGCGAGCGTTAATCGGAATAACTGGGCGTAAAGG
Proteobacteria; Gammaprote GCACGCAGGCGGTGACTTAAGTGAGGTGTGAAAGCCCCGGGCT
0.46
± obacteria;Pasteurellales;Pas TAACCTGGGAATTGCATTTCATACTGGGTCGCTAGAGTACTTTAG
9514
teurellaceae;Haemophilus;H GGAGGGGTAGAATTCCACGTGTAGCGGTGAAATGCGTAGAGAT 0.06
aemophilus_parainfluenzae GTGGAGGAATACCGAAGGCGAAGGCAGCCCCTTGGGAATGTAC
TGACGCTCATGTGCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTCCCGAGCGTTATCCGGATTTATTGGGCGTAAAGCG
AGCGCAGGCGGTTAGATAAGTCTGAAGTTAAAGGCTGTGGCTTA
0.42 Firmicutes;Bacilli;Lactobacill
ACCATAGTACGCTTTGGAAACTGTTTAACTTGAGTGCAAGAGGG
561636 ± ales ; Streptococcaceae ; Stre
GAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATG
0.05 ptococcus
GAGGAACACCGGTGGCGAAAGCGGCTCTCTGGCTTGTAACTGA
CGCTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGGGGCTAGCGTTATCCGGAATTACTGGGCGTAAAGG
GTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTC
0.42 Firmicutes;Clostridia;Clostrid
± AACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGA
312461 iales;Clostridiaceae;Clostridi
GGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTA
0.04 um
GGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTG
ACACTGAGGCACGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGAG
Firmicutes;Erysipelotrichi;Er
GGAGCAGGCGGCAGCAAGGGTCTGTGGTGAAAGCCTGAAGCTT
0.40 ysipelotrichales; Erysipelotric
± AACTTCAGTAAGCCATAGAAACCAGGCAGCTAGAGTGCAGGAGA
470139 haceae;unclassified_Erysipe
GGATCGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATAT
0.05 lotrichaceae;Clostridium_ra
GGAGGAACACCAGTGGCGAAGGCGACGATCTGGCCTGCAACTG
mosum
ACGCTCAGTCCCGAAAGCGTGGGGAGCAAATAGG
TACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGG
GAGCGCAGACGGCGATGCAAGTCTGAAGTGAAAGCCCGGGGCT
0.38 Firmicutes;Clostridia;Clostrid
CAACCCCGGGACTGCTTTGGAAACTGTATGGCTAGAGTGCTGG
181834 ± iales;Clostridiaceae;Clostridi
AGAGGCAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATA
0.08 um
TTAGGAAGAACACCAGTGGCGAAGGCGGCTTGCTGGACAGTAA
CTGACGTTCAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTATGTTCCAAGCGTTATCCGGATTTATTGGGCGTAAAGCG AGCGCAGACGGTTATTTAAGTCTGAAGTGAAAGCCCTCAGCTCA
0.38 Firmicutes;Bacilli;Lactobacill
± ACTG AG G AATTG CTTTG G AAACTG GATGACTTGAGTGCAGTAGA
148099 ales;Leuconostocaceae;Wei
GGAAAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATAT
0.05 ssella;Weissella_cibaria
GGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTG ACGTTGAGGCTCGAAAGTGTGGGTAGCAAACAGG
TACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGGG
CTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTT
0.36 Actinobacteria;1760;Bifidoba
± AACGGTGGATCTGCGCCGGGTACGGGCGGGCTGGAGTGCGGT
469873 cteriales;Bifidobacteriaceae;
AGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGAT
0.05 Bifidobacterium
ATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTT
ACTGACGCTGAGGAGCGAAAGCGTGGGGAGCGAACAGG
TACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGG
GAGCGTAGACGGATGGACAAGTCTGATGTGAAAGGCTGGGGCT
0.35
Firmicutes;Clostridia;Clostrid CAACCCCGGGACTGCATTGGAAACTGCCCGTCTTGAGTGCCGG
185951 ±
iales AGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATA 0.06
TTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAA
CTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
AACGTAGGGTGCAAGCGTTGTCCGGAATTACTGGGTGTAAAGG
GAGCGCAGGCGGACCGGCAAGTTGGAAGTGAAAACTATGGGCT
0.31
Firmicutes;Clostridia;Clostrid CAACCCATAAATTGCTTTCAAAACTGCTGGCCTTGAGTAGTGCA
212619 ±
iales; Ruminococcaceae GAGGTAGGTGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATA 0.04
TCGGGAGGAACACCAGTGGCGAAGGCGACCTACTGGGCACCAA
CTGACGCTGAGGCTCGAAAGCATGGGTAGCAAACAGG
TACGTAGGGCGCAAGCGTTATCCGGATTTATTGGGCGTAAAGG
Actinobacteria;1760;Bifidoba GCTCGTAGGCGGCTCGTCGCGTCCGGTGTGAAAGTCCATCGCT
0.29
cteriales;Bifidobacteriaceae; TAACGGTGGATCTGCGCCGGGTACGGGCGGGCTGGAGTGCGG
469852 ±
Bifidobacterium; Bifidobacteri TAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGA 0.05
um_bifidum TATCGGGAAGAACACCGATGGCGAAGGCAGGTCTCTGGGCCGT
CACTGACGCTGAGGAGCGAAAGCGTGGGGAGCGAACAGG
170124 0.28 Firmicutes;Clostridia;Clostrid TACGTAGGGAGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGG ± iales; Eubacteriaceae; Eubact CGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTT 0.06 erium;Eubacterium_desmola AACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAG ns AGGCAGGCGGAATTCCGTGTGTAGCGGTGAAATGCGTAGATAT
ACGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACATTAAC
TGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGG
Firmicutes;Clostridia;Clostrid AGCGCAGGCGGGAGAGCAAGTTGGAAGTGAAATCCATGGGCTC
0.28
iales; Ruminococcaceae;Fae AACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGA
187010 ±
calibacterium;Faecalibacteri GGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATC 0.06
um_prausnitzii GGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAAC
TGACGCTGAGGCTCGAAAGTGTGGGTAGCAAACAGG
TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGG
AGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCA
0.27 Bacteroidetes; Bacteroidia; B
± ACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAA
303304 acteroidales;Prevotellaceae;
AGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCA
0.05 Prevotella;Prevotella_copri
CGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTG
ACGCTGAAGCTCGAAAGTGCGGGTATCGAACAGG
TACGTAGGTGGCAAGCGTTATCCGGATTTATTGGGCGTAAAGCG
Firmicutes;Bacilli;Lactobacill AGCGCAGGCGGTTGCTTAGGTCTGATGTGAAAGCCTTCGGCTTA
0.26
± ales;Lactobacillaceae;Lacto ACCGAAGAAGTGCATCGGAAACCGGGCGACTTGAGTGCAGAAG
470527
bacillus;Lactobacillus_reuter AGGACAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATA 0.03
i TGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGCAACT
GACGCTGAGGCTCGAAAGCATGGGTAGCGAACAGG
TACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGC
GCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCT
0.25 Proteobacteria; Gammaprote
CAACCTGGGAACTGCATTCGAAACTGGCAGGCTAGAGTCTTGTA
210269 ± obacteria;Enterobacteriales;
GAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGA
0.04 Enterobacteriaceae
TCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGA
CTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGGGGCTAGCGTTATCCGGATTTACTGGGCGTAAAGG
GTGCGTAGGTGGTTTCTTAAGTCAGGAGTGAAAGGCTACGGCTC
0.24 Firmicutes;Clostridia;Clostrid
AACCGTAGTAAGCTCTTGAAACTGGGAAACTTGAGTGCAGGAGA
182202 ± iales;Clostridiaceae;Clostridi
GGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTA
0.03 um;Clostridium_glycolicum
GGAGGAACACCAGTAGCGAAGGCGGCTTTCTGGACTGTAACTG
ACACTGAGGCACGAAAGCGTGGGGAGCAAACAGG
TACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGG
Firmicutes;Clostridia;Clostrid GAGCGTAGACGGACTGGCAAGTCTGATGTGAAAGGCGGGGGCT
0.23
iales; Ruminococcaceae;Ru CAACCCCTGGACTGCATTGGAAACTGTTAGTCTTGAGTGCCGGA
178122 ±
minococcus;Ruminococcus_ GAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATAT 0.06
obeum TAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAAC
TGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTGGCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCG
Firmicutes;Bacilli;Lactobacill AGCGCAGGCGGTTTCTTAAGTCTGATGTGAAAGCCCCCGGCTC
0.21
ales;Enterococcaceae;Enter AACCGGGGAGGGTCATTGGAAACTGGGAGACTTGAGTGCAGAA
540230 ±
ococcus;Enterococcus_faec GAGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATAT 0.04
alis ATGGAGGAACACCAGTGGCGAAGGCGGCTCTCTGGTCTGTAAC
TGACGCTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGG AGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCA
0.18 Bacteroidetes; Bacteroidia; B
± ACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAA
309068 acteroidales;Prevotellaceae;
AGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCA
0.04 Prevotella;Prevotella_copri
CGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTG ACGCTGAAGCTCGAAAGTGCGGGTATCGAACAGG
TACGTAGGGTGCAAGCGTTATCCGGAATTATTGGGCGTAAAGGG
CTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTT
0.16 Actinobacteria;1760;Bifidoba
± AACGGTGGATCTGCGCCGGGTACGGGCGGGCTGGAGTGCGGT
24773 cteriales;Bifidobacteriaceae;
AGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGAT
0.03 Bifidobacterium
ATCGGGAAGAACACCGATGGCGAAGGCAGGTCTCTGGGCCGTC
ACTGACGCTGAGGAGCGAAAGCGTGGGGAGCGAACAGG
TACGTAGGTGGCAAGCGTTATCCGGATTTATTGGGCGTAAAGCG
AGCGCAGGCGGTTTCTTAAGTCTGATGTGAAAGCCCCCGGCTC
0.16 Firmicutes;Bacilli;Lactobacill
AACCGGGGAGGGTCATTGGAAACTGGGAAACTTGAGTGCAGAA
554755 ± ales;Enterococcaceae;Enter
GAGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATAT
0.03 ococcus
ATGGAGGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAAC
TGACGCTGAGGTTCGAAAGCGTGGGTAGCAAACAGG
0.15 Proteobacteria; Gammaprote TACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGC
305760 ± obacteria;Enterobacteriales; GCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCT
0.04 Enterobacteriaceae;Escheri CAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTA chia;Escherichia_coli GAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGA
TCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAG
ACTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGG
TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGG AGCGCAGGCGGACCTTTAAGTCAGCTGTGAAATACGGCGGCTC
0.15 Bacteroidetes; Bacteroidia; B
± AACCGTCGAACTGCAGTTGATACTGGAGGTCTTGAGTGCACACA
268604 acteroidales;Prevotellaceae;
GGGATGCTGGAATTCATGGTGTAGCGGTGAAATGCTCAGATATC
0.03 Prevotella
ATGAAGAACTCCGATCGCGAAGGCAGGCATCCGGGGTGCAACT GACGCTGAGGCTCGAAAGTGCGGGTATCAAACAGG
AACGTAGGTCACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGG
Firmicutes;Clostridia;Clostrid AGCGCAGGCGGGAGAACAAGTTGGAAGTGGAATCCATGGGCTC
0.15
188900 ± iales;Ruminococcaceae;Fae AACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGA calibacterium;Faecalibacteri GGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATC 0.04
um_prausnitzii GGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAAC
TGACGCTGAGGCTCGAAAGTGTGGGTAGCAAACAGG
TACGTATGGAGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGG
TGCGTAGGTGGCAGTGCAAGTCAGATGTGAAAGGCCGGGGCTC
0.14 Firmicutes;Clostridia;Clostrid
AACCCCGGAGCTGCATTTGAAACTGCTCGGCTAGAGTACAGGA
174256 ± iales; Eubacteriaceae; Eubact
GAGGCAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATA
0.03 erium;Eubacterium_hallii
TTAGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACTGTTA
CTGACACTGAGGCACGAAAGCGTGGGGAGCAAACAGG
TACGGAGGATCCGAGCGTTATCCGGATTTATTGGGTTTAAAGGG
Bacteroidetes; Bacteroidia; B AGCGTAGGTGGACTGGTAAGTCAGTTGTGAAAGTTTGCGGCTCA
0.14
± acteroidales;Bacteroidaceae ACCGTAAAATTGCAGTTGATACTGTCAGTCTTGAGTACAGTAGA
130663
;Bacteroides;Bacteroides_fr GGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCA 0.03
agilis CGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTG
ACACTGATGCTCGAAAGTGTGGGTATCAAACAGG
TACGTAGGTCCCGAGCGTTGTCCGGATTTATTGGGCGTAAAGCG
AGCGCAGGCGGTTTAATAAGTCTGAAGTTAAAGGCAGTGGCTTA
0.14 Firmicutes;Bacilli;Lactobacill
± ACCATTGTTCGCTTTGGAAACTGTTAGACTTGAGTGCAGAAGGG
292424 ales ; Streptococcaceae ; Stre
GAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATG
0.04 ptococcus
GAGGAACACCGGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGA
CGCTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGCG
CGCGTAGGCGGTTTTTTAAGTCTGATGTGAAAGCCCACGGCTCA
0.14 Firmicutes;Bacilli;Bacillales;
ACCGTGGAGGGTCATTGGAAACTGGAAAACTTGAGTGCAGAAG
269541 ± Staphylococcaceae;Staphyl
AGGAAAGTGGAATTCCATGTGTAGCGGTGAAATGCGCAGAGATA
0.03 ococcus
TGGAGGAACACCAGTGGCGAAGGCGACTTTCTGGTCTGTAACT
GACGCTGATATGCGAAAGCGTGGGGATCAAACAGG
TACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGG
Firmicutes;Clostridia;Clostrid GAGCGTAGACGGTGCGGCAAGTCTGATGTGAAAGGCATGGGCT
0.13
± iales; Ruminococcaceae;Ru CAACCTGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGA
191900
minococcus;Ruminococcus GGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATAT 0.03
sp_5_1_39BFAA TAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGCAAC
TGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTGGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGC
GCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCT
0.13 Firmicutes;Negativicutes;Sel
± TAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGA
48207 enomonadales;Veillonellace
GAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGAT
0.03 ae;Dialister
TAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAAC
TGACGCTGAGGCGCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGGGGCGAGCGTTATCCGGATTCATTGGGCGTAAAGC
Actinobacteria;1760;Corioba GCGCGTAGGCGGCCCGGCAGGCCGGGGGTCGAAGCGGGGGG
0.12
cteriales;Coriobacteriaceae; CTCAACCCCCCGAAGCCCCCGGAACCTCCGCGGCTTGGGTCCG
186029 ±
Collinsella;Collinsella_aerof GTAGGGGAGGGTGGAACACCCGGTGTAGCGGTGGAATGCGCA 0.03
aciens GATATCGGGTGGAACACCGGTGGCGAAGGCGGCCCTCTGGGC
CGAGACCGACGCTGAGGCGCGAAAGCTGGGGGAGCGAACAGG
TACGTAGGGGGCTAGCGTTATCCGGATTTACTGGGCGTAAAGG
GTGCGTAGGCGGTC I I I I AAGTCAGGAGTGAAAGGCTACGGCT
0.1 1 Firmicutes;Clostridia;Clostrid
CAACCGTAGTAAGCTCTTGAAACTGGAGGACTTGAGTGCAGGAG
325608 ± iales;Clostridiaceae;Clostridi
AGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATT
0.04 um;Clostridium_bartlettii
AGGAGGAACACCAGTAGCGAAGGCGGCTCTCTGGACTGTAACT
GACGCTGAGGCACGAAAGCGTGGGGAGCAAACAGG
TACGTAGGGTGCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCG
0.1 1 Firmicutes;Bacilli;Lactobacill AGCGCAGGCGGAAGAATAAGTCTGATGTGAAAGCCCTCGGCTT
252321 ± ales;Lactobacillaceae;Lacto AACCGAGGAACTGCATCGGAAACTGTTTTTCTTGAGTGCAGAAG
0.02 bacillus AGGAGAATGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATA
TGGAAGAACACCAGTGGCGAAGGCGGCTCTCTGGTCTGCAACT GACGCTGAGGCTCGAAAGCATGGGTAGCGAACAGG
TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGG
AGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGAGGCTC
0.10 Bacteroidetes; Bacteroidia; B
AACCTCTGCACTGCAGCGCGAACTGGTCTTCTTGAGTACGCACA
195574 ± acteroidales;Prevotellaceae;
ACGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATC
0.02 Prevotella
ACGAAGAACTCCGATTGCGAAGGCAGCTCACGGGAGCGCAACT
GACGCTGAAGCTCGAAAGTGCGGGTATCGAACAGG
TACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGG
Firmicutes;Clostridia;Clostrid GAGCGTAGACGGTGTGGCAAGTCTGATGTGAAAGGCATGGGCT
0.10
iales;Ruminococcaceae;Ru CAACCTGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGA
365047 ±
minococcus;Ruminococcus GGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATAT 0.03
sp_5_1_39BFAA TAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAAC
TGACGTTGAGGCTCGAAAGCGTGGGTAGCAAACAGG
TACGTAGGTGGCAGGCGTTGTCCGGATTTATTGGGCGTAAAGG
Firmicutes;Bacilli;Lactobacill GAACGCAGGCGGTC I I I I AAGTCTGATGTGAAAGCCTTCGGCTT
0.09
ales;Lactobacillaceae;Lacto AACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAG
471308 ±
bacillus;Lactobacillus_rumini AGGAGAGTGGAGCTCCATGTGTAGCGGTGAAATGCGTAGATATA 0.03
s TGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACT
GACGCTGAGGTTCGAAAGCGTGGGTAGCAAACAGG
TACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGC
GCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCT
0.09 Proteobacteria; Gammaprote
CAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTA
307981 ± obacteria;Enterobacteriales;
GAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGA
0.03 Enterobacteriaceae
TCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGA
CTGACGCTCAGGTGCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTCCCGAGCGTTGTCCGGATTTATTGGGCGTAAAGCG AGCGCAGGCGGTTTGATAAGTCTGAAGTTAAAGGCTGTGGCTCA
0.08 Firmicutes;Bacilli;Lactobacill
ACCATAGTTCGCTTTGGAAACTGTCAAACTTGAGTGCAGAAGGG
15382 ± ales ; Streptococcaceae ; Stre
GAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATG
0.02 ptococcus
GAGGAACACCGGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGA CGCTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTGGCGAGCGTTGTCCGGATTTACTGGGCGTAAAGG
GAGCGTAGGCGGAC I I I I AAGTGAGATGTGAAATACCCGGGCT
0.08 Firmicutes;Clostridia;Clostrid
CAACTTGGGTGCTGCATTTCAAACTGGAAGTCTAGAGTGCAGGA
302844 ± iales;Clostridiaceae;Clostridi
GAGGAGAATGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGAT
0.02 um;Clostridium_disporicum
TAGGAAGAACACCAGTGGCGAAGGCGATTCTCTGGACTGTAACT
GACGCTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTCCCGAGCGTTATCCGGATTTATTGGGCGTAAAGCG
Firmicutes;Bacilli;Lactobacill AGCGCAGGCGGTTAGATAAGTCTGAAGTTAAAGGCTGTGGCTTA
0.08
ales ; Streptococcaceae ; Stre ACCATAGTACGCTTTGGAAACTGTTTAACTTGAGTGCAGAAGGG
528842 ±
ptococcus ; Streptococcus_p GAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATG 0.02
arasanguinis GAGGAACACCGGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGA
CGCTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTCCCGAGCGTTGTCCGGATTTATTGGGCGTAAAGG
GCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCT
0.08 Actinobacteria;1760;Bifidoba
TAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGG
131391 ± cteriales;Bifidobacteriaceae;
TAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGA
0.03 Bifidobacterium
TATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGT
TACTGACGCTGAGGAGCGAAAGCGTGGGGAGCGAACAGG
TACGTAGGCGGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGG
GAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCT
0.08 Firmicutes;Negativicutes;Sel
± CAACCCCGTGATGGGGTCCGAACTGG I I I I CTTGAGTGCAGGA
259261 enomonadales;Veillonellace
GAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATAT
0.02 ae;Megamonas
TGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAAC
TGACGCTGAGGCTCGAAAGCTAGGGTAGCGAACGGG
TACGTAGGTGGCGAGCGTTGTCCGGATTTACTGGGCGTAAAGG
GAGCGTAGGCGGATTTTTAAGTGAGATGTGAAATACTCGGGCTT
0.08 Firmicutes;Clostridia;Clostrid
± AACCTGAGTGCTGCATTTCAAACTGGAAGTCTAGAGTGCAGGAG
248563 iales;Clostridiaceae;Clostridi
AGGAGAAGGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATT
0.02 um
AGGAAGAACACCAGTGGCGAAGGCGCTTCTCTGGACTGTAACT
GACGCTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
TACGTAGGTGGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGG
GCGCGCAGGCGGCATCGCAAGTCGGTCTTAAAAGTGCGGGGCT
0.08 Firmicutes;Negativicutes;Sel
TAACCCCGTGAGGGGACCGAAACTGTGAAGCTCGAGTGTCGGA
162427 ± enomonadales;Veillonellace
GAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATAT
0.02 ae;Megasphaera
TAGGAGGAACACCAGTGGCGAAAGCGGCTTTCTGGACGACAAC
TGACGCTGAGGCGCGAAAGCCAGGGGAGCAAACGGG
545371 0.07 Firmicutes;Bacilli;Lactobacill TACGTAGGTGGCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCG ± ales;Lactobacillaceae;Lacto AGCGCAGGCGGAAAGATAAGTCTGAAGTGAAAGCCCCCGGCTT 0.01 bacillus AACCGGGGAACTGCTTCGGAAACTGTCCTTCTTGAGTGCAGAAG
AGGAGAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATA
TGGAAGAACACCAGTGGCGAAGGCGGCTCTCTGGTCTGCAACT
GACGCTGAGGCTCGAAAGCATGGGTAGCGAACAGG
The larger the age-discriminatory importance score the more important the indicated taxon is for age discrimination (determined by permutation using unsealed feature importance scores)
Figure imgf000130_0001
Figure imgf000131_0001
Figure imgf000131_0002
Figure imgf000132_0001
Halwa
Khichuri- lost to follow-
Bgmal17 Male -4.2 na 2 (LAMA) -4.16 3.78 none detected
Halwa up
Khichuri- Aeromona
Bgmal19 Male edema 7.8 25 -0.09 7.06 1.17
Halwa hydrophila
Khichuri-
Bgmal21 Male A.76 8.83 22 -3.89 3.31 none detected 2.74
Halwa
Khichuri- lost to follow-
Bgmal23 Male -3.15 9.97 6 (LAMA) -3.1 4 not done
Halwa up
Khichuri-
Bgmal24 Male ^.63 8.43 14 -3.94 4.81 Shigella sonnei 0.46
Halwa
Khichuri-
Bgmal28 Female -3.56 15.87 18 -2.26 8.89 Shigella flexneri 5.94
Halwa
Khichuri- lost to follow-
Bgmal30 Female -3.35 9.2 13 -2.13 11.31 none detected
Halwa up
Khichuri-
Bgmal31 Male ^.47 13.9 13 -3 12.5 none detected 5.67
Halwa
Khichuri-
Bgmal32 Male Α.5Λ 19.9 15 -3.05 11.4 none detected 5.9
Halwa
Khichuri-
Bgmal36 Male -3.96 15.37 27 -2.61 5.9 none detected 3.4
Halwa
Khichuri-
Bgmal37 Male -3.37 13.13 26 3.01 2.6 none detected 5.43
Halwa
Khichuri- Salmonella
Bgmal39 Female -5.59 14.13 10 ^.93 15.9 9.1
Halwa enterica group C1
Khichuri-
Bgmal40 Female -4.21 10.1 27 -3.76 3.3 not done 5.57
Halwa
Khichuri-
Bgmal45 Male -5.32 8.6 26 ^.58 5.6 none detected 5.9
Halwa
Khichuri-
Bgmal46 Female -5.4 18.5 15 ^.03 12.3 none detected 3.87
Halwa
Khichuri-
Bgmal47 Female ^.76 12.33 10 -3.91 10.8 Shigella flexneri 5.9
Halwa
Khichuri-
Bgmal50 Male -3.79 13.97 13 -2.23 12.9 not done 6.03
Halwa
Khichuri-
Bgmal51 Male ^.03 14.93 10 -2.22 14.7 Vibrio cholerae 1.96
Halwa
Khichuri- lost to follow-
Bgmal53 Male Α.5Λ 14.53 11 -2.76 16.1 not done
Halwa up
Khichuri- lost to follow-
Bgmal55 Male -5.58 8.4 4 (LAMA) -4.9 23.2 not done
Halwa up
Khichuri-
Bgmal56 Male -3.35 9 11 -1 .87 13.5 not done 7.3
Halwa
Khichuri-
Bgmal60 Male edema 8.97 15 -0.71 11.7 not done 5.9
Halwa
Khichuri-
Bgmal63 Male -3.02 8.47 12 -1 .59 14.74 not done 6
Halwa
Khichuri-
Bgmal64 Female -3.1 11.17 8 -1 .55 19.77 4.5
Halwa not done
LAMA - left against medical advice
Figure imgf000133_0001
No 55 90 145
Total 139 169 308 0.02
(c) 'Antibiotics by Food-intervention' during the post-intervention follow-up r. eriod
Khic urt-
Antibiotics RUTF Total p value
Halwa
Yes 22 23 45
No 78 80 158
Total 100 103 203 1
Fisher's Exact test (two-sided) p-value are presented
Figure imgf000134_0001
Table 12. Relative microbiota maturitv. Microbiota-for-Aae Z-score and Aae-adiusted
Shannon Diversity Index co mparisons in each ti eatment phi ase of each inl ervention arm in the SAM trial (compared to healthy controls and to values at enrollment)
(a) Effect size and significance of diffe rences in microbiota maturation metrics between each treatment phase relative to healthy children
Relative microbiota maturity Intervention Arm Estimate Std. Error p value
Enrollment -5.62 0.78 <0.001 During -5.09 0.68 <0.001
End of Intervention ^.75 0.82 <0.001 < 1 month -2.85 0.83 <0.01
RUTF
1 - 2 months -2.19 0.87 0.07
2 - 3 months -0.31 1.03 1.00
3 - 4 months -1.96 1.11 0.35 > 4 months -3.43 0.96 <0.01
Enrollment -6.55 0.67 <0.001 During -5.88 0.53 <0.001
End of Intervention -5.63 0.72 <0.001 < 1 month -3.98 0.74 <0.001
Khichuri-Halwa
1 - 2 months -1.72 0.79 0.18
2 - 3 months -2.67 0.89 0.02
3 - 4 months -1.58 1.05 0.59 > 4 months -2.29 0.82 0.04
Microbiota-for-Age
Z-score
Enrollment -1.63 0.26 <0.001 During -1.49 0.23 <0.001
End of Intervention -1.39 0.27 <0.001 < 1 month -0.84 0.27 0.01
RUTF
1 - 2 months -0.75 0.28 0.04
2 - 3 months -0.42 0.33 0.68
3 - 4 months -0.73 0.35 0.19 > 4 months -1.43 0.31 <0.001
Enrollment -1.80 0.23 <0.001 During -1.68 0.19 <0.001
End of Intervention -1.66 0.24 <0.001 < 1 month -1.30 0.25 <0.001
Khichuri-Halwa
1 - 2 months -0.69 0.26 0.06
2 - 3 months -1.05 0.29 <0.01
3 - 4 months -0.56 0.34 0.46 > 4 months -0.96 0.27 <0.01
Age-Adjusted Shannon Diversity
Intervention Arm Estimate Std. Error p value Index
Enrollment -1.18 0.16 <0.001 During -0.82 0.12 <0.001
End of Intervention -0.89 0.17 <0.001 < 1 month -0.56 0.18 0.01
RUTF
1 - 2 months -0.30 0.19 0.55
2 - 3 months -0.46 0.24 0.32
3 - 4 months -1.18 0.26 <0.001 > 4 months -0.79 0.22 <0.01
Enrollment -1.03 0.15 <0.001 During -0.95 0.11 <0.001
End of Intervention -0.90 0.16 <0.001 < 1 month -0.83 0.17 <0.001
Khichuri-Halwa
1 - 2 months -0.89 0.18 <0.001
2 - 3 months -0.73 0.21 <0.01
3 - 4 months -0.41 0.25 0.54 > 4 months -0.58 0.19 0.02
(b) Effect size and significance of differences in microbiota maturation metrics between each treatment phase relative to enrollment
Relative microbiota maturity Intervention Arm Estimate Std. Error p value
Healthy 5.62 0.78 <0.001
RUTF
During 0.53 0.57 0.93
Figure imgf000136_0001
comparisons Table 13. 220 bacterial taxa whose abundances are significantly altered in the microbiota of children with SAM compared to similarly aged healthy children
fa Taxa altered in children with SAM relative to healthy controls at enrollment prior to intervention phase
Rank order of
importance in
16S rRNA Unabbreviated
FDR- Random
OTU ID OTU ID in Beta RDP 2.4 Taxonomic Annotation
corrected Forests-based
(as shown in deposited OTU Coefficient (Pnylum;Class;Qrder;Fa*nily;G6nus;Species) p value age- FIG. 8 & 9) table
discriminatory
model
Proteobacteria; Gammaproteobacteria; Enterobacteriales; Ente
142054 142054 0.000 0.0286 robacteriaceae
Proteobacteria; Gammaproteobacteria; Enterobacteriales; Ente
210269 210269 0.000 0.0278 robacteriaceae
Proteobacteria; Gammaproteobacteria; Enterobacteriales; Ente
9715 9715 0.000 0.0263 robacteriaceae
Proteobacteria;Gammaproteobacteria; Enterobacteriales; Ente
563485 563485 0.002 0.0236 robacteriaceae
Proteobacteria;Gammaproteobacteria; Enterobacteriales; Ente
436723 436723 0.000 0.0232 robacteriaceae
Proteobacteria; Gammaproteobacteria; Enterobacteriales; Ente
512914 512914 0.000 0.0215 robacteriaceae
Proteobacteria; Gammaproteobacteria; Enterobacteriales; Ente
310265 310265 0.000 0.0207 robacteriaceae
Proteobacteria; Gammaproteobacteria; Enterobacteriales; Ente
307981 307981 0.000 0.0174 robacteriaceae
Proteobacteria;Gamrnaproteobacteria;Enterobacteriales;Ente
307080 307080 0.000 0.0152 robacteriaceae;Escherichia
Proteobacteria;Gammaproteobacteria; Enterobacteriales; Ente
305760 305760 0.000 0.0131 robacteriaceae;Escherichia;Escherichia_coli
Proteobacteria;Gammaproteobacteria; Enterobacteriales; Ente
113558 113558 0.000 0.0115 robacteriaceae
Proteobacteria; Gammaproteobacteria; Enterobacteriales; Ente
280706 280706 0.000 0.0090 robacteriaceae
Firmicutes;Bacilli;Lactobacillales;Enterococcaceae;Enterococ
540230 540230 0.002 0.0079 cus;Enterococcus faecalis
Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptoco
15382 15382 0.027 0.0068 ecus
New.O.CIeanUp.
ReferenceOTU24 Firmicutes;Bacilli;Lactobacillales;Leuconostocaceae;Leucono
249155.C0 9155 0.027 0.0067 stoc
Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptoco
316587 316587 0.007 0.0063 ccus;Streptococcus gallolyticus
Actinobacteria;1760;Bifidobacteriales;Bifidobacteriaceae;Bifid
469852 469852 0.000 -0.0163 obacterium; Bifidobacterium bifidum
Actinobacteria;1760;Bifidobacteriales;Bifidobacteriaceae;Bifid
15
533785 533785 0.000 -0.0146 obacterium
Actinobacteria;1760;Bifidobacteriales;Bifidobacteriaceae;Bifid
24773 24773 0.000 -0.0137 obacterium
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
1
326792 326792 0.000 -0.01 18 acterium;Faecalibacterium prausnitzii
Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae;Olse
301004 301004 0.001 -0.01 15 nella
181834 181834 0.000 -0.01 12 20 Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium
Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Dorea;Do
12
261912 261912 0.000 -0.0109 rea formicigenerans
Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;V
13823 13823 0.005 -0.0104 eillonella;Veillonella ratti
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
188900 188900 0.000 -0.0 03 acterium;Faecalibacterium prausnitzii
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
187010 187010 0.000 -0.0102 acterium;Faecalibacterium prausnitzii
Actinobacteria;1760;Bifidobacteriales;Bifidobacteriaceae;Bifid
131391 131391 0.000 -0.0098 obacterium
New.0. Reference
576.d0 OTU576 0.001 -0.0097 Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae; M
162427 162427 0.020 -0.0096 egasphaera
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
303304 303304 0.000 -0.0094 otella;Prevotella copri
Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Lactobacill
3
470663 470663 0.000 -0.0094 us;Lactobacillus ruminis
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
309068 309068 0.000 -0.0093 otella;Prevotella copri
Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae;Colli
186029 186029 0.000 -0.0092 nsella;Collinsella aerofaciens
Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae;V
145149 145149 0.001 -0.0090 eillonella
Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bact
130663 130663 0.020 -0.0088 eroides;Bacteroides fragilis
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
6
194745 194745 0.000 -0.0088 coccus; Ruminococcus sp 5 1 39BFAA
212503 212503 0.000 -0.0088 Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
184464 184464 0.001 -0.0085 otella;Prevotella copri
New.O.CIeanUp.
ReferenceOTU89 Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae;Colli
89679 679 0.007 -0.0085 nsella;Collinsella aerofaciens
Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae; M
274208 274208 0.014 -0.0084 egasphaera; Megasphaera elsdenii
Actinobacteria;1760;Bifidobacteriales;Bifidobacteriaceae;Bifid
22
469873 469873 0.000 -0.0081 obacterium
139221 139221 0.017 -0.0080 Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae
Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Lactobacill
7
15141 15141 0.016 -0.0079 us;Lactobacillus mucosae
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
10
364234 364234 0.000 -0.0077 coccus; Ruminococcus sp 5 1 39BFAA
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
198251 198251 0.022 -0.0077 coccus; Ruminococcus gnavus
Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae; M
259261 259261 0.01 1 -0.0077 egamonas
Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Dorea;Do
4
191687 191687 0.000 -0.0076 rea longicatena
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
2
189827 189827 0.000 -0.0073 coccus;Ruminococcus sp 5 1 39BFAA
Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Lactobacill
292302 292302 0.002 -0.0072 us
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
365047 365047 0.000 -0.0072 coccus; Ruminococcus sp 5 1 39BFAA
Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Lactobacill
250395 250395 0.000 -0.0072 us
New.O.CIeanUp.
ReferenceOTU25
258806 8806 0.005 -0.0071 Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae
Actinobacteria;1760;Bifidobacteriales;Bifidobacteriaceae;Bifid
326977 326977 0.000 -0.0070 obacterium
165261 165261 0.005 -0.0069 Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium
Actinobacteria;1760;Actinomycetales;Actinomycetaceae;Actin
370431 370431 0.003 -0.0068 omyces;Actinomyces odontolyticus
Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bact
2000 2000 0.038 -0.0068 eroides;Bacteroides fragilis
Actinobacteria;1760;Bifidobacteriales;Bifidobacteriaceae;Bifid
8
561483 561483 0.006 -0.0067 obacterium
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
177351 177351 0.004 -0.0064 otella
Actinobacteria;1760;Bifidobacteriales;Bifidobacteriaceae;Bifid
5
72820 72820 0.005 -0.0064 obacterium; Bifidobacterium longum
Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae;AI
58262 58262 0.012 -0.0064 lisonella;Allisonella histaminiformans
212619 212619 0.000 -0.0063 24 Firmicutes;Clostridia;Clostridiales;Ruminococcaceae
Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Lactobacill
142448 142448 0.006 -0.0060 us Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae; Di
48207 48207 0.001 -0.0060 alister
Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bact
158660 158660 0.038 -0.0059 eroides
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
195574 195574 0.005 -0.0059 otella
Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Lactobacill
28727 28727 0.009 -0.0059 us
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
170124 170124 0.000 -0.0057 m;Eubacterium desmolans
Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae;Egge
1 1372 11372 0.019 -0.0057 rthella;Eggerthella lenta
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
365758 365758 0.003 -0.0057 otella
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
13
36 809 361809 0.000 -0.0056 coccus; Ruminococcus torques
Firmicutes;Erysipelotrichi;Erysipelotrichales;Erysipelotrichace
11
287510 287510 0.000 -0.0055 ae;Catenibacterium;Catenibacterium mitsuokai
177005 177005 0.000 -0.0054 Firmicutes;Clostridia;Clostridiales
185951 185951 0.000 -0.0054 23 Firmicutes;Clostridia;Clostridiales
New.O. Reference
73.d0 OTU73 0.026 -0.0054 Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae
New.O.CIeanUp.
ReferenceOTU15 Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
155355.C0 5355 0.045 -0.0052 acterium
Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium;
325969 325969 0.002 -0.0051 Clostridium sp SS2 1
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
268604 268604 0.016 -0.0050 otella
182804 182804 0.000 -0.0050 Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
71685 71685 0.006 -0.0049 coccus; Ruminococcus torques
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
181003 181003 0.001 -0.0048 coccus
266274 266274 0.007 -0.0047 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
198941 198941 0.004 -0.0045 m;Eubacterium desmolans
Firmicutes;Bacilli;Lactobacillales;Carnobacteriaceae;Granulic
470477 470477 0.009 -0.0044 atella;Granulicatella adiacens
Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium;
184037 184037 0.004 -0.0044 Clostridium sp SS2 1
Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium;
325608 325608 0.003 -0.0044 Clostridium bartlettii
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
367433 367433 0.009 -0.0044 acterium; Faecalibacterium prausnitzii
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
517331 517331 0.006 -0.0043 otella
Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium;
302844 302844 0.015 -0.0042 Clostridium disporicum
Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Coprococ
189396 189396 0.019 -0.0042 cus;Coprococcus comes
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
24916 24916 0.019 -0.0042 otella
369164 369164 0.007 -0.0040 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
191306 191306 0.003 -0.0039 coccus; Ruminococcus sp 5 1 39BFAA
Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteur
16
9514 9514 0.01 1 -0.0038 ellaceae;Haemophilus;Haemophilus parainfluenzae
Firmicutes;Erysipelotrichi;Erysipelotrichales;Erysipelotrichace
470369 470369 0.009 -0.0038 ae; unclassified Erysipelotrichaceae;Eubacterium biforme
Firmicutes;Erysipelotrichi;Erysipelotrichales;Erysipelotrichace
295024 295024 0.009 -0.0037 ae; unclassified Erysipelotrichaceae;Eubacterium biforme
185281 185281 0.009 -0.0037 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium;
579564 579564 0.039 -0.0036 Clostridium disporicum
178146 178146 0.019 -0.0034 Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu m;Eubacterium hallii
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
199293 199293 0.005 -0.0033 acterium;Faecalibacterium prausnitzii
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
177772 177772 0.009 -0.0032 m;Eubacterium rectale
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
174256 174256 0.016 -0.0031 m;Eubacterium hallii
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
182994 182994 0.034 -0.0031 m;Eubacterium rectale
179460 179460 0.042 -0.0030 Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium
212304 212304 0.017 -0.0029 Firmicutes;Clostridia;Clostridiales
Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae;Slac
594084 594084 0.001 -0.0029 kia;Slackia isoflavoniconvertens
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
175682 175682 0.019 -0.0028 m;Eubacterium rectale
Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bact
325738 325738 0.049 -0.0027 eroides;Bacteroides galacturonicus
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
178122 178122 0.007 -0.0027 coccus; Ruminococcus obeum
Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium;
182202 182202 0.036 -0.0026 Clostridium glycolicum
168716 168716 0.025 -0.0026 Firmicutes;Clostridia;Clostridiales
206931 206931 0.014 -0.0025 Firmicutes;Clostridia;Clostridiales
560141 560141 0.01 1 -0.0023 Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae
212787 212787 0.030 -0.0022 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
100258 100258 0.026 -0.0021 m;Eubacterium sp cL 10 1 3
Firmicutes;Clostridia;Clostridiales;unclassified_Clostridiales;B
194648 194648 0.038 -0.0018 lautia;Blautia sp M25
Actinobacteria;1760;Bifidobacteriales;Bifidobacteriaceae;Bifid
471180 471 180 0.027 -0.0017 obacterium
(b) Taxa altered in children with SAM relative to healthy controls during the post-intervention period
Rank order of
importance in
16S rRNA Unabbreviated
FDR- Random
OTU ID OTU ID in Beta RDP 2.4 Taxonomic Annotation
corrected Forests-based
(as shown in deposited OTU Coefficient (Phylum;Class;Order;Family;Genus;Species) p value age- FIG. 8 & 9) table
discriminatory
model
Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptoco
292424 292424 0.0000 0.0105 ecus
Firmicutes;Bacilli;Lactobacillales;Leuconostocaceae;Weissell
21
148099 148099 0.0000 0.0101 a;Weissella cibaria
New.0. CleanUp.
ReferenceOTU24 Firmicutes;Bacilli;Lactobacillales;Leuconostocaceae;Leucono
249155.C0 9155 0.0035 0.0100 stoc
Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptoco
15382 15382 0.0001 0.0096 ecus
New.0. Reference Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptoco
628.d0 OTU628 0.001 1 0.0080 ecus
New.0. Reference Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Lactobacill
239.d0 OTU239 0.0045 0.0065 us
New.0. CleanUp.
ReferenceOTU28 Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae;Lactobacill
282068/cO 2068 0.0051 0.0063 us
Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptoco
528842 528842 0.0029 0.0059 ccus;Streptococcus parasanguinis
Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptoco
14
108747 108747 0.0018 0.0057 ccus;Streptococcus thermophilus
New.0. Reference Actinobacteria ; 1760; Bifidobacteriales; Bifidobacteriaceae; Bifid
340.d0 OTU 340 0.0043 0.0055 obacterium
New.0. Reference
73.d0 OTU73 0.0417 0.0054 Firmicutes;Bacilli;Lactobacillales;Lactobacillaceae
Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptoco
294794 294794 0.0173 0.0045 ecus Firmicutes;Erysipelotrichi;Erysipelotrichales;Erysipelotrichace
233573 233573 0.0172 0.0006 ae
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
1
326792 326792 0.0000 -0.0136 acterium;Faecalibacterium prausnitzii
181834 181834 0.0000 -0.0126 20 Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
187010 187010 0.0000 -0.01 14 acterium; Faecalibacterium prausnitzii
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
188900 188900 0.0000 -0.0106 acterium; Faecalibacterium prausnitzii
Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae; M
162427 162427 0.0008 -0.0095 egasphaera
Actinobacteria;1760;Bifidobacteriales;Bifidobacteriaceae;Bifid
15
533785 533785 0.0008 -0.0093 obacterium
New.O. Reference
576.d0 OTU576 0.0002 -0.0090 Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae
New.O. Reference Actinobacteria;1760;Bifidobacteriales;Bifidobacteriaceae;Bifid
417.d0 OTU417 0.0000 -0.0090 obacterium
Actinobacteria ; 1760; Bifidobacteriales; Bifidobacteriaceae; Bifid
469852 469852 0.0000 -0.0090 obacterium; Bifidobacterium bifidum
212503 212503 0.0000 -0.0089 Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium
Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Dorea;Do
12
261912 261912 0.0000 -0.0088 rea formicigenerans
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
309068 309068 0.0000 -0.0086 otella;Prevotella copri
Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae;Olse
301004 301004 0.0034 -0.0084 nella
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
184464 184464 0.0000 -0.0083 otella;Prevotella copri
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
177351 1 7351 0.0000 -0.0082 otella
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
303304 303304 0.0000 -0.0081 otella;Prevotella copri
Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae; Di
48207 48207 0.0000 -0.0081 alister
Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae;V
13823 13823 0.0024 -0.0079 eillonella;Veillonella ratti
Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bact
130663 130663 0.0017 -0.0079 eroides;Bacteroides fragilis
Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae;AI
58262 58262 0.0000 -0.0076 lisonella;Allisonella histaminiformans
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
195574 195574 0.0000 -0.0076 otella
Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae; M
259261 259261 0.0001 -0.0075 egamonas
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
365758 365758 0.0000 -0.0072 otella
165261 165261 0.0000 -0.0072 Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium
212619 212619 0.0000 -0.0069 24 Firmicutes;Clostridia;Clostridiales;Ruminococcaceae
Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bact
196757 196757 0.0092 -0.0067 eroides;Bacteroides ovatus
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
181330 181330 0.0000 -0.0067 coccus; Ruminococcus sp 5 1 39BFAA
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
170124 170124 0.0000 -0.0067 m;Eubacterium desmolans
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
6
194745 194745 0.0001 -0.0066 coccus; Ruminococcus sp 5 1 39BFAA
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
189862 189862 0.0046 -0.0066 otella;Prevotella sp DJF B1 16
Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Dorea;Do
4
191687 191687 0.0000 -0.0066 rea longicatena
Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bact
158660 158660 0.0006 -0.0064 eroides
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
268604 268604 0.0000 -0.0064 otella
Proteobacteria;Deltaproteobacteria;Desulfovibrionales;Desulf
192132 192132 0.0006 -0.0063 ovibrionaceae;Bilophila;Bilophila wadsworthia Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae; M
274208 274208 0.0172 -0.0062 egasphaera;Megasphaera elsdenii
New.O.CIeanUp.
ReferenceOTU15 Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
155355 5355 0.0008 -0.0062 acterium
266274 266274 0.0000 -0.0061 Firmicutes;Clostridia;Clostridiales
Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bact
2000 2000 0.0059 -0.0059 eroides;Bacteroides fragilis
Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bact
331820 331820 0.0043 -0.0058 eroides;Bacteroides vulgatus
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
10
364234 364234 0.0000 -0.0057 coccus; Ruminococcus sp 5 1 39BFAA
New.O. Reference
11. dO OTU1 1 0.0305 -0.0057 Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
198941 198941 0.0000 -0.0057 m;Eubacterium desmolans
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
365047 365047 0.0000 -0.0057 coccus; Ruminococcus sp 5 1 39BFAA
New.O.CIeanUp.
ReferenceOTU25
258806 8806 0.0051 -0.0056 Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
298533 298533 0.0012 -0.0055 otella
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
367433 367433 0.0000 -0.0053 acterium; Faecalibacterium prausnitzii
Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae;Egge
11372 11372 0.0052 -0.0053 rthella;Eggerthella lenta
Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Coprococ
189396 189396 0.0000 -0.0052 cus;Coprococcus comes
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
24916 24916 0.0000 -0.0052 otella
Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bact
348374 348374 0.0089 -0.0052 eroides;Bacteroides thetaiotaomicron
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
517331 517331 0.0000 -0.0052 otella
New.O. Reference
574.d0 OTU574 0.0356 -0.0049 Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
2
189827 189827 0.0002 -0.0049 coccus; Ruminococcus sp 5 1 39BFAA
New.O.CIeanUp.
ReferenceOTU23
235476 5476 0.0449 -0.0049 Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae
294710 294710 0.0002 -0.0048 Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium
369164 369164 0.0000 -0.0047 Firmicutes;Clostridia;Clostridiales
Firmicutes; Negativicutes;Selenomonadales;Veillonellaceae;V
145149 145149 0.0335 -0.0046 eillonella
Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Coprococ
369502 369502 0.0025 -0.0043 cus;Coprococcus catus
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
199293 199293 0.0000 -0.0042 acterium; Faecalibacterium prausnitzii
209122 209122 0.0001 -0.0041 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium;
184037 184037 0.0003 -0.0041 Clostridium sp SS2 1
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
174902 1 4902 0.0021 -0.0041 acterium; Faecalibacterium prausnitzii
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
177772 177772 0.0000 -0.0041 m;Eubacterium rectale
185281 185281 0.0000 -0.0041 Firmicutes;Clostridia;Clostridiales
Proteobacteria; Gammaproteobacteria; Enterobacteriales; Ente
305760 305760 0.0087 -0.0040 robacteriaceae;Escherichia;Escherichia coli
179460 179460 0.0001 -0.0040 Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium
198161 198161 0.0001 -0.0040 Bacteroidetes;Bacteroidia;Bacteroidales
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
182994 182994 0.0000 -0.0039 m;Eubacterium rectale
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
179287 179287 0.0006 -0.0038 acterium; Faecalibacterium prausnitzii Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
294196 294196 0.0000 -0.0037 otella;Prevotella copri
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
208539 208539 0.0002 -0.0037 m
177005 177005 0.0017 -0.0037 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
191306 191306 0.0001 -0.0037 coccus; Ruminococcus sp 5 1 39BFAA
Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Roseburi
195493 195493 0.0023 -0.0037 a;Roseburia intestinalis
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Subdoli
177495 177495 0.0002 -0.0037 granulum;Subdoligranulum variabile
Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium;
325608 325608 0.0009 -0.0036 Clostridium bartlettii
168716 168716 0.0000 -0.0036 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium;
325969 325969 0.0110 -0.0036 Clostridium sp SS2 1
181170 181170 0.0007 -0.0035 Firmicutes;Clostridia;Clostridiales
Bacteroidetes;Bacteroidia;Bacteroidales;Porphyromonadacea
541301 541301 0.0035 -0.0035 e;Parabacteroides;Parabacteroides merdae
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
340615 340615 0.0110 -0.0035 m;Eubacterium hallii
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
204593 204593 0.0049 -0.0035 m;Eubacterium coprostanoligenes
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
193067 193067 0.0000 -0.0034 m;Eubacterium rectale
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
181003 181003 0.0017 -0.0034 coccus
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
172962 172962 0.0042 -0.0034 otella
Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium;
363400 363400 0.0108 -0.0034 Clostridium clostridioforme
New.O. Reference
171.d0 OTU171 0.0367 -0.0034 Proteobacteria
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
172274 172274 0.0042 -0.0034 acterium;Faecalibacterium prausnitzii
212304 212304 0.0001 -0.0034 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
175682 175682 0.0000 -0.0034 m;Eubacterium rectale
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
13
361809 361809 0.0093 -0.0034 coccus; Ruminococcus torques
316732 316732 0.0022 -0.0034 Firmicutes;Clostridia;Clostridiales
Proteobacteria; Betaproteobacteria;Burkholderiales;Sutterellac
111135 111135 0.0069 -0.0034 eae;Sutterella;Sutterella wadsworthensis
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
528303 528303 0.0000 -0.0033 otella
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
182087 182087 0.0033 -0.0032 acterium;Faecalibacterium prausnitzii
Proteobacteria; Gammaproteobacteria; Enterobacteriales; Ente
210269 210269 0.0489 -0.0031 robacteriaceae
New.1.Reference Firmicutes;Negativicutes;Selenomonadales;Veillonellaceae;
188.d1 OTU188 0.0372 -0.0031 egamonas
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
71685 71685 0.0262 -0.0031 coccus; Ruminococcus torques
162623 162623 0.0002 -0.0031 Firmicutes;Clostridia;Clostridiales
185951 185951 0.0108 -0.0031 23 Firmicutes;Clostridia;Clostridiales
203590 203590 0.0110 -0.0030 Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Subdoli
291266 291266 0.0000 -0.0030 qranulum;Subdoligranulum variabile
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
184511 18451 1 0.0112 -0.0030 acterium;Faecalibacterium prausnitzii
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
188236 188236 0.0002 -0.0030 acterium;Faecalibacterium prausnitzii
263461 263461 0.0223 -0.0029 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
181139 181139 0.0001 -0.0029 acterium;Faecalibacterium prausnitzii
442.d0 New.O. Reference 0.0051 -0.0029 Bacteroidetes OTU442
Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Roseburi
352304 352304 0.0035 -0.0029 a
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
178146 178146 0.0075 -0.0029 m;Eubacterium allii
181882 181882 0.0002 -0.0029 Firmicutes;Clostridia;Clostridiales
323253 323253 0.0298 -0.0028 Unknown bacteria
Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium;
207570 207570 0.0015 -0.0028 Clostridium lactatifermentans
Proteobacteria;Gammaproteobacteria;Pasteurellales;Pasteur
16
9514 9514 0.0335 -0.0028 ellaceae;Haemophilus;Haemophilus parainfluenzae
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
174256 174256 0.0039 -0.0028 m;Eubacterium hallii
206931 206931 0.0001 -0.0028 Firmicutes;Clostridia;Clostridiales
212787 212787 0.0000 -0.0027 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
173135 173135 0.0018 -0.0027 acterium;Faecalibacterium prausnitzii
203620 203620 0.0060 -0.0026 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Subdoli
354737 354737 0.0017 -0.0026 granulum;Subdoligranulum variabile
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
69909 69909 0.0107 -0.0026 m;Eubacterium ramulus
Firmicutes;Negativicutes;Selenomonadales;Acidaminococcac eae;Phascolarctobacterium;Phascolarctobacterium_succinatu
555326 555326 0.0190 -0.0026 tens
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
16054 16054 0.0092 -0.0026 coccus; Ruminococcus callidus
Bacteroidetes;Bacteroidia;Bacteroidales;Bacteroidaceae;Bact
325738 325738 0.0043 -0.0025 eroides;Bacteroides galacturonicus
Proteobacteria; Gammaproteobacteria; Enterobacteriales; Ente
329096 329096 0.0335 -0.0025 robacteriaceae
Firmicutes;Erysipelotrichi;Erysipelotrichales;Erysipelotricriace
11
287510 287510 0.0338 -0.0025 ae;Catenibacterium;Catenibacterium mitsuokai
175537 1 5537 0.0001 -0.0025 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
187846 187846 0.0022 -0.0025 m;Eubacterium desmolans
183879 183879 0.0312 -0.0024 Firmicutes;Clostridia;Clostridiales
179795 179795 0.0017 -0.0024 Firmicutes;Clostridia;Clostridiales;Clostridiaceae;Clostridium
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
191547 191547 0.0013 -0.0023 acterium;Faecalibacterium prausnitzii
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
260352 260352 0.0301 -0.0023 m;Eubacterium coprostanoligenes
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
329728 329728 0.0071 -0.0023 otella
Proteobacteria;Gammaproteobacteria;Aeromonadales;Succin
205408 205408 0.0335 -0.0023 ivibrionaceae;Succini vibrio; Succinivibrio dextrinosolvens
Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Roseburi
293221 293221 0.0223 -0.0022 a;Roseburia intestinalis
293896 293896 0.0087 -0.0022 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
113909 113909 0.0007 -0.0022 m;Eubacterium rectale
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
338889 338889 0.0291 -0.0021 otella
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
259959 259959 0.0293 -0.0021 otella;Prevotella sp oral taxon 302
Firmicutes;Clostridia;Clostridiales;Oscillospiraceae;Oscillibact
193632 193632 0.0013 -0.0021 er;Oscillibacter sp G2
186640 186640 0.0015 -0.0020 Firmicutes;Clostridia;Clostridiales
Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
529733 529733 0.0307 -0.0020 otella
321016 321016 0.0135 -0.0019 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
16076 16076 0.0335 -0.0018 coccus; Ruminococcus bromii
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Rumino
209578 209578 0.0092 -0.0018 coccus
215433 215433 0.0489 -0.0017 Firmicutes;Clostridia;Clostridiales Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
192252 192252 0.0054 -0.0017 m;Eubacterium_rectale
New.O.CIeanUp.
ReferenceOTU19 Bacteroidetes;Bacteroidia;Bacteroidales;Prevotellaceae;Prev
196225 6225 0.0449 -0.0016 otella
Firmicutes;Clostridia;Clostridiales;unclassified_Clostridiales;B
194648 194648 0.0136 -0.0015 lautia;Blautia_sp_M25
560141 560141 0.0356 -0.0015 Actinobacteria;1760;Coriobacteriales;Coriobacteriaceae
Firmicutes;Clostridia;Clostridiales;Eubacteriaceae;Eubacteriu
172603 172603 0.0219 -0.0014 m;Eubacterium_hallii
187524 187524 0.0027 -0.0014 Firmicutes;Clostridia;Clostridiales
195102 195102 0.0060 -0.0013 Firmicutes;Clostridia;Clostridiales
343985 343985 0.0032 -0.0013 Firmicutes
207065 207065 0.0108 -0.0013 Firmicutes;Clostridia;Clostridiales
189047 189047 0.0177 -0.0013 Firmicutes;Clostridia;Clostridiales;Ruminococcaceae
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
310301 310301 0.0075 -0.0013 acterium;Faecalibacterium_prausnitzii
364261 364261 0.0244 -0.0013 Firmicutes;Clostridia;Clostridiales
516022 516022 0.0276 -0.0012 Firmicutes
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
190572 190572 0.0018 -0.0012 acterium;Faecalibacterium prausnitzii
571220 571220 0.0194 -0.0012 Firmicutes;Clostridia;Clostridiales
293360 293360 0.0414 -0.0009 Firmicutes;Clostridia;Clostridiales
298079 298079 0.0383 -0.0007 Firmicutes;Clostridia;Clostridiales
Firmicutes;Clostridia;Clostridiales;Ruminococcaceae;Faecalib
179291 179291 0.0299 -0.0007 acterium;Faecalibacterium prausnitzii
54730 54730 0.0394 -0.0007 Unknown bacteria
352215 352215 0.0298 -0.0007 Firmicutes;Clostridia;Clostridiales
43267 43267 0.0258 -0.0006 Firmicutes;Clostridia;Clostridiales
New.O.CIeanUp.
ReferenceOTU31
312816 2816 0.0449 -0.0005 Firmicutes;Clostridia;Clostridiales;Clostridiaceae
Figure imgf000145_0001
Figure imgf000146_0001
Oral rehydration
Bgsng7074 Yes Yes No Yes No 15,006 5 saline, Multi Vitamin
Bgsng7081 Yes Yes No No No 17,264 5
Bgsng7087 Yes Yes No No No 19,084 5
Amoxycillin trihydrate,
Bgsng7090 Yes Yes No No Yes 20,562 5
Sulbutamol
Bgsng7096 Yes Yes No No No 27,208 5
Bgsng7108 Yes Yes No No No 21 ,247 6
Bgsng7130 Yes Yes No No No 26,268 5
Bgsng7131 Yes Yes No No No 39,288 5
Azithromycin
Dihydrate, Oral
Bgsng7133 Yes Yes No Yes Yes rehydration saline, 23,886 5
Chlorpheniramine
Maleate
Bgsng7135 Yes Yes No No No 18,644 5
Bgsng7145 Yes Yes No No No 24,350 4
Bgsng7149 Yes Yes No No No 21 ,384 4
Bgsng7152 Yes Yes No No No 22,772 4
Amoxicillin trihydrate +
Clavulanic acid,
Bgsng7173 Yes Yes No No Yes 22,652 9
Chloramphenicol,
Sulbutamol
Bgsng7178 Yes Yes No No No 26,494 9
Bgsng7203 Yes Yes No No No 15,174 7
Figure imgf000147_0001
h18 h18A female MZ h18A.4 16.2 0.51 76,375 Malawi6 h18 h18B female MZ h18B.4 16.2 0.08 70,390 Malawi6 h68 h68A male DZ h68A.4 20.4 -0.09 267,547 Malawi3 h68 h68B male DZ h68B.4 20.4 0 70,403 Malawi3 h35 h35A male MZ h35A.3 22.7 -2.16 46,022 Malawi6 h78 h78A male DZ h78A.4 22.7 -0.7 241 ,999 Malawi3 h78 h78B male DZ h78B.4 22.7 0.95 216,988 Malawi3 h186 h186A male MZ h186A.1 24.2 0.09 120,553 Malawi89 h186 h186B female DZ h186B.1 24.2 1.36 62,791 Malawi7 h186 h186C male MZ h186C1 24.2 -0.07 121 ,883 Malawi89 h60 h60A female MZ h60A.2 24.5 0.04 232,045 Malawi7 h60 h60B female MZ h60B.2 24.5 -0.29 93,313 Malawi89 h101 h101A female MZ h101A.3 25.1 -0.55 111 ,821 Malawi89 h101 h101 B female MZ h101 B.3 25.1 -0.16 122,049 Malawi89
Figure imgf000148_0001
Bgsng7149.m21 Healthy Singleton Birth Cohort - + +
Bgsng7149.m24 Healthy Singleton Birth Cohort - + -
Bgsng7173.m12 Healthy Singleton Birth Cohort - + -
Bgsng7173.m22 Healthy Singleton Birth Cohort - + -
Bgsng7173.m24 Healthy Singleton Birth Cohort + - -
Bgsng7178.m14 Healthy Singleton Birth Cohort - + -
Bgsng7178.m18 Healthy Singleton Birth Cohort - + -
Bgsng7178.m23 Healthy Singleton Birth Cohort - + -
Bgtw1.T1 .m18 Healthy Twins & Triplets - + -
Bgtw2.T1 .m21 Healthy Twins & Triplets - + -
Bgtw2.T1 .m22 Healthy Twins & Triplets - + -
Bgtw5.T1.m3 Healthy Twins & Triplets + - -
Bgtw11 .T1.m1 Healthy Twins & Triplets - + -
Bgtw1.T2.m21 Healthy Twins & Triplets - + -
Bgtw2.T2.m21 Healthy Twins & Triplets - + -
Bgtw5.T2.m3 Healthy Twins & Triplets - - +
Bgtw8.T2.m7 Healthy Twins & Triplets - + +
Additional singletons sampled with and
Bgsng7013.m19 +
without MAM at 18 months - -
Additional singletons sampled with and
Bgsng7040.m19 - +
without MAM at 18 months -
Additional singletons sampled with and
Bgsng7148.m19 +
without MAM at 18 months - Samples for which the enteropathogens tested for were not ( letected:
Bgsng7035.m1 Bgsng7202.m21 Bgsng7096.m15 Bgtw4.T1.m5 Bgtw4.T2.m4
Bgsng7035.m2 Bgsng7202.m22 Bgsng7096.m17 Bgtw4.T1.m6 Bgtw4.T2.m5
Bgsng7035.m3 Bgsng7202.m23 Bgsng7096.m20 Bgtw4.T1.m7 Bgtw4.T2.m6
Bgsng7035.m4 Bgsng7202.m24 Bgsng7096.m23 Bgtw4.T1.m8 Bgtw4.T2.m8
Bgsng7035.m5 Bgsng7204.m1 Bgsng7114.m1 Bgtw4.T1.m9 Bgtw4.T2.m9
Bgsng7035.m6 Bgsng7204.m2 Bgsng71 14.m2 Bgtw4.T1.m10 Bgtw4.T2.m10
Bgsng7035.m7 Bgsng7204.m3 Bgsng71 14.m3 Bgtw4.T1.m1 1 Bgtw4.T2.m1 1
Bgsng7035.m8 Bgsng7204.m4 Bgsng7114.m4 Bgtw4.T1.m12 Bgtw4.T2.m12
Bgsng7035.m9 Bgsng7204.m5 Bgsng7114.m5 Bgtw4.T1.m13 Bgtw4.T2.m13
Bgsng7035.m10 Bgsng7204.m6 Bgsng7114.m6 Bgtw4.T1.m14 Bgtw4.T2.m14
Bgsng7035.m1 1 Bgsng7204.m7 Bgsng7114.m7 Bgtw4.T1.m15 Bgtw4.T2.m15
Bgsng7035.m12 Bgsng7204.m8 Bgsng7114.m8 Bgtw4.T1.m16 Bgtw4.T2.m15.dr
Bgsng7035.m13 Bgsng7204.m9 Bgsng7114.m10 Bgtw4.T1.m18 Bgtw4.T2.m17
Bgsng7035.m14 Bgsng7204.m10 Bgsng7114.m11 Bgtw4.T1.m18.drb Bgtw4.T2.m18
Bgsng7035.m15 Bgsng7204.m1 1 Bgsng7114.m12 Bgtw4.T1.m18.drc Bgtw4.T2.m19
Bgsng7035.m16 Bgsng7204.m12 Bgsng7114.m13 Bgtw4.T1.m20 Bgtw5.T2.m1
Bgsng7035.m17 Bgsng7204.m13 Bgsng7114.m16 Bgtw5.T1.m2 Bgtw5.T2.m2
Bgsng7035.m19 Bgsng7204.m14 Bgsng7114.m17 Bgtw5.T1.m4 Bgtw5.T2.m4
Bgsng7035.m20 Bgsng7204.m15 Bgsng7114.m18 Bgtw5.T1.m5 Bgtw5.T2.m5
Bgsng7035.m21 Bgsng7204.m16 Bgsng71 14.m20 Bgtw5.T1.m6 Bgtw5.T2.m6
Bgsng7035.m22 Bgsng7204.m18 Bgsng71 14.m22 Bgtw5.T1.m7 Bgtw5.T2.m8
Bgsng7035.m24 Bgsng7204.m21 Bgsng7114.m24 Bgtw5.T1.m8 Bgtw5.T2.m9
Bgsng7106.m1 Bgsng7204.m24 Bgsng7131.m1 Bgtw5.T1.m9 Bgtw5.T2.m10
Bgsng7106.m2 Bgsng8064.m1 Bgsng7131.m2 Bgtw5.T1.m10 Bgtw5.T2.m1 1
Bgsng7106.m3 Bgsng8064.m2 Bgsng7131.m3 Bgtw5.T1.m11 Bgtw5.T2.m12
Bgsng7106.m4 Bgsng8064.m3 Bgsng7131.m4 Bgtw5.T1.m12 Bgtw5.T2.m13
Bgsng7106.m5 Bgsng8064.m4 Bgsng7131.m5 Bgtw5.T1.m13 Bgtw5.T2.m14
Bgsng7106.m6 Bgsng8064.m5 Bgsng7131.m6 Bgtw5.T1.m14 Bgtw5.T2.m15
Bgsng7106.m9 Bgsng8064.m6 Bgsng7131.m7 Bgtw5.T1.m15 Bgtw5.T2.m16
Bgsng7106.m10 Bgsng8064.m7 Bgsng7131.m8 Bgtw5.T1.m16 Bgtw5.T2.m17
Bgsng7106.m1 1 Bgsng8064.m8 Bgsng7131.m9 Bgtw5.T1.m17 Bgtw5.T2.m18
Bgsng7106.m14 Bgsng8064.m9 Bgsng7131.m10 Bgtw5.T1.m18 Bgtw5.T2.m19
Bgsng7106.m18 Bgsng8064.m10 Bgsng7131.m11 Bgtw5.T1.m19 Bgtw5.T2.m20
Bgsng7106.m19 Bgsng8064.m1 1 Bgsng7131.m12 Bgtw5.T1.m20 Bgtw5.T2.m21
Bgsng7106.m20 Bgsng8064.m12 Bgsng7131.m13 Bgtw5.T1.m21 Bgtw5.T2.m22
Bgsng7106.m21 Bgsng8064.m13 Bgsng7131.m15 Bgtw5.T1.m22 Bgtw6.T2.m1
Bgsng7106.m22 Bgsng8064.m14 Bgsng7131.m16 Bgtw6.T1.m1 Bgtw6.T2.m2
Bgsng7106.m23 Bgsng8064.m15 Bgsng7131.m17 Bgtw6.T1.m2 Bgtw6.T2.m3
Bgsng7115.m1 Bgsng8064.m16 Bgsng7131.m18 Bgtw6.T1.m3 Bgtw6.T2.m4
Bgsng7115.m2 Bgsng8064.m17 Bgsng7131.m19 Bgtw6.T1.m4 Bgtw6.T2.m6
Bgsng7115.m3 Bgsng8064.m18 Bgsng7131.m20 Bgtw6.T1.m7 Bgtw6.T2.m7
Bgsng7115.m4 Bgsng8064.m19 Bgsng7131.m21 Bgtw6.T1.m8 Bgtw6.T2.m8
Bgsng7115.m5 Bgsng8064.m20 Bgsng7131.m22 Bgtw6.T1.m9 Bgtw6.T2.m9
Bgsng7115.m6 Bgsng8064.m21 Bgsng7131.m23 Bgtw6.T1.m10 Bgtw6.T2.m10
Bgsng7115.m7 Bgsng8064.m22 Bgsng7131.m24 Bgtw7.T1.m1 Bgtw6.T2.m15.dr
Bgsng7115.m8 Bgsng8064.m23 Bgsng7142.m1 Bgtw7.T1.m2 Bgtw7.T2.m1
Bgsng7115.m9 Bgsng8064.m24 Bgsng7142.m2 Bgtw7.T1.m3 Bgtw7.T2.m2
Bgsng71 15.m10 Bgsng8169.m1 Bgsng7142.m3 Bgtw7.T1.m4 Bgtw7.T2.m3
Bgsng71 15. ml 1 Bgsng8169.m3 Bgsng7142.m4 Bgtw7.T1.m5 Bgtw7.T2.m4
Bgsng71 15.m12 Bgsng8169.m5 Bgsng7142.m5 Bgtw7.T1.m6 Bgtw7.T2.m5
Bgsng71 15.m13 Bgsng8169.m6 Bgsng7142.m6 Bgtw7.T1 .m6.dra Bgtw7.T2.m6
Bgsng71 15.m14 Bgsng8169.m7 Bgsng7142.m7 Bgtw7.T1 .m6.drb Bgtw7.T2.m6.dra
Bgsng71 15.m15 Bgsng8169.m8 Bgsng7142.m8 Bgtw7.T1.m7 Bgtw7.T2.m6.drb
Bgsng71 15.m16 Bgsng8169.m9 Bgsng7142.m9 Bgtw7.T1.m8 Bgtw7.T2.m8
Bgsng71 15.m17 Bgsng8169.m10 Bgsng7142.m11 Bgtw7.T1.m9 Bgtw7.T2.m9
Bgsng71 15.m18 Bgsng8169.m1 1 Bgsng7142.m12 Bgtw7.T1.m10 Bgtw7.T2.m10
Bgsng71 15.m20 Bgsng8169.m12 Bgsng7142.m13 Bgtw7.T1.m11 Bgtw7.T2.m1 1
Bgsng71 15.m23 Bgsng8169.m13 Bgsng7142.m14 Bgtw7.T1.m12 Bgtw7.T2.m12
Bgsng71 15.m24 Bgsng8169.m14 Bgsng7142.m15 Bgtw7.T1.m13 Bgtw7.T2.m13
Bgsng7128.m1 Bgsng8169.m15 Bgsng7142.m16 Bgtw7.T1.m14 Bgtw7.T2.m14
Bgsng7128.m2 Bgsng8169.m16 Bgsng7142.m17 Bgtw7.T1.m15 Bgtw7.T2.m15
Bgsng7128.m3 Bgsng8169.m20 Bgsng7142.m21 Bgtw7.T1.m16 Bgtw7.T2.m16
Bgsng7128.m4 Bgsng8169.m21 Bgsng7142.m22 Bgtw8.T1.m1 Bgtw8.T2.m1
Bgsng7128.m5 Bgsng7018.m1 Bgsng7142.m23 Bgtw8.T1.m2 Bgtw8.T2.m2
Bgsng7128.m6 Bgsng7018.m2 Bgsng7149.m1 Bgtw8.T1.m3 Bgtw8.T2.m3
Bgsng7128.m7 Bgsng7018.m3 Bgsng7149.m2 Bgtw8.T1.m4 Bgtw8.T2.m4
Bgsng7128.m8 Bgsng7018.m4 Bgsng7149.m3 Bgtw8.T1.m5 Bgtw8.T2.m5
Bgsng7128.m9 Bgsng7018.m5 Bgsng7149.m4 Bgtw8.T1.m6 Bgtw8.T2.m6 Bgsng7128.m10 Bgsng7018.m6 Bgsng7149.m5 Bgtw8.T1.m7 Bgtw8.T2.m8
Bgsng7128.m13 Bgsng7018.m7 Bgsng7149.m6 Bgtw8.T1.m8 Bgtw8.T2.m9
Bgsng7128.m14 Bgsng7018.m8 Bgsng7149.m7 Bgtw8.T1.m9 Bgtw8.T2.m10
Bgsng7128.m16 Bgsng7018.m9 Bgsng7149.m8 Bgtw8.T1.m10 Bgtw8.T2.m1 1
Bgsng7128.m17 Bgsng7018.m10 Bgsng7149.m9 Bgtw8.T1.m11 Bgtw8.T2.m12
Bgsng7128.m20 Bgsng7018.m1 1 Bgsng7149.m10 Bgtw8.T1.m12 Bgtw8.T2.m13
Bgsng7128.m21 Bgsng7018.m12 Bgsng7149.m11 Bgtw8.T1.m13 Bgtw9.T2.m1
Bgsng7128.m22 Bgsng7018.m13 Bgsng7149.m12 Bgtw9.T1.m2 Bgtw9.T2.m2
Bgsng7128.m23 Bgsng7018.m14 Bgsng7149.m13 Bgtw9.T1.m3 Bgtw9.T2.m3
Bgsng7128.m24 Bgsng7018.m15 Bgsng7149.m17 Bgtw9.T1.m4 Bgtw9.T2.m4
Bgsng7150.m1 Bgsng7018.m16 Bgsng7149.m23 Bgtw9.T1.m5 Bgtw9.T2.m5
Bgsng7150.m2 Bgsng7018.m17 Bgsng7173.m1 Bgtw9.T1.m6 Bgtw9.T2.m6
Bgsng7150.m3 Bgsng7018.m18 Bgsng7173.m2 Bgtw9.T1.m8 Bgtw9.T2.m8
Bgsng7150.m4 Bgsng7018.m19 Bgsng7173.m3 Bgtw9.T1.m9 Bgtw9.T2.m9
Bgsng7150.m5 Bgsng7018.m20 Bgsng7173.m4 Bgtw9.T1.m10 Bgtw9.T2.m10
Bgsng7150.m6 Bgsng7018.m24 Bgsng7173.m5 Bgtw9.T1.m11 Bgtw9.T2.m1 1
Bgsng7150.m7 Bgsng7052.m1 Bgsng7173.m6 Bgtw9.T1.m12 Bgtw9.T2.m12
Bgsng7150.m8 Bgsng7052.m2 Bgsng7173.m7 Bgtw9.T1.m13 Bgtw9.T2.m13
Bgsng7150.m9 Bgsng7052.m3 Bgsng7173.m8 Bgtw10.T1 .m1 Bgtw10.T2.m1
Bgsng7150.m10 Bgsng7052.m4 Bgsng7173.m9 Bgtw10.T1 .m2 Bgtw10.T2.m2
Bgsng7150.m1 1 Bgsng7052.m5 Bgsng7173.m10 Bgtw10.T1 .m4 Bgtw10.T2.m4
Bgsng7150.m12 Bgsng7052.m6 Bgsng7173.m11 Bgtw10.T1 .m5 Bgtw10.T2.m5
Bgsng7150.m13 Bgsng7052.m7 Bgsng7173.m13 Bgtw10.T1 .m6 Bgtw10.T2.m6
Bgsng7150.m14 Bgsng7052.m8 Bgsng7173.m14 Bgtw10.T1 .m8 Bgtw10.T2.m7
Bgsng7150.m15 Bgsng7052.m9 Bgsng7173.m15 Bgtw10.T1 .m9 Bgtw10.T2.m8
Bgsng7150.m16 Bgsng7052.m10 Bgsng7173.m16 Bgtw10.T1.m10 Bgtw10.T2.m9
Bgsng7150.m17 Bgsng7052.m1 1 Bgsng7173.m17 Bgtw10.T1.m11 Bgtwl O.T2.m10
Bgsng7150.m18 Bgsng7052.m12 Bgsng7173.m18 Bgtw10.T1.m12 Bgtwl O.T2.m1 1
Bgsng7150.m19 Bgsng7052.m14 Bgsng7173.m19 Bgtw10.T1.m13 Bgtw10.T2.m12
Bgsng7150.m20 Bgsng7052.m16 Bgsng7173.m20 Bgtw1 1.T1 .m2 Bgtw10.T2.m13
Bgsng7150.m21 Bgsng7052.m18 Bgsng7173.m21 Bgtw1 1.T1 .m3 Bgtwl 1 .T2.m1
Bgsng7150.m24 Bgsng7052.m19 Bgsng7173.m23 Bgtw1 1.T1 .m4 Bgtwl 1 .T2.m2
Bgsng7155.m1 Bgsng7052.m20 Bgsng7178.m1 Bgtw1 1.T1 .m5 Bgtwl 1 .T2.m3
Bgsng7155.m2 Bgsng7063.m1 Bgsng7178.m2 Bgtw1 1.T1 .m6 Bgtwl 1 .T2.m4
Bgsng7155.m3 Bgsng7063.m2 Bgsng7178.m3 Bgtw1 1.T1 .m7 Bgtwl 1 .T2.m5
Bgsng7155.m4 Bgsng7063.m3 Bgsng7178.m4 Bgtw1 1.T1 .m8 Bgtwl 1 .T2.m6
Bgsng7155.m5 Bgsng7063.m4 Bgsng7178.m5 Bgtw1 1.T1 .m9 Bgtwl 1.T2.m6.dr
Bgsng7155.m6 Bgsng7063.m5 Bgsng7178.m6 Bgtw11 .T1.m10 Bgtwl 1 .T2.m7
Bgsng7155.m7 Bgsng7063.m6 Bgsng7178.m7 Bgtw11 .T1.m11 Bgtwl 1 .T2.m8
Bgsng7155.m8 Bgsng7063.m7 Bgsng7178.m8 Bgtw11 .T1.m12 Bgtwl 1 .T2.m9
Bgsng7155.m9 Bgsng7063.m8 Bgsng7178.m9 Bgtw12.T1 .m1 Bgtwl 1.T2.m10
Bgsng7155.m10 Bgsng7063.m10 Bgsng7178.m10 Bgtw12.T1 .m2 Bgtw1 1.T2.m12
Bgsng7155.m1 1 Bgsng7063.m1 1 Bgsng7178.m11 Bgtw12.T1 .m3 Bgtwl 1 .T2.m12.dr
Bgsng7155.m12 Bgsng7063.m12 Bgsng7178.m12 Bgtw12.T1 .m4 Bgtw12.T2.m1
Bgsng7155.m13 Bgsng7063.m15 Bgsng7178.m13 Bgtw12.T1 .m5 Bgtw12.T2.m2
Bgsng7155.m14 Bgsng7063.m16 Bgsng7178.m15 Bgtw12.T1 .m6 Bgtw12.T2.m3
Bgsng7155.m15 Bgsng7063.m17 Bgsng7178.m16 Bgtw12.T1 .m7 Bgtw12.T2.m4
Bgsng7155.m16 Bgsng7063.m18 Bgsng7178.m17 Bgtw12.T1 .m8 Bgtw12.T2.m5
Bgsng7155.m17 Bgsng7063.m19 Bgsng7178.m19 Bgtw12.T1 .m9 Bgtw12.T2.m6
Bgsng7155.m18 Bgsng7063.m20 Bgsng7178.m20 Bgtw12.T1.m10 Bgtw12.T2.m7
Bgsng7155.m19 Bgsng7063.m21 Bgsng7178.m21 Bgtw12.T1.m11 Bgtw12.T2.m8
Bgsng7155.m20 Bgsng7063.m22 Bgsng7178.m22 Bgtw12.T1.m12 Bgtw12.T2.m9
Bgsng7155.m21 Bgsng7063.m24 Bgsng7178.m24 Bgtw12.T1.m13 Bgtwl 2.T2.m10
Bgsng7155.m22 Bgsng7071.m1 Bgtw1 .T1.m2 Bgtwl 2.m2 Bgtwl 2.T2.m1 1
Bgsng7155.m23 Bgsng7071.m2 Bgtw1 .T1.m3 Bgtwl 2.m3 Bgtw12.T2.m12
Bgsng7155.m24 Bgsng7071.m4 Bgtw1 .T1.m4 Bgtwl 2.m4 Bgtw12.T2.m13
Bgsng7177.m1 Bgsng7071.m5 Bgtw1 .T1.m5 Bgtwl 2.m5 Bgtw4.T3.m1
Bgsng7177.m2 Bgsng7071.m6 Bgtw1 .T1.m6 Bgtwl 2.m6 Bgtw4.T3.m2
Bgsng7177.m3 Bgsng7071.m7 Bgtw1.T1 .m6.dr Bgtwl 2.m7 Bgtw4.T3.m3
Bgsng7177.m4 Bgsng7071.m9 Bgtw1 .T1.m7 Bgtwl 2.m8 Bgtw4.T3.m4
Bgsng7177.m5 Bgsng7071 .m10 Bgtw1 .T1.m8 Bgtwl 2.m9 Bgtw4.T3.m5
Bgsng7177.m6 Bgsng7071 .m12 Bgtw1 .T1.m9 Bgtwl 2.m10 Bgtw4.T3.m6
Bgsng7177.m7 Bgsng7071 .m14 Bgtw1 .T1.m10 Bgtwl 2.m11 Bgtw4.T3.m8
Bgsng7177.m8 Bgsng7071 .m15 Bgtw1 .T1.m11 Bgtwl 2.m12 Bgtw4.T3.m8.dr
Bgsng7177.m9 Bgsng7071 .m16 Bgtw1 .T1.m12 Bgtw1 .T2.m13 Bgtw4.T3.m9
Bgsng7177.m1 1 Bgsng7071 .m19 Bgtw1 .T1.m13 Bgtwl 2.m14 Bgtw4.T3.m10
Bgsng7177.m12 Bgsng7071 .m20 Bgtw1 .T1.m14 Bgtw1 .T2.m15 Bgtw4.T3.m1 1
Bgsng7177.m13 Bgsng7071 .m22 Bgtw1 .T1.m15 Bgtwl 2.m16 Bgtw4.T3.m12 Bgsng7177.m14 Bgsng7071 .m23 Bgtw1 .T1.m16 Bgtw1 .T2.m18 Bgtw4.T3.m13
Bgsng7177.m15 Bgsng7071 .m24 Bgtw1 .T1.m19 Bgtw1 .T2.m19 Bgtw4.T3.m14
Bgsng7177.m16 Bgsng7082.m1 Bgtw1 .T1.m20 Bgtwl 2.m22 Bgtw4.T3.m15
Bgsng7177.m17 Bgsng7082.m2 Bgtw1 .T1.m21 Bgtwl 2.m23 Bgtw4.T3.m16
Bgsng7177.m18 Bgsng7082.m3 Bgtw1 .T1.m22 Bgtwl 2.m24 Bgtw4.T3.m17
Bgsng7177.m21 Bgsng7082.m4 Bgtw1 .T1.m23 Bgtwl 2.m25 Bgtw4.T3.m18
Bgsng7177.m22 Bgsng7082.m5 Bgtw1 .T1.m24 Bgtw2.T2.m2 Bgtw4.T3.m19
Bgsng7177.m23 Bgsng7082.m6 Bgtw1.T1 .m24.dr Bgtw2.T2.m2.dr Bgtw4.T3.m20
Bgsng7177.m24 Bgsng7082.m7 Bgtw1 .T1.m25 Bgtw2.T2.m3 Bgtw1.T1 .m17
Bgsng7192.m1 Bgsng7082.m8 Bgtw2.T1.m2 Bgtw2.T2.m4 Bgtw2.T1.m6.dr
Bgsng7192.m2 Bgsng7082.m9 Bgtw2.T1 .m2.dr Bgtw2.T2.m5 Bgtw3.T1.m4.dr
Bgsng7192.m3 Bgsng7082.m10 Bgtw2.T1.m3 Bgtw2.T2.m6 Bgtw3.T1.m5.dr
Bgsng7192.m4 Bgsng7082.m1 1 Bgtw2.T1.m4 Bgtw2.T2.m7 Bgtw3.T1.m9.dr
Bgsng7192.m5 Bgsng7082.m12 Bgtw2.T1.m5 Bgtw2.T2.m8 Bgtw3.T1 .m10
Bgsng7192.m6 Bgsng7082.m15 Bgtw2.T1.m6 Bgtw2.T2.m9 Bgtw3.T1.m18.dr
Bgsng7192.m7 Bgsng7082.m18 Bgtw2.T1.m7 Bgtw2.T2.m10 Bgtw4.T1 .m18.dra
Bgsng7192.m8 Bgsng7082.m19 Bgtw2.T1.m8 Bgtw2.T2.m11 Bgtw6.T1.m8.dr
Bgsng7192.m9 Bgsng7082.m20 Bgtw2.T1.m9 Bgtw2.T2.m12 Bgtw8.T1.m5.dr
Bgsng7192.m10 Bgsng7082.m24 Bgtw2.T1.m10 Bgtw2.T2.m13 Bgtw9.T1 .m7
Bgsng7192.m1 1 Bgsng7090.m1 Bgtw2.T1.m11 Bgtw2.T2.m14 Bgtwl O.T1 .m2.dr
Bgsng7192.m12 Bgsng7090.m2 Bgtw2.T1.m12 Bgtw2.T2.m15 Bgtw10.T1.m3
Bgsng7192.m13 Bgsng7090.m3 Bgtw2.T1.m13 Bgtw2.T2.m16 Bgtwl O.T1.m4.dra
Bgsng7192.m14 Bgsng7090.m4 Bgtw2.T1.m14 Bgtw2.T2.m17 Bgtwl O.T1.m4.drb
Bgsng7192.m15 Bgsng7090.m5 Bgtw2.T1.m15 Bgtw2.T2.m18 Bgtw10.T1.m7
Bgsng7192.m16 Bgsng7090.m6 Bgtw2.T1.m16 Bgtw2.T2.m19 Bgtwl O.T1 .m8.dr
Bgsng7192.m17 Bgsng7090.m7 Bgtw2.T1.m17 Bgtw2.T2.m20 Bgtwl 2.T1 .m9.dr
Bgsng7192.m18 Bgsng7090.m8 Bgtw2.T1.m18 Bgtw2.T2.m22 Bgtwl 2.m16.dr
Bgsng7192.m20 Bgsng7090.m9 Bgtw2.T1.m19 Bgtw2.T2.m23 Bgtw1.T2.m17
Bgsng7192.m21 Bgsng7090.m10 Bgtw2.T1.m20 Bgtw2.T2.m24 Bgtw2.T2.m6.dr
Bgsng7192.m22 Bgsng7090.m1 1 Bgtw2.T1.m23 Bgtw3.T2.m1 Bgtw3.T2.m4.dr
Bgsng7192.m23 Bgsng7090.m12 Bgtw2.T1.m24 Bgtw3.T2.m2 Bgtw3.T2.m5.dra
Bgsng7192.m24 Bgsng7090.m13 Bgtw3.T1.m1 Bgtw3.T2.m3 Bgtw3.T2.m5.drb
Bgsng7202.m1 Bgsng7090.m14 Bgtw3.T1.m3 Bgtw3.T2.m4 Bgtw3.T2.m11.dr
Bgsng7202.m2 Bgsng7090.m15 Bgtw3.T1.m4 Bgtw3.T2.m5 Bgtw4.T2.m19.dr
Bgsng7202.m3 Bgsng7090.m16 Bgtw3.T1.m5 Bgtw3.T2.m6 Bgtw7.T2.m12.dr
Bgsng7202.m4 Bgsng7090.m17 Bgtw3.T1.m6 Bgtw3.T2.m7 Bgtw9.T2.m2.dr
Bgsng7202.m5 Bgsng7090.m18 Bgtw3.T1.m7 Bgtw3.T2.m8 Bgtw9.T2.m7
Bgsng7202.m6 Bgsng7090.m19 Bgtw3.T1.m8 Bgtw3.T2.m9 Bgtwl O.T2.m2.dr
Bgsng7202.m7 Bgsng7090.m20 Bgtw3.T1.m9 Bgtw3.T2.m9.dr Bgtw10.T2.m3
Bgsng7202.m8 Bgsng7090.m22 Bgtw3.T1.m11 Bgtw3.T2.m10 Bgtwl O.T2.m4.dra
Bgsng7202.m9 Bgsng7090.m23 Bgtw3.T1.m12 Bgtw3.T2.m11 Bgtwl O.T2.m4.drb
Bgsng7202.m10 Bgsng7090.m24 Bgtw3.T1.m13 Bgtw3.T2.m12 Bgtwl O.T2.m9.dr
Bgsng7202.m1 1 Bgsng7096.m1 Bgtw3.T1.m14 Bgtw3.T2.m13 Bgtw4.T3.m5.dr
Bgsng7202.m12 Bgsng7096.m2 Bgtw3.T1.m15 Bgtw3.T2.m14 Bgtw4.T3.m10.dr
Bgsng7202.m13 Bgsng7096.m3 Bgtw3.T1.m16 Bgtw3.T2.m15 Bgsng7001 .m19
Bgsng7202.m14 Bgsng7096.m4 Bgtw3.T1.m17 Bgtw3.T2.m16 Bgsng7004.m19
Bgsng7202.m15 Bgsng7096.m5 Bgtw3.T1.m19 Bgtw3.T2.m17 Bgsng7031 .m19
Bgsng7202.m16 Bgsng7096.m6 Bgtw3.T1.m20 Bgtw3.T2.m19 Bgsng7050.m19
Bgsng7202.m17 Bgsng7096.m7 Bgtw3.T1.m21 Bgtw3.T2.m20 Bgsng7074.m19
Bgsng7202.m18 Bgsng7096.m8 Bgtw3.T1.m22 Bgtw3.T2.m21 Bgsng7081 .m19
Bgsng7202.m20 Bgsng7096.m10 Bgtw4.T1.m1 Bgtw3.T2.m22 Bgsng7087.m19
Bgsng7145.m19 Bgsng7096.m1 1 Bgtw4.T1.m2 Bgtw4.T2.m1 Bgsng7094.m19
Bgsng7152.m19 Bgsng7096.m12 Bgtw4.T1.m3 Bgtw4.T2.m2 Bgsng7108.m18
Bgsng7203.m19 Bgsng7096.m14 Bgtw4.T1.m4 Bgtw4.T2.m3 Bgsng7109.m18
Bgsng7133.m19 Bgsng7123.m18 Bgsng71 10.m18
Bgsng7135.m19 Bgsng7130.m19 Bgsng71 16.m19 Table 18. History of recruitment for patients for cholera study
Reason for Exclusion:
History of Number of
No Negative Presentation
antibiotic Residence individuals
Total permane Dark Field >24h after
Date usage <1 outside of satisfying screened nt test for V. initiation of
week prior to Dhaka enrollment address cholerae diarrhea
presentation criteria
July, 2010 30 7 1 1 1 7 3 1
August, 2010 38 6 19 3 5 3 2 September, 2010 30 4 10 4 8 4 0 October, 2010 94 1 1 55 7 7 7 7
November, 2010 163 9 141 0 7 5 1
December, 2010 50 6 36 0 4 4 0
January, 2011 49 4 38 0 5 2 0 February, 201 1 51 7 37 0 3 4 0 March, 2011 59 4 47 0 3 5 0
April, 2011 94 6 77 0 7 4 0
May, 2011 84 9 65 0 5 5 0
June, 201 1 96 1 1 76 0 4 5 0
July, 2011 116 17 82 0 9 8 0
August, 2011 199 31 102 0 25 41 0
Total 1153 132 796 15 99 100 11*
*4 individuals lost to follow-up after hospital phase
Figure imgf000153_0001
Table 20. Correlation of relative abundance of species in fecal samples to time in healthy Ban ladeshi children and adults with cholera
Figure imgf000153_0002
Dorea formicigenerans 0.207317889 0.642324047 1.90E-03 3.69E-88
Prevotella;Other 0.239978967 0.529655297 2.84E-04 2.90E-55
Eubacterium desmolans 0.25699301 1 0.6126782 9.63E-05 3.55E-78
Ruminococcus obeum 0.272979694 0.538363585 3.25E-05 2.60E-57
Megamonas; Other 0.280339656 0.262466461 1.97E-05 3.20E-13
Bifidobacterium bifidum 0.304172965 0.202363768 3.18E-06 2.62E-08
Collinsella aerofaciens 0.307743017 0.4323211 19 2.48E-06 1.97E-35
Prevotella copri 0.316303623 0.544177019 1.26E-06 1.02E-58
Megasphaera;Other 0.333802576 0.164470597 2.83E-07 6.53E-06
Lactobacillus ruminis 0.333863734 0.535215561 2.83E-07 1.41 E-56
Ruminococcus sp 5 1 39BFAA 0.354025816 0.661928886 5.24E-08 3.15E-95
Catenibacterium mitsuokai 0.384683801 0.50376204 2.54E-09 2.24E-49
Lactobacillus;Other 0.400088891 0.171080853 4.91 E-10 2.84E-06
Eubacterium hallii 0.443162269 0.442782454 2.68E-12 3.19E-37
Ruminococcaceae;Other 0.449971243 0.433092184 1.18E-12 1.52E-35
Clostridium;Other 0.474086343 0.504843768 4.91 E-14 1.39E-49
Faecalibacterium prausnitzii 0.481962341 0.710727898 1.73E-14 1.54E-115
Clostridium bartlettii 0.502239897 0.441173741 8.58E-16 5.88E-37
Clostridiales;Other 0.502335879 0.697003842 8.58E-16 1.44E-109
Clostridium disporicum 0.562421244 0.366422526 3.61 E-20 3.64E-25
Bifidobacterium longum 0.614285922 -0.455197243 7.47E-25 1.72E-39
Clostridium glycolicum 0.621583288 0.335500578 2.00E-25 4.26E-21
Bifidobacterium; Other 0.683619978 0.368987869 2.18E-32 1.66E-25
[Table 21. Indicator value analysis results
Indicator value(Recovery) - Indicator value(Diarrhea)*
Subject
Figure imgf000154_0001
Bacillales;Other NA -0.500 -1.000 NA -0.857 -0.593 -0.572 -0.529
Bacilli;Other NA NA NA NA NA -0.667 NA -0.274
Bacillus sp 01082 NA NA NA -0.949 NA -0.962 -0.877 -0.667
Bacillus;Other NA NA NA NA NA NA -0.471 -0.196
Bacteria; Other 0.803 0.786 0.574 NA NA 0.875 0.567 0.672
Bacteriovorax sp EPA NA NA NA NA NA NA NA -0.065
Bacteriovorax sp F2 NA NA NA NA NA NA NA NA
Bacteroidales;Other 0.422 0.999 0.500 NA NA 0.911 0.920 0.700
Bacteroides caccae NA NA NA NA NA NA NA 0.143
Bacteroides fragilis 0.625 NA 0.440 0.676 NA NA NA 0.445
Bacteroides galacturonicus 0.563 NA 0.834 0.929 NA 0.674 0.421 0.547
Bacteroides ovatus 0.438 0.833 NA NA 0.712 NA 0.736 0.460
Bacteroides plebeius 0.250 0.431 NA 0.784 0.713 0.830 0.937 0.600
Bacteroides sp CIP103040 NA NA NA NA NA 0.496 NA 0.1 12
Bacteroides stercoris 0.250 NA NA NA 0.571 NA NA 0.208
Bacteroides uniformis NA NA NA NA NA 0.250 NA 0.087
Bacteroides vulgatus NA NA NA NA NA 0.450 NA 0.127
Bacteroides;Other 0.302 0.937 NA 0.784 NA NA 0.498 0.549
Bacteroidetes;Other NA 0.938 NA NA NA NA 0.618 0.377
Betaproteobacteria; Other NA NA NA NA NA NA NA 0.092
Bifidobacteriaceae;Other NA NA NA NA NA NA NA NA
Bifidobacterium bifidum NA NA 0.462 0.714 NA 0.301 NA 0.275
Bifidobacterium dentium NA NA NA NA -0.923 NA NA -0.158
Bifidobacterium; Other 0.954 0.981 0.995 0.999 0.981 0.985 0.998 0.987
Bilophila wadsworthia NA 0.938 NA NA NA NA 0.735 0.312
Blautia producta NA 0.571 NA NA 0.857 0.313 NA 0.337
Blautia sp M25 NA NA NA NA NA NA -0.353 NA
Blautia;Other 0.537 0.676 -0.845 0.848 0.857 0.890 NA 0.669
Brachybacterium paraconglomeratum NA NA NA NA NA NA NA -0.109
Brachybacterium;Other NA NA NA NA NA NA NA -0.114
Brachyspira aalborgi NA NA NA NA -1.000 NA NA -0.101
Brachyspira pilosicoli NA NA NA NA -0.857 NA -0.588 -0.158
Bradyrhizobiaceae;Other NA NA NA NA NA NA NA NA
Bre vibacterium; Other NA NA NA NA NA NA -0.529 -0.221
Brevundimonas diminuta NA NA NA NA NA NA -0.588 -0.241
Brevundimonas terrae NA NA NA NA NA NA -0.647 -0.202
Brevundimonas vesicularis NA NA NA NA NA NA NA -0.167
Bru eel laceae; Other NA NA NA NA NA NA NA -0.086
Butyrivibrio crossotus NA 0.840 NA NA NA NA NA 0.273
Campylobacter hyointestinalis 0.737 NA NA -0.887 NA NA -0.999 -0.686
Campylobacter jejuni NA NA NA -0.889 NA -0.481 NA -0.203
Campylobacter;Other -0.940 NA NA -0.778 NA NA -0.706 -0.476
Catenibacterium mitsuokai 0.745 0.895 0.776 NA NA NA 0.994 0.791
Cerasicoccus arenae NA NA NA NA NA NA NA NA
Cetobacterium somerae 0.625 NA NA NA NA -0.684 NA NA
Chryseobacterium hominis NA NA NA NA NA NA NA -0.123
Chryseobacterium;Other NA NA NA NA NA NA NA 0.061
Clostridiaceae;Other 0.431 0.805 0.923 NA 0.857 0.373 0.812 0.641
Clostridiales;Other 0.972 0.825 NA NA 0.996 0.695 0.852 0.832
Clostridium bartlettii 0.875 0.903 NA 1.000 0.857 NA NA 0.341
Clostridium bifermentans NA NA NA NA NA NA NA NA
Clostridium bolteae NA NA NA NA NA NA 0.796 0.278
Clostridium clostridioforme 0.496 0.553 NA -0.888 0.857 NA 0.963 0.334
Clostridium disporicum 0.749 0.966 0.923 0.999 0.999 0.551 NA 0.699
Clostridium glycolicum 0.563 0.750 0.462 0.643 0.857 0.500 0.750 0.633
Clostridium glycyrrhizinilyticum NA NA -0.680 NA 0.714 0.489 NA NA
Clostridium hathewayi NA 0.374 NA NA NA NA 0.875 0.234
Clostridium hylemonae NA NA NA NA NA NA NA NA
Clostridium innocuum NA NA NA NA NA NA NA 0.061
Clostridium neonatale NA NA NA NA NA NA NA NA
Clostridium nexile 0.250 NA NA NA NA NA NA 0.233
Clostridium paraputrificum 0.983 NA 0.874 NA 0.999 0.472 NA 0.521
Clostridium perfringens NA 0.313 NA 0.714 NA NA NA 0.286
Clostridium ramosum 0.250 NA NA 0.786 NA NA NA 0.247
Clostridium sp L2 50 NA NA NA 0.786 NA NA NA 0.242 Clostridium sp SS2 1 NA 0.563 NA NA NA NA NA 0.122
Clostridium;Other NA 0.681 NA NA NA 0.681 0.820 0.387
Collinsella aerofaciens 0.918 0.883 0.890 NA NA 0.525 NA 0.584
Collinsella;Other 0.438 0.703 0.538 NA NA NA 0.920 0.438
Comamonadaceae;Other NA NA NA NA NA -0.704 -0.824 NA
Comamonas aquatica NA NA NA NA NA -0.370 -0.765 -0.343
Comamonas;Other NA NA NA NA NA NA NA 0.082
Coprococcus catus NA 0.950 NA -0.778 0.835 NA 0.772 0.199
Coprococcus comes NA 0.627 NA -0.778 NA 0.652 0.745 0.378
Coprococcus eutactus 0.375 0.812 NA NA NA 0.747 0.851 0.526
Coprococcus;Other 0.250 0.625 NA NA NA 0.625 0.869 0.438
Coriobacteriaceae;Other 0.499 0.956 0.799 NA NA 0.631 0.545 0.600
Corynebacterium aurimucosum NA NA NA NA NA NA NA -0.164
Corynebacterium durum NA NA NA NA NA NA -0.584 -0.245
Corynebacterium imitans NA NA NA NA NA NA -0.412 -0.217
Corynebacterium matruchotii NA NA NA NA NA NA -0.641 -0.31 1
Corynebacterium mucifaciens NA NA NA NA NA -0.519 NA -0.326
Corynebacterium tuberculostearicum NA NA NA NA NA NA -0.647 -0.492
Corynebacterium;Other -0.300 NA -0.695 NA NA -0.481 -0.706 -0.488
Deltaproteobacteria;Other NA NA NA NA NA NA NA NA
Desulfovibrio;Other 0.438 NA NA NA NA NA 0.860 0.274
Dialister invisus NA NA NA NA NA NA NA NA
Dialister pneumosintes 0.749 NA NA NA NA 0.551 NA 0.400
Dialister;Other NA NA NA NA NA NA -0.353 -0.109
Dolosiqranulum piqrum NA NA NA NA NA NA NA NA
Dorea formiciqenerans 0.522 0.765 NA -0.836 0.998 0.513 0.909 0.494
Dorea lonqicatena 0.656 0.624 0.702 -0.751 0.855 NA NA 0.358
Edwardsiella ictaluri NA NA NA NA NA NA NA NA
Eqqerthella lenta NA NA NA NA NA NA 0.500 0.227
Elusimicrobium minutum NA NA NA NA NA NA -0.706 NA
Enterobacteriaceae; Other 0.470 NA NA NA NA -0.510 0.737 NA
Enterococcaceae; Other NA NA NA NA NA NA NA -0.177
Enterococcus cecorum NA 0.629 NA NA NA NA NA 0.1 16
Enterococcus faecalis NA NA NA NA NA NA NA NA
Enterococcus; Other NA NA -0.753 NA -0.525 -0.765 -0.750 -0.460
Erysipelotrichaceae;Other NA NA NA NA NA NA NA NA
Escherichia albertii NA NA NA NA NA NA NA NA
Escherichia coli -0.518 -0.409 NA NA NA -0.361 0.951 -0.345
Escherichia;Other NA NA NA NA NA -0.428 0.890 -0.256
Eubacterium biforme 0.864 NA 0.774 NA 0.996 0.583 0.459 0.712
Eubacterium coprostanoliqenes 0.563 0.812 NA NA NA 0.873 0.874 0.530
Eubacterium desmolans NA 0.819 NA NA 0.856 0.589 0.591 0.258
Eubacterium eliqens 0.875 0.981 NA 0.786 0.429 0.845 NA 0.696
Eubacterium hallii 0.548 0.993 NA NA NA 0.989 0.969 0.673
Eubacterium limosum NA NA NA NA NA NA NA 0.041
Eubacterium ramulus 0.250 0.872 NA NA NA NA 0.844 0.353
Eubacterium rectale NA 0.919 NA NA NA 0.929 0.830 0.457
Eubacterium saburreum 0.962 0.952 NA NA NA 0.891 0.944 0.769
Eubacterium siraeum NA NA NA NA NA NA -0.529 -0.216
Eubacterium sp cL 10 1 3 NA 0.435 NA NA NA NA 0.869 0.223
Eubacterium;Other 0.500 0.438 0.686 NA NA 0.625 0.931 0.508
Faecalibacterium prausnitzii 0.972 1.000 NA NA 0.857 0.835 0.801 0.860
Faecalibacterium;Other 0.953 0.999 0.876 NA NA 0.697 0.660 0.819
Fineqoldia maqna NA NA NA NA NA NA NA NA
Firmicutes;Other NA 0.902 NA NA NA 0.748 0.947 0.725
Flavobacteriaceae;Other NA NA NA NA NA NA NA NA
Flavobacteriales;Other NA NA NA NA NA NA NA NA
Fla vobacterium; Other NA NA NA NA NA NA NA NA
Flexibacter;Other NA NA NA NA NA NA NA 0.091
Fusobacteriaceae;Other NA NA NA NA NA -0.481 NA -0.094
Fusobacterium mortiferum NA -0.996 NA NA NA NA NA -0.786
Fusobacterium nucleatum NA -0.444 NA -1 .000 NA -0.641 -0.641 -0.51 1
Fusobacterium;Other NA -0.996 -0.981 -0.836 NA NA NA -0.879
Gammaproteobacteria;Other NA -0.943 NA NA -0.820 NA -0.412 -0.439
Granulicatella adiacens NA NA -0.746 NA NA -0.705 -0.925 -0.615 Granulicatella elegans NA NA NA -0.742 -0.923 -0.838 NA -0.452
Haemophilus haemolyticus NA NA NA -0.965 NA -0.852 NA -0.463
Haemophilus;Other NA NA NA NA NA -0.667 NA -0.217
Halomonas;Other NA -0.333 NA NA NA NA -0.353 -0.167
Helicobacter cinaedi NA NA NA NA NA NA NA NA
Helicobacter winghamensis NA NA NA NA NA -0.556 NA -0.210
Helicobacter;Other NA NA NA NA NA NA NA NA lntrasporangiaceae;Other NA NA NA NA NA NA -0.706 -0.101
Jeotgalicoccus;Other NA -0.444 NA NA NA NA NA -0.217
Kocuria marina NA NA NA NA NA NA NA NA
Lachnospira pectinoschiza NA 0.582 NA NA NA NA NA 0.176
Lachnospiraceae;Other 0.563 0.755 NA NA NA 0.708 0.460 0.492
Lactobacillaceae;Other NA NA NA NA NA NA NA NA
Lactobacillales;Other NA NA NA NA NA NA NA NA
Lactobacillus delbrueckii NA NA NA NA NA NA NA -0.072
Lactobacillus fermentum -0.832 NA NA NA NA NA NA -0.31 1
Lactobacillus mucosae NA NA 0.308 NA NA NA NA NA
Lactobacillus ruminis -0.823 NA 0.769 NA NA NA NA NA
Lactobacillus saerimneri NA NA 0.846 NA NA NA NA 0.619
Lactobacillus sp Atm5 NA NA NA NA NA NA NA NA
Lactobacillus sp KLDS 1 0716 NA NA NA NA NA NA NA -0.065
Lactobacillus;Other NA NA NA NA NA NA NA NA
Lactococcus garvieae NA NA NA NA NA NA NA 0.082
Leptotrichia 591114 NA NA NA NA NA NA NA -0.109
Leptotrichia buccalis NA NA NA NA NA -0.481 NA -0.283
Leptotrichia shahii -0.667 -0.389 -0.609 -0.889 -0.928 -0.908 -0.706 -0.716
Leptotrichia sp PG10 NA NA NA NA NA NA -0.471 -0.130
Leptotrichia;Other NA NA NA NA -0.857 NA NA -0.188
Leptotrichiaceae;Other NA NA NA NA -0.929 -0.741 NA -0.399
Leucobacter komagatae NA NA NA NA NA NA NA 0.102
Leucobacter;Other NA NA NA NA NA NA NA 0.061
Leuconostoc;Other NA NA NA NA NA NA NA NA
Luteimonas;Other NA NA NA NA NA NA -0.647 NA
Megamonas rupellensis NA NA 0.921 0.786 NA 0.310 NA 0.336
Megamonas;Other NA NA 0.762 NA NA NA NA 0.192
Megasphaera elsdenii 0.687 NA 0.919 NA NA -0.333 NA 0.315
Megasphaera hominis NA NA 0.538 NA NA NA NA 0.102
Megasphaera sp TrE9262 0.437 NA 0.846 NA NA NA NA 0.234
Megasphaera;Other 0.313 NA NA NA NA NA NA 0.092
Micrococcus luteus NA NA NA NA NA NA NA -0.123
Mitsuokella multacida 0.625 0.563 NA NA NA NA NA 0.255
Mitsuokella;Other 0.375 NA NA NA NA NA NA 0.091
Mogibacterium neglectum NA NA NA NA NA NA -0.412 NA
Moraxella osloensis NA NA NA NA NA NA NA -0.188
Moraxellaceae;Other NA NA NA NA NA NA NA -0.080
Morganella morganii NA NA NA NA NA NA NA NA
Neisseria elongate NA NA NA NA NA NA NA -0.158
Neisseria subflava NA NA NA -0.667 NA -0.444 NA -0.275
Neisseria;Other -0.633 -0.444 -0.564 -0.994 NA -0.660 -0.412 -0.613
Nevskia;Other NA NA NA NA NA NA NA NA
Novosphingobium;Other NA NA NA NA NA NA NA NA
Odoribacter splanchnicus NA NA NA NA NA NA 0.419 0.129
Olsenella sp A2 0.500 0.995 NA NA NA NA NA 0.306
Olsenella;Other 0.500 NA NA NA NA NA NA 0.092
Oribacterium sp oral taxon 108 NA NA NA NA NA NA -0.353 -0.180
Oscillibacter sp G2 NA 0.563 NA NA NA NA 0.681 0.244
Oscillibacter;Other NA 0.938 NA -0.667 NA 0.791 NA 0.304
Oxalobacter formigenes NA NA NA NA NA NA NA 0.051
Pantoea ananatis NA NA NA NA NA NA NA NA
Parabacteroides distasonis NA 0.375 NA NA NA 0.616 0.500 0.326
Parabacteroides merdae NA NA NA NA NA 0.375 NA 0.092
Parabacteroides;Other 0.438 0.688 NA NA NA 0.500 0.938 0.429
Paracoccus denitrificans NA NA NA NA -0.786 NA -0.471 -0.167
Paracoccus; Other NA NA NA NA NA NA NA NA
Pasteurellaceae;Other NA NA -0.770 NA NA -0.870 -0.689 -0.568 Pediococcus;Other NA NA NA NA NA NA NA NA
Pedobacter terrae NA NA NA NA NA NA NA -0.116
Pedobacter;Other NA NA NA NA NA NA NA 0.071
Peptoniphilus harei NA NA NA NA NA NA NA NA
Peptostreptococcus stomatis NA NA NA NA NA NA NA NA
Peptostreptococcus;Other NA NA NA -0.889 NA -0.543 -0.647 -0.396
Peredibacter starrii NA NA NA NA NA NA NA NA
Phascolarctobacterium faecium NA NA NA NA NA 0.313 0.419 0.122
Phascolarctobacterium succinatutens NA 0.998 0.615 -0.667 NA NA NA 0.383
Porphyromonas catoniae NA NA NA -0.778 NA NA NA -0.290
Prevotella buccae NA NA NA NA NA NA NA NA
Prevotella copri 0.920 0.994 0.900 NA NA 0.925 NA 0.776
Prevotella denticola NA NA NA NA NA NA NA -0.094
Prevotella histicola NA -0.611 -0.478 -1 .000 -0.929 -0.963 -0.706 -0.667
Prevotella nanceiensis NA NA NA -0.889 NA -0.519 NA -0.268
Prevotella oulorum 0.966 0.969 NA NA NA 0.742 NA 0.520
Prevotella salivae NA NA NA NA NA NA NA -0.145
Prevotella sp DJF B112 NA -0.389 NA NA NA -0.630 -0.588 -0.341
Prevotella sp DJF B116 0.560 0.938 0.754 -0.540 NA 0.915 0.726 0.633
Prevotella sp DJF CP65 0.492 0.927 NA -0.641 NA 0.789 0.481 0.453
Prevotella sp DJF LS16 0.500 NA NA NA NA NA NA 0.092
Prevotella sp oral taxon 302 0.531 0.426 NA -0.674 NA 0.857 NA 0.280
Prevotella sp RS2 0.312 0.937 NA NA NA NA NA 0.214
Prevotella stercorea NA 0.976 NA -0.889 NA 0.788 -0.684 NA
Prevotella veroralis NA NA NA NA NA NA NA 0.139
Prevotella; Other NA -0.500 NA NA NA -0.593 -0.647 -0.391
Prevotellaceae;Other NA 0.500 NA NA NA NA NA 0.199
Prosthecobacter;Other NA NA NA NA NA NA NA NA
Proteobacteria;Other -0.691 -0.450 NA NA NA -0.634 0.911 -0.501
Proteus mirabilis NA NA NA NA NA NA NA NA
Pseudomonas mendocina NA -0.778 NA NA -0.857 NA -0.647 -0.449
Pseudomonas stutzeri -0.400 -0.888 -0.565 NA -1.000 -0.601 -0.913 -0.653
Pseudomonas;Other NA NA NA NA -0.786 NA -0.471 -0.225
Ralstonia;Other NA -0.611 NA NA -0.786 NA NA -0.246
Rheinheimera sp 09BSZB 9 NA NA NA NA NA NA NA -0.130
Rheinheimera sp TPS16 NA NA NA NA NA NA NA NA
Rheinheimera;Other NA NA NA NA NA NA -0.353 -0.181
Rhizobiaceae;Other NA NA NA NA NA NA NA 0.081
Rhizobiales;Other NA NA NA NA NA NA NA NA
Rhodobacteraceae;Other NA NA NA NA NA NA NA 0.092
Rhodocyclaceae;Other NA NA NA NA NA NA NA NA
Roseburia intestinalis 0.561 NA NA NA NA 0.709 0.773 0.562
Roseburia;Other 0.870 0.917 NA NA 0.857 0.628 NA 0.674
Rothia dentocariosa -0.689 NA -0.696 NA NA -0.370 -0.824 -0.557
Rothia mucilaginosa -0.694 NA -0.895 -0.905 -0.947 NA -0.824 -0.706
Ruminococcaceae;Other 0.545 0.999 NA NA NA 0.892 0.979 0.686
Ruminococcus bromii 0.562 0.562 NA NA NA 0.687 0.499 0.479
Ruminococcus callidus 0.500 1.000 NA NA NA 0.872 0.562 0.479
Ruminococcus flavefaciens NA 0.375 NA NA NA NA 0.375 0.133
Ruminococcus gnavus 0.784 NA 0.610 0.714 NA NA NA 0.430
Ruminococcus obeum 0.494 0.908 NA NA 0.851 NA NA 0.301
Ruminococcus sp 5 1 39BFAA 0.812 NA -0.443 NA 0.997 0.950 0.888 0.671
Ruminococcus sp DJF VR70k1 0.874 0.946 NA NA 0.856 0.917 0.668 0.762
Ruminococcus sp ZS2 15 NA NA NA NA 0.857 NA NA 0.124
Ruminococcus torques 0.369 0.688 NA NA NA 0.687 0.250 0.337
Ruminococcus;Other NA NA NA NA 0.992 0.563 0.801 NA
Salmonella enterica NA NA NA NA NA NA NA NA
Sarcina ventriculi NA NA NA NA NA NA NA NA
Sarcina;Other NA NA NA NA NA NA NA 0.092
Scardovia wiggsiae NA NA NA NA NA NA NA -0.130
Shinella;Other NA NA NA NA NA NA NA NA
Shuttleworthia satelles NA NA NA NA NA NA NA -0.085
Slackia isoflavoniconvertens 0.250 0.757 0.613 NA NA 0.646 0.611 0.465
Solobacterium moorei NA NA NA NA NA -0.538 -0.529 -0.320
Sphingobacterium mizutaii NA NA NA NA NA NA -0.706 -0.123 Sphingobacterium multivorum NA NA NA NA NA NA -0.294 -0.130
Sphingobacterium sp P 7 NA NA NA NA NA NA NA NA
Sphingobium yanoikuyae NA NA NA NA NA NA NA -0.196
Sphingomonadaceae;Other NA NA NA NA NA NA NA -0.058
Sphingomonadales;Other NA NA NA NA NA NA NA 0.061
Sphingomonas sp BAC84 NA -0.535 NA NA NA NA NA -0.156
Sphingomonas yabuuchiae NA NA NA NA NA NA NA NA
Sphingomonas;Other NA NA NA NA NA NA NA -0.072
Staphylococcaceae; Other NA NA NA NA NA NA NA -0.087
Staphylococcus aureus NA -0.667 -0.957 NA -1.000 -0.535 -0.699 -0.621
Staphylococcus;Other NA -0.333 -0.913 NA -1.000 NA -0.646 -0.478
Stenotrophomonas maltophilia NA NA NA NA -0.857 NA -0.471 -0.283
Stenotrophomonas rhizophila NA NA NA NA NA NA -0.765 -0.275
Stenotrophomonas;Other -0.733 -0.936 -0.669 NA NA NA NA -0.423
Streptococcaceae;Other -0.585 NA 0.385 NA NA NA NA NA
Streptococcus anginosus -0.926 NA NA NA -0.779 -0.810 -0.584 -0.629
Streptococcus gordonii -0.790 NA NA NA -0.867 -0.740 -0.880 -0.679
Streptococcus mutans NA NA NA NA NA NA NA -0.203
Streptococcus parasanguinis -0.797 -0.624 -0.665 NA NA -0.856 -0.718 -0.663
Streptococcus peroris -0.944 NA -0.818 NA NA NA -0.997 -0.738
Streptococcus sanguinis NA NA NA NA NA NA NA NA
Streptococcus thermophilus -0.913 -0.333 -0.901 NA -0.962 NA -0.882 -0.721
Streptococcus;Other -0.953 NA NA NA -0.941 NA -0.984 -0.562
Subdoligranulum variabile NA 1.000 NA -0.622 NA 0.986 0.652 0.498
Succinivibrio dextrinosolvens NA NA NA NA NA NA -0.765 NA
Sutterella sp YIT 12072 NA 0.791 NA NA NA 0.583 NA NA
Sutterella stercoricanis NA 0.563 NA NA NA NA -0.601 NA
Sutterella;Other 0.704 -0.883 NA -0.881 NA NA -0.825 -0.393
Thauera terpenica NA NA NA NA NA NA NA 0.071
Thauera;Other NA NA NA NA NA NA NA NA
Thermus igniterrae NA NA NA NA NA NA NA NA
Treponema porcinum NA NA NA NA NA NA NA NA
Treponema succinifaciens NA NA NA NA NA NA NA NA
Turicibacter sanguinis 0.563 0.812 0.692 NA 1 .000 0.438 0.375 0.612 unclassified Erysipelotrichaceae;Other 0.402 NA NA NA NA NA NA 0.156
Ureaplasma;Other NA NA NA NA NA NA NA NA
Varibaculum cambriense NA NA NA NA NA NA NA NA
Veillonella dispar NA NA NA NA NA -0.81 1 -0.999 -0.537
Veillonella parvula NA NA NA -0.822 NA -0.762 -0.812 NA
Veillonella sp S101 NA NA NA -0.865 NA -0.669 -0.761 -0.302
Veillonella;Other NA NA NA NA NA NA NA -0.130
Veillonellaceae;Other 0.611 NA 0.766 NA NA NA -0.641 0.259
Verrucomicrobium sp IRVE NA NA NA NA NA NA NA NA
Vibrio cholerae -0.997 -0.997 NA -0.998 -0.999 -0.966 -0.999 -0.990
Vibrio;Other -0.499 NA NA -0.888 -0.786 -0.481 -0.941 -0.521
Wautersiella falsenii NA NA NA NA NA NA NA NA
Weissella cibaria NA 0.313 NA NA NA NA NA NA
Weissella;Other NA NA NA NA NA 0.309 NA NA
X1760;Other 0.500 0.938 0.385 NA NA 0.492 0.674 0.486
Xanthomonadaceae;Other NA NA NA NA NA NA -0.353 NA
FDR-adjusted p-value for indicator value analysis (NA = not observed in individual)
Subject
Combined (not differentiated by
Species A B C D E F G
subject, ns = not significant)
Acidaminococcus intestini 0.171 NA 0.479 NA NA NA NA ns
Acidaminococcus;Other 0.013 0.341 1.000 NA NA NA NA 0.003
Acidovorax avenae NA NA NA 0.21 1 1 .000 NA NA ns
Acinetobacter baumannii 0.395 0.341 0.054 0.044 0.024 0.012 0.011 0.000
Acinetobacter calcoaceticus NA 1 .000 0.130 NA 0.121 0.572 0.011 0.000
Acinetobacter johnsonii 0.099 0.039 0.077 1 .000 0.041 0.217 0.011 0.000
Acinetobacter junii 0.163 0.085 0.571 0.398 0.024 0.012 0.011 0.000
Acinetobacter; Other 0.223 0.091 0.096 0.267 0.024 0.023 0.011 0.000 Actinobacillus porcinus 0.422 NA 0.125 0.026 NA 0.041 0.127 0.000
Actinomyces georgiae NA NA NA NA 0.570 NA 0.133 ns
Actinomyces graevenitzii 0.107 0.212 0.194 0.600 0.024 0.110 0.011 0.000
Actinomyces odontolyticus 0.707 0.624 0.096 0.791 0.329 0.081 0.011 0.000
Actinomyces oris 0.268 0.978 0.624 1.000 0.202 0.410 0.011 0.001
Actinomycetales;Other NA NA NA 0.078 1 .000 NA NA 0.007
Aerococcus; Other NA 0.028 0.040 NA 0.024 0.126 0.011 0.000
Aeromonas;Other 0.613 0.166 0.254 0.968 0.024 0.012 0.011 0.000
Afipia genosp 9 NA 1 .000 1.000 0.085 0.196 NA 0.137 ns
Aggregatibacter segnis 1.000 0.787 0.314 0.026 0.446 0.012 0.159 0.000
Agrobacterium tumefaciens NA NA 0.637 0.168 0.041 NA 0.019 ns
Alcaligenaceae;Other NA 0.166 0.644 0.422 0.121 1 .000 1 .000 0.009
Alistipes finegoldii NA NA NA NA NA 0.041 NA 0.021
Alistipes shahii NA 0.079 0.479 NA NA 0.041 0.011 0.000
Alistipes sp NML05A004 NA 0.185 NA NA NA 0.012 NA 0.000
Allisonella histaminiformans 0.013 0.091 1.000 1 .000 NA 0.176 1.000 0.001
Anaerobiospirillum succiniciproducens 0.546 NA NA 1.000 NA NA NA ns
Anaerococcus octavius 1.000 0.028 NA NA NA NA NA 0.008
Anaerococcus vaginalis NA NA 0.357 0.648 0.329 NA 0.011 0.000
Anaeroglobus geminatus NA 1 .000 NA NA 0.626 NA 0.079 0.036
Arcobacter butzleri NA NA NA NA NA 0.643 0.011 0.021
Atopobium sp F0209 0.671 0.624 0.470 0.600 0.041 0.081 0.011 0.000
Atopobium;Other NA NA NA NA 0.224 NA 1 .000 0.040
Azospirillum;Other NA 0.403 NA NA 1 .000 NA NA ns
Bacillaceae;Other 0.436 0.687 NA NA NA NA NA ns
Bacillales;Other 1.000 0.015 0.026 0.130 0.041 0.012 0.011 0.000
Bacilli;Other 0.436 0.832 0.561 0.568 0.219 0.012 0.133 0.000
Bacillus sp 01082 0.065 0.127 0.308 0.026 0.104 0.012 0.011 0.000
Bacillus;Other NA 0.415 0.615 NA 0.070 0.553 0.011 0.000
Bacteria; Other 0.013 0.015 0.040 0.068 0.129 0.012 0.011 0.000
Bacteriovorax sp EPA NA 0.296 1.000 NA 0.353 NA 1 .000 0.027
Bacteriovorax sp F2 NA 0.683 NA NA 0.657 NA NA ns
Bacteroidales;Other 0.013 0.015 0.040 0.648 0.570 0.012 0.011 0.000
Bacteroides caccae 0.469 0.059 NA 1.000 0.157 0.232 0.261 0.000
Bacteroides fragilis 0.013 0.789 0.040 0.026 0.612 0.982 1 .000 0.001
Bacteroides galacturonicus 0.013 1.000 0.026 0.026 0.471 0.012 0.019 0.000
Bacteroides ovatus 0.013 0.015 0.130 0.981 0.024 0.51 1 0.011 0.000
Bacteroides plebeius 0.035 0.039 0.117 0.026 0.024 0.012 0.011 0.000
Bacteroides sp CIP103040 NA NA NA 0.541 NA 0.012 NA 0.001
Bacteroides stercoris 0.045 1.000 NA 0.055 0.024 0.952 0.147 0.001
Bacteroides uniformis NA 0.334 0.479 NA NA 0.041 NA 0.004
Bacteroides vulgatus 0.436 0.069 NA NA NA 0.012 NA 0.000
Bacteroides;Other 0.045 0.015 0.088 0.044 NA 0.095 0.011 0.000
Bacteroidetes;Other 0.436 0.015 0.644 0.403 0.657 0.958 0.019 0.000
Betaproteobacteria; Other NA NA NA 0.055 NA NA NA 0.003
Bifidobacteriaceae;Other NA NA NA NA 0.471 NA NA ns
Bifidobacterium bifidum 0.436 0.085 0.026 0.044 0.866 0.041 1.000 0.000
Bifidobacterium dentium 0.324 1 .000 0.130 0.127 0.024 NA 0.065 0.041
Bifidobacterium; Other 0.013 0.015 0.026 0.026 0.024 0.012 0.011 0.000
Bilophila wadsworthia NA 0.015 0.479 NA 1.000 0.102 0.011 0.000
Blautia producta 0.123 0.015 0.188 0.068 0.024 0.033 0.099 0.000
Blautia sp M25 NA NA NA NA NA 0.088 0.036 ns
Blautia;Other 0.013 0.015 0.026 0.026 0.024 0.012 0.835 0.000
Brachybacterium paraconglomeratum 1.000 0.624 1.000 NA 0.250 1 .000 0.284 0.001
Brachybacterium;Other NA 1 .000 0.590 1.000 0.121 1 .000 1 .000 0.000
Brachyspira aalborgi NA NA NA NA 0.024 NA NA 0.000
Brachyspira pilosicoli NA NA NA 1.000 0.024 NA 0.011 0.000
Bradyrhizobiaceae;Other NA NA 0.644 0.085 0.580 NA NA ns
Bre vibacterium; Other NA 1 .000 0.117 0.434 0.070 0.646 0.011 0.000
Brevundimonas diminuta 1.000 0.212 0.117 0.648 0.139 0.581 0.011 0.000
Brevundimonas terrae 1.000 1 .000 0.200 1.000 0.224 0.620 0.011 0.000
Brevundimonas vesicularis NA 0.1 19 0.637 NA 0.104 0.397 0.072 0.000
Bru eel laceae; Other NA 1 .000 NA 1.000 0.121 NA 0.314 0.009
Butyrivibrio crossotus NA 0.015 NA NA NA 0.067 0.223 0.000
Campylobacter hyointestinalis 0.013 0.172 0.832 0.044 0.189 0.782 0.011 0.001 Campylobacter jejuni 0.555 NA 0.479 0.026 1.000 0.033 0.057 0.001
Campylobacter;Other 0.013 0.624 NA 0.026 0.104 0.769 0.011 0.000
Catenibacterium mitsuokai 0.013 0.015 0.026 1.000 0.525 0.515 0.011 0.000
Cerasicoccus arenae NA 0.613 NA NA NA NA 0.566 ns
Cetobacterium somerae 0.013 0.212 0.652 0.434 1 .000 0.012 1.000 ns
Chryseobacterium hominis NA NA 0.644 0.600 0.248 0.190 1 .000 0.001
Chryseobacterium;Other NA NA NA 0.168 NA NA NA 0.040
Clostridiaceae;Other 0.013 0.015 0.026 0.127 0.024 0.012 0.011 0.000
Clostridiales;Other 0.013 0.015 0.791 0.355 0.024 0.012 0.011 0.000
Clostridium bartlettii 0.013 0.015 0.605 0.026 0.024 0.531 1 .000 0.007
Clostridium bifermentans 0.436 NA NA NA NA NA 0.142 ns
Clostridium bolteae 0.207 0.1 10 0.479 0.666 0.242 0.297 0.011 0.000
Clostridium clostridioforme 0.013 0.039 0.776 0.026 0.024 0.705 0.011 0.006
Clostridium disporicum 0.013 0.015 0.026 0.026 0.024 0.012 0.284 0.000
Clostridium glycolicum 0.013 0.015 0.026 0.044 0.024 0.012 0.011 0.000
Clostridium glycyrrhizinilyticum NA 0.637 0.026 0.055 0.024 0.012 0.137 ns
Clostridium hathewayi 0.436 0.039 NA 1 .000 0.446 NA 0.011 0.000
Clostridium hylemonae NA NA 0.517 NA NA NA NA ns
Clostridium innocuum NA NA NA 0.127 NA NA NA 0.036
Clostridium neonatale NA NA NA NA NA NA 0.476 ns
Clostridium nexile 0.013 0.310 NA 0.648 NA 0.851 0.133 0.008
Clostridium paraputrificum 0.013 0.624 0.026 0.597 0.024 0.033 0.072 0.000
Clostridium perfringens 0.218 0.049 0.054 0.026 0.084 0.060 NA 0.000
Clostridium ramosum 0.035 0.140 0.117 0.026 NA 0.508 0.576 0.001
Clostridium sp L2 50 0.436 0.420 1.000 0.026 0.094 0.236 0.057 0.006
Clostridium sp SS2 1 0.436 0.015 NA NA NA 0.232 NA 0.000
Clostridium;Other 0.679 0.015 0.164 1 .000 NA 0.012 0.011 0.000
Collinsella aerofaciens 0.013 0.015 0.026 0.095 0.582 0.012 0.142 0.000
Collinsella;Other 0.013 0.015 0.026 NA NA 0.102 0.011 0.000
Comamonadaceae;Other 1.000 0.637 0.593 0.055 0.094 0.012 0.011 ns
Comamonas aquatica 0.657 0.231 0.117 0.648 0.070 0.041 0.011 0.000
Comamonas;Other NA NA NA 0.078 NA NA NA 0.008
Coprococcus catus 0.838 0.015 0.054 0.026 0.024 0.901 0.011 0.023
Coprococcus comes 0.082 0.015 0.155 0.026 1.000 0.012 0.011 0.000
Coprococcus eutactus 0.013 0.015 NA 0.130 0.471 0.012 0.011 0.000
Coprococcus;Other 0.013 0.015 NA 0.203 NA 0.012 0.011 0.000
Coriobacteriaceae;Other 0.013 0.015 0.026 0.125 0.582 0.012 0.011 0.000
Corynebactenum aurimucosum NA 0.687 0.212 1.000 0.157 0.140 1 .000 0.000
Corynebactenum durum 1.000 0.624 0.637 0.722 0.487 0.657 0.011 0.018
Corynebactenum imitans NA 0.133 0.077 NA 0.129 NA 0.019 0.000
Corynebactenum matruchotii 0.056 NA 0.130 0.130 0.369 0.183 0.011 0.000
Corynebactenum mucifaciens 0.630 0.687 0.066 0.575 0.056 0.012 0.052 0.000
Corynebactenum tuberculostearicum 0.240 0.624 0.066 0.068 0.056 0.081 0.011 0.000
Corynebacterium;Other 0.045 0.091 0.026 0.055 0.471 0.012 0.011 0.000
Deltaproteobacteria;Other NA NA NA NA 1 .000 NA NA ns
Desulfovibrio;Other 0.013 0.091 0.479 NA NA 0.976 0.011 0.000
Dialister invisus 1.000 0.385 1.000 NA 1 .000 1 .000 0.133 ns
Dialister pneumosintes 0.013 0.085 0.096 1 .000 0.695 0.041 1.000 0.001
Dialister;Other NA 1 .000 1.000 0.327 0.626 0.429 0.019 0.003
Dolosigranulum pigrum NA NA NA NA 0.692 NA NA ns
Dorea formicigenerans 0.013 0.015 0.479 0.026 0.024 0.012 0.011 0.000
Dorea longicatena 0.013 0.015 0.040 0.044 0.024 0.864 1 .000 0.000
Edwardsiella ictaluri NA NA NA 0.571 NA NA NA ns
Eggerthella lenta NA 0.314 NA 0.078 0.471 0.204 0.011 0.000
Elusimicrobium minutum 0.436 0.613 NA NA NA NA 0.011 ns
Enterobacteriaceae; Other 0.013 0.337 0.112 0.078 0.721 0.033 0.019 ns
Enterococcaceae; Other NA NA 0.096 0.257 0.129 0.556 0.133 0.000
Enterococcus cecorum 0.218 0.028 0.370 0.427 NA 0.076 NA 0.041
Enterococcus faecalis 0.073 0.185 0.992 0.068 0.309 0.095 0.085 ns
Enterococcus; Other 0.149 0.314 0.026 1.000 0.041 0.012 0.011 0.000
Erysipelotrichaceae;Other 0.207 0.613 0.096 0.929 1 .000 0.140 0.127 ns
Escherichia albertii NA NA NA 1.000 NA 0.353 NA ns
Escherichia coli 0.025 0.049 0.538 1 .000 0.821 0.012 0.011 0.000
Escherichia;Other 0.383 0.085 0.791 0.713 0.657 0.012 0.011 0.006
Eubacterium biforme 0.013 1.000 0.040 0.264 0.024 0.012 0.011 0.000 Eubactenum coprostanoliqenes 0.013 0.015 0.479 NA 0.471 0.012 0.011 0.000
Eubactenum desmolans 0.163 0.015 0.787 0.897 0.024 0.012 0.011 0.008
Eubactenum eliqens 0.013 0.015 0.125 0.026 0.041 0.012 0.425 0.000
Eubactenum hallii 0.013 0.015 0.125 0.812 1.000 0.012 0.011 0.000
Eubactenum limosum 0.436 NA 0.517 NA NA NA 0.287 0.039
Eubactenum ramulus 0.025 0.015 NA NA NA 0.088 0.011 0.000
Eubactenum rectale 0.679 0.015 1.000 1 .000 NA 0.012 0.011 0.000
Eubactenum saburreum 0.013 0.015 0.308 0.466 0.149 0.012 0.011 0.000
Eubactenum siraeum NA 0.218 NA NA 0.070 0.095 0.011 0.000
Eubactenum sp cL 10 1 3 0.436 0.039 NA NA NA NA 0.011 0.000
Eubacterium;Other 0.013 0.015 0.026 1.000 NA 0.012 0.011 0.000
Faecalibacterium prausnitzii 0.013 0.015 0.054 0.095 0.024 0.012 0.011 0.000
Faecalibacterium;Other 0.013 0.015 0.040 0.314 0.115 0.012 0.011 0.000
Fineqoldia maqna NA 0.542 0.066 NA 0.297 1 .000 NA ns
Firmicutes;Other 0.683 0.015 0.225 0.373 0.467 0.012 0.011 0.000
Flavobacteriaceae;Other NA 0.655 1.000 NA 0.336 NA 0.576 ns
Flavobacteriales;Other NA NA NA 0.689 1 .000 NA NA ns
Fla vobacterium; Other NA NA NA NA 0.657 NA NA ns
Flexibacter;Other NA NA NA 0.078 NA NA 0.284 0.032
Fusobacteriaceae;Other 0.453 0.655 NA NA NA 0.023 NA 0.007
Fusobacterium mortiferum 0.345 0.015 0.138 0.130 0.340 0.349 0.137 0.000
Fusobacterium nucleatum 0.082 0.015 0.320 0.026 0.104 0.012 0.011 0.000
Fusobacterium;Other 0.065 0.015 0.026 0.044 0.231 1.000 0.323 0.000
Gammaproteobacteria;Other 0.254 0.015 0.077 0.541 0.024 0.076 0.028 0.000
Granulicatella adiacens 0.065 0.953 0.040 0.373 0.302 0.012 0.011 0.000
Granulicatella eleqans NA 0.069 0.066 0.026 0.024 0.012 0.121 0.000
Haemophilus haemolyticus 0.073 0.687 0.112 0.026 0.157 0.012 0.133 0.000
Haemophilus;Other NA NA NA 0.115 0.369 0.012 0.318 0.000
Halomonas;Other NA 0.049 0.637 NA 0.250 0.343 0.044 0.000
Helicobacter cinaedi 0.742 NA 0.066 1.000 NA 0.465 NA ns
Helicobacter winqhamensis 1.000 1 .000 0.428 NA 0.250 0.012 0.273 0.000
Helicobacter;Other NA NA 0.479 NA 0.242 NA NA ns lntrasporanqiaceae;Other NA NA NA 1.000 0.657 NA 0.011 0.007
Jeotqalicoccus;Other NA 0.015 0.269 NA 0.115 0.584 0.052 0.000
Kocuria marina 0.657 1 .000 NA NA NA NA 1 .000 ns
Lachnospira pectinoschiza 0.933 0.039 0.249 NA NA 0.572 0.278 0.001
Lachnospiraceae;Other 0.013 0.015 0.244 0.267 0.471 0.012 0.028 0.000
Lactobacillaceae;Other NA NA 0.112 0.373 0.636 NA NA ns
Lactobacillales;Other 0.218 0.687 0.249 0.055 0.056 0.654 0.057 ns
Lactobacillus delbrueckii NA NA NA NA 0.094 NA NA 0.022
Lactobacillus fermentum 0.013 1.000 0.479 NA 0.056 0.346 NA 0.000
Lactobacillus mucosae 0.502 0.708 0.040 0.095 0.139 NA 1.000 ns
Lactobacillus ruminis 0.013 0.085 0.026 0.085 0.471 0.508 0.866 ns
Lactobacillus saerimneri 0.621 0.172 0.026 0.095 0.219 0.277 0.462 0.002
Lactobacillus sp Atm5 NA NA NA 1.000 NA NA NA ns
Lactobacillus sp KLDS 1 0716 0.123 NA NA NA NA NA NA 0.018
Lactobacillus;Other 0.598 NA 0.171 0.106 0.104 NA NA ns
Lactococcus qarvieae NA 0.624 NA 0.203 1 .000 0.463 0.566 0.019
Leptotrichia 591114 NA NA 1.000 NA 0.056 NA 0.133 0.000
Leptotrichia buccalis 0.193 0.344 0.244 0.358 0.248 0.012 0.133 0.000
Leptotrichia shahii 0.013 0.028 0.040 0.026 0.024 0.012 0.011 0.000
Leptotrichia sp PG10 NA 0.624 NA NA 0.202 1 .000 0.011 0.001
Leptotrichia;Other NA 0.344 NA 0.288 0.024 0.154 0.133 0.000
Leptotrichiaceae;Other 0.282 0.127 0.225 0.115 0.024 0.012 0.284 0.000
Leucobacter komaqatae NA NA NA 0.055 NA NA NA 0.002
Leucobacter;Other NA NA NA 0.186 NA NA NA 0.039
Leuconostoc;Other 0.214 0.613 NA 0.384 1 .000 0.401 NA ns
Luteimonas;Other NA NA NA 0.249 0.582 NA 0.011 ns
Meqamonas rupellensis 0.073 1 .000 0.026 0.026 0.196 0.012 NA 0.000
Meqamonas;Other NA NA 0.026 0.1 15 0.471 1.000 NA 0.000
Meqasphaera elsdenii 0.013 0.133 0.026 1.000 0.483 0.023 NA 0.000
Meqasphaera hominis 0.131 NA 0.026 NA NA NA NA 0.000
Meqasphaera sp TrE9262 0.013 0.091 0.026 NA 1.000 1.000 NA 0.000
Meqasphaera;Other 0.013 0.091 NA NA NA NA NA 0.000
Micrococcus luteus NA 0.208 0.479 NA 0.275 1 .000 0.566 0.001 Mitsuokella multacida 0.013 0.015 0.858 NA NA 0.067 0.566 0.000
Mitsuokella;Other 0.013 0.613 1.000 NA NA NA 0.566 0.001
Moqibacterium neqlectum NA 0.236 0.833 0.130 0.666 1 .000 0.011 ns
Moraxella osloensis 0.240 0.059 0.637 0.648 0.237 0.611 1 .000 0.000
Moraxellaceae;Other NA 0.133 1.000 NA 0.372 NA 0.576 0.015
Morqanella morqanii NA NA NA 1.000 1 .000 NA NA ns
Neisseria elonqata 0.653 1 .000 0.275 0.597 0.626 0.346 0.057 0.001
Neisseria subflava 0.123 0.687 0.117 0.026 0.626 0.012 NA 0.000
Neisseria;Other 0.035 0.015 0.026 0.026 0.084 0.012 0.019 0.000
Nevskia;Other NA NA NA NA 1 .000 NA NA ns
Novosphinqobium;Other NA NA NA NA 0.372 NA NA ns
Odoribacter splanchnicus NA NA NA 0.408 1 .000 0.081 0.028 0.001
Olsenella sp A2 0.013 0.015 0.066 NA 0.897 NA 0.794 0.000
Olsenella;Other 0.013 0.613 NA NA NA NA NA 0.000
Oribacterium sp oral taxon 108 0.531 0.266 0.496 1.000 0.094 1 .000 0.028 0.000
Oscillibacter sp G2 0.436 0.015 NA NA NA 0.088 0.011 0.000
Oscillibacter;Other 0.370 0.015 0.840 0.026 NA 0.012 0.435 0.000
Oxalobacter formiqenes 0.436 0.091 NA NA NA NA NA 0.021
Pantoea ananatis 0.653 1 .000 NA 1.000 0.613 NA 1 .000 ns
Parabacteroides distasonis 0.436 0.015 NA 0.125 NA 0.012 0.019 0.000
Parabacteroides merdae 0.436 NA 0.212 NA NA 0.012 NA 0.000
Parabacteroides;Other 0.013 0.015 0.527 NA NA 0.012 0.011 0.000
Paracoccus denitrificans NA NA NA 0.648 0.024 0.429 0.011 0.000
Paracoccus; Other NA 1 .000 NA 0.055 0.369 1 .000 0.292 ns
Pasteurellaceae;Other 0.157 0.909 0.026 1.000 0.329 0.033 0.011 0.000
Pediococcus;Other NA NA 0.479 NA NA NA NA ns
Pedobacter terrae NA 0.208 0.644 NA 0.378 1 .000 0.137 0.001
Pedobacter;Other NA NA NA 0.127 NA NA NA 0.019
Peptoniphilus harei NA 1 .000 0.077 NA 0.471 0.140 0.079 ns
Peptostreptococcus stomatis NA 0.687 1.000 NA 0.866 0.228 0.065 ns
Peptostreptococcus;Other 1.000 1 .000 0.054 0.026 0.237 0.012 0.011 0.000
Peredibacter starrii NA NA NA NA 0.657 NA NA ns
Phascolarctobacterium faecium NA NA NA NA NA 0.012 0.011 0.000
Phascolarctobacterium succinatutens NA 0.015 0.026 0.026 0.589 NA 0.085 0.000
Porphyromonas catoniae 0.679 0.069 0.096 0.026 0.570 0.060 0.133 0.000
Prevotella buccae 0.436 NA NA NA NA NA NA ns
Prevotella copri 0.013 0.015 0.026 0.479 0.557 0.012 0.165 0.000
Prevotella denticola NA NA 1.000 NA 0.340 0.572 0.057 0.005
Prevotella histicola 0.091 0.015 0.040 0.026 0.024 0.012 0.011 0.000
Prevotella nanceiensis 0.531 0.687 0.200 0.026 0.570 0.012 1.000 0.000
Prevotella oulorum 0.013 0.015 0.112 0.130 0.525 0.012 0.334 0.000
Prevotella salivae NA 0.385 1.000 NA 0.282 0.236 0.057 0.000
Prevotella sp DJF B112 0.531 0.049 0.652 NA 0.129 0.012 0.011 0.000
Prevotella sp DJF B116 0.013 0.015 0.026 0.044 0.471 0.012 0.011 0.000
Prevotella sp DJF CP65 0.013 0.015 0.088 0.026 NA 0.012 0.044 0.000
Prevotella sp DJF LS16 0.013 0.613 NA NA 0.568 NA NA 0.000
Prevotella sp oral taxon 302 0.013 0.039 0.088 0.026 NA 0.012 0.338 0.000
Prevotella sp RS2 0.013 0.015 NA 1 .000 0.582 1 .000 NA 0.000
Prevotella stercorea 0.229 0.015 0.785 0.026 0.692 0.012 0.036 ns
Prevotella veroralis 0.099 1 .000 NA NA NA 0.050 0.085 0.002
Prevotella; Other 0.679 0.028 0.288 0.130 0.115 0.012 0.011 0.000
Prevotellaceae;Other 0.436 0.015 0.112 1 .000 NA 0.081 0.147 0.003
Prosthecobacter;Other NA 1 .000 NA NA 1 .000 1 .000 NA ns
Proteobacteria;Other 0.013 0.028 0.171 1 .000 0.860 0.012 0.011 0.000
Proteus mirabilis NA NA NA 1.000 NA NA NA ns
Pseudomonas mendocina 0.365 0.015 0.130 0.648 0.024 0.067 0.011 0.000
Pseudomonas stutzeri 0.035 0.015 0.040 0.274 0.024 0.012 0.011 0.000
Pseudomonas;Other NA 0.231 0.593 NA 0.041 0.232 0.019 0.000
Ralstonia;Other NA 0.015 0.146 0.342 0.041 0.372 NA 0.000
Rheinheimera sp 09BSZB 9 NA 0.613 0.593 NA 0.211 0.308 0.297 0.001
Rheinheimera sp TPS16 NA NA NA NA 0.657 NA NA ns
Rheinheimera;Other NA 0.403 0.415 0.648 0.224 0.349 0.028 0.000
Rhizobiaceae;Other NA NA NA 0.085 NA NA 0.566 0.032
Rhizobiales;Other NA NA NA NA 0.302 NA NA ns
Rhodobacteraceae;Other NA NA NA 0.068 NA NA NA 0.003 Rhodocyclaceae; Other NA NA NA NA 1 .000 NA NA ns
Roseburia intestinalis 0.013 0.079 0.107 0.650 0.471 0.012 0.011 0.000
Roseburia;Other 0.013 0.015 0.605 0.267 0.024 0.012 0.361 0.000
Rothia dentocariosa 0.013 0.385 0.026 0.358 0.070 0.023 0.011 0.000
Rothia mucilaginosa 0.013 0.185 0.026 0.026 0.024 0.877 0.011 0.000
Ruminococcaceae;Other 0.013 0.015 0.188 0.648 0.471 0.012 0.011 0.000
Ruminococcus bromii 0.013 0.015 NA 0.085 0.681 0.012 0.028 0.000
Ruminococcus callidus 0.013 0.015 NA NA NA 0.012 0.011 0.000
Ruminococcus flavefaciens NA 0.015 NA NA NA 0.475 0.011 0.000
Ruminococcus gnavus 0.013 0.140 0.026 0.026 NA 0.308 0.072 0.000
Ruminococcus obeum 0.013 0.015 0.442 1 .000 0.024 0.21 1 0.570 0.001
Ruminococcus sp 5 1 39BFAA 0.013 0.085 0.040 0.466 0.024 0.012 0.011 0.000
Ruminococcus sp DJF VR70k1 0.013 0.015 0.785 0.085 0.024 0.012 0.011 0.000
Ruminococcus sp ZS2 15 NA NA NA NA 0.024 0.147 NA 0.009
Ruminococcus torques 0.035 0.015 NA NA 0.471 0.012 0.036 0.000
Ruminococcus;Other 0.888 0.960 0.208 0.127 0.024 0.012 0.019 ns
Salmonella enterica 0.082 0.357 0.212 0.150 0.896 0.081 0.866 ns
Sarcina ventriculi 0.436 0.337 NA NA NA NA NA ns
Sarcina;Other 0.177 0.059 NA NA NA 0.228 NA 0.001
Scardovia wiggsiae 0.994 1 .000 NA 0.597 0.297 0.410 0.057 0.005
Shinella;Other NA NA NA 0.106 0.582 NA 0.133 ns
Shuttleworthia satelles 1.000 1 .000 NA NA 0.268 NA 0.284 0.006
Slackia isoflavoniconvertens 0.025 0.015 0.026 1.000 NA 0.012 0.011 0.000
Solobacterium moorei 0.966 0.602 0.138 1.000 0.202 0.012 0.011 0.000
Sphingobacterium mizutaii NA NA NA NA 0.359 NA 0.011 0.001
Sphingobacterium multivorum NA 1 .000 0.446 NA 0.167 0.643 0.044 0.001
Sphingobacterium sp P 7 NA NA NA NA 1 .000 NA NA ns
Sphingobium yanoikuyae NA 0.218 0.254 NA 0.084 0.333 0.099 0.000
Sphingomonadaceae;Other NA 0.624 1.000 NA 0.392 NA 1 .000 0.050
Sphingomonadales;Other NA NA NA 0.186 NA NA NA 0.039
Sphingomonas sp BAC84 0.598 0.015 0.239 0.597 0.471 1 .000 NA 0.001
Sphingomonas yabuuchiae NA NA NA NA 1 .000 NA NA ns
Sphingomonas;Other NA NA NA NA 0.398 0.333 0.566 0.014
Staphylococcaceae; Other NA 0.334 1.000 NA 0.443 NA 0.159 0.010
Staphylococcus aureus 0.177 0.015 0.026 0.327 0.024 0.033 0.011 0.000
Staphylococcus;Other 1.000 0.039 0.026 1.000 0.024 0.050 0.011 0.000
Stenotrophomonas maltophilia 1.000 0.377 0.112 0.648 0.024 0.249 0.011 0.000
Stenotrophomonas rhizophila 1.000 1 .000 0.107 1.000 0.094 0.343 0.011 0.000
Stenotrophomonas;Other 0.013 0.015 0.026 NA 0.589 NA 1.000 0.000
Streptococcaceae;Other 0.013 NA 0.040 0.127 0.250 NA NA ns
Streptococcus anginosus 0.013 1.000 0.194 1 .000 0.024 0.012 0.011 0.000
Streptococcus gordonii 0.013 0.079 0.220 0.355 0.041 0.012 0.011 0.000
Streptococcus mutans 0.107 1 .000 NA 1.000 0.353 1 .000 0.133 0.002
Streptococcus parasanguinis 0.013 0.015 0.026 0.373 0.196 0.012 0.044 0.000
Streptococcus peroris 0.013 0.059 0.026 0.288 0.231 0.176 0.011 0.000
Streptococcus sanguinis NA NA NA NA 1 .000 NA NA ns
Streptococcus thermophilus 0.013 0.049 0.026 1.000 0.024 0.459 0.011 0.000
Streptococcus;Other 0.013 0.266 0.475 0.889 0.024 0.769 0.011 0.001
Subdoligranulum variabile 0.683 0.015 0.077 0.026 1.000 0.012 0.011 0.000
Succinivibrio dextrinosolvens 1.000 1 .000 0.652 1.000 0.626 0.421 0.011 ns
Sutterella sp YIT 12072 NA 0.015 0.054 0.994 0.626 0.023 0.472 ns
Sutterella stercoricanis 0.679 0.015 NA 0.434 1.000 0.465 0.028 ns
Sutterella;Other 0.013 0.015 0.581 0.026 1.000 0.224 0.011 0.004
Thauera terpenica NA NA NA 0.127 NA NA NA 0.019
Thauera;Other NA NA NA 0.378 NA NA NA ns
Thermus igniterrae NA NA NA NA 1 .000 NA 0.057 ns
Treponema porcinum NA NA NA NA 1 .000 NA NA ns
Treponema succinifaciens NA 0.613 NA NA NA NA 0.287 ns
Turicibacter sanguinis 0.013 0.015 0.026 0.055 0.024 0.012 0.011 0.000 unclassified_Erysipelotrichaceae;Other 0.013 0.453 NA 0.085 NA 0.769 0.576 0.004
Ureaplasma;Other 0.679 NA 0.239 NA NA NA NA ns
Varibaculum cambriense NA NA NA NA 0.626 NA NA ns
Veillonella dispar 0.295 0.133 1.000 0.078 0.121 0.012 0.011 0.001
Veillonella parvula 0.426 0.951 1.000 0.026 0.242 0.012 0.011 ns
Veillonella sp S101 0.223 0.991 0.442 0.026 0.149 0.012 0.011 0.015 Veillonella;Other NA 0.180 NA NA 0.056 NA NA 0.000
Veillonellaceae;Other 0.013 0.236 0.026 0.731 0.121 0.983 0.011 0.027
Verrucomicrobium sp IRVE NA NA NA NA 0.626 NA NA ns
Vibrio cholerae 0.013 0.015 0.146 0.026 0.024 0.012 0.011 0.000
Vibrio;Other 0.025 0.208 0.288 0.026 0.041 0.041 0.011 0.000
Wautersiella falsenii NA NA 1.000 NA 0.613 NA 0.503 ns
Weissella cibaria 0.502 0.028 0.440 1 .000 0.847 0.067 0.927 ns
Weissella;Other 0.235 0.624 0.466 0.713 0.866 0.041 NA ns
X1760;Other 0.013 0.015 0.026 1.000 NA 0.012 0.011 0.000
Xanthomonadaceae;Other NA NA NA 0.055 0.582 NA 0.019 ns
Table 22. Correlation between bacterial species abundance in fecal microbiota of cholera patients and the samples' UniFrac distance to healthy adult Bangladeshi fecal communities
Figure imgf000165_0001
Corynebactenum tuberculosteancum 0.3303 0.0001 0.0002 -0.492
Acinetobacter baumannii 0.3287 0.0001 0.0002 -0.478
Paracoccus denitrificans 0.3256 0.0001 0.0002 -0.167
Fusobacterium;Other 0.3223 0.0001 0.0002 -0.879
Bacillales;Other 0.3213 0.0001 0.0002 -0.529
Veillonella sp S101 0.3209 0.0001 0.0002 -0.302
Acinetobacter junii 0.3192 0.0001 0.0002 -0.428
Leptotrichia sp PG10 0.3153 0.0001 0.0002 -0.130
Moraxellaceae;Other 0.31 12 0.0001 0.0002 -0.080
Bacillus;Other 0.3104 0.0001 0.0002 -0.196
Rheinheimera;Other 0.3101 0.0001 0.0002 -0.181
Corynebacterium;Other 0.3083 0.0001 0.0002 -0.488
Leptotrichia shahii 0.3053 0.0001 0.0002 -0.716
Corynebactenum mucifaciens 0.303 0.0001 0.0002 -0.326
Sphinqobacterium multivorum 0.3027 0.0001 0.0002 -0.130
Dialister invisus 0.3018 0.0001 0.0002 NA
Prevotella oulorum 0.2992 0.0001 0.0002 0.520
Pedobacter;Other 0.2984 0.0001 0.0002 0.071
Brachybacterium;Other 0.2972 0.0001 0.0002 -0.114
Brevundimonas terrae 0.2942 0.0001 0.0002 -0.202
Haemophilus;Other 0.2914 0.0001 0.0002 -0.217
Brevibacterium;Other 0.2885 0.0001 0.0002 -0.221
Neisseria;Other 0.285 0.0001 0.0002 -0.613
Bacteriovorax sp EPA 0.2827 0.0001 0.0002 -0.065
Helicobacter;Other 0.2808 0.0001 0.0002 NA
Solobacterium moorei 0.2799 0.0001 0.0002 -0.320
Sphinqobacterium mizutaii 0.2785 0.0001 0.0002 -0.123
Brachyspira aalborqi 0.2711 0.0001 0.0002 -0.101
Campylobacter;Other 0.2708 0.0001 0.0002 -0.476
Peptostreptococcus stomatis 0.2705 0.0001 0.0002 NA
Shuttleworthia satelles 0.2702 0.0001 0.0002 -0.085
Leptotrichia;591114 0.2688 0.0001 0.0002 #N/A lntrasporanqiaceae;Other 0.2666 0.0001 0.0002 -0.101
Anaerococcus octavius 0.2663 0.0001 0.0002 0.088
Succinivibrio dextrinosolvens 0.2643 0.0001 0.0002 NA
Streptococcus sanquinis 0.2634 0.0001 0.0002 NA
Leptotrichia buccalis 0.2627 0.0001 0.0002 -0.283
Porphyromonas catoniae 0.2616 0.0001 0.0002 -0.290
Luteimonas;Other 0.2611 0.0001 0.0002 NA
Micrococcus luteus 0.261 0.0001 0.0002 -0.123
Moraxella osloensis 0.2598 0.0001 0.0002 -0.188
Alcaliqenaceae;Other 0.2581 0.0001 0.0002 -0.11 1
Atopobium;Other 0.2545 0.0001 0.0002 -0.051
Brachybacterium paraconqlomeratum 0.2535 0.0001 0.0002 -0.109
Staphylococcaceae;Other 0.2517 0.0001 0.0002 -0.087
Streptococcus parasanquinis 0.2515 0.0001 0.0002 -0.663
Rothia mucilaqinosa 0.2512 0.0001 0.0002 -0.706
Dialister pneumosintes 0.2495 0.0001 0.0002 0.400
Bifidobacterium dentium 0.2485 0.0001 0.0002 -0.158
Actinomyces oris 0.246 0.0001 0.0002 -0.315
Aqrobacterium tumefaciens 0.2441 0.0002 0.0004 NA
Corynebactenum aurimucosum 0.2402 0.0002 0.0004 -0.164
Neisseria elonqata 0.2371 0.0002 0.0004 -0.158
Prevotella nanceiensis 0.2361 0.0003 0.0006 -0.268
Aqqreqatibacter seqnis 0.2357 0.0003 0.0006 -0.288
Brucellaceae; Other 0.2354 0.0003 0.0006 -0.086
Prevotella denticola 0.2351 0.0003 0.0006 -0.094
Flavobacteriaceae;Other 0.2321 0.0003 0.0006 NA
Fusobacterium mortiferum 0.2319 0.0003 0.0006 -0.786
Sphinqomonas;Other 0.2259 0.0005 0.0009 -0.072
Bacilli;Other 0.2258 0.0005 0.0009 -0.274
Arcobacter butzleri 0.2245 0.0005 0.0009 -0.072
Elusimicrobium minutum 0.2224 0.0006 0.0011 NA
Enterococcus;Other 0.2221 0.0006 0.0011 -0.460
Streptococcus thermophilus 0.2201 0.0007 0.0013 -0.721 Chryseobacterium hominis 0.2187 0.0007 0.0013 -0.123
Azospirillum;Other 0.2168 0.0008 0.0014 NA
Thermus igniterrae 0.2156 0.0009 0.0016 NA
Wautersiella falsenii 0.2092 0.0012 0.0021 NA
Corynebacterium matruchotii 0.208 0.0013 0.0022 -0.31 1
Lactobacillales; Other 0.2053 0.0015 0.0025 NA
Bacteriovorax sp F2 0.2043 0.0016 0.0027 NA
No vosphingobium; Other 0.1972 0.0023 0.0038 NA
Scardovia wiggsiae 0.1923 0.003 0.0048 -0.130
Anaeroglobus geminatus 0.1921 0.003 0.0048 -0.064
Sphingomonadaceae;Other 0.1907 0.0033 0.0053 -0.058
Lactobacillus fermentum 0.1902 0.0034 0.0054 -0.31 1
Actinobacillus porcinus 0.189 0.0036 0.0057 -0.230
Helicobacter winghamensis 0.1871 0.0039 0.0061 -0.210
Actinomyces georgiae 0.1851 0.0043 0.0067 NA
Veillonella parvula 0.1845 0.0045 0.0070 NA
Pantoea ananatis 0.1842 0.0045 0.0070 NA
Finegoldia magna 0.1773 0.0063 0.0096 NA
Actinomyces graevenitzii 0.1764 0.0066 0.0100 -0.380
Veillonella;Other 0.1764 0.0066 0.0100 -0.130
Rhizobiales;Other 0.1762 0.0067 0.0101 NA
Granulicatella adiacens 0.1754 0.0069 0.0104 -0.615
Pasteurellaceae;Other 0.173 0.0077 0.01 15 -0.568
Streptococcus; Other 0.1679 0.0098 0.0145 -0.562
Atopobium sp F0209 0.1667 0.0103 0.0152 -0.387
Lactobacillus delbrueckii 0.1658 0.0107 0.0157 -0.072
Bifidobacteriaceae;Other 0.1642 0.01 15 0.0166 NA
Afipia genosp 9 0.1623 0.0125 0.0179 NA
Streptococcus mutans 0.1613 0.0131 0.0187 -0.203
Prosthecobacter; Other 0.1592 0.0143 0.0204 NA
Rheinheimera sp 09BSZB 9 0.1567 0.016 0.0226 -0.130
Xanthomonadaceae;Other 0.1567 0.016 0.0226 NA
Sphingomonas yabuuchiae 0.1563 0.0163 0.0229 NA
Paracoccus;Other 0.1517 0.0197 0.0275 NA
Kocuria marina 0.1507 0.0205 0.0285 NA
Flavobacterium;Other 0.1486 0.0224 0.0309 NA
Peredibacter starrii 0.1486 0.0224 0.0309 NA
Streptococcaceae;Other 0.1469 0.024 0.0329 NA
Varibaculum cambriense 0.1452 0.0257 0.0350 NA
Verrucomicrobium sp IRVE 0.1452 0.0257 0.0350 NA
Lactobacillus sp KLDS 1 0716 0.1421 0.029 0.0393 -0.065
Faecalibacterium;Other -0.6171 0.0001 0.0002 0.819
Clostridiales;Other -0.614 0.0001 0.0002 0.832
Faecalibacterium prausnitzii -0.5937 0.0001 0.0002 0.860
Prevotella sp DJF B112 -0.5859 0.0001 0.0002 -0.341 p Bacteroides vulgatus -0.539 0.0001 0.0002 0.127
Ruminococcus callidus -0.5327 0.0001 0.0002 0.479
Ruminococcus;Other -0.5226 0.0001 0.0002 NA
Ruminococcus sp 5 1 39BFAA -0.522 0.0001 0.0002 0.671
Eubacterium coprostanoligenes -0.5186 0.0001 0.0002 0.530
Parabacteroides;Other -0.5137 0.0001 0.0002 0.429
1760;Other -0.5099 0.0001 0.0002 #N/A
Coprococcus;Other -0.5065 0.0001 0.0002 0.438
Clostridium disporicum -0.5027 0.0001 0.0002 0.699
I Clostridium glycolicum -0.5001 0.0001 0.0002 0.633 ¾ Clostridium bartlettii -0.4979 0.0001 0.0002 0.341 ■8
■SS Firmicutes;Other -0.4909 0.0001 0.0002 0.725 e Eubacterium eligens -0.4878 0.0001 0.0002 0.696 o o Eubacterium sp cL 10 1 3 -0.486 0.0001 0.0002 0.223
Turicibacter sanguinis -0.4765 0.0001 0.0002 0.612
Ruminococcaceae;Other -0.4708 0.0001 0.0002 0.686
Eubacterium ramulus -0.4649 0.0001 0.0002 0.353
Roseburia;Other -0.4528 0.0001 0.0002 0.674
Lachnospiraceae;Other -0.4518 0.0001 0.0002 0.492
Clostridium clostridioforme -0.4498 0.0001 0.0002 0.334 Eubacterium rectale -0.442 0.0001 0.0002 0.457
Bacteroides galacturonicus -0.4347 0.0001 0.0002 0.547
Eubacterium biforme -0.4315 0.0001 0.0002 0.712
Bacteroides;Other -0.4309 0.0001 0.0002 0.549
Slackia isoflavoniconvertens -0.4305 0.0001 0.0002 0.465
Bifidobacterium;Other -0.4284 0.0001 0.0002 0.987
Blautia sp M25 -0.4267 0.0001 0.0002 NA
Eubacterium hallii -0.4251 0.0001 0.0002 0.673
Ruminococcus sp ZS2 15 -0.4231 0.0001 0.0002 0.124
Eubacterium desmolans -0.4227 0.0001 0.0002 0.258
Dorea formicigenerans -0.4197 0.0001 0.0002 0.494
Prevotella copri -0.4098 0.0001 0.0002 0.776
Collinsella;Other -0.406 0.0001 0.0002 0.438
Prevotella sp DJF B116 -0.4032 0.0001 0.0002 0.633
Coprococcus eutactus -0.4031 0.0001 0.0002 0.526
Bacteroides ovatus -0.4028 0.0001 0.0002 0.460
Ruminococcus obeum -0.4012 0.0001 0.0002 0.301
Catenibacterium mitsuokai -0.3988 0.0001 0.0002 0.791
Subdoligranulum variabile -0.3979 0.0001 0.0002 0.498
Oscillibacter;Other -0.3949 0.0001 0.0002 0.304
Coriobacteriaceae;Other -0.3948 0.0001 0.0002 0.600
Bilophila wadsworthia -0.3942 0.0001 0.0002 0.312
Ruminococcus bromii -0.386 0.0001 0.0002 0.479
Clostridiaceae;Other -0.3807 0.0001 0.0002 0.641
Parabacteroides distasonis -0.3787 0.0001 0.0002 0.326
Collinsella aerofaciens -0.3754 0.0001 0.0002 0.584
Bacteroidales;Other -0.3752 0.0001 0.0002 0.700
Prevotella;Other -0.3565 0.0001 0.0002 -0.391
Clostridium;Other -0.3414 0.0001 0.0002 0.387
Dorea longicatena -0.334 0.0001 0.0002 0.358
Eubacterium;Other -0.3318 0.0001 0.0002 0.508
Clostridium hathewayi -0.3296 0.0001 0.0002 0.234
Alistipes shahii -0.3229 0.0001 0.0002 0.184
Mitsuokella multacida -0.3184 0.0001 0.0002 0.255
Clostridium sp SS2 1 -0.3175 0.0001 0.0002 0.122
Clostridium bolteae -0.3131 0.0001 0.0002 0.278
Oscillibacter sp G2 -0.3101 0.0001 0.0002 0.244
Ruminococcus flavefaciens -0.2997 0.0001 0.0002 0.133
Olsenella;Other -0.2933 0.0001 0.0002 0.092
Phascolarctobacterium faecium -0.2906 0.0001 0.0002 0.122
Alistipes sp NML05A004 -0.2905 0.0001 0.0002 0.092
Bacteroides uniformis -0.2895 0.0001 0.0002 0.087
Coprococcus comes -0.2874 0.0001 0.0002 0.378
Prevotella sp DJF LS16 -0.2814 0.0001 0.0002 0.092
Clostridium sp L2 50 -0.281 0.0001 0.0002 0.242
Bacteria;Other -0.269 0.0001 0.0002 0.672
Coprococcus catus -0.2667 0.0001 0.0002 0.199
Prevotella stercorea -0.2662 0.0001 0.0002 NA
Bacteroides caccae -0.2596 0.0001 0.0002 0.143
Roseburia intestinalis -0.2593 0.0001 0.0002 0.562
Blautia;Other -0.2567 0.0001 0.0002 0.669
Desulfovibrio;Other -0.2443 0.0002 0.0004 0.274
Butyrivibrio crossotus -0.2353 0.0003 0.0006 0.273
Clostridium paraputrificum -0.2223 0.0006 0.0011 0.521
Parabacteroides merdae -0.2213 0.0006 0.0011 0.092
Ruminococcus sp DJF VR70k1 -0.2213 0.0006 0.0011 0.762
Clostridium perfringens -0.221 1 0.0006 0.0011 0.286
Prevotella sp oral taxon 302 -0.2157 0.0009 0.0016 0.280
Sarcina ventriculi -0.2143 0.0009 0.0016 NA
Clostridium nexile -0.2127 0.001 0.0018 0.233
Bacteroides sp CIP103040 -0.2075 0.0013 0.0022 0.1 12
Bacteroidetes;Other -0.2055 0.0015 0.0025 0.377
Prevotella sp RS2 -0.2055 0.0015 0.0025 0.214
Enterococcus cecorum -0.2033 0.0017 0.0029 0.1 16
Bacteroides stercoris -0.2003 0.002 0.0033 0.208 Ruminococcus gnavus -0.1991 0.0021 0.0035 0.430
Clostridium bifermentans -0.199 0.0021 0.0035 NA
Phascolarctobacterium succinatutens -0.1973 0.0023 0.0038 0.383
Ruminococcus torques -0.1971 0.0023 0.0038 0.337
Alistipes finegoldii -0.1958 0.0025 0.0041 0.041
Megasphaera sp TrE9262 -0.1953 0.0026 0.0042 0.234
Olsenella sp A2 -0.1851 0.0043 0.0067 0.306 unclassified Erysipelotrichaceae;Other -0.1807 0.0054 0.0083 0.156
Bacteroides fragilis -0.1727 0.0078 0.01 16 0.445
Odoribacter splanchnicus -0.1717 0.0082 0.0122 0.129
Allisonella histaminiformans -0.1645 0.01 14 0.0166 0.177
Eubacterium siraeum -0.1645 0.01 14 0.0166 -0.216
Bacteroides plebeius -0.1638 0.01 17 0.0169 0.600
Megamonas; Other -0.1532 0.0185 0.0259 0.192
Sutterella;Other -0.1414 0.0299 0.0404 -0.393
Megasphaera;Other -0.1362 0.0365 0.0491 0.092
Figure imgf000169_0001
Figure imgf000169_0002
2,3,4,5-tetrahydropyridine-
EC2.3.1.1 1
2,6-dicarboxylate N- 7
succinyl transferase 1 .8 1.8 1 .5 1 .2 0.7 0.0000 0.0000 0.0000 0.0000 0.6294 -0.5308 0.0047
Glutamate N-
EC2.3.1.35
acetyltransferase 0.6 0.9 1 .0 1 .3 0.9 0.0149 0.0000 0.0000 0.0000 0.1313 0.5616 0.0047
Hydroxymethylglutaryl-CoA
EC2.3.3.10
synthase 1 .5 1.4 1 .4 1 .1 0.8 0.0000 0.0000 0.0000 0.0000 0.4787 -0.1355 0.4877
Amidophosphoribosyltransfe
EC2.4.2.14
rase 0.5 0.8 0.9 1 .2 1 .0 0.0000 0.0000 0.0000 0.0000 0.4790 0.5388 0.0047
O-acetyl homoseri ne
EC2.5.1.49 aminocarboxypropyltransfera
se 0.8 0.8 1 .1 0.9 0.9 0.0000 0.0000 0.4459 0.0721 0.6926 0.4012 0.0278
EC2.6.1.2 Alanine transaminase 0.8 1.0 1 .0 1 .1 0.9 0.0000 0.0000 0.0000 0.0000 0.0008 0.3441 0.0608
Valine-pyruvate
EC2.6.1.66
transaminase 2.3 2.2 1 .2 1 .8 0.8 0.0000 0.0000 0.0001 0.0000 0.0000 -0.3271 0.0761
LL-diaminopimelate
EC2.6.1.83
aminotransferase 1 .3 1.1 1 .0 1 .1 0.6 0.0000 0.0000 0.0002 0.0000 0.3916 -0.1562 0.4516
EC2.7.1.39 Homoserine kinase 0.6 0.6 0.9 0.8 0.9 0.0000 0.0000 0.0600 0.0006 0.0047 0.4455 0.0144
EC2.7.2.4 Aspartate kinase 1 .2 2.1 1 .9 1 .4 1 .0 0.0014 0.0000 0.0000 0.0000 0.7149 -0.1063 0.5965
EC3.4.-.- NA 0.7 0.9 1 .1 1.1 1.0 0.0000 0.0000 0.0000 0.0000 0.3482 0.3816 0.0349
EC3.4.11 .5 Prolyl aminopeptidase 0.6 0.8 1 .3 1 .2 0.8 0.0000 0.0000 0.0000 0.0421 1.0000 0.2676 0.1734
EC3.5.1.16 Acetylornithine deacetylase 1.4 1 .8 1.6 0.9 0.8 0.0000 0.0000 0.0000 0.0065 0.7787 -0.5281 0.0047
N-carbamoylputrescine
EC3.5.1.53
amidase 4.2 25.9 10.1 0.7 1 .0 0.0000 0.0000 0.0000 0.0003 1.0000 -0.1753 0.4062
EC3.5.4.1 Cytosine deaminase 0.9 1.1 1 .3 0.9 0.7 0.0000 0.0000 0.0000 0.0000 0.5662 -0.0406 0.8571
Imidazoleglycerol-phosphate
EC4.2.1.19
dehydratase 0.9 1.1 1 .0 1 .0 0.8 0.0000 0.0001 0.0000 0.0592 0.2813 -0.1084 0.5934
EC4.2.1.20 Tryptophan synthase 0.6 1.1 1 .3 0.6 0.7 0.0000 0.0001 0.0000 0.4848 0.0252 0.1446 0.4703
EC4.2.1.22 Cystathionine beta-synthase 0.8 1 .1 1.2 1.3 1 .0 0.0000 0.0000 0.0001 0.0006 0.0006 0.4040 0.0271
EC4.3.1.1 Aspartate ammonia-lyase 0.6 6.9 3.2 0.3 0.4 0.0000 0.0000 0.0000 0.3475 1.0000 -0.0492 0.8212
S-methyl-5-thioribose-1 -
EC5.3.1.23
phosphate isomerase 1 .3 1.9 1 .7 1 .4 1.1 0.0000 0.0000 0.0039 0.0000 0.0021 0.0127 0.9363
EC5.4.99.5 Chorismate mutase 0.8 0.8 0.7 0.8 1 .0 0.0000 0.0000 0.0000 0.0000 0.4790 0.1436 0.4703
EC6.3.1.2 Glutamate-ammonia ligase 1.2 1 .5 1.0 1.2 0.8 0.0000 0.0000 0.0000 0.0000 1.0000 -0.1954 0.3426
Asparagine synthase
EC6.3.5.4
(glutamine-hydrolyzing) 0.5 0.2 0.5 0.9 0.9 0.0000 0.0000 0.01 16 0.0000 0.9039 0.5007 0.0075
Carbamoyl-phosphate
EC6.3.5.5 synthase (glutamine- hydrolyzing) 2.1 3.3 2.3 1 .6 0.9 0.0000 0.0000 0.0000 0.0000 0.7708 -0.4774 0.0083
EC1.1.1.30
NA
3 4.4 1 .2 1.6 2.0 0.5 0.0000 0.0000 0.0001 0.0000 1.0000 -0.3922 0.0325
3-hydroxyacyl-CoA
EC1.1.1.35
dehydrogenase 0.6 0.3 0.7 0.8 1 .1 0.0000 1.0000 0.0000 0.0000 0.0000 0.3626 0.0464
EC1.1.1.37 Malate dehydrogenase 0.7 0.8 0.9 1 .0 0.8 0.0509 0.0000 0.0000 0.0000 0.6533 0.2915 0.1246
Acetaldehyde
EC1.2.1.10
dehydrogenase (acetylating) 1.7 1 .3 1.3 1.0 0.4 0.0000 0.0000 0.0000 0.0000 0.0135 -0.3376 0.0667
EC1 .2.1.2 Formate dehydrogenase 1.3 1.4 1 .8 1 .0 0.7 0.0000 0.2032 0.0000 0.0309 0.0000 -0.1387 0.4859
Aldehyde dehydrogenase
EC1 .2.1.3
(NAD(+)) 0.7 0.9 0.8 0.8 0.9 0.0000 0.2606 0.0000 0.2333 0.0136 0.2328 0.2424
Pyruvate dehydrogenase
EC1 .2.4.1
(acetyl-transferring) 0.4 0.2 0.4 0.6 0.8 0.0000 0.0000 0.0000 0.0000 0.0178 0.4347 0.0164
EC1.3.1.- NA 0.8 1.0 1 .0 0.9 0.8 0.0000 0.0000 0.0000 0.0000 0.0184 0.0728 0.7097
EC2.3.1.- NA 5.0 6.6 2.0 2.7 1.1 0.1 143 0.0000 0.1184 0.6833 0.0000 -0.3261 0.0761
Hydroxymethylglutaryl-CoA
EC2.3.3.10
synthase 1 .5 1.4 1 .4 1 .1 0.8 0.0000 0.0000 0.0000 0.0000 0.4787 -0.1355 0.4877
EC2.4.1.10 Levansucrase 4.5 9.2 2.3 1 .8 0.4 0.0000 0.6783 1.0000 0.0000 1.0000 -0.3879 0.0345
EC2.4.1.25 4-alpha-glucanotransferase 0.8 1 .5 3.8 1.9 0.9 0.0000 0.0000 0.4014 0.9467 0.4638 0.1739 0.4062
EC2.7.1.12 Gluconokinase 2.1 4.4 1 .5 1 .1 0.3 0.0000 0.2574 0.0421 0.0000 0.2079 -0.4349 0.0164
EC2.7.1.16 Ribulokinase 0.8 1.2 1 .1 1 .0 1 .0 0.0000 0.0000 0.0000 0.0004 0.4638 0.0952 0.6347
2-dehydro-3-
EC2.7.1.45
deoxygluconokinase 0.8 0.4 1 .5 0.9 0.8 0.0000 0.0000 0.0000 0.0441 0.0137 0.1231 0.5309
UDP-glucose-hexose-1 -
EC2.7.7.12 phosphate
uridylyl transferase 0.8 1.0 0.9 1 .0 0.8 0.0003 0.0000 0.0000 0.0000 0.0005 0.0197 0.9363
Mannose-1 -phosphate
EC2.7.7.13
guanylyltransferase 0.6 0.3 0.6 1 .0 1 .0 0.0000 0.0000 0.0000 0.0009 0.6704 0.4360 0.0164 Mannose-1 -phosphate
EC2.7.7.22
guanylyltransferase (GDP) 0.6 0.3 0.5 0.7 0.9 0.0000 0.0000 0.0000 0.0041 0.0415 0.2147 0.2925
EC2.7.8.30 NA 1 .0 0.4 0.5 0.6 0.4 0.0000 0.0392 0.0000 0.0000 0.1225 -0.1518 0.4648
Pyruvate, phosphate
EC2.7.9.1
dikinase 0.9 1.3 1 .5 1 .1 0.9 0.0000 0.0000 0.0000 0.0000 0.0930 0.0342 0.8815
EC2.7.9.2 Pyruvate, water dikinase 0.8 1.2 1 .1 1 .2 0.9 0.8899 0.0000 0.0000 0.0004 0.0000 0.1449 0.4703
Hydroxyacylglutathione
EC3.1 .2.6
hydrolase 1 .0 1.0 0.9 1 .1 1 .0 0.0000 0.0000 0.0571 0.8598 0.3280 0.0875 0.6592
EC3.1.3.- NA 1.3 1.6 1 .7 1.7 0.9 0.0000 0.0000 0.0000 0.0000 0.8949 -0.0748 0.7097
EC3.2.1.1 Alpha-amylase 1 .0 2.6 1 .8 1 .6 0.5 0.0562 0.0000 0.0000 1.0000 0.0032 -0.0129 0.9363
EC3.2.1.14 Chitinase 0.4 0.7 0.9 0.9 1 .0 0.0000 0.0000 0.0000 0.0674 1.0000 0.4655 0.0102
EC3.2.1.20 Alpha-glucosidase 2.4 4.6 2.0 1 .7 0.8 0.0000 0.0000 0.0000 0.0000 0.0000 -0.4073 0.0261
EC3.2.1.23 Beta-galactosidase 0.6 1.9 2.4 0.9 0.8 0.0000 0.0000 0.0000 0.0000 0.0000 0.0940 0.6347
EC3.2.1.26 Beta-fructofuranosidase 0.3 0.3 0.8 0.5 0.9 0.0000 0.0000 0.0000 0.0000 0.1481 0.4862 0.0075
Beta-N-
EC3.2.1.52
acetylhexosaminidase 0.5 0.2 0.7 0.8 1.1 0.0295 0.0000 0.0000 0.0000 0.3981 0.3615 0.0464
6-phospho-beta-
EC3.2.1.85
galactosidase 0.3 0.2 0.3 0.8 1 .0 0.0000 0.0000 0.0000 0.0000 0.0613 0.4896 0.0075
EC3.2.1.86 6-phospho-beta-glucosidase 0.9 1 .1 0.9 1.0 0.8 0.0000 0.0000 0.0000 0.0000 0.0000 -0.1238 0.5309
EC3.6.1.7 Acylphosphatase 0.7 1.0 1 .2 1 .0 0.9 0.0000 0.0000 0.2792 0.0005 0.2843 0.2195 0.2808
Tagatose-bisphosphate
EC4.1.2.40
aldolase 0.5 0.6 0.9 0.8 1 .2 0.0000 0.0000 0.0000 0.0000 0.0003 0.4712 0.0093
Phosphogluconate
EC4.2.1.12
dehydratase 0.5 0.7 0.7 1 .0 1 .0 0.0000 0.2685 0.0000 0.0448 0.0030 0.5441 0.0047
EC4.2.1.7 Altronate dehydratase 1 .0 1.1 1 .2 1 .1 0.9 0.0000 0.0000 0.1942 0.0393 0.7318 -0.0138 0.9363
3-hydroxybutyryl-CoA
EC5.1 .2.3
epimerase 0.6 0.5 0.8 0.9 0.8 0.0000 1.0000 0.0000 0.0000 0.0000 0.3819 0.0349
N-acylglucosamine 2-
EC5.1 .3.8
epimerase 1 .0 0.7 0.8 1 .0 1 .0 0.0000 0.0000 0.0000 0.0000 1.0000 0.1501 0.4660
EC5.3.1.14 L-rhamnose isomerase 0.6 0.9 1 .2 0.9 0.7 0.0000 0.0000 0.0001 0.0002 0.1062 0.1039 0.6008
EC5.3.1.25 L-fucose isomerase 1 .0 1.2 1 .0 1 .0 1 .2 0.3334 0.0000 0.0000 0.0026 0.1453 -0.0139 0.9363
Galactose-6-phosphate
EC5.3.1.26
isomerase 0.7 1.0 1 .0 0.9 0.8 0.0000 0.0000 0.0000 0.0002 0.0453 0.2018 0.3344
EC5.4.2.7 Phosphopentomutase 1 .2 0.5 0.8 1 .0 0.9 0.0000 0.0000 0.0000 0.0000 0.1024 -0.0206 0.9363
EC5.4.99.1 Maltose alpha-D-
6 glucosyltransferase 0.4 0.6 1 .0 1 .1 0.7 0.0000 0.0000 0.0000 0.0000 0.7244 0.4121 0.0244 lnositol-3-phosphate
EC5.5.1.4
synthase 0.6 0.8 0.8 0.9 0.9 0.0000 0.0000 0.0000 0.0000 0.0659 0.4126 0.0244
EC6.3.1.2 Glutamate-ammonia ligase 1.2 1 .5 1.0 1.2 0.8 0.0000 0.0000 0.0000 0.0000 1.0000 -0.1954 0.3426
EC6.4.1.2 Acetyl-CoA carboxylase 0.7 0.8 0.9 0.9 0.9 0.0000 0.0000 0.0000 0.0000 0.1821 0.2656 0.1749
EC2.1.1.- NA 1881.2 290.3 35.5 13.3 1380.7 0.0000 0.0000 0.0000 0.0000 0.0000 -0.4938 0.0075
Nicotinate
EC2.4.2.1 1
phosphoribosyl transferase 0.7 0.9 1 .3 1 .2 1.0 0.0000 0.0000 0.0001 0.0000 0.0030 0.1976 0.3426
Adenosylmethionine-8-
EC2.6.1.62 amino-7-oxononanoate
transaminase 0.3 0.3 0.9 0.9 1 .0 0.0000 0.0554 0.0000 0.1438 0.3047 0.5389 0.0047
EC2.7.4.16 Thiamine-phosphate kinase 81.3 1 1.4 2.2 1.6 61.1 0.0000 0.0000 0.0000 0.0000 0.6166 -0.3854 0.0349
EC2.7.7.63 Lipoate-protein ligase 0.7 0.8 0.8 0.9 0.9 0.0000 0.0000 0.4459 0.0000 0.0007 0.5012 0.0075
EC3.4.-.- NA 0.7 0.9 1 .1 1.1 1.0 0.0000 0.0000 0.0000 0.0000 0.3482 0.3816 0.0349
EC3.6.1.- NA 0.6 0.2 0.8 1.1 1.1 0.0000 0.0000 0.2258 0.0143 0.1579 0.5522 0.0047
EC4.1.2.25 Dihydroneopterin aldolase 0.7 1.0 1 .0 1 .0 0.9 0.0241 0.0000 0.0505 0.0000 1.0000 0.1750 0.4062
EC4.2.1.1 1
o-succinylbenzoate synthase
3 0.6 0.5 0.9 0.8 0.7 0.0040 0.0000 0.0000 0.0039 0.4056 0.3061 0.1035
EC4.99.1 .1 Ferrochelatase 0.7 0.6 0.7 0.8 0.9 0.0000 0.0031 0.0087 0.0000 0.2843 0.3563 0.0499
EC6.1.1.17 Glutamate-tRNA ligase 0.6 0.8 0.9 0.7 0.8 0.0000 0.0000 0.0000 0.0000 0.2131 0.1590 0.4516
EC6.3.1.5 NAD(+) synthase 0.7 0.8 0.9 1 .1 0.9 0.0000 0.0000 0.0000 0.0018 0.6387 0.4611 0.0103
Pantoate-beta-alanine
EC6.3.2.1
ligase 5.4 9.5 2.0 2.5 1 .0 0.0000 0.0000 0.0000 0.0018 0.0096 -0.4800 0.0083
NAD(+) synthase (glutamine-
EC6.3.5.1
hydrolyzing) 1 .2 2.1 1 .3 1 .3 0.8 0.0000 0.0000 0.0000 0.0000 0.1520 -0.2431 0.2283
EC6.1 .1.1 Tyrosine-tRNA ligase 0.7 1.0 0.9 0.9 0.8 0.0000 0.0001 0.0000 0.2745 1.0000 0.2399 0.2337
EC6.1.1.16 Cysteine-tRNA ligase 0.6 0.8 0.7 0.7 0.8 0.0000 0.0004 0.0000 0.0243 0.3930 0.1439 0.4703
EC6.1.1.17 Glutamate-tRNA ligase 0.6 0.8 0.9 0.7 0.8 0.0000 0.0000 0.0000 0.0000 0.2131 0.1590 0.4516
EC6.1.1.18 Glutamine-tRNA ligase 0.7 1.9 2.9 1 .1 1.2 0.0000 0.0000 0.01 11 0.0000 0.3139 0.1562 0.4516
EC6.1.1.21 Histidine-tRNA ligase 0.6 0.8 0.9 0.9 0.9 0.0000 0.3446 0.0000 0.0000 0.0048 0.4881 0.0075
EC6.1.1.22 Asparagine-tRNA ligase 0.7 0.8 1 .0 0.9 0.8 0.0000 0.0029 0.0901 0.0000 0.0703 0.2500 0.2122 EC6.1.1.24 Glutamate-tRNA(Gln) ligase 0.7 0.9 0.9 1.0 0.9 0.0000 0.0000 0.0000 0.0000 0.0295 0.3712 0.0416
EC6.1 .1.4 Leucine-tRNA ligase 0.5 0.3 0.5 0.9 1 .0 0.0000 0.0000 0.0000 0.0000 0.3546 0.4628 0.0103
Asparaginyl-tRNA synthase
EC6.3.5.6
(glutamine-hydrolyzing) 0.5 0.4 0.8 0.8 1.1 0.0000 0.0000 0.0000 0.0000 0.2595 0.4322 0.0167
Figure imgf000172_0001
D1 invasion 4 3 50.43% 8.51 % 4.47% 11.59% 5.58% 8.91 % 0.33%
D1 invasion 4 4 41.72% 9.22% 5.93% 14.56% 4.44% 11.49% 0.54%
D1 invasion 4 7 42.60% 4.79% 4.85% 9.85% 5.14% 19.10% 1.19%
D1 invasion 4 11 46.70% 6.36% 5.88% 7.62% 5.21 % 19.27% 0.96%
D1 invasion 4 12 42.68% 9.20% 6.62% 7.91 % 4.71 % 17.96% 0.82%
D1 invasion 4 13 40.68% 8.38% 7.76% 8.71 % 4.32% 17.70% 1.01 %
D1 invasion 4 14 39.69% 8.49% 12.31 % 8.38% 5.43% 12.26% 0.96%
D1 invasion 4 15 33.88% 11.47% 13.42% 11.89% 6.74% 13.27% 0.59%
D1 invasion 4 16 20.32% 9.34% 19.63% 12.49% 7.78% 16.73% 0.33%
D1 invasion 4 17 25.35% 7.58% 13.31 % 11.25% 6.05% 22.12% 0.92%
D1 invasion 4 18 28.54% 6.71 % 12.92% 10.25% 5.09% 22.76% 0.56%
D1 invasion 4 19 32.47% 5.99% 13.27% 9.70% 4.63% 17.77% 1.07%
D1 invasion 4 21 36.80% 6.39% 16.06% 9.59% 4.07% 16.10% 0.26%
D1 invasion 4 25 32.94% 5.32% 14.27% 9.38% 1.63% 19.03% 0.19%
D1 invasion 4 28 35.31 % 4.82% 15.15% 11.37% 2.64% 16.14% 0.62%
D1 invasion 5 1 40.24% 15.55% 2.78% 19.56% 4.19% 7.33% 0.17%
D1 invasion 5 3 54.84% 6.67% 2.94% 17.70% 4.48% 7.43% 0.35%
D1 invasion 5 4 47.75% 5.82% 5.27% 10.37% 7.03% 11.37% 0.59%
D1 invasion 5 7 43.60% 6.42% 6.07% 10.45% 5.85% 13.78% 1.04%
D1 invasion 5 11 45.52% 9.75% 7.19% 8.69% 5.00% 11.72% 2.06%
D1 invasion 5 12 44.42% 9.52% 10.92% 10.34% 5.17% 10.88% 0.60%
D1 invasion 5 13 38.34% 10.18% 10.06% 10.11 % 5.34% 13.84% 0.94%
D1 invasion 6 4 44.49% 5.47% 5.10% 9.55% 6.74% 14.83% 0.31 %
D1 invasion 6 7 43.82% 6.48% 5.08% 12.20% 5.06% 14.68% 0.37%
D1 invasion 6 11 42.54% 6.52% 7.44% 9.52% 4.90% 16.32% 2.06%
D1 invasion 6 12 45.23% 7.75% 6.81 % 9.35% 4.90% 16.21 % 0.81 %
D1 invasion 6 13 37.94% 6.86% 7.41 % 9.51 % 6.55% 15.19% 2.46%
D1 invasion 6 14 37.84% 7.77% 9.52% 11.64% 10.42% 14.86% 0.48%
D1 invasion 6 15 35.54% 7.85% 9.83% 9.48% 7.06% 14.64% 1.97%
D1 invasion 6 16 15.30% 8.32% 19.04% 13.69% 11.01 % 21.80% 0.37%
D1 invasion 6 17 24.00% 6.26% 17.26% 11.93% 6.62% 19.60% 0.82%
D1 invasion 6 18 30.30% 5.56% 14.01 % 10.11 % 4.25% 20.63% 0.39%
D1 invasion 6 19 32.54% 6.36% 15.80% 10.66% 4.86% 19.71 % 0.13%
D1 invasion 6 21 34.77% 6.88% 16.81 % 10.25% 6.56% 17.09% 0.17%
D1 invasion 6 25 35.40% 5.89% 14.34% 9.38% 4.29% 19.76% 0.38%
D1 invasion 6 28 36.30% 5.47% 16.04% 10.41 % 1.68% 18.48% 0.44%
D14invasion 1 1 35.91 % 26.51 % 0.75% 10.36% 7.94% 7.63% 0.56%
D14invasion 1 2 46.85% 15.62% 2.62% 17.83% 1.97% 6.38% 0.18%
D14invasion 1 3 50.37% 10.69% 4.49% 10.97% 2.15% 11.80% 0.15%
D14invasion 1 4 43.53% 8.05% 7.31 % 12.49% 3.28% 11.19% 0.26%
D14invasion 1 7 43.83% 7.28% 8.32% 9.87% 2.85% 17.29% 0.47%
D14invasion 1 11 41.04% 8.25% 9.10% 10.86% 5.62% 13.64% 1.01 %
D14invasion 1 12 45.29% 9.92% 11.61 % 9.49% 6.05% 13.09% 0.21 %
D14invasion 1 13 44.37% 8.18% 12.41 % 8.71 % 4.64% 14.29% 0.31 %
D14invasion 1 14 45.20% 7.21 % 13.31 % 9.16% 5.54% 13.33% 0.27%
D14invasion 1 15 41.13% 7.49% 14.29% 8.60% 5.05% 15.83% 0.34%
D14invasion 1 16 28.88% 10.95% 11.14% 15.91 % 4.83% 14.59% 0.81 %
D14invasion 1 17 35.92% 6.53% 9.69% 16.28% 6.23% 12.98% 0.68%
D14invasion 1 18 46.69% 4.65% 6.10% 10.58% 8.28% 12.14% 0.54%
D14invasion 1 19 47.28% 3.69% 7.85% 5.43% 9.85% 14.17% 0.64%
D14invasion 1 21 46.25% 2.52% 6.91 % 7.53% 4.52% 21.60% 0.43%
D14invasion 1 25 41.60% 4.50% 15.85% 10.64% 5.60% 13.57% 0.15%
D14invasion 1 28 37.07% 4.70% 13.85% 9.86% 3.34% 21.00% 0.34%
D14invasion 2 1 45.03% 12.28% 1.65% 14.64% 7.41 % 8.82% 0.64%
D14invasion 2 2 45.45% 16.10% 3.77% 17.66% 2.27% 6.49% 0.24%
D14invasion 2 3 47.44% 11.72% 5.22% 12.22% 1.76% 10.67% 0.27%
D14invasion 2 4 45.60% 8.41 % 7.34% 12.57% 2.64% 12.87% 0.22%
D14invasion 2 7 42.00% 7.95% 7.85% 9.11 % 3.98% 17.07% 0.67%
D14invasion 2 11 47.26% 7.79% 10.59% 8.69% 6.07% 11.21 % 0.45%
D14invasion 2 12 47.34% 3.90% 10.70% 9.64% 4.39% 13.46% 0.57%
D14invasion 2 13 43.55% 4.43% 11.75% 9.92% 5.95% 12.54% 0.86%
D14invasion 3 1 44.41 % 14.52% 1.76% 14.48% 6.46% 11.12% 0.37%
D14invasion 3 2 51.66% 15.83% 2.53% 14.07% 2.16% 7.50% 0.14%
D14invasion 3 3 48.22% 12.97% 5.49% 13.48% 2.84% 8.91 % 0.20%
D14invasion 3 11 43.53% 9.84% 9.98% 10.25% 4.85% 10.91 % 0.98% D14invasion 3 12 43.27% 10.75% 12.53% 9.79% 4.28% 12.73% 0.20%
D14invasion 3 13 42.55% 8.79% 10.35% 8.27% 3.72% 20.50% 0.44%
D14invasion 3 14 40.81 % 8.53% 12.97% 7.74% 5.68% 16.94% 0.47%
D14invasion 3 15 39.01 % 7.47% 12.66% 8.29% 4.44% 22.35% 0.21 %
D14invasion 3 16 37.77% 6.02% 11 .21 % 7.88% 2.93% 25.83% 0.31 %
D14invasion 3 17 39.81 % 6.37% 13.37% 8.84% 4.17% 18.52% 0.24%
D14invasion 3 18 43.57% 4.40% 10.67% 9.12% 6.76% 16.60% 0.35%
D14invasion 3 19 46.20% 3.24% 6.24% 7.63% 7.49% 16.19% 0.74%
D14invasion 3 21 42.41 % 1.94% 7.97% 8.90% 9.00% 17.77% 0.45%
D14invasion 3 25 35.52% 3.96% 12.86% 10.60% 4.37% 23.09% 0.41 %
D14invasion 3 28 33.25% 5.90% 16.15% 11 .26% 2.65% 21.89% 0.20%
D14invasion 4 1 35.85% 12.52% 1.96% 17.65% 2.89% 4.78% 1 .06%
D14invasion 4 2 46.23% 14.12% 3.80% 17.60% 2.20% 6.45% 0.27%
D14invasion 4 3 49.19% 1 1.71 % 5.16% 14.42% 2.44% 8.23% 0.31 %
D14invasion 4 4 43.59% 8.83% 6.43% 13.67% 3.16% 13.70% 0.33%
D14invasion 4 7 43.31 % 8.71 % 7.49% 11 .04% 2.97% 15.53% 0.39%
D14invasion 4 1 1 45.61 % 10.14% 10.05% 10.26% 4.89% 1 1.03% 0.58%
D14invasion 4 12 45.01 % 9.37% 9.35% 9.67% 5.1 1 % 12.09% 0.45%
D14invasion 4 13 39.30% 8.58% 10.40% 9.52% 4.10% 14.97% 0.76%
D14invasion 4 14 41 .36% 5.28% 10.50% 8.69% 3.93% 21.59% 0.43%
D14invasion 4 15 38.42% 4.75% 12.50% 9.50% 5.07% 19.32% 0.63%
D14invasion 4 16 34.70% 6.91 % 16.65% 9.65% 5.50% 16.89% 0.34%
D14invasion 4 17 37.31 % 3.76% 18.12% 9.21 % 5.92% 15.18% 0.42%
D14invasion 4 18 42.93% 3.13% 10.52% 9.68% 7.74% 16.09% 0.59%
D14invasion 4 19 45.19% 3.27% 8.62% 8.13% 7.58% 15.10% 0.66%
D14invasion 4 21 46.07% 3.72% 11 .57% 8.61 % 4.47% 16.55% 0.1 1 %
D14invasion 4 25 35.75% 3.92% 10.07% 9.60% 4.79% 20.84% 0.56%
D14invasion 4 28 32.70% 5.37% 13.07% 9.94% 6.38% 18.43% 0.73%
D14invasion 5 4 45.02% 10.35% 6.05% 13.62% 2.65% 10.70% 0.29%
D14invasion 5 7 43.01 % 8.15% 6.79% 10.38% 2.19% 18.09% 0.52%
D14invasion 5 1 1 46.58% 9.38% 9.52% 8.96% 3.02% 15.06% 0.68%
D14invasion 5 12 38.51 % 9.64% 8.66% 7.65% 3.90% 23.56% 0.99%
D14invasion 5 13 38.32% 7.32% 9.20% 8.47% 5.44% 20.57% 2.36%
D14invasion 5 14 35.30% 7.52% 12.70% 10.81 % 7.30% 17.25% 0.93%
D14invasion 5 15 35.63% 7.14% 12.46% 8.74% 5.96% 21.53% 0.63%
D14invasion 5 16 36.52% 5.28% 14.27% 7.87% 6.85% 21.60% 0.56%
D14invasion 5 17 38.59% 3.14% 14.1 1 % 7.45% 5.56% 20.68% 0.60%
D14invasion 5 18 40.69% 2.78% 14.36% 9.01 % 4.31 % 19.08% 0.31 %
D14invasion 5 19 42.87% 2.63% 12.97% 8.54% 2.26% 17.91 % 0.20%
D14invasion 5 21 44.13% 2.05% 8.21 % 7.26% 3.73% 21.21 % 0.50%
D14invasion 5 25 35.94% 3.37% 9.58% 6.89% 4.81 % 24.06% 0.96%
D14invasion 5 28 33.66% 4.45% 11 .66% 7.14% 4.36% 25.19% 0.60%
Figure imgf000174_0001
D1 invasion 1 25 0.28% 0.39% 1.44% 0.00% 5.82% 2.09% 1.00% 0.06%
D1 invasion 1 28 0.40% 0.40% 1.93% 0.00% 6.64% 2.14% 1.50% 0.07%
D1 invasion 2 1 0.04% 0.74% 0.01 % 0.00% 2.89% 1.06% 5.77% 34.75%
D1 invasion 2 2 0.86% 0.05% 0.18% 0.00% 4.40% 8.48% 1.41 % 15.44%
D1 invasion 2 3 0.50% 0.01 % 0.16% 0.00% 4.16% 1.98% 0.08% 17.17%
D1 invasion 2 4 0.55% 0.00% 0.18% 0.00% 9.64% 2.41 % 0.70% 10.07%
D1 invasion 2 7 0.55% 0.00% 0.80% 0.00% 8.87% 3.58% 0.63% 2.32%
D1 invasion 2 11 0.48% 0.09% 0.87% 0.00% 1.57% 1.88% 0.81 % 0.67%
D1 invasion 2 12 0.45% 0.31 % 1.03% 0.00% 1.97% 1.80% 1.26% 0.39%
D1 invasion 2 13 0.52% 0.37% 2.11 % 0.00% 2.69% 2.07% 1.07% 0.27%
D1 invasion 2 14 0.67% 0.58% 1.63% 0.00% 3.02% 3.00% 1.09% 0.23%
D1 invasion 2 15 0.90% 0.92% 2.37% 0.00% 3.28% 3.44% 1.74% 0.16%
D1 invasion 2 16 0.44% 0.72% 1.90% 0.00% 3.78% 2.33% 1.87% 4.71 %
D1 invasion 2 17 0.35% 0.54% 1.57% 0.00% 4.99% 1.46% 2.92% 1.16%
D1 invasion 2 18 0.32% 0.32% 1.30% 0.00% 4.00% 0.97% 2.45% 0.87%
D1 invasion 2 19 0.36% 0.51 % 1.02% 0.00% 4.71 % 1.57% 1.25% 0.38%
D1 invasion 2 21 0.41 % 0.53% 1.73% 0.00% 4.07% 2.83% 1.14% 0.12%
D1 invasion 2 25 0.33% 0.40% 2.95% 0.00% 4.68% 1.93% 1.69% 0.04%
D1 invasion 2 28 0.61 % 0.68% 1.99% 0.00% 5.75% 3.13% 2.03% 0.06%
D1 invasion 3 1 0.05% 1.13% 0.06% 0.00% 9.53% 1.53% 2.58% 48.83%
D1 invasion 3 2 0.45% 0.17% 0.09% 0.00% 3.97% 1.84% 2.61 % 50.51 %
D1 invasion 3 3 0.85% 0.00% 0.25% 0.00% 5.08% 1.72% 0.34% 58.83%
D1 invasion 3 4 0.60% 0.00% 0.31 % 0.00% 5.79% 1.82% 0.82% 13.11 %
D1 invasion 3 7 0.22% 0.00% 0.72% 0.00% 7.88% 3.53% 0.31 % 1.52%
D1 invasion 3 11 0.20% 0.08% 0.92% 0.00% 2.74% 2.60% 0.39% 1.05%
D1 invasion 3 12 0.46% 0.33% 1.90% 0.00% 2.24% 2.30% 0.72% 0.82%
D1 invasion 3 13 1.08% 0.71 % 2.10% 0.00% 3.25% 4.76% 1.38% 0.43%
D1 invasion 3 14 0.64% 0.39% 1.69% 0.00% 4.69% 2.27% 1.01 % 0.70%
D1 invasion 3 15 1.03% 0.89% 2.59% 0.00% 5.19% 3.96% 1.43% 0.57%
D1 invasion 3 16 0.53% 0.46% 2.50% 0.00% 6.35% 2.84% 1.14% 0.13%
D1 invasion 3 17 0.50% 0.27% 0.88% 0.00% 7.55% 1.93% 0.54% 0.33%
D1 invasion 3 18 0.34% 0.17% 2.09% 0.00% 6.14% 0.87% 0.88% 0.13%
D1 invasion 3 19 0.54% 0.43% 1.64% 0.00% 5.15% 2.41 % 0.99% 0.12%
D1 invasion 3 21 0.42% 0.25% 1.68% 0.00% 5.41 % 2.08% 0.90% 0.10%
D1 invasion 3 25 0.22% 0.29% 1.25% 0.00% 5.22% 1.03% 0.68% 0.09%
D1 invasion 3 28 0.42% 0.23% 1.68% 0.00% 5.13% 2.48% 0.60% 0.04%
D1 invasion 4 1 0.06% 1.65% 0.01 % 0.00% 5.03% 2.41 % 5.22% 51.81 %
D1 invasion 4 3 0.61 % 0.00% 0.21 % 0.00% 6.40% 2.77% 0.18% 29.25%
D1 invasion 4 4 0.48% 0.65% 0.35% 0.00% 6.64% 2.66% 1.32% 7.25%
D1 invasion 4 7 0.82% 0.00% 1.10% 0.00% 4.92% 4.86% 0.79% 0.88%
D1 invasion 4 11 0.29% 0.20% 1.15% 0.00% 2.88% 3.03% 0.45% 0.44%
D1 invasion 4 12 0.42% 0.26% 2.22% 0.00% 2.61 % 3.79% 0.78% 0.47%
D1 invasion 4 13 0.70% 0.44% 2.84% 0.00% 3.29% 3.29% 0.88% 0.45%
D1 invasion 4 14 0.69% 0.75% 2.69% 0.00% 4.07% 3.09% 1.19% 0.61 %
D1 invasion 4 15 0.26% 0.23% 1.96% 0.00% 4.76% 0.97% 0.56% 0.20%
D1 invasion 4 16 0.48% 0.51 % 4.04% 0.00% 4.09% 2.13% 2.12% 0.16%
D1 invasion 4 17 0.59% 0.31 % 3.26% 0.00% 6.15% 2.46% 0.65% 0.15%
D1 invasion 4 18 0.39% 0.32% 4.24% 0.00% 5.48% 2.12% 0.63% 0.08%
D1 invasion 4 19 0.76% 0.88% 2.66% 0.00% 4.26% 5.42% 1.11 % 0.13%
D1 invasion 4 21 0.31 % 0.31 % 2.86% 0.00% 4.43% 1.88% 0.95% 0.06%
D1 invasion 4 25 0.31 % 0.23% 7.09% 0.00% 6.50% 2.11 % 1.00% 0.03%
D1 invasion 4 28 0.45% 0.35% 3.04% 0.00% 7.01 % 2.36% 0.74% 0.05%
D1 invasion 5 1 0.12% 1.03% 0.05% 0.00% 3.85% 1.27% 3.87% 38.48%
D1 invasion 5 3 0.39% 0.00% 0.08% 0.00% 2.73% 2.30% 0.10% 13.45%
D1 invasion 5 4 0.57% 0.00% 0.13% 0.00% 8.49% 2.30% 0.29% 23.28%
D1 invasion 5 7 0.54% 0.00% 2.06% 0.00% 5.48% 2.97% 1.74% 0.84%
D1 invasion 5 11 0.76% 0.39% 2.05% 0.00% 2.28% 3.64% 0.94% 0.47%
D1 invasion 5 12 0.29% 0.34% 2.01 % 0.00% 3.16% 1.42% 0.93% 0.31 %
D1 invasion 5 13 0.70% 0.67% 2.02% 0.00% 3.35% 3.07% 1.39% 0.22%
D1 invasion 6 4 0.48% 0.00% 0.01 % 0.00% 7.26% 3.29% 2.49% 2.49%
D1 invasion 6 7 0.31 % 0.00% 0.38% 0.00% 6.00% 3.96% 1.66% 1.24%
D1 invasion 6 11 0.61 % 0.00% 0.45% 0.00% 4.17% 3.89% 1.58% 0.52%
D1 invasion 6 12 0.39% 0.00% 0.22% 0.00% 3.85% 3.24% 1.24% 0.68%
D1 invasion 6 13 1.18% 0.00% 0.35% 0.00% 4.50% 6.26% 1.79% 0.48%
D1 invasion 6 14 0.16% 0.00% 0.05% 0.00% 6.23% 0.81 % 0.21 % 0.18% D1 invasion 6 15 1.19% 0.00% 0.48% 0.00% 5.07% 4.86% 2.02% 1.00%
D1 invasion 6 16 0.45% 0.33% 0.42% 0.00% 5.66% 2.57% 1.04% 0.22%
D1 invasion 6 17 0.89% 0.74% 0.97% 0.00% 5.78% 3.52% 1.62% 0.08%
D1 invasion 6 18 0.66% 0.71 % 0.57% 0.00% 6.44% 4.87% 1.50% 0.07%
D1 invasion 6 19 0.37% 0.38% 0.65% 0.00% 4.37% 2.43% 1.73% 0.05%
D1 invasion 6 21 0.27% 0.31 % 0.52% 0.00% 3.99% 1.97% 0.41 % 0.11 %
D1 invasion 6 25 0.39% 0.18% 1.35% 0.00% 5.20% 2.30% 1.15% 0.05%
D1 invasion 6 28 0.30% 0.29% 1.36% 0.00% 6.95% 1.56% 0.72% 0.04%
D14invasion 1 1 0.19% 2.29% 0.00% 0.07% 2.43% 1.32% 4.05% 2.11 %
D14invasion 1 2 0.10% 1.09% 0.31 % 0.00% 3.93% 1.00% 2.12% 0.00%
D14invasion 1 3 0.06% 1.01 % 0.42% 0.00% 5.72% 1.06% 1.11 % 0.01 %
D14invasion 1 4 0.14% 1.44% 3.65% 0.00% 4.56% 1.10% 3.01 % 0.00%
D14invasion 1 7 0.14% 0.17% 1.34% 0.00% 6.76% 1.13% 0.55% 0.00%
D14invasion 1 11 0.46% 1.03% 1.18% 0.00% 1.89% 4.03% 1.90% 0.00%
D14invasion 1 12 0.12% 0.32% 0.62% 0.00% 1.58% 1.09% 0.60% 0.00%
D14invasion 1 13 0.27% 0.48% 1.26% 0.00% 2.41 % 1.58% 1.09% 0.00%
D14invasion 1 14 0.20% 0.34% 0.53% 0.00% 3.36% 1.11 % 0.44% 0.00%
D14invasion 1 15 0.26% 0.57% 0.65% 0.00% 2.91 % 1.20% 1.68% 4.57%
D14invasion 1 16 0.47% 0.53% 0.63% 0.00% 2.78% 3.13% 5.35% 6.29%
D14invasion 1 17 0.74% 0.32% 0.34% 0.00% 4.52% 3.50% 2.29% 9.63%
D14invasion 1 18 0.71 % 0.22% 0.38% 0.00% 6.57% 2.00% 1.15% 5.27%
D14invasion 1 19 0.81 % 0.22% 0.32% 0.00% 6.28% 2.47% 0.99% 1.51 %
D14invasion 1 21 0.75% 0.23% 0.91 % 0.00% 4.03% 3.61 % 0.72% 0.21 %
D14invasion 1 25 0.26% 0.40% 0.50% 0.00% 5.20% 1.29% 0.43% 0.21 %
D14invasion 1 28 0.38% 0.39% 1.00% 0.00% 5.15% 1.57% 1.35% 0.05%
D14invasion 2 1 0.07% 2.01 % 0.00% 0.00% 1.39% 0.63% 5.41 % 0.00%
D14invasion 2 2 0.08% 0.94% 0.10% 0.00% 3.64% 0.66% 2.60% 0.02%
D14invasion 2 3 0.24% 1.75% 0.38% 0.00% 4.22% 2.07% 2.05% 0.00%
D14invasion 2 4 0.13% 1.34% 1.66% 0.00% 4.27% 0.82% 2.12% 0.00%
D14invasion 2 7 0.32% 0.37% 1.23% 0.00% 6.23% 2.01 % 1.22% 0.00%
D14invasion 2 11 0.30% 1.01 % 1.41 % 0.00% 1.82% 1.85% 1.54% 0.00%
D14invasion 2 12 0.20% 0.68% 2.44% 0.00% 2.10% 3.57% 1.01 % 0.00%
D14invasion 2 13 0.68% 1.24% 1.61 % 0.00% 2.79% 3.21 % 1.47% 0.00%
D14invasion 3 1 0.05% 1.33% 0.00% 0.00% 0.99% 0.50% 4.01 % 0.01 %
D14invasion 3 2 0.10% 0.68% 0.30% 0.00% 2.63% 0.69% 1.71 % 0.00%
D14invasion 3 3 0.06% 0.85% 0.55% 0.00% 4.27% 0.80% 1.37% 0.00%
D14invasion 3 11 0.36% 0.66% 1.42% 0.00% 3.58% 2.09% 1.54% 0.00%
D14invasion 3 12 0.25% 0.31 % 1.80% 0.00% 1.92% 0.96% 1.23% 0.00%
D14invasion 3 13 0.31 % 0.43% 0.64% 0.00% 1.87% 1.56% 0.58% 0.00%
D14invasion 3 14 0.26% 0.67% 0.68% 0.00% 2.68% 1.65% 0.92% 0.00%
D14invasion 3 15 0.15% 0.24% 0.51 % 0.00% 3.52% 0.75% 0.38% 0.20%
D14invasion 3 16 0.27% 0.38% 0.21 % 0.00% 4.60% 1.61 % 0.97% 0.29%
D14invasion 3 17 0.40% 0.57% 0.82% 0.00% 3.39% 2.01 % 1.50% 0.22%
D14invasion 3 18 0.50% 0.25% 0.60% 0.00% 4.66% 1.58% 0.94% 0.52%
D14invasion 3 19 0.95% 0.11 % 0.22% 0.00% 5.77% 4.55% 0.67% 1.64%
D14invasion 3 21 0.74% 0.31 % 1.29% 0.00% 4.51 % 3.95% 0.77% 0.19%
D14invasion 3 25 0.53% 0.28% 1.29% 0.00% 3.87% 2.19% 1.02% 0.03%
D14invasion 3 28 0.25% 0.24% 0.96% 0.00% 5.10% 1.04% 1.10% 0.06%
D14invasion 4 1 0.09% 8.35% 0.01 % 0.00% 0.73% 4.11 % 10.00% 0.01 %
D14invasion 4 2 0.10% 1.16% 0.94% 0.00% 2.93% 1.08% 3.14% 0.01 %
D14invasion 4 3 0.11 % 0.93% 0.79% 0.00% 3.61 % 1.20% 1.88% 0.00%
D14invasion 4 4 0.12% 1.08% 0.72% 0.00% 5.40% 1.34% 1.63% 0.00%
D14invasion 4 7 0.21 % 0.20% 1.63% 0.00% 6.41 % 0.79% 1.32% 0.00%
D14invasion 4 11 0.22% 0.80% 1.24% 0.00% 2.37% 1.36% 1.46% 0.00%
D14invasion 4 12 0.22% 0.59% 3.06% 0.00% 2.56% 1.18% 1.33% 0.00%
D14invasion 4 13 0.52% 1.90% 1.97% 0.00% 2.79% 3.21 % 1.98% 0.00%
D14invasion 4 14 0.22% 0.48% 0.90% 0.00% 4.58% 1.12% 0.93% 0.00%
D14invasion 4 15 0.41 % 0.99% 0.92% 0.00% 4.12% 1.92% 1.47% 0.12%
D14invasion 4 16 0.22% 0.67% 0.63% 0.00% 3.45% 1.38% 2.99% 0.36%
D14invasion 4 17 0.42% 0.62% 1.86% 0.00% 3.56% 1.43% 2.19% 0.43%
D14invasion 4 18 0.50% 0.11 % 0.62% 0.00% 5.82% 1.29% 0.96% 1.10%
D14invasion 4 19 0.49% 0.11 % 0.40% 0.00% 7.90% 1.95% 0.59% 1.87%
D14invasion 4 21 0.22% 0.16% 0.43% 0.00% 6.51 % 0.99% 0.58% 0.27%
D14invasion 4 25 0.61 % 0.31 % 1.70% 0.00% 5.47% 3.11 % 3.26% 0.06%
D14invasion 4 28 0.66% 0.97% 1.63% 0.00% 4.86% 2.64% 2.63% 0.05% D14invasion 5 4 0.09% 1.06% 2.67% 0.00% 3.63% 0.76% 3.1 1 % 0.00%
D14invasion 5 7 0.14% 0.20% 3.50% 0.00% 5.37% 0.69% 0.97% 0.00%
D14invasion 5 1 1 0.17% 0.56% 2.20% 0.00% 1.74% 1 .30% 0.83% 0.00%
D14invasion 5 12 0.23% 0.45% 2.48% 0.00% 2.02% 1 .37% 0.53% 0.00%
D14invasion 5 13 0.36% 0.67% 2.07% 0.00% 3.02% 1 .37% 0.83% 0.00%
D14invasion 5 14 0.27% 0.45% 0.53% 0.00% 4.48% 2.01 % 0.45% 0.00%
D14invasion 5 15 0.27% 0.72% 1 .21 % 0.00% 3.08% 1 .79% 0.83% 0.08%
D14invasion 5 16 0.23% 0.31 % 1 .25% 0.00% 3.51 % 0.86% 0.89% 0.34%
D14invasion 5 17 0.40% 0.59% 2.04% 0.00% 4.09% 1 .93% 0.80% 0.39%
D14invasion 5 18 0.33% 0.19% 1 .99% 0.00% 4.86% 1 .32% 0.76% 0.10%
D14invasion 5 19 0.61 % 0.51 % 3.21 % 0.00% 3.52% 3.03% 1 .74% 0.10%
D14invasion 5 21 0.62% 0.16% 1 .95% 0.00% 5.16% 3.49% 1 .52% 0.09%
D14invasion 5 25 0.79% 0.54% 2.36% 0.00% 6.10% 3.12% 1 .48% 0.02%
D14invasion 5 28 0.43% 0.30% 2.88% 0.00% 6.41 % 1 .56% 1 .36% 0.05%
Table 26. Comparative analysis of in vivo V. cholerae and R. obeum transcriptional responses to co-colonization
a. Transcriptional responses of known V. cholerae C6706 AluxS strain (MM883) virulence genes
Virulence gene transcripts with mean RPKM >20 in at least one group of mice. P-values based on unpaired two-tailed Student's t-test.
Figure imgf000177_0001
b. V. cholerae C6706 AluxS strain (MM883) transcriptional responses to co-colonization with R. obeum
DESeq analysis of transcript differences d2 after gavage of V. cholerae into R. obeum mono-colonized mice vs d2 after mono- colonization of mice with V. cholerae
Figure imgf000177_0002
VC1143 5.4 0.0000 0.0033 ATP-dependent Clp protease adaptor protein ClpS
VC1445 5.2 0.0001 0.0039 sensor histidine kinase/response regulator
VC0365 5.0 0.0000 0.0002 bacterioferritin
VCA0827 4.6 0.0000 0.0001 pterin-4-alpha-carbinolamine dehydratase
VC1962 3.7 0.0009 0.0307 lipoprotein
VC0242 3.7 0.0000 0.0004 phosphomannomutase
VC1838 3.5 0.0005 0.0201 toIR membrane protein
VC2399 3.3 0.0000 0.0002 cell division protein FtsQ
VCA0947 3.3 0.0007 0.0270 spermidine n1-acetyltransferase
VC1914 3.3 0.0012 0.0404 integration host factor subunit beta
VC2587 3.3 0.0000 0.0001 30S ribosomal protein S17
VC0364 3.1 0.0010 0.0348 bacterioferritin-associated ferredoxin
VC1595 3.1 0.0015 0.0453 galactokinase
VCA0198 3.1 0.0000 0.0025 site-specific DNA-methy transferase, putative
VC1854 3.0 0.0014 0.0425 porin, putative
VCA0130 3.0 0.0013 0.0404 D-ribose transporter subunit RbsB
VC2078 2.8 0.0001 0.0058 ferrous iron transport protein A
VC1144 2.8 0.0000 0.0012 ATP-dependent Clp protease ATP-binding subunit
VC2679 2.8 0.0001 0.0073 50S ribosomal protein L31
VCA0867 2.8 0.0016 0.0485 outer membrane protein W
VC0243 2.8 0.0002 0.0097 GDP-mannose 4,6-dehydratase
VC0562 2.7 0.0000 0.0007 16S rRNA-processing protein RimM
VC0608 2.7 0.0001 0.0044 iron(lll) ABC transporter, periplasmic iron-compound-binding protein
VC0947 2.6 0.0001 0.0047 D-alanyl-D-alanine carboxypeptidase
VC1910 2.6 0.0004 0.0168 tRNA-(ms[2lio[6lA)-hydroxylase
VC2249 2.5 0.0005 0.0206 (3R)-hydroxymyristoyl-ACP dehydratase
VCA0566 2.5 0.0002 0.0106 transcriptional regulator
VC0750 2.4 0.0001 0.0073 hesB family protein
VC0241 2.4 0.0012 0.0404 mannose-1 -phosphate guanylyltransferase
VC2291 2.3 0.0003 0.0118 Na(+)-translocating NADH-quinone reductase subunit E
VC2634 2.2 0.0012 0.0404 fimbrial assembly protein PilM, putative
VC0046 2.2 0.0013 0.0420 peptide deformylase
VC2561 0.4 0.0001 0.0045 uroporphyrin-lll C-methyltransferase
VC2607 0.4 0.0004 0.0165 glutathione-regulated potassium-efflux system ancillary protein KefG
VC1409 0.4 0.0002 0.0098 multidrug resistance protein, putative
VC0719 0.4 0.0012 0.0404 DNA-binding response regulator PhoB
VC2182 0.4 0.0000 0.0009 4-diphosphocytidyl-2-C-methyl-D-erythritol kinase
VCA0758 0.4 0.0003 0.0151 arginine transporter permease subunit ArtQ
VC1391 0.4 0.0002 0.0108 multidrug transporter, putative
VC2759 0.3 0.0002 0.0114 3-ketoacyl-CoA thiolase
VCA0665 0.3 0.0012 0.0404 C4-dicarboxylate transporter DcuC
VC0676 0.3 0.0012 0.0399 nptA protein
VC0720 0.3 0.0001 0.0066 phosphate regulon sensor protein
VC1823 0.2 0.0012 0.0399 PTS system, fructose-specific MB component
phosphate ABC transporter, periplasmic phosphate-binding protein,
VC0721 0.2 0.0001 0.0057 putative
VC0298 0.2 0.0001 0.0047 acetyl-CoA synthetase
VC1337 0.2 0.0005 0.0209 methylcitrate synthase
VC0299 0.2 0.0001 0.0047 DNA polymerase III subunit epsilon
VC1335 0.1 0.0001 0.0058 GntR family transcriptional regulator
c. R. obeum transcriptional responses to co-colonization with V. cholerae C6706 AluxS
DESeq analysis of transcript differences d2 after gavage of V. cholerae into R. obeum mono-colonized mice vs d7 of mono-colonization with R. obeum time oint Immediatel rior to ava e of V. cholerae
Figure imgf000178_0001
Figure imgf000179_0001
Figure imgf000179_0002
EC2.6.1.83 LL-diaminopimelate aminotransferase 0.58 1.09E-12
EC2.7.4.14 Cytidylate kinase 0.59 7.15E-59
EC3.5.1.24 Choloylglycine hydrolase 0.60 4.58E-12
EC1 .4.3.5 Pyridoxal 5'-phosphate synthase 0.60 8.39E-06
EC5.1.3.- NA 0.60 2.99E-78
EC2.5.1.17 Cob(l)yrinic acid a,c-diamide adenosyltransferase 0.61 6.76E-19
EC5.1 .1.7 Diaminopimelate epimerase 0.62 1.16E-25
EC2.1.1.63 Methylated-DNA-fproteinl-cysteine S-methyltransferase 0.62 9.40E-06
EC1.4.1.13 Glutamate synthase (NADPH) 0.63 1.09E-27
EC1.4.1.14 Glutamate synthase (NADH) 0.63 1.09E-27
EC3.6.3.12 Potassium-transporting ATPase 0.63 7.27E-158
EC1.1.1.85 3-isopropylmalate dehydrogenase 0.64 7.80E-10
EC1.1.1.86 Ketol-acid reductoisomerase 0.65 5.89E-24
EC1.1.1.44 Phosphogluconate dehydrogenase (decarboxylating) 0.66 3.12E-34
EC3.4.23.36 Signal peptidase II 0.67 5.83E-14
EC1.11 .1 .15 Peroxiredoxin 0.67 1.47E-108
EC3.2.2.- NA 0.68 1.17E-21
EC1.2.1.21 Glycolaldehyde dehydrogenase 0.68 6.60E-06
EC1.2.1.22 Lactaldehyde dehydrogenase 0.68 6.60E-06
EC1.1.1.49 Glucose-6-phosphate dehydrogenase 0.68 2.90E-20
EC2.3.1.79 Maltose O-acetyltransferase 0.68 2.17E-06
EC2.5.1.47 Cysteine synthase 0.68 7.21 E-15
EC1.6.99.3 NADH dehydrogenase 0.69 3.35E-09
EC1.11 .1 .9 Glutathione peroxidase 0.70 2.11 E-07
EC3.4.23.- NA 0.71 9.75E-13
EC1.15.1 .1 Superoxide dismutase 0.71 2.23E-79
EC4.2.1.1 1 Phosphopyruvate hydratase 0.72 5.14E-24
EC1.1.1.28 D-lactate dehydrogenase 0.74 3.38E-06
EC2.6.1.1 Aspartate transaminase 0.75 2.16E-08
EC1 .5.1.2 Pyrroline-5-carboxylate reductase 0.76 1.83E-06
EC3.6.3.4 Copper-exporting ATPase 0.76 2.66E-07
EC2.7.7.3 Pantetheine-phosphate adenylyltransferase 0.76 3.68E-06
EC3.1.3.18 Phosphoglycolate phosphatase 0.77 1.36E-16
EC3.1.1.31 6-phosphogluconolactonase 0.77 6.57E-16
EC1.1.1.42 Isocitrate dehydrogenase (NADP(+)) 0.78 4.31 E-08
EC1 .6.5.3 NADH dehydrogenase (ubiquinone) 0.78 4.30E-18
EC6.5.1.2 DNA ligase (NAD(+)) 0.78 6.60E-06
EC2.7.1.4 Fructokinase 0.78 3.70E-09
EC3.4.21 .- NA 0.78 1.85E-15
EC3.4.24.- NA 0.79 6.33E-26
EC4.2.1.3 Aconitate hydratase 0.79 2.18E-09
EC2.7.13.3 Histidine kinase 0.79 7.26E-18
EC2.7.8.- NA 0.80 6.11 E-06
EC2.3.3.1 Citrate (Si)-synthase 0.81 5.24E-09
EC1.2.1.12 Glyceraldehyde-3-phosphate dehydrogenase (phosphorylating) 0.83 1 .89E-39
EC2.4.2.10 Orotate phosphoribosyltransferase 0.85 1.92E-07
EC4.1.2.13 Fructose-bisphosphate aldolase 0.87 5.37E-17
EC4.2.1.52 Dihydrodipicolinate synthase 0.87 8.05E-06
EC3.6.3.14 H(+)-transporting two-sector ATPase 0.89 9.63E-36
EC4.1.1.49 Phosphoenolpyruvate carboxykinase (ATP) 0.92 1.33E-06
EC3.6.5.3 Protein-synthesizing GTPase 1 .14 2.77E-33
EC3.5.1.28 N-acetylmuramoyl-L-alanine amidase 1 .16 6.46E-08
EC5.3.1.6 Ribose-5-phosphate isomerase 1 .18 4.62E-06
EC2.7.7.6 DNA-directed RNA polymerase 1 .22 2.64E-26
EC3.2.1.23 Beta-galactosidase 1 .22 1.16E-17
EC1.3.99.1 Succinate dehydrogenase 1 .23 4.12E-14
EC3.2.1.21 Beta-glucosidase 1 .25 1.40E-12
EC2.3.1.179 Beta-ketoacyl-acyl-carrier-protein synthase II 1.25 5.80E-1 1
EC3.2.1.51 Alpha-L-fucosidase 1 .26 2.81 E-10
EC3.6.1.1 Inorganic diphosphatase 1.26 7.71 E-06
EC3.4.11 .18 Methionyl aminopeptidase 1 .27 5.04E-16
EC4.1.2.17 L-fuculose-phosphate aldolase 1 .28 2.92E-07
EC3.2.1.52 Beta-N-acetylhexosaminidase 1 .29 2.10E-22
EC1.6.5.- NA 1.30 2.81 E-21
EC3.5.4.5 Cytidine deaminase 1 .31 9.56E-08 EC3.4.11 .4 Tripeptide aminopeptidase 1 .31 2.78E-06
EC5.4.2.8 Phosphomannomutase 1 .32 4.48E-14
EC6.1.1.15 Proline-tRNA ligase 1 .32 3.43E-10
EC2.4.1.25 4-alpha-glucanotransferase 1 .34 3.23E-09
EC1.21 .4.2 Glycine reductase 1 .34 2.61 E-17
Acyl-[acyl-carrier-protein]-UDP-N-acetylglucosamine 0-
EC2.3.1.129 acyl transferase 1 .36 4.80E-11
EC3.5.99.6 Glucosamine-6-phosphate deaminase 1 .37 4.71 E-43
EC2.7.1.69 Protein-N(pi)-phosphohistidine-sugar phosphotransferase 1.38 6.38E-90
EC2.4.1.1 Phosphorylase 1.39 2.87E-15
EC2.1 .2.5 Glutamate formimidoyltransferase 1 .39 8.70E-08
EC5.3.1.25 L-fucose isomerase 1 .40 9.32E-46
EC3.1.1.53 Sialate O-acetylesterase 1 .41 2.76E-07
EC2.1.2.10 Aminomethyltransferase 1 .42 2.07E-15
EC2.7.2.7 Butyrate kinase 1 .46 2.55E-07
EC3.4.13.9 Xaa-Pro dipeptidase 1 .49 1.20E-12
EC1 .4.4.2 Glycine dehydrogenase (decarboxylating) 1.50 1.21 E-07
EC2.7.1.- NA 1.52 3.48E-06
EC5.3.1.4 L-arabinose isomerase 1 .52 1.73E-40
EC2.7.2.2 Carbamate kinase 1 .52 1.15E-11
EC2.7.1.15 Ribokinase 1 .53 2.77E-10
EC4.1.1.15 Glutamate decarboxylase 1 .55 6.66E-23
EC4.1 .3.3 N-acetylneuraminate lyase 1 .55 1.35E-78
EC2.7.1.5 Rhamnulokinase 1 .59 2.55E-07
EC2.3.1.54 Formate C-acetyltransferase 1 .61 5.51 E-25
EC1.2.99.2 Carbon-monoxide dehydrogenase (acceptor) 1.61 1.06E-82
EC3.2.1.89 Arabinogalactan endo-1 ,4-beta-galactosidase 1 .66 6.47E-23
EC5.1 .3.8 N-acylglucosamine 2-epimerase 1 .68 5.47E-1 14
EC3.2.1.55 Alpha-N-arabinofuranosidase 1.71 8.19E-26
EC3.4.11 .9 Xaa-Pro aminopeptidase 1 .76 1.11 E-40
EC3.5.2.7 Imidazolonepropionase 1.77 3.84E-17
EC6.3.4.3 Formate-tetrahydrofolate ligase 1 .78 2.53E-120
EC4.3.1.12 Ornithine cyclodeaminase 1 .81 1.44E-10
EC2.7.1.30 Glycerol kinase 1 .81 1.43E-16
EC3.5.4.2 Adenine deaminase 1 .84 4.36E-10
EC1.1.1.14 L-iditol 2-dehydrogenase 1.89 3.73E-06
EC3.4.11 .- NA 2.04 2.42E-12
EC1.97.1 .9 Selenate reductase 2.18 3.68E-06
EC2.3.1.9 Acetyl-CoA C-acetyltransferase 2.27 3.97E-15
EC1.21 .4.1 D-proline reductase (dithiol) 2.38 0
EC5.1 .1.4 Proline racemase 2.49 6.89E-1 16
EC1.17.1 .4 Xanthine dehydrogenase 2.55 1.03E-26
EC2.7.1.39 Homoserine kinase 2.73 1.20E-07
EC3.5.4.3 Guanine deaminase 2.79 7.29E-44
EC1 .1 .1.6 Glycerol dehydrogenase 2.83 3.27E-08
EC3.5.2.10 Creatininase 2.84 4.40E-17
EC1.1.1.157 3-hydroxybutyryl-CoA dehydrogenase 3.22 2.43E-22
EC4.1.2.- NA 5.05 7.58E-13
EC3.4.11 .5 Prolyl aminopeptidase 5.18 2.20E-84
EC2.7.1.92 5-dehydro-2-deoxygluconokinase 7.26 1.14E-06
EC5.3.1.- NA 7.27 1.27E-06
EC2.8.3.1 Propionate CoA-transferase 7.80 3.77E-08
EC3.2.1.26 Beta-fructofuranosidase 10.71 1 .93E-240
EC3.7.1.- NA 18.78 3.73E-10
EC2.7.10.1 Receptor protein-tyrosine kinase 19.32 1.88E-08 Table 29. PGR primers and bacterial strains
Target gene Primer name Sequence SEQ ID NO:
R. obeum luxS TA-ROIux-f CACCATGAAAAAAATTGCAAGTTTTACC 61
(RUMOBE02774) TA-ROIux-r TTATTCCGGATAAGTCAGACGTTC 62
TA-VC0557-f CACCATGCCATTATTAGACAGTTTTACC 63
V. cholerae luxS (VC0557)
TA-VC0557-r TTAGTGAACCTTCAGCTCATTG 64
Antibiotics used for
Strain
Strain Plasmid description maintenance of plasmid in
Background
host bacterial strain
Ptcp-lux C6706 pJZ376 Cm, 1 ug/mL
PBAD-VCIuxS DH5a VC0557 in pBAD202-TOPO Km, 50ug/mL
PBAD-ROIuxS DH5a RUMOBE02774 in pBAD202-TOPO Km, 50ug/mL
PBAD-lacZ DH5a lacZ in pBAD202-TOPO Km, 50ug/mL
Cm, chloramphenicol; Km, kanamycin

Claims

CLAIMS What is claimed is:
1 . A method to determine the maturity of a subject's gut microbiota, the method comprising:
(a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least the first 6 bacterial taxa listed in Table A;
(b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was obtained; and
(c) calculating the maturity of the subject's gut microbiota, wherein the
calculation for maturity is defined as relative maturity, and relative maturity = (microbiota age of the subject ) - (microbiota age of a healthy subject of a similar chronological age).
2. The method of claim 1 , wherein the group comprises at least the first 6 bacterial taxa listed in Table A and at least one bacterial taxon listed in rows 7 to 24 of Table A.
3. A method to determine the maturity of a subject's gut microbiota, the method comprising:
(a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least 24 bacterial taxa listed in Table A;
(b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was obtained; and (c) calculating the maturity of the subject's gut microbiota, wherein the
calculation for maturity is defined as relative maturity, and relative maturity = (microbiota age of the subject ) - (microbiota age of a healthy subject of a similar chronological age).
The method of any of the claims 1 to 3, wherein the calculation for maturity is defined as a Microbiota-for-Age Z score (MAZ), wherein MAZ = ((microbiota age of the subject) - (median microbiota age of a healthy subject of a similar chronological age))/(standard deviation of microbiota age of healthy subjects of the similar chronological age).
The method of any of the claims 1 to 4, wherein the age of the subject and the age of the healthy subject(s) are within about 1 month, about 2 months, or about 3 months.
A method to classify a subject, the method comprising:
(a) (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least the first 6 bacterial taxa listed in Table A;
(b) (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was obtained; and (c) (c) classifying the subject as having normal gut maturation when the microbiota age of the subject is substantially similar to the microbiota age of a healthy subject with a similar chronological age.
The method of claim 6, wherein the group comprises at least the first 6 bacterial taxa listed in Table A and at least one bacterial taxon listed listed in rows 7 to 24 of Table A.
A method to classify a subject, the method comprising:
(a) (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least 24 bacterial taxa listed in Table A;
(b) (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was obtained; and
(c) (c) classifying the subject as having normal gut maturation when the
microbiota age of the subject is substantially similar to the microbiota age of a healthy subject with a similar chronological age.
A method to classify a subject, the method comprising:
(a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least the first 6 bacterial taxa listed in Table A;
(b) applying the relative abundances of the bacterial taxa from step (a) and chronological age of the subject to a classification model, wherein the classification model is trained on datasets comprising measurements obtained from a plurality of healthy subjects and a plurality of
undernourished subjects, and the measurements including (i) relative abundances of the same bacterial taxa in step (a), as determined from a plurality of gut microbiota samples obtained over time for each healthy and undernourished subject, and (ii) chronological age of the subject at the time the gut microbiota sample was obtained; and
wherein the classification model assigns the subject to a category.
10. The method of claim 9, wherein the group comprises at least the first 6 bacterial taxa listed in Table A and at least one bacterial taxon listed listed in rows 7 to 24 of Table A.
1 1 . A method to classify a subject, the method comprising:
(a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject, wherein the group comprises at least 24 bacterial taxa listed in Table A;
(b) applying the relative abundances of the bacterial taxa from step (a) and chronological age of the subject to a classification model, wherein the classification model is trained on datasets comprising measurements obtained from a plurality of healthy subjects and a plurality of undernourished subjects, and the measurements including (i) relative abundances of the same bacterial taxa in step (a), as determined from a plurality of gut microbiota samples obtained over time for each healthy and undernourished subject, and (ii) chronological age of the subject at the time the gut microbiota sample was obtained; and
wherein the classification model assigns the subject to a category.
12. A method for identifying an effect of a therapy, the method comprising:
(a) (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject before and after administration of the therapy, wherein the group comprises at least the first 6 bacterial taxa listed in Table A;
(b) (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota before and after therapy, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected;
wherein the therapy has an effect when the microbiota age of the subject changes after therapy.
13. The method of claim 12, wherein the group comprises at least the first 6 bacterial taxa listed in Table A and at least one bacterial taxon listed listed in rows 7 to 24 of Table A.
14. A method for identifying an effect of a therapy, the method comprising:
(a) (a) calculating a relative abundance for each bacterial taxon in a group, from a fecal sample obtained from the subject before and after administration of the therapy, wherein the group comprises at least 24 bacterial taxa listed in Table A;
(b) (b) applying the relative abundances of the bacterial taxa from step (a) to a regression model to determine a microbiota age for the subject's gut microbiota before and after therapy, wherein the model regresses, for a group of healthy subjects, relative abundances of the same bacterial taxa, as determined from a plurality of gut microbiota samples obtained over time for each healthy subject in the group, against the chronological age of each healthy subject in the group at the time the gut microbiota sample was collected;
wherein the therapy has an effect when the microbiota age of the subject changes after therapy.
15. The method of any of the claims 12 to 14, further comprising calculating the maturity of the subject's gut microbiota, wherein the calculation for maturity is defined as relative maturity or Microbiota-for-Age Z score.
16. The method of any of claims 1 to 15, wherein the group of bacterial taxa comprises at least 12 of the bacterial taxa listed in Table B.
17. The method of any of claims 1 to 15, wherein the group of bacterial taxa comprises at least the bacterial taxa listed in Table B.
18. A method for identifying an effect of a therapy, the method comprising:
(a) identifying a set of age-discriminatory bacterial taxa within the bacterial taxa comprising the gut microbiota a group of healthy subject's gut microbiota, the method comprising
(i) providing, for each healthy subject, a relative abundance for the bacterial taxa comprising the subject's gut microbiota, wherein the relative abundance of the bacterial taxa in the healthy subject's gut microbiota was determined from a plurality of gut microbiota samples obtained at intervals of time;
(ii) regressing the relative abundances of the bacterial taxa comprising each healthy subject's gut microbiota against the chronological age of the healthy subject at the time the gut microbiota sample was collected, thereby producing a prediction of a gut microbiota age that is based only on the relative abundance of age-discriminatory bacterial taxa present in the subject's gut microbiota; and
(iii) selecting the minimum number of bacterial taxa from step (ii) that is needed to produce a prediction that is substantially similar to or better than the prediction of step (ii);
(b) calculating a relative abundance for each bacterial taxon in the set of age- discriminatory taxa from step (a)(iii), using a fecal sample obtained from a subject before and after administration of the therapy; and
(c) applying the relative abundances from step (b) to the prediction of gut microbiota age from step (a)(iii) to determine the microbiota age of the subject's gut microbiota before and after therapy; wherein the therapy has an effect when the microbiota age of the subject changes after therapy.
19. The method of claim 18, wherein the method further comprises after step (a)(ii) and before step (a)(iii), validating the model developed in a(ii) in a separate group of subjects to estimate the performance of the model.
20. The method of any of claims 12 to 19, wherein the method further comprises calculating a metric for maturity using the microbiota age of the subject, the therapy has an effect when the metric for maturity changes after therapy, and wherein the metric for maturity is
(i) (microbiota age of the subject) - (microbiota age of a healthy subject of a similar chronological age), or
(ii) (microbiota age of the subject) - (microbiota age of a healthy subject of a similar chronological age))/(standard deviation of microbiota age of healthy subjects of the same chronological age).
21 . The method of claims 12 to 20, wherein the effect of the therapy is positive, and the microbiota age of the subject increases.
22. The method of any of claims 12 to 21 , wherein the subject has acute malnutrition and the therapy is selected from the group consisting of therapeutic foods, prebiotics, probiotics, synbiotics, antibiotics and vaccines.
23. The method of any of claims 12 to 21 , wherein the subject has acute diarrhea and the therapy is selected from the group consisting of antibiotics, antimoti!ity agents, antisecretory agents, bulk-forming agents, supplemental zinc therapy, nonsteroidal antiinflammatory drugs, mucosal protectants, mucosal absorbents, prebiotics, probiotics, synbiotics, and/or rehydration therapy.
24. The method of claim 23, wherein the acute diarrhea is associated with an enteropathogen infection.
25. The method of claim 24, wherein the enteropathogen is selected from the group consisting of Escherichia coli, Salmonella enterica, Shigella flexneri, Shigella sonnei, Shigella dysenteriae, Camplobacter jejuni, Camplobacter coli, Camplobacter
upsaliensis, Yersinia enterocolitica, Yersinia pseudotuberculosis, Vibrio cholerae, Vibrio parahaemolyticus, Clostridum difficile, Entamoeba histolytica, Entamoeba dispar, Endolimax nana, lodamoeba butschii, Chilomastix mesnili, Blastocystis hominis, Trichomonas hominis, Giardia intestinalis, Giardia lamblia, Cryptosporidium parvum, Isospora belli, Dientamoeba fragilis, Microsporidia, Strongyloides stercoralis,
Angiostrongylus costaricensis, Schistosoma mansoni, Schistosoma japonicum,
Cyclospora cayetanensis, Enterocytozoon bieneusi, Enterocytozoon helium,
Encephalitozoon intestinalis, Encephalitozoon cuniculi, Ascaris lumbricoides, Trichuris tricuria, Ancylostoma duodenale, Necator americanus, Hymenolepsis nana, rotaviruses, human caliciviruses, astroviruses, and cytolomegaloviruses.
26. The method of claim 25, wherein the enteropathogen is Vibrio cholerae.
27. The method of any of claims 12 to 21 , wherein the subject has chronic diarrhea, and the therapy is selected from the group consisting of prebiotics, probiotics, synbiotics, immune-modulating agents, and antibiotics.
28. The method of claim 27, wherein the chronic diarrhea is a symptom of antibiotic use, use of a pharmacologic agent, ulcerative colitis, Crohn's disease, chemotherapy, radiation therapy, colon cancer, an enteropathogen infection, or necrotizing
enterocolitis.
29. The method of claim 28, wherein the enteropathogen is selected from the group consisting of Clostridum difficile, Trichomonas hominis, Giardia intestinalis, Giardia lamblia, Cryptosporidium parvum, Isospora belli, Dientamoeba fragilis, Microsporidia, Strongyloides stercoralis, Angiostrongylus costaricensis, Schistosoma mansoni, Schistosoma japonicum, Cyclospora cayetanensis, Enterocytozoon bieneusi,
Enterocytozoon helium, Encephalitozoon intestinalis, Encephalitozoon cuniculi, Ascaris lumbricoides, Trichuris tricuria, Ancylostoma duodenale, Necator americanus, Hymenolepsis nana, rotaviruses, human caliciviruses, astroviruses, and
cytolomegaloviruses.
30. The method of any of the preceding claims, wherein the subject is a human.
31 . A composition comprising at least one of the bacterial taxa listed in Table A.
32. A composition comprising a combination of bacterial taxa, the combination comprising of at least two bacterial taxa listed in Table B.
33. A composition comprising a combination of bacterial taxa, the combination selected from those listed in Table D.
34. A method of preventing acute malnutrition, acute diarrhea, or chronic diarrhea in a subject in need thereof, the method comprising administering to the subject a composition of any of the claims 31 to 33.
35. A method of treating acute malnutrition, acute diarrhea, or chronic diarrhea in a subject in need thereof, the method comprising administering to the subject a composition of any of the claims 31 to 33.
36. The method of claim 34 or 35, wherein the method further comprises
administering to the subject a therapeutic agent.
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