EP3346912A1 - Procédé et système pour des diagnostics dérivés du microbiome et agents thérapeutiques pour des affections associées à la santé cérébro-carniofaciale - Google Patents

Procédé et système pour des diagnostics dérivés du microbiome et agents thérapeutiques pour des affections associées à la santé cérébro-carniofaciale

Info

Publication number
EP3346912A1
EP3346912A1 EP16845218.3A EP16845218A EP3346912A1 EP 3346912 A1 EP3346912 A1 EP 3346912A1 EP 16845218 A EP16845218 A EP 16845218A EP 3346912 A1 EP3346912 A1 EP 3346912A1
Authority
EP
European Patent Office
Prior art keywords
cerebro
microbiome
sequence
health issue
characterization
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP16845218.3A
Other languages
German (de)
English (en)
Other versions
EP3346912A4 (fr
Inventor
Zachary APTE
Jessica RICHMAN
Daniel Almonacid
Siavosh Rezvan BEHBAHANI
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Psomagen Inc
Original Assignee
uBiome Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by uBiome Inc filed Critical uBiome Inc
Publication of EP3346912A1 publication Critical patent/EP3346912A1/fr
Publication of EP3346912A4 publication Critical patent/EP3346912A4/fr
Pending legal-status Critical Current

Links

Classifications

    • 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
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • 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
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • 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
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • 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

  • a microbiome is an ecological community of commensal, symbiotic, and pathogenic microorganisms that are associated with an organism.
  • the human microbiome comprises more microbial cells than human cells, but characterization of the human microbiome is still in nascent stages due to limitations in sample processing techniques, genetic analysis techniques, and resources for processing large amounts of data. Nonetheless, the microbiome is suspected to play at least a partial role in a number of health/disease- related states (e.g., preparation for childbirth, diabetes, auto-immune disorders,
  • This invention creates such a new and useful method and system.
  • a method for identification and classification of occurrence of a microbiome associated with a cerebro-craniofacial health issue or screening for the presence or absence of a microbiome associated with a cerebro-craniofacial health issue in an individual and/or determining a course of treatment for an individual human having a microbiome composition associated with a cerebro-craniofacial health issue comprising: providing a sample comprising microorganisms from the individual human; determining an amount(s) of one or more of the following in the sample:
  • bacteria and "bacterial material” (e.g., DNA). Additionally or alternatively, other microorganisms and their material (e.g., DNA) can be detected, classified, and used in the methods and compositions described herein and thus every occurrence of "bacterial” or “bacterial material” or equivalents thereof apply equally to other microorganisms, including but not limited to archaea, unicellular eukaryotic organisms, viruses, or the combinations thereof.
  • the method comprising, providing a sample comprising microorganisms including bacteria (or at least one of the following microorganisms including: bacteria, archaea, unicellular eukaryotic organisms and viruses, or the combinations thereof) from the individual human; determining an amount(s) of one or more of the following in the sample: bacteria taxon or gene sequence corresponding to gene functionality as set forth in Tables A, B, C, D, or E; comparing the determined amount(s) to a disease signature having cut-off or probability values for amounts of the bacteria taxon and/or gene sequence for an individual having a microbiome indicative of a cerebro-craniofacial health issue or an individual not having a microbiome indicative of a cerebro-craniofacial health issue or both; and determining a classification of the presence or absence of the microbiome indicative of a cerebro-cran
  • the determining comprises preparing DNA from the sample and performing nucleotide sequencing of the DNA.
  • the determining comprises deep sequencing bacterial DNA from the sample to generate sequencing reads, receiving at a computer system the sequencing reads; and mapping, with the computer system, the reads to bacterial genomes to determine whether the reads map to a sequence from the bacterial taxon or a gene sequence from Tables A, B, C, D, or E; and determining a relative amount of different sequences in the sample that correspond to a sequence from the bacteria taxon or gene sequence corresponding to gene functionality from Tables A, B, C, D, or E.
  • the deep sequencing is random deep sequencing.
  • the deep sequencing comprises deep sequencing of 16S rRNA coding sequences.
  • the method further comprises obtaining physiological, demographic or behavioral information from the individual human, wherein the disease signature comprises physiological, demographi
  • determining comprises comparing the obtained physiological, demographic or behavioral information to corresponding information in the disease signature.
  • the sample is a fecal, blood, saliva, cheek swab, urine or bodily fluid from the individual human.
  • the treating comprises administering a dose of one of more of the bacteria taxon listed in Tables A, B, C, D, or E to the individual human for which the individual human is deficient.
  • the method comprises performing, by a computer system: receiving sequence reads of bacterial DNA obtained from analyzing a test sample from the individual human; mapping the sequence reads to a bacterial sequence database to obtain a plurality of mapped sequence reads, the bacterial sequence database including a plurality of reference sequences of a plurality of bacteria; assigning the mapped sequence reads to sequence groups based on the mapping to obtain assigned sequence reads assigned to at least one sequence group, wherein a sequence group includes one or more of the plurality of reference sequences; determining a total number of assigned sequence reads; for each sequence group of a disease signature set of one or more sequence groups selected from Tables A, B, C, D, or E: determining a relative abundance value of assigned sequence reads assigned to the sequence group relative to the total number of assigned sequence reads, the relative abundance values forming a test feature vector; comparing the test feature vector to calibration
  • the comparing includes: clustering the calibration feature vectors into a control cluster not having the microbiome indicative of a cerebro-craniofacial health issue and a disease cluster having the microbiome indicative of a cerebro-craniofacial health issue; and determining which cluster the test feature vector belongs.
  • the clustering includes using a Bray-Curtis dissimilarity.
  • the comparing includes comparing each of the relative abundance values of the test feature vector to a respective cutoff value determined from the calibration feature vectors generated from the calibration samples.
  • the comparing includes: comparing a first relative abundance value of the test feature vector to a disease probability distribution to obtain a disease probability for the individual human having a microbiome indicative of a cerebro-craniofacial health issue, the disease probability distribution determined from a plurality of samples having the microbiome indicative of a cerebro- craniofacial health issue and exhibiting the sequence group; comparing the first relative abundance value to a control probability distribution to obtain a control probability for the individual human not having a microbiome indicative of a cerebro- craniofacial health issue , wherein the disease probabilities and the control probabilities are used to determine the classification of the presence or absence of the microbiome indicative of a cerebro-craniofacial health issue and/or determining the course of treatment for the individual human having the microbiome indicative of a cerebro-craniofacial health issue .
  • the sequence reads are mapped to one or more predetermined regions of the reference sequences.
  • the disease sig the disease sig ⁇ sig ⁇ sig ⁇ sig ⁇ sig ⁇ sig ⁇ sig ⁇ sig ⁇ sig ⁇
  • the analyzing comprises deep sequencing.
  • the deep sequencing reads are random deep sequencing reads.
  • the deep sequencing reads comprise 16S rRNA deep sequencing reads.
  • FIG. 1 A is a flowchart of an embodiment of a method for determining a
  • FIG. IB is a flowchart of an embodiment of a method for determining a
  • FIG. 1C is a flowchart of an embodim
  • FIG. ID is a flowchart of an embodiment of a method for generating features derived from composition and/or functional components of a biological sample or an aggregate of biological samples.
  • FIG. IE is a flowchart of an embodiment of a method for characterizing a microbiome-associated condition and identifying therapeutic measures.
  • FIG. IF is a flow chart of an embodiment of a method for generating microbiome- derived diagnostics.
  • FIG. 2 depicts an embodiment of a method and system for generating microbiome- derived diagnostics and therapeutics.
  • FIG. 3 depicts variations of a portion of an embodiment of a method for generating microbiome-derived diagnostics and therapeutics.
  • FIG. 4 depicts a variation of a process for generation of a model in an embodiment of a method and system for generating microbiome-derived diagnostics and therapeutics.
  • FIG. 5 depicts variations of mechanisms by which therapies (e.g., probiotic-based or prebiotic-based therapies) operate in an embodiment of a method for characterizing a health condition.
  • therapies e.g., probiotic-based or prebiotic-based therapies
  • FIG. 6 depicts examples of therapy-related notification provision in an example of a method for generating microbiome-derived diagnostics and therapeutics.
  • FIG. 7 shows a plot illustrating the control distribution and the disease distribution for insomnia where the sequence group is Moryella for the Genus taxonomic group according to embodiments of the present invention.
  • FIG. 8 shows a plot illustrating the control distribution and the disease distribution for insomnia where the sequence group is Selenocompound metabolism for the function taxonomic group according to embodiments of the present invention.
  • FIG. 9 shows a plot illustrating the control distribution and the disease distribution for light sleep where the sequence group is Lactobacillaceae for the Family taxonomic group according to embodiments of the present invention.
  • FIG. 10 shows a plot illustrating the c
  • sequence group is Translation for the function taxonomic group according to embodiments of the present invention.
  • FIG. 11 shows a plot illustrating the control distribution and the disease distribution for headache where the sequence group is Marvinbryantia for the Genus taxonomic group according to embodiments of the present invention.
  • FIG. 12 shows a plot illustrating the control distribution and the disease distribution for headache where the sequence group is Selenocompound metabolism for the function taxonomic group according to embodiments of the present invention.
  • FIG. 13 shows a plot illustrating the control distribution and the disease distribution for sinusitis where the sequence group is Clostridiales for the Genus taxonomic group according to embodiments of the present invention.
  • FIG. 14 shows a plot illustrating the control distribution and the disease distribution for poor concentration where the sequence group is Moryella for the Genus taxonomic group according to embodiments of the present invention.
  • FIG. 15 shows a plot illustrating the control distribution and the disease distribution for poor concentration where the sequence group is Propanoate metabolism for the function taxonomic group according to embodiments of the present invention.
  • characterization of the microbiome of individuals is useful for detecting a microbiome indicative of insomnia, light sleep, headache, sinusitis, or poor concentration.
  • a microbiome indicative of insomnia, light sleep, headache, sinusitis, or poor concentration can be tested to confirm or provide further evidence to support or refute a diagnosis of the subject.
  • an individual can be assayed to determine whether they have a microbiome that is likely to increase the risk of insomnia, light sleep, headache, sinusitis, or poor concentration.
  • an individual having, or suspected of having, or having a history of, insomnia, light sleep, headache, sinusitis, or poor concentration can be assayed to determine whether the microbiome is likely to be a causative agent, or contribute to the frequency or severity of the insomnia, light sleep, headache, sinusitis, or poor concentration.
  • insomnia e.g., a gut or stool microbiome
  • a microbiome e.g., a gut or stool microbiome
  • an individual having symptoms of insomnia, or has insomnia, or has a microbiome e.g., a gut or stool
  • an individual having symptoms of light sleep, or has light sleep, or has a microbiome (e.g., a gut or stool microbiome) that causes or contributes to the frequency or severity of light sleep is referred to herein as having a "light sleep issue.”
  • An individual having symptoms of a headache, or has a headache, or has a microbiome (e.g., a gut or stool microbiome) that causes or contributes to the frequency or severity of a headache is referred to herein as having a "headache issue.”
  • An individual having symptoms of sinusitis, or has sinusitis, or has a microbiome (e.g., a gut or stool microbiome) that causes or contributes to the frequency or severity of sinusitis is referred to herein as having a "sinusitis issue.”
  • the inventors have discovered that the amount of certain bacteria and/or bacterial sequences corresponding to certain genetic pathways can be used to predict the presence or absence of a cerebro-craniofacial health issue.
  • the bacteria and genetic pathways in some cases are present in a certain abundance in individuals having a cerebro-craniofacial health issue, or having a specific cerebro-craniofacial health issue, as discussed in more detail below whereas the bacteria and genetic pathways are at a statistically different abundance in control individuals that do not have a cerebro-craniofacial health issue, or do not have a specific cerebro-craniofacial health issue.
  • Scoring of a particular bacteria or genetic pathway can be determined according to a
  • a detected abundance value less than a certain value is associated with a insomnia issue and above the certain value is scored as associated with a lack of a insomnia issue, depending on the particular criterion.
  • a detected abundance value greater than a certain value can be associated with a insomnia issue and below the certain value can be scored as associated with a lack of a insomnia issue or a microbiome that is not indicative of a insomnia issue.
  • the scoring for various bacteria or genetic pathways can be combined to provide a classification for a subject.
  • Insomnia (901) vs control subjects subjects abundance for abundance for (4865) detected detected disease control
  • Taxa microbiome
  • composition (a):
  • Flavonifractor plautii_292800 6.44E-09 518 2401 0.369 0.278
  • a detected abundance value greater than a certain value can be associated with a light sleep issue and below the certain value can be scored as associated with a lack of a light sleep issue or a microbiome that is not indicative of a light sleep issue.
  • the scoring for various bacteria or genetic pathways can be combined to provide a classification for a subject.
  • Taxa microbiome
  • composition (a):
  • a detected abundance value greater than a certain value can be associated with a headache issue and below the certain value can be scored as associated with a lack of a headache issue or a microbiome that is not indicative of a headache issue.
  • the scoring for various bacteria or genetic pathways can be combined to provide a classification for a subject.
  • Flavonifractor plautii_292800 7.45E-09 j 456 21 75 0.420 0.284
  • prokaryotes 4.40E-16 j 795 4346 1 .001 1 .027
  • Scoring of a particular bacteria or genetic pathway can be determined according to a comparison of an abundance value to one or more reference (calibration) abundance values for known samples, e.g., where a detected abundance value less than a certain value is associated with a sinusitis issue and above the certain value is scored as associated with a lack of a sinusitis issue, depending on the particular criterion. Similarly, depending on the particular criterion, a detected abundance value greater than a certain value can be associated with a sinusitis issue and below the certain value can be scored as associated with a lack of a sinusitis issue or a microbiome that is not indicative of a sinusitis issue.
  • the scoring for various bacteria or genetic pathways can be combined to provide a classification for a subject.
  • a detected abundance value greater than a certain value can be associated with a poor concentration issue and below the certain value can be scored as associated with a lack of a poor concentration issue or a microbiome that is not indicative of a poor concentration issue.
  • the scoring for various bacteria or genetic pathways can be combined to provide a classification for a subject.
  • Taxa microbiome
  • composition (a):
  • prokaryotes 1 .64E-07 1396 6269 1 .01 1 1 .025
  • the comparison of an abundance value to one or more reference abundance values can involve a comparison to a cutoff value determined from the one or more reference values.
  • Such cutoff value(s) can be part of a decision tree or a clustering technique (where a cutoff value is used to determine which cluster the abundance value(s) belong) that are determined using the reference abundance values.
  • the comparison can include intermediate
  • the comparison can also include a comparison of an abundance value to a probability distribution of the reference abundance values, and thus a comparison to probability values.
  • the inventors have identified the specific bacteria taxa and genetic pathways listed in TABLE A by deep sequencing of bacterial DNA associated with samples from test individuals having a insomnia issue and control individuals that do not have a insomnia issue and determining those criteria that readily distinguish test individuals from control individuals.
  • the inventors have identified the specific bacteria taxa and genetic pathways listed in TABLE B by deep sequencing of bacterial DNA associated with samples from test individuals having a light sleep issue and control individuals that do not have a light sleep issue and determining those criteria that readily distinguish test individuals from control individuals.
  • the inventors have identified the specific bacteria taxa and genetic pathways listed in TABLE C by deep sequencing of bacterial DNA associated with samples from test individuals having a headache issue and control individuals that do not have a headache issue and determining those criteria that readily distinguish test individuals from control individuals.
  • the inventors have identified the specific bacteria taxa and genetic pathways listed in TABLE D by deep sequencing of bacterial DNA associated with samples from test individuals having a sinusitis
  • Deep sequencing allows for determination of a sufficient number of copies of DNA sequences to determine relative amount of corresponding bacteria or genetic pathways in the sample. Having identified the criteria in TABLEs A, B, C, D, and E, one can now detect an individual that has a cerebro-craniofacial health issue by detecting one or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or more) of the options in TABLEs A, B, C, D, or E by any quantitative detection method.
  • one or more e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or more
  • deep sequencing can be used to detect the presence, absence or amount of one or more option in TABLEs A, B, C, D, or E
  • protein-based diagnostics such as immunoassays to detect bacterial taxons by detecting taxon-specific protein markers.
  • a targeted therapy that reduces the abundance of such bacteria (e.g., bacteriophage therapy or selective antibiotic therapy) can be administered to the individual.
  • a targeted therapy that reduces the abundance of such bacteria e.g., bacteriophage therapy or selective antibiotic therapy
  • a targeted therapy that reduces the abundance of such bacteria (e.g., bacteriophage therapy or selective antibiotic therapy) can be administered to the individual.
  • a method of determining whether, or the likelihood whether, an individual has a cerebro-craniofacial health issue is provided.
  • an individual having a cerebro-craniofacial health issue can exhibit an increase in one or more taxonomic groups in the microbiome, a decrease in one or more taxonomic groups in the microbiome, an increase in one or more functional groups in the microbiome, a decrease in one or more functional groups in the microbiome, or a combination thereof (e.g., relative to a control/healthy individual or population of control or healthy individuals).
  • the method can include one or more of the following steps: obtaining a sample from the individual; purifying nucleic acids (e.g., DNA) from the sample; deep sequencing nucleic acids from the sample so as to determine the amount of one or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more, e.g., 1-20, 2-15, 3-10, 1-10, 1-15, 1-5, or 5-30) of the features listed in TABLEs A, B, C, D, or E; and comparing the resulting amount of each feature to one or more reference amounts of the one or more of the features listed in TABLEs A, B, C, D, or E as occurs in an average individual having a cerebro-craniofacial health issue or an individual not having a cerebro-craniofacial health issue or both.
  • the compilation of features can sometimes be referred to as a "disease signature" for a specific disease (i.e., a cerebro-crani
  • the disease signature can act as a characterization model, and may include probability distributions for control population (no cerebro-craniofacial health issue) or disease populations having the disease (a cerebro-craniofacial health issue) or both.
  • the disease signature can include one or more of the features (e.g., bacterial taxa or genetic pathways) in TABLEs A, B, C, D, or E and can optionally include criteria determined from abundance values of the control and/or disease populations.
  • Example criteria can include cutoff or probability values for amounts of those features associated with average control individuals (no cerebro-craniofacial health issue) or individuals having the disease (a cerebro-craniofacial health issue).
  • the likelihood of an individual having a microbiome indicative of a cerebro- craniofacial health issue refers to the chance (degree of confidence) that the results from the individual's sample can be correlated with a cerebro-craniofacial health issue.
  • a cerebro- craniofacial health issue e.g., one can simply screen for a cerebro- craniofacial health issue, i.e., one can generate a yes or no indication for the presence or absence of a microbiome indicative of insomnia, light sleep, headache, sinusitis, or poor concentration.
  • the individual will not yet have been diagnosed with insomnia, light sleep, headache, sinusitis, or poor concentration or a insomnia issue, light sleep issue, headache issue, sinusitis issue, or poor concentration issue.
  • the individual can have been initially diagnosed by other methods and the methods described herein can be used to provide better (or worse) confidence of the initial diagnosis.
  • sample containing bacteria can be used from the individual.
  • sample types include, for example, a fecal sample, blood sample, saliva sample, throat swab, cheek swab, gum swab, urine or other bodily fluid from the individual.
  • Nucleic acids e.g., DNA and/or RNA
  • Basic texts disclosing the general molecular biology methods include Sambrook and Russell, Molecular Cloning, A Laboratory Manual (3rd ed. 2001); Kriegler, Gene Transfer and Expression: A Laboratory Manual (1990); and Current Protocols in Molecular Biology (Ausubel et al., eds., 1994-1999).
  • nucleic acids may also be obtained through in vitro amplification methods such as those described herein and in Berger, Sambrook, and Ausubel, as well as Mullis et al., (1987) U.S. Pat. No. 4,683,202; PCR Protocols A Guide to Methods and Applications (Innis et al, eds) Academic Press Inc. San Diego, Calif. (1990) (Innis); Arnheim & Levinson (Oct. 1, 1990) C&EN 36-47; The Journal Of NIH Research (1991) 3 : 81-94; Kwoh et al. (1989) Proc. Natl. Acad. Sci. USA 86: 1173; Guatelli et al. (1990) Proc. Natl. Acad. Sci. USA 87, 1874; Lomell et al. (1989) J. Clin. Chem., 35: 1826; Landegn
  • nucleic acids will not be amplified before they are quantified.
  • any of a variety of detection methods can be used to screen an individual's sample for one or more of the features listed in TABLEs A, B, C, D, or E .
  • nucleic acid hybridization and/or amplification methods are used to detect and quantify one or more of the features.
  • an immunoassay or other assay to detect and quantify one or more specific proteins determinative of one or more of the criteria can be used.
  • solid-phase ELISA immunoassays, Western blots, or immunohistochemistry are routinely used to specifically detect a protein.
  • nucleotide sequencing is used to identify and quantify one or more of the criteria.
  • DNA sequencing can be performed as desired. Such sequencing can be performed using known sequencing methodologies, e.g., Illumina, Life Technologies, and Roche 454 sequencing systems. In typical embodiments, a sample is sequenced using a large-scale sequencing method that provides the ability to obtain sequence information from many reads. Such sequencing platforms include those commercialized by Roche 454 Life Sciences (GS systems), Illumina (e.g., HiSeq, MiSeq) and Life Technologies (e.g., SOLiD systems).
  • GS systems Roche 454 Life Sciences
  • Illumina e.g., HiSeq, MiSeq
  • Life Technologies e.g., SOLiD systems
  • the Roche 454 Life Sciences sequencing platform involves using emulsion PCR and immobilizing DNA fragments onto bead. Incorporation of nucleotides during synthesis is detected by measuring light that is generated when a nucleotide is incorporated.
  • the Illumina technology involves the attachment of genomic DNA to a planar, optically transparent surface. Attached DNA fragments are extended and bridge amplified to create an ultra-high density sequencing flow cell with clusters containing copies of the same template. These templates are sequenced using a sequencing-by-synthesis technology that employs reversible terminators with removable fluorescent dyes.
  • Methods that employ sequencing by hybridization may also be used. Such methods, e.g., used in the Life Technologies SOLiD4+ technology uses a pool of all possible oligonucleotides of a fixed length, labeled according to the sequence. Oligonucleotides are annealed and ligated; the preferential ligation b
  • the sequence can be determined using any other DNA sequencing method including, e.g., methods that use semiconductor technology to detect nucleotides that are incorporated into an extended primer by measuring changes in current that occur when a nucleotide is incorporated (see, e.g., U.S. Patent Application Publication Nos. 20090127589 and 20100035252).
  • Deep sequencing can be used to quantify the number of copies of a particular sequence in a sample and then also be used to determine the relative abundance of different sequences in a sample.
  • Deep sequencing refers to highly redundant sequencing of a nucleic acid sequence, for example such that the original number of copies of a sequence in a sample can be determined or estimated.
  • the redundancy (i.e., depth) of the sequencing is determined by the length of the sequence to be determined (X), the number of sequencing reads (N), and the average read length (L). The redundancy is then NxL/X.
  • the sequencing depth can be, or be at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 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, 60, 70, 80, 90, 100, 110, 120, 130, 150, 200, 300, 500, 500, 700, 1000, 2000, 3000, 4000, 5000 or more. See, e.g., Mirebrahim, Hamid et al, Bioinformatics 31 (12): i9-il6 (2015).
  • specific sequences in the sample can be targeted for amplification and/or sequencing.
  • specific primers can be used to detect and sequence bacterial sequences of interest.
  • Exemplary target sequences can include, but are not limited to, the 16S rRNA coding sequence (e.g., gene families mentioned in the discussion of Block S120), as well as gene sequences involved in one or more genetic pathway as shown in TABLEs A, B, C, D, or E.
  • whole genome sequencing methods that randomly sequence DNA fragments in a sample can be used.
  • sequencing raw data is generated, the resulting sequence reads can be
  • mapped to known sequences in a genomic database.
  • Exemplary algorithms that are suitable for determining percent sequence identity and sequence similarity and thus aligning and identifying sequence reads are the BLAST
  • a read can be designated as from a genetic pathway if that read has the best alignment to a DNA sequence from that genetic pathway in the database.
  • KEGG Kyoto Encyclopedia of Genes and Genomes
  • COG Clusters of Orthologous Groups
  • KEGGs are described more at genome.jp/kegg/.
  • COGs are described in, e.g., Tatusov, et al, Nucleic Acids Res. 2000 Jan 1; 28(1): 33-36.
  • TABLEs provided herein lists various KEGG and COG categories that are correlated with the presence or absence of a microbiome indicative of a cerebro-craniofacial health issue. Different levels of KEGG or COG categories are provided in TABLEs A, B, C, D, or E. Values in TABLEs A, B, C, D, and E for particular criteria are proportional values compared to totals at that taxonomic or functional designation level.
  • An exemplary relative amount calculation is to determine the amount of 16S rRNA coding sequence reads for a particular bacterial taxon (e.g., genus , family, order, class, or phylum) relative to the total number of 16S rRNA coding sequence reads assigned to the bacterial domain.
  • a value indicative of amount of a feature in the sample can then be compared to a cut-off value or a probability distribution in a disease signature for a microbiome indicative of a cerebro-craniofacial health issue. For example, if the signature indicates that a relative amount of feature #1 of 50% or more of all features possible at that level indicates the likelihood of
  • quantification of gene sequences associated with feature #1 less than 50% in a sample would indicate a higher likelihood of a microbiome that is not indicative of a cerebro-craniofacial health issue and alternatively, quantification of gene sequences associated with feature #1 more than 50% in a sample would indicate a higher likelihood of a microbiome indicative of a cerebro-craniofacial health issue.
  • Disease signatures can include criteria corresponding to one or at least one of the features set forth in TABLEs A, B, C, D, or E .
  • 2, 3, or 4 of the criteria of TABLE A can be used in a disease signature for a microbiome indicative of a insomnia issue.
  • 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more (e.g., all) of the criteria of TABLE B can be used in a disease signature for a microbiome indicative of a light sleep issue.
  • various numbers of the criteria of TABLE C can be used in a disease signature for a microbiome indicative of a headache issue.
  • various numbers of the criteria of TABLE D can be used in a disease signature for a microbiome indicative of a sinusitis issue.
  • various numbers of the criteria of TABLE E can be used in a disease signature for a microbiome indicative of a poor concentration issue.
  • supplementary information about the individual can also be used in the disease signature and thus also for determining the likelihood of occurrence of a microbiome indicative of a cerebro-craniofacial health issue in the individual.
  • Supplementary information can include, for example, different demographics (e.g., genders, ages, marital statuses, ethnicities, nationalities, socioeconomic statuses, sexual orientations, etc.), different health conditions (e.g., health and disease states), different living situations (e.g., living alone, living with pets, living with a significant other, living with children, etc.), different dietary habits (e.g., omnivorous, vegetarian, vegan, sugar consumption, acid consumption, etc.), different behavioral tendencies (e.g., levels of physical activity, drug use, alcohol use, etc.), different levels of mobility (e.g., related to distance traveled within a given time period), biomarker states (e.g., cholesterol levels, lipid levels, etc.), weight, height, body mass index, genotypic factors, and any other suitable trait that has an effect on microbiome composition.
  • FIG. 1 A is a flowchart of an embodin
  • a microbiome indicative of a cerebro-craniofacial health issue such as insomnia, light sleep, headache, sinusitis, or poor concentration and/or determining the course of treatment for the individual human having the microbiome indicative of a cerebro-craniofacial health issue, such as insomnia, light sleep, headache, sinusitis, or poor concentration.
  • samples can comprise stool samples, blood samples, saliva samples, plasma/serum samples (e.g., to enable extraction of cell-free DNA), cerebrospinal fluid, and tissue samples.
  • the sample is an oral sample (e.g., a throat, tongue, or gum swab, or saliva), or a sample (e.g., a nucleic acid sample, such as a DNA sample) extracted from an oral sample.
  • an amount(s) of bacteria taxon and/or gene sequence corresponding to gene functionality as set forth in TABLEs A, B, C, D, or E is determined.
  • an amount of one bacteria taxon can be determined; an amount of one gene sequence corresponding to gene functionality can be determined; an amount of one bacteria taxon and an amount one gene sequence corresponding to gene functionality can be determined; multiple amounts (e.g., 2-4) of bacteria taxa can be determined; multiple amounts (e.g., 2-6) of gene sequences corresponding to gene functionalities can be determined; and multiple amounts of both can be determined.
  • the amount can be determined in various ways, e.g., by sequencing nucleic acids in the sample, using a hybridization array, and PCR. As examples, the amounts can correspond to levels of a signal or a count of numbers of nucleic acids corresponding to each taxa. The amount can be a relative abundance value. [0084] At block 12, the determined amount(s) are compared to a condition signature having cut-off or probability values for amounts of the bacteria taxon and/or gene sequence for an individual having a microbiome indicative of a cerebro-craniofacial health issue or an individual not having a microbiome indicative of a cerebro-craniofacial health issue or both.
  • each amount can be compared to a separate value, and a number of taxa exceeding that value can be compared to a threshold for determining whether a sufficient number of the taxa provide the condition signature.
  • a threshold for determining whether a sufficient number of the taxa provide the condition signature.
  • the amount can be transformed (e.g., via a probability distribution).
  • the amounts can be used to determine a measure probability, which can be compared to
  • a classification of the presence or absence of the microbiome indicative of a cerebro-craniofacial health issue is determined based on the comparing, and/or the course of treatment for the individual human having the microbiome indicative of a cerebro- craniofacial health issue is determined based on the comparing.
  • the classification can be binary or includes more levels, e.g., corresponding to a probability.
  • a condition/disease i.e., individuals having a microbiome indicative of a cerebro-craniofacial health issue
  • healthy individuals i.e., individuals having a microbiome that is not indicative of
  • the individual will have been diagnosed, optionally by other methods, of having a microbiome associated with a cerebro- craniofacial health issue, or symptoms thereof, and the methods described herein (e.g., comparison to the disease signature) will reveal excessive amounts and/or deficient amounts of one or more of the features that can then be used to guide treatment.
  • a possible treatment is providing a probiotic or prebiotic treatment that provides or stimulates growth of the particular bacteria type.
  • antibiotics can be administered to reduce the target bacterial population.
  • other treatments can be administered including promoting (by administration of probiotics or prebiotics) bacteria that compete with the target bacteria.
  • bacteriophage targeting the particular bacteria can be administered to the individual.
  • a cerebro-craniofacial health issue e.g., prebiotic, probiotic, or bacteriophage therapy
  • progression of the cerebro-craniofacial health issue e.g., monitor progression of insomnia, light sleep, headache, sinusitis, or poor concentration.
  • levels of one or more of the criteria in TABLEs A, B, C, D, or E are determined one or more (e.g., 2 or more, 3, 4, 5 or more) times and the dosage of a pre-biotic and/or pro-biotic treatment can be adjusted up or down depending on how the criteria respond to the treatment.
  • sequence information can be received.
  • the sequence information can correspond to one or more sequence reads per nucleic acid molecule (e.g., a DNA fragment).
  • the sequence reads can be obtained in a variety of ways. For example, a hybridization array, PCR, or sequencing techniques can be used.
  • a sequence read can be aligned (mapped) to a plurality of reference bacterial genomes (also called reference genomes) to determine which reference bacterial genome the sequence read aligns and where on that reference genome the sequence read aligns.
  • the alignment can be to a particular region (e.g., 16S region) of a reference genome, and thus to a reference sequence, which can be all or part of the reference genome.
  • both sequence reads can be aligned as a pair, with an expected length of the nucleic acid molecule being used to aid in the alignment.
  • a particular DNA fragment is derived from a particular gene of a particular bacterial taxonomic group (also called taxon) based on the aligned location of a sequence read to the particular gene of the particular bacterial taxonomic group.
  • the same determination may be made by various hybridization probes using a variety of techniques, as will be known by one skilled in the art.
  • the mapping can be performed in a variety of ways. [0095] In this manner, a count of the number
  • a relative abundance value (RAV) of a particular taxonomic group can be determined based on a fraction (proportion) of sequence reads aligning to that taxonomic group relative to other taxonomic groups.
  • the RAV can correspond to the proportion of reads assigned to a particular taxonomic or functional group.
  • the proportion can be relative to various denominator values, e.g., relative to all of the sequence reads, relative to all assigned to at least one group (taxonomic or functional), or all assigned to for a given level in the hierarchy.
  • the alignment can be implemented in any manner that can assign a sequence read to a particular taxonomic or functional group. For example, based on the mappings to the reference sequence(s) in the 16S region, a taxonomic group with the best match for the alignment can be identified. The RAV can then be determined for that taxonomic group using the number of sequence reads (or votes of sequence reads) for a particular sequence group divided by the number of sequence reads identified as being bacterial, which may be for a specific region or even for a given level of a hierarchy.
  • a taxonomic group can include one or more bacteria and their corresponding reference sequences.
  • a taxonomic group can correspond to any set of one or more reference sequences for one or more loci (e.g., genes) that represent the taxonomic group. Any given level of a taxonomic hierarchy would include a plurality of taxonomic groups. For instance, a reference sequence in the one group at the genus level can be in another group at the family level.
  • a sequence read can be assigned based on the alignment to a taxonomic group when the sequence read aligns to a reference sequence of the taxonomic group.
  • a functional group can correspond to one or more genes labeled as having a similar function.
  • a functional group can be represented by reference sequences of the genes in the functional group, where the reference sequences of a particular gene can correspond to various bacteria.
  • the taxonomic and functional groups can collectively be referred to as sequence groups, as each group includes one or more reference sequences that represent the group.
  • a taxonomic group of multiple bacteria can be represented by multiple reference sequence, e.g., one reference sequence per bacteria species in the taxonomic group.
  • Embodiments can use the degree of alignment of a sequence read to multiple reference sequences to determine which sequence group to assign the sequence read based on the alignment.
  • a particular genomic region e.g., gene 16S
  • the region can be amplified, and a portion of the amplified DNA fragments can be sequenced.
  • the amplification can be to such
  • regions can be smaller than a gene, e.g., variable regions within a gene. The longer the region, more resolution can be obtained to determine voting to assign a sequence read to a group. Multiple non-contiguous regions can be analyzed, e.g., by amplifying multiple regions.
  • a relative abundance value can correspond to a proportion of sequence reads that align to at least one reference sequence of a sequence group, also referred to as a feature herein.
  • a sequence read can be assigned to one or more sequence groups based on the alignment to the reference sequence(s) for each sequence group.
  • a sequence read can be assigned to more than one sequence group if the assigned groups are in different categories (e.g., taxonomic or functional) or in different levels of a hierarchy (e.g., genus and family).
  • a sequence group can include multiple sequences for different regions or a same region, e.g., a sequence group can include more than one base at a particular position, e.g., if the group encompasses various polymorphisms at a genomic position.
  • a sequence group is an example of a feature that can be used to characterize a sample, e.g., when the sequence group has a statistically significant separation between the control population and the disease population. 1. Assignment to a sequence group
  • sequence reads can be obtained for two ends of a nucleic acid molecule, e.g., via paired-end sequencing.
  • Embodiments can identify whether each sequence read of a pair of sequence reads corresponds to a particular sequence group. Each sequence read can effectively have a vote, and the nucleic acid molecule can be identified as corresponding to a particular sequence group only if both sequence reads are aligned to that sequence group (alignment may allow mismatches when less than 100% sequence identity is used). In such embodiments, molecules that do not have both sequence reads aligning to the same sequence group can be discarded.
  • the alignment to a reference sequence may be required to be perfect (i.e., no mismatches), while other embodiments can allow mismatches. Further, the alignment can be required to be unique, or else the read is discarded.
  • a partial vote can be attributed to each sequence group to which a sequence read aligns.
  • a weight of the partial vote based on the degree of alignment, e.g., whether there are any mismatches.
  • each sequence read can get a vote when it does
  • a total weight for a read being assigned to a particular reference sequence can be determined by various factors, each providing a weight.
  • the total votes to the reference sequence of a group can be determined and compared to the total votes for other groups in the same level.
  • the sequence group at a given level with the highest percentage for assignment to the read can be assigned the read.
  • Various techniques of partial assignment can be used, e.g., Dirichlet partial assignment.
  • Sequencing can be advantageous for assigning sequence reads to a group, as sequencing provides the actual sequence of at least a portion of a nucleic acid molecule.
  • the sequence might be slightly different than what has already been known for a particular taxonomic group, but it may be similar enough to assign to a particular taxonomic group. If predetermined probes were used, then that nucleic acid molecule might not be identified. Thus, one can identify unknown bacteria, but whose sequence is similar enough to an existing taxonomic group, or even assigned to an unknown group.
  • the proportion can be the total of sequence reads, even if some are not assigned, or equivalently assigned to an unknown group.
  • the 16S gene can be analyzed, and a read can be determined to align to one or more reference sequences in the region, e.g., with a certain number of mismatches below a threshold, but with a high enough variations to not correspond to any known taxonomic group (or functional group as discussed below).
  • embodiments can include unassigned reads that contribute to the denominator for determining the proportion of reads of a certain sequence group relative to the sequence reads identified, e.g., as being bacterial.
  • a proportion of the bacterial population of sequence reads can be determined. Using predetermined probes would generally not allow one to identify unknown bacterial sequences.
  • Sequence group corresponds to a particular taxonomic group
  • a taxonomic group can correspond to any set of one or more reference sequences for one or more loci (e.g., genes) that represent the taxonomic group.
  • Any given level of a taxonomic hierarchy would include a plurality of taxonomic groups.
  • the taxonomic groups of a given level of the taxonomic hierarchy would typically be mutually exclusive.
  • a reference sequence of one taxonomic group would not be included in another taxonomic group in the same level.
  • a reference sequence in one group at the genus level would not be included in another group at the g
  • one group at the genus level can be in another group at the family level.
  • the RAV can correspond to the proportion of reads assigned to a particular taxonomic group.
  • the proportion can be relative to various denominator values, e.g., relative to all of the sequence reads, relative to all assigned to at least one group (taxonomic or functional), or all assigned to for a given level in the hierarchy.
  • the alignment can be implemented in any manner that can assign a sequence read to a particular taxonomic group.
  • a taxonomic group with the best match for the alignment can be identified.
  • the RAV can then be determined for that taxonomic group using the number of sequence reads (or votes of sequence reads) for a particular sequence group divided by the number of sequence reads identified, e.g., as being bacterial, which may be for a specific region or even for a given level of a hierarchy.
  • Sequence group corresponds to a particular gene or functional group
  • embodiments can use a count of a number of sequence reads that correspond to a particular gene or a collection of genes having an annotation of a particular function, where the collection is called a functional group.
  • the RAV can be determined in a similar manner as for a taxonomic group.
  • functional group can include a plurality of reference sequences corresponding to one or more genes of the functional group. Reference sequences of multiple bacteria for a same gene can correspond to a same functional group. Then, to determine the RAV, the number of sequence reads assigned to the functional group can be used to determine a proportion for the functional group.
  • a function group which may include a single gene, can help to identify situations where there is a small change (e.g., increase) in many taxonomic groups such that the change is too small to be statistically significant. But, the changes may all be for a same gene or set of genes of a same functional group, and thus the change for that functional group can be statistically significant, even though the changes for the taxonomic groups may not be significant. The reverse can be true of a taxonomic group being more predictive than a particular functional group, e.g., when a single taxonomic group includes many genes that have changed by a relatively small amount. [0108] As an example, if 10 taxonomic grouf
  • the functional group can act to provide a sum of small changes for various taxonomic groups. And, small changes for various functional groups, which happen to all be on a same taxonomic group, can sum to provide high statistical power for that particular taxonomic group.
  • the taxonomic groups and functional groups can supplement each other as the information can be orthogonal, or at least partially orthogonal as there still may be some relationship between the RAVs of each group.
  • the RAVs of one or more taxonomic groups and functional groups can be used together as multiple features of a feature vector, which is analyzed to provide a diagnosis, as is described herein.
  • the feature vector can be compared to a disease signature as part of a characterization model.
  • Embodiments can use the relative abundance values (RAVs) for populations of subjects that have a disease (condition population; i.e., individuals having a microbiome indicative of a cerebro-craniofacial health issue) and that do not have the disease (control population; i.e., individuals having a microbiome that is not indicative of a cerebro- craniofacial health issue). If the distribution of RAVs of a particular sequence group for the disease population is statistically different than the distribution of RAVs for the control population, then the particular sequence group can be identified for including in a disease signature.
  • condition population i.e., individuals having a microbiome indicative of a cerebro-craniofacial health issue
  • control population i.e., individuals having a microbiome that is not indicative of a cerebro- craniofacial health issue.
  • the RAV for a new sample for a sequence group in the disease signature can be used to classify (e.g., determine a probability) of whether the sample does or does not have the disease.
  • the classification can also be used to determine a treatment, as is described herein.
  • a discrimination level can be used to identify sequence groups that have a high predictive value.
  • embodiment can filter out taxonomic groups that are not very accurate for providing a diagnosis.
  • the Kolmogorov- Smirnov (KS) test can be used to provide a probability value (p-value) that the two distributions are actually identical. The smaller the p-value the greater the probability to correctly identify which population a sample belongs. The larger the separation in the mean values between the two populations generally results in a smaller p-value (an example of a discrimination level).
  • Other tests for comparing distributions can be used.
  • the Welch's t-test presumes that the distributions are Gaussian, which is not necessarily true for a particular sequence group.
  • the KS test as it is a non-parametric test, is well suited for comparing distributions of taxa or functions for which the probability distributions are unknown.
  • the distribution of the RAVs for the control and condition populations can be analyzed to identify sequence groups with a large separation between the two distributions.
  • the separation can be measured as a p-value (See example section).
  • the relative abundance values for the control population may have a distribution peaked at a first value with a certain width and decay for the distribution.
  • the disease population can have another distribution that is peaked a second value that is statistically different than the first value.
  • an abundance value of a control sample has a lower probability to be within the distribution of abundance values encountered for the disease samples.
  • the larger the separation between the two distributions the more accurate the discrimination is for determining whether a given sample belongs to the control population or the disease population.
  • the distributions can be used to determine a probability for an RAV as being in the control population and determine a probability for the RAV being in the disease population.
  • FIG. 7 shows a plot illustrating the control distribution and the disease distribution for insomnia where the sequence group is Moryella for the Genus taxonomic group according to embodiments of the present invention.
  • the RAVs for the disease group having a microbiome indicative of insomnia tend to have higher values than the control distribution.
  • Moryella is present, a higher RAV would have a higher probability of being in the insomnia population.
  • the p-value in this instance is 9.34 x 10 "9 , as indicated in TABLE A.
  • the RAVs for the disease having a microbiome indicative of a cerebro-craniofacial health issue can have lower values than the control distribution.
  • the RAVs of the genus taxonomic group can have lower values than the control distribution.
  • Roseburia for the insomnia condition group tend to have lower values than the control group. Thus, if Roseburia is present, a lower RAV woi
  • insomnia population The p-value in this instance is 4.53 x 10 "7 , as indicated in TABLE A.
  • FIG. 8 shows a plot illustrating the control distribution and the disease distribution for insomnia where the sequence group is Selenocompound metabolism for the function taxonomic group according to embodiments of the present invention.
  • the RAVs for the disease group having a microbiome indicative of insomnia tend to have lower values than the control distribution.
  • the p-value in this instance is 1.99 x 10 "13 , as indicated in TABLE A.
  • FIG. 9 shows a plot illustrating the control distribution and the disease distribution for light sleep where the sequence group is Lactobacillaceae for the Family taxonomic group according to embodiments of the present invention.
  • the RAVs for the disease group having a microbiome indicative of light sleep tend to have higher values than the control distribution.
  • Lactobacillaceae is present, a higher RAV would have a higher probability of being in the light sleep population.
  • the p-value in this instance is 4.72 x 10 "8 , as indicated in TABLE B.
  • FIG. 10 shows a plot illustrating the control distribution and the disease distribution for light sleep where the sequence group is Translation for the function taxonomic group according to embodiments of the present invention.
  • the RAVs for the disease group having a microbiome indicative of light sleep tend to have lower values than the control distribution.
  • the p-value in this instance is 1.13 x 10 "7 , as indicated in TABLE B.
  • FIG. 11 shows a plot illustrating the control distribution and the disease distribution for headache where the sequence group is Marvinbryantia for the Genus taxonomic group according to embodiments of the present invention.
  • the RAVs for the disease group having a microbiome indicative of headache tend to have lower values than the control distribution.
  • Marvinbryantia is present, a lower RAV would have a higher probability of being in the headache population.
  • the p-value in this instance is 1.31 x 10 "6 , as indicated in TABLE C.
  • FIG. 12 shows a plot illustrating the control distribution and the disease distribution for headache where the sequence group is Selenocompound metabolism for the function taxonomic group according to embodiments of the present invention.
  • FIG. 13 shows a plot illustrating the control distribution and the disease distribution for sinusitis where the sequence group is Clostridiales for the Genus taxonomic group according to embodiments of the present invention.
  • the RAVs for the disease group having a microbiome indicative of sinusitis tend to have lower values than the control distribution.
  • the p-value in this instance is 5.22 x 10 "5 , as indicated in TABLE D.
  • FIG. 14 shows a plot illustrating the control distribution and the disease distribution for poor concentration where the sequence group is Moryella for the Genus taxonomic group according to embodiments of the present invention.
  • the RAVs for the disease group having a microbiome indicative of poor concentration tend to have higher values than the control distribution.
  • the p-value in this instance is 5.06 x 10 "7 , as indicated in TABLE E.
  • FIG. 15 shows a plot illustrating the control distribution and the disease distribution for poor concentration where the sequence group is Propanoate metabolism for the function taxonomic group according to embodiments of the present invention.
  • the RAVs for the disease group having a microbiome indicative of poor concentration tend to have lower values than the control distribution.
  • certain samples may not have any presence of a particular taxonomic group, or at least not a presence above a relatively low threshold (i.e., a threshold below either of the two distributions for the control and condition population).
  • a particular sequence group may be prevalent in the population, e.g., more than 30% of the population may have the taxonomic group.
  • Another sequence group may be less prevalent in the population, e.g., showing up in only 5% of the population.
  • sequence group may be used to determine a diagnosis.
  • the sequence group can be used to determine a status of the disease (e.g., diagnose for the disease) when the subject falls within the 30%. But, when the subject does not fall within the 30%, such that the taxonomic group is simply not present, the particular taxonomic group may not be helpful in determining a diagnosis of the subject. Thus, whether a particular taxonomic group or functional group is useful in diagnosing a particular subject can be dependent on whether nucleic acid molecules corresponding to the sequence group are actually sequenced.
  • the disease signature can include more sequence groups that are used for a given subject. As an example, the disease signature can include 100 sequence groups, but only 60 of sequence groups may be detected in a sample. The classification of the subject (including any probability for being in the application) would be determined based on the 60 sequence groups.
  • sequence groups with high discrimination levels e.g., low p-values
  • a given condition e.g., a cerebro-craniofacial health issue
  • the disease signature can include a set of sequence groups as well as discriminating criteria (e.g., cutoff values and/or probability distributions) used to provide a classification of the subject.
  • the classification can be binary (e.g., indicative of a cerebro- craniofacial health issue or not indicative of a cerebro-craniofacial health issue) or have more classifications (e.g., probability of being indicative of a cerebro-craniofacial health issue or not being indicative of a cerebro-craniofacial health issue).
  • classifications e.g., probability of being indicative of a cerebro-craniofacial health issue or not being indicative of a cerebro-craniofacial health issue.
  • a separate characterization model can be determined for different populations, e.g., by geography where the subject is currently residing (e.g., country, region, or continent), the generic history of the subject (e.g., ethnicity), or other factors. 1. Selection of sequence gi
  • sequence groups having at least a specified discrimination level can be selected for inclusion in the characterization model.
  • the specified discrimination level can be an absolute level (e.g., having a p-value below a specified value), a percentage (e.g., being in the top 10% of discriminating levels), or a specified number of the top discrimination levels (e.g., the top 100 discriminating levels).
  • the characterization model can include a network graph, where each node in a graph corresponds to a sequence group having at least a specified discrimination level.
  • the sequence groups used in a disease signature of a characterization model can also be selected based on other factors.
  • a particular sequence group may only be detected in a certain percentage of the population, referred to as a coverage percentage.
  • An ideal sequence group would be detected in a high percentage of the population and have a high discriminating level (e.g., a low p-value).
  • a minimum percentage may be required before adding the sequence group to the characterization model for a particular disease (e.g., a cerebro-craniofacial health issue). The minimum percentage can vary based on the accompanying discriminating level. For instance, a lower coverage percentage may be tolerated if the discriminating level is higher.
  • 95% of the patients with a disease may be classified with one or a combination of a few sequence groups, and the 5% remaining can be explained based on one sequence group, which relates to the orthogonality or overlap between the coverage of sequence groups.
  • a sequence group that provides discriminating power for 5% of the individuals having the disease e.g., a cerebro- craniofacial health issue
  • Another factor for determining which sequence to include in a disease signature of the characterization model is the overlap in the subjects exhibiting the sequence groups of a disease signature. For example, to sequence groups can both have a high coverage percentage, but sequence groups may cover the exact same subjects. Thus, adding one of the sequence groups does increase the overall coverage of the disease signature. In such a situation, the two sequence groups can be considered parallel to each other. Another sequence group can be selected to add to the characterization model based on the sequence group covering different subjects than other sequence groups already in the characterization model. Such a sequence group can be considered orthogonal to the already existing sequence groups in the characterization model. [0130] As examples, selecting a sequence grc
  • a taxa that appears in only 20% of individuals not having the disease and 30% of individuals having the disease can have distributions of relative abundance that are so different from one another, it allows to catalogue 20% of individuals not having the disease and 30%) of individuals having the disease (i.e. it has a high discriminating level).
  • machine learning techniques can allow the automatic identification of the best combination of features (e.g., sequence groups). For instance, a Principal Component Analysis can reduce the number of features used for classification to only those that are the most orthogonal to each other and can explain most of the variance in the data. The same is true for a network theory approach, where one can create multiple distance metrics based on different features and evaluate which distance metric is the one that best separates individuals having the disease ( a cerebro-craniofacial health issue) from individuals that do not have the disease.
  • features e.g., sequence groups
  • a Principal Component Analysis can reduce the number of features used for classification to only those that are the most orthogonal to each other and can explain most of the variance in the data. The same is true for a network theory approach, where one can create multiple distance metrics based on different features and evaluate which distance metric is the one that best separates individuals having the disease ( a cerebro-craniofacial health issue) from individuals that do not have the disease.
  • the discrimination criteria for the sequence groups included in the disease signature of a characterization model can be determined based on the disease distributions and the control distributions for the disease.
  • a discrimination criterion for a sequence group can be a cutoff value that is between the mean values for the two distributions.
  • discrimination criteria for a sequence group can include probability distributions for the control and disease populations. The probability distributions can be determined in a separate manner from the process of determining the discrimination level.
  • the probability distributions can be determined based on the distribution of RAVs for the two populations.
  • the probability distribution for the disease population can have its peak at 20%.
  • the width or other shape parameters e.g., the decay
  • the same can be done for the control population.
  • the sequence groups included in the disease signature of the characterization can be used to classify a new subject.
  • the sequence groups can be considered features of the feature vector, or the RAVs of the sequence groups considered as features of a feature vector, where the feature vector can be compared to the discriminating criteria of the disease signature. For instance, the RAVs of the sequence groups for the new subject can be compared to the probability distributions for each sequence group of the disease signature. If an RAV is zero or nearly zero, then the sequence group may be skipped and not used in the classification.
  • the RAVs for sequence groups that are exhibited in the new subject can be used to determine the classification.
  • the result e.g., a probability value
  • clustering of the RAVs can be performed, and the clusters can be used to determine a classification of a disease.
  • Embodiments can provide a method for determining a classification of the presence or absence for a disease and/or determine a course of treatment for an individual human having the disease ( a cerebro-craniofacial health issue such as insomnia, light sleep, headache, sinusitis, or poor concentration).
  • the method can be performed by a computer system, as described herein.
  • FIG. IB is a flowchart of an embodiment of a method for determining a classification of the presence or absence of a microbiome indicative of a cerebro-craniofacial health issue and/or determining the course of treatment for an individual human having the microbiome indicative of a cerebro-craniofacial health issue.
  • sequence reads of bacterial DNA obtained from analyzing a test sample from the individual human are received.
  • the analysis can be done with various techniques, e.g., as described herein, such as sequencing or hybridization arrays.
  • the sequence reads can be received at a computer system, e.g., from a detection apparatus, such as a sequencing machine that provides data to a storage device (which can be loaded into the computer system) or across a network to the computer system.
  • the sequence reads are mapped to a bacterial sequence database to obtain a plurality of mapped sequence reads.
  • the bacterial sequence database includes a plurality of reference sequences of a plurality of bacteria.
  • the reference sequences can be for predetermined region(s) of the bacteria, e.g., the 16S region.
  • a sequence group includes one or more of the plurality of reference sequences.
  • the mapping can involve the sequence reads being mapped to one or more predetermined regions of the reference sequences. For example, the sequence reads can be mapped to the 16S gene. Thus, the sequence reads do not have to be mapped to the whole genome, but only to the region(s) covered by the reference sequences of a sequence group.
  • a total number of assigned sequence reads is determined.
  • the total number of assigned reads can include reads identified as being, e.g., bacterial, but not assigned to a known sequence group.
  • the total number can be a sum of sequence reads assigned to known sequence groups, where the sum may include any sequence read assigned to at least one sequence group.
  • relative abundance value(s) can be determined. For example, for each sequence group of a disease signature set of one or more sequence groups selected from TABLEs A, B, C, D, or E, a relative abundance value of assigned sequence reads assigned to the sequence group relative to the total number of assigned sequence reads can be determined.
  • the relative abundance values can form a test feature vector, where each values of the test feature vector is an RAV of a different sequence group.
  • the test feature vector is compared to calibration feature vectors generated from relative abundance values of calibration samples having a known status of the disease.
  • the calibration samples may be samples of a disease population and samples of a control population.
  • the comparison can involve various machine learning techniques, such as supervised machine learning (e.g. decision trees, nearest neighbor, support vector machines, neural networks, naive Bayes classifier, etc%) and unsupervised machine learning (e.g., clustering, principal component analysis, etc).
  • clustering can use a network approach, where the distance between each pair of samples in the network is computed based on the relative abundance of the sequence groups that are relevant for each disease. Then, a new sample can be compared to all samples in the network, using the same metric based on relative abundance, and it can be decided to which cluster it should belong.
  • a meaningful distance metric would allow all individuals having the disease ( a cerebro-craniofacial health issue) to form one or a few clusters and all individuals lacking the disease to form one or a few clusters.
  • One distance metric is the Bray-Curtis dissimilarity, or equiv
  • the feature vectors may be compared by transforming the RAVs into probability values, thereby forming probability vectors. Similar processing for the feature vectors can be performed for the probability, with such a process still involving a comparison of the feature vectors since the probability vectors are generated from the feature vectors.
  • Block 26 can determine a classification of the presence or absence of the disease (e.g., a cerebro-craniofacial health issue) and/or determine a course of treatment for an individual human having the disease based on the comparing.
  • the cluster to which the test feature vector is assigned may be a disease cluster, and the classification can be made that the individual human has the disease or a certain probability for having the disease.
  • the calibration feature vectors can be clustered into a control cluster not having the disease and a disease cluster having the disease. Then, which cluster the test feature vector belongs can be determined. The identified cluster can be used to determine the classification or select a course of treatment.
  • the clustering can use a Bray-Curtis dissimilarity.
  • the comparison may be performed to by comparing the test feature vector to one or more cutoff values (e.g., as a corresponding cutoff vector), where the one or more cutoff values are determined from the calibration feature vectors, thereby providing the comparison.
  • the comparison can include comparing each of the relative abundance values of the test feature vector to a respective cutoff value determined from the calibration feature vectors generated from the calibration samples. The respective cutoff values can be determined to provide an optimal discrimination for each sequence group.
  • a new sample can be measured to detect the RAVs for the sequence groups in the disease signature.
  • the RAV for each sequence group can be compared to the probability distributions for the control and disease populations for the particular sequence group.
  • the probability distribution for the disease population can provide an output of a probability (e.g., a conditional probability) of having the disease (condition) for a given input of the RAV.
  • the probability distribut e.g., a conditional probability
  • the value of the probability distribution at the RAV can provide the probability of the sample being in each of the populations. Thus, it can be determined which population the sample is more likely to belong to, by taking the maximum probability.
  • just the maximum probability is used in further steps of a characterization process. In other embodiments, both the disease probability and the control probability are used. As noted above, the probability distributions used here for classification may be different than the statistical test used to determine whether the distribution of RAV values are separated, e.g., the KS test.
  • a total probability across sequence groups of a disease signature can be used. For all of the sequence groups that are measured, a disease probability can be determined for whether the sample is in the disease group and a control probability can be determined for whether the sample is in the control population. In other embodiments, just the disease probabilities or just the control probabilities can be determined.
  • the probabilities across the sequence groups can be used to determine a total probability. For example, an average of the conditional probabilities can be determined, thereby obtaining a final disease probability of the subject having the disease based on the disease signature. An average of the control probabilities can be determined, thereby obtaining a final control probability of the subject not having the disease based on the disease signature.
  • the final disease probability and final control probability can be compared to each other to determine the final classification. For instance, a difference between the two final probabilities can be determined, and a final classification probability determined from the difference. A large positive difference with final disease probability being higher would result in a higher final classification probability of the subject having the disease.
  • the final classification probability can be the final disease probability.
  • the final classification probability can be one minus the final control probability, or 100% minus the final control probability depending on the formatting of the probabilities.
  • a final classifk can be the final disease probability.
  • the final classification probability can be one minus the final control probability, or 100% minus the final control probability depending on the formatting of the probabilities.
  • embodiments can be combined with other final classification probabilities of other disease of the same class.
  • the aggregated probability can then be used to determine whether the subject has at least one of the class of diseases.
  • embodiments can determine whether a subject has a health issue that may include a plurality of diseases associated with that health issue.
  • the classification can be one of the final probabilities.
  • embodiments can compare a final probability to a threshold value to make a determination of whether the disease exists.
  • the respective conditional probabilities can be averaged, and an average can be compared to a threshold value to determine whether the disease exists.
  • the comparison of the average to the threshold value can provide a treatment for treating the subject.
  • a first method 100 for diagnosing and treating an individual having a microbiome indicative of a cerebro-craniofacial health issue can comprise:
  • the method can further comprise: receiving a supplementary dataset, associated with at least a subset of the population of subjects, wherein the supplementary dataset is informative of characteristics associated with a cerebro-craniofacial health issue S130.
  • the method further comprises: and transforming the features extracted from the at least one microbiome composition dataset, microbiome functional diversity dataset, or the combination thereof, into a characterization model of a cerebro-craniofacial health issue S140.
  • the transforming includes transforming the supplementary dataset, if received.
  • the first method 100 can further include: based upon the characterization, generating a therapy model configured to in
  • the first method 100 functions to generate models that can be used to characterize and/or diagnose subjects according to at least one of their microbiome composition and functional features (e.g., as a clinical diagnostic, as a companion diagnostic, etc.), and provide therapeutic measures (e.g., probiotic-based therapeutic measures, phage-based therapeutic measures, small-molecule-based therapeutic measures, prebiotic-based therapeutic measures, clinical measures, etc.) to subjects based upon microbiome analysis for a population of subjects.
  • therapeutic measures e.g., probiotic-based therapeutic measures, phage-based therapeutic measures, small-molecule-based therapeutic measures, prebiotic-based therapeutic measures, clinical measures, etc.
  • data from the population of subjects can be used to characterize subjects according to their microbiome composition and/or functional features, indicate states of health and areas of improvement based upon the characterization(s), and promote one or more therapies that can modulate the composition of a subject's
  • microbiome toward one or more of a set of desired equilibrium states.
  • the method 100 can be used to promote targeted therapies to subjects having a microbiome indicative of a cerebro-craniofacial health issue.
  • the targeted therapies are promoted when the cerebro-craniofacial health issue produces observed differences in insomnia, light sleep, headache, sinusitis, or poor concentration or at least one of social behavior, motor behavior, and energy levels, gastrointestinal heath, etc.
  • diagnostics associated with a cerebro-craniofacial health issue can be typically assessed using one or more of: a survey instrument or study, such as a sleep study, and any other standard tool.
  • the method 100 can be used to characterize the effects of a cerebro-craniofacial health issue, including disorders, and/or adverse states in an entirely non-typical method.
  • the inventors propose that characterization of the microbiome of individuals can be useful for predicting the likelihood of a cerebro- craniofacial health issue in subjects. Such characterizations can also be useful for screening for symptoms related to a cerebro-craniofacial health issue and/or determining a course of treatment for an individual human having a microbiome indicative of a cerebro-craniofacial health issue.
  • the inventors propose that features associated with certain microbiome compositional and/or functional features (e.g., the amount of certain bacteria and/or bacterial sequences corresponding to certain genetic pathways) can be used to predict the presence or absence of a microbiome indicative of a cerebro-craniofacial health issue.
  • the bacteria and genetic pathways in some cases are present in a certain abundance in individuals having a microbiome indicative of a cerebro- craniofacial health issue as discussed in more
  • outputs of the first method 100 can be used to generate diagnostics and/or provide therapeutic measures for a subject based upon an analysis of the subject's microbiome composition and/or functional features of the subject's microbiome.
  • a second method 200 derived from at least one output of the first method 100 can include: receiving a biological sample from a subject S210; characterizing the subject as having or not having a microbiome indicative of a cerebro-craniofacial health issue based upon processing a microbiome dataset derived from the biological sample S220; and promoting a therapy to the subject with the microbiome indicative of a cerebro-craniofacial health issue based upon the characterization and the therapy model S230.
  • Variations of the method 200 can further facilitate monitoring and/or adjusting of therapies provided to a subject, for instance, through reception, processing, and analysis of additional samples from a subject throughout the course of therapy.
  • Embodiments, variations, and examples of the second method 200 are described in more detail below.
  • methods 100 and/or 200 can function to generate models that can be used to classify individuals and/or provide therapeutic measures (e.g., therapy recommendations, therapies, therapy regimens, etc.) to individuals based upon microbiome analysis for a population of individuals.
  • therapeutic measures e.g., therapy recommendations, therapies, therapy regimens, etc.
  • data from the population of individuals can be used to generate models that can classify individuals according to their microbiome
  • compositions (e.g., as a diagnostic measure), indicate states of health and areas of improvement based upon the classification(s), and/or provide therapeutic measures that can push the composition of an individual's microbiome toward one or more of a set of improved equilibrium states.
  • Variations of the second method 200 can further facilitate monitoring and/or adjusting of therapies provided to an individual, for instance, through reception, processing, and analysis of additional samples from an individual throughout the course of therapy.
  • at least one of the methods 100, 200 is implemented, at least in part, at a system 300, as shown in FIG.
  • a processing system implementing a characterization process and a therapy model configured to positively influence a microorganism distribution in the subject (e.g., human, non-hi
  • the processing system can be configured to generate and/or improve the characterization process and the therapy model based upon sample data received from a population of subjects.
  • the method 100 can, however, alternatively be implemented using any other suitable system(s) configured to receive and process microbiome-related data of subjects, in aggregation with other information, in order to generate models for microbiome-derived diagnostics and associated therapeutics.
  • the method 100 can be implemented for a population of subjects (e.g., including the subject, excluding the subject), wherein the population of subjects can include patients dissimilar to and/or similar to the subject (e.g., in health condition, in dietary needs, in demographic features, etc.).
  • information derived from the population of subjects can be used to provide additional insight into connections between behaviors of a subject and effects on the subject's microbiome, due to aggregation of data from a population of subjects.
  • the methods 100, 200 can be implemented for a population of subjects (e.g., including the subject, excluding the subject), wherein the population of subjects can include subjects dissimilar to and/or similar to the subject (e.g., health condition, in dietary needs, in demographic features, etc.).
  • the population of subjects can include subjects dissimilar to and/or similar to the subject (e.g., health condition, in dietary needs, in demographic features, etc.).
  • information derived from the population of subjects can be used to provide additional insight into connections between behaviors of a subject and effects on the subject's microbiome, due to aggregation of data from a population of subjects.
  • Block SI 10 recites: receiving an aggregate set of biological samples from a population of subjects, which functions to enable generation of data from which models for characterizing subjects and/or providing therapeutic measures to subjects can be generated.
  • biological samples are preferably received from subjects of the population of subjects in a non-invasive manner.
  • non-invasive manners of sample reception can use any one or more of: a permeable substrate (e.g., a swab configured to wipe a region of a subject's body, toilet paper, a sponge, etc.), a non-permeable substrate (e.g., a slide, tape, etc.), a container (e.g., vial, tube, bag, etc.) configured to receive a sample from a region of a subject's body, and any other suitable sample-reception element.
  • samples can be collected from one or more of a subject's nose, skin, genitals, mouth, and gut in a non-invasive manner (e.g., using a swab and a vial).
  • one or more biological samples of the set of biological samples can additionally or alternatively be received in a semi-invasive manner or an invasive manner.
  • invasive manners of sample reception can use any one or more of: a permeable substrate (e.g., a swab
  • samples can comprise blood samples, plasma/serum samples (e.g., to enable extraction of cell-free DNA), cerebrospinal fluid, and tissue samples.
  • the sample is a stool sample, or a sample (e.g., a nucleic acid sample, such as a DNA sample) extracted from a stool sample.
  • samples can be taken from the bodies of subjects without facilitation by another entity (e.g., a caretaker associated with an individual, a health care professional, an automated or semi-automated sample collection apparatus, etc.), or can alternatively be taken from bodies of individuals with the assistance of another entity.
  • a sample-provision kit can be provided to a subject.
  • the kit can include one or more swabs or sample vials for sample acquisition, one or more containers configured to receive the swab(s) or sample vials for storage, instructions for sample provision and setup of a user account, elements configured to associate the sample(s) with the subject (e.g., barcode identifiers, tags, etc.), and a receptacle that allows the sample(s) from the individual to be delivered to a sample processing operation (e.g., by a mail delivery system).
  • a sample processing operation e.g., by a mail delivery system.
  • samples are extracted from the user with the help of another entity
  • one or more samples can be collected in a clinical or research setting from a subject (e.g., during a clinical appointment).
  • Block SI 10 the aggregate set of biological samples is preferably received from a wide variety of subjects, and can involve samples from human subjects and/or non- human subjects.
  • Block S I 10 can include receiving samples from a wide variety of human subjects, collectively including subjects of one or more of: different demographics (e.g., genders, ages, marital statuses, ethnicities, nationalities, socioeconomic statuses, sexual orientations, etc.), different health conditions (e.g., health and disease states), different living situations (e.g., living alone, living with pets, living with a significant other, living with children, etc.), different dietary habits (e.g., omnivorous, vegetarian, vegan, sugar consumption, acid consumption, etc.), different behavioral tendencies (e.g., levels of physical activity, drug use, alcohol use, etc.), different levels of mobility (e.g., related to distance traveled within a given time period), biomarker states (e.g., cholesterol levels, lipid levels, etc.), weight, height,
  • different demographics e.
  • the aggregate set of biological samples received in Block SI 10 can include receiving biological samples from a targeted group of similar subjects in one or more of: demographic traits, health conditions, living situations, dietary habits, behavior tendencies, levels of mobility, age range (e.g., pediatric, adulthood, geriatric), and any other suitable trait that has an effect on microbiome composition.
  • the methods 100, and/or 200 can be adapted to characterize diseases typically detected by way of lab tests (e.g., polymerase chain reaction based tests, cell culture based tests, blood tests, biopsies, chemical tests, etc.), physical detection methods (e.g., manometry), medical history based assessments, behavioral assessments, and imagenology based assessments. Additionally or alternatively, the methods 100, 200 can be adapted to characterization of acute conditions, chronic conditions, conditions with difference in prevalence for different demographics, conditions having characteristic disease areas (e.g., the head, the gut, endocrine system diseases, the heart, nervous system diseases, respiratory diseases, immune system diseases, circulatory system diseases, renal system diseases, locomotor system diseases, etc.), and comorbid conditions.
  • lab tests e.g., polymerase chain reaction based tests, cell culture based tests, blood tests, biopsies, chemical tests, etc.
  • physical detection methods e.g., manometry
  • medical history based assessments e.g., behavioral assessments, and
  • receiving the aggregate set of biological samples in Block SI 10 can be performed according to embodiments, variations, and examples of sample reception as described in U.S. App. No. 14/593,424 filed on 09-JAN-2015 and entitled
  • Block SI 10 receives the aggregate set of biological samples in Block SI 10 in any other suitable manner.
  • some alternative variations of the first method 100 can omit Block SI 10, with processing of data derived from a set of biological samples performed as described below in subsequent blocks of the method 100.
  • Block S120 recites: characterizing a microbiome composition and/or functional features for each of the aggregate set of biological samples associated with a population of subjects, thereby generating at least one of a microbiome composition dataset and a microbiome functional diversity dataset for the population of subjects.
  • Block SI 20 functions to process each of the aggregate set of biological samples, in order to determine
  • compositional and/or functional aspects associated with the microbiome of each of a population of subjects can include compositional aspects at the microorganism level, including p
  • compositional and functional aspects can also be represented in terms of operational taxonomic units (OTUs).
  • Compositional and functional aspects can additionally or alternatively include compositional aspects at the genetic level (e.g., regions determined by multilocus sequence typing, 16S sequences, 18S sequences, ITS sequences, other genetic markers, other phylogenetic markers, etc.).
  • Compositional and functional aspects can include the presence or absence or the quantity of genes associated with specific functions (e.g., enzyme activities, transport functions, immune activities, etc.).
  • Outputs of Block S120 can thus be used to provide features of interest for the characterization process of Block SI 40, wherein the features can be microorganism-based (e.g., presence of a genus of bacteria), genetic-based (e.g., based upon representation of specific genetic regions and/or sequences) and/or functional-based (e.g., presence of a specific catalytic activity, presence of metabolic pathways, etc.).
  • Block S120 can include characterization of features based upon identification of phylogenetic markers derived from bacteria and/or archaea in relation to gene families associated with one or more of: ribosomal protein S2, ribosomal protein S3, ribosomal protein S5, ribosomal protein S7, ribosomal protein S8, ribosomal protein S9, ribosomal protein S10, ribosomal protein SI 1, ribosomal protein S12/S23, ribosomal protein S13, ribosomal protein S15P/S13e, ribosomal protein S17, ribosomal protein S19, ribosomal protein LI, ribosomal protein L2, ribosomal protein L3, ribosomal protein L4/Lle, ribosomal protein L5, ribosomal protein L6, ribosomal protein L10, ribosomal protein LI 1, ribosomal protein L13, ribosomal protein
  • markers can include any other suitable marker(s).
  • Characterizing the microbiome composition and/or functional features for each of the aggregate set of biological samples in Block S120 thus can include a combination of sample processing techniques (e.g., wet laborat
  • sample processing in Block S120 can include any one or more of: lysing a biological sample, disrupting membranes in cells of a biological sample, separation of undesired elements (e.g., RNA, proteins) from the biological sample, purification of nucleic acids (e.g., DNA) in a biological sample, amplification of nucleic acids from the biological sample, further purification of amplified nucleic acids of the biological sample, and sequencing of amplified nucleic acids of the biological sample.
  • undesired elements e.g., RNA, proteins
  • Block S120 can be implemented using embodiments, variations, and examples of the sample handling network and/or computing system as described in U.S. App. No. 14/593,424 filed on 09-JAN-2015 and entitled “Method and System for microbiome Analysis", which is incorporated herein in its entirety by this reference.
  • the computing system
  • lysing a biological sample and/or disrupting membranes in cells of a biological sample preferably includes physical methods (e.g., bead beating, nitrogen decompression, homogenization, sonication), which omit certain reagents that produce bias in representation of certain bacterial groups upon sequencing.
  • lysing or disrupting in Block S120 can involve chemical methods (e.g., using a detergent, using a solvent, using a surfactant, etc.). Additionally or alternatively, lysing or disrupting in Block S120 can involve biological methods. In variations, separation of undesired elements can include removal of RNA using RNases and/or removal of proteins using proteases.
  • purification of nucleic acids can include one or more of: precipitation of nucleic acids from the biological samples (e.g., using alcohol-based precipitation methods), liquid- liquid based purification techniques (e.g., phenol-chloroform extraction), chromatography- based purification techniques (e.g., column adsorption), purification techniques involving use of binding moiety-bound particles (e.g., magnetic beads, buoyant beads, beads with size distributions, ultrasonically responsive beads, etc.) configured to bind nucleic acids and configured to release nucleic acids in the presence of an elution environment (e.g., having an elution solution, providing a pH shift, providing
  • an elution environment e.g., having an elution solution, providing a pH shift
  • performing an amplification operation S 123 on purified nucleic acids can include performing one or more of: polymerase chain reaction (PCR)-based techniques (e.g., solid-phase PCR, RT-PCR, qPCR, multiplex PCR, touchdown PCR, nanoPCR, nested PCR, hot start PCR, etc.), helicase-dependent amplification (HDA), loop mediated isothermal amplification (LAMP), self-sustained sequence replication (3 SR), nucleic acid sequence based amplification (NASBA), strand displacement amplification (SDA), rolling circle amplification (RCA), ligase chain reaction (LCR), and any other suitable amplification technique.
  • PCR polymerase chain reaction
  • HDA helicase-dependent amplification
  • LAMP loop mediated isothermal amplification
  • SR self-sustained sequence replication
  • NASBA nucleic acid sequence based amplification
  • SDA strand displacement amplification
  • RCA rolling circle amplification
  • LCR liga
  • the primers used are preferably selected to prevent or minimize amplification bias, as well as configured to amplify nucleic acid regions/sequences (e.g., of the 16S region, the 18S region, the ITS region, etc.) that are informative taxonomically, phylogenetically, for diagnostics, for formulations (e.g., for probiotic formulations), and/or for any other suitable purpose.
  • amplification bias e.g., a F27-R338 primer set for 16S rRNA, a F515-R806 primer set for 16S RNA, etc.
  • Primers used in variations of Block S 120 can additionally or alternatively include incorporated barcode sequences specific to each biological sample, which can facilitate identification of biological samples post-amplification.
  • Primers used in variations of Block S120 can additionally or alternatively include adaptor regions configured to cooperate with sequencing techniques involving complementary adaptors (e.g., according to protocols for Illumina Sequencing).
  • Identification of a primer set for a multiplexed amplification operation can be performed according to embodiments, variations, and examples of methods described in U.S. App. No. 62/206,654 filed 18-AUG-2015 and entitled "Method and System for Multiplex Primer Design", which is herein incorporated in its entirety by this reference.
  • Performing a multiplexed amplification operation using a set of primers in Block SI 23 can additionally or alternatively be performed in any other suitable manner.
  • Block S 120 can implement any other step configured to facilitate processing (e.g., using a Nextera kit) for performance of a fragmentation operation S 122 (e.g., fragmentation and tagging with sequencing adaptors) in cooperation with the amplification operation S 123 (e.g., S 122 can be performed after S I 23, S I 22 can be performed before S I 23, S 122 can be performed substantially contemporaneously with S 123, etc.).
  • a fragmentation operation S 122 e.g., fragmentation and tagging with sequencing adaptors
  • S 123 e.g., S 122 can be performed after S I 23, S I 22 can be performed before S I 23, S 122 can be performed substantially contemporaneously with S 123, etc.
  • Blocks S 122 and/or S 123 can be performed with or without a nucleic a ⁇
  • Block S123 can be performed prior to amplification of nucleic acids, followed by fragmentation, and then amplification of fragments. Alternatively, extraction can be performed, followed by fragmentation and then amplification of fragments. As such, in some embodiments, performing an amplification operation in Block S123 can be performed according to embodiments, variations, and examples of amplification as described in U.S. App. No. 14/593,424 filed on 09-JAN-2015 and entitled "Method and System for microbiome Analysis". Furthermore, amplification in Block S123 can additionally or alternatively be performed in any other suitable manner.
  • amplification and sequencing of nucleic acids from biological samples of the set of biological samples includes: solid-phase PCR involving bridge amplification of DNA fragments of the biological samples on a substrate with oligo adapters, wherein amplification involves primers having a forward index sequence (e.g., corresponding to an illumina forward index for miSeq/NextSeq/HiSeq platforms) and/or a reverse index sequence (e.g., corresponding to an Illumina reverse index for
  • a forward barcode sequence and/or a reverse barcode sequence optionally a transposase sequence (e.g., corresponding to a transposase binding site for MiSeq/NextSeq/HiSeq platforms), optionally a linker (e.g., a zero, one, or two-base fragment configured to reduce homogeneity and improve sequence results), optionally an additional random base, and optionally a sequence for targeting a specific target region (e.g., 16S region, 18S region, ITS region).
  • amplification involves one or both primers having any combination of the foregoing elements, or all of the foregoing elements.
  • sequencing comprises Illumina sequencing (e.g., with a HiSeq platform, with a MiSeq platform, with a NextSeq platform, etc.) using a sequencing-by-synthesis technique.
  • any other suitable next generation sequencing technology e.g., PacBio platform, MinlON platform, Oxford Nanopore platform, etc.
  • any other suitable sequencing platform or method can be used (e.g., a Roche 454 Life Sciences platform, a Life Technologies SOLiD platform, etc.).
  • sequencing can include deep sequencing to quantify the number of copies of a particular sequence in a sample and then also be used to determine the relative abundance of different sequences in a sample.
  • the sequencing depth can be, or be at least about 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, 60, 70, 80, 90, 100, 1 10
  • sample processing in Block S I 20 can include further purification of amplified nucleic acids (e.g., PCR products) prior to sequencing, which functions to remove excess amplification elements (e.g., primers, dNTPs, enzymes, salts, etc.).
  • additional purification can be facilitated using any one or more of: purification kits, buffers, alcohols, pH indicators, chaotropic salts, nucleic acid binding filters, centrifugation, and any other suitable purification technique.
  • computational processing in Block S 120 can include any one or more of: performing a sequencing analysis operation S 124 including identification of microbiome-derived sequences (e.g., as opposed to subject sequences and contaminants), performing an alignment and/or mapping operation SI 25 of microbiome-derived sequences (e.g., alignment of fragmented sequences using one or more of single-ended alignment, ungapped alignment, gapped alignment, pairing), and generating features S 126 derived from compositional and/or functional aspects of the microbiome associated with a biological sample.
  • a sequencing analysis operation S 124 including identification of microbiome-derived sequences (e.g., as opposed to subject sequences and contaminants), performing an alignment and/or mapping operation SI 25 of microbiome-derived sequences (e.g., alignment of fragmented sequences using one or more of single-ended alignment, ungapped alignment, gapped alignment, pairing), and generating features S 126 derived from compositional and/or functional aspects of the microbiome associated with a biological sample.
  • Performing the sequencing analysis operation S 124 with identification of microbiome-derived sequences can include mapping of sequence data from sample processing to a subject reference genome (e.g., provided by the Genome Reference
  • Unidentified sequences remaining after mapping of sequence data to the subject reference genome can then be further clustered into operational taxonomic units (OTUs) based upon sequence similarity and/or reference-based approaches (e.g., using VAMPS, using MG-RAST, and/or using QIIME databases), aligned (e.g., using a genome hashing approach, using a
  • Needleman- Wunsch algorithm using a Smith-Waterman algorithm
  • mapped to reference bacterial genomes e.g., provided by the National Center for Biotechnology Information
  • an alignment algorithm e.g., Basic Local Alignment Search Tool, FPGA accelerated alignment tool, BWT-indexing with BWA, BWT-indexing with SOAP, BWT-indexing with Bowtie, etc.
  • Mapping of unidentified sequences can additionally or alternatively include mapping to reference archaeal genomes, viral genomes and/or eukaryotic genomes.
  • mapping of taxa can be performed in relation to existing databases, and/or in relation to custom-generated databases. [0180] Additionally or alternatively, in relatic
  • Block S120 can include extracting candidate features associated with functional aspects of one or more microbiome components of the aggregate set of biological samples S127, as indicated in the microbiome composition dataset. Extracting candidate functional features can include identifying functional features associated with one or more of: prokaryotic clusters of orthologous groups of proteins (COGs); eukaryotic clusters of orthologous groups of proteins (KOGs); any other suitable type of gene product; an RNA processing and modification functional classification; a chromatin structure and dynamics functional classification; an energy production and conversion functional classification; a cell cycle control and mitosis functional classification; an amino acid metabolism and transport functional classification; a nucleotide metabolism and transport functional classification; a carbohydrate metabolism and transport functional classification; a coenzyme metabolism functional classification; a lipid metabolism functional classification; a translation functional classification; a transcription functional classification; a replication and repair functional classification; a cell wall/membrane/envelop biogenesis functional classification; a cell motility functional classification; a post-translational modification, protein turnover
  • extracting candidate functional features in Block S127 can include identifying functional features associated with one or more of: systems information (e.g., pathway maps for cellular and organismal functions, modules or functional units of genes, hierarchical classifications of biological entities); genomic information (e.g., complete genomes, genes and proteins in the complete genomes, orthologous groups of genes in the complete genomes); chemical information (e.g., chemical compounds and glycans, chemical reactions, enzyme nomenclature); health information (e.g., human diseases, approved drugs, crude drugs and health-related substances); metabolism pathway maps; genetic information processing (e.g., transcription, translation, replication and repair, etc.) pathway maps; environmental information processing (e.g., membrane transport, signal transduction, etc.) pathway maps; cellular processes (e.g., cell growth, cell death, cell membrane functions, etc.) pathway maps; organismal systems (e.g., immune system, endocrine system, nervous system, etc.) pathwa
  • systems information e.g., pathway maps for cellular and
  • Block S127 can comprise performing a search of one or more databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and/or the Clusters of Orthologous Groups (COGs) database managed by the KEGG.
  • KEGG Kyoto Encyclopedia of Genes and Genomes
  • COGs Clusters of Orthologous Groups
  • Block S127 can include implementation of a data-oriented entry point to a KEGG database including one or more of a KEGG pathway tool, a KEGG BRITE tool, a KEGG module tool, a KEGG ORTHOLOGY (KO) tool, a KEGG genome tool, a KEGG genes tool, a KEGG compound tool, a KEGG glycan tool, a KEGG reaction tool, a KEGG disease tool, a KEGG drug tool, or a KEGG medicus tool. Searching can additionally or alternatively be performed according to any other suitable filters. Additionally or
  • Block S127 can include implementation of an organism-specific entry point to a KEGG database including a KEGG organisms tool. Additionally or alternatively, Block S127 can include implementation of an analysis tool including one or more of: a KEGG mapper tool that maps KEGG pathway, BRITE, or module data; a KEGG atlas tool for exploring KEGG global maps, a BlastKOALA tool for genome annotation and KEGG mapping, a BLAST/FASTA sequence similarity search tool, a SIMCOMP chemical structure similarity search tool, and a SUBCOMP chemical substructure search tool.
  • a KEGG mapper tool that maps KEGG pathway, BRITE, or module data
  • a KEGG atlas tool for exploring KEGG global maps
  • a BlastKOALA tool for genome annotation and KEGG mapping
  • BLAST/FASTA sequence similarity search tool for genome annotation and KEGG mapping
  • SIMCOMP chemical structure similarity search tool a SIMCOMP chemical structure similarity search tool
  • Block S127 can include extracting candidate functional features, based on the microbiome composition dataset, from a KEGG database resource and a COG database resource; moreover, Block S127 can comprise extracting candidate functional features in any other suitable manner.
  • Block S127 can include extracting candidate functional features, including functional features derived from a Gene Ontology functional
  • a taxonomic group can include one or more bacteria and their corresponding reference sequences.
  • a sequence read can be assigned based on the alignment to a taxonomic group when the sequence read aligns to a reference sequence of the taxonomic group.
  • a functional group can correspond to one or more genes labeled as having a similar function.
  • a functional group can be represented by reference sequences of the genes in the functional group, where the reference sequences of a particular gene can correspond to various bacteria.
  • the taxonomic and functional groups can collectively be referred to as sequence groups, as each group includes one or
  • a taxonomic group of multiple bacteria can be represented by multiple reference sequence, e.g., one reference sequence per bacteria species in the taxonomic group.
  • Embodiments can use the degree of alignment of a sequence read to multiple reference sequences to determine which sequence group to assign the sequence read based on the alignment.
  • embodiments can use a count of a number of sequence reads that correspond to a particular gene or a collection of genes having an annotation of a particular function, where the collection is called a functional group.
  • the RAV can be determined in a similar manner as for a taxonomic group.
  • functional group can include a plurality of reference sequences corresponding to one or more genes of the functional group. Reference sequences of multiple bacteria for a same gene can correspond to a same functional group. Then, to determine the RAV, the number of sequence reads assigned to the functional group can be used to determine a proportion for the functional group.
  • the functional group is a KEGG or COG group.
  • a functional group which may include a single gene, can help to identify situations where there is a small change (e.g., increase) in many taxonomic groups such that the individual changes are too small to be statistically significant.
  • the changes may all be for a same gene or set of genes of a same functional group, and thus the change for that functional group can be statistically significant, even though the changes for the taxonomic groups may not be statistically significant for a given sequence dataset.
  • the reverse can be true of a taxonomic group being more predictive than a particular functional group, e.g., when a single taxonomic group includes many genes that have changed by a relatively small amount.
  • the functional group can act to provide a sum of small changes for various taxonomic groups. And, small changes for various functional groups, which happen to all be on a same taxon
  • Embodiments can provide a bioinformatics pipeline that taxonomically annotates the microorganisms present in a sample.
  • the example clinical annotation pipeline can comprise the following procedures described herein.
  • FIG. 1C is a flowchart of an
  • the samples can be identified and the sequence data can be loaded.
  • the pipeline can begin with demultiplexed fastq files (or other suitable files) that are the product of pair-end sequencing of amplicons (e.g., of the V4 region of the 16S gene). All samples can be identified for a given input sequencing file, and the corresponding fastq files can be obtained from the fastq repository server and loaded into the pipeline.
  • the reads can be filtered. For example, a global quality filtering of reads in the fastq files can accept reads with a global Q-score > 30. In one implementation, for each read, the per-position Q-scores are averaged, and if the average is equal or higher than 30, then the read is accepted, else the read is discarded, as is its paired read.
  • primers can be identified and removed.
  • only forward reads that contain the forward primer and reverse reads that contain the reverse primer are further considered.
  • Primers and any sequences 5' to them are removed from the reads.
  • the 125 bp (or other suitable number) towards the 3' of the forward primer are considered from the forward reads, and only 124 bp (or other suitable number) towards the 3' of the reverse primer are considered for the reverse reads. All processed forward reads that are ⁇ 125bp and reverse reads that are ⁇ 124bp are eliminated from further processing as are their paired reads.
  • the forward and reverse reads can be written to files (e.g., FASTA files).
  • files e.g., FASTA files
  • the forward and reverse reads that remained paired can be used to generate files that contain 125bp from the forward read, concatenated to 124bp from the reverse read (in the reverse complement direction).
  • the sequence reads can b(
  • sequences or determine a consensus sequence for a bacterium can be subjected to clustering using the Swarm algorithm [Mahe, F. et al. 2014] with a distance of 1.
  • This treatment allows the generation of cluster composed of a central biological entity, surrounded by sequences which are 1 mutation away from the biological entity, which are less abundant and the result of the normal base calling error associated to high throughput sequencing. Singletons are removed from further analyses. In the remaining clusters, the most abundant sequence per cluster is then used as the representative and assigned the counts of all members in the cluster.
  • chimeric sequences can be removed. For example, amplification of gene superfamilies can produce the formation of chimeric DNA sequences.
  • VSEARCH chimera detection algorithm uses abundance of PCR products to identify reference "real" sequences as those most abundant, and chimeric products as those less abundant and displaying local similarity to two or more of the reference sequences. All chimeric sequences can be removed from further analysis. [0194] In block 36, taxonomy annotation can be assigned to sequences using sequence identity searches.
  • some embodiments can perform identity searches against a database that contains bacterial strains (e.g., reference sequences) annotated to phylum, class, order, family, genus and species level, at least to a subsection of those taxonomic levels, or any other taxonomic levels.
  • the most specific level of taxonomic annotation for a sequence can be kept, given that higher order taxonomy designations for a lower level taxonomy level can be inferred.
  • the sequence identity search can be performed using the algorithm VSEARCH [Rognes, T. et al.
  • FIG. ID is a flowchart of an embodiment of a method for generating features derived from composition and/or functional components of a biological sample or an aggregate of biological samples.
  • sample OTUs (Operational Taxonomic Units) can be found. This may occur, e.g., after the sixth block described above in section V.B.2. After sample OTUs are found, sequences can be clustered, e.g., based on sequence identity (e.g., 97% sequence identity).
  • a taxonomy can be assigned, e.g., by comparing OTUs with reference sequences of known taxonomy. The comparison can be based on sequence identity (e.g., 97%).
  • taxonomic abundance can be adjusted for 16S copy number, or whatever genomic regions may be analyzed. Different species may have different number of copies of the 16S gene, so those possessing a higher number of copies will have more 16S material for PCR amplification at same number of cells than other species. Therefore, abundance can be normalized by adjusting the number of 16S copies.
  • a pre-computed genomic lookup table can be used to relate taxonomy to functions, and amount of function.
  • a pre-computed genomic lookup table that shows the number of genes for important KEGG or COG functional categories per taxonomic group can be used to estimate the abundance of those functional categories based on the normalized 16S abundance data.
  • generating features can include generating features derived from multilocus sequence typing (MLST), which can be performed experimentally at any stage in relation to implementation of the methods 100, 200, in order to identify markers useful for characterization in subsequent blocks of the method 100. Additionally or alternatively, generating features can include generating features that describe the presence or absence of certain taxonomic groups of microorganisms, and/or ratios between exhibited taxonomic groups of microorganisms.
  • MMT multilocus sequence typing
  • generating features can include generating features describing one or more of: quantities of represented taxonomic groups, networks of represented taxonomic groups, correlations in representation of different taxonomic groups, interactions between different taxonomic groups, products produced by different taxonomic groups, interactions between products produced by different taxonomic groups, ratios between dead and alive microorganisms (e.g., for different represented taxonomic groups, e.g., based upon analysis of RNAs), phylogenetic distance (e.g., in terms of Kantorovich-Rubinstein distances, Wasserstein distances etc.), any other suitable taxonomic group-related feature(s), or any other suitable genetic or functional feature(s).
  • generating features can include generating features describing relative abundance of different microorganism groups, for instance, using a sparCC approach, using Genome Relative Abundance and Average size (GAAS) approach and/or using a genome Relative Abundance using Mixture Model theory (GRAMM) approach that uses sequence-similarity data to perform a maximum likelihood estimation of the relative abundance of one or more groups of microorganisms.
  • generating features can include generating statistical measures of taxonomic variation, as derived from abundance metrics.
  • generating features can include generating features derived from relative abundance factors (e.g., in relation to changes in abundance of a taxon, which affects abundance of other taxa).
  • generating features can include generation of qualitative features describing presence of one or more taxonomic groups, in isolation and/or in combination. Additionally or alternatively, generating features can include generation of features related to genetic markers (e.g., representative 16S, 18S, and/or ITS sequences) characterizing microorganisms of the microbiome associated with a biological sample. Additionally or alternatively, generating features can include generation of features related to functional associations of specific genes and/or organisms having the specific genes. Additionally or alternatively, generating featun
  • Block S120 can, however, include generation of any other suitable feature(s) derived from sequencing and mapping of nucleic acids of a biological sample.
  • the feature(s) can be combinatory (e.g., involving pairs, triplets), correlative (e.g., related to correlations between different features), and/or related to changes in features (i.e., temporal changes, changes across sample sites, spatial changes, etc.).
  • Features can, however, be generated in any other suitable manner in Block S120.
  • Block S130 recites: receiving a supplementary dataset, associated with at least a subset of the population of subjects, wherein the supplementary dataset is informative of characteristics associated with the disease or condition.
  • the supplementary dataset can thus be informative of presence of the disease within the population of subjects.
  • Block S130 functions to acquire additional data associated with one or more subjects of the set of subjects, which can be used to train and/or validate the characterization processes performed in block S140.
  • the supplementary dataset can include survey-derived data, and can additionally or alternatively include any one or more of: contextual data derived from sensors, medical data (e.g., current and historical medical data associated with a cerebro- craniofacial health issue or health conditions associated with a cerebro-craniofacial health issue, brain scan data (e.g., imaging or electrocardiogram, EKG), behavioral instrument data, data derived from a tool derived from the Diagnostic and Statistical Manual of Mental Disorders, etc.), and any other suitable type of data.
  • medical data e.g., current and historical medical data associated with a cerebro- craniofacial health issue or health conditions associated with a cerebro-craniofacial health issue
  • brain scan data e.g., imaging or electrocardiogram, EKG
  • behavioral instrument data e.g., data derived from a tool derived from the Diagnostic and Statistical Manual of Mental Disorders, etc.
  • the survey- derived data preferably provides physiological, demographic, and behavioral information in association with a subject.
  • Physiological information can include information related to physiological features (e.g., height, weight, body mass index, body fat percent, body hair level, etc.).
  • Demographic information can include information related to demographic features (e.g., gender, age, ethnicity, marital status, number of siblings, socioeconomic status, sexual orientation, etc.).
  • Behavioral information can include information related to one or more of: health conditions (e.g., health and disease states), living situations (e.g., living alone, living with pets, living with a significant other, living with children, etc.), dietary habits (e.g., omnivorous, vegetarian, vegan, sugar consumption, acid consumption, etc.), behavioral tendencies (e.g., levels of physical activity, drug use, alcohol use, etc.), different levels of mobility (e.g., related to distance traveled within a given time period), different levels of sexual activity (e.g., related to numbers of parti
  • health conditions e.g., health and disease states
  • living situations e.g., living alone, living with pets, living with a significant other, living with children, etc.
  • dietary habits e.g., omnivorous, vegetarian, vegan, sugar consumption, acid consumption, etc.
  • behavioral tendencies e.g., levels of physical activity, drug use, alcohol use, etc.
  • different levels of mobility e.g., related to distance traveled
  • Survey-derived data can include quantitative data and/or qualitative data that can be converted to quantitative data (e.g., using scales of severity, mapping of qualitative responses to quantified scores, etc.).
  • Block SI 30 can include providing one or more surveys to a subject of the population of subjects, or to an entity associated with a subject of the population of subjects. Surveys can be provided in person (e.g., in coordination with sample provision and/or reception from a subject), electronically (e.g., during account setup by a subject, at an application executing at an electronic device of a subject, at a web application accessible through an internet connection, etc.), and/or in any other suitable manner.
  • portions of the supplementary dataset received in Block S130 can be derived from sensors associated with the subject(s) (e.g., sensors of wearable computing devices, sensors of mobile devices, biometric sensors associated with the user, etc.).
  • Block S130 can include receiving one or more of: physical activity- or physical action-related data (e.g., accelerometer and gyroscope data from a mobile device or wearable electronic device of a subject), environmental data (e.g., temperature data, elevation data, climate data, light parameter data, etc.), patient nutrition or diet-related data (e.g., data from food establishment check-ins, data from spectrophotometric analysis, etc.), biometric data (e.g., data recorded through sensors within the patient's mobile computing device, data recorded through a wearable or other peripheral device in communication with the patient's mobile computing device), location data (e.g., using GPS elements), and any other suitable data.
  • portions of the supplementary dataset can be derived from medical record
  • the supplementary dataset of Block SI 30 can include any other suitable diagnostic information (e.g., clinical diagnosis information), which can be combined with analyses derived from features to support characterization of subjects in subsequent blocks of the method 100.
  • diagnostic information e.g., clinical diagnosis information
  • information derived from a colonoscopy, biopsy, blood test, diagnostic imaging, survey-related information, and any other suitable test can be used to supplement Block S130. 5. Characterization of ceritol
  • Block S140 recites: transforming the supplementary dataset and features extracted from at least one of the microbiome composition dataset and the microbiome functional diversity dataset into a characterization model of the disease or condition.
  • Block S140 functions to perform a characterization process for identifying features and/or feature combinations that can be used to characterize subjects or groups with a cerebro-craniofacial health issue based upon their microbiome composition and/or functional features.
  • the characterization process can be used as a diagnostic tool that can characterize a subject (e.g., in terms of behavioral traits, in terms of medical conditions, in terms of demographic traits, etc.) based upon their microbiome composition and/or functional features, in relation to other health condition states, behavioral traits, medical conditions, demographic traits, and/or any other suitable traits. Such characterization can then be used to suggest or provide personalized therapies by way of the therapy model of Block SI 50.
  • Block S140 can use computational methods (e.g., statistical methods, machine learning methods, artificial intelligence methods, bioinformatics methods, etc.) to characterize a subject as exhibiting features characteristic of a group of subjects with a cerebro-craniofacial health issue.
  • characterization can be based upon features derived from a statistical analysis (e.g., an analysis of probability distributions) of similarities and/or differences between a first group of subjects exhibiting a target state (e.g., a health condition state) associated with the cerebro-craniofacial health issue, and a second group of subjects not exhibiting the target state (e.g., a "normal" state) associated with absence of a cerebro- craniofacial health issue, or the absence of a microbiome indicative of a cerebro-craniofacial health issue, or the absence of a microbiome indicative of a health and/or quality of life issue caused by a cerebro-craniofacial health issue.
  • a target state e.g., a health condition state
  • a second group of subjects not exhibiting the target state e.g., a "normal" state
  • KS Kolmogorov-Smirnov
  • permutation test e.g., a permutation test
  • Cramer-von Mises test e.g., t-test, Welch's t-test, z-test, chi-squared test, test associated with distributions, etc.
  • any other statistical test e.g., t-test, Welch's t-test, z-test, chi-squared test, test associated with distributions, etc.
  • one or more such statistical hypothesis tests can be used to assess a set of features having varying degrees of abundance in (or variations across) a first group of subjects exhibiting a target state (e.g., an adverse state) associated with the a cerebro-craniofacial health issue and a second group of subjects not exhibiting the target state (e.g., having a normal state) associated with cerebro-craniofacial health issue.
  • a target state e.g., an adverse state
  • the set of features assessed can be constrained based upon percent abundance and/or any other suitable parameter pertaining 1
  • a feature can be derived from a taxon of microorganism and/or presence of a functional feature that is abundant in a certain percentage of subjects of the first group and subjects of the second group, wherein a relative abundance of the taxon between the first group of subjects and the second group of subjects can be determined from one or more of a KS test or a Welch's t-test (e.g., a t-test with a log normal transformation), with an indication of significance (e.g., in terms of p- value).
  • an output of Block S140 can comprise a normalized relative abundance value (e.g., 25% greater abundance of a taxon-derived feature and/or a functional feature in cerebro-craniofacial health issue subjects vs. control subjects) with an indication of significance (e.g., a p-value of 0.0013).
  • Variations of feature generation can additionally or alternatively implement or be derived from functional features or metadata features (e.g., non-bacterial markers).
  • characterization can use the relative abundance values (RAVs) for populations of subjects that have the disease ( a cerebro-craniofacial health issue) and that do not have the disease (control population).
  • the particular sequence group can be identified for including in a disease signature. Since the two populations have different distributions, the RAV for a new sample for a sequence group in the disease signature can be used to classify (e.g., determine a probability) of whether the sample does or does not have, or is indicative of, the disease. The classification can also be used to determine a treatment, as is described herein. A discrimination level can be used to identify sequence groups that have a high predictive value. Thus, embodiment can filter out taxonomic groups and/or functional groups that are not very accurate for providing a diagnosis.
  • KS Kolmogorov-Smirnov
  • p-value a probability value that the two distributions are actually identical. The smaller the p-value the greater the probability to correctly identify which population a sample belongs. The larger the separation in the mean values between the two populations generally results in a smaller p-value (an example of a discrimination level).
  • Other tests for comparing distributions can be ⁇
  • the distribution of the RAVs for the control and disease populations can be analyzed to identify sequence groups with a large separation between the two distributions.
  • the separation can be measured as a p-value (See example section).
  • the RAVs for the control population may have a distribution peaked at a first value with a certain width and decay for the distribution.
  • the disease population can have another distribution that is peaked a second value that is statistically different than the first value.
  • an abundance value of a control sample has a lower probability to be within the distribution of abundance values encountered for the disease samples.
  • the distributions can be used to determine a probability for an RAV as being in the control population and determine a probability for the RAV being in the disease population, where sequence groups associated with the largest percentage difference between two means have the smallest p-value, signifying a greater separation between the two populations.
  • Block SI 40 can additionally or alternatively transform input data from at least one of the microbiome composition datasets and/or microbiome functional diversity datasets into feature vectors that can be tested for efficacy in predicting characterizations of the population of subjects.
  • Data from the supplementary dataset can be used to inform characterizations of the cerebro-craniofacial health issue, wherein the characterization process is trained with a training dataset of candidate features and candidate classifications to identify features and/or feature
  • refinement of the characterization process with the training dataset identifies feature sets (e.g., of subject features, of combinations of features) having high correlation with a cerebro-craniofacial health issue or a health issue (e.g., symptom) associated with a cerebro-craniofacial health issue.
  • feature sets e.g., of subject features, of combinations of features
  • feature vectors effective in predicting classifications of the characterization process can include features related to one or more of: microbiome diversity metrics (e.g., in relation to distribution across taxonomic groups, in relation to distribution across archaeal, bacterial, viral, and/or eukaryotic groups), presence of taxonomic groups in one's microbiome, representation of specific ge
  • microbiome resilience metrics e.g., in response to a perturbation determined from the supplementary dataset
  • abundance of genes that encode proteins or RNAs with given functions e.g., enzymes, transporters, proteins from the immune system, hormones, interference RNAs, etc.
  • any other suitable features derived from the microbiome composition dataset e.g., COG-derived features, KEGG derived features, other functional features, etc.
  • the microbiome functional diversity dataset e.g., COG-derived features, KEGG derived features, other functional features, etc.
  • combinations of features can be used in a feature vector, wherein features can be grouped and/or weighted in providing a combined feature as part of a feature set.
  • one feature or feature set can include a weighted composite of the number of represented classes of bacteria in one's microbiome, presence of a specific genus of bacteria in one's microbiome, representation of a specific 16S sequence in one's microbiome, and relative abundance of a first phylum over a second phylum of bacteria.
  • the feature vectors can additionally or alternatively be determined in any other suitable manner.
  • Block S140 In examples of Block S140, assuming sequencing has occurred at a sufficient depth, one can quantify the number of reads for sequences indicative of the presence of a feature, thereby allowing one to set a value for an estimated amount of one of the criteria.
  • the number of reads or other measures of amount of one of the features can be provided as an absolute or relative value.
  • An example of an absolute value is the number of reads of 16S rRNA coding sequence reads that map to the genus of Lachnospira. Alternatively, relative amounts can be determined.
  • An exemplary relative amount calculation is to determine the amount of 16S rRNA coding sequence reads for a particular bacterial taxon (e.g., genus , family, order, class, or phylum) relative to the total number of 16S rRNA coding sequence reads assigned to the bacterial domain.
  • a value indicative of amount of a feature in the sample can then be compared to a cut-off value or a probability distribution in a disease signature for a cerebro-craniofacial health issue.
  • the disease signature indicates that a relative amount of feature #1 of 50% or more of all features possible at that level indicates the likelihood of a cerebro-craniofacial health issue or a health or quality of life issue attributable to, indicative of, or caused by a cerebro-craniofacial health issue
  • quantification of gene sequences associated with feature #1 less than 50% in a sample would indicate a higher likelihood of being from a healthy subject (or at least from a subject that does not have a cerebro-craniofacial health , or does not have a specific a cerebro-craniofacial health issue) and alternatively, quantification of gene sequences associated with feature #1 of more than 50% in a sample would indicate a higher likelihood of the disease.
  • the taxonomic groups are examples of the taxonomic groups ;
  • scoring of a particular bacteria or genetic pathway can be determined according to a comparison of an abundance value to one or more reference (calibration) abundance values for known samples, e.g., where a detected abundance value less than a certain value is associated with the cerebro-craniofacial health issue in question and above the certain value is scored as associated with healthy, or vice versa depending on the particular criterion.
  • the scoring for various bacteria or genetic pathways can be combined to provide a classification for a subject.
  • the comparison of an abundance value to one or more reference abundance values can include a comparison to a cutoff value determined from the one or more reference values.
  • Such cutoff value(s) can be part of a decision tree or a clustering technique (where a cutoff value is used to determine which cluster the abundance value(s) belong) that are determined using the reference abundance values.
  • the comparison can include intermediate determination of other values, (e.g., probability values).
  • the comparison can also include a comparison of an abundance value to a probability distribution of the reference abundance values, and thus a comparison to probability values.
  • a disease signature can include more sequence groups than are used for a given subject.
  • the disease signature can include 100 sequence groups, but only 60 of sequence groups may be detected in a sample, or detected above a threshold cutoff.
  • the classification of the subject can be determined based on the 60 sequence groups.
  • the sequence groups with high discrimination levels (e.g., low p-values) for a given disease can be identified and used as part of a characterization model, e.g., which uses a disease signature to determine a probability of a subject having a cerebro-craniofacial health issue.
  • the disease signature can include a set of sequence groups as well as discriminating criteria (e.g., cutoff values and/or probability distributions) used to provide a classification of the subject.
  • the classification can be binary (e.g., disease or control) or have more classifications (e.g., probability values for having the disease of a cerebro-craniofacial health issue, or not having the disease).
  • a separate characterization model can be determined for different populations, e.g., by geography where the subject is currently residing (e.g., country, r
  • the characterization process can be generated and trained according to a random forest predictor (RFP) algorithm that combines bagging (i.e., bootstrap aggregation) and selection of random sets of features from a training dataset to construct a set of decision trees, T, associated with the random sets of features.
  • RFP random forest predictor
  • N cases from the set of decision trees are sampled at random with replacement to create a subset of decision trees, and for each node, m prediction features are selected from all of the prediction features for assessment.
  • the prediction feature that provides the best split at the node (e.g., according to an objective function) is used to perform the split (e.g., as a bifurcation at the node, as a trifurcation at the node).
  • the strength of the characterization process, in identifying features that are strong in predicting classifications can be increased substantially.
  • measures to prevent bias e.g., sampling bias
  • account for an amount of bias can be included during processing to increase robustness of the model.
  • a characterization process of Block SI 40 based upon statistical analyses can identify the sets of features that have the highest correlations with a cerebro-craniofacial health issue, for which one or more therapies would have a positive effect, based upon an algorithm trained and validated with a validation dataset derived from a subset of the population of subjects.
  • a cerebro-craniofacial health issue in this first variation is characterized by an alteration of the microbiome that is predictive of the presence or absence of insomnia, light sleep, headache, sinusitis, or poor concentration.
  • a set of features useful for diagnostics associated with cerebro- craniofacial disorders includes features derived from one or more of the taxa of TABLEs A, B, C, D, or E (e.g., one or more of the family, order, class, and/or phylum of TABLE A, or the species of TABLE B) and/or one or more of the functional groups of TABLE B (e.g., one or more of the KEGG level 2 (KEGG L2) functional groups and/or one or more of the KEGG level 3 (KEGG L3) functional groups of TABLE B).
  • the functional groups of TABLE B e.g., one or more of the KEGG level 2 (KEGG L2) functional groups and/or one or more of the KEGG level 3 (KEGG L3) functional groups of TABLE B.
  • outputs of the first method 100 can be used to generate diagnostics and/or provide therapeutic measures for an individual based upon an analysis of the individual's microbiome.
  • a second method 200 derived from at least one output of the first method 100 can include: receiving a biological sample from a subject S210; characterizing the subject with a form of a cerebro-craniofacial health issue based upon the characterization and the therapy model S230.
  • Block S210 recites: receiving a biological sample from the subject, which functions to facilitate generation of a microbiome composition dataset and/or a microbiome functional diversity dataset for the subject.
  • processing and analyzing the biological sample preferably facilitates generation of a microbiome composition dataset and/or a microbiome functional diversity dataset for the subject, which can be used to provide inputs that can be used to characterize the individual in relation to diagnosis of the cerebro-craniofacial health issue, as in Block S220.
  • Receiving a biological sample from the subject is preferably performed in a manner similar to that of one of the embodiments, variations, and/or examples of sample reception described in relation to Block SI 10 above.
  • reception and processing of the biological sample in Block S210 can be performed for the subject using similar processes as those for receiving and processing biological samples used to generate the characterization(s) and/or the therapy provision model of the first method 100, in order to provide consistency of process.
  • biological sample reception and processing in Block S210 can alternatively be performed in any other suitable manner.
  • Block S220 recites: characterizing the subject characterizing the subject with a form of a disease or condition based upon processing a microbiome dataset derived from the biological sample.
  • Block S220 functions to extract features from microbiome-derived data of the subject, and use the features to positively or negatively characterize the individual as having a form of the cerebro-craniofacial health issue. Characterizing the subject in Block S220 thus preferably includes identifying features and/or combinations of features associated with the microbiome composition and/or functional features of the microbiome of the subject, and comparing such features with features characteristic of subjects with the cerebro- craniofacial health issue.
  • Block S220 can further include generation of and/or output of a confidence metric associated with the characterization for the individual. For instance, a confidence metric can be derived from the number of features used to generate the
  • features extracted from the microbiome dataset can be supplemented with survey-derived and/or medical history-derived features from the individual, which can be used to further refine the characterization operation(s) of Block S220.
  • the microbiome composition dataset and/or the microbiome functional diversity dataset of the individual can additionally or alternatively be used in any other suitable manner to enhance the first method 100 and/or the second method 200.
  • Block S230 recites: promoting a therapy to the subject with the disease or condition based upon the characterization and the therapy model.
  • Block S230 functions to recommend or provide a personalized therapeutic measure to the subject, in order to shift the microbiome composition of the individual toward a desired equilibrium state.
  • Block S230 can include correcting the cerebro-craniofacial health issue, or otherwise positively affecting the user's health in relation to the cerebro-craniofacial health issue.
  • Block S230 can thus include promoting one or more therapeutic measures to the subject based upon their characterization in relation to the cerebro-craniofacial health issue, as described herein, wherein the therapy is configured to modulate taxonomic makeup of the subject's microbiome and/or modulate functional feature aspects of the subject in a desired manner toward a "normal” or "control” state in relation to the characterizations described above.
  • providing the therapeutic measure to the subject can include recommendation of available therapeutic measures configured to modulate microbiome composition of the subject toward a desired state (e.g. , having a microbiome that is not indicative of (e.g., altered by) a cerebro-craniofacial health issue).
  • Block S230 can include provision of customized therapy to the subject according to their characterization (e.g., in relation to a specific type of a cerebro-craniofacial health issue, such as insomnia, light sleep, headache, sinusitis, or poor concentration).
  • therapeutic measures for adjusting a microbiome composition of the subject, in order to improve a state of the cerebro-craniofacial health issue can include one or more of: probiotics, prebiotics, bacteriophage-based therapies, consumables, suggested activities, topical therapies, adjustments to hygienic product usage, adjustments to diet, adjustments to sleep behavior, living arrangement, adjustments to level of sexual activity, nutritional supplements, medications, antibiotics, and any other suitable therapeutic measure.
  • Therapy provision in Block S230 can include provision of notifications by way of an electronic device, through an entity associated with the individual, and/or in any other suitable manner. [0230] In more detail, therapy provision in B
  • Notifications can be provided to an individual by way of an electronic device (e.g., personal computer, mobile device, tablet, head-mounted wearable computing device, wrist-mounted wearable computing device, etc.) that executes an application, web interface, and/or messaging client configured for notification provision.
  • an electronic device e.g., personal computer, mobile device, tablet, head-mounted wearable computing device, wrist-mounted wearable computing device, etc.
  • a web interface of a personal computer or laptop associated with a subject can provide access, by the subject, to a user account of the subject, wherein the user account includes information regarding the subject' s characterization, detailed characterization of aspects of the subject' s microbiome composition and/or functional features, and notifications regarding suggested therapeutic measures generated in Block S I 50.
  • an application executing at a personal electronic device can be configured to provide notifications (e.g., at a display, haptically, in an auditory manner, etc.) regarding therapeutic suggestions generated by the therapy model of Block S I 50.
  • Notifications can additionally or alternatively be provided directly through an entity associated with a subject (e.g., a caretaker, a spouse, a significant other, a healthcare professional, etc.).
  • notifications can additionally or alternatively be provided to an entity (e.g., healthcare professional) associated with the subject, wherein the entity is able to administer the therapeutic measure (e.g., by way of prescription, by way of conducting a therapeutic session, etc.).
  • Notifications can, however, be provided for therapy administration to the subject in any other suitable manner.
  • monitoring of the subject during the course of a therapeutic regimen e.g., by receiving and analyzing biological samples from the subject throughout therapy, by receiving survey-derived data from the subject throughout therapy
  • the first method 100 can further include Block S I 50, which recites: based upon the characterization model, generating a therapy model configured to correct or otherwise improve a state of the disease or condition.
  • Block S I 50 functions to identify or predict therapies (e.g., probiotic-based therapies, prebiotic- based therapies, phage-based therapies, small molecule-based therapies (e.g., selective, pan- selective, or non-selective antibiotics), etc.) that can shift a subject' s microbiome composition and/or functional features toward a desired equ
  • therapies e.g., probiotic-based therapies, prebiotic- based therapies, phage-based therapies, small molecule-based therapies (e.g., selective, pan- selective, or non-selective antibiotics), etc.
  • the therapies can be selected from therapies including one or more of:
  • probiotic therapies phage-based therapies, prebiotic therapies, small molecule-based therapies, cognitive/behavioral therapies, physical rehabilitation therapies, clinical therapies, medication-based therapies, diet-related therapies, and/or any other suitable therapy designed to operate in any other suitable manner in promoting a user' s health.
  • a bacteriophage-based therapy one or more populations (e.g., in terms of colony forming units) of bacteriophages specific to a certain bacteria (or other microorganism) represented in a subject with the cerebro-craniofacial health issue can be used to down-regulate or otherwise eliminate populations of the certain bacteria.
  • bacteriophage-based therapies can be used to reduce the size(s) of the undesired population(s) of bacteria represented in the subject.
  • bacteriophage-based therapies can be used to increase the relative abundances of bacterial populations not targeted by the bacteriophage(s) used.
  • therapies e.g., probiotic therapies, bacteriophage-based therapies, prebiotic therapies, etc.
  • therapies can be configured to downregulate and/or upregulate microorganism populations or subpopulations (and/or functions thereof) associated with features
  • the Block SI 50 can include one or more of the following steps: obtaining a sample from the subject; purifying nucleic acids (e.g., DNA) from the sample; deep sequencing nucleic acids from the sample so as to determine the amount of one or more of the features of TABLEs A, B, C, D, or E ; and comparing the resulting amount of each feature to one or more reference amounts of the one or more of the features listed in one or more of TABLEs A, B, C, D, or E as occurs in an average individual having a cerebro- craniofacial health issue or an individual not having the cerebro-craniofacial health issue or both.
  • nucleic acids e.g., DNA
  • the compilation of features can sometimes be referred to as a "disease signature" for a specific condition related to a cerebro-craniofacial health issue.
  • the disease signature can act as a characterization model, and may include probability distributions for control population (no cerebro-craniofacial health issue) or disease populations having the condition or both.
  • the disease signature can include one or more of the features (e.g., bacterial taxa or genetic pathways) listed and can optionally include criteria determined from abundance values of the control and/or disease populations
  • probability values for amounts of those features associated with average control or disease e.g., insomnia, light sleep, headache, sinusitis, or poor concentration
  • individuals e.g., insomnia, light sleep, headache, sinusitis, or poor concentration
  • candidate therapies of the therapy model can perform one or more of: blocking pathogen entry into an epithelial cell by providing a physical barrier (e.g., by way of colonization resistance), inducing formation of a mucous barrier by stimulation of goblet cells, enhance integrity of apical tight junctions between epithelial cells of a subject (e.g., by stimulating up regulation of zona- occludens 1, by preventing tight junction protein redistribution), producing antimicrobial factors, stimulating production of anti-inflammatory cytokines (e.g., by signaling of dendritic cells and induction of regulatory T-cells), triggering an immune response, and performing any other suitable function that adjusts a subject's microbiome away from a state of dysbiosis.
  • a physical barrier e.g., by way of colonization resistance
  • inducing formation of a mucous barrier by stimulation of goblet cells e.g., by stimulating up regulation of zona- occludens 1, by preventing tight junction protein redistribution
  • the therapy model is preferably based upon data from a large population of subjects, which can comprise the population of subjects from which the microbiome-related datasets are derived in Block SI 10, wherein microbiome composition and/or functional features or states of health, prior exposure to and post exposure to a variety of therapeutic measures, are well characterized.
  • data can be used to train and validate the therapy provision model, in identifying therapeutic measures that provide desired outcomes for subjects based upon different microbiome characterizations.
  • support vector machines as a supervised machine learning algorithm, can be used to generate the therapy provision model.
  • any other suitable machine learning algorithm described above can facilitate generation of the therapy provision model.
  • the algorithm(s) can be characterized by a learning style including any one or more of: supervised learning (e.g., using logistic regression, using back propagation neural networks), unsupervised learning (e.g., using an Apriori algorithm, using K-means clustering), semi-supervised learning, reinforcement learning (e.g., using a Q-learning algorithm, using temporal difference learning), and any other suitable learning style.
  • supervised learning e.g., using logistic regression, using back propagation neural networks
  • unsupervised learning e.g., using an Apriori algorithm, using K-means clustering
  • semi-supervised learning e.g., using a Q-learning algorithm, using temporal difference learning
  • reinforcement learning e.g., using a Q-learning algorithm, using temporal difference learning
  • the algorithm(s) can implement any one or more of: a regression algorithm (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), an instance-based method (e.g., k-nearest
  • a regression algorithm e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.
  • an instance-based method e.g., k-nearest
  • a regularization method e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, etc.
  • a decision tree learning method e.g., classification and regression tree, iterative dichotomiser 3, C4.5, chi-squared automatic interaction detection, decision stump, random forest, multivariate adaptive regression splines, gradient boosting machines, etc.
  • a Bayesian method e.g., naive Bayes, averaged one-dependence estimators, Bayesian belief network, etc.
  • a kernel method e.g., a support vector machine, a radial basis function, a linear discriminant analysis, etc.
  • a clustering method e.g., k-means clustering, expectation maximization, etc.
  • an associated rule learning algorithm e.g., an Apriori algorithm, an Eclat algorithm, etc.
  • an artificial neural network model e.g., a Perceptron method, a back-propagation method, a Hopfield network
  • convolutional network method e.g., a convolutional network method, a stacked autoencoder method, etc.
  • a dimensionality reduction method e.g., principal component analysis, partial least squares regression
  • Sammon mapping, multidimensional scaling, projection pursuit, etc. an ensemble method (e.g., boosting, bootstrapped aggregation, AdaBoost, stacked generalization, gradient boosting machine method, random forest method, etc.), and any suitable form of algorithm.
  • AdaBoost bootstrapped aggregation
  • stacked generalization gradient boosting machine method
  • random forest method random forest method
  • the therapy model can be derived in relation to identification of a "normal" or baseline microbiome composition and/or functional features, as assessed from subjects of a population of subjects who are identified to be in good health.
  • identification of a subset of subjects of the population of subjects who are characterized to be in good health e.g., characterized as not having an altered microbiome caused by, or indicative of, a cerebro-craniofacial health issue, e.g., using features of the characterization process
  • therapies that modulate microbiome compositions and/or functional features toward those of subjects in good health can be generated in Block SI 50.
  • Block SI 50 can thus include identification of one or more baseline microbiome compositions and/or functional features (e.g., one baseline microbiome for each of a set of demographics), and potential therapy formulations and therapy regimens that can shift microbiomes of subjects who are in a state of dysbiosis toward one of the identified baseline microbiome compositions and/or functional features.
  • the therapy model can, however, be generated and/or refined in any other suitable manner.
  • Microorganism compositions associated with probiotic therapies associated with the therapy model preferably include microorganisms that are culturable (e.g., able to be expanded to provide a scalable therapy) and no
  • microorganism compositions can comprise a single type of microorganism that has an acute or moderated effect upon a subject's microbiome.
  • microorganism compositions can comprise balanced
  • a combination of multiple types of bacteria in a probiotic therapy can comprise a first bacteria type that generates products that are used by a second bacteria type that has a strong effect in positively affecting a subject's microbiome.
  • a combination of multiple types of bacteria in a probiotic therapy e.g., can comprise several bacteria types that produce proteins with the same functions that positively affect a subject's microbiome.
  • probiotic compositions can comprise
  • the therapy can comprise dosages of proteins resulting from functional presence in the microbiome compositions of subjects without the cerebro-craniofacial health issue.
  • a subject can be instructed to ingest capsules comprising the probiotic formulation according to a regimen tailored to one or more of his/her: physiology (e.g., body mass index, weight, height), demographics (e.g., gender, age), severity of dysbiosis, sensitivity to medications, and any other suitable factor.
  • physiology e.g., body mass index, weight, height
  • demographics e.g., gender, age
  • probiotic compositions of probiotic-based therapies can be naturally or synthetically derived.
  • a probiotic composition can be naturally derived from fecal matter or other biological matter (e.g., of one or more subjects having a baseline microbiome composition and/or functional features, as identified using the characterization process and the therapy model).
  • probiotic compositions can be synthetically derived (e.g., derived using a benchtop method) based upon a baseline microbiome composition and/or functional features, as identified using the characterization process and the therapy model.
  • the probiotic composition is or is derived from the subject's own fecal matter that has been stored or "banked" from a period during which the subject is in a healthy state for use when the microbiome is imbalanced (e.g., due to antibiotic usage, or due to a cerebro-craniofacial health issue).
  • microorganism agents tl are or is derived from the subject's own fecal matter that has been stored or "banked" from a period during which the subject is in a healthy state for use when the microbiome is imbalanced (e.g., due to antibiotic usage, or due to a cerebro-craniofacial health issue).
  • yeast e.g., Saccharomyces boulardii
  • gram-negative bacteria e.g., E. coli Nissle, Akkermansia muciniphila, Prevotella bryantii, etc.
  • gram-positive bacteria e.g., Bifidobacterium animalis (including subspecies lactis), Bifidobacterium longum (including subspecies infantis), Bifidobacterium bifidum, Bifidobacterium pseudolongum, Bifidobacterium thermophilum, Bifidobacterium breve, Lactobacillus rhamnosus,
  • Lactobacillus acidophilus Lactobacillus casei, Lactobacillus helveticus, Lactobacillus plantarum, Lactobacillus fermentum, Lactobacillus salivarius, Lactobacillus delbrueckii (including subspecies bulgaricus), Lactobacillus johnsonii, Lactobacillus reuteri,
  • Lactobacillus gasseri Lactobacillus brevis (including subspecies coagulans), Bacillus cereus, Bacillus subtilis (including var. Natto), Bacillus polyfermenticus, Bacillus clausii, Bacillus licheniformis, Bacillus coagulans, Bacillus pumilus, Faecalibacterium prausnitzii, Streptococcus thermophilus, Brevibacillus brevis, Lactococcus lactis, Leuconostoc mesenteroides, Enterococcus faecium, Enterococcus faecalis, Enterococcus durans,
  • therapies promoted by the therapy model of Block SI 50 can include one or more of: consumables (e.g., food items, beverage items, nutritional supplements), suggested activities (e.g., exercise regimens, adjustments to alcohol consumption, adjustments to cigarette usage, adjustments to drug usage), topical therapies (e.g., lotions, ointments, antiseptics, etc.), adjustments to hygienic product usage (e.g., use of shampoo products, use of conditioner products, use of soaps, use of makeup products, etc.), adjustments to diet (e.g., sugar consumption, fat consumption, salt consumption, acid consumption, etc.), adjustments to sleep behavior, living arrangement adjustments (e.g., adjustments to living with pets, adjustments to living with plants in one's home environment, adjustments to light and temperature in one's home environment, etc.), nutritional supplements (e.g., vitamins, minerals, fiber, fatty acids, amino acids, prebiotics, probiotics, etc.), medications, antibiotics, and any other suitable therapeutic measure.
  • consumables e.g., food
  • DHNA l,4-dihydroxy-2-naphthoic acid
  • Inulin trans- Galactooligosaccharides
  • Lactulose Lactulose
  • Mannan oligosaccharides MOS
  • Fructooligosaccharides (FOS), Neoagaro-oligosaccharides (NAOS), Pyrodextrins, Xylo- oligosaccharides (XOS), Isomalto-oligosaccharides (IMOS), Amylose-resistant starch, Soybean oligosaccharides (SBOS), Lactitol, Lactosucrose (LS), Isomaltulose (including Palatinose), Arabinoxylooligosaccharides (AX (FOS), Neoagaro-oligosaccharides (NAOS), Pyrodextrins, Xylo- oligosaccharides (XOS), Isomalto-oligosaccharides (IMOS), Amylose-resistant starch, Soybean oligosaccharides (SBOS), Lactitol, Lactosucrose (LS), Isomaltulose (including Palatinose), Arabinoxylooligosaccharides (AX (
  • Arabinoxylans (AX), Polyphenols or any other compound capable of changing the microbiota composition with a desirable effect.
  • therapies promoted by the therapy model of Block S I 50 can include one or more of: different forms of therapy having different therapy orientations (e.g., motivational, increase energy level, reduce weight gain, improve diet, psychoeducational, cognitive behavioral, biological, physical, mindfulness-related, relaxation-related, dialectical behavioral, acceptance-related, commitment-related, etc.) configured to address a variety of factors contributing to an adverse states due to a microbiome that is altered by a cerebro-craniofacial health issue or a microbiome that is caused by or indicative of a cerebro-craniofacial health issue; weight management interventions (e.g., to prevent adverse weight-related (e.g., weight gain or loss) side effects due to insomnia, light sleep, headache, sinusitis, or poor concentration, or a therapy to prevent, mitigate, or reduce the frequency or severity of insomnia, light sleep, headache, sinusitis, or poor concentration); physical therapy; rehabilitation measures; and any other suitable therapeutic measure.
  • different therapy orientations e.g., motivational, increase energy level, reduce
  • the first method 100 can, however, include any other suitable blocks or steps configured to facilitate reception of biological samples from individuals, processing of biological samples from individuals, analyzing data derived from biological samples, and generating models that can be used to provide customized diagnostics and/or therapeutics according to specific microbiome compositions of individuals.
  • the methods 100, 200 and/or system of the embodiments can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions.
  • the instructions can be executed by computer- executable components integrated with the application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software elements of a patient computer or mobile device, or any suitable combination thereof.
  • Other systems and methods of the embodiments can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions.
  • the instructions can be executed by computer-executable components integrated with apparatuses and networks of the type described above.
  • the computer-readable medium can be stored on any suitable computer readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device.
  • the computer-executable component can be a ⁇
  • hardware device can (alternatively or additionally) execute the instructions.
  • each block in the flowchart or block diagrams may represent a module, segment, step, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
  • the functions noted in the block can occur out of the order noted in the Figs.
  • two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
  • TABLE A shows data for insomnia. The data was obtained from 901 subjects in the condition population and 4865 subjects in the control population. TABLE A shows taxonomic groups for Species, Genus, and Family all in the first column of TABLE A. Each of the rows containing data corresponds to a different sequence group. For example, Parabacteroides distasonis corresponds to a sequence group in the Species level of the taxonomic hierarchy.
  • a level can have many sequence groups.
  • the p-values are determined via either the Kolmogorov-Smirnov test, or the Welch's t-test.
  • Sequence groups having a p-value less than 0.01 are shown in the second column. Other sequence groups may exist, but likely would not be selected for inclusion into a disease signature.
  • the third column (“# disease subjects detected") shows the number of samples tested that had the condition of insomnia and w
  • the fourth column (“# control subjects detected") shows the number of samples tested that did not have the disease (control) and where the sample exhibited bacteria in the sequence group.
  • the coverage percentage of the sequence group can be determined from the values in the third and fourth columns.
  • the fifth column shows the mean percentage for the abundance for the subjects having the disease and where the sample exhibited bacteria in the sequence group.
  • the sixth column shows the mean percentage for the abundance for the subjects not having the disease and where the sample exhibited bacteria in the sequence group.
  • a set of sequence groups can be selected from TABLE A for forming a disease signature that can be used to classify a sample regarding a presence or absence of a microbiome indicative of a insomnia issue.
  • all taxonomic sequence groups can be selected, or just the 2, 3, 4, 5, or 6 ones with the smallest p-value, as may include the function groups as well.
  • the sequence groups for the disease signature can be selected to optimize accuracy for discriminating between the two groups and coverage of the population such that a likelihood of being able to provide a classification is higher (e.g., if a sequence group is not present then that sequence group cannot be used to determine the classification).
  • the total coverage can dependent on the individual coverage percentages and based on the overlap in the coverages among the sequence groups, as described above.
  • sequence groups discriminating levels, coverage percentages, and discriminating criteria are provided in TABLE B.
  • TABLE B shows data for light sleep. 627 subjects are in the condition population and 4471 subjects are in the control population. TABLE B shows the taxonomic group for Species, Genus, and Family and shows functional groups all in the first column of TABLE B. As mentioned above, the functional groups correspond to one or more genes with the function. Each of the rows containing data corresponds to a different sequence group.
  • a set of sequence groups can be selected from TABLE B for forming a disease signature that can be used to classify a sample regarding a presence or absence of a microbiome indicative
  • sequence groups can be selected, e.g., with the smallest p-value.
  • the sequence groups for the disease signature can be selected to optimize accuracy for discriminating between the two groups and coverage of the population such that a likelihood of being able to provide a classification is higher (e.g., if a sequence group is not present then that sequence group cannot be used to determine the classification).
  • the total coverage can dependent on the individual coverage percentages and based on the overlap in the coverages among the sequence groups, as described above.
  • TABLE C shows data for headache. 795 subjects are in the condition population and 4349 subjects are in the control population.
  • TABLE C shows the taxonomic group for Species, Genus, and Family and shows functional groups all in the first column of TABLE C. As mentioned above, the functional groups correspond to one or more genes with the function. Each of the rows containing data corresponds to a different sequence group.
  • a set of sequence groups can be selected from TABLE C for forming a disease signature that can be used to classify a sample regarding a presence or absence of a microbiome indicative of a headache issue.
  • 6 (or other number) sequence groups can be selected, e.g., with the smallest p-value.
  • the sequence groups for the disease signature can be selected to optimize accuracy for discriminating between the two groups and coverage of the population such that a likelihood of being able to provide a classification is higher (e.g., if a sequence group is not present then that sequence group cannot be used to determine the classification).
  • the total coverage can dependent on the individual coverage percentages and based on the overlap in the coverages among the sequence groups, as described above.
  • TABLE D shows data for sinusitis. 218 subjects are in the condition population and 1049 subjects are in the control population. TABLE D shows the taxonomic group for Species, Genus, and Family and shows functional groups all in the first column of TABLE D. As mentioned above, the functional groups con
  • Each of the rows containing data corresponds to a different sequence group.
  • a set of sequence groups can be selected from TABLE D for forming a disease signature that can be used to classify a sample regarding a presence or absence of a microbiome indicative of a sinusitis issue.
  • 6 (or other number) sequence groups can be selected, e.g., with the smallest p-value.
  • the sequence groups for the disease signature can be selected to optimize accuracy for discriminating between the two groups and coverage of the population such that a likelihood of being able to provide a classification is higher (e.g., if a sequence group is not present then that sequence group cannot be used to determine the classification).
  • the total coverage can dependent on the individual coverage percentages and based on the overlap in the coverages among the sequence groups, as described above.
  • sequence groups discriminating levels, coverage percentages, and discriminating criteria are provided in TABLE E.
  • TABLE E shows data for poor concentration. 1396 subjects are in the condition population and 6276 subjects are in the control population. TABLE E shows the taxonomic group for Species, Genus, and Family and shows functional groups all in the first column of TABLE E. As mentioned above, the functional groups correspond to one or more genes with the function. Each of the rows containing data corresponds to a different sequence group.
  • a set of sequence groups can be selected from TABLE E for forming a disease signature that can be used to classify a sample regarding a presence or absence of a microbiome indicative of a light sleep issue.
  • 6 (or other number) sequence groups can be selected, e.g., with the smallest p-value.
  • the sequence groups for the disease signature can be selected to optimize accuracy for discriminating between the two groups and coverage of the population such that a likelihood of being able to provide a classification is higher (e.g., if a sequence group is not present then that sequence group cannot be used to determine the classification).
  • the total coverage can dependent on the individual coverage percentages and based on the overlap in the coverages among the sequence groups, as described above.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Medical Informatics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biotechnology (AREA)
  • Biophysics (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Theoretical Computer Science (AREA)
  • Public Health (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Data Mining & Analysis (AREA)
  • Epidemiology (AREA)
  • Databases & Information Systems (AREA)
  • Molecular Biology (AREA)
  • Genetics & Genomics (AREA)
  • Primary Health Care (AREA)
  • Organic Chemistry (AREA)
  • Biomedical Technology (AREA)
  • Wood Science & Technology (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Zoology (AREA)
  • Evolutionary Computation (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Pathology (AREA)
  • Bioethics (AREA)
  • Immunology (AREA)
  • Hospice & Palliative Care (AREA)
  • Psychiatry (AREA)
  • Psychology (AREA)
  • Social Psychology (AREA)
  • Developmental Disabilities (AREA)
  • Child & Adolescent Psychology (AREA)

Abstract

L'invention concerne des procédés, des compositions et des systèmes pour détecter un ou plusieurs problèmes de santé cognitive par la caractérisation du microbiome d'un individu, surveiller ces effets et/ou déterminer, afficher ou améliorer une thérapie pour le problème de santé cognitive. L'invention concerne également des procédés, des compositions et des systèmes pour créer et comparer des ensembles de données sur la composition et/ou la diversité fonctionnelle du microbiome. L'invention concerne également des procédés, des compositions et des systèmes pour créer un modèle de caractérisation et/ou un modèle de thérapie pour des problèmes d'insomnie, des problèmes de sommeil léger, des problèmes de maux de tête, des problèmes de sinusite et des problèmes de mauvaise concentration.
EP16845218.3A 2015-09-09 2016-09-09 Procédé et système pour des diagnostics dérivés du microbiome et agents thérapeutiques pour des affections associées à la santé cérébro-carniofaciale Pending EP3346912A4 (fr)

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
US201562215939P 2015-09-09 2015-09-09
US201562216028P 2015-09-09 2015-09-09
US201562216016P 2015-09-09 2015-09-09
US201562216035P 2015-09-09 2015-09-09
US201562216042P 2015-09-09 2015-09-09
PCT/US2016/051155 WO2017044885A1 (fr) 2015-09-09 2016-09-09 Procédé et système pour des diagnostics dérivés du microbiome et agents thérapeutiques pour des affections associées à la santé cérébro-carniofaciale

Publications (2)

Publication Number Publication Date
EP3346912A1 true EP3346912A1 (fr) 2018-07-18
EP3346912A4 EP3346912A4 (fr) 2019-08-21

Family

ID=58240338

Family Applications (1)

Application Number Title Priority Date Filing Date
EP16845218.3A Pending EP3346912A4 (fr) 2015-09-09 2016-09-09 Procédé et système pour des diagnostics dérivés du microbiome et agents thérapeutiques pour des affections associées à la santé cérébro-carniofaciale

Country Status (5)

Country Link
EP (1) EP3346912A4 (fr)
CN (1) CN108348167B (fr)
AU (1) AU2016321333A1 (fr)
CA (1) CA3006044A1 (fr)
WO (1) WO2017044885A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113096818A (zh) * 2021-04-21 2021-07-09 西安电子科技大学 基于ode和grud的急性病症发生几率的评估方法

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9703929B2 (en) 2014-10-21 2017-07-11 uBiome, Inc. Method and system for microbiome-derived diagnostics and therapeutics
US10777320B2 (en) 2014-10-21 2020-09-15 Psomagen, Inc. Method and system for microbiome-derived diagnostics and therapeutics for mental health associated conditions
US10395777B2 (en) 2014-10-21 2019-08-27 uBiome, Inc. Method and system for characterizing microorganism-associated sleep-related conditions
US10366793B2 (en) 2014-10-21 2019-07-30 uBiome, Inc. Method and system for characterizing microorganism-related conditions
WO2018094376A1 (fr) * 2016-11-21 2018-05-24 uBiome, Inc. Procédé et système pour caractériser une affection liée à une céphalée
WO2018223146A1 (fr) * 2017-06-02 2018-12-06 uBiome, Inc. Procédé et système destinés à la caractérisation de troubles liés au sommeil apparentés à un microorganisme
US11154240B2 (en) 2019-04-02 2021-10-26 Kpn Innovations Llc Methods and systems for utilizing diagnostics for informed vibrant constitutional guidance
US11289206B2 (en) 2020-06-02 2022-03-29 Kpn Innovations, Llc. Artificial intelligence methods and systems for constitutional analysis using objective functions
US11211158B1 (en) 2020-08-31 2021-12-28 Kpn Innovations, Llc. System and method for representing an arranged list of provider aliment possibilities

Family Cites Families (26)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060228721A1 (en) * 2005-04-12 2006-10-12 Leamon John H Methods for determining sequence variants using ultra-deep sequencing
WO2009038754A2 (fr) * 2007-09-19 2009-03-26 The Research Foundation Of State University Of New York Signatures de l'expression génique dans des échantillons de cellules tumorales enrichis
EP2719774B8 (fr) * 2008-11-07 2020-04-22 Adaptive Biotechnologies Corporation Procédés de surveillance de maladies par l'analyse de séquence
WO2011022660A1 (fr) * 2009-08-21 2011-02-24 Puretech Ventures, Llc Procédés de diagnostic et de traitement de maladie associée au microbiome au moyen de paramètres de réseau d’interaction
CN106951732B (zh) * 2010-05-25 2020-03-10 加利福尼亚大学董事会 基于计算机的基因组序列分析系统
CA2815259A1 (fr) * 2010-10-27 2012-05-03 Quantibact A/S Capture d'adn et d'arn cibles par des sondes comprenant des molecules intercalaires
US9689044B2 (en) * 2011-01-26 2017-06-27 The Brigham And Women's Hospital, Inc. Assays and methods to sequence microbes directly from immune complexes
CA2829606C (fr) * 2011-03-09 2020-06-02 Cell Signaling Technology, Inc. Procedes et reactifs pour creer des anticorps monoclonaux
US20130121968A1 (en) * 2011-10-03 2013-05-16 Atossa Genetics, Inc. Methods of combining metagenome and the metatranscriptome in multiplex profiles
FI126711B (fi) * 2011-10-12 2017-04-13 Gut Guide Oy Serotoniinivajeeseen liittyvän terveysriskin arvioiminen
JP2015512255A (ja) * 2012-03-17 2015-04-27 ザ リージェンツ オブ ザ ユニバーシティ オブ カリフォルニア ざ瘡の高速診断および個人化された治療
CN204440396U (zh) * 2012-04-12 2015-07-01 维里纳塔健康公司 用于确定胎儿分数的试剂盒
CA2873176C (fr) * 2012-05-10 2024-02-27 The General Hospital Corporation Procedes pour determiner une sequence nucleotidique
EP4289948A3 (fr) * 2012-05-25 2024-04-17 The Regents of the University of California Procédés et compositions permettant la modification de l'adn cible dirigée par l'arn et la modulation de la transcription dirigée par l'arn
WO2013176774A1 (fr) * 2012-05-25 2013-11-28 Arizona Board Of Regents Marqueurs de microbiome et thérapies pour troubles du spectre autistique
US20150125438A1 (en) * 2012-07-20 2015-05-07 Sang Jae Kim Anti-Inflammatory Peptides and Composition Comprising the Same
CN104540962B (zh) * 2012-08-01 2017-09-19 深圳华大基因研究院 糖尿病生物标志物及其应用
EP3584308A3 (fr) * 2013-02-04 2020-03-04 Seres Therapeutics, Inc. Compositions et procédés
WO2014165810A2 (fr) * 2013-04-05 2014-10-09 Akins Robert A Systèmes et procédés d'évaluation de microbiomes et de traitement de ceux-ci
MX2015015532A (es) * 2013-05-09 2016-02-05 Procter & Gamble Metodo y sistema de identificacion de biomarcadores.
WO2015074054A1 (fr) * 2013-11-18 2015-05-21 The Trustees Of Columbia University In The City Of New York Amélioration de la santé microbienne dans l'intestin de mammifères
CA2936933A1 (fr) * 2014-01-25 2015-07-30 uBiome, Inc. Procede et systeme d'analyse du microbiome
CN104195145B (zh) * 2014-07-15 2017-01-18 浙江大学 肝硬化的生物标志物及其应用
US9703929B2 (en) * 2014-10-21 2017-07-11 uBiome, Inc. Method and system for microbiome-derived diagnostics and therapeutics
US9754080B2 (en) * 2014-10-21 2017-09-05 uBiome, Inc. Method and system for microbiome-derived characterization, diagnostics and therapeutics for cardiovascular disease conditions
US10265009B2 (en) * 2014-10-21 2019-04-23 uBiome, Inc. Method and system for microbiome-derived diagnostics and therapeutics for conditions associated with microbiome taxonomic features

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113096818A (zh) * 2021-04-21 2021-07-09 西安电子科技大学 基于ode和grud的急性病症发生几率的评估方法
CN113096818B (zh) * 2021-04-21 2023-05-30 西安电子科技大学 基于ode和grud的急性病症发生几率的评估方法

Also Published As

Publication number Publication date
CN108348167A (zh) 2018-07-31
EP3346912A4 (fr) 2019-08-21
WO2017044885A1 (fr) 2017-03-16
CN108348167B (zh) 2022-06-03
CA3006044A1 (fr) 2017-03-16
AU2016321333A1 (en) 2018-04-26

Similar Documents

Publication Publication Date Title
AU2016321349B2 (en) Method and system for microbiome-derived diagnostics and therapeutics for conditions associated with gastrointestinal health
US10327642B2 (en) Method and system for microbiome-derived characterization, diagnostics and therapeutics for conditions associated with functional features
CN108350502B (zh) 用于口腔健康的源自微生物群系的诊断及治疗方法和系统
US10340045B2 (en) Method and system for microbiome-derived diagnostics and therapeutics for autoimmune system conditions
CN108350019B (zh) 用于细菌性阴道病的源自微生物群系的诊断及治疗方法和系统
CN108348168B (zh) 用于湿疹的源自微生物群系的诊断及治疗方法和系统
US11773455B2 (en) Method and system for microbiome-derived diagnostics and therapeutics infectious disease and other health conditions associated with antibiotic usage
CN108348167B (zh) 用于脑-颅面健康相关病症的源自微生物群系的诊断及治疗方法和系统
EP3283650A1 (fr) Procédé et système pour la caractérisation, le diagnostic et le traitement dérivés du microbiome d'affections associées à des caractéristiques fonctionnelles
CN108350503B (zh) 用于甲状腺健康问题相关病症的源自微生物群系的诊断及治疗方法和系统
US20190211378A1 (en) Method and system for microbiome-derived diagnostics and therapeutics for cerebro-craniofacial health

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20180406

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

AX Request for extension of the european patent

Extension state: BA ME

DAV Request for validation of the european patent (deleted)
DAX Request for extension of the european patent (deleted)
RIC1 Information provided on ipc code assigned before grant

Ipc: G16B 20/00 20190101ALI20190410BHEP

Ipc: G16B 40/20 20190101ALI20190410BHEP

Ipc: C12Q 1/68 20180101ALI20190410BHEP

Ipc: G16H 50/20 20180101AFI20190410BHEP

Ipc: G16H 50/70 20180101ALI20190410BHEP

Ipc: A61B 5/00 20060101ALI20190410BHEP

REG Reference to a national code

Ref country code: DE

Ref legal event code: R079

Free format text: PREVIOUS MAIN CLASS: A61B0005000000

Ipc: G16H0050200000

A4 Supplementary search report drawn up and despatched

Effective date: 20190718

RIC1 Information provided on ipc code assigned before grant

Ipc: A61B 5/00 20060101ALI20190712BHEP

Ipc: C12Q 1/68 20180101ALI20190712BHEP

Ipc: G16H 50/70 20180101ALI20190712BHEP

Ipc: G16B 20/00 20190101ALI20190712BHEP

Ipc: G16B 40/20 20190101ALI20190712BHEP

Ipc: G16H 50/20 20180101AFI20190712BHEP

19U Interruption of proceedings before grant

Effective date: 20191011

19W Proceedings resumed before grant after interruption of proceedings

Effective date: 20201201

RAP1 Party data changed (applicant data changed or rights of an application transferred)

Owner name: PSOMAGEN, INC.

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: EXAMINATION IS IN PROGRESS

17Q First examination report despatched

Effective date: 20231122