WO2012116289A2 - Signatures microbiennes comme indicateurs d'exposition aux radiations - Google Patents

Signatures microbiennes comme indicateurs d'exposition aux radiations Download PDF

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WO2012116289A2
WO2012116289A2 PCT/US2012/026541 US2012026541W WO2012116289A2 WO 2012116289 A2 WO2012116289 A2 WO 2012116289A2 US 2012026541 W US2012026541 W US 2012026541W WO 2012116289 A2 WO2012116289 A2 WO 2012116289A2
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bacteria
sfa
firmicutes
lactobacillales
clostridiales
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PCT/US2012/026541
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WO2012116289A3 (fr
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John Edward Baker
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Tricorder Diagnostics, Llc
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Priority to US13/499,256 priority Critical patent/US20130330728A1/en
Publication of WO2012116289A2 publication Critical patent/WO2012116289A2/fr
Publication of WO2012116289A3 publication Critical patent/WO2012116289A3/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/025—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/40—Disorders due to exposure to physical agents, e.g. heat disorders, motion sickness, radiation injuries, altitude sickness, decompression illness

Definitions

  • the present invention encompasses the recognition that reproducible and detectable changes occur in microbiome composition and/or activity in response to radiation exposure.
  • the present invention permits identification and/or characterization of microbial signatures reflecting such changes, and also provides systems for using such microbial signatures, for example to assess or detect extent and/or type of radiation to which an individual or area may have been exposed.
  • a microbial signature comprises a level or levels of one or more microbes or components or products thereof and is sufficient to distinguish or characterize a microbiome exposed to radiation (and/or to a particular extent or type of radiation) relative to a microbiome that has not been so exposed (e.g., has not been exposed at all, or has been exposed to a different extent and/or type), or has been exposed to a known reference dose and/or type of radiation.
  • microbial signatures obtained from gastrointestinal microbiomes of individuals suspected of or suffering from radiation exposure are sufficient to diagnose individuals when compared with microbial signatures of gastrointestinal microbiomes of unexposed individuals and/or of reference exposed individuals.
  • microbial signatures are defined for particular microbiota samples relative to appropriate reference microbiota samples.
  • particular microbiota samples share a common feature of radiation exposure that is not shared by reference microbiota samples.
  • particular microbiota samples differ from reference microbiota samples in that they are samples of a different source.
  • particular microbiota samples differ from reference microbiota samples in that the microbiota reference samples are historical microbiota samples of the same or a different source.
  • the present disclosure provides methods for identifying and/or characterizing exposure to radiation comprising providing a reference microbial signature that correlates with extent and/or type of exposure to radiation and determining a microbial signature present in a microbiota sample from an individual whose exposure to radiation is to be identified or characterized.
  • a microbiota sample comprises a sample of one or more types of microbes found in a gastrointestinal tract of a subject.
  • the microbial signature comprises a level or set of levels of one or more 16S rRNA gene sequences of one or more types of microbes.
  • the present disclosure provides methods for defining a microbial signature that correlates with an aspect of radiation exposure. For example, in some embodiments, the present disclosure provides methods comprising steps of determining a first set of levels of one or more types of microbes, or components or products thereof, in a first collection of microbiota samples, where each sample in the first collection of microbiota samples shares a common feature of radiation exposure, determining a second set of levels of the one or more types of microbes or components or products thereof in a second collection of microbiota samples, which second collection of microbiota samples does not share the common feature of radiation exposure but is otherwise comparable to the first set of microbiota samples, and identifying a microbial signature comprising levels within the first or second set that correlates with presence or absence of the common feature of radiation exposure.
  • a common feature of radiation exposure comprises an intensity of exposure ranging from 0 to 10 Grays (Gy).
  • a set of levels of one or more types of microbes or components or products thereof comprises a set of levels of 16S rRNA gene sequences of one or more types of microbes found in a gastrointestinal tract from which microbiota samples are collected
  • Figure 1 shows a scatter plot of data from 6 rats to show data variance amongst irradiated rats. Data shown herein was used to generate data shown in Figures 7 A and 7B. Approximately 5% of the 432 values are missing due to a rat not being able to produce feces at time of sampling. Each data point consist of at least 4 biological replicates.
  • Figure 2 presents a bar chart showing proportions of Operational
  • Taxonomical Units present in rat feces classified at family level. For each sample, the 6 richest members of family rank are shown. Each color block represents a percentage of OTUs detected within a family compared to total number of OTUs detected within the 6 richest families.
  • Figures 3A-3B illustrate intestinal microbial community analysis in feces pre- and post- irradiation.
  • Figure 3 A differences in composition of 16S rRNA sequences measured by PhyloChip are used to calculate the Bray-Curtis distance between rat feces samples. Presence-absence scoring for each hybridizing signal in all 7484 OTUs was incorporated in the analysis. Non-metric multidimensional scaling ordination of samples showed microbial communities were significantly different by day (p ⁇ 0.001) but not by rat (p ⁇ 0.09), as determined by the Adonis test, and delineated with lines for clarity.
  • Figure 3B demonstrates hierarchical clustering showing phylogenetic relationships of microbiota in rat feces. Samples were clustered using the farthest neighbor distance (complete linkage) algorithm to show strong dependence of microbiota on day post irradiation.
  • Figures 4A-4B illustrate candidate biomarkers for radiation exposure.
  • Figure 4A shows a Venn diagram illustrating abundance of OTUs exhibiting statistically significant changes between background, day 0, and day 11 (Day 1 1); background and day 21 (Day 21); and background and combined days 4-21 (All Days). Numbers in black indicate number of OTUs that are shared between each analysis.
  • Nonmetric multidimensional scaling ordination of samples based on the 147 common OTUs found in Figure 4A shown in Figure 4B showed distance separation by day (p ⁇ 0.001) but not by rat (p ⁇ 0.09). Data points are delineated with lines for clarity.
  • Figure 5 presents heatmaps highlighting trends of OTUs that increase
  • Figure 6 presents a line graph showing persistent changes in specific
  • Figure 8 presents a collection of line graphs illustrating abundance of biomarkers in feces of rats exposed to 10 and 18 Gy irradiation at 0, 2, 4, 8, 1 1, 15, and 21 days post exposure.
  • Figures 10A-10B show bar graphs illustrating the stability of bacterial populations across age (Figure 10A) strain and diet (Figure 10B) in rats not exposed to radiation.
  • Figure 11 presents a chart mapping rat biomarker OTUs to human microbiome project pyrosequencing data.
  • Figure 12 shows a bar graph illustrating abundance of different microbe types in rats treated with different antibiotics. Orally administered vancomycin and a mixture of streptomycin, bacitracin polymyxin B and neomycin alter abundance of intestinal microbiota present in rat feces.
  • Antibiotic agent means any of a group of chemical substances, isolated from natural sources or derived from antibiotic agents isolated from natural sources, having a capacity to inhibit growth of, or to destroy bacteria, and other microorganisms, used chiefly in treatment of infectious diseases.
  • antibiotic agents include, but are not limited to, Penicillin G; Methicillin; Nafcillin; Oxacillin; Cloxacillin; Dicloxacillin; Ampicillin; Amoxicillin; Ticarcillin; Carbenicillin; Mezlocillin; Azlocillin; Piperacillin; Imipenem; Aztreonam; Cephalothin; Cefaclor; Cefoxitin; Cefuroxime; Cefonicid; Cefinetazole; Cefotetan; Cefprozil; Loracarbef; Cefetamet; Cefoperazone; Cefotaxime; Ceftizoxime;
  • Ceftriaxone Ceftazidime; Cefepime; Cefixime; Cefpodoxime; Cefsulodin;
  • Lomefloxacin Cinoxacin; Doxycycline; Minocycline; Tetracycline; Amikacin; Gentamicin; Kanamycin; Netilmicin; Tobramycin; Streptomycin; Azithromycin; Clarithromycin; Erythromycin; Erythromycin estolate; Erythromycin ethyl succinate; Erythromycin glucoheptonate; Erythromycin lactobionate; Erythromycin stearate; Vancomycin; Teicoplanin; Chloramphenicol; Clindamycin; Trimethoprim;
  • Anti-bacterial antibiotic agents include, but are not limited to, penicillins, cephalosporins, carbacephems, cephamycins, carbapenems, monobactams, aminoglycosides, glycopeptides, quinolones, tetracyclines, macrolides, sulfonamides, fluoroquinolones, and lincosamides.
  • Antibacterial agents also include antibacterial peptides. Examples include but are not limited to maximum H5, dermcidin, cecropins, andropin, moricin, ceratotoxin, melittin, magainin, dermaseptin, bombinin, brevinin-1, esculentins, buforin II, CAP 18, LL37, abaecin, apidaecins, prophenin, indolicidin, brevinins, protegrin, tachyplesins, defensins, and or drosomycin.
  • Comparable Sufficiently similar to permit comparison, but differing in at least one feature.
  • Correlates The term "correlates”, as used herein, has its ordinary meaning of "showing a correlation with”. Those of ordinary skill in the art will appreciate that two features, items or values show a correlation with one another if they show a tendency to appear and/or to vary, together. In some embodiments, a correlation is statistically significant when its p-value is less than 0.05; in some embodiments, a correlation is statistically significant when its p-value is less than 0.01. In some embodiments, correlation is assessed by regression analysis. In some embodiments, a correlation is a correlation coefficient. [0025] Differentiates: The term “differentiates”, as used herein, indicates defining or distinguishing from other entities (e.g., comparable entities). In some embodiments, differentiates means distinguishing from other types with which present together in source and/or sample.
  • Microbe is typically used in the art to refer to a microscopically small organisms such as a bacterium, fungus, protozoan, or virus.
  • a microbe is a bacterium, archaeon, unicellular fungus (e.g., yeast), alga, or a protozoa (e.g., plasmodia as a malaria pathogen).
  • microbes are characterized according to their kingdom.
  • microbes are characterized according to their phylum.
  • microbes are characterized according to their class.
  • microbes are characterized according to their family.
  • microbes are characterized according to their genus. In some embodiments, microbes are characterized according to their species. In some embodiments, microbes are characterized according to their subspecies. In some embodiments, microbes are characterized according to their strain. Occasionally additional taxonomic class(es), e.g., serovars or serotypes, are used for differentiating microbes, such as bacteria, included within a subspecies. Serovars and serotypes are distinguished by their different types of attachment behavior at a cell membrane. In some embodiments, genus and species are utilized to identify and/or characterize a microbe (e.g., in a sample).
  • subspecies, serotype and/or strain are utilized to identify and/or characterize a microbe (e.g., in a sample).
  • a microbe e.g., in a sample
  • a microbe is identified and/or characterized using one or more distinguishing characteristics such as pathogenicity (i.e., an ability to bring on a particular illness), or resistance to one or more antibiotics, metabolic profiles, morphology, etc.
  • Microbial Types As will be understood from the context, the term
  • microbial types or “types of microbes” is used herein to indicate a grouping of microbes with a common feature.
  • a microbial type is a group of microbes sharing a common detectable feature.
  • a common detectable feature is or comprises presence or amount of a particular DNA sequence.
  • a common detectable feature is or comprises presence or amount of a particular RNA transcript.
  • a common detectable feature is or comprises presence or amount of a polypeptide (e.g., a microbially- produced polypeptide).
  • a common detectable feature is or comprises presence or level of an enzymatic activity (e.g., of a microbial enzyme).
  • microbes of a common type are microbes of a particular classification, according to standard taxonomy. Those of skill in the art will understand that the term "microbial type" as used herein is not restricted to a specific degree of resolution; different features may be detected using technologies that achieve different levels of resolution.
  • microbes of a common type are microbes of the same microbial kingdom.
  • microbes of a common type are microbes of the same microbial phylum.
  • microbes of a common type are microbes of the same microbial class.
  • microbes of a common type are microbes of the same microbial family.
  • microbes of a common type are microbes of the same microbial genus. In some embodiments, microbes of a common type are microbes of the same microbial species. In some embodiments, microbes of a common type are microbes of the same microbial subspecies. In some embodiments, microbes of a common type are microbes of the same microbial serovar. In some embodiments microbes of a common type are microbes of the same microbial serotype. In some embodiments, microbes of a common type are microbes of the same strain.
  • radiation can refer to any type of emission of energy as electromagnetic waves or as moving subatomic particles.
  • radiation comprises ionizing radiation.
  • Ionizing radiation is radiation of sufficiently high energy to ionize atoms.
  • Types of ionizing radiation include but are not limited to alpha radiation, beta radiation, cosmic radiation, neutron radiation, X- ray radiation, and gamma radiation.
  • radiation comprises nonionizing radiation.
  • Types of non-ionizing radiation include but are not limited to visible light, infrared light, microwave radiation, radiowaves, very low frequency radiation, extremely low frequency radiation, thermal radiation, and black body radiation.
  • a reference sample or individual is one that is sufficiently similar to a particular sample or individual of interest to permit a relevant comparison.
  • information about a reference sample is obtained simultaneously with information about a particular sample.
  • information about a reference sample is historical.
  • information about a reference sample is stored for example in a computer-readable medium.
  • comparison of a particular sample of interest with a reference sample establishes identity with, similarity to, or difference of the particular sample of interest relative to the reference.
  • sample refers to a biological or environmental sample obtained from a source of interest.
  • a source of interest comprises an organism, such as an insect, animal, human, or plant; in some embodiments, a source of interest comprises soil, sediment, ground water, surface water and/or air from a geographic location.
  • a biological sample comprises biological tissue or fluid.
  • a biological sample may be or comprise bone marrow; blood; blood cells; ascites; tissue or fine needle biopsy samples; cell-containing body fluids; free floating nucleic acids;
  • a biological sample is or comprises cells obtained from an individual.
  • obtained cells are or include cells from the individual from whom the sample is obtained.
  • obtained cells are or include microbial cells of the individual's microbiome.
  • a sample is a "primary sample" obtained directly from a source of interest by any appropriate means.
  • a primary biological sample is obtained by a method selected from the group consisting of biopsy (e.g., fine needle aspiration or tissue biopsy), surgery, collection of body fluid (e.g., blood, lymph, feces etc.), etc.
  • a primary environmental sample is obtained by digging, core sampling, and/or extracting or combinations thereof.
  • sample refers to a preparation that is obtained by processing (e.g., by removing one or more components of and/or by adding one or more agents to) a primary sample. For example, filtering using a semi-permeable membrane.
  • processing e.g., by removing one or more components of and/or by adding one or more agents to
  • a primary sample For example, filtering using a semi-permeable membrane.
  • Such a “processed sample” may comprise, for example nucleic acids or proteins extracted from a sample or obtained by subjecting a primary sample to techniques such as amplification or reverse transcription of mRNA, isolation and/or purification of certain components, etc.
  • substantially refers to a qualitative condition of exhibiting total or near-total extent or degree of a
  • Transcript refers to a molecule as transcribed or alternately as processed in one or more steps of splicing, ect.
  • Methods in accordance with the present invention provide a means for identifying and/or characterizing exposure to radiation.
  • fast and reliable means are needed to identify radiation-exposed individuals and characterize their exposure.
  • Humans are highly sensitive to radiation exposure, but appropriate medical treatment can have a dramatic impact on chances of survival and/or extent of disease or suffering. In certain situations, it may be critical to not only identify, but also to quantify radiation dose because appropriate medical treatment can be highly dose dependent.
  • sources of radiation exposure include but are not limited to nuclear power plants, nuclear weapons, cosmic rays, radiation therapy, nuclear materials, radiopharmaceuticals, X-ray tubes, particle accelerators, exposure to radon -222, exposure to thorium-232, exposure to uranium-235 and -238, exposure to potassium-40, exposure to radium-226, smoke detectors, airport luggage screeners, radiation diagnostics (CT scans), radiologic dirty bombs and space travel or any combination thereof.
  • CT scans radiation diagnostics
  • cytokines for treatment include but are not limited to granulocyte colony-stimulating factor, filgrastim, pegylated granulocyte colony-stimulating factor, pegfilgrastim, granulocyte macrophage colony-stimulating factor, and/or
  • radiation exposure comprises any amount of radiation to which an individual or object has been exposed.
  • radiation exposure comprises exposure to non-ionizing radiation.
  • radiation exposure comprises exposure to ionizing radiation.
  • radiation exposure comprises exposure to between 0 and 1 Gy of ionizing radiation.
  • radiation exposure comprises exposure to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more Grays of ionizing radiation.
  • a Gray is a measure of radiation exposure defined as absorption of one joule of ionizing radiation by one kilogram of matter.
  • Clinical manifestations of radiation exposure include but are not limited to loss of and/or damage to bone marrow cells, decreased lymphocytes, altered levels of granulocytes, gastrointestinal symptoms including loss of intestinal crypts and gastrointestinal barrier breakdown, loss of and/or damage to epidermal and/or dermal cells and combinations thereof.
  • Affected individuals may immediately show symptoms of radiation exposure.
  • Affected individuals may be initially asymptomatic and then begin to show symptoms of exposure after a period of time.
  • Affected individuals may begin to show symptoms after 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more seconds.
  • Affected individuals may begin to show symptoms after 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more minutes.
  • Affected individuals may begin to show symptoms after 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more hours.
  • Affected individuals may begin to show symptoms after 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more days.
  • Affected individuals may be asymptomatic.
  • symptoms of radiation exposure include but are not limited to nasal bleeding, mouth bleeding, gum bleeding, rectum bleeding, bloody stool, bruising, confusion, dehydration, diarrhea, fainting, fatigue, fever, hair loss, inflammation of exposed areas (redness, tenderness, swelling, bleeding), mouth ulcers, nausea and vomiting, open sores on the skin, skin burns (redness, blistering), sloughing of skin, esophageal ulcers, stomach ulcers, intestinal ulcers, vomiting blood, weakness and combinations thereof.
  • Current methods of assessing radiation exposure include but are not limited to assessment of symptoms present, obtaining biological samples for radiological monitoring, determination of absolute lymphocyte counts, lymphocyte depletion kinetics, chromosome-aberration cytogenetic assays, assaying eukaryotic gene and protein expression in blood, assaying eukaryotic gene and protein expression in urine, and electron spin resonance of dental enamel and nail clippings.
  • lymphocyte depletion kinetics is generally believed to be a practical method to assess radiation dose within hours or days following a radiation exposure. Lymphocyte depletion kinetics is able to detect doses of 1-10 Gy with a resolution of 2 Gy. However, assaying lymphocyte depletion kinetics requires hematology laboratory capabilities, and a minimum of 3 complete blood counts over four days immediately following radiation exposure. For more accurate results, ideally 6 complete blood counts are needed within 2-3 days of exposure with a first blood count obtained within 4 hours of exposure.
  • chromosome-aberration cytogenetic assays remain the gold standard for quantifying radiation exposure following a major nuclear event.
  • a major disadvantage of this assay is that results are not available for several days. Blood samples cannot be taken until 24 hours after exposure and then take between 48 and 72 hours to process.
  • a human body typically contains ten times as many microbial (and particularly bacterial) cells as it has human cells. Many or most of such microbes are harmless, or even beneficial, to their human host. Increasingly, research demonstrates that such microbes play a significant role in maintaining and/or promoting human health. Gastrointestinal bacteria are a well studied example. These bacteria are thought to provide a variety of important functions including but not limited to aiding in carbohydrate digestion, regulating of intestinal cell growth, repressing pathogenic microbial growth, promoting development of intestinal mucosal immunity, metabolizing carcinogens, and preventing allergies and inflammatory bowel diseases.
  • microbiome All types and abundances of microbes in a particular environment comprise a microbiome. As microbes are nearly ubiquitous, microbiomes exist in most locations.
  • a microbiome comprises microbes associated with any defined location.
  • a microbiome comprises microbes associated with a non-living component of a natural environment. Examples include but are not limited rocks, soil, and water in any form, including water in natural bodies of water, puddles, pools, or droplets.
  • a microbiome comprises microbes associated with a non-living component of a manufactured environment. Examples include but are not limited to a surface of a computer keyboard or mouse, a surface of manufacturing equipment, or a door handle.
  • a microbiome comprises microbes associated with a living organism, or a particular portion, organ, tissue, or component thereof.
  • such an organism is a non-human multicellular organism that shares an environment with humans.
  • such an organism is a plant.
  • such an organism is an insect.
  • such an organism is an animal.
  • an animal is a mouse, rat, bird, cat, dog, wolf, coyote, deer, fox, skunk, rabbit, chipmunk, squirrel, horse, cow, goat, sheep, pig, possum, and cockroach.
  • an animal is a non-human primate.
  • an organism is a human.
  • Content e.g., type and/or abundance of microbes present
  • behavior e.g., production of one or more markers, rate of respiration and/or proliferation, extent of migration, etc
  • a microbiome can be shaped by local environments; in some embodiments; a single organism contains multiple different microbiomes, for example in different locations within or portions of their bodies.
  • the human microbiome project http://commonfund.nih.gov/hmp/
  • the human microbiome project is characterizing the microbial communities found at several different sites on the human body, including nasal passages, oral cavities, skin, gastrointestinal tract, and urogenital tract.
  • a microbiome for use in accordance with the present invention is one associated with a particular site or location (e.g., tissue or organ) of an organism body.
  • a microbiome comprises microbes associated with skin.
  • a microbiome comprises microbes associated with teeth.
  • a microbiome comprises microbes associated with oral mucosa.
  • a microbiome comprises microbes associated with nasal passages.
  • a microbiome comprises microbes associated with a urogenital system.
  • a microbiome comprises microbes associated with a gastrointestinal tract.
  • a microbiome comprises a single microbe. In some embodiments a microbiome comprises between 1 and a trillion or more individual microbes. In some embodiments, a microbiome comprises a single type of microbe. In some embodiments, a microbiome comprises between 1 and a million or more types of microbes. In some embodiments, a microbiome comprises between 500 and 5, 000 types of microbes. In some embodiments, a microbiome comprises between 1000 and 2, 000 types of microbes. Types of microbes that reside in the intestines are generally described at the phylum, class, order and family levels. In some embodiments, there are between 1000 - 1500 types of bacteria in
  • microbiome composition and/or activity and more particularly that changes in microbiome composition and/or activity can be informative about particular environmental conditions.
  • the invention presented herein encompasses the finding that microbiome composition and/or activity can change in detectable and reproducible ways that are correlated with exposure to radiation.
  • a change in microbiome composition and/or activity comprises any change in abundance and/or type of one or more types of microbes in a microbiome, and/or of one of more components produced thereby.
  • a change in microbiome composition and/or activity comprises an increase in abundance of one or more types of microbes in a microbiome, or of one or more components produced thereby.
  • a change in microbiome composition and/or activity comprises a decrease in abundance of one or more types of microbes in a microbiome, and/or of one or more components produced thereby.
  • a change in microbiome composition and/or activity comprises an increase in abundance of one or more types of microbes, and/or of components ) produced thereby, and also a decrease in abundance of one or more types of microbes in a microbiome, and/or of component(s) produced thereby.
  • microbiome changes that correlate with extent and/or type of radiation exposure are identified, characterized, and/or detected.
  • analysis of such changes involves controlling for and/or subtracting out effects of one or more other alterations in microbiome composition and/or activity.
  • Microbiome composition and/or activity can be detectably altered by events external or internal to a host organism. For example, oral ingestion of antibiotics by individuals can dramatically alter composition and/or activity of their gastrointestinal microbiomes.
  • a change in microbiome composition and/or activity occurs in response to disease in a host organism. In some embodiments a change in microbiome composition and/or activity occurs in response to infection of a host organism with pathogenic bacteria. In some embodiments a change in microbiome composition and/or activity occurs in response to a change in diet of a host organism. In some embodiments a change in microbiome composition and/or activity occurs in response to a change in water source of a host organism. In some embodiments a change in microbiome composition and/or activity occurs in response to a change in environment of a host organism, for example a person may move to a new city or country.
  • a change in microbiome composition and/or activity occurs in response to a change in personal hygiene habits of a host organism. In some embodiments a change in microbiome composition and/or activity occurs in response to a change in weight of a host organism. In some embodiments a change in microbiome composition and/or activity occurs in response to a change in age of a host organism. In some embodiments a change in microbiome composition and/or activity occurs in response to a change in chemical exposure of a host organism.
  • microbiome altering agents comprise chemicals.
  • microbiome altering agents comprise antimicrobials.
  • microbiome altering agents comprise antibiotics.
  • microbiome altering agents comprise bacteria.
  • microbiome altering agents comprise probiotic bacteria.
  • microbe altering agents comprise antimicrobial peptides.
  • microbe altering agents comprise antifungals.
  • microbe altering agents comprise bacteriophages.
  • the present invention encompasses the recognition that microbial signatures can be relied upon as proxy for microbiome composition and/or activity.
  • Microbial signatures comprise data points that are indicators of microbiome composition and/or activity.
  • changes in microbiomes can be detected and/or analyzed through detection of one or more features of microbial signatures.
  • a microbial signature includes information relating to absolute amount of one or more types of microbes, and/or products thereof. In some embodiments, a microbial signature includes information relating to relative amounts of one or more types of microbes and/or products thereof.
  • a microbial signature includes information relating to presence, level, and/or activity of at least one type of microbes. In some embodiments, a microbial signature includes information relating to presence, level, and/or activity of between one and 10 types of microbes. In some embodiments, a microbial signature includes information relating to presence, level, and/or activity of between one and 100 types of microbes. In some embodiments, a microbial signature includes information relating to presence, level, and/or activity of between one and 1000 or more types of microbes. In some embodiments, a microbial signature includes information relating to presence, level, and/or activity of substantially all types of microbes within a microbiome.
  • a microbial signature comprises a level or set of levels of one or more types of microbes or components or products thereof. In some embodiments, a microbial signature comprises a level or set of levels of one or more DNA sequences. In some embodiments, a microbial signature comprises a level or set of levels of one or more 16S rRNA gene sequences. In some embodiments, a microbial signature comprises a level or set of levels of 18S rRNA gene sequences. In some embodiments, a microbial signature comprises a level or set of levels of one or more RNA transcripts. In some embodiments, a microbial signature comprises a level or set of levels of one or more proteins. In some embodiments, a microbial signature comprises a level or set of levels of one or more metabolites.
  • 16S and 18S rRNA gene sequences encode small subunit components of prokaryotic and eukaryotic ribsosomes respectively. rRNA genes are particularly useful in distinguishing between types of microbes because, although sequences of these genes differs between microbial species, the genes have highly conserved regions for primer binding. This specificity between conserved primer binding regions allows the rRNA genes of many different types of microbes to be amplified with a single set of primers and then to be distinguished by amplified sequences.
  • a microbial signature is obtained and/or determined using a microbiota sample.
  • a microbiota sample comprises a sample of microbes and or components or products thereof from a microbiome.
  • a microbiota sample is collected by any means that allows recovery of microbes or components or products thereof of a microbiome and is appropriate to the relevant microbiome source. For example, where the microbiota sample of the gastrointestinal tract is obtained from a fecal sample.
  • a microbial signature is obtained and/or determined by quantifying microbial levels. Methods of quantifying levels of microbes of various types are described herein. [0064] In some embodiments, determining a level or set of levels of one or more types of microbes or components or products thereof comprises determining a level or set of levels of one or more DNA sequences. In some embodiments, one or more DNA sequences comprises any DNA sequence that can be used to differentiate between different microbial types. In certain embodiments, one or more DNA sequences comprises 16S rRNA gene sequences. In certain embodiments, one or more DNA sequences comprises 18S rRNA gene sequences. In some embodiments, 1, 2, 3, 4, 5, 10, 15, 20, 25, 50, 100, 1,000, 5,000 or more sequences are amplified.
  • a microbiota sample is directly assayed for a level or set of levels of one or more DNA sequences.
  • DNA is isolated from a microbiota sample and isolated DNA is assayed for a level or set of levels of one or more DNA sequences.
  • Methods of isolating microbial DNA are well known in the art. Examples include but are not limited to phenol-chloroform extraction and a wide variety of commercially available kits, including QIAamp DNA Stool Mini Kit (Qiagen, Valencia, CA).
  • a level or set of levels of one or more DNA sequences is determined by amplifying DNA sequences using PCR (e.g., standard PCR, semi-quantitative, or quantitative PCR). In some embodiments, a level or set of levels of one or more DNA sequences is determined by amplifying DNA sequences using quantitative PCR.
  • PCR e.g., standard PCR, semi-quantitative, or quantitative PCR.
  • a level or set of levels of one or more DNA sequences is determined by amplifying DNA sequences using quantitative PCR.
  • DNA sequences are amplified using primers specific for one or more sequence that differentiate(s) individual microbial types from other, different microbial types.
  • 16S rRNA gene sequences or fragments thereof are amplified using primers specific for 16S rRNA gene sequences.
  • 18S DNA sequences are amplified using primers specific for 18S DNA sequences.
  • 16S rRNA gene sequences are amplified using primer sequences listed in Table 1 or 2.
  • a level or set of levels of one or more 16S rRNA gene sequences is determined using phylochip technology. Use of phylochips is well known in the art and is described in Hazen et al.
  • 16S rRNA genes sequences are amplified and labeled from DNA extracted from a microbiota sample. Amplified DNA is then hybridized to an array containing probes for microbial 16S rRNA genes. Level of binding to each probe is then quantified providing a sample level of microbial type corresponding to 16S rRNA gene sequence probed.
  • phylochip analysis is performed by a commercial vendor. Examples include but are not limited to Second Genome Inc. (San Francisco, CA).
  • determining a level or set of levels of one or more types of microbes or components or products thereof comprises determining a level or set of levels of one or more microbial RNA molecules (e.g., transcripts).
  • microbial RNA molecules e.g., transcripts.
  • Methods of quantifying levels of RNA transcripts are well known in the art and include but are not limited to northern analysis, semi-quantitative reverse transcriptase PCR, quantitative reverse transcriptase PCR, and microarray analysis. These and other basic RNA transcript detection procedures are described in Ausebel et al.
  • determining a level or set of levels of one or more types of microbes or components or products thereof comprises determining a level or set of levels of one or more microbial proteins.
  • Methods of quantifying protein levels are well known in the art and include but are not limited to western analysis and mass spectrometry. These and all other basic protein detection procedures are described in Ausebel et al. (Ausubel FM, Brent R, Kingston RE, Moore DD, Seidman JG, Smith JA, Struhl K (eds). 1998. Current Protocols in Molecular Biology. Wiley: New York).
  • determining a level or set of levels of one or more types of microbes or components or products thereof comprises determining a level or set of levels of one or more microbial metabolites.
  • levels of metabolites are determined by mass spectrometry.
  • levels of metabolites are determined by nuclear magnetic resonance spectroscopy.
  • levels of metabolites are determined by enzyme-linked immunosorbent assay (ELISA).
  • ELISA enzyme-linked immunosorbent assay
  • levels of metabolites are determined by colorimetry.
  • levels of metabolites are determined by spectrophotometry.
  • the present invention encompasses the recognition that changes in microbial signature can be relied upon as proxy for changes in microbiome composition and/or activity. Thus, specific changes in a microbiome to be detected and/or analyzed will contribute to features of a microbial signature.
  • the present invention is drawn to a method for defining a microbial signature indicative of radiation exposure by identifying those components of the microbiome that are affected by radiation exposure.
  • defining a microbial signature that correlates with a feature of radiation exposure comprises any method that allows identification of types of microbes or components or products thereof that differ between exposed and non-exposed and/or that define or classify exposed microbiomes.
  • defining a microbial signature that correlates with an aspect of radiation exposure comprises determining a first set of levels of one or more types of microbes or components or products thereof in a first collection of microbiota samples, where each microbiota sample in the first collection of microbiota samples shares a common feature of radiation exposure; determining a second set of levels of the one or more types of microbes or components or products thereof in a second collection of microbiota samples, which second collection of microbiota samples does not share the common feature of radiation exposure but is otherwise comparable to the first set of microbiota samples; and identifying a microbial signature comprising levels within the first or second set that correlates with presence or absence of the common feature of radiation exposure.
  • a collection of microbiota samples comprises at least one microbiota sample.
  • a microbiota sample comprises 1, 2, 3, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 100, or 1,000 or more samples.
  • the first and second collections of microbiota samples are any two collections of microbiota samples that differ in a feature of radiation exposure but are otherwise comparable.
  • the first and second collections of microbiota samples are obtained from different host organisms.
  • the first and second collections of microbiota samples are obtained at from a same collection of hosts at different times.
  • the first and second collections of microbiota samples are obtained.
  • a feature of radiation exposure comprises a dose of radiation exposure to a host from which a microbiota sample is obtained.
  • a dose of radiation exposure comprises between 0 and 1 Gy.
  • dose of radiation exposure comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 Gy or more.
  • a feature of radiation exposure comprises a duration of radiation exposure to a host from which a microbiota sample is obtained. In some embodiments, the duration is between 0 and 1 seconds. In some
  • the duration is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more seconds. In some embodiments, the duration is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more minutes. In some embodiments, the duration is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more hours. In some embodiments, the duration is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more days.
  • a feature of radiation exposure comprises a duration of time post-exposure to a host from which a microbiota sample is obtained.
  • the duration is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more minutes.
  • the duration is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more hours.
  • the duration is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more days.
  • the duration is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more weeks.
  • a feature of radiation exposure comprises a frequency of exposure to radiation to a host from which a microbiota sample is obtained.
  • a frequency of exposure to radiation comprises exposure of a host to radiation one or more times per second.
  • a frequency of exposure to radiation comprises exposure of a host to radiation one or more times per minute.
  • a frequency of exposure to radiation comprises exposure of a host to radiation one or more times per hour.
  • a frequency of exposure to radiation comprises exposure of a host to radiation one or more times per day.
  • a frequency of exposure to radiation comprises exposure of a host to radiation one or more times per week.
  • a frequency of exposure to radiation comprises exposure of a host to radiation one or more times per month. In some embodiments, a frequency of exposure to radiation comprises exposure of a host to radiation one or more times per year. In some embodiments, a frequency of exposure to radiation comprises exposure of a host to radiation one or more times per lifetime of a host.
  • a feature of radiation exposure comprises a type of radiation exposure.
  • Types of radiation exposure in accordance with the present invention include but are not limited to ionizing radiation, alpha radiation, beta radiation, cosmic radiation, neutron radiation, X-ray radiation, and gamma radiation or combinations thereof.
  • identifying a microbial signature comprises any means that allows a signature correlated with a feature of radiation exposure to be identified. In some embodiments, identifying a microbial signature comprises identifying one or more levels in a first set of levels in the first collection of microbiota samples that are increased and/or decreased when compared to the second set of levels of the second collection of microbiota samples. In some embodiments, identifying a microbial signature comprises identifying levels of one or more DNA sequences that are increased and/or decreased in the first collection of microbiota samples when compared to the second collection of microbiota samples.
  • DNA sequences are identified by comparing semi-quantitative or quantitative real time PCR data for the first and second collections of microbiota samples. In some embodiments, DNA sequences are identified by performing cluster analysis on phylochip data generated from the first and second collections of microbiota samples. In some embodiments, identifying a microbial signature comprises identifying levels of one or more RNA transcripts that are increased and/or decreased in the first collection of microbiota samples when compared to the second collection of microbiota samples. In some embodiments, RNA transcripts are identified by comparing semi-quantitative or quantitative real time reverse transcriptase PCR data for the first and second collections of microbiota samples.
  • RNA sequences are identified by performing cluster analysis on microarray data generated from the first and second collections of microbiota samples.
  • identifying a microbial signature comprises identifying levels of one or more proteins that are increased and/or decreased in the first collection of microbiota samples when compared to the second collection of microbiota samples.
  • the present invention encompasses the recognition that changes in microbial signature can be relied upon as a diagnostic tool to identify and characterize radiation exposure.
  • current tests for detecting radiation exposure either require extensive repeated testing or take upwards of three days post-exposure.
  • the current invention provides methods of identifying and/or characterizing exposure to radiation comprising determining a microbial signature in a microbiota sample from an individual whose exposure to radiation is to be identified or characterized, and comparing it to a reference microbial signature that correlates with one or more features of exposure to radiation.
  • an individual comprises any individual exposed to, suspected of being exposed to, and/or at risk of exposure to radiation.
  • a reference microbial signature comprises any value that is correlated with a known feature of exposure to radiation.
  • a reference microbial signature comprises a microbial signature obtained from an individual who has not been exposed to radiation.
  • a reference microbial signature comprises a microbial signature from an individual who has been exposed to a known feature of radiation.
  • a reference microbial signature comprises a microbial signature from an individual who is comparable to the individual whose exposure to radiation is to be identified or characterized.
  • a reference microbial signature comprises a microbial signature that was obtained at a different time from the individual whose exposure to radiation is to be identified or characterized. In some embodiments, the different time occurred before exposure to radiation.
  • a reference microbial signature is from a microbiota sample of an individual whose exposure to radiation is to be identified.
  • a reference microbial signature comprises a level and/or activity one or more microbes.
  • a reference microbial signature comprises a level and/or activity one or more microbes, wherein the level and/or activity of the one or more microbes remains substantially unchanged in response to radiation exposure.
  • comparing a microbial signature in a microbiota sample from an individual whose exposure to radiation is to be identified or characterized, to a reference microbial signature comprises comparing microbial signatures obtained from two separate individuals. In some embodiments, comparing microbial signatures comprises comparing microbial signatures obtained from the same individual at separate time points. In some embodiments, comparing microbial signatures comprises comparing microbial signatures of the same microbial sample. In some embodiments, comparing microbial signatures comprises comparing relative levels and/or activities of two or more microbes.
  • comparing microbial signatures comprises comparing relative levels and/or activities of two or more microbes, wherein at least one first microbe (i.e., level and/or activity of at least one first microbe) remains substantially constant. In some such embodiments, comparing microbial signatures comprises comparing relative levels and/or activities of two or more microbes, wherein at least one second microbe changes.
  • Example 1 Obtaining microbial samples of irradiated rats
  • Example 2 Phylochip analysis of rat microbiomes
  • Microbial diversity and comparative community structure of rat fecal DNA samples is characterized by Second Genome Inc. (San Francisco, CA) using high-density G3 PhyloChipTM 16S rRNA microarray-based assays (PN49-0002A) and bioinformatic methods.
  • Microbiota analysis is focused on calculating inter-sample distances and assessing significance of microbiome dissimilarity (Hazen et al. Deep-sea oil plume enriches indigenous oil-degrading bacteria. Science, 330(6001):204-8, 2010). Data analysis incorporates several separate stages; pre-processing and data reduction,
  • Pre-processing and data reduction To calculate a summary intensity for each feature on each array, 9 central pixels of individual features are ranked by intensity and 75% percentile is used. Probe intensities are background-subtracted and scaled to PhyloChipTM Control MixTM (Standard-Scaling) (Second Genome, Inc., San Francisco). A hybridization score (HybScore) for an operational taxonomic unit (OTU) is calculated as a mean intensity of perfectly matching probes exclusive of maximum and minimum values. Data was reduced to consider taxa deemed present as described in Hazen et al. and filtered to taxa present in at least one sample or to taxa present in a majority of profiles of exactly one category and in zero other categories.
  • HybScore operational taxonomic unit
  • Sample-to-Sample Distance Function All profiles are inter-compared in a pair-wise fashion to determine a dissimilarity score and to store it in a distance matrix. Distance functions are chosen to allow similar biological samples to produce only small dissimilarity scores.
  • the Bray-Curtis Index utilizes taxon abundance differences across samples but employs a pair-wise normalization by dividing the sum of differences by the sum of all abundances.
  • Non-Metric Multidimensional Scaling is a method of two- dimensional ordination plotting that is used to visualize complex relationships between samples. NMDS uses only rank order of dissimilarity values to position points relative to each other. Lists of significant taxa whose abundance characterizes each class is performed using Prediction Analysis for Microarrays which utilizes a nearest shrunken centroid method described in Tibshirani et al. in Pre-validation and inference in microarrays. (Stat Appl Genet Mol Biol, l :Articlel, 2002).
  • Hybridization Scoring and Saturation To calculate summary intensities for each feature on each array, 9 central pixels of individual features are ranked by intensity and 75% percentile is used as probe intensity. Probe intensities are background-subtracted and scaled via Standard Scaling to PhyloChipTM Control Mix (Second Genome Inc., San Francisco) so that mean probe intensity of all probes complimentary to any target in the control mix will equal 10,000 units. Since the same concentration of spike mix is added to each PhyloChip assay, scaled probe intensities are directly comparable to each other across arrays. When a probe's scaled intensity changes from array-to-array it indicates a change in target DNA concentration.
  • the summary score for an operational taxonomic unit is calculated as a mean intensity of perfectly matching probes exclusive of the maximum and minimum. These trimmed means can theoretically range from 0 to 65,536, but in real microbiome samples we commonly observe a range from -100 to -17,000.
  • a common practice with microarray data is to logarithmically transform scores so that variance is constant over a broad concentration range. Log base 2 of scores was used which, for example, converts 100 to 6.644 and 17,000 to 14.053.
  • HybScores such as 6,644 or 14,053.
  • DNA from 26 different taxa were applied to G3 PhyloChip assays across a range of concentrations from 0 to 480 pM.
  • PCR reaction mixture consists of 50% iQ SYBR Green Supermix (Bio-Rad), 0.4 ⁇ forward and reverse primers, and 3.8% template solution in RNase/DNase free water. Primer combinations shown allow for detection of bacterial taxons indicated (table 1) or biomarker (table 2) indicated. A paired Students t-test was used to find significant differences among variables in qPCR data. PCR data variance is shown in representative scatter plots ( Figure 1).
  • Example 4 Identifying Candidate biomarkers of prior radiation exposure
  • 16S ribosomal RNA (rRNA) gene sequences are thought to be unique to each eubacteria taxon and changes in quantity of 16S rRNA genes across total DNA extraction products are thought to be indicative of changes in species abundance.
  • Example 1 from five independent rats at all time points (0, 4, 1 1 and 21 days) after exposure to 10 Gy single-fraction total body irradiation for analysis. Microbial diversity and comparative community structure of rat fecal DNA samples were characterized using G3 PhyloChip 16S microarray -based assay and bioinformatic methods described in Example 2.
  • OTUs were identified that exhibited changes in abundance that were persistent from day 4 through day 21 post irradiation. Abundance levels of 276 OTUs were found that were changed at days 4 through 21 when the number of false discoveries were limited to 5 (total), as estimated by the q-value (All Days, Figure 4) (Turnbaugh, P.J. et al. "The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice.” Sci Transl Med, l(6):6ral4, 2009). These 276 OTUs were then compared with 3855 and 237 OTUs that were significantly altered on days 11 and 21 as compared to background (Days 11 and 21, Figure 4).
  • OTUs were found with unchanged abundance that may serve as internal controls, and 47 separate Clostridiaceae OTUs with decreased expression were identified.
  • a complete listing of OTUs that increased, decreased or were unchanged following irradiation is provided in the Appendix. Results from microarray studies, described herein, are also specific to individual bacteria. For example, abundance of OTU 31902 (Cyanobacteria) increased, OTU 39153 (Clostridia) decreased, and OTU 42924 (Clostridia) was unchanged in the 4-21 day period post radiation exposure (Figure 6).
  • the "increased/decreased" ratio of 31902/39153 increased from -4 to +2 log 2 difference indicating a 64 fold change at days 4, 11, and 21 post irradiation ( Figure 6), and may also be used as a possible biomarker of prior radiation exposure.
  • Use of a ratio in developing intestinal microbiota as biomarkers for radiation biodosimetry may be advantageous as preexposure samples will not be available during or after a radiological device being detonated.
  • Proteobacteria over 5 days (Figure 7B), as compared to 3 days observed after 10 Gy ( Figure 7A). Further, 18 Gy irradiation induced a 10 fold reduction in Clostridia at days 1-3 that was not observed with 10 Gy irradiation. Increases in Proteobacteria at two days post 18 Gy irradiation correspond with equivalent responses observed at four days post 10 Gy irradiation since 6 fractions of the 18 Gy regimen were administered over three days instead of one.
  • the biomarker ratio "acute increase/decrease” was increased from 2 to 8 days following 10 and 18 Gy irradiation, while the ratio “chronic increase/decrease” was increased from 8 to 21 days post irradiation ( Figure 9A and 9B).
  • the present disclosure therefore confirms existence of individuals or groups of microbes that can serve as biomarkers of prior radiation exposure.
  • Example 7 Impact of factors other than radiation on microbial populations
  • the present disclosure therefore indicates that genetic background and age do not appear to exert changes in abundance for multiple bacterial taxa including Bacteroidetes, Proteobacteria and Clostridia in control rats not exposed to radiation. Abundance of multiple intestinal bacterial taxa were also unaffected by diet in control rats.
  • Example 8 Human and rat similarities
  • human gastrointestinal tract microbiomes are studied to identify similarities with data found in rats. 6 human fecal samples were analyzed using G3 PhyloChips. The present disclosure reveals that, when comparing this data to data for rats from Example 4, all 47 OTUs found to decreased in rats are present in humans, 98 of 142 stable OTUs in rat are present in humans, and 12 of 165 OTUs that increased in rat are found in humans. The present disclosure therefore indicates that these 157 OTUs form a microbial signature that correlates with and appears to be diagnostic of prior exposure to radiation.
  • Rat-to-human analysis was further broadened by comparing rat OTUs from Example 4 to bacterial taxa detected in 373 stool samples collected during the human microbiome project (http://www.hmpdacc.org/data_browser.php).
  • Rat fecal OTUs were binned at genus-level to match pyrosequencing data from human samples (Figure 9).
  • 47 OTUs that decreased in rats were mapped to two genera: Clostridium and Sarcina (both are present in humans).
  • Eighty nine of 142 stable OTUs were mapped to 14 genera in the Firmicutes phylum ( Figure 1 1) of which 13 are present in humans.
  • One hundred and forty one of 165 OTUs that increased in rat were mapped to three genera: Barnesiella, Lactobacillus, and Streptococcus, and all three are present in humans.
  • the present disclosure therefore indicates that more than 96% of classified rat biomarkers are matched to bacterial genera present in humans.
  • Example 9 Determining minimum dose detectable by microbial signatures
  • the following example describes an experiment to determine a minimum dose of radiation detectable using microbial signatures.
  • the following experimental method allows determination of a minimum dose detectable by biomarkers of the present invention.
  • a dose of 1 Gy is first used and dosage is then progressively increase in 1 Gy increments.
  • a lowest dose and earliest response from rat and dog is confirmed.
  • Feces is collected daily and analyzed with a microarray analysis signature specific for radiation exposure described herein. Findings are confirmed using qPCR. This method will allow identification of a dose- and time -response characteristics for intestinal microbiota to detect prior exposure to ionizing radiation.
  • Example 10 Determine impact of health status on intestinal microbiota after irradiation.
  • the following experimental method allows determination of whether microbes affected by antibiotics are the same as those affected by radiation by examining impact of pre-existing use of azithromycin on abundance of intestinal microbiota following exposure to radiation.
  • Three groups are irradiated with a single fraction exposure with X-rays and their feces are examined for changes in intestinal microbiota signature.
  • Table 2 Taxonomy corresponding to OTUs increased following irradiation
  • Clostridia SP Clostridiales
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Abstract

La présente invention définit une méthode d'identification et de caractérisation d'une exposition aux radiations après, par exemple, un incident nucléaire tel que la détonation d'une arme nucléaire ou la fusion du cœur d'une centrale nucléaire, à l'aide des modifications de microbiomes. La présente invention prouve que les microbiomes sont modifiés par l'exposition aux radiations de façon reproductible et détectable. Selon l'invention, les signatures microbiennes peuvent être utilisées pour caractériser les composants de microbiomes qui sont modifiés par une exposition aux radiations.
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