METHOD FOR ANALYSIS OF MICROBIOTA

Description

Technical Field

[0001] The present application relates generally to methods for meta-omic analysis and more specifically methods for microbiota protein composition analysis.Background

[0002] The human body harbors trillions of microbes which together comprise the human microbiome. Accumulating evidence has associated changes in microbiota composition with many diseases including inflammatory bowel diseases (IBD), obesity, diabetes, cancer, heart disease, urolithiasis, allergies etc. [2]. The microbiome is a highly complex and extremely sensitive system and it has been demonstrated that the microbiome composition is susceptible to alterations due to exposure to various compounds including but not limited to therapeutics, excipients, additives, preservatives, chemicals, stress, exercise, foods and beverages, which can impact both maintenance of health as well as development of disease. In addition to the overall microbiome, organ and region specific microbiomes have been described, in which subsets of these microbes form populations, such as the intestines, skin, vagina, oral cavity, kidney, bladder, eyes, lungs and breasts. Furthermore, it has been shown that the changes induced by many of these environmental, chemical and alimentary compounds have the potential to induce positive changes to these regional microbiomes which improve the composition and diversity of the microbiome, while others induce negative changes consistent with a diseased or unhealthy microbiome. For example, it has been demonstrated that emulsifiers, a ubiquitous component of processed food, causes negative changes in the intestinal microbiome composition indicative of inflammation and disease, while prebiotics have been shown to increase the levels of beneficial microbes, increasing the health of the intestinal microbiome. Exactly how these compositional changes affect overall function, however, remains an outstanding and important question.

[0003] One of the largest populations of microbes resides in the gastrointestinal tract and constitutes the intestinal microbiota [1]. The importance of intestinal microbiota for human health was illustrated with the use of fecal microbiota transplantation (FMT) for treating recurrent Clostridium difficile infections [3]. Furthermore, it has been shown that the microbiome continues to undergo changes throughout the course of a disease, which was recently demonstrated in IBD in which changes in the intestinal microbiome composition were tightly correlated with disease severity in Crohn's disease. Clearly, the microbiota appears to be involved in the development, progression and resolution of multiple diseases and in some instances, can be modulated to impact disease outcome, making the microbiota of global interest in both scientific and public health communities.

[0004] Beyond the human microbiome many additional microbial communities, or microbiomes, have been identified and described. One of the most prominent is the soil microbiome, which is a complex microbial community that can differ by region, climate and cultivation. The composition of the soil microbiome can be assessed to determine biodiversity and the subsequent function of these microbes in the generation and breakdown of nutrients holds wide-ranging implications for agriculture. Biofilms are another well-studied microbiome, which can be of varying complexity, occur in numerous environmental and industrial settings, and can contribute to contamination or toxicity. Understanding the composition and function of the microbes within biofilms is important for understanding biofilm prevention and dissolution. Finally, the microbiome of animals, both in agricultural and lab settings are important to the advancement of food production and research respectively. The microbiome of agricultural animals can be effected by antibiotics as well as feed and these changes have the potential to impact, breeding, animal health and subsequently food production. Additionally, it has recently been recognized that the microbiome of laboratory research animals can have profound effects on their response to therapeutics. A deeper understanding of the microbiome composition and function of these animals could possibly lead to an improved understanding of drug metabolism and the microbiome effect of therapeutic response.

[0005] Next-generation sequencing (NGS), such as metagenomics and metatranscriptomics, is well suited for examining the microbiota composition and predicting potential functions, however, it does not provide direct evidence on whether the genes are translated into proteins or not. This information, therefore, does not provide data on changes in the function of the microbiota, which are needed to understand how the alterations in composition impact function and if it is impacted in a physiologically meaningful way. Instead, metaproteomics can provide invaluable information on the functional activities of the microbiome by directly profiling protein expression levels, which are indicative of function [4, 5]. In stark contrast to metagenomics, however, metaproteomics approaches have only been applied to a limited number of studies on the microbiota. This is due, at least in part, to challenges related to both identification and quantification of the microbial peptides / proteins. Peptide / protein identification algorithms have recently been significantly improved by the use of iterative searching of large microbial protein databases [6]. In contrast, accurate methods for peptide / protein quantification are still lacking. Most current metaproteomic approaches are based on label free quantification (LFQ) which suffers from significant variability during the separate sample processing and mass spectrometry runs, making data extremely difficult to compare across experiments or datasets. Stable isotope labeling by amino acids in cell culture (SILAC) and stable isotope labeling of mammals (SILAM) are currently the most widely used approach for quantitative proteomics and provides lower variability [7]. In SILAC / SILAM, proteins in one test or reference sample are metabolically labeled with isotopically heavy amino acids, enabling for quantitative comparison between different samples. However, the application of this approach in bacteria, particularly complex bacterial populations such as the microbiome, has been limited. One of the challenges of applying these metabolic labelling approaches to the microbiota is the inherently high species diversity, which is not present in mammalian cells. Furthermore, the diverse microbiota populations frequently include both aerobic and many anaerobic species, which are extremely difficult to culture in order to achieve sufficient labeling. These complex populations also almost inevitably result in a diverse metabolic capacity to biosynthesize amino acids, which hampers the full incorporation of heavy-labelled amino acids into microbial proteins. Instead, complete metabolic labeling of nitrogen or carbon has only been applied to single bacteria [8], and environmental microbial communities, such as acid mine drainage biofilms [9]. However, its application to characterize the microbiota proteome is lacking.

[0006] WO2012170478 discloses high throughput pyrosequencing to explore the gut microbiome of patients with colorectal adenoma.

[0007] There is therefore a need for better meta-omics, e. g. metaproteomics approach for the analysis of the microbiota.Summary

[0008] The invention relating to a method of highthroughput meta-omic analysis of a plurality of microbiota samples is defined in its broadest sense by the appended independent claim. Further embodiments are detailed in the dependent claims.

[0009] Aspects detailed below, not relating to this method, are for illustrative purposes only and not part of the invention.

[0010] A first broad aspect is a fast and cost-effective strategy for metabolic labeling of the whole human microbiota, termed stable isotopically labelled microbiota (SILAMi). It will be understood that by whole human microbiota, it is meant that SILAMi may be used to provide metabolic labeling of any microbiota sample taken from the human microbiota. By samples from the human microbiota, it is meant such samples as, but not limited to samples form the intestinal microbiota, samples from the vaginal microbiota, samples from the oral microbiota, samples from the cutis microbiota, samples from the vaginal microbiota, samples from the bladder microbiota, samples from the kidney microbiota, samples from the lung microbiota, samples from the eye microbiota, samples from the breast microbiota, samples from the penile microbiota, a microbiota mucosal sample, etc. A skilled person will also readily understand that SILAMi can also be applied to a microbiota originating from an animal sample, wherein that animal is, for instance, a mammal, a bird, a reptile, etc.

[0011] Applicant has discovered that it is possible to use an isotope-labelling standard for a given microbiota sample having a large microbe population. Prior to the Applicant's discovery, it was believed that the diverse microbiota population could not be labelled simultaneously and that the microbiota would change too rapidly during incorporation of the labelling to achieve a reliable standard that is representative of the original microbiota sampled. However, applicant has discovered that the isotope-labelled standard achieved following the isotope-incorporation process is in fact representative of a significant population of the microbiota of the original sample and the proteins representative thereof. This isotope labelling of the microbiota is entitled SILAMi.

[0012] Therefore, SILAMi is a method for achieving the successful labelling of a large microbe population (a microbiota), such as one found in a human or animal subject. Difficulties in obtaining such a standard lie for instance in the fragility and low abundance of some of the microbe species found in the desired microbiota sample, for instance sensitive to changes in environment or exposure to oxygen (e.g. as some of these microbes grow in anaerobic conditions). Moreover, not all of these microbes have the same cell cycle or life cycle, and some take longer to replicate and incorporate the isotope. However, the more time that is taken to grow and incorporate the isotopes in culture, the greater the risk that certain of the species found in the microbiota sample will die off or disproportionately proliferate, not providing a faithful depiction of the microbiota found in the sample as obtained. SILAMi has overcome these prior problems and successfully provides an isotope-labelled standard for a given microbiota from a microbiota sample, where the sample may be taken from a human or an animal.

[0013] Moreover, Applicant has discovered that using an isotope-labelled standard, such as the one achieved using SILAMi, allows for the study of a large population of microbes in a given microbiota sample. The standard allows for the determination of the functionality and the composition, including changes in the determination of the functionality and composition of the microbiota sample. Furthermore, the standard provides a means of reducing variability when performing a metaproteomic analysis of the microbiota sample. This may be achieved, for instance, by adding a known amount of the standard to the microbiota sample (with a known ratio and measuring the heavy to light ratios for the sample, while comparing the ratios to theoretical known results).

[0014] In some examples, the analysis of the microbiota of a subject, i.e. a human or animal subject, may provide an indication of a disease, illness or other condition afflicting the subject, where these conditions have a measured effect on the microbiota of the subject found at different locations on the subject (e.g. the subject's organs). A comparison between the isotope-labelled standard and the microbiota sample may provide indication, for instance, as to the effectiveness of a treatment, the nature, including diagnosis and therapeutic response to of the disease or illness, the potential for weight gain or loss of the subject, etc.

[0015] Another broad aspect is a human microbiota labelled-proteins standard having labelled proteins representative of a metaproteome from a human microbiota as described herein. In some examples, said labelled proteins have at least about 50%, preferable 90%, and more preferably at least about 95 % average heavy isotopic enrichment rate.

[0016] In a further aspect there is provided a human intestinal microbiota labelled-proteins standard that may have labelled proteins representative of a metaproteome from an intestinal microbiota. In some aspects, said labelled proteins may have at least about 90% and preferably at least about 95 % average heavy isotopic enrichment rate.

[0017] In yet another aspect the microbiota labelled-proteins standard may be taxon-specific for at least about 90% and preferably at least 95% of the microbes present in the microbiota sample. Preferably the labelled proteins are taxon-specific for 100% of Kingdoms present in the microbiota sample. Preferably the labelled proteins are also taxon-specific for 95% and preferably at least 100% of Phyla present in the microbiota sample. Preferably the labelled proteins are also taxon-specific for at least about 90% and preferably at least 95% of Genera present in the microbiota sample. Preferably the labelled proteins are also taxon-specific for at least about 90% and preferably at least 95% of species present in the microbiota sample.

[0018] In yet another aspect the microbiota labelled-proteins standard may be taxon-specific for at least about 90% and preferably at least 95% of the microbes present in the intestinal microbiota. The labelled proteins may be taxon-specific for 100% of Kingdoms present in the intestinal microbiota. The labelled proteins may also be taxon-specific for 95% and preferably at least 100% of Phyla present in the intestinal microbiota. The labelled proteins may also be taxon-specific for at least about 90% and preferably at least 95% of Genera present in the intestinal microbiota. The labelled proteins may also be taxon-specific for at least about 70% and preferably at least 95% of species present in the intestinal microbiota.

[0019] In yet another aspect the microbiota labelled-proteins standard may be taxon-specific for at least about 90% and preferably at least 95% of the microbes present in the microbiota sample from including but not limited to vaginal, oral, skin, bladder, kidney, lung, eye and breast. The labelled proteins may be taxon-specific for 100% of Kingdoms present in the microbiota. The labelled proteins may also be taxon-specific for 95% and preferably at least 100% of Phyla present in the microbiota. The labelled proteins may be also taxon-specific for at least about 90% and preferably at least 95% of Genera present in the microbiota. The labelled proteins may also be taxon-specific for at least about 90% and preferably at least 95% of the species present in the microbiota.

[0020] For an exemplary intestinal microbiota samples, the Domains may be Bacteria, Eukaryota and Archaea, the Phyla are, but not limited to, Bacteroidetes, Proteobacteria, Verrucomicrobia, Fusobacteria, Synergistetes, Thaumarchaeota, Fimicutes, Actinobacteria, Ascomycota, Basidiomycota, Euryarchaeota, Apicomplexa, Arthropoda, Chordata, Nematoda, Streptophyta, the Genera are those listed in table 1, the Species are those listed in table 2. Table 1: An exemplary set of genera that can be found in an exemplary intestinal microbiota sample.AbiotrophiaEdwardsiellaNitrososphaeraTyzzerellaAcidaminococcusEggerthellaOdoribacterVeillonellaAcidovoraxEnterobacterOribacteriumVibrioAcinetobacterEnterococcusOscillibacterWeissellaActinobacillusErwiniaOxalobacterXanthomonasActinomycesErysipelatoclostridiumPaenibacillusYokenellaAdlercreutziaEscherichiaParabacteroidesAerococcusEubacteriumParaprevotellaAeromonasFacklamiaParasutterellaAkkermansiaFaecalibacteriumParvimonasAlcanivoraxFaecalitaleaPediococcusAlistipesFerrimonasPeptoclostridiumAlteromonasFilobasidiellaPeptostreptococcusAnaerobaculumFinegoldiaPhascolarctobacteriumAnaerococcusFlavonifractorPhotobacteriumAnaerofustisFusariumPiscirickettsiaAnaerostipesFusobacteriumPlasmodiumAnaerotruncusGemellaPorphyromonasArcobacterGordonibacterPrevotellaAspergillusGranulicatellaPropionibacteriumAtopobiumHaemophilusProteusBacillusHafniaProvidenciaBacteroidesHahellaPseudoflavonifractorBarnesiellaHelicobacterPseudomonasBifidobacteriumHoldemanellaPseudoxanthomonasBilophilaHoldemaniaRalstoniaBlautiaHungatellaRhodotorulaBurkholderiaIntestinibacterRoseburiaButyrivibrioKlebsiellaRothiaCampylobacterLachnoanaerobaculumRuminiclostridiumCandidaLachnoclostridiumRuminococcusCarnobacteriumLactobacillusSalmonellaCatenibacteriumLeptotrichiaSelenomonasCellvibrioLeuconostocSerratiaCitrobacterListeriaShewanellaClostridiumMarinomonasSlackiaCollinsellaMarvinbryantiaStaphylococcusCoprobacillusMegamonasStreptococcusCoprococcusMethanobrevibacterSubdoligranulumCorynebacteriumMethanosphaeraSuccinatimonasDebaryomycesMethylobacteriumSutterellaDesulfitobacteriumMeyerozymaSynergistesDesulfovibrioMitsuokellaTannerellaDialisterMogibacteriumTerrisporobacterDoreaMoraxellaThermoplasmaDysgonomonasNeisseriaTuricibacter Table 2: An exemplary set of species that can be found in an exemplary intestinal microbiota sample. Abiotrophia defectivaBacteroides intestinalisAcidaminococcus intestiniBacteroides oleiciplenusAcidovorax avenaeBacteroides ovatusAcinetobacter juniiBacteroides pectinophilusActinobacillus suisBacteroides plebeiusActinomyces georgiaeBacteroides stercorisActinomyces massiliensisBacteroides thetaiotaomicronActinomyces odontolyticusBacteroides uniformisAdlercreutzia equolifaciensBacteroides vulgatusAerococcus viridansBacteroides xylanisolvensAeromonas hydrophilaBarnesiella intestinihominisAeromonas veroniiBifidobacterium adolescentisAkkermansia muciniphilaBifidobacterium angulatumAlcanivorax dieseloleiBifidobacterium bifidumAlistipes finegoldiiBifidobacterium breveAlistipes indistinctusBifidobacterium catenulatumAlistipes putredinisBifidobacterium dentiumAlistipes shahiiBifidobacterium gallicumAlteromonas macleodiiBifidobacterium longumAnaerobaculum hydrogeniformansBifidobacterium pseudocatenulatumAnaerococcus hydrogenalisBilophila wadsworthiaAnaerofustis stercorihominisBlautia hanseniiAnaerostipes caccaeBlautia hydrogenotrophicaAnaerostipes hadrusBlautia obeumAnaerotruncus colihominisButyrivibrio crossotusArcobacter butzleriButyrivibrio fibrisolvensAspergillus fumigatusCampylobacter concisusAtopobium minutumCampylobacter upsaliensisAtopobium parvulumCandida albicansAtopobium rimaeCandidatus Nitrososphaera gargensisBacillus cereusCatenibacterium mitsuokaiBacillus smithiiCellvibrio japonicasBacteroides caccaeCitrobacter freundiiBacteroides cellulosilyticusCitrobacter youngaeBacteroides clarusClostridium asparagiformeBacteroides coprocolaClostridium bolteaeBacteroides coprophilusClostridium butyricumBacteroides doreiClostridium citroniaeBacteroides eggerthiiClostridium clostridioformeBacteroides finegoldiiClostridium hiranonisBacteroides fluxusClostridium hylemonaeBacteroides fragilisBacteroides intestinalisClostridium innocuumEubacterium siraeumClostridium leptumEubacterium ventriosumClostridium methylpentosumFacklamia ignavaClostridium perfringensFaecalibacterium prausnitziiClostridium saccharolyticumFaecalitalea cylindroidesClostridium scindensFerrimonas balearicaClostridium spiroformeFinegoldia magnaClostridium symbiosumFlavonifractor plautiiCollinsella aerofaciensFusarium graminearumCollinsella intestinalisFusobacterium gonidiaformansCollinsella stercorisFusobacterium mortiferumCollinsella tanakaeiFusobacterium necrophorumCoprococcus catusFusobacterium nucleatumCoprococcus comesFusobacterium periodonticumCoprococcus eutactusFusobacterium ulceransCorynebacterium ammoniagenesFusobacterium variumCorynebacterium durumGemella sanguinisCryptococcus gattiiGordonibacter pamelaeaeDebaryomyces hanseniiGranulicatella adiacensDesulfitobacterium hafnienseHafnia alveiDesulfovibrio desulfuricansHahella chejuensisDesulfovibrio pigerHelicobacter bilisDialister invisusHelicobacter CanadensisDialister succinatiphilusHelicobacter cinaediDorea formicigeneransHelicobacter pullorumDorea longicatenaHelicobacter pyloriDysgonomonas gadeiHelicobacter winghamensisDysgonomonas mossiiHoldemanella biformisEdwardsiella tardaHoldemania filiformisEggerthella lentaHungatella hathewayiEnterobacter cancerogenusIntestinibacter bartlettiiEnterobacter cloacaeKlebsiella pneumoniaeEnterococcus faecalisLachnoanaerobaculum saburreumEnterococcus faeciumLactobacillus acidophilusEnterococcus haemoperoxidusLactobacillus amylolyticusEnterococcus saccharolyticusLactobacillus antriErwinia amylovoraLactobacillus brevisEscherichia coliLactobacillus delbrueckiiEubacterium dolichumLactobacillus fermentumEubacterium halliiLactobacillus helveticusEubacterium rectaleLactobacillus inersLactobacillus plantarumPrevotella stercoreaLactobacillus reuteriPrevotella veroralisLactobacillus rhamnosusPropionibacterium acnesLactobacillus ruminisProteus mirabilisLactobacillus ultunensisProteus penneriLeptotrichia goodfellowiiProvidencia alcalifaciensLeuconostoc mesenteroidesProvidencia rettgeriListeria grayiProvidencia rustigianiiListeria innocuaProvidencia stuartiiMarinomonas profundimarisPseudoflavonifractor capillosusMarvinbryantia formatexigensPseudomonas aeruginosaMegamonas funiformisPseudoxanthomonas spadixMegamonas hypermegaleRalstonia pickettiiMethanobrevibacter smithiiRhodotorula glutinisMethanosphaera stadtmanaeRoseburia intestinalisMethylobacterium nodulansRoseburia inulinivoransMeyerozyma guilliermondiiRothia aeriaMitsuokella multacidaRothia mucilaginosaMogibacterium timidumRuminococcus bromiiMoraxella catarrhalisRuminococcus champanellensisNeisseria bacilliformisRuminococcus gnavusOdoribacter laneusRuminococcus lactarisOribacterium sinusRuminococcus torquesOxalobacter formigenesSalmonella entericaPaenibacillus lactisSelenomonas sputigenaParabacteroides distasonisSerratia marcescensParabacteroides johnsoniiShewanella putrefaciensParabacteroides merdaeSlackia exiguaParaprevotella claraSlackia piriformisParaprevotella xylaniphilaStaphylococcus aureusParasutterella excrementihominisStreptococcus equinusParvimonas micraStreptococcus thermophilesPediococcus acidilacticiSubdoligranulum variabilePeptoclostridium difficileSuccinatimonas hippiePeptostreptococcus anaerobiusSutterella parvirubraPhascolarctobacterium succinatutensSutterella wadsworthensisPhotobacterium damselaeTerrisporobacter othiniensisPiscirickettsia salmonisThermoplasma volcaniumPorphyromonas endodontalisTuricibacter sanguinisPrevotella copriTyzzerella nexilisPrevotella salivaeVeillonella disparVeillonella parvulaWeissella paramesenteroidesYokenella regensburgei

[0021] In another aspect, the labelled-proteins standard may have isotope(s) labelled proteins and wherein the isotopes(s) can be stable or radioactive isotopes. The isotopes can be selected, for example, from 13< C, 14< C, 15< N, 32< S, 35< S, 32< P and Deuterium, and combination thereof.

[0022] In a further aspect, there is provided a method for obtaining a microbiota labelled-proteins standard, as described above, comprising: obtaining a microbiota sample from an individual; exposing said sample to an isotope enriching growth medium (i.e. an enriched media, such as an isotope enriched media, also defined herein as an isotope enriched medium); and culturing said exposed sample for a period of time sufficient to obtain a predetermined level of enrichment.

[0023] In a further aspect, there is provided a method for obtaining a microbiota labelled-proteins standard, as described above, comprising: obtaining a microbiota sample from an individual including but not limited to intestinal, vaginal, oral, skin, bladder, kidney, lung, eye or breast microbiota; exposing said sample to an isotope enriching medium; and culturing said exposed sample for a period of time sufficient to obtain a predetermined level of enrichment.

[0024] In another aspect, there is provided a method for measuring an amount of one or more proteins in a microbiota sample comprising obtaining a protein extract from the microbiota sample and spiking the protein extract with the standard, as described above, and obtaining labelled / unlabeled protein ratios of the standard and the one or more bacteria (or, as the case may be, other forms of microbes) in the microbiota sample.

[0025] In another aspect, there is also provided a method for measuring an amount of one or more proteins in an intestinal microbiota sample comprising obtaining a protein extract from the microbiota sample and spiking the protein extract with the standard, as described above, and obtaining labelled / unlabeled protein ratios of the standard and the one or more bacteria in the intestinal microbiota sample.

[0026] In another aspect, there is also provided a method for measuring an amount of one or more proteins in a microbiota sample comprising obtaining a protein extract from the microbiota sample including but not limited to vaginal, oral, skin, bladder, kidney, lung, eye, or bladder microbiota and spiking the protein extract with the standard, as described above, and obtaining labelled / unlabeled protein ratios of the standard and the one or more bacteria in the microbiota sample.

[0027] The method for measuring an amount of one or more proteins in an microbiota sample may further comprise obtaining a label free quantification (LFQ) of the microbiota sample. The SILAMi and LFQ method can be combined to improve the accuracy of protein measurement in a sample. The method may also involve performing gas chromatography / mass spectrometry. In some embodiments, the method may involve performing mass spectrometry.

[0028] In some aspects, the method for measuring an amount of one or more proteins in an intestinal microbiota sample may further comprise obtaining a label free quantification (LFQ) of the intestinal microbiota sample. The SILAMi and LFQ method can be combined to improve the accuracy of protein measurement in a sample.

[0029] The method for measuring an amount of one or more proteins in a microbiota sample including but not limited to vaginal, oral, skin, bladder, kidney, lung, eye, or bladder microbiota may further comprise obtaining a label free quantification (LFQ) of the microbiota sample. The SILAMi and LFQ method can be combined to improve the accuracy of protein measurement in a sample

[0030] In yet another aspect there is provided a method for diagnosing a disease such as, but not limited to, an intestinal disease (IBD for example) comprising measuring an amount of one or more proteins in a microbiota sample (wherein the measuring is performed using the standard such as described with respect to the method for measuring an amount of one or more proteins in a microbiota sample as described herein) from a patient and wherein deviation from normal is indicative of disease. In an aspect of this method the measuring is performed at one or more time point and is compared to control samples optimally obtained at a predetermined time in the life of an individual or from an individual in a predetermined state of health.

[0031] A method for treating a patient with a disease is also provided that involves assessing said patient's microbiota as described above to diagnose the disease and treating the patient according to the diagnostic.

[0032] In yet another aspect, there is provided a method for determining treatment response in a patient with a disease comprising measuring one or more proteins in a microbiota sample from a patient and wherein derivation away from diseased and / or toward normal is indicative of favorable treatment response. In an aspect of this method the measuring is performed at a one or more time point and is compared to control samples optimally obtained at a predetermined time in the life of an individual or from an individual in a predetermined state of health or disease.

[0033] In yet another aspect, there is provided a method for determining remission in a patient with a disease comprising measuring one or more proteins in a microbiota sample from a patient and wherein normal levels are indicative of the absence of a previously present disease. In an aspect of this method the measuring is performed at a one or more time point and is compared to control and / or disease samples optimally obtained at a predetermined time in the life of an individual or from an individual in a predetermined state of health or disease.

[0034] In another aspect there is provided a method for screening xenobiotics effect on a human microbiota comprising exposing the microbiota to one or more xenobiotics and measuring an amount of one or more protein as described above.

[0035] In another aspect, there is provided a method for screening xenobiotics effect on a human microbiota, including but not limited to intestinal, vaginal, oral, skin, bladder, kidney, lung, eye and breast microbiome, comprising exposing the microbiota to one or more xenobiotics and measuring an amount of one or more protein as described above.

[0036] In another aspect, there is provided a method for screening xenobiotics effect on an intestinal human microbiota comprising exposing the microbiota to one or more xenobiotics, including but not limited to chemicals, toxins, environmental toxins, and poisons and measuring an amount of one or more protein as described above.

[0037] From the screening of xenobiotics effect a profile may be generated based on proteins measurements. The profile can be integrated into a method of diagnostic or prognostic.

[0038] In a further aspect, there is provided a method for screening the effect of therapeutics on a human microbiota, including but not limited to immunotherapies, antibiotics, checkpoint inhibitors, chemotherapies, antidepressants, antiepileptic, antiemetic, analgesics, antivirals, sedatives, antidiabetic, antipsychotics, and anticoagulants, comprising exposing the microbiota to one or more drugs and measuring the amount of one or more proteins as described above.

[0039] In a further aspect, there is provided a method for screening the effect of therapeutics on a human microbiota, using the RapidAIM and / or SILAMi technique disclosed herein, including but not limited to therapies or antibodies targeted to, PD-1 / PDCD1 / CD279; PD-L1 / CD274; PD-L2 / PDCD1LG2; CTLA-4 / CD152; CD80 / B7 / B7-1; CD86; TIM-3 / HAVCR2; Galectin-9 / GAL9 / LGALS9; TIGIT; CD155 / PVR; LAG3; VISTA / C10orf54; B7-H3 / CD276; B7-H4 / VTCN1; BTLA / CD272; HVEM / TR2 / TNFRSF14; A2AR; CD28; CD80 / B7 / B7-1; CD86; ICOS / CD278; CD275 / ICOSLG / B7RP1; CD40L / CD154; CD40; CD137 / 4-1BB; CD137L; CD27; CD70 / CD27L; OX40 / CD134 / TNFRSF4; OX40L / TNFSF4; GITR; GITRL; SIRPα; CD47 comprising exposing the microbiota to one or more drugs and measuring the amount of one or more proteins as described above.

[0040] In a further aspect, there is provided a method for screening the effect of foods on a human microbiota comprising exposing the microbiota to one or more foods and measuring the amount of one or more proteins as described above.

[0041] In a further aspect, there is provided a method for screening the effect of food ingredients on a human microbiota, including but not limited to food additives, amino acids, flavorings, dyes, emulsifiers, sweetners, hydrocolloids and preservatives, comprising exposing the microbiota to one or more ingredients and measuring the amount of one or more proteins as described above.

[0042] In a further aspect, there is provided a method for screening the effect of beverages on a human microbiota, including but not limited to soda, sports beverages, infant formula, milk, alcohol, juice, drinkable yogurt, and fermented teas, comprising exposing the microbiota to one or more beverages and measuring the amount of one or more proteins as described above.

[0043] In another aspect, there is provided a method for screening the effect of packaging components on a human microbiota, including but not limited to coatings and plastics, comprising exposing the microbiota to one or more packaging component and measuring the amount of one or more proteins as described above.

[0044] In another aspect, there is provided a method for screening the effect of cosmetics and cosmetic components including but not limited to excipients, natural and synthetic pigments, thickeners, and emulsifiers, on a human microbiota, comprising exposing the microbiota to one or more cosmetics or cosmetic components and measuring the amount of one or more proteins as described above.

[0045] In another aspect, there is provided a method for screening the effect of consumer products including but not limited to infant products, household cleaners, lotions, shampoos and perfumes on a human microbiota, comprising exposing the microbiota to one or more consumer products and measuring the amount of one or more proteins as described above.

[0046] In another aspect, there is provided a method for screening the effect of consumer health products including but not limited to supplements, vitamins, amino acids, and plant extracts on a human microbiota, comprising exposing the microbiota to one or more consumer products and measuring the amount of one or more proteins as described above

[0047] In another aspect, there is provided a fast and cost-effective method for metabolic labeling of a soil microbiota. The method for obtaining a soil microbiota labelled-proteins standard, as described above, involves obtaining a soil microbiota sample; exposing said sample to an isotope enriching medium; and culturing said exposed sample for a period of time sufficient to obtain a pre-determined level of enrichment.

[0048] Another aspect is a method for measuring an amount of one or more proteins in a soil microbiota sample comprising obtaining a protein extract from the microbiota sample and spiking the protein extract with the standard, as described above, and obtaining labelled / unlabeled protein ratios of the standard and the one or more bacteria in the microbiota sample.

[0049] In another aspect, there is provided a method for screening xenobiotics effect on a soil microbiota, involving exposing the microbiota to one or more xenobiotics including but not limited to pesticides, toxins, amino acids, and nitrates and then measuring an amount of one or more proteins.

[0050] In another aspect, there is provided a fast and cost-effective method for metabolic labeling of an animal microbiota, wherein the animal microbiota may originate from a microbiota sample from an animal, such as a cow, pig, chicken, llama, sheep, goat, rabbit, mouse, rat, etc.

[0051] In a further aspect, there is provided a method for obtaining an animal microbiota labelled-proteins standard, as described above, involving obtaining an animal microbiota sample such as a cow, pig, chicken, llama, sheep, goat, rabbit, mouse, rat, etc; exposing said sample to an isotope enriching medium; and culturing said exposed sample for a period of time sufficient to obtain a pre-determined level of enrichment.

[0052] In yet another aspect there is provided a method for diagnosing a disease including measuring an amount of one or more proteins in a microbiota sample from an animal, including but not limited to cows, pigs, chickens, llamas, sheep, goats, rabbits, mice and rats and wherein deviation from normal is indicative of disease. In an aspect of this method the measuring is performed at a one or more time point and is compared to control samples optionally obtained at a predetermined time in the life of an animal or from an animal in a predetermined state of health.

[0053] In another aspect there is provided a method for screening xenobiotics effect on an animal microbiota, including but not limited to cows, pigs, chickens, llamas, sheep, goats, rabbits, mice and rats; comprising exposing the microbiota to one or more xenobiotics, including but not limited to feed, amino acids, supplements, pesticides, and toxins, and measuring an amount of one or more proteins.

[0054] In one aspect, there is provided a fast and cost-effective method for metabolic labeling of a biofilm microbiota. The method for screening xenobiotics effect on a biofilm microbiota; includes exposing the microbiota to one or more xenobiotics, including but not limited to chemicals, pesticides, toxins, and soaps, and measuring an amount of one or more proteins.

[0055] In another aspect, there is provided a fast and cost-effective strategy for metabolic labeling of a microbiota from an industrial manufacturing facility.

[0056] Another broad aspect is a method of labelling a microbiota sample that includes providing a microbiota sample that was obtained from a given source. The method involves exposing the microbiota sample to an enriched medium, and culturing the microbiota sample to obtain a microbiota sample with a labeled proteome. In some embodiments, the labelled microbiota sample may be taxon specific for taxa present in the first microbiota sample when initially obtained from the given source.

[0057] In some aspects, the enriched medium may be an isotope enriched medium, wherein the proteome of the microbiota sample may be isotope-labelled. However, the label enriched medium may provide for labelling other than isotopes.

[0058] Another broad aspect is a microbiota labelled-proteins standard obtained by performing the method of obtaining a labelled microbiota sample as defined herein, wherein the microbiota labelled-proteins standard has labelled proteins representative of a proteome from a selected microbiota.

[0059] Another broad aspect is a method for labelling protein of a microbiota sample comprising providing a first microbiota sample that was obtained from a given source; exposing the first microbiota sample to an enriched medium; and culturing the exposed first microbiota sample in the enriched medium to obtain an labelled microbiota sample, wherein the labelled metaproteome of the labelled microbiota sample is taxon specific for taxa present in the first microbiota sample when initially obtained from the given source. In some embodiments, the method may further comprise characterizing said labelled microbiota sample by performing a metaproteomic analysis of said labelled microbiota sample. In some embodiments, said labelled microbiota sample may be taxon specific for a predetermined proportion of microbe populations present in the first microbiota sample when initially obtained from the given source. In some embodiments, the enriched medium may be an isotope enriched medium.

[0060] . In some aspects, the labelled microbiota sample is taxon specific for a predetermined proportion of microbe populations present in the first microbiota sample when initially obtained from the given source. By pre-determined proportion it is meant that some taxa of microbes are specifically sought to be labelled in the labelling of the labelled sample. For instance, a user may be searching for specific bacterial species that are associated with a given disease (e.g. atopobium parvulum in the case of certain intestinal disease). In this example, the pre-determined proportion would be or would include the bacterial species that are known for that disease. Moreover, certain microbial populations may be known to react positively or negatively when a patient is given a specific compound (e.g. a drug) or when a patient is responding to a given diagnostic treatment. In these examples, the pre-determined populations may be or may include those reactive microbial populations or taxa.

[0061] In some embodiments, the method may involve characterizing the labelled microbiota sample by performing a metaproteomic analysis of the -labelled microbiota sample. The culturing of the exposed first microbiota sample may be for a period to obtain an average level of enrichment of the labelled proteins representative of the metaproteome of at least 70% and to be taxon specific for a predetermined proportion of microbe populations present in the first microbiota sample when initially obtained from the given source.

[0062] The culturing of the exposed first microbiota sample may be for a period to obtain an average level of enrichment of the labelled proteins representative of the metaproteome of at least 90% and to be taxon specific for a predetermined proportion of microbe populations present in the first microbiota sample when initially obtained from the given source. The culturing of the exposed first microbiota sample may be for a period to obtain an average level of enrichment of the labelled proteins representative of the metaproteome of at least 95% and to be taxon specific for a predetermined proportion of microbe populations present in the first microbiota sample when initially obtained from the given source. The culturing the exposed first microbiota sample may be for a period to obtain a predetermined average level of enrichment of the labelled proteins representative of the metaproteome of the exposed first microbiota sample and to be taxon specific for at least 50% of the microbe populations present in the first microbiota sample when initially obtained from the given source. The culturing the exposed first microbiota sample may be for a period to obtain a predetermined average level of enrichment of the labelled proteins representative of the metaproteome of the exposed first microbiota sample and to be taxon specific for at least 90% of the microbe populations present in the first microbiota sample when initially obtained from the given source. The culturing the exposed first microbiota sample may be for a period to obtain a predetermined average level of enrichment of the labelled proteins representative of the metaproteome of the exposed first microbiota sample and to be taxon specific for 90% of Phyla present in the first microbiota sample when initially obtained from the given source. The culturing the exposed first microbiota sample may be for a period to obtain a predetermined average level of enrichment of the labelled proteins representative of the metaproteome of the exposed first microbiota sample and to be taxon specific for at least about 90% of Genera present in the first microbiota sample when initially obtained from the given source. The culturing the exposed first microbiota sample may be for a period to obtain a predetermined average level of enrichment of the labelled proteins representative of the metaproteome of the exposed first microbiota sample and to be taxon specific for at least about 90% of species present in the first microbiota sample when initially obtained from the given source.

[0063] In some aspects, an isotope enriched medium to which the first microbiota sample is exposed may contain an isotope selected from 13C, 14C, 15N, 32S, 35S, 32P and Deuterium, and combination thereof. The isotope enriched medium to which the microbiota sample is exposed may contain as an isotope 15N.

[0064] In some embodiments, the first microbiota sample that is provided may be obtained from a human subject. In other embodiments, the first microbiota sample that is provided may be obtained from an animal subject.

[0065] The providing a first microbiota sample may be providing a type of microbiota sample, wherein the microbiota sample type may be an intestinal microbiota sample, a cutis microbiota sample, a vaginal microbiota sample, an oral microbiota sample, a lung microbiota sample, a mucosal microbiota sample, a bladder microbiota sample, a kidney microbiota sample, an eye microbiota sample, a penile microbiota sample, or a breast microbiota sample.

[0066] Another broad aspect may be a method of performing a compositional analysis of a second microbiota sample that involves using a labelled microbiota sample, obtained by performing a method such a sample as described herein, as a labelled standard to perform compositional analysis of a second microbiota sample, wherein the compositional analysis is enhanced as a result of the employment of the labelled-standard.

[0067] The compositional analysis may be performed on a second microbiota sample having a same microbiota sample type as that of the first microbiota sample. The method may include, prior to the employing the labelled-standard to perform compositional analysis of a second microbiota sample, providing the second microbiota sample that was obtained from the same source as the microbiota sample used to obtain the labelled standard. The using a labelled microbiota sample as a labelled standard to perform compositional analysis of a second microbiota sample may include performing metaproteomic analysis, and the metaproteomic analysis may be for measuring an amount of one or more protein in the second microbiota sample. The metaproteomic analysis may include obtaining a protein extract from the second microbiota sample, spiking the protein extract with the labelled standard; and obtaining labelled / unlabelled protein ratios of the labelled standard and the one or more protein in the second microbiota sample. The metaproteomic analysis may also involve obtaining a label free quantification (LFQ) of the second microbiota sample. The using a labelled microbiota sample as a labelled standard to perform compositional analysis of a second microbiota sample may involve performing metagenomic analysis. In some embodiments, the metagenomic analysis may involve 16S-based sequencing. The metagenomic analysis may involve shotgun sequencing.

[0068] The compositional analysis may be performed to achieve disease diagnosis in a target subject, assessing treatment response in a target subject, assessing remission in a subject receiving treatment, screening for xenobiotic effects on a microbiome of a target subject, screening for effects of a compound on a microbiome of a target subject, wherein the compound is one of a food, a drug, a chemical, a therapeutic agent, a toxin, a poison, a beverage, a food additive, a cosmetic, a cosmetic ingredient, packaging material, a pesticide, a herbicide, a consumer product, and / or screening a microbiome to identify the responsiveness of a subject to a therapy or treatment.

[0069] The compositional analysis may be performed to achieve the screening for xenobiotic effects on a microbiome of a target subject, and the second microbiota sample may be obtained from the target subject, and the compositional analysis may be performed subsequent to the target subject being exposed to one or more xenobiotics. The compositional analysis may be performed to achieve screening for effects of a compound on a microbiome of a target subject, wherein the second microbiota sample may be obtained from the target subject, and the compositional analysis may be performed subsequent to the target subject being exposed to one or more compounds. A profile may be generated based on the compositional analysis. The profile may be integrated into a method of diagnosis or prognosis. The compositional analysis may be performed to achieve the disease diagnosis in a target subject, wherein the using a labelled microbiota sample as a labelled standard to perform compositional analysis may also include measuring an amount of the one or more protein in the second microbiota sample and wherein deviation from normal is indicative of the disease. The metaproteomic analysis may be performed at one or more time points using a time-point microbiota sample taken at the one or more time points, and, following a metaproteomic analysis performed on the time-point microbiota sample, a measured one or more proteins from the time-point microbiota sample may be compared to a control sample. The control sample may be a standard control sample taken from a subject in a predetermined state of health, and / or a control sample obtained at a predetermined time in the life of the target subject. Another broad aspect is a method for treating a patient with a disease comprising assessing the patient's microbiota to diagnose the disease and treat the patient in accordance with the diagnostic.

[0070] Another broad aspect is a method of high throughput screening of multiple microbiota samples for metaproteomic analysis of the samples. The method entails culturing multiple microbiota samples wherein each sample of the multiple microbiota samples is cultured in a well of a multi-well receptacle. The method involves washing the cells of the multiple microbiota culture samples, re-suspending in lysis buffer with a protease inhibitor the microbiota culture samples, lysing the cells of the multiple microbiota culture samples, diluting the multiple microbiota culture samples, and digesting the proteins contained in the microbiota culture samples. The method adds performing simultaneous metaproteome identification and quantification of the multiple microbiota samples by using a microbial gene catalog of a given subject type and an iterative database search strategy.

[0071] In some embodiments, the microbial gene catalog of a given subject type is a microbial gene catalog of a human. In some embodiments, the microbial gene catalog of a given subject type may be a microbial gene catalog of an animal.

[0072] According to the invention, prior to digesting the proteins contained in the microbiota culture samples, the method may involve spiking the microbiota culture samples with an isotope labelled standard corresponding to a given microbiota sample. In some embodiments, prior to the digesting, the method may involve reducing and alkylating of cysteines in the proteins contained in the microbiota culture samples. The spiking may involve adding sufficient isotope labelled-standard to reach a 1:1 protein mass ratio with the protein contained in the microbiota culture samples.

[0073] In some embodiments, the multi-well receptacle is a multi-well plate.

[0074] In some aspects, the method may involve assessing the results of the metaproteome identification and quantification of the multiple microbiota samples to perform disease diagnosis in a target subject, assessing treatment response in a target subject, assessing remission in a subject receiving treatment, screening for xenobiotic effects on a microbiome of a target subject, screening for effects of a compound on a microbiome of a target subject, wherein the compound is one of a food, a drug, a chemical, a therapeutic agent, a toxin, a poison, a beverage, a food additive, a cosmetic, a cosmetic ingredient, packaging material, a pesticide, a herbicide, a consumer product, and / or screening a microbiome to identify the responsiveness of a subject to a therapy or treatment.

[0075] The invention relates to a method of high throughput meta-omic analysis of a plurality of microbiota samples for effects of compounds on a microbiome of a target subject comprising: providing a plurality of microbiota samples from an animal or a human being, simultaneously culturing said microbiota samples; treating said plurality of microbiota samples with selected compounds; performing a screening using one or more of a metagenomic technique, metatranscriptomic technique, metaproteomic technique and metabolomic technique to identify expression changes in microbiomes of said microbiota samples following said treating; selecting, based on the expression changes identified during said screening, said microbiomes exhibiting said predetermined expression changes; and analyzing the selected microbiomes to characterize the changes to identify compounds that are biota-affectors for at least one of specific microbes, groups of microbes and metabolic activities of microbes, characterized in that the method further comprises, after said providing, spiking said plurality of microbiota culture samples with an isotope labelled standard corresponding to a given microbiota sample from the animal or the human being. The provided microbiota samples may be cultured in micro-well receptacles. The provided microbiota samples may be cultured in micro-well plates.

[0076] In some embodiments, the analyzing may involve using a microbial gene catalog of a given subject type and an iterative database search strategy. The analyzing may involve performing a metaproteomic analysis combined with a metagenomic analysis. The microbial gene catalog of a given subject type may be a microbial gene catalog of a human. The microbial gene catalog of a given subject type may be a microbial gene catalog of an animal.

[0077] According to the invention, the method involves, after the providing, spiking the plurality microbiota culture samples with an isotope labelled standard corresponding to a given microbiota sample from the animal or the human being The spiking may involve adding sufficient isotope labelled-standard to reach a 1:1 protein mass ratio with the protein contained in the plurality of microbiota culture samples. The performing a pre-screening using a meta-omic technique may involve performing metaproteomics.

[0078] The method may involve assessing the results of the analysis of the selected microbiomes to perform disease diagnosis in a target subject; assessing treatment response in a target subject; assessing remission in a subject receiving treatment; screening for xenobiotic effects on a microbiome of a target subject; screening for effects of a compound on a microbiome of a target subject, wherein the compound is one of a food, a drug, a chemical, a therapeutic agent, a toxin, a poison, a beverage, a food additive, a cosmetic, a cosmetic ingredient, packaging material, a pesticide, a herbicide, a consumer product; and / or screening a microbiome to identify the responsiveness of a subject to a therapy or treatment.

[0079] Another aspect is a method for isotope-labelling protein of a microbiota sample comprising: providing a first microbiota sample that was obtained from a given source; exposing said first microbiota sample to an isotope enriched medium; and culturing said exposed first microbiota sample in said isotope enriched medium to obtain an isotope-labelled microbiota sample, wherein the isotope labelled metaproteome of said isotope-labelled microbiota sample is taxon specific for taxa present in said first microbiota sample when initially obtained from said given source.Brief Description of the Drawings

[0080] Figure 1 is a schematic diagram of the exemplary labelling method comprising isotopic 15< N metabolic labeling of human microbiota for quantitative metaproteomics. (A) Brief workflow of the stable isotope labeling of microbiota (SILAMi), and the SILAMi-based quantitative metaproteomic approaches, which can be applied to any human microbiota sample. (B) 15< N isotopic enrichment of identified intestinal microbial peptides. Mucosal-luminal interface aspirate samples, or stool samples yielding slurries from five different individuals were labelled separately with three technical replicates. The average 15< N enrichment rates of all the identified peptides for each individual's microbiota were shown. It will be understood that even though a Mucosal-luminal interface aspirate samples or a stool sample (slurry) were used as a microbiota sample, any intestinal microbiota sample may be used to perform the following method. Moreover, a skilled person will understand that any microbiota sample used from a human or animal may be used when performing the following method. Figure 2 describes the quantitation accuracy of the SILAMi-based metaproteomics. (A) Density plot showing the calculated L / H ratios of quantified protein groups in samples with different L / H spike-in ratios (1:1, 1.25: 1, 2: 1, and 5: 1). Scatter plot shows the correlation between the calculated L / H protein ratios (median) and spike-in ratios. Pearson's r-value was indicated; (B) Density plot showing the distribution of fold changes when compared to the sample with 1:1 spike-in ratio. Log2-transformed L / H ratios or fold changes were used for generating the density plots with a band width of 0.2. Dashed lines indicate median values. The percentage of proteins within two-fold difference to median was shown in the brackets. Figure 3 illustrates examples of SILAMi-based quantitative metaproteomics for microbiota studies and the use of this technique for screening the effect of chemicals and / or compounds on microbiota protein expression overtime. (A) Principal component analysis score plot of FOS-mediated metaproteome changes. (B) Representative total ion currents (TICs) of quantified peptides of protein EF-Tu. Both heavy (red) and light (blue) are shown. (C) Heatmap of 246 microbial protein which significantly changed upon the supplementation of monosaccharides during in vitro cultivation. Both column and row clusterings were based on Euclidean distance. Blue square, N-acetyl glucosamine (GlcNAc); blue circle, glucose; yellow circle, galactose; green circle, mannose; red triangle, fucose; C, control. (D) Quantified proteins involved in bacterial fucose utilization pathway. Mean ± SD was shown in the bar charts. DHAP, dihydroxyacetone phosphate; FucP, L-fucose:H+ symporter permease; FucM, L-fucose mutarotase; FucI, L-fucose isomerase; FucA, L-fuculose-1-phosphate aldolase; FucK, L-fuculokinase; FucO, L-1,2-propanediol oxidoreductase or lactaldehyde reductase. It will be understood that while microbiota samples were treated with monosaccharides, any compound could be used in this method to treat the microbiota samples and the subsequent effect on microbiome protein expression assessed. Figure 4 is a graph illustrating 14< N and 15< N peptide identification for each passage of the five human intestinal microbiota samples during metabolic labeling. The mean and standard error of the identified unique peptide sequences are shown. Figure 5 illustrates microbiota composition at the initial inoculum (Passage 0) and SILAMi at phylum and genus levels. Taxonomic analysis was performed using metaproteomics. For metaproteomic analysis, phyla (A) and genera (B) were considered present if they had ≥ 2detected unique peptide sequences. Orange indicates the presence of the taxa in only Passage 0, while purple indicates the taxa are present at both Passage 0 and in SILAMi. Figure 6 represents a heat map of the 187 identified protein groups altered by fructo-oligosacchride (FOS) treatment. Complete protein names are listed in Table 3; a few proteins of interest are indicated. The clustering of rows was generated based on Euclidean distance in Perseus. It will be understood that while this example demonstrates changes from treatment with FOS, a heatmap of changes could be generated from microbiome protein changes following treatment with any compound using the methods described herein. Figure 7 illustrates the influence of monosaccharides on the relative abundance of N-acetyl glucosamine-degrading related proteins in human microbiome samples. Log2-transformed L / H ratios are shown and expressed as mean ± SD. A two sample t-test was used to compare differences between the non-treated control group (n=3) and the treated sample (n=3). * P < 0.05, ** P < 0.01, *** P < 0.001. Figure 8 illustrates an exemplary workflow of RapidAIM. Rapid Analysis of Individual Microbiota (RapidAIM) through fast-pass metaproteomics for rapid screening and in-depth metaproteomics / metagenomics for mechanism interpretation. The workflow includes high-performance, easy-to-use software platforms for rapidly identifying positive hits and providing functional insights into microbiome changes. Figure 9 illustrates an exemplary 96-well-based culturing and metaproteomic experimental workflow for the RapidAIM assay, which can be used to assess any set of microbiome samples, including those from multiple individuals, in a multi-well format. Briefly, the microbiome samples cultured in 96 deepwells are washed with PBS and resuspended in urea lysis buffer. Then the samples are sonicated with a multi-channel sonicator for cell lysis. The protein concentrations of control samples (in triplicates) are tested and all samples will be digested in a volume equivalent to 100 µg proteins in the control. 1:1 protein content of SILAMi reference can be spiked-in in this step (but spiking with the SILAMi reference is not required). After reduction and alkylation, the proteins are digested by trypsin and are desalted. This workflow allows for accurate and reproducible high throughput metaproteomic analysis in a multi-well. This provides a screening platform that is capable of assessing changes due to disease, drug treatment or any other manipulation or treatment of the microbiome samples while in culture. Furthermore, this allow for the simultaneous culture and assessment of the microbiome samples from multiple individuals in a multi-well format, allowing for high-throughput screening in a compact and time-efficient manner. Figure 10 illustrates exemplary results from a RapidAIM assay for samples treated with a high (4-BBH), medium (3-BBM) or low (2-BBL) dose of berberine compared to a sample that is normal control (1-CN). The taxonomic composition at the species level were quantified in each of the cultured microbiome sample on the MetaLab bioinformatics platform: Figure 10A shows the results from the loadings plot of a principal component analysis (PCA) with all bacterial species. Figure10B shows that the abundances of the species originating from the Akkermansia genus (identified in Figure10B as Akkermansia species 1, Akkermansia species 2 and Akkermansia species 3) were significantly increased when treated with high concentration of Berberine. Akkermansia spp has been reported to be beneficial bacterial in the gut microbiome, which has been shown in other literatures to be increased by another antidiabetic drug, Metformin. It will be understood that while microbiota samples were treated with Berberine, any compound could be used in this screening platform to treat the microbiome samples and the subsequent effect on microbiome protein expression assessed with a system to perform functional and quantitative analysis of the samples. Detailed Description

[0081] SILAMi is a labelling technique that yields an isotope-labelled standard for a given microbiota sample. The original microbiota sample may have a large diversity of microbes. The microbe populations contained in the sample may range from prokaryotes (bacteria and archaea) to eukaryotes, where the eukaryotes may include fungi, protists.

[0082] In SILAMi, microbiota samples are inoculated into 15< N-labeled bacterial growth media, cultured under anaerobic conditions and passaged every 24 hours. Once the 15< N isotope is incorporated, the labelled microbiota can be used as an internal standard for the study of unlabelled samples. In the examples provided herein, a fresh intestinal microbiota sample was used. However, the skilled person will readily understand that other microbiota samples may be obtained and used in SILAMi without departing from the present teachings.

[0083] In the present application, by "compositional analysis" it is meant an analysis technique to determine the composition of a microbiota sample. Such analysis may involve, for example, metaproteomic analysis, metagenomic analysis or any other analytical technique employed to determine the composition (may it be the protein composition, the microbe composition), or a combination thereof, of the microbiota sample.

[0084] Moreover, by "microbe populations" it is meant the different taxa present in a microbiota (this includes, for example, the Domain, Kingdom, Phyla, Class, Order, Family Genera, Species found in the sample). In some examples, the microbe populations as herein defined may relate to the microdiversity of a microbiota sample, or to the diverse taxa found in the microbiota sample.

[0085] By "microbiota sample" it is meant a sample that contains a microbiota from a particular source. Even though the experiments described herein focus upon microbiota samples originating from a human (e.g. an intestine of a human as shown in Figures 1A and following), it will be understood that these are but examples of the fact that an isotope-labelled standard may be achieved for such a diverse population as that found in a human subject. Therefore, it will be appreciated by a skilled person in the art that other microbiota samples may similarly be obtained and cultured to reach an isotope-labelled standard by employing the SILAMi technique as described herein. For example, it will be readily understood that such microbiota samples may originate from animals.

[0086] By "isotopically metabolic labelling" it is meant the technique of incorporating isotopes into a given microbiota population as described herein.Experiment 1: Effectiveness of the SILAMi technique to label a diverse microbe population:

[0087] In an experiment to demonstrate the efficacy of the SILAMi microbiome labeling technique in a diverse mixed microbe populations, it was first examined whether intestinal metaproteomes could be efficiently labeled with 15< N. The intestinal metaproteome was selected for this experiment due to its diverse microbiome - indicative that other diverse microbiomes may similarly be labelled to provide isotope-labelled standards and importance in intestinal disease.Experiment Protocol:

[0088] Five intestinal microbiome samples were aspirated from colons. In some examples, as shown in Figure 1A, the samples may be obtained from stool 101 of subjects. The microbiota samples were transported to an anaerobic workstation (37°C, 10% H2, 10% CO2, and 80% N2) for processing and culturing. The samples were individually cultured in 15N-labeled growth media for 5 days 102 (passages) and kept during this time in anaerobic conditions. An optimized enriched media that is used may be modified dependent upon the microbiota sample used. In some embodiments, a skilled person will understand the enriched media used may be adapted to be suitable for the particular microbiota sample. For instance, in the case of the present intestinal microbiota samples, a 15N bacteria growth medium was supplemented with 0.1% w / v sodium thioglycolate and a 0.5 g / L bile salts mixture to accommodate intestinal bacteria. It will be understood that other adjustments may be made in order to provide a suitable growth medium that may be used in combination with other and / or additional labelled isotopes for the microbiome found in the sample. Moreover, it will be understood that the growth medium may include other isotopes that are to be incorporated into the microbiota. Even though 15N was used for the present intestinal microbiota sample, other isotopes may be used depending upon the nature of the microbiota present within the sample, the point of origination of the sample itself, and the desired labelling (e.g. a sulfur isotope may be used if only cysteine is to be labelled).Results:

[0089] After each passage 102, as shown in Figure 1A, the microbiota sample was analyzed by mass spectrometry, as is known in the art, to determine the 15N enrichment level of the microbiota sample. These results are shown in Figure 1B. As illustrated in Figure 1B, for the five individuals tested, the 15N enrichment rate of the samples was approximately 95% after two passages. This illustrates that it is possible to achieve a high isotope enrichment rate for a microbiota sample with a diverse microbe population (e.g. a microbiota sample originating from the intestine). A skilled person will appreciate that even though an intestinal microbiota sample was used in the present experiment to achieve this high enrichment rate, such isotope enrichment may be achieved by using other microbiota populations with a diverse microbe population, other than the one found in the intestine of a human subject (e.g. lung microbiota, cutis microbiota). Moreover, based upon these results, such microbiota samples may also be obtained from animal subjects, wherein animal subjects similarly have diverse microbial populations.

[0090] Moreover, for certain microbiota samples exposed to air (and oxygen), it may not be necessary to maintain anaerobic conditions.

[0091] Furthermore, a skilled person will also understand that by using other isotope enriched growth media, where the isotope is one other that 15N, it is appreciated that labelling a microbiota sample with other isotopes can be performed while still yielding a high enrichment rate, based upon these results, as presented in Figure 1B.

[0092] The metaproteomes were analyzed by mass spectrometry. It will be readily understood that gas chromatography-mass spectrometry may also be used. It will be readily understood that the intestinal microbiota sample was selected because of its diversity of microbiota to demonstrate SILAMi's ability to provide a standard for such a complex microbiota population. However, it will be apparent that any other microbiota population with a diverse microbiota may be similarly used without departing from the present teachings (e.g. mucosal, lung, cutis, etc.).

[0093] Moreover, the number of peptides identified with complete 15< N labeling increased (up to 11,800 peptides / sample after three days labeling), while the unlabeled peptides were minimally identified (less than 100 peptides / sample; Figure 4). Percent atomic enrichment calculation using Census

[10] also showed that all five microbiota tested reached an average 15< N enrichment of >95%, which is more than sufficient for 15< N-based quantitative proteomics, within three passages / days (Figure 1B). However, even though over 95% enrichment rate is explained herein as being sufficient for quantitative proteomics as illustrated in the example of Figure 1B, showing a very high and optimal enrichment rate, it will be understood by a person skilled in the art that the enrichment rate for quantitative proteomics may be anywhere over 50% and still provide acceptable results. In some examples, an enrichment rate of over 90%, as shown for the majority of subjects after 1 passage in Figure 1B, is sufficient for quantitative proteomics or any other compositional analysis. These data demonstrate that this technique is capable of efficiently labeling a complex and metabolically diverse population of microbes.

[0094] In order for this labeling approach to have broad applicability to a microbiome, the labeling is to be occurring across the various phyla and species represented within the microbiome samples. For instance, in some examples, in order to examine the representability of the 15< N-labelled SILAMi, the SILAMi microbial composition was compared to the initial inoculum (Passage 0) using metaproteomics-based methods. This demonstrates if the SILAMi labeled proteins were representative of the initial population in the microbiota sample. Briefly, all the identified peptide sequences (i.e. 15< N peptides in SILAMi and 14< N peptides in Passage 0) were phylogenetically classified using Unipept, which assigns taxonomic information for peptides based on lowest common ancestor (LCA) algorithm, UniProt database and NCBI taxonomy

[11] . As shown in Figure 5, 16 of 18 microbial phyla (including those belong to Bacteria, Archaea, and Eukaryota kingdoms), and 138 of 142 genera that were detected in Passage 0 remained in the SILAMi reference (Figure 5). This demonstrates that this labeling approach can efficiently label across all kingdoms as well as the majority of genera which are present in human microbiome samples. It will be appreciate that such an isotope-labelling standard may be used to perform accurate and reproducible metaproteomics.

[0095] In some examples, an isotope-labelled standard that has labelled 50% or more of the microbe population corresponding to an initial microbiota sample (an initial microbiota sample being the microbe population of the sample when initially obtained from the patient) may be used. However, it will be understood that the percentage of the microbe population of an initial microbiota sample that is to labelled to obtain an effective standard may vary depending upon the nature of the experiment (if only certain populations are desirable, such as the study of hydrogen sulfide producing bacteria in the study of inflammatory bowel disease).Experiment 2: Accurate Ratio Measurement using SILAMi labeled samples

[0096] It was next tested whether accurate ratio measurement could be obtained using the SILAMi-based quantitative metaproteomics. Briefly, the same amount of SILAMi proteomes were spiked into different amounts of the unlabelled human gut metaproteome samples at L / H ratios of 1:1, 1.25:1, 2:1, and 5:1, respectively (e.g. Figure 1A, 103). The mixtures were then processed for 4 hr gradient MS analysis on an Orbitrap Elite. A total of 6,943 unique peptide sequences corresponding to 4,014 protein groups were quantified and the L / H ratios 104 were calculated using Census. The distributions of calculated L / H ratios for all the quantified protein groups are shown in Figure 2A, which demonstrated that the median L / H ratios were in great agreement with the spike-in ratios (Pearson's r = 0.99). The fold change (FC) of each protein group between the four samples was then calculated by the "ratio of the L / H ratios" (Figure 2B), which showed that narrow FC distributions were obtained, with 81-92% of the protein groups having less than two-fold difference to the median. Moreover, the median protein FCs (1.23, 1.93 and 4.39 folds; Figure 2B) were in great agreement with the theoretical FC values (1.25, 2 and 5 folds, respectively). This demonstrates that metabolically labeled SILAMi microbiome can be used efficiently as an internal standard to reduce interexperiment and / or intraexperiment variability and optimize quantification in metaproteomics analysis. For instance, the use of an isotope-labelled standard for a microbiota population as described herein may identify false positives, changes in protein levels due to or experimental error or provide an indication if certain increases or reductions in the presence of certain proteins is a result of the preparation itself, or may be in fact due to a change in the microbiota as found in the original microbiota sample.

[0097] In summary, SILAMi represents a fast (3 days or less), efficient and cost-effective approach for generating metabolically labelled proteomes of intestinal microbial community, and allows accurate metaproteomic analysis of multiple samples with highly flexible experimental designs and implementations. SILAMi allows for highly standardized and quantitative analysis of the metaproteome, which will facilitate the use of metaproteomics analysis in the characterization of microbiome composition and function.Example 1: Use of SILAMi to assess changes in a microbiome as a result of treatment with a compound:

[0098] As a proof-of-principle example demonstrating the application of SILAMi to assess changes in a microbiome treated with a compound overtime, the approach was applied for evaluating the effects of fructooligosaccharide (FOS), a known prebiotic, on the microbiota. Briefly, unlabelled intestinal microbiota were cultured in basal culture medium (BCM) with or without 10 mg / ml FOS for 13 and 36 hours. The proteomes extracted from each microbial culture were spiked with the labelled SILAMi reference and analyzed by mass spectrometry. Principal component analysis of the 2,280 quantified proteins showed that FOS markedly shifted the overall metaproteome along the first principal component (explains 37.5% of the total variance; Figure 3A). 187 proteins were significantly changed, as shown in Table 3: Table 3: 187 identified protein groups altered by fructo-oligosacchride (FOS) treatmentProtein_ID Protein_name Taxa YP_007784111.1LSU ribosomal protein L12PRuminococcus sp. SR1 / 5CBK99399.1pyruvate: ferredoxin (flavodoxin) oxidoreductase, homodimericFaecalibacterium prausnitzii L2-6EEU98313.1pyruvate synthaseFaecalibacterium prausnitzii A2-165EFQ07801.1pyruvate synthaseFaecalibacterium cf. prausnitzii KLE1255EFJ59195.1DNA-binding protein H-NSEscherichia coli MS 200-1CBL23538.1Phosphotransferase system, HPr-related proteinsRuminococcus obeum A2-162EET15673.1rubredoxinBacteroides sp. 4_3_47FAAEHJ38221.1glutamate dehydrogenase, NAD-specificPrevotella stercorea DSM 18206EEJ50816.1pyruvate, phosphate dikinaseOribacterium sinus F0268EDR45788.1pyruvate, phosphate dikinaseDorea formicigenerans ATCC 27755EFE12305.1rubredoxinClostridium sp. M62 / 1EDM52622.1rubredoxinEubacterium ventriosum ATCC 27560EEJ51973.1chaperonin GroLOribacterium sinus F0268CBK95920.1glutamate dehydrogenase (NADP)Eubacterium siraeum 70 / 3EDP26881.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinCoprococcus eutactus ATCC 27759EEC56529.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinBacteroides pectinophilus ATCC 43243WP_008981262.1glutamate dehydrogenaseRuminococcaceae bacterium D16EEO57674.1ribosomal protein L7 / L12Bacteroides sp. 2_2_4EET14416.1ribosomal protein L7 / L12Bacteroides sp. 4_3_47FAAEDO55515.1ribosomal protein L7 / L12Bacteroides uniformis ATCC 8492EKU90753.1acyl carrier proteinBacteroides oleiciplenus YIT 12058EFV67501.1glyceraldehyde 3-phosphate dehydrogenaseBacteroides sp. 3_1_40AEDO52619.1glyceraldehyde-3-phosphate dehydrogenase, type IBacteroides uniformis ATCC 8492EDS13393.1glyceraldehyde-3-phosphate dehydrogenase, type IBacteroides stercoris ATCC 43183CBL23659.1Glutamate dehydrogenase / leucine dehydrogenaseRuminococcus obeum A2-162CBK78198.1glyceraldehyde-3-phosphate dehydrogenase, type IClostridium cf. saccharolyticum K10EJZ69496.1glyceraldehyde-3-phosphate dehydrogenase, type ILachnoanaerobaculum sp. OBRC5-5EDO59009.1glyceraldehyde-3-phosphate dehydrogenase, type IClostridium sp. L2-50YP_007775284.1glyceraldehyde-3-phosphate dehydrogenase, type IEubacterium siraeum 70 / 3EEG49252.1hypothetical protein RUMHYD_01832Blautia hydrogenotrophica DSM 10507EDP25141.1hypothetical protein COPEUT_02635Coprococcus eutactus ATCC 27759EFE14625.1hypothetical protein CLOM621_05456Clostridium sp. M62 / 1WP_009005634.1hypothetical proteinClostridium sp. D5EGG85123.1hypothetical protein HMPREF0992_00050Lachnospiraceae bacterium 6_1_63FAAEEG49378.1ketol-acid reductoisomeraseBlautia hydrogenotrophica DSM 10507CBK94839.1chaperonin GroLEubacterium rectale M104 / 1EFC97322.1chaperonin GroLClostridium hathewayi DSM 13479EFU71919.1chaperone GroELCampylobacter upsaliensis JV21EDO56757.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinClostridium sp. L2-50ACD05861.1Glutamate dehydrogenase (NADP(+))Akkermansia muciniphila ATCC BAA-835EEZ62164.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinSlackia exigua ATCC 700122EGW52698.1NADP-specific glutamate dehydrogenaseDesulfovibrio sp. 6_1_46AFAAEHH00248.1hypothetical protein HMPREF9441_01482Paraprevotella clara YIT 11840EJZ66498.1hypothetical protein HMPREF9448_00677Barnesiella intestinihominis YIT 11860EMZ42216.1hypothetical protein HMPREF1091_01190Atopobium minutum 10063974EFI05647.1conserved hypothetical proteinBacteroides sp. 1_1_14WP_010168870.1RubrerythrinEpulopiscium sp. 'N.t. morphotype B'YP_007784205.1RubrerythrinRuminococcus sp. SR1 / 5EID25347.1glyceraldehyde-3-phosphate dehydrogenase, type IStreptococcus pseudopneumoniae ATCC BAA-960AGJ88568.1ADP-L-glycero-D-mannoheptose-6-epimeraseRaoultella omithinolytica B6EFJ91797.1ADP-glyceromanno-heptose 6-epimeraseEscherichia coli MS 45-1EGB20520.1ribosomal protein S8Clostridium symbiosum WAL-14673EEG56094.1hypothetical protein CLOSTASPAR_01820Clostridium asparagiforme DSM 15981EHP50162.1reverse rubrerythrin-1Clostridium perfringens WAL-14572EGX68663.1triosephosphate isomeraseDorea formicigenerans 4_6_53AFAAEDM62185.1triose-phosphate isomeraseDorea longicatena DSM 13814EHL68608.1triosephosphate isomeraseBacillus sp. 7_6_55CFAA_CT2WP_009261992.1triosephosphate isomeraseLachnospiraceae bacterium 9_1_43BFAAEES65026.1butyryl-CoA dehydrogenaseFusobacterium varium ATCC 27725CBK80400.1Acyl-CoA dehydrogenasesCoprococcus catus GD / 7YP_007771689.1Acyl-CoA dehydrogenasesEubacterium rectale DSM 17629EEG92096.1acyl-CoA dehydrogenase, C-terminal domain proteinRoseburia inulinivorans DSM 16841YP_007789048.1Acyl-CoA dehydrogenasesbutyrate-producing bacterium SSC / 2EDO58999.1acyl-CoA dehydrogenase, C-terminal domain proteinClostridium sp. L2-50EGB17912.1acyl-CoA dehydrogenase, C-terminal domain proteinClostridium symbiosum WAL-14673WP_008981913.1acyl-CoA dehydrogenaseRuminococcaceae bacterium D16CBL15057.1hypothetical protein RBR_06960Ruminococcus bromii L2-63EEG90243.1acetyl-CoA C-acetyltransferaseCoprococcus comes ATCC 27758EGB17915.1acetyl-CoA C-acetyltransferaseClostridium symbiosum WAL-14673EEG35102.1pyridoxal-phosphate dependent TrpB-like enzymeEubacterium hallii DSM 3353EGB18784.1cell wall-binding repeat proteinClostridium symbiosum WAL-14673ADG61750.1chaperonin protein Cpn60Moraxella catarrhalis BBH18EFU70513.1chaperone GroELArcobacter butzleri JV22WP_010167160.1molecular chaperone GroELEpulopiscium sp. 'N.t. morphotype B'YP_007849205.1chaperonin GroLClostridium cf. saccharolyticum K10EGA92696.1hypothetical protein HMPREF9474_03417Clostridium symbiosum WAL-14163CBK80101.1LSU ribosomal protein L10PCoprococcus catus GD / 7YP_007785100.1Formate-tetrahydrofolate ligaseRuminococcus sp. SR1 / 5EES75500.1formate-tetrahydrofolate ligaseRuminococcus sp. 5_1_39BFAACBL24477.1Formate-tetrahydrofolate ligaseRuminococcus obeum A2-162WP_009644236.1rubrerythrin domain proteinMogibacterium sp. CM50EGB17993.1chaperonin GroLClostridium symbiosum WAL-14673EDY32295.1triose-phosphate isomeraseRuminococcus lactaris ATCC 29176YP_007786820.1triosephosphate isomeraseRuminococcus torques L2-14ACV56754.1ribosomal protein S13Eggerthella lenta DSM 2243EEX17882.1glutamate dehydrogenase, NAD-specificPrevotella veroralis F0319EGN46805.150S ribosomal protein L7 / L12Lachnospiraceae bacterium 2_1_58FAAEEG51161.1Rubrerythrin, partialClostridium asparagiforme DSM 15981EEX22906.1formate--tetrahydrofolate ligase, partialBlautia hansenii DSM 20583YP_007782188.1Glutamate dehydrogenase / leucine dehydrogenaseRuminococcus sp. SR1 / 5EEG47401.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinBlautia hydrogenotrophica DSM 10507EDQ97591.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinIntestinibacter bartlettii DSM 16795YP_007768517.1Glutamate dehydrogenase / leucine dehydrogenaseCoprococcus catus GD / 7YP_007830561.1Glutamate dehydrogenase / leucine dehydrogenaseRoseburia intestinalis M50 / 1EHP49125.1hypothetical proteinClostridium perfringens WAL-14572HMPREF9476_01168EFW89087.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinStreptococcus equinus ATCC 9812YP_007839796.1glutamate dehydrogenase (NADP)Eubacterium siraeum V10Sc8aEEG31752.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinClostridium methylpentosum DSM 5476EDS03463.1glutamate dehydrogenase, NAD-specificAlistipes putredinis DSM 17216EKA95101.1NADP-specific glutamate dehydrogenaseProteus mirabilis WGLW6EEG86298.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinProteus penneri ATCC 35198EES78421.1hypothetical protein RSAG_00378Ruminococcus sp. 5_1_39BFAAEHG28402.1hypothetical protein HMPREF9478_01803Enterococcus saccharolyticus 30_1EHO80265.1hypothetical protein HMPREF0402_02090Fusobacterium ulcerans 12-1BCBK99448.1Electron transfer flavoprotein, beta subunitFaecalibacterium prausnitzii L2-6EHJ31916.1electron transfer flavoprotein subunit betaPeptoclostridium difficile 002-P50-2011EFB77236.1electron transfer flavoprotein domain proteinSubdoligranulum variabile DSM 15176WP_020989365.1electron transfer flavoprotein beta subunitRuminococcaceae bacterium D16YP_008664299.1glutamate dehydrogenaseAdlercreutzia equolifaciens DSM 19450YP_007801881.1glutamate dehydrogenase (NADP)Gordonibacter pamelaeae 7-10-1-bCBL18303.1glutamate dehydrogenase (NADP)Ruminococcus champanellensis 18P13 = JCM 17042WP_019893516.1glutamate dehydrogenaseAllobaculum stercoricanisEMZ41672.1glutamate dehydrogenase (NADP+)Atopobium minutum 10063974YP_007837627.1glutamate dehydrogenase (NADP)Faecalibacterium prausnitzii L2-6EDM50401.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinEubacterium ventriosum ATCC 27560EFF68264.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinButyrivibrio crossotus DSM 2876EEG37302.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinEubacterium hallii DSM 3353EFR58785.1glutamate dehydrogenase, NAD-specificAlistipes sp. HGB5CBK64456.1glutamate dehydrogenase (NAD)Alistipes shahii WAL 8301EFW05566.1Glutamate:leucine:phenylalanine:valin e dehydrogenaseCoprobacillus sp. 29_1CBL26527.1Glutamate dehydrogenase / leucine dehydrogenaseRuminococcus torques L2-14EET58217.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinMarvinbryantia formatexigens DSM 14469EFK29209.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinLactobacillus plantarum subsp. plantarum ATCC 14917EEQ44915.1NADP-specific glutamate dehydrogenaseCandida albicans WO-1WP_009733626.1glutamate dehydrogenaseBilophila sp. 4_1_30CBK74255.1glutamate dehydrogenase (NADP)Butyrivibrio fibrisolvens 16 / 4EFC93334.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinMethanobrevibacter smithii DSM 2374EFI84299.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinListeria grayi DSM 20601EEB34074.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinDesulfovibrio piger ATCC 29098EJF41864.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinActinomyces massiliensis F0489YP_007781565.1Glutamate dehydrogenase / leucineRuminococcus bromii L2-63dehydrogenaseEHL05236.1NAD(P)-specific glutamate dehydrogenaseDesulfitobacterium hafniense DP7EKX90337.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinCorynebacterium durum F0235EGG79390.1NADP-specific glutamate dehydrogenaseLachnospiraceae bacterium 6_1_63FAAEJU21607.1glutamate dehydrogenase, NAD-specificMogibacterium sp. CM50EGX98849.1glutamate dehydrogenaseLactobacillus ruminis ATCC 25644WP_002582046.1glutamate dehydrogenaseClostridium butyricumEHN61579.1Glu / Leu / Phe / Val dehydrogenase, dimerization domain proteinListeria innocua ATCC 33091YP_004374626.1cryptic glutamate dehydrogenaseCamobacterium sp. 17-4CBL42834.1Glutamate dehydrogenase / leucine dehydrogenasebutyrate-producing bacterium SS3 / 4EES65252.1translation elongation factor TsFusobacterium varium ATCC 27725EEG47205.1formate--tetrahydrofolate ligase, partialBlautia hydrogenotrophica DSM 10507EFV22449.1rubredoxinAnaerostipes sp. 3_2_56FAAEHO34579.1hypothetical protein HMPREF0995 _01217Lachnospiraceae bacterium 7_1_58FAACBL23222.1Carbon dioxide concentrating mechanism / carboxysome shell proteinRuminococcus obeum A2-162EHI57300.1hypothetical protein HMPREF9473_04409Clostridium hathewayi WAL-18680CBL25604.1Carbon dioxide concentrating mechanism / carboxysome shell proteinRuminococcus torques L2-14YP_007783749.1Carbon dioxide concentrating mechanism / carboxysome shell proteinRuminococcus sp. SR1 / 5EFV41475.1propanediol utilization protein PduAEnterobacteriaceae bacterium 9_2_54FAAEEG37602.1BMC domain proteinEubacterium hallii DSM 3353EEB47897.1ribosomal protein S4Providencia alcalifaciens DSM 30120EEQ62196.1ribosomal protein S5Clostridiales bacterium 1_7_47FAAWP_009461300.130S ribosomal protein S5Lachnospiraceae bacterium 2_1_46FAAEEF93821.1translation elongation factor TuCatenibacterium mitsuokai DSM 15897EEY85351.1translation elongation factor TuAcinetobacter radioresistens SH164EFG16453.1transporter, MotA / TolQ / ExbB proton channel family proteinBacteroides vulgatus PC510EFV65278.1fructose-bisphosphate aldolaseBacteroides sp. 3_1_40AEEC95866.1fructose-1,6-bisphosphate aldolase, class IIParabacteroides johnsonii DSM 18315CBK74269.1phosphoenolpyruvate carboxykinase (ATP)Butyrivibrio fibrisolvens 16 / 4EEU32089.1chaperoninFusobacterium nucleatum subsp. vincentii 3_1_36A2WP_018590022.1molecular chaperone GroELTerrisporobacter glycolicusEFS23154.1chaperonin GroLFusobacterium necrophorum D12EFY04737.1B12 binding domain proteinPhascolarctobacterium succinatutens YIT 12067EEQ65050.1elongation factor TuLactobacillus paracasei subsp. paracasei 8700:2CBL22274.1phosphoenolpyruvate carboxykinase (ATP)Ruminococcus obeum A2-162CBL01371.1phosphoenolpyruvate carboxykinase (ATP)Faecalibacterium prausnitzii SL3 / 3EDM63411.1phosphoenolpyruvate carboxykinase (ATP)Dorea longicatena DSM 13814EEA80919.1phosphoenolpyruvate carboxykinase (ATP)Tyzzerella nexilis DSM 1787EFV68881.1plasminogen binding proteinBacteroides sp. 3_1_40AEEO45868.1tetratricopeptide repeat proteinBacteroides dorei 5_1_36 / D4 Lachnospiraceae bacteriumEGN48017.150S ribosomal protein L7 / L123_1_57FAA_CT1EGX69049.1elongation factor TuCollinsella tanakaei YIT 12063EEP44307.1translation elongation factor TuCollinsella intestinalis DSM 13280ACV51606.1translation elongation factor TuAtopobium parvulum DSM 20469YP_002294143.1autonomous glycyl radical cofactor GrcAEscherichia coli SE11EER73923.1translation elongation factor TuWeissella paramesenteroides ATCC 33313EES64428.1DNA-binding protein, YbaB / EbfC familyFusobacterium varium ATCC 27725EFK66259.1phosphoenolpyruvate-protein phosphotransferaseEscherichia coli MS 124-1EEX68873.1ATP synthase F1, beta subunitMitsuokella multacida DSM 20544EFY04736.1methylmalonyl-CoA mutase domain proteinPhascolarctobacterium succinatutens YIT 12067ACV56138.1ribosomal protein L2Eggerthella lenta DSM 2243EFY04939.1ribosomal protein S3Phascolarctobacterium succinatutens YIT 12067EHR32371.1ATP synthase subunit betaMegamonas funiformis YIT 11815EBA39887.1translation elongation factor TuCollinsella aerofaciens ATCC 25986EES64070.1glyceraldehyde-3-phosphate dehydrogenase, type IFusobacterium varium ATCC 27725EBA39912.1ribosomal protein L7 / L12Collinsella aerofaciens ATCC 25986EGX68988.130S ribosomal protein S10Collinsella tanakaei YIT 12063

[0099] Among the identified 187 significantly changed proteins (Figure 6), eight orthologs of the translation elongation factor Tu (EF-Tu) from different bacterial species were found to be increased by FOS (up to 6 fold, Figure 3B). Additionally, nine orthologs of glyceraldehyde-3-phosphate dehydrogenase protein from different species were also quantified, among which eight isoforms were decreased while the one from Fusobacterium varium was increased. It also found that the proteins involved in endotoxin synthesis (eg., ADP-glyceromanno-heptose 6-epimerase) were decreased, suggesting an inhibiting role of FOS on potential endotoxin-producing pathogens in the intestinal tract. This demonstrates that SILAMi can be used to quantitatively assess changes in metaproteomic expression following treatment with a compound and that these changes can indicate specific pathways and functions that are effected by the treatment of these compounds. This approach could be used to identify therapeutic targets, diagnostics and assess treatment response.

[0100] Finally, it was tested whether the SILAMi labelled-standard could be used to distinguish the effects of different monosaccharides on the microbiota. It will be understood that monosaccharides are used herein as an example of a compound that may have an effect on the microbiota. However, other compounds that have be introduced to a microbiota sample or to a subject of which a microbiota sample has been obtained, that may impact the microbiota, may be similarly analyzed as described herein.

[0101] Overall, 18 samples cultured with or without 2.5 g / L of each monosaccharide (N-acetyl glucosamine or GlcNAc, mannose, galactose, fucose, or glucose) were analyzed, by SILAMi-based metaproteomics which led to 3,158 quantified proteins. Two hundred and forty-six protein groups were identified as being differentially abundant as compared to the non-treated control group (Table 4): Table 4: the two hundred and forty-six protein groups were identified as being differentially abundant as compared to the non-treated control group following metaproteomics with SILAMi.Protein_ ID Protein_name taxonomy EFY04736.1methylmalonyl-CoA mutase domain proteinPhascolarcto bacterium succinatutens YIT 12067EFF50472.1chaperonin GroLBacteroides ovatus SD CMC 3fEFG25764.1methylmalonyl-CoA mutaseVeillonella sp. 6_1_27EFG19106.1pyruvate, phosphate dikinaseBacteroides vulgatus PC510EFD84126.1lactaldehyde reductaseKlebsiella sp. 1_1_55AGJ89383.1L-fucose isomeraseRaoultella ornithinolytica B6EFK03838.1arabinose isomeraseEscherichia coli MS 182-1EKN23949.1hypothetical protein HMPREF1059_02856Parabacteroides distasonis CL09T03C24EEZ28128.1pyruvate, phosphate dikinaseBacteroides sp. 2_1_16EET14319.1phosphoenolpyruvate carboxykinase (ATP)Bacteroides sp. 4_3_47FAAEET60019.1ribosomal protein L7 L12Marvinbryantia formatexigens DSM 14469EFK61747.1hypothetical protein HMPREF9008_02015Parabacteroides sp. 20_3EKN23143.1hypothetical protein HMPREF1059_03287Parabacteroides distasonis CL09T03C24YP_00229278 2.1hypothetical protein ECSE_1507Escherichia coli SE11EEJ51224.1arabinose isomeraseOribacterium sinus F0268EFJ64969.1lactaldehyde reductaseEscherichia coli MS 175-1EEG94073.1ribosomal protein L7 L12Roseburia inulinivorans DSM 16841EFG23247.1translation elongation factor GVeillonella sp. 3_1_44YP_00335058 1.1L-fuculose-1-phosphate aldolaseEscherichia coli SE15EHM50183.1glutamine-fructose-6-phosphate transaminaseYokenella regensburgei ATCC 43003YP_00229433 5.1L-fuculose phosphate aldolaseEscherichia coli SE11EHP45389.1phosphoenolpyruvate carboxykinase [ATP]Odoribacter laneus YIT 12061EFY05605.1translation elongation factor GPhascolarctobacterium succinatutens YIT 12067EEA80457.1hypothetical protein CLONEX_03662Tyzzerella nexilis DSM 1787EEO62927.1Xaa-His dipeptidaseBacteroides sp. 9_1_42FAAEFK60382.1phosphoenolpyruvate carboxykinase (ATP)Parabacteroides sp. 20_3EDV01624.1ribosomal protein S12Bacteroides coprocola DSM 17136EFU58095.1L-fucose:H+ symporter permeaseEscherichia coli MS 16-3EFD04604.1ribosomal protein S12Peptostreptococcus anaerobius 653-LEBA39920.1ribosomal protein S12Collinsella aerofaciens ATCC 25986EHL76277.130S ribosomal protein S12Bacillus smithii 7_3_47FAAEFG23245.1ribosomal protein S12Veillonella sp. 3_1_44EEU51870.1phosphoglycerate kinaseParabacteroides sp. D13EEZ25115.1phosphoglycerate kinaseBacteroides sp. 2_1_16EGB 19009.1pyruvate, phosphate dikinaseClostridium symbiosum WAL-14673EHP48549.1pyruvate, phosphate dikinaseClostridium perfringens WAL-14572EFU52197.1glutamine-fructose-6-phosphate transaminase (isomerizing)Escherichia coli MS 153-1CDM03263.1SSU ribosomal protein S11p (S14e)Bacteroides xylanisolvens SD CC 1bEEU49302.1tetratricopeptide repeat proteinParabacteroides sp. D13EDM19917.1transporter, MotA TolQ ExbB proton channel family proteinBacteroides caccae ATCC 43185EHE95587.1ketol-acid reductoisomeraseClostridium citroniae WAL-17108EDM88222.1pyruvate, phosphate dikinaseRuminococcus obeum ATCC 29174EFC98279.1pyruvate, phosphate dikinaseClostridium hathewayi DSM 13479EHI58090.1pyruvate, phosphate dikinaseClostridium hathewayi WAL-18680EFV23852.1hypothetical protein HMPREF1011_00311Anaerostipes sp.3_2_56FAAACA17149.1pyruvate, phosphate dikinaseMethylobacterium sp. 4-46EDS09607.1pyruvate, phosphate dikinaseAnaerotruncus colihominis DSM 17241EFJ97558.1glycerol-3-phosphate dehydrogenase, anaerobic, A subunitEscherichia coli MS 115-1EDN88029.1hypothetical protein PARMER_00607Parabacteroides merdae ATCC 43184EHP67661.1succinate dehydrogenase flavoprotein subunitEscherichia coli 4_1_47FAAEFK60341.1tetratricopeptide repeat proteinParabacteroides sp. 20_3EFK61507.1malate dehydrogenase, NAD-dependentParabacteroides sp. 20_3EEQ57746.1pyruvate, phosphate dikinaseClostridiales bacterium 1_7_47FAAEDP13424.1hypothetical protein CLOBOL_06339Clostridium bolteae ATCC BAA-613EEZ23361.1rubredoxinBacteroides sp. 3_1_33FAAEET16778.1trigger factorBacteroides sp. 4_3_47FAAEEY84376.1TonB-linked outer membrane protein, SusC RagA familyBacteroides sp. 2_1_33BB6I7Z9.1Succinyl-CoA ligase [ADP-forming] subunit betaEscherichia coli SE11EEZ25084.1fructose-1,6-bisphosphate aldolase, class IIBacteroides sp. 2_1_16AGH75904.1glycerol kinaseXanthomonas axonopodis Xac29-1EEZ20650.1malate dehydrogenase, NAD-dependentBacteroides sp. 3_1_33FAAEGN42283.1phosphoenolpyruvate carboxykinaseLachnospiraceae bacterium 1_1_57FAAEEU51305.1rubredoxinParabacteroides sp. D13YP_00779470 8.1RubrerythrinBacteroides xylanisolvens XB1AEFI88374.1transcriptional regulator, PadR familyEscherichia coli MS 196-1EET17854.2fructose-1,6-bisphosphate aldolase, class IIBacteroides sp. 4_3_47FAAEFU52050.1glycerol kinaseEscherichia coli MS 153-1EEH85689.1L-asparaginaseEscherichia sp. 3_2_53FAAEGB18784.1cell wall-binding repeat proteinClostridium symbiosum WAL-14673EEH89155.1glycerol kinaseEscherichia sp. 3_2_53FAAEEO47368.1succinate dehydrogenase flavoprotein subunitBacteroides dorei 5_1_36 D4EFR55977.1succinate dehydrogenase flavoprotein subunitBacteroides fragilis 3_1_12EFV69103.1fumarate reductase flavoprotein subunitBacteroides sp. 3_1_40AEFK60272.1succinate dehydrogenase flavoprotein subunitParabacteroides sp. 20_3YP_00335003 6.1hypothetical protein ECSF_2046Escherichia coli SE15EES78774.1triosephosphate isomeraseRuminococcus sp. 5_1_39BFAAEFJ74438.1glycerophosphodiester phosphodiesterase family proteinEscherichia coli MS 198-1EFD83709.1universal stress family proteinKlebsiella sp. 1_1_55EFK02128.1L-asparaginase, type IIEscherichia coli MS 182-1EFU51004.1phosphoenolpyruvate carboxykinase (ATP)Escherichia coli MS 153-1EFG23975.1methylmalonyl-CoA carboxyltransferase 12S subunitVeillonella sp. 3_1_44EFK64468.1transporter, MotA TolQ ExbB proton channel family proteinParabacteroides sp. 20_3EFJ83172.1arylsulfataseEscherichia coli MS 69-1EEH87575.1oxidoreductase, short chain dehydrogenase reductase family proteinEscherichia sp. 3_2_53FAAEFY04740.1methylmalonyl-CoA decarboxylase alpha subunitPhascolarctobacteriumsuccinatutens YIT 12067EHP67477.1galactokinaseEscherichia coli 4_1_47FAAEHI58078.1glyceraldehyde-3-phosphate dehydrogenaseClostridium hathewayi WAL-18680EGB74319.1carbon starvation protein CstAEscherichia coli MS 57-2EFJ62757.1threonine ammonia-lyaseEscherichia coli MS 200-1YP_00782531 1.1Glutamate dehydrogenase leucine dehydrogenasebutyrate-producing bacterium SS3 4EFJ73903.1galactose mutarotaseEscherichia coli MS 198-1AGJ85389.1threonine dehydrataseRaoultella ornithinolytica B6EFE22430.1L-asparaginase, type IIEdwardsiella tarda ATCC 23685EHC27190.1chaperonin 1Propionibacterium sp. 5_U_42AFAAEDN83951.1chaperonin GroLBifidobacterium adolescentis L2-32YP_00776684 3.1chaperonin GroLBifidobacterium longum subsp. longum F8EEZ21779.1ribosomal protein L22Bacteroides sp. 3_1_33FAAEFJ96310.1UDP-glucose 4-epimeraseEscherichia coli MS 115-1EEY85226.1outer membrane protein 40Bacteroides sp. 2_1_33BEFK20636.1PTS system, N-acetylglucosamine-specific IIBC componentEscherichia coli MS 21-1EFJ79729.1PTS system, N-acetylglucosamine-specific IIBC componentEscherichia coli MS 69-1YP_00334860 8.1glutaminyl-tRNA synthetaseEscherichia coli SE15EEH84887.1universal stress family proteinEscherichia sp. 3_2_53FAAEDY33848.1ribosomal protein L13Ruminococcus lactaris ATCC 29176EGB20384.1ribosomal protein L13Clostridium symbiosum WAL-14673EDQ97629.1hypothetical protein CLOBAR_00369Intestinibacter bartlettii DSM 16795EFJ75883.1ABC transporter, substrate-binding protein, family 5Escherichia coli MS 198-1EFU59091.1Glu Leu Phe Val dehydrogenase, dimerization domain proteinEscherichia coli MS 16-3B6I3D0.1Glycine--tRNA ligase alpha subunitEscherichia coli SE11EEH85049.1hypothetical protein ESAG_00761Escherichia sp. 3_2_53FAAB6I227.150S ribosomal protein L16Escherichia coli SE11EFK74482.1tyrosine--tRNA ligaseEscherichia coli MS 78-1WP_00129508 0.1lysyl-tRNA synthetaseEscherichia sp. 4_1_40BAFU19253.1chaperonin GroELActinobacillus suis H91-0380YP_00334938 5.130S ribosomal protein S22Escherichia coli SE15EFV40188.12,3,4,5-tetrahydropyridine-2,6-dicarboxylate N-succinyltransferaseEnterobacteriaceae bacterium 9_2_54FAAEFK88351.1isoleucine--tRNA ligaseEscherichia coli MS 146-1EFD82816.12,3,4,5-tetrahydropyridine-2,6-dicarboxylate N-succinyltransferaseKlebsiella sp. 1_1_55EHP65572.1phenylalanyl-tRNA synthetase alpha chainEscherichia coli 4_1_47FAAEEH89206.1LOW QUALITY PROTEIN: hypothetical protein ESAG _04918, partialEscherichia sp. 3_2_53FAAEFJ71500.1GMP reductaseEscherichia coli MS 198-1ADX45418.1anti-sigma H sporulation factor, LonBAcidovorax avenae subsp.avenae ATCC 19860EGG53815.1putative transcriptional regulatory protein FixJParasutterella excrementihominis YIT 11859B6I152.1LPS-assembly lipoprotein LptEEscherichia coli SE11EFC57150.1hypothetical protein ENTCAN_05663Enterobacter cancerogenus ATCC 35316EKA96042.1adenylosuccinate synthetaseProteus mirabilis WGLW6EHM49114.1lysine--tRNA ligaseYokenella regensburgei ATCC43003EFJ58662.1glutamate--tRNA ligaseEscherichia coli MS 200-1YP_00335194 7.1peptide ABC transporter substrate binding componentEscherichia coli SE15EFU57211.1hypothetical protein HMPREF9545_03054Escherichia coli MS 16-3EGK61283.1chaperone GroELEnterobacter hormaechei ATCC 49162EDU60353.1peptidyl-prolyl cis-trans isomerase BProvidencia stuartii ATCC 25827EFJ74879.1Dyp-type peroxidase family proteinEscherichia coli MS 198-1YP_00334970 0.1glucose-6-phosphate dehydrogenaseEscherichia coli SE15EHM47365.1glyceraldehyde-3-phosphate dehydrogenase, type IYokenella regensburgei ATCC 43003YP_00334949 4.1outer membrane lipoproteinEscherichia coli SE15EEH72503.1phosphopentomutaseEscherichia sp. 1_1_43YP_00353907 5.1pyruvate kinase IIErwinia amylovora ATCC 49946YP_00334818 3.1hypothetical protein ECSF_0193Escherichia coli SE15EFJ68155.1ATP-dependent protease HslVU, ATPase subunitEscherichia coli MS 175-1YP_00334899 5.1hypothetical protein ECSF_1005Escherichia coli SE15YP_00353751 2.1molecular chaperone GroELErwinia amylovora ATCC 49946YP_00784719 5.1glutamyl-tRNA synthetaseEnterobacter cloacae subsp. cloacae NCTC 9394EEH92664.2glyceraldehyde-3-phosphate dehydrogenase ACitrobacter sp. 30_2EKA97512.1glycyl-tRNA synthetase alpha subunitProteus mirabilis WGLW6EFK92190.1ADP-glyceromanno-heptose 6-epimeraseEscherichia coli MS 146-1YP_00334841 3.1acridine efflux pump protein AcrAEscherichia coli SE15B6I615.1AltName: Full=GroEL protein;AltName: Full=Protein Cpn60;CH60_ECOSE RecName: Full=60 kDa chaperonin0YP_00353719 8.1serine acetyltransferaseErwinia amylovora ATCC 49946EFK17770.1co-chaperone GrpEEscherichia coli MS 21-1ADN76170.1pyruvate kinaseFerrimonas balearica DSM 9799YP_00335027 0.1PTS system enzyme IEscherichia coli SE15EFP65709.1phosphopyruvate hydrataseRalstonia sp. 5_7_47FAAB3PJB3.12-phospho-D-glycerate hydro-lyaseCellvibrio japonicus Ueda107EEH94022.2autonomous glycyl radical cofactorCitrobacter sp. 30_2EEH95287.2aspartate-semialdehyde dehydrogenaseCitrobacter sp. 30_2EHP49497.1enolaseClostridium perfringens WAL-14572EFJ73315.1sporulation and cell division repeat proteinEscherichia coli MS 198-1YP_00229207 5.1translocation protein TolBEscherichia coli SE11EFU37439.1hexose kinase, 1-phosphofructokinase familyEscherichia coli MS 85-1B6I4S5.1Heat shock protein HslVEscherichia coli SE11EFU37381.1glyceraldehyde-3-phosphate dehydrogenase, type IEscherichia coli MS 85-1YP_00334913 8.1putative PTS system enzyme IEscherichia coli SE15EGJ08498.16-phosphofructokinaseShigella sp. D9EJZ47828.1fructose-bisphosphate aldolase class 1Escherichia sp. 1_1_43EFK73994.1NAD(P)H:quinone oxidoreductase, type IVEscherichia coli MS 78-1YP_00334817 3.1methionine aminopeptidaseEscherichia coli SE15WP_01859209 2.1hypothetical proteinTerrisporobacter glycolicusEDO60098.1formate C-acetyltransferaseClostridium leptum DSM 753WP_02403910 7.1Formate acetyltransferaseClostridium butyricumEGU99618.12,3-bisphosphoglycerate-independent phosphoglycerate mutaseEscherichia coli MS 79-10EFU56329.12,3-bisphosphoglycerate-independent phosphoglycerate mutaseEscherichia coli MS 16-3B6I2X8.1Fe S biogenesis protein NfuAEscherichia coli SE11EEH87714.1autonomous glycyl radical cofactorEscherichia sp. 3_2_53FAAA6VUU9.12-phospho-D-glycerate hydro-lyaseMarinomonas sp. MWYL1YP_00334853 8.1alkyl hydroperoxide reductase subunit FEscherichia coli SE15YP_00229194 9.1alkyl hydroperoxide reductase subunit FEscherichia coli SE11EFJ56595.1stringent starvation protein AEscherichia coli MS 185-1YP_00334853alkyl hydroperoxide reductase subunit CEscherichia coli SE157.1 EFK73356.1chaperonin GroSEscherichia coli MS 78-1WP_00900803 9.1autonomous glycyl radical cofactor GrcAShigella sp. D9EFK71210.1glucose-6-phosphate isomeraseEscherichia coli MS 78-1AGJ88568.1ADP-L-glycero-D-mannoheptose-6-epimeraseRaoultella omithinolytica B6EKB82759.1chaperoninKlebsiella pneumoniae subsp. pneumoniae WGLW5YP_00334809 5.1cell division protein FtsZEscherichia coli SE15YP_00229177 3.1heat shock protein 90Escherichia coli SE11EFK25863.1deoxyribose-phosphate aldolaseEscherichia coli MS 187-1EFU57607.1deoxyribose-phosphate aldolaseEscherichia coli MS 16-3EFJ62792.1hypothetical protein HMPREF9553_01102Escherichia coli MS 200-1YP_00335047 5.1autoinducer-2 production proteinEscherichia coli SE15EFU58344.1peptidyl-prolyl cis-trans isomerase BEscherichia coli MS 16-3EGF14772.1formate acetyltransferaseHaemophilus aegyptius ATCC 11116YP_00334881 4.1formate acetyltransferase 1Escherichia coli SE15EFE22433.1formate C-acetyltransferaseEdwardsiella tarda ATCC 23685EGU97855.1formate acetyltransferaseEscherichia coli MS 79-10EFK20865.1phosphoserine transaminaseEscherichia coli MS 21-1A6VZ92.1Phosphohydroxythreonine aminotransferaseMarinomonas sp. MWYL1EFD85361.1formate C-acetyltransferaseKlebsiella sp. 1_1_55EFJ53806.1rhodanese-like proteinEscherichia coli MS 185-1EFK44911.1formate C-acetyltransferaseEscherichia coli MS 119-7YP_00422042 8.1formate acetyltransferaseBifidobacterium longum subsp. longum JCM 1217AGJ89504.1S-ribosylhomocysteinaseRaoultella omithinolytica B6YP_00334971 6.1aspartyl-tRNA synthetaseEscherichia coli SE15EEH86836.1aspartate--tRNA ligaseEscherichia sp. 3_2_53FAAEDP16360.1hypothetical protein CLOBOL_03126Clostridium bolteae ATCC BAA-613EFR46519.1chaperone protein ClpBHelicobacter cinaedi CCUG 18818EFK89002.1glycerol-3-phosphate dehydrogenase, anaerobic, C subunitEscherichia coli MS 146-1EFK00860.1beta-aspartyl peptidaseEscherichia coli MS 182-1EFO55869.1chaperone protein DnaKEscherichia coli MS 145-7YP_00335013 7.1hypothetical protein ECSF_2147Escherichia coli SE15YP_00335194 9.1truncated formate dehydrogenase H, partialEscherichia coli SE15EGU96599.1ATP-dependent chaperone protein ClpBEscherichia coli MS 79-10EFE53197.1methionine adenosyltransferaseProvidencia rettgeri DSM 1131YP_00353773 9.1chaperone proteinErwinia amylovora ATCC 49946EEH84693.1outer membrane protein XEscherichia sp. 3_2_53FAAEFU58863.1pyrroline-5-carboxylate reductaseEscherichia coli MS 16-3EFK20818.1curved DNA-binding proteinEscherichia coli MS 21-1EFD82978.1chaperone protein DnaKKlebsiella sp. 1_1_55YP_00335133 1.1putative lipoproteinEscherichia coli SE15EEH85525.1TIGR00156 family proteinEscherichia sp. 3_2_53FAAEFO56144.1outer membrane protein slpEscherichia coli MS 145-7EHP49037.1chaperone ClpBClostridium perfringens WAL-14572EFB70971.1chaperone protein DnaKProvidencia rustigianii DSM 4541EEZ20102.1hypothetical protein HMPREF0105_3492Bacteroides sp. 3_1_33FAAEFJ65716.1putative protein HdeBEscherichia coli MS 175-1EFU51921.1DNA protection during starvation proteinEscherichia coli MS 153-1EJZ49312.1N-acetylglucosamine-6-phosphate deacetylaseEscherichia sp. 1_1_43EFU58593.1DNA protection during starvation proteinEscherichia coli MS 16-3EFO58101.1N-acetylglucosamine-6-phosphate deacetylaseEscherichia coli MS 145-7YP_00334942 6.1hypothetical protein ECSF_1436Escherichia coli SE15EFU51394.1glucosamine-6-phosphate deaminaseEscherichia coli MS 153-1EEH70955.1aspartate-ammonia ligaseEscherichia sp. 1_1_43YP_00334939 1.1amino acid antiporterEscherichia coli SE15EFK92597.1oxygen-insensitive NAD(P)H nitroreductaseEscherichia coli MS 146-1EFK24351.1aminotransferase AlaTEscherichia coli MS 187-1EFK90487.1glutamate decarboxylaseEscherichia coli MS 146-1YP_00335133 5.1glutamate decarboxylaseEscherichia coli SE15EFC55519.1asparagine synthase (glutamine-hydrolyzing)Enterobacter cancerogenus ATCC 35316EFK50572.1asparagine synthase (glutamine-hydrolyzing)Escherichia coli MS 107-1EFR55608.1ribosomal protein S10Bacteroides fragilis 3_1_12EFR55609.150S ribosomal protein L3Bacteroides fragilis 3_1_12EFK61943.1SusD family proteinParabacteroides sp. 20_3EBA38760.1ribosomal protein S20Collinsella aerofaciens ATCC 25986EGB75313.1indole-3-glycerol phosphate synthaseEscherichia coli MS 57-2EFK61740.1Tat pathway signal sequence domain proteinParabacteroides sp. 20_3EET16977.1phosphoglucomutaseBacteroides sp. 4_3_47FAA

[0102] Unique metaproteome patterns were observed in response to the different monosaccharide treatments (Figure 3C). The monosaccharide fucose showed the smallest effect on the metaproteome, however cluster 206, mainly consisting of fucose utilizationrelated proteins, was increased only in the fucose-treated microbiota (Figure 3C). Moreover, all of the six quantified fucose utilizing proteins in the fucose utilization pathway were increased upon the supplementation of fucose (Figure 3D)

[12] . On the other hand, GlcNAc resulted in the most dramatic alterations of the metaproteome with significant increase of GlcNAc degrading proteins (Figure 3A-C), which produce fructose 6-phosphate and NH3. The latter may be used for asparagine synthesis since asparagine synthase was also significantly increased (Figure 3D).

[0103] As a result, it is shown that use of the heavy-labelled standard obtained via SILAMi may be used to assess changes in a microbiome as a result of a given compound. More specifically, this approach allows for identification of specific pathways and metabolic processes which may be altered in a treated microbiome sample. This data could be used by one of skill in the art to determine who changes in composition effect function as well as identify pathways effected in disease or by drug and chemical treatment. It will be understood that such compounds may include xenobiotics, but also drugs, chemicals, therapeutic agents, toxins, poisons, beverages, food additives, cosmetics, cosmetic ingredients, packaging materials, pesticides, herbicides, consumer products. A skilled person recognizes that a given microbiome is very sensitive to change, and therefore such a compound may have an impact upon the microbiome. Such an impact is now quantifiable as a result of the heavy-labelled standard developed using the SILAMi technique. Taken together, a fast and cost-effective approach is provided, namely SILAMi, to perform accurate and large-scale quantitative metaproteomic studies on the microbiota. Moreover, it was successfully applied to screen and evaluate the effects of different compounds on human microbiota. More interestingly, new insights on the interactions between drug, microbe and host may be acquired through experimentsbenefiting from the heavy-labelled standard obtained with SILAMi. Thus, the application of SILAMi can help to improve the accuracy of metaproteomics, thereby largely promoting its application in studying the microbiota in the context of health and disease. It will be understood that such study in the context and disease may include determining for a given patient if the disease is in remission or if the disease is worsening in severity. The study may also involve determining if a patient is responding to a given treatment, or even determining which treatment should be given for a specific patient. Furthermore, diagnosis of disease is also possible with SILAMi. It is known in that changes in health and disease often yield a change in the microbiota of the patient. These changes, in particular in depth metaproteomic changes, can now be quantified and analyzed as a result of the heavy-labelled standard obtained using SILAMi.Example 2: RapidAIM, a high throughput screening platform to assess the effect of drugs:

[0104] Reference is now made to RapidAIM, an experimental and computational framework to rapidly assay an individual's microbiome (called RapidAIM), a platform to assess the effects of compounds including but not limited to drugs on the microbiome and drug metabolism is described. The use of RapidAIM to validate the platform for compounds, specifically, in this example, those used in IBD, is described (Figures 8, 9). Briefly, RapidAIM consists of panel of microbiomes derived from multiple individuals that are treated with selected compounds and screened in a multi-well format; This approach allows for rapid classification of compounds that have no affect or affect the microbiota composition (biota-affectors), or compounds that are affected by the microbiome (biota-altered) using metagenomic ( 16< S-based sequencing), and / or fast-pass metaproteomics, and / or metabolomics assessment of the microbiome's metabolic activities on the compound; Optionally, the RapidAIM platform will be utilized to gain mechanistic insights on these compounds in combination with functional metagenomics, metatranscriptomics and in depth metaproteomics. It will be appreciated that certain steps of the RapidAIM can be performed separately. Finally, this bioinformatics platform can be used to rapidly guide the selection of positive hit compounds based on the metaOMICS analyses of an individual's microbiome or broad screening of many microbiomes. Currently, no technology exists to rapidly assay individual microbiome, particularly with respect to metaproteomics. The RapidAIM project is transformative to the pharmaceutical and biotechnology development and microbiology field, as it allows for the rapid screening of candidate drugs against human microbiome before the drugs are commercialized, to screen current drugs for potential adverse microbiome effects, to stratify patients based on their response to drug treatment, and to screen compounds that would have efficacy across a wider population.

[0105] RapidAIM can be used to screen a panel of microbiomes derived from IBD and control patients in multi-well plates against selected xenobiotics. However, it will be understood that RapidAIM may also be used in the context of selected therapeutics, amino acids, and dietary supplements, etc. Assessment of the changes in the metaproteome upon treatment with any such compounds in the microbiota of healthy individuals or those associated with a disease other than inflammatory bowel disease may be similarly performed without departing from the present teachings. Biota-affectors can be selected by metagenomic ( 16< S-based sequencing) analysis of microbial composition changes and fast-pass metaproteomics to identify impacts on the top 1,500 most abundant proteins. Biota-altered compounds can be identified by metabolomics. Each multi-well plate takes approximately 2 days for screening and can identify compounds that either target specific microbes or group of microbes and / or their metabolic activities. Furthermore, this screening can be done to determine the effect of any compound upon the microbiome. The assay can be repeated on a reduced pool of compounds to generate functional metagenomics, metatranscriptomics and more in-depth metaproteomics (4000-5000 proteins / sample). A modeling algorithm can be used to rapidly guide selection of compounds based on the metaOMICS analyses, and pathway databases.

[0106] Developing RapidAIM in a multi-well plate format: microbiota can be inoculated and grown in culture media. Assays, performed in any multiwall format (e.g. 6 well to 96 well plate formats, or any other type of format, for example, using tubes) and can be titrated, examining at each stage whether the yield per well provides sufficient material for downstream analyses. The analysis can be performed using a workflow for metaproteome as described in Zhang et al. "MetaPro-IQ: a universal metaproteomic approach to studying human and mouse gut microbiota", Microbiome, 2016 Jun 24:4(1):31, doi: 10.1186 / s40168-016-0176-z. The workflow uses the close-to-complete human or mouse gut microbial gene catalog as a database and uses an iterative database search strategy. An example of a highthrough put experimental workflow for the RapidAIM has been established based on a 96-well format (Figure 9). This workflow includes, (A) 96-well based microbiome culturing, and (B) 96-well based metaproteomics analysis.

[0107] As shown in Figure 9, one of the steps in the workflow describes how "the SILAMi spike in can be added here", where the SILAMi spike may be optionally added. The preparation of the "SILAMi spike" or the "SILAMi Reference Standard" can be prepared as described herein. Alternatively, a representative superSILAMi standard for quantitative metaproteomics can be used. To prepare a superSILAMi standard, multiple SILAMi reference samples can be combined using strategic additions to the culture media and samples from many individuals. The superSILAMi reference can be used as a comprehensive quantitative spike-in for diverse samples.

[0108] The performance of RapidAIM may also involve parameter setting for time in order to measure (i) microbiota changes and (ii) generation of drug metabolites. These can be guided for example by current literature including from in vitro liver system drug metabolism tests

[13] . Briefly, microbiota can be inoculated and grown in basal culture media with or without compounds for different times (ranging from 30 min to 24 hrs), and samples collected for analyses.

[0109] The performance of RapidAIM may also involve parameter setting for the dosage of each compound in the pool which can be tested and pre-determined using the clinical dosage or reported concentrations for culturing as guidance. Microbiota from multiple individuals (including both male and female) can be used to negate inter-individual variability of intestinal microbiota.

[0110] As an example, RapidAIM was used to assay an individual a microbiome treated with a high, medium or low dose of berberine compared to the sample cultured without drug treatment (Figure 10). The taxonomic composition at the species level were quantified in each of the cultured microbiome sample on the MetaLab bioinformatics platform. Figure 10A shows the results from the loadings plot of a principal component analysis (PCA) with all bacterial species. Figure10B shows that the abundances of the species originating from the Akkermansia genus (identified in Figure10B as Akkermansia species 1, Akkermansia species 2 and Akkermansia species 3) were significantly increased when treated with high concentration of Berberine. Akkermansia spp has been reported to be beneficial bacterial in the gut microbiome, which has been shown to be increased by another antidiabetic drug, Metformin.References

[0111] 1. Qin, J., et al. Nature, 2010. 464 (7285), 59-65. 2. Clemente, J.C., et al. Cell, 2012. 148 (6), 1258-70. 3. Kelly, C.P. N Engl J Med, 2013. 368 (5), 474-5. 4. Verberkmoes, N.C., et al. ISME J, 2009. 3 (2), 179-89. 5. Juste, C., et al. Gut, 2014. 63 (10), 1566-77. 6. Jagtap, P., et al. J Proteomics, 2013. 13, 1352-1357. 7. Ong, S.E., et al. Mol Cell Proteomics, 2002. 1 (5), 376-86. 8. Krijgsveld, J., et al. Nat Biotechnol, 2003. 21 (8), 927-31. 9. Mueller, R. S., et al. Environ Microbiol, 2011. 13, 2279-2292. 10. Park, S.K., et al. Nat Methods, 2008. 5 (4), 319-22. 11. Mesuere, B., et al. J Proteome Res, 2012. 11 (12), 5773-80. 12. Stahl, M., et al. Proc Natl Acad Sci U S A, 2011. 108 (17), 7194-9. 13. Zhong, S. et al. Drug Metab Dispos, 2015.

Claims

1. A method of high throughput meta-omic analysis of a plurality of microbiota samples for effects of compounds on a microbiome of a target subject comprising: - providing a plurality of microbiota samples from an animal or a human being, - simultaneously culturing said microbiota samples; - treating said plurality of microbiota samples with selected compounds; - performing a screening using one or more of a metagenomic technique, metatranscriptomic technique, metaproteomic technique and metabolomic technique to identify expression changes in microbiomes of said microbiota samples following said treating; - selecting, based on the expression changes identified during said screening, said microbiomes exhibiting said predetermined expression changes; and - analyzing the selected microbiomes to characterize the changes to identify compounds that are biota-affectors for at least one of specific microbes, groups of microbes and metabolic activities of microbes, characterized in that the method further comprises, after said providing, spiking said plurality of microbiota culture samples with an isotope labelled standard corresponding to a given microbiota sample from the animal or the human being.

2. The method as defined in claim 1, wherein said provided plurality of microbiota samples are cultured in micro-well receptacles.

3. The method as defined in claim 2, wherein said provided plurality of microbiota samples are cultured in micro-well plates.

4. The method as defined in any one of claims 1 to 3, wherein said analyzing comprises using a microbial gene catalog of a given subject type and an iterative database search strategy.

5. The method as defined in any one of claims 1 to 4, wherein said analyzing comprises performing a metaproteomic analysis combined with a metagenomic analysis.

6. The method as defined in claim 4, wherein said microbial gene catalog of a given subject type is a microbial gene catalog of a human.

7. The method as defined in claim 4, wherein said microbial gene catalog of a given subject type is a microbial gene catalog of an animal.

8. The method as defined in claim 1, wherein said spiking comprises adding sufficient isotope labelled-standard to reach a 1:1 protein mass ratio with the protein contained in said plurality of microbiota samples.

9. The method as defined in any one of claims 1 to 8, wherein said performing a screening comprises performing metaproteomics.

10. The method as defined in any one of claims 1 to 9, wherein said compound is one of a food, a drug, a chemical, a therapeutic agent, a toxin, a poison, a beverage, a food additive, a cosmetic, a cosmetic ingredient, packaging material, a pesticide, a herbicide, a consumer product.