{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T04:43:55Z","timestamp":1791434635718,"version":"4.3.3"},"reference-count":69,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T00:00:00Z","timestamp":1607040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:p>Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.<\/jats:p>","DOI":"10.3389\/fmicb.2020.595910","type":"journal-article","created":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T05:30:15Z","timestamp":1607059815000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data"],"prefix":"10.3389","volume":"11","author":[{"given":"Xiaochen","family":"Yin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomer","family":"Altman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erica","family":"Rutherford","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiana A.","family":"West","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonggan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinlyung","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul L.","family":"Beck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gilaad G.","family":"Kaplan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karim","family":"Dabbagh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Todd Z.","family":"DeSantis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoko","family":"Iwai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2020,12,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-14-112","article-title":"A systematic comparison of the MetaCyc and KEGG pathway databases.","volume":"14","author":"Altman","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.3390\/ht7020009","article-title":"Recent advances in targeted and untargeted metabolomics by NMR and MS\/NMR methods.","volume":"7","author":"Bingol","year":"2018","journal-title":"High Throughput"},{"key":"B3","doi-asserted-by":"publisher","first-page":"2114","DOI":"10.1093\/bioinformatics\/btu170","article-title":"Trimmomatic: a flexible trimmer for Illumina sequence data.","volume":"30","author":"Bolger","year":"2014","journal-title":"Bioinformatics"},{"key":"B4","doi-asserted-by":"publisher","first-page":"16050","DOI":"10.1073\/pnas.1102999108","article-title":"Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve.","volume":"108","author":"Bravo","year":"2011","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"B5","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1038\/nmeth.3176","article-title":"Fast and sensitive protein alignment using DIAMOND.","volume":"12","author":"Buchfink","year":"2014","journal-title":"Nat. Methods"},{"key":"B6","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1038\/nmeth.3869","article-title":"DADA2: high-resolution sample inference from Illumina amplicon data.","volume":"13","author":"Callahan","year":"2016","journal-title":"Nat. Methods"},{"key":"B7","doi-asserted-by":"publisher","first-page":"D742","DOI":"10.1093\/nar\/gkr1014","article-title":"The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway\/genome databases.","volume":"40","author":"Caspi","year":"2011","journal-title":"Nucleic Acids Res."},{"key":"B8","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1038\/s41385-018-0022-7","article-title":"A screen of Crohn\u2019s disease-associated microbial metabolites identifies ascorbate as a novel metabolic inhibitor of activated human T cells.","volume":"12","author":"Chang","year":"2019","journal-title":"Mucosal Immunol."},{"key":"B9","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.2174\/138920111795909078","article-title":"How unbiased is non-targeted metabolomics and is targeted pathway screening the solution?","volume":"12","author":"Christians","year":"2011","journal-title":"Curr. Pharm. Biotechnol."},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0148028","article-title":"Comparing apples and oranges? Next generation sequencing and its impact on microbiome analysis.","volume":"11","author":"Clooney","year":"2016","journal-title":"PLoS One"},{"key":"B11","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1016\/j.ebiom.2019.05.063","article-title":"The path toward using microbial metabolites as therapies.","volume":"44","author":"Descamps","year":"2019","journal-title":"EBioMedicine"},{"key":"B12","doi-asserted-by":"publisher","first-page":"2460","DOI":"10.1093\/bioinformatics\/btq461","article-title":"Search and clustering orders of magnitude faster than BLAST.","volume":"26","author":"Edgar","year":"2010","journal-title":"Bioinformatics"},{"key":"B13","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1038\/s41564-018-0306-4","article-title":"Gut microbiome structure and metabolic activity in inflammatory bowel disease.","volume":"4","author":"Franzosa","year":"2019","journal-title":"Nat. Microbiol."},{"key":"B14","doi-asserted-by":"publisher","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","article-title":"CD-HIT: accelerated for clustering the next-generation sequencing data.","volume":"28","author":"Fu","year":"2012","journal-title":"Bioinformatics"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.3390\/ijms17060816","article-title":"Applications of fourier transform ion cyclotron resonance (FT-ICR) and orbitrap based high resolution mass spectrometry in metabolomics and lipidomics.","volume":"17","author":"Ghaste","year":"2016","journal-title":"Int. J. Mol. Sci."},{"key":"B16","doi-asserted-by":"publisher","first-page":"8226","DOI":"10.1021\/ac1016612","article-title":"Does the mass spectrometer define the marker? A comparison of global metabolite profiling data generated simultaneously via UPLC-MS on two different mass spectrometers.","volume":"82","author":"Gika","year":"2010","journal-title":"Anal. Chem."},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2019.00647","article-title":"Untargeted metabolomics for autism spectrum disorders: current status and future directions.","volume":"10","author":"Glinton","year":"2019","journal-title":"Front. Psychiatry"},{"key":"B18","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.ymeth.2018.04.024","article-title":"Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer.","volume":"149","author":"Hale","year":"2018","journal-title":"Methods"},{"key":"B19","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.chom.2014.08.014","article-title":"The integrative human microbiome project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease.","volume":"16","year":"2014","journal-title":"Cell Host Microbe"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0166104","article-title":"Piphillin: improved prediction of metagenomic content by direct inference from human microbiomes.","volume":"11","author":"Iwai","year":"2016","journal-title":"PLoS One"},{"key":"B21","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1038\/nrd2505","article-title":"Gut microbiota: a potential new territory for drug targeting.","volume":"7","author":"Jia","year":"2008","journal-title":"Nat. Rev. Drug Discov."},{"key":"B22","doi-asserted-by":"publisher","first-page":"D109","DOI":"10.1093\/nar\/gkr988","article-title":"KEGG for integration and interpretation of large-scale molecular data sets.","volume":"40","author":"Kanehisa","year":"2011","journal-title":"Nucleic Acids Res."},{"key":"B23","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.anaerobe.2017.12.007","article-title":"Differences in fecal microbial metabolites and microbiota of children with autism spectrum disorders.","volume":"49","author":"Kang","year":"2018","journal-title":"Anaerobe"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1093\/bib\/bbx085","article-title":"The BioCyc collection of microbial genomes and metabolic pathways.","volume":"20","author":"Karp","year":"2017","journal-title":"Brief. Bioinform."},{"key":"B25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.aca.2018.05.031","article-title":"A review on human fecal metabolomics: methods, applications and the human fecal metabolome database.","volume":"1030","author":"Karu","year":"2018","journal-title":"Anal. Chim. Acta"},{"key":"B26","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.1093\/bioinformatics\/bts611","article-title":"SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data.","volume":"28","author":"Kopylova","year":"2012","journal-title":"Bioinformatics"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1038\/s41564-019-0491-9","article-title":"Modelling approaches for studying the microbiome.","volume":"4","author":"Kumar","year":"2019","journal-title":"Nat. Microbiol."},{"key":"B28","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/nmeth.1923","article-title":"Fast gapped-read alignment with Bowtie 2.","volume":"9","author":"Langmead","year":"2012","journal-title":"Nat. Methods"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1186\/2042-5783-1-4","article-title":"Predicted Relative Metabolomic Turnover (PRMT): determining metabolic turnover from a coastal marine metagenomic dataset.","volume":"1","author":"Larsen","year":"2011","journal-title":"Microb. Inform. Exp."},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.3033","article-title":"Sets and intersections.","volume":"11","author":"Lex","year":"2014","journal-title":"Nat. Methods"},{"key":"B31","doi-asserted-by":"publisher","first-page":"3425","DOI":"10.1039\/C6MB00339G","article-title":"An LC-MS based untargeted metabolomics study identified novel biomarkers for coronary heart disease.","volume":"12","author":"Li","year":"2016","journal-title":"Mol. Biosyst."},{"key":"B32","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","article-title":"Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences.","volume":"22","author":"Li","year":"2006","journal-title":"Bioinformatics"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1172\/jci.insight.99096","article-title":"Untargeted metabolomics identifies trimethyllysine, a TMAO-producing nutrient precursor, as a predictor of incident cardiovascular disease risk.","volume":"3","author":"Li","year":"2018","journal-title":"JCI Insight"},{"key":"B34","doi-asserted-by":"publisher","first-page":"14424","DOI":"10.1021\/acs.analchem.9b02948","article-title":"Strategy for intercorrelation identification between metabolome and microbiome.","volume":"91","author":"Liang","year":"2019","journal-title":"Anal. Chem."},{"key":"B35","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1038\/s41586-019-1237-9","article-title":"Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases.","volume":"569","author":"Lloyd-Price","year":"2019","journal-title":"Nature"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-014-0550-8","article-title":"Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.","volume":"15","author":"Love","year":"2014","journal-title":"Genome Biol."},{"key":"B37","doi-asserted-by":"publisher","first-page":"e1343","DOI":"10.1128\/mBio.01343-17","article-title":"Impact of dietary resistant starch on the human gut microbiome, metaproteome, and metabolome.","volume":"8","author":"Maier","year":"2017","journal-title":"mBio"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-10927-1","article-title":"Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences.","volume":"10","author":"Mallick","year":"2019","journal-title":"Nat. Commun."},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-015-0610-8","article-title":"MUSiCC: a marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome.","volume":"16","author":"Manor","year":"2015","journal-title":"Genome Biol."},{"key":"B40","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1038\/s41575-019-0188-9","article-title":"Multi-omics in IBD biomarker discovery: the missing links.","volume":"16","author":"Metwaly","year":"2019","journal-title":"Nat. Rev. Gastroenterol. Hepatol."},{"key":"B41","year":"2020","journal-title":"MIMOSA2."},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2019.00185","article-title":"Intestinal bacteria interplay with bile and cholesterol metabolism: implications on host physiology.","volume":"10","author":"Molinero","year":"2019","journal-title":"Front. Physiol."},{"key":"B43","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.1038\/s41592-019-0616-3","article-title":"Learning representations of microbe\u2013metabolite interactions.","volume":"16","author":"Morton","year":"2019","journal-title":"Nat. Methods"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-020-6537-9","article-title":"Piphillin predicts metagenomic composition and dynamics from DADA2- corrected 16S rDNA sequences.","volume":"21","author":"Narayan","year":"2020","journal-title":"BMC Genomics"},{"key":"B45","doi-asserted-by":"publisher","first-page":"e00013","DOI":"10.1128\/mSystems.00013-15","article-title":"Microbiome taxonomic and metabolomic profiles elucidates mechanistic links between ecological and metablic variations.","volume":"1","author":"Noecker","year":"2016","journal-title":"mSystems"},{"key":"B46","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/j.trac.2016.06.011","article-title":"Increasing selectivity and coverage in LC-MS based metabolome analysis.","volume":"82","author":"Ortmayr","year":"2016","journal-title":"TrAC Trends Anal. Chem."},{"key":"B47","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.jpba.2014.12.020","article-title":"Emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery.","volume":"107","author":"Patel","year":"2015","journal-title":"J. Pharm. Biomed. Anal."},{"key":"B48","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s004420100720","article-title":"How well do multivariate data sets match? The advantages of a procrustean superimposition approach over the Mantel test.","volume":"129","author":"Peres-Neto","year":"2001","journal-title":"Oecologia"},{"key":"B49","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-019-3176-8","article-title":"AMON: annotation of metabolite origins via networks to integrate microbiome and metabolome data.","volume":"20","author":"Shaffer","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"B50","doi-asserted-by":"publisher","first-page":"1600","DOI":"10.1016\/j.cell.2019.05.004","article-title":"Human gut microbiota from autism spectrum disorder promote behavioral symptoms in mice.","volume":"177","author":"Sharon","year":"2019","journal-title":"Cell"},{"key":"B51","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1038\/s41586-019-1865-0","article-title":"Microbial bile acid metabolites modulate gut ROR\u03b3 + regulatory T cell homeostasis.","volume":"577","author":"Song","year":"2019","journal-title":"Nature"},{"key":"B52","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1093\/infdis\/jix680","article-title":"Respiratory syncytial virus and rhinovirus bronchiolitis are associated with distinct metabolic pathways.","volume":"217","author":"Stewart","year":"2018","journal-title":"J. Infect. Dis."},{"key":"B53","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1038\/s41564-018-0307-3","article-title":"GABA-modulating bacteria of the human gut microbiota.","volume":"4","author":"Strandwitz","year":"2019","journal-title":"Nat. Microbiol."},{"key":"B54","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1038\/nmeth.2693","article-title":"Metagenomic species profiling using universal phylogenetic marker genes.","volume":"10","author":"Sunagawa","year":"2013","journal-title":"Nat. Methods"},{"key":"B55","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1021\/ac302919t","article-title":"Comparative analysis of sample preparation methods to handle the complexity of the blood fluid metabolome: when less is more.","volume":"85","author":"Tulipani","year":"2013","journal-title":"Anal. Chem."},{"key":"B56","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1016\/j.cell.2008.08.025","article-title":"An invitation to the marriage of metagenomics and metabolomics.","volume":"134","author":"Turnbaugh","year":"2008","journal-title":"Cell"},{"key":"B57","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1038\/nature06244","article-title":"The human microbiome project.","volume":"449","author":"Turnbaugh","year":"2007","journal-title":"Nature"},{"key":"B58","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1002\/elps.201600366","article-title":"Recent advances in CE-MS coupling: instrumentation, methodology, and applications.","volume":"38","author":"T\u00fd\u010dov\u00e1","year":"2017","journal-title":"Electrophoresis"},{"key":"B59","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1016\/j.nut.2015.05.006","article-title":"TMAO: a small molecule of great expectations.","volume":"31","author":"Ufnal","year":"2015","journal-title":"Nutrition"},{"key":"B60","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.diabet.2018.02.006","article-title":"Non-targeted metabolomic biomarkers and metabotypes of type 2 diabetes: a cross-sectional study of PREDIMED trial participants.","volume":"45","author":"Urpi-Sarda","year":"2019","journal-title":"Diabetes Metab."},{"key":"B61","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.atherosclerosis.2013.10.013","article-title":"Gut microbiota metabolism of l-carnitine and cardiovascular risk.","volume":"231","author":"Ussher","year":"2013","journal-title":"Atherosclerosis"},{"key":"B62","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.tplants.2016.01.008","article-title":"Metabolomics in the rhizosphere: tapping into belowground chemical communication.","volume":"21","author":"van Dam","year":"2016","journal-title":"Trends Plant Sci."},{"key":"B63","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1146\/annurev-pathol-020117-043559","article-title":"Microbial contribution to the human metabolome: implications for health and disease.","volume":"15","author":"Van Treuren","year":"2020","journal-title":"Annu. Rev. Pathol. Mech. Dis."},{"key":"B64","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1007\/s00216-012-6039-y","article-title":"Current trends and challenges in sample preparation for global metabolomics using liquid chromatography\u2013mass spectrometry.","volume":"403","author":"Vuckovic","year":"2012","journal-title":"Anal. Bioanal. Chem."},{"key":"B65","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1038\/nrd.2016.32","article-title":"Emerging applications of metabolomics in drug discovery and precision medicine.","volume":"15","author":"Wishart","year":"2016","journal-title":"Nat. Rev. Drug Discov."},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2014-15-3-r46","article-title":"Kraken: ultrafast metagenomic sequence classification using exact alignments.","volume":"15","author":"Wood","year":"2014","journal-title":"Genome Biol."},{"key":"B67","doi-asserted-by":"publisher","DOI":"10.1186\/s40168-019-0767-6","article-title":"Advancing functional and translational microbiome research using meta-omics approaches.","volume":"7","author":"Zhang","year":"2019","journal-title":"Microbiome"},{"key":"B68","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aau8317","article-title":"The gut microbiome from patients with schizophrenia modulates the glutamate-glutamine-GABA cycle and schizophrenia-relevant behaviors in mice.","volume":"5","author":"Zheng","year":"2019","journal-title":"Sci. Adv."},{"key":"B69","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1038\/s41588-018-0135-7","article-title":"The fecal metabolome as a functional readout of the gut microbiome.","volume":"50","author":"Zierer","year":"2018","journal-title":"Nat. Genet."}],"container-title":["Frontiers in Microbiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmicb.2020.595910\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T05:30:18Z","timestamp":1607059818000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmicb.2020.595910\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,4]]},"references-count":69,"alternative-id":["10.3389\/fmicb.2020.595910"],"URL":"https:\/\/doi.org\/10.3389\/fmicb.2020.595910","relation":{},"ISSN":["1664-302X"],"issn-type":[{"value":"1664-302X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,4]]},"article-number":"595910"}}