Gut microorganisms modulate host phenotypes and are associated with numerous health effects in humans, ranging from host responses to cancer immunotherapy to metabolic disease and obesity. However, difficulty in accurate and high-throughput functional analysis of human gut microorganisms has hindered efforts to define mechanistic connections between individual microbial strains and host phenotypes. One key way in which the gut microbiome influences host physiology is through the production of small molecules1,2,3, yet progress in elucidating this chemical interplay has been hindered by limited tools calibrated to detect the products of anaerobic biochemistry in the gut. Here we construct a microbiome-focused, integrated mass-spectrometry pipeline to accelerate the identification of microbiota-dependent metabolites in diverse sample types. We report the metabolic profiles of 178 gut microorganism strains using our library of 833 metabolites. Using this metabolomics resource, we establish deviations in the relationships between phylogeny and metabolism, use machine learning to discover a previously undescribed type of metabolism in Bacteroides, and reveal candidate biochemical pathways using comparative genomics. Microbiota-dependent metabolites can be detected in diverse biological fluids from gnotobiotic and conventionally colonized mice and traced back to the corresponding metabolomic profiles of cultured bacteria. Collectively, our microbiome-focused metabolomics pipeline and interactive metabolomics profile explorer are a powerful tool for characterizing microorganisms and interactions between microorganisms and their host.
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All raw data from metabolomics are publicly available from the Metabolomics Workbench under study number ST001683 for in vivo data and study number ST001688 for in vitro data. MS/MS libraries generated using the qTOF and QE instruments are publicly accessible in the MoNA spectrum database (https://mona.fiehnlab.ucdavis.edu) and can be queried using the keywords ‘Sonnenburg Lab MS2 Library’.
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We thank C. Khosla, Y. Dai and the Stanford ChEM-H Metabolomics Knowledge Center for use of the LC–MS-qTOF instrument; A. Shiver, K. C. Huang and A. Cheng for sharing bacterial strains; T. Meyers and T. Cowan for sharing chemical standards; H. C. Wastyk and G. K. Fragiadakis for sharing their metabolite data before publication; J. K. Yang for consultation on web design tools; and T. Le for discussion on MS methods. This work is supported by R01-DK085025, DP1-AT009892, R01-DK101674, gifts from M. and J. Pasquesi, H. Buhr and J. Feiber, a Stanford Discovery Innovation Fund Award (J.L.S.), Chan Zuckerberg Biohub (M.A.F. and J.L.S.), DP1-DK113598 and P01-HL147823 (M.A.F.), K08DK110335 (D.D.), Stanford Dean’s Postdoctoral Fellowship and NRSA F32AG062119 (S.H.), NSF-GRFP DGE-114747 (W.V.T.) and 5T32AI007328-32 (L.G.).
M.A.F. is a co-founder and director of Federation Bio and Viralogic, a co-founder of Revolution Medicines, and a member of the scientific advisory boards of NGM Bio and Zymergen.
Peer review information Nature thanks Gary Siuzdak and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Summary statistics for the MS reference library metabolites, their detection and validation.
a, Chemical similarity network of the compound library. Network nodes, library compounds coloured by their superclasses. Node size, monoisotopic mass. Edges between nodes, substructure similarity values above a z-score threshold of 1 s.d. from the mean. b, Scatter plots and histograms of chemical properties of 833 library metabolites. c, Venn diagram of library compounds that are detected by each of the three methods. d, Venn diagram of compounds (by PubChem CID) identified in the reference compound library (Supplementary Table 1), in vitro conditions (Supplementary Table 7, ‘count.ps’) and in vivo conditions (Supplementary Table 8, ‘istd_corr_ion_count_matrix’). In vitro conditions include all medium types, and in vivo conditions include all sample types: urine, serum, faeces and caecal contents, and all colonization states. e, Scatterplot of all pairwise similarity scores (biological sample versus library) of the same compound searched against the MoNA spectrum database. All library standards (median similarity score = 992) and 97.3% of the corresponding compounds from biological samples (median similarity score = 923) had similarity scores of ≥600, and 2.7% of those compounds from biological samples scored below 600. Confidence levels were determined based on both similarity scores and visual validation of the MS/MS spectra. f, Schematic of the data collection and analysis workflow of the metabolomics pipeline. Panel created with Biorender.com.
Extended Data Fig. 2 Schematic of a custom bioinformatics analysis pipeline that generates a metabolite fold-change matrix.
The pipeline integrates data across multiple experimental runs and minimizes intra-replicate, intra-experiment and inter-experiment variability. The four steps detailed here are explained in depth in the Supplementary Methods (see ‘Custom bioinformatics: in vitro pipeline’ section). Step 1, a database recording sample metadata (organism, media, growth data, and so on) and MS-DIAL output files are integrated into data matrices that are specific to each analytical method. Step 2, all data are grouped by replicate (biological sample groups (BSGs)) and analysed to remove replicates with low intra-replicate correlation. Replicates are then grouped by experiment (EXPs) to assess inter-experiment variability. Transformations reducing inter-experiment variability are identified and compared. For metabolites that are detected by multiple methods, their ion counts are compared on a per-replicate and per-experiment basis to identify one or more methods that consistently detect these metabolites. Step 3, using an internal standard-based correction, ion counts for individual samples are adjusted and transformed into different fold-change data matrices. Step 4, data matrices corresponding to each method are combined into a single data matrix representing all detected metabolites.
Extended Data Fig. 3 High-throughput identification and analysis of diverse metabolites in complex biological matrices.
a, Number of unique compounds (by PubChem CID) within distinct chemical superclasses detected in the m/z-RT reference library (n = 815, 11 superclasses), in vitro dataset (n = 458, 9 superclasses) or in vivo dataset (n = 551, 9 superclasses), excluding internal standards. Nine of the eleven chemical superclasses in the reference library are represented in the metabolites detected in vitro and in vivo. The two remaining library superclasses (organosulfur and organometallic compounds) not represented in the experimental data contain one compound each. b, Diverse classes of metabolites identified in the conventional mouse caecum. Representative metabolites shown are significantly elevated (≥4-fold, corrected P < 0.05) in conventional mice versus germ-free controls in one experiment with n = 3 (conventional) and n = 4 (germ-free) mice. P values were calculated using two-tailed Student’s t-tests with Benjamini–Hochberg correction for multiple comparisons. c, Examples of precursors, intermediates and products from the tryptophan fermentation pathway that were identified by our methods both in vitro (C. sporogenes culture supernatant) and in vivo (C. sporogenes mono-colonization caecal contents). Extracted ion chromatogram peaks representing relative ion counts for each metabolite are shown. d, e, Histograms of changes in RT (d) and total ion count (e) for 132 spike-in metabolites in five complex biological matrices using three analytical methods. All spiked-in metabolites show minimal change in RT, falling within a conservative ±0.1-min search window from their RTs as determined in the library control condition (d). The majority of spiked-in metabolites (for example, 97% in faeces) exhibit less than fourfold change in ion counts relative to those detected in the library control condition (e). Representative examples of RT shifts (d) and changes in total ion counts (e) in individual metabolites in the mouse faecal matrix are shown. Data are mean ± s.e.m. of one experiment with n = 3 biological replicates. f, Histograms of linear ranges of 377 reference library metabolites measured in serial dilutions. A representative linear range of 5-hydroxyindole is shown. g, Violin plots (median, quartiles) of differences in RTs measured by three analytical methods between distinct MS instruments: the qTOF 6454, with which the library was built, was compared with a second instrument: a qTOF 6530 for a shared panel of 219 reference library metabolites (top) or a Orbitrap QE for a shared panel of 773 reference library metabolites (bottom). Mean RT differences (in min) between two instruments by each method (C18-positive, C18-negative, HILIC-positive, respectively) were as follows: qTOF versus qTOF, pre-correction: 0.238, 0.044, −0.110; post-correction: −0.023, −0.020, 0.015; qTOF versus QE, pre-correction: 0.151, 0.027, 0.196; post-correction: −0.040, −0.021, 0.026). Per method, RT correction was performed by polynomial transformation of the library based on inter-instrumental RT shifts of 10–20 robustly detected metabolites. Per method, using the corrected library with a RT tolerance window of 0.2 min, around 99% of the 219 metabolites tested on the second qTOF and about 94% of the 773 metabolites tested on the QE were correctly identified.
Extended Data Fig. 4 Conserved and unique metabolomic signatures across bacterial taxa.
a, Schematic of our high-throughput bacterial culture and sample collection workflow. Panel created with Biorender.com. b, Intra-replicate Pearson correlation coefficients (triplicates and greater) stratified by fourteen independent bacterial culture experiments and three analytical methods. For each experiment, Pearson correlation r values were calculated for all supernatant and medium sample replicate groups: n = 346 (C18-positive), n = 344 (C18-negative) and n = 344 (HILIC-positive). Total ion count data were corrected by internal standards and log-transformed, standardized and scaled, before computing Pearson correlation values. Box, median, 25th and 75th percentiles; whiskers, Tukey’s method. c, Left, number of medium-specific or common metabolites detected in the same bacterial strain grown in two different media (29 strains cultured in two or more of the 12 different media). Each dot represents the total number of metabolites from a single comparison between two media in which a strain has been grown: n = 58 (co-detected in two media), n = 116 (detected in one of the two media), n = 33 (detected in the mega medium) and n = 16 (detected in polyamine-free medium). Box, median, 25th and 75th percentiles; whiskers, minimum and maximum. Right, agmatine production levels by B. eggerthii. Data are mean ± s.e.m. from 2–3 independent experiments, each with n = 3 biological replicates. P values, two-tailed t-test with Benjamini–Hochberg correction for multiple comparisons. d, Heat map of metabolomic profiles of 158 bacterial strains grown in mega medium, clustered by 16S phylogenetic distance. Individual metabolites are hierarchically clustered (Ward’s method) using Euclidean distance between the fold-change (log2-transformed) values across all taxonomies. Metabolites shown are detected in at least 50% of the 158 taxonomies to enable Ward clustering. e, f, Production or consumption patterns of tyramine and pantothenic acid across 158 strains grown in mega medium. Data are mean ± s.e.m. from 1–3 independent experiments (identified by dot colour), each with n ≥ 3 biological replicates.
Extended Data Fig. 5 Metabolic profile variation among related bacteria.
a, Pairwise metabolomic profile comparisons between two closely related strains grown in mega medium: C. sporogenes ATCC 15579 and C. cadaveris HM-1039 (subpanel 1), and among four strains of Bacteroides fragilis (subpanels 2–7): HM-710, HM-711, HM-714 and HM-20. Each dot represents an averaged fold-change value (log2-transformed) from 1–3 independent experiments, each with n = 3 biological replicates. Pearson correlation r values of pairwise metabolomic profile comparisons, performed on standardized and scaled data: ATCC 15579 versus HM-1039 (r = 0.063), HM-711 versus HM-710 (r = 0.859), HM-714 versus HM-710 (r = 0.866), HM-714 versus HM-711 (r = 0.880), HM-20 versus HM-710 (r = 0.829), HM-20 versus HM-711 (r = 0.845) and HM-20 versus HM-714 (r = 0.807). b, Metabolic similarities and variations among closely related species of C. sporogenes and C. cadaveris, and among different strains of the same species of B. fragilis grown in mega medium. Taxonomies shown are clustered by 16S phylogenetic distance, and are coloured according to the distinct phyla. Data are mean ± s.e.m. from 1–3 independent experiments, each with n = 3 biological replicates.
Extended Data Fig. 6 Relationships between phylogeny, taxonomy and metabolome.
a, Metabolomic profiles of 158 bacterial strains grown in mega medium. Individual taxonomies are clustered by metabolomic profile distances (fold change, log2-transformed) across all metabolites. Individual metabolites are hierarchically clustered (Ward’s method) using Euclidean distance between the fold-change (log2-transformed) values across all taxonomies. Metabolites shown are detected in at least 50% of the 158 taxonomies to enable Ward clustering. b, Metabolic similarities between two phylogenetically distant species grown in mega medium. Taxonomies are clustered by metabolomic profile distances (fold change, log2-transformed) across all metabolites. Data are mean ± s.e.m. of one experiment with n = 3 biological replicates. c, Scatter plot of pairwise metabolomic profile comparison between two phylogenetically distant species. Each dot represents an averaged fold-change value (log2-transformed) of one experiment with n = 3 biological replicates. Pearson correlation of pairwise metabolomic profile comparison between these two species, performed on standardized and scaled fold-change data, r = 0.7090. d, Venn diagram of unique and overlapping compounds (by PubChem CID) identified in the culture supernatant of 158 mega-medium grown strains and caecal contents of conventional mice.
Extended Data Fig. 7 Multiple data transformations identify nonlinear relationship between phylogenetic and metabolomic distance.
a, Heat map showing the comparison of phylogenetic and metabolomic tree topologies. Cells record the number of tips for which the neighbourhoods share more overlap than expected (P < 0.05; one-sided permutation test). Data are stratified by fractional overlap of neighbourhoods and permutation probability (see Supplementary Methods, ‘Distance comparisons’). b, Histogram of chemical similarity scores (based on Tanimoto 2D structures) between each unique pair of compounds (by PubChem CID) detected in the in vitro dataset. For this pairwise comparison, 359 non-co-eluting compounds were used. c, Metabolomic distance tree with each metabolite weighted based on their chemical similarity (left) or unweighted control metabolomic distance tree (right). The weighted and unweighted matrices were calculated using uniquely detected, non-co-eluting compounds in the in vitro dataset, for which a unique PubChem CID identifier can be assigned to each compound. Two-sided Mantel test for comparison between the weighted and unweighted distance matrices: r2 = 0.863, P = 0.001. d, Left, correlation of phylogenetic and metabolomic distance across pairs of strains coloured by lowest shared taxonomic rank with a LOESS fit shown. Dashed vertical line occurs at x = 0.11 as referenced in the text. Right, Metabolomic distance between pairs of strains binned by the lowest shared taxonomic rank. Species (n = 111), genus (n = 1,386), family (n = 159), order (n = 1,222), class (n = 34), phylum (n = 1,442) and kingdom (n = 8,442). Box, median, 25th and 75th percentiles; whiskers, Tukey’s method. e–i, Internal-standard-corrected fold-change data (e–g) and internal-standard-corrected total ion count data (h, i) were log-transformed and used to calculate pairwise metabolomic distances between microbial taxa. These distances were compared to the corresponding pairwise phylogenetic distances generated from a tree built with the V4 region of 16S (left) or the full-length 16S gene (right). Data are plotted with a LOESS fit. Set 1, microorganisms grown in at least one experiment simultaneously. Set 2, microorganisms grown in the same experiment only. j, Phylogenetic tree constructed using the full 16S sequences of a subset of the strains grown in mega medium. Only strains with available full 16S sequences are shown (Supplementary Table 6). k, Left, schematic of the pathway that synthesizes citrulline and ornithine, or synthesizes agmatine and/or putrescine. Right, the top six matches identified by the comparative genomics tool MultiGeneBlast within a 40-kb search window, when searched against a genomic database of our strain collection with sequenced genomes. Horizontal dashed lines between genes represent multiple other genes present within the search window.
Extended Data Fig. 8 Asparagine and glutamine can be used as sole nitrogen sources by most tested Bacteroidetes.
a, Top, an example decision tree from a forest that can differentiate Bacteroidetes versus bacteria from the other four represented phyla with >97% accuracy. For each decision node, phylum-level increases and decreases based on metabolite levels are shown (relative fold change compared to the bacterial medium controls, log2-transformed). Actinobacteria (n = 20), Bacteroidetes (n = 57), Firmicutes (n = 83), Fusobacteria (n = 3) and Proteobacteria (n = 10). Dashed line, metabolite threshold. Box, median, 25th and 75th percentiles; whiskers: Tukey’s method. Bottom, the 10 most important features differentiating the five tested phyla. Data are shown as median metabolite log2-fold-change values for each phylum; metabolites and phyla are ordered by Ward linkage distance. b, Representative growth curves from two independent experiments, each with n = 3 biological replicates for a subset of Bacteroides spp. using modified SMM with the indicated nitrogen source. Legend colours for the sole nitrogen source are the same in b–d. c, Representative growth curves of one experiment with n = 5 biological replicates for 60 Bacteroidetes using modified SMM with the indicated nitrogen sources. d, Growth curves of wild-type and mutant B. thetaiotaomicron (Bt) grown in defined minimal media with either cysteine (top) (one experiment, n = 3 biological replicates) or sodium sulfide (Na2S, bottom) as sole reduced sulfur sources (one experiment, n = 3 biological replicates). e, Amino acid production and consumption levels in gnotobiotic mice mono-colonized with B. thetaiotaomicron (one experiment, n = 5 mice). Box, median, 25th and 75th percentiles; whiskers, Tukey’s method. Numeric labels in b and c correspond to the following: 1, B. acidifaciens DSMZ 15896; 2, B. caccae ATCC 43185; 3, B. caccae BEI HM-728; 4, B. cellulosilyticus BEI HM-726; 5, B. cellulosilyticus DSMZ 14838; 6, B. coprophilus DSMZ 18228; 7, B. dorei BEI HM-29; 8, B. dorei BEI HM-717; 9, B. dorei BEI HM-718; 10, B. dorei BEI HM-719; 11, B. dorei DSMZ 17855; 12, B. eggerthii ATCC 27754; 13, B. eggerthii DSMZ 20697; 14, B. finegoldii BEI HM-727; 15, B. finegoldii DSMZ 17565; 16, B. fragilis BEI HM-20; 17, B. fragilis BEI HM-710; 18, B. fragilis BEI HM-711; 19, B. fragilis BEI HM-714; 20, B. fragilis NCTC 9343; 21, B. intestinalis DSMZ 17393; 22, B. ovatus ATCC 8483; 23, B. ovatus BEI HM-222; 24, B. pectinophilus ATCC 43243; 25, B. plebeius DSMZ 17135; 26, B. salyersiae BEI HM-725; 27, Bacteroides sp. BEI HM-18; 28, Bacteroides sp. BEI HM-189; 29, Bacteroides sp. BEI HM-19; 30, Bacteroides sp. BEI HM-22; 31, Bacteroides sp. BEI HM-23; 32, Bacteroides sp. BEI HM-258; 33, Bacteroides sp. BEI HM-27; 34, Bacteroides sp. BEI HM-28; 35, Bacteroides sp. BEI HM-58; 36, B. stercoris ATCC 43183; 37, B. stercoris BEI HM-1036; 38, B. thetaiotaomicron 3730; 39, B. thetaiotaomicron 3731; 40, B. thetaiotaomicron 633; 41, B. thetaiotaomicron 7330; 42, B. thetaiotaomicron 7853; 43, B. thetaiotaomicron 8702; 44, B. thetaiotaomicron 8713; 45, B. thetaiotaomicron 8736; 46, B. thetaiotaomicron 940; 47, B. thetaiotaomicron VPI 5482; 48, B. thetaiotaomicron WH302; 49, B. thetaiotaomicron WH305; 50, B. uniformis ATCC 8492; 51, B. vulgatus ATCC 8482; 52, B. vulgatus BEI HM-720; 53, B. xylanisolvens DSMZ 18836; 54, P. distasonis ATCC 8503; 55, P. distasonis BEI HM-169; 56, P. johnsonii BEI HM-731; 57, P. johnsonii DSMZ 18315; 58, P. merdae ATCC 43184; 59, P. merdae BEI HM-729; 60, P. merdae BEI HM-730.
Extended Data Fig. 9 Metabolic contribution by individual gut microorganisms in a multi-species community.
a, α-Ketoglutaric acid levels in faeces of mice mono-colonized with Anaerostipes sp. BEI HM-220. Data are mean ± s.e.m. of two independent experiments, each with n = 4 mice (germ-free) or n = 5 or 7 mice (Anaerostipes mono-colonized). b, Left, MDMs were associated with specific bacterial phyla leveraging both in vivo and in vitro metabolomic data. Right, number of bacterial strains grown in mega medium by phylum that produce MDMs identified in the caecal contents of mice colonized with B. thetaiotaomicron (Bt, n = 5) or C. sporogenes (Cs, n = 3), or with a six-member community (n = 3). Numbers of strains that produce at least one of these metabolites in vitro by phylum: Bacteroidetes, n = 52; Firmicutes, n = 60; Proteobacteria, n = 8; Actinobacteria, n = 16; and Fusobacteria, n = 3. Each metabolite shown was significantly produced both in vitro and in vivo (≥4-fold, corrected P < 0.05). Uniquely detected (non-co-eluting) metabolites are shown (Supplementary Table 9). c, Spearman correlation between metabolomic profiles (standardized and scaled, log2-transformed, fold-change data) of individual B. thetaiotaomicron- or C. sporogenes-mono-colonizesd host biofluids (caecal contents, faeces, serum or urine) and individual bacterial culture (158 strains grown in mega medium). Coloured dots, Spearman’s ρ values calculated by comparing metabolomic profiles of individual bacterial culture versus individual biofluid of either B. thetaiotaomicron- or C. sporogenes-mono-colonized mice. Black dots, Spearman’s ρ calculated using metabolomic profiles of B. thetaiotaomicron or C. sporogenes, the same strains used for mono-colonization in mice. d, Venn diagram of overlapping metabolites that are significantly produced (≥4-fold, corrected P < 0.05) in culture and in the caecum of colonized mice. e, Principal component analysis separates metabolomic profiles of identified metabolites by sample type in each colonization state. P values on metabolomic profile comparisons between different sample types of the same colonization state were determined using PERMANOVA: six-member community (P = 0.073) and all other colonization states (P = 0.001). f, Principal component analysis separates metabolomic profiles of identified metabolites by colonization states. P values on metabolomic profile comparisons between different colonization states of the same sample type were determined using PERMANOVA: P = 0.001 for all four sample types. g, h, Example chemical structures of significantly produced metabolites (≥4-fold, corrected P < 0.05) in serum (g) or urine (h) by each colonization state corresponding to Fig. 4b. a, b, d, g, h, P values were determined using two-tailed Student’s t-tests with Benjamini–Hochberg correction for multiple comparisons.
Extended Data Fig. 10 Metabolic contribution of multi-species communities in gnotobiotic mice.
a, Proposed host–microbial co-metabolism pathways that could lead to the synthesis of specific host–microbial co-metabolites in the urine and serum of mice colonized with the six-member community. b, c, Metabolite levels in urine (b) and caecal contents (c) of mice colonized with the six-member community (+Cs) or the five-member community (−Cs). Metabolites shown represent a panel of significantly elevated or reduced metabolites (≥4-fold, corrected P < 0.05) in the six-member community. Superscript ‘1’ in metabolite names, co-eluting metabolites as annotated in the MS reference library (Supplementary Table 1). Superscript ‘2’ in metabolite names, co-eluting isomeric metabolites with truncated names in the figure (2-hydroxy-3-methylpentanoic acid, 2-hydroxy-4-methylpentanoic acid; and α-galactose 1-phosphate, α-glucose 1-phosphate, glucose-6-phosphate, mannose 6-phosphate). Data are mean ± s.e.m. of one experiment with n = 6 (urine, six-member community), n = 7 (urine, five-member community) and n = 3 (caecal, both six-member and five-member communities). b, c, P values were calculated using two-tailed Student’s t-tests with Benjamini–Hochberg correction for multiple comparisons. *P < 0.05, **P < 0.01, ***P < 0.001. b, Venn diagram (right) of significantly elevated and reduced metabolites in individual host biofluids (caecal contents, serum and urine) using the same threshold in b (left).
This file contains Supplementary Methods and a full guide to Supplementary Tables 1-9. The Supplementary Methods include mass spectrometry LC/MS methods, data analysis, bacterial sequencing and phylogenetics, distance comparisons and classifiers, the Mega Medium (MM) preparation protocol and Salyer’s Minimal Medium (SMM) preparation protocol.
Supplementary Table 1
Mass spectrometry compound m/z-RT reference library.
Supplementary Table 2
MS/MS validation of biological metabolites and library standards, matrix effects on ion count and RT, and linear range of individual metabolites.
Supplementary Table 3
MS/MS spectra library constructed on qTOF and QE instruments.
Supplementary Table 4
Inter-instrumental retention time shift and correction.
Supplementary Table 5
In vitro sample metadata.
Supplementary Table 6
Culture and strain information.
Supplementary Table 7
In vitro data matrices.
Supplementary Table 8
The in vivo database and in vivo pipeline output data matrices.
Supplementary Table 9
List of “phylum-associated” metabolites.
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Han, S., Van Treuren, W., Fischer, C.R. et al. A metabolomics pipeline for the mechanistic interrogation of the gut microbiome. Nature 595, 415–420 (2021). https://doi.org/10.1038/s41586-021-03707-9
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