A mosaic of cross-phylum chemical interactions occurs between all metazoans and their microbiomes. A number of molecular families that are known to be produced by the microbiome have a marked effect on the balance between health and disease1,2,3,4,5,6,7,8,9. Considering the diversity of the human microbiome (which numbers over 40,000 operational taxonomic units10), the effect of the microbiome on the chemistry of an entire animal remains underexplored. Here we use mass spectrometry informatics and data visualization approaches11,12,13 to provide an assessment of the effects of the microbiome on the chemistry of an entire mammal by comparing metabolomics data from germ-free and specific-pathogen-free mice. We found that the microbiota affects the chemistry of all organs. This included the amino acid conjugations of host bile acids that were used to produce phenylalanocholic acid, tyrosocholic acid and leucocholic acid, which have not previously been characterized despite extensive research on bile-acid chemistry14. These bile-acid conjugates were also found in humans, and were enriched in patients with inflammatory bowel disease or cystic fibrosis. These compounds agonized the farnesoid X receptor in vitro, and mice gavaged with the compounds showed reduced expression of bile-acid synthesis genes in vivo. Further studies are required to confirm whether these compounds have a physiological role in the host, and whether they contribute to gut diseases that are associated with microbiome dysbiosis.
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All metabolomics data that support the findings of this study are available at GNPS (https://gnps.ucsd.edu/) under MassIVE ID numbers: MSV000079949 (original germ-free and SPF mouse data), MSV000082480, MSV000082467, MSV000079134, MSV000082406, MSV000083032, MSV000083004 and MSV000083446. The sequencing data for the germ-free and SPF mouse study are available on the Qiita microbiome data analysis platform at https://qiita.ucsd.edu/ under study ID 10801 and through the European Bioinformatics Institute accession number ERP109688. Source Data for Figs. 1–3, Extended Data Fig. 7 are provided with the paper.
MASST can be accessed at https://masst.ucsd.edu/; the development of MASST is described in ref. 43. The code for MS/MS-based MASST searching is available at https://github.com/CCMS-UCSD/GNPS_Workflows/tree/master/search_single_spectrum.
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The authors acknowledge funding from the National Institutes of Health (NIH), grants 5U01AI124316-03, 1R03CA211211-01, 1R01HL116235 U54DE023798, R24DK110499, GMS10RR029121, 1 DP1 AT010885, P30 DK120515 and R01HD084163. Additionally, B.S.B. was supported by UCSD KL2 (1KL2TR001444), T.D.A. by the National Library of Medicine Training Grant NIH grant T15LM011271. R.M.E. is an investigator of the Howard Hughes Medical Institute and March of Dimes Chair in Molecular and Developmental Biology at the Salk Institute. R.M.E. was funded by grants from the NIH (DK057978, HL105278, HL088093 and ES010337), and Samuel Waxman Cancer Research Foundation. We acknowledge G. Ackermann for her contributions. This work was also supported in part by Seed Grants from the UC San Diego Center for Microbiome Innovation. This work was funded by grants from the NIH (DK057978, HL105278 and HL088093), National Cancer Institute (CA014195), the Leona M. and Harry B. Helmsley Charitable Trust (2017PG-MED001), SWCRF Investigator Award and Ipsen/Biomeasure. J.L. is supported by grant EIA14660045, an American Heart Association Established Investigator Award. T.F. is supported by a Hewitt Medical Foundation Fellowship, a Salk Alumni Fellowship. T.F., R.K. and P.C.D. acknowledge support from the Crohn’s & Colitis Foundation (CCFA). R.M.E. and M.D. are supported in part by a Stand Up to Cancer (SU2C) - Cancer Research UK-Lustgarten Foundation Pancreatic Cancer Dream Team Research Grant (SU2C-AACR-DT-20-16). SU2C is a programme of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research, the scientific partner of SU2C. Research reported in this publication was also supported by the National Institute of Environmental Health Sciences of the NIH under Award Number P42ES010337. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
C.H. is on the scientific advisory board of Seres Therapeutics. M.W. is founder of, and A.A. is a consultant for, Ometa Laboratories LLC. P.C.D. and M.W. are consultants for Sirenas Therapeutics. W.J.S. consults for Abbvie, Allergan, Amgen, Arena Pharmaceuticals, Avexegen Therapeutics, BeiGene, Boehringer Ingelheim, Celgene, Celltrion, Conatus, Cosmo, Escalier Biosciences, Ferring, Forbion, Genentech, Gilead Sciences, Gossamer Bio, Incyte, Janssen, Kyowa Kirin Pharmaceutical Research, Landos Biopharma, Lilly, Oppilan Pharma, Otsuka, Pfizer, Progenity, Prometheus Biosciences (merger of Precision IBD and Prometheus Laboratories), Reistone, Ritter Pharmaceuticals, Robarts Clinical Trials (owned by Health Academic Research Trust, HART), Series Therapeutics, Shire, Sienna Biopharmaceuticals, Sigmoid Biotechnologies, Sterna Biologicals, Sublimity Therapeutics, Takeda, Theravance Biopharma, Tigenix, Tillotts Pharma, UCB Pharma, Ventyx Biosciences, Vimalan Biosciences and Vivelix Pharmaceuticals; and holds stock or stock options from BeiGene, Escalier Biosciences, Gossamer Bio, Oppilan Pharma, Prometheus Biosciences (merger of Precision IBD and Prometheus Laboratories), Progenity, Ritter Pharmaceuticals, Ventyx Biosciences and Vimalan Biosciences.
Peer review information Nature thanks Hanns-Ulrich Marschall, Trent Northen 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
a, Principal coordinate (PC) analysis of microbiome and mass-spectrometry data highlighted by sample source as germ-free (GF) or SPF (n = 4 mice in each group). The microbial signatures from the germ-free mice are an important control, which represents background reads found in buffers, tips and tubes and other experimental materials. b, Data from a highlighted by organ source (n = 4 mice in each group). c, Bray–Curtis dissimilarities of the metabolome data collected from mouse organs. The dissimilarities are calculated within individual mice of the same group (germ-free or SPF, ‘within’) or across the germ-free and SPF groups (‘GF-SPF’) (n = 4 mice in each group). Only samples collected from exact same location (subsection) are compared. Significance was tested with a two-sided Mann–Whitney U-test. Boxes represent the interquartile range (IQR), the notch is the 95% confidence interval of the mean, the centre is the median and whiskers are 1.5× the IQR. d, Microbiome profile of the gastrointestinal tracts of SPF mice. Data were generated by sequencing 16S rRNA gene amplicons from each organ and organ section, and analysed through the Qiita Deblur pipeline as described in the Supplementary Methods. Bacterial taxa of relevance are colour-coded according to the legend. e, Molecular network of LC–MS/MS data with nodes coloured by source as germ-free, SPF, shared or detected in blanks. Molecular families with metabolites annotated by spectral matching in GNPS are listed by a number that corresponds to the molecular family. These are level-2 or -3 annotations according to the metabolomics standards consortium16. 12-OAHSA, 12-(9Z-octadecenoyloxy)-octadecanoic acid.
Extended Data Fig. 2 Microbial metabolism of soyasaponins in metabolomics data from germ-free and SPF mice.
n = 4 mice in each group. a, Molecular network cluster of soyasaponins, coloured by source of each node as germ-free, SPF or shared. Structures of corresponding molecules are shown in nodes highlighted in yellow, according to the numbering scheme. Mean total-ion-current-normalized abundance of each soyasaponin metabolite from the gastrointestinal tracts of germ-free and SPF mice. Ce, caecum; co, colon; D, duodenum; I, ileum; J, jejunum; stl, stool; sto, stomach. Boxes represent the IQR, the centre is the median and whiskers are 1.5× the IQR. n = 4 mice in each group. b, Molecular family of soyasapogenols, their structures and relative abundances in gut organs of germ-free and SPF mice (data are in the same format as in a). c, Three-dimesional model visualization (generated using ’ili) of the normalized abundance of soyasaponin I in the mouse gastrointestinal tract. The abundance of the metabolite is indicated according to the rainbow spectrum (high, red; low, blue). n = 4 mice in each group. d, Three-dimensional cartography (generated using ’ili) of the normalized abundance of soyasapogenol B onto an magnetic resonance imaging organ model of the mice. e, Mean normalized abundance of soyasaponin I through all gastrointestinal sample locations in the germ-free and SPF mice. f, Mean normalized abundance of soyasapogenol through all gastrointestinal sample locations. The annotations are level two or three16.
Extended Data Fig. 3 Microbial metabolism of plant isoflavones in metabolomics data from germ-free and SPF mice.
a, Structures, molecular network and total-ion-chromatogram-normalized abundance of glycone isoflavanoids in the mouse gastrointestinal tract. Nodes are coloured according to their source in germ-free or SPF mice (n = 4 mice each), and known library hits are shaped as arrowheads. Boxes represent the IQR, the centre is the median and whiskers are 1.5× the IQR. b, Same information as in a, for the aglycones. c, Three-dimensional molecular cartography mapping the abundance of the daidzein and glycitein glycone and sulfated forms through entire 3D mouse model. The normalized abundance of a particular molecule is indicated as a heat map. Red, most abundant; blue, least abundant. d, Three-dimesional molecular cartography mapping the abundance of the daidzein and glycitein aglycone forms through entire 3D mouse model. The gastrointestinal-tract model is inset for reference. The annotations are level two or three16.
Extended Data Fig. 4 Microbial metabolism of known bile acids in metabolomics data from germ-free and SPF mice.
n = 4 mice in each group. a, Total-ion-chromatogram-normalized abundance of taurocholic acid and secondary bile acids in gastrointestinal tract samples from germ-free and SPF mice. Gall, gall bladder; liv, liver. Boxes represent the IQR, the centre is the median and whiskers are 1.5× the IQR. b, Three-dimesional molecular cartography mapping the abundance of the same bile acids as in a through the mouse gastrointestinal-tract model; liver is separated for better visualization. The normalized abundance of a particular molecule is indicated as a heat map. Red, most abundant; blue, least abundance. The annotations are level two or three16.
a, Extracted-ion-chromatogram MS1 traces of Tyr-chol (m/z 572.37 ± 0.05 Da), Phe-chol (m/z 556.37 ± 0.05 Da) and Leu-chol (m/z 522.37 ± 0.05 Da). Experiments were performed four times. b, Extracted ion chromatograms for the synthetic muricholic and cholic acid versions of the Phe (m/z 556.37 ± 0.05), Tyr (572.37 ± 0.05) and Leu (522.37 ± 0.05) conjugates, showing the different retention times from the muricholic- and cholic-acid forms. c, Retention time alignments of synthetic muricholic- and cholic-acid conjugates with the newly identified conjugates found in a sample from the jejunum of a colonized mouse. The isoleucocholic- and leucocholic-acid analysis was run on a long-gradient high-performance liquid-chromatography column to separate isomeric Ile and Leu conjugates, and to compare to those detected in vivo. d, Annotation of MS/MS fragmentation patterns for the three conjugated bile acids and GCA. Structures of the immonium ions from amino acid fragmentation, whole amino acid fragments and the major sterol fragment are shown. Loss of the amino acid mass on the bile-acid steroid backbone is also highlighted.
a, Molecular network of MS/MS data from synthesized amino acid conjugated bile acids and the duodenum of SPF mice. LC–MS/MS data from synthetic standards were networked with mouse samples and spectral matching. Molecular networking is indicated by node colouring. Mirror plots show the alignment between the mouse and the synthetic standards. Nodes shaped as arrowheads had hits in the GNPS libraries, and node size is scaled to the spectral count. Tauro, taurocholic acid. These experiments were performed twice. b, Three-dimensional molecular cartography of the mean abundance of the newly discovered conjugates mapped onto a 3D-rendered model of the mouse gastrointestinal tract, as a heat map according to the colour scale. Organs are labelled as described in Fig. 1. c, Molecular network of conjugated bile acids from portal and peripheral blood of germ-free and SPF mice. Nodes are coloured by source as germ-free portal, germ-free portal and peripheral blood, SPF portal and peripheral blood, GF portal and peripheral blood and SPF peripheral blood, and all. Arrowhead nodes represent known compounds in the GNPS spectral database; circular nodes represent unknown compounds. The annotations were obtained through spectral matches against reference libraries (level two or three16). d, Mean area-under-the-curve abundance and s.d. of bile acids of interest during incubation with an actively growing batch human faecal culture for 24 h (n = 3 independent incubations). e, Molecular network of newly identified conjugated bile acids after incubation in a human faecal batch culture experiment. Each node represents a unique tandem mass spectrum; arrowhead-shaped nodes indicate known spectra in the GNPS database. The nodes are coloured by their retention time according to the legend, and the mass shifts between nodes are mapped onto the edge representing the cosine connection between related spectra. The H2 mass shift representing oxidation of the newly identified conjugates is shown. f, Mean ion intensity and s.d. of the oxidized forms of Phe-chol, Tyr-chol and Leu-chol through the 24-h batch faecal culture incubation (n = 3 independent incubations).
Extended Data Fig. 7 MASST search results and associations of newly identified conjugated bile acids with high-fat diet.
a, Proportion of samples in which Phe-chol, Tyr-chol and Leu-chol were found from a single-spectrum MASST search of publicly available data on GNPS. Massive dataset identifers are shown for each dataset, are divided into mouse (‘murine’) or human gastrointestinal samples. b, Box plots of the newly identified conjugates in a previously published mouse study, in which mice were fed high-fat diet (HFD) (n = 14 mice) or normal chow (NC) (n = 19 mice) (Gly, P = 0.72; Phe, P = 0.038; Tyr, P = 0.083; Leu P = 9.4 × 10−5) and dot plot of mice treated with (n = 27 mice) or without antibiotics (Ab) (n = 415 mice)29. Boxes represent the IQR, the line is the median and whiskers are 1.5× the IQR. Colour legend applies to both a and b. c, Mean normalized abundance of the three newly identified conjugated bile acids compared to taurocholic acid in mice (Apoe-knockout on a C57BL/6J background) fed either a high-fat diet (n = 12 mice) or normal chow (n = 12 mice) for 10 weeks. Faecal samples were collected and extracted in 50:50 methanol:water and analysed with LC–MS/MS metabolomics, as described in the Supplementary Methods. The s.d. around the mean is shown, and significance between a high-fat diet and normal chow at each time point is tested with two-sided Student’s t-test. ***P < 0.001. d, Correlations between rarefied reads of a deblurred read assigned to a Clostridium sp. from atherosclerosis-prone mice fed a high-fat diet over time (n = 12 mice). The line of best fit is plotted using the lm method in the R statistical software; grey area around the line of best fit is the 95% confidence interval.
a, Dot plot of the measured production of Phe-chol and Tyr-chol using a targeted liquid chromatography–mass spectrometry method for two C. bolteae strains grown in faecal culture medium (FCM) with or without labelled Phe (n = 2 independent cultures). b, The mean ratio and s.e.m. of 13C-Phe-chol:12C-phe-chol from the same C. bolteae strains when grown with faecal culture medium with 13C-labelled phenylalanine (bottom left) (n = 2 cultures). c, Mean and s.d. of the Shannon index of human faecal batch culture (n = 3 cultures) before and after 24-h growth exposed to conjugated bile acids or a mock control. NS, not significant by Mann–Whitney U-test. d, Box-and-whisker plots of concentration of Phe-chol and Tyr-chol in original samples from the gut of SPF mice. Boxes represent the IQR, the centre is the median and whiskers are 1.5× the IQR. n = 4 mice.
a, Mean normalized luciferase activity as a readout of human FXR stimulation when exposed to various conjugated and unconjugated bile acids, as a function of the compound dose. n = 8 measurements, ± s.e.m. DCA, deoxycholic acid; CDCA, chenodeoxycholic acid; T-βMCA, tauro-β-muricholic acid. b, Ileum mean fold expression change compared to 36B4 control of various bile acids after gavage in mice. Error bars are s.e.m. c, Liver fold expression change compared to 36B4 control of various bile acids after gavage in mice. Significance was tested with two-tailed t-test compared to the mock corn-oil control. Error bars are s.e.m.
This file contains Methods, Supplementary Data, Supplementary Tables, Supplementary NMR spectra and Supplementary 3D mouse model. Supplemental 3D-Mouse model: provided in the SI are .stl files that comprise the 3D mouse model for 3D-molecular cartography mapping. These include a full mouse model, the liver only, the GI tract only, and the GI tract without the liver. Also included are x,y,z coordinates that will enable mapping of multi-omics data to locations of interest on all four .stl files.
This file contains Supplementary Table S1: Sample information from GF and SPF mice analyzed in this study.
This file contains Supplementary Table S3: Deblurred read, taxonomic assignment and Pearson’s r correlation for bacterial assigned sequence variants (ASVs) with the three novel conjugated bile acids in the HFD feeding experiment (n=12).
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Quinn, R.A., Melnik, A.V., Vrbanac, A. et al. Global chemical effects of the microbiome include new bile-acid conjugations. Nature 579, 123–129 (2020). https://doi.org/10.1038/s41586-020-2047-9
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