The microbiota modulates gut immune homeostasis. Bacteria influence the development and function of host immune cells, including T helper cells expressing interleukin-17A (TH17 cells). We previously reported that the bile acid metabolite 3-oxolithocholic acid (3-oxoLCA) inhibits TH17 cell differentiation1. Although it was suggested that gut-residing bacteria produce 3-oxoLCA, the identity of such bacteria was unknown, and it was unclear whether 3-oxoLCA and other immunomodulatory bile acids are associated with inflammatory pathologies in humans. Here we identify human gut bacteria and corresponding enzymes that convert the secondary bile acid lithocholic acid into 3-oxoLCA as well as the abundant gut metabolite isolithocholic acid (isoLCA). Similar to 3-oxoLCA, isoLCA suppressed TH17 cell differentiation by inhibiting retinoic acid receptor-related orphan nuclear receptor-γt, a key TH17-cell-promoting transcription factor. The levels of both 3-oxoLCA and isoLCA and the 3α-hydroxysteroid dehydrogenase genes that are required for their biosynthesis were significantly reduced in patients with inflammatory bowel disease. Moreover, the levels of these bile acids were inversely correlated with the expression of TH17-cell-associated genes. Overall, our data suggest that bacterially produced bile acids inhibit TH17 cell function, an activity that may be relevant to the pathophysiology of inflammatory disorders such as inflammatory bowel disease.
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The 16S amplicon and RNA-seq datasets are available through NCBI under BioProject ID PRJNA675599 and GEO accession number GSE179740, respectively. All mass spectra acquired for standards in this study were deposited at MoNA (https://mona.fiehnlab.ucdavis.edu/) under IDs MoNA031840 to MoNA031854 (Supplementary Table 12) and the https://ibdmdb.org/ dataset and the metabolomics workbench study ST000923. Source data are provided with this paper.
The software packages used in this study are free and open source. Source code for ElenMatchR is available at GitHub (https://github.com/turnbaughlab/ElenMatchR). MaAsLin2 is available online (http://huttenhower.sph.harvard.edu/maaslin) as source code and installable packages. The R package limma is available online (https://www.bioconductor.org/packages/release/bioc/html/limma.html). Analysis scripts using these packages are available from the authors on request.
Hang, S. et al. Bile acid metabolites control TH17 and Treg cell differentiation. Nature 576, 143–148 (2019).
Fiorucci, S. & Distrutti, E. Bile acid-activated receptors, intestinal microbiota, and the treatment of metabolic disorders. Trends Mol. Med. 21, 702–714 (2015).
Ridlon, J. M., Kang, D.-J. & Hylemon, P. B. Bile salt biotransformations by human intestinal bacteria. J. Lipid Res. 47, 241–259 (2005).
Modica, S., Gadaleta, R. M. & Moschetta, A. Deciphering the nuclear bile acid receptor FXR paradigm. Nucl. Recept. Signal. 8, e005 (2010).
Schaap, F. G., Trauner, M. & Jansen, P. L. M. Bile acid receptors as targets for drug development. Nat. Rev. Gastroenterol. 11, 55–67 (2014).
Guo, C. et al. Bile acids control inflammation and metabolic disorder through inhibition of NLRP3 inflammasome. Immunity 45, 944 (2016).
Ma, C. et al. Gut microbiome-mediated bile acid metabolism regulates liver cancer via NKT cells. Science 360, eaan5931 (2018).
Cao, W. et al. The xenobiotic transporter Mdr1 enforces T cell homeostasis in the presence of intestinal bile acids. Immunity 47, 1182–1196 (2017).
Duerr, R. H. et al. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science 314, 1461–1463 (2006).
Nair, R. P. et al. Genome-wide scan reveals association of psoriasis with IL-23 and NF-κB pathways. Nat. Genet. 41, 199–204 (2009).
Stahl, E. A. et al. Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat. Genet. 42, 508–514 (2010).
Sakaguchi, S. Naturally arising CD4+ regulatory T cells for immunologic self-tolerance and negative control of immune responses. Annu. Rev. Immunol. 22, 531–562 (2004).
Josefowicz, S. Z., Lu, L.-F. & Rudensky, A. Y. Regulatory T cells: mechanisms of differentiation and function. Immunology 30, 531–564 (2012).
Campbell, C. et al. Bacterial metabolism of bile acids promotes generation of peripheral regulatory T cells. Nature 581, 475–479 (2020).
Song, X. et al. Microbial bile acid metabolites modulate gut RORγ+ regulatory T cell homeostasis. Nature 577, 410–415 (2020).
Ivanov, I. I. et al. The orphan nuclear receptor RORγt directs the differentiation program of proinflammatory IL-17+ T helper cells. Cell 126, 1121–1133 (2006).
Yang, X. O. et al. T helper 17 lineage differentiation is programmed by orphan nuclear receptors RORα and RORγ. Immunity 28, 29–39 (2008).
Hamilton, J. P. et al. Human cecal bile acids: concentration and spectrum. Am. J. Physiol. Gastrointest. Liver Physiol. 293, G256–G263 (2007).
Hirano, S. & Masuda, N. Transformation of bile acids by Eubacterium lentum. Appl. Environ. Microbiol. 42, 912–915 (1981).
Devlin, A. S. & Fischbach, M. A. A biosynthetic pathway for a prominent class of microbiota-derived bile acids. Nat. Chem. Biol. 11, 685–690 (2015).
Ivanov, I. I. et al. Induction of intestinal TH17 Cells by segmented filamentous bacteria. Cell 139, 485–498 (2009).
Esplugues, E. et al. Control of TH17 cells occurs in the small intestine. Nature 475, 514–518 (2011).
Hong, P.-Y., Wu, J.-H. & Liu, W.-T. Relative abundance of Bacteroides spp. in stools and wastewaters as determined by hierarchical oligonucleotide primer extension. Appl. Environ. Microbiol. 74, 2882–2893 (2008).
García-Bayona, L. & Comstock, L. E. Streamlined genetic manipulation of diverse Bacteroides and Parabacteroides isolates from the human gut microbiota. mBio 10, e01762-19 (2019).
Sayin, S. I. et al. Gut microbiota regulates bile acid metabolism by reducing the levels of tauro-beta-muricholic acid, a naturally occurring FXR antagonist. Cell Metab. 17, 225–235 (2013).
Franzosa, E. A. et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat. Microbiol. 4, 293–305 (2019).
Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).
Revu, S. et al. IL-23 and IL-1β drive human TH17 cell differentiation and metabolic reprogramming in absence of CD28 costimulation. Cell Rep. 22, 2642–2653 (2018).
Lee, W. et al. Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease. Cell Host Microbe 29, 1294–1304 (2021).
Sato, Y. et al. Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians. Nature 599, 458–464 (2021).
Bouladoux, N., Harrison, O. J. & Belkaid, Y. The mouse model of infection with Citrobacter rodentium. Curr. Protoc. Immunol. 119, 19.15.1–19.15.25 (2017).
Huh, J. R. & Littman, D. R. Small molecule inhibitors of RORγt: targeting TH17 cells and other applications. Eur. J. Immunol. 42, 2232–2237 (2012).
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Mi, H., Muruganujan, A. & Thomas, P. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 41, D377–D386 (2013).
Browne, H. P. et al. Culturing of “unculturable” human microbiota reveals novel taxa and extensive sporulation. Nature 533, 543–546 (2016).
Hall, A. B. et al. A novel Ruminococcus gnavus clade enriched in inflammatory bowel disease patients. Genome Med. 9, 103 (2017).
Yao, L. et al. A selective gut bacterial bile salt hydrolase alters host metabolism. eLife 7, e37182 (2018).
Swann, J. R. et al. Systemic gut microbial modulation of bile acid metabolism in host tissue compartments. Proc. Natl Acad. Sci. USA 108, 4523–4530 (2011).
Chen, I.-M. A. et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 47, D666–D677 (2019).
Mukherjee, S. et al. Genomes OnLine database (GOLD) v.7: updates and new features. Nucleic Acids Res. 47, D649–D659 (2019).
Drozdetskiy, A., Cole, C., Procter, J. & Barton, G. J. JPred4: a protein secondary structure prediction server. Nucleic Acids Res. 43, W389–W394 (2015).
Bisanz, J. E. et al. A genomic toolkit for the mechanistic dissection of intractable human gut bacteria. Cell Host Microbe 27, 1001–1013 (2020).
Segata, N., Börnigen, D., Morgan, X. C. & Huttenhower, C. PhyloPhlAn is a new method for improved phylogenetic and taxonomic placement of microbes. Nat. Commun. 4, 2304 (2013).
Yu, G., Lam, T. T.-Y., Zhu, H. & Guan, Y. Two methods for mapping and visualizing associated data on phylogeny using Ggtree. Mol. Biol. Evol. 35, 3041–3043 (2018).
Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).
Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23, 1282–1288 (2007).
Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).
Suzek, B. E. et al. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2015).
Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol. 17, e1009442 (2021).
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
Smyth, G. K. in Bioinformatics and Computational Biology Solutions using R and Bioconductor (eds Gentleman, R. et al.) 397–420 (Springer, 2015).
Barman, M. et al. Enteric salmonellosis disrupts the microbial ecology of the murine gastrointestinal tract. Infect. Immun. 76, 907–915 (2008).
Sagaidak, S., Taibi, A., Wen, B. & Comelli, E. M. Development of a real-time PCR assay for quantification of Citrobacter rodentium. J. Microbiol. Methods 126, 76–77 (2016).
We thank the members of the Devlin, Huh and Clardy laboratories (Harvard Medical School (HMS)) for discussions; the staff at the HMS ICCB-Longwood Screening Facility, BPF Genomics Core Facility at Harvard Medical School for their expertise and instrument support; N. Lee, J. Vasquez, C. Powell, B. Russell and M. Henke for technical support and advice; M. Trombly and S. Blacklow for reading the manuscript; L. E. Comstock for the pLGB30 plasmid (Addgene plasmid 126620); and L. García-Bayona for her technical support. We are grateful to the human patients who participated in the human stool screen, PRISM and HMP2 studies. We acknowledge NIH grant P30DK034854 and the use of the Harvard Digestive Disease Center’s core services, resources, technology and expertise. This work was supported by National Institutes of Health grants R01 DK110559 (to J.R.H. and A.S.D.), R01AR074500 (P.J.T.), U54DE023798 (C.H.), R24DK110499 (C.H.), T32GM095450 and MIRA R35 GM128618 (A.S.D.), a Harvard Medical School Dean’s Innovation Grant in the Basic and Social Sciences (A.S.D. and J.R.H.), a John and Virginia Kaneb Fellowship (A.S.D.), a Harvard Medical School Christopher Walsh Fellowship (L.Y.) and a Wellington Postdoctoral Fellowship (L.Y.). J.E.B. was the recipient of a Natural Sciences and Engineering Research Council of Canada Postdoctoral Fellowship and is supported by the National Institute of Allergy and Infectious Diseases (K99AI147165). P.J.T. is a Chan Zuckerberg Biohub investigator. The computations in this paper were run in part on the FASRC Cannon cluster supported by the FAS Division of Science Research Computing Group at Harvard University. The images in Figs. 2c, 4a, d, Extended Data Figs. 1b, c, 3i, l were created using BioRender.
A.S.D. is a consultant for Takeda Pharmaceuticals and Axial Therapeutics. J.R.H. is a consultant for CJ Research Center and Interon Laboratories and on the scientific advisory board for ChunLab. P.J.T. is on the scientific advisory board for Kaleido, Pendulum, Seres and SNIPRbiome. C.H. is on the scientific advisory boards of Seres Therapeutics, Empress Therapeutics, and ZOE Nutrition.
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Extended data figures and tables
a, Quantification of 3-oxoLCA and isoLCA in stool samples from patients after faecal microbiota transplant (FMT) (n = 15). Stool samples from patient p#3 (3-oxoLCA: 44 picomol/mg, isoLCA: 136 picomol/mg) and patient p#27 (3-oxoLCA: 83 picomol/mg, isoLCA: 213 picomol/mg) were used to screen for 3-oxoLCA producers. b, Schematic of the screen for bacterial producers of the LCA metabolite 3-oxoLCA from human stool samples. In total, 990 bacterial colonies were isolated, restreaked, and archived from two human stool samples. ① Replicate plates (assay plates) were then used for the screen. ② Individual isolates were incubated anaerobically with LCA (100 μM) (see Fig. 1b) or 3-oxoLCA (100 μM) (see Fig. 2b) for 48 h. Cultures were harvested, acidified, extracted, and BA metabolites were quantified by UPLC-MS. ③ Positive hits containing 3-oxoLCA were re-selected from the archived stock plates, and recovered on new plates. ④ Activity was verified and each producer species was identified by full-length 16S rRNA sequencing. Finally, bacterial enzymes responsible for the LCA metabolite production were identified (see Fig. 3), and ⑤ corresponding genes were utilized as query sequences in BLASTP searches for novel putative bacterial producers and enzymes. c, Sample preparation workflow for the determination of cultured bacteria from the human stool sample screen. For each patient, individual isolates were recovered and cultured for 48 h. These isolates were then pooled together, and genomic DNA was extracted from the pooled pellet. Illumina® MiSeq sequencing on the V3 and V4 hypervariable regions of 16S rRNA was then performed. d, Genus and phylum-level microbial community composition for each human stool sample. e, 3-oxoLCA and/or isoLCA production was verified in the type strains of a subset of 3-oxoLCA-producing human isolates (n = 3 biological replicates per group, data are mean ± SEM).
Extended Data Fig. 2 Supernatants from LCA metabolite-producing bacteria do not affect Treg cell differentiation in vitro.
a, b, Representative FACS plots (a) and population frequencies (b) of CD4+ T cells, cultured under Treg polarization conditions in vitro are presented. Bacterial culture supernatants were added 18 h after TCR activation (n = 3 biologically independent samples per group. Data are mean ± SEM, one-way ANOVA followed by Tukey’s multiple comparison test). c, A pure standard of isoLCA was spiked into a subset of bacterial culture extracts containing the new peak (#). Co-elution and an identical m/z match confirmed that the new compound (#) in Fig. 1b was isoLCA. Total ion chromatograms are shown. d, isoLCA production from 3-oxoLCA (100 μM) was verified in the type strains of a subset of isoLCA-producing human isolates (n = 3 biological replicates per group, data are mean ± SEM).
Extended Data Fig. 3 IsoLCA neither affects T cell viability nor inhibits Treg and TH1 cell differentiation in vitro.
a-c, IsoLCA does not reduce T cell viability or proliferation. Percentages of TH17 cells (a), viable cells (b) and total cell numbers (c) at the end of T cell culture under TH17 polarization conditions in the presence of LCA, 3-oxoLCA, or isoLCA at 40, 20, 10, 5, 2.5, 1.25 and 0.625 μM (n = 3 biologically independent samples, data are mean ± SEM, one-way ANOVA with Dunnett’s multiple comparisons). d–g, IsoLCA does not affect Treg or TH1 cell differentiation in vitro. Flow cytometry and quantification of intracellular staining for FoxP3 (d, e) or IFN-γ (f, g). Mouse naive CD4 T cells from wild-type C57BL/6J mice were cultured under TH1- or Treg- polarizing conditions and DMSO or isoLCA was added 18 h after TCR activation (n = 3 biologically independent samples per condition, data are mean ± SEM, two-tailed unpaired t-test). h, SFB colonization measured by qPCR analysis in Fig. 2c–f, calculated as SFB 16s rRNA copy number (n = 8 mice per group, pooled from two experiments, data are mean ± SEM, two-tailed unpaired t-test). i–k, Experimental scheme of Th17 induction by SFB (i), representative FACS plots (j) and population frequencies of TH17 cells (k), isolated from the ileal lamina propria of control or isoLCA-treated mice (n = 8 mice for control, n = 6 mice for isoLCA-treated groups, pooled from two experiments). B6 Tac mice were fed a control or an isoLCA (0.3% w/w)-containing diet for 7 days (data are mean ± SEM, two-tailed unpaired t-test). l–o, Experimental scheme of anti-CD3 experiment (l), representative FACS plots (m) and population frequencies of TH17 (n) and Treg cells (o) of the ileal lamina propria of control or isoLCA-treated mice (n = 15 mice for control, 13 mice for isoLCA-treated groups, pooled from three experiments). B6 Tac mice were intraperitoneally injected with anti-CD3 and fed a control diet or an isoLCA-containing (0.3% w/w) diet during the experiments (data are mean ± SEM, two-tailed unpaired t-test). p, RORγt luciferase reporter assay in HEK293 cells, treated with a synthetic RORγ inhibitor ML209 (1 μM), isoLCA (20 μM, 10 μM, 5 μM), isoDCA (20 μM, 10 μM, 5 μM) or DMSO. The fold ratio of firefly luciferase (FLuc) to Renilla luciferase (RLuc) activity is presented on the y-axis. DMSO-treated group set to 1 (n = 7 independent transfections per group, pooled from two experiments. Data are mean ± SEM, one-way ANOVA with Dunnett’s multiple comparison test, vehicle set as control). q, r, Differential scanning fluorimetry (DSF) analyses indicated robust binding of isoLCA (q), but not of isoDCA (r) to the RORγt ligand-binding domain (LBD). s–v, Surface plasmon resonance (SPR) indicated robust binding of isoLCA to the RORγt LBD. Sensorgrams for affinity (s) and kinetics (t) of isoLCA and affinity (u) and kinetics (v) of isoDCA with the RORγt LBD. w, Transcriptional profiling of wild-type (WT) T cells and RORγ deficient (KO) T cells, cultured under TH17 cell polarization conditions. DMSO or BAs were added to cells 18 h after TCR activation. Cells were then harvested, and RNA-sequencing was performed. Heat map represents 46 genes that are regulated by either 3-oxoLCA or isoLCA as well as RORγ (n = 3 mice per condition, the Wald test with Benjamini-Hochberg correction was used to determine FDR-adjusted p value < 0.05, genes that were differentially expressed by both isoLCA and 3-oxoLCA are shown in magenta). x, Gene ontology enrichment analysis was performed on the 46 genes that were differentially regulated by either 3-oxoLCA or isoLCA and RORγt revealed that these BA treatments resulted in changes in the expression of genes involved in several biological processes, including IL-17-mediated signalling and cytokine production pathways.
a–c, Results of lysis assay in which the E. lenta DSM2243 (Elen), R. gnavus ATCC29149 (Rumgna), and B. fragilis NCTC9343 (BF) candidate HSDH enzymes were expressed in E. coli BL21 pLysS and their ability to convert LCA to 3-oxoLCA (a, 3α-HSDH activity), 3-oxoLCA to isoLCA (b, and c, left, 3β-HSDH activity), and 3-oxoLCA back to LCA (d, right, reverse 3α-HSDH activity) was analyzed by UPLC-MS. Data are reported as percent conversion to product (n = 3 biological replicates per group, data are mean ± SEM). d–g, SDS-PAGE analysis of candidate gene expression from E. lenta DSM 2243 and R. gnavus ATCC 29149 (Elen_0358, Elen_690, Elen_1325, Elen_2515, Rumgna_00694, and Rumgna_02133) (n = 3 replicates) (d). Western blot of the expression of Elen_0198, Elen_0359, Elen_0360, and Rumgna_02133. Anti-His tag labeling (left). Amido black total protein stain of membrane (right) (n = 2 replicates) (e). Western blot of the expression of BF0083, BF0143, BF1060, BF1669, BF2144, and BF3320. Anti-His tag labeling (left). Amido black total protein stain of membrane (right) (n = 2 replicates) (f). Western blot of the expression of Bf3538 and Bf3932. Anti-His tag labeling (left). Amido black total protein stain of membrane (right) (n = 2 replicates) (g). For source gel data for d–g, see Fig. S1. h, DNA gel for the B. fragilis genetic knockout mutants’ diagnostic PCR. IntF-UHF-BF3538/ Int-R-DHF-BF3538 PCR primers: lane 1–3 are B. fragilis Δ3538 mutant colonies #1-#3; lane 4, 5, 7 are B. fragilis Δ3932 mutant colonies #1-#3; lanes 6 and 8 are B. fragilis WT; lane 9 is a non-template control. IntF-UHF-BF3932/ Int-R-DHF-BF3932 PCR primers: lane 11-13 are B. fragilis Δ3538 mutant colonies #1-#3; lane 14, 15, 17 are B. fragilis Δ3932 mutant colonies #1-#3; lanes 16 and 18 are B. fragilis WT; lane 19 is a non-template control. UNIV-16s-F/ UNIV-16s-R PCR primers: lane 21-23 are B. fragilis Δ3538 mutant colonies #1-#3; lane 24, 25, 27 are B. fragilis Δ3932 mutant colonies #1-#3; lanes 26 and 28 are B. fragilis WT; lane 29 is a non-template control. Lane 10, 20, 30 are the 1kb DNA ladder (n = 2 replicates). For source gel data, see Fig. S1. i, j, R. gnavus isolates in red (R. gnavus RJX1118, R. gnavus RJX1119, R. gnavus RJX1124, R. gnavus RJX1125, R. gnavus RJX1126, R. gnavus RJX1128) that lack a homologue of Rumgna_02133 (Table S5) did not synthesize 3-oxoLCA or isoLCA from LCA (i). R. gnavus isolates in red that lack a homologue of Rumgna_02133 (Table S5) only produced isoLCA from 3-oxoLCA (j). All strains were incubated with 100 μM LCA as a substrate for 48 h (n = 3 biological replicates per group). k, l, The 3α-HSDH gene of E. lenta is required to suppress TH17 cell differentiation in vitro. Representative FACS plots (l) and population frequencies of TH17 cells (k) are presented. Naive CD4+ T cells from wild-type C57BL/6J mice were cultured under TH17 cell polarizing conditions for 3 days. Culture supernatants of E. lenta DSM2243 or E. lenta DSM15644, an isolate lacking a 3α-HSDH, were added 18 h after TCR activation (n = 3 biologically independent samples per group, data are mean ± SEM, one-way ANOVA followed by Tukey’s multiple comparison test. p = 0.000081 between column 4 and 6(l)). m, Production of 3-oxoLCA and isoLCA by “high” and “low” producer co-cultures. Production of 3-oxoLCA and isoLCA from LCA (100 µM) by co-cultures of human gut bacteria type strains in vitro are shown (high producer group: E. lenta DSM2243 + B. fragilis NCTC9343; low producer group: E. lenta DSM15644 + B. fragilis NCTC9343 ΔBF3538 and C. citroniae human isolate P2-B6 + B. fragilis NCTC9343 ΔBF3538; n = 3 biological replicates per co-culture, data are mean ± SEM).
a, Representative FACS plots for IL-17A or IFNγ- producing CD4 T cells in the colonic lamina propria of GF mice (left) or in C.rodentium infected mice 5 days after infection (right). b, IsoLCA reduced IFNγ+ TH17 cell level but did not affect TH1 and Treg cell levels in GF mice following C. rodentium infection (n = 8 for control and isoLCA groups, data are mean ± SEM pooled from two experiments followed by two-tailed unpaired t test). c, IsoLCA inhibited TH17 and IFNγ+ TH17 cell levels in a dose-dependent manner but not TH1 and Treg cell levels in GF mice treated with 0.08% or 0.4% (w/w) isoLCA-containing diet (linear regression, n = 12 mice pooled from two experiments; TH17, R-squared = 0.4877, p = 0.0115; IFNγ+ TH17, R-squared = 0.5083, p = 0.0093; TH1, R-squared = 0.0848, p = 0.3715; Treg, R-squared = 0.006924, p = 0.7971). d, LCA did not affect IFNγ+ TH17 level while TH1 and Treg cell levels were negatively impacted in GF mice following C. rodentium infection. Mice were sorted into quartile groups based on LCA levels in caecal contents (see Methods for details, n = 5 mice for Q1, n = 6 for Q2, n = 6 for Q3 and n = 5 for Q4, data are mean ± SEM pooled from three experiments, one-way ANOVA followed by Tukey’s multiple comparison test). e, LCA treatment did not affect TH17 and IFNγ+ TH17 cell levels but negatively impacted TH1 and Treg cell levels in GF mice treated with 0.012%, 0.06%, 0.25% or 0.3% (w/w) LCA-containing diets (linear regression, n = 22 mice; TH17, R-squared = 0.01291, p = 0.6141; IFNγ+ TH17, R-squared = 0.1783, p = 0.0503; TH1, R-squared = 0.3818, p = 0.0022; Treg, R-squared = 0.3989, p = 0016). f, 3-oxoLCA and isoLCA levels in mice colonized with the high producer bacterial group were significantly higher than those colonized with the low producer groups (linear regression, R-squared = 0.1434, p = 0.0564, n = 26 mice for low producers; R-squared = 0.4727, p = 0.0011, n = 19 for high producers; p = 0.0033 for the difference between two lines). g, GF mice colonized with bacterial producers of 3-oxoLCA and isoLCA affected IFNγ+ TH17 but not TH1 or Treg cell levels. Mice were sorted into quartile groups based on 3-oxoLCA+isoLCA levels in caecal contents (see Methods for details, n = 11 mice for Q1, n = 12 for Q2, n = 11 for Q3 and n = 11 for Q4, data are mean ± SEM pooled from six experiments, one-way ANOVA followed by Tukey’s multiple comparison test). h, GF mice colonized with low and high bacterial producers of 3-oxoLCA and isoLCA affected TH17 and IFNγ+ TH17 but not TH1 or Treg cell levels (linear regression, n = 26 for low producers, n = 19 mice for high producers; TH17, R-squared = 0.02255, p = 0.4640 for low producers, R-squared = 0.3699, p = 0.0057 for high producers, p = 0.3007 for the interaction term (slope*bacterial groups); IFNγ+ TH17, R-squared = 0.03817, p = 0.3389 for low producers, R-squared = 0.3079, p = 0.0137 for high producers, p = 0.7402 for the interaction term (slope*bacterial groups); TH1, R-squared = 0.1533, p = 0.0647 for low producers, R-squared = 0.006748, p = 0.2430 for high producers, p = 0.3013 for the interaction term (slope*bacterial groups); Treg, R-squared = 0.0539, p = 0.2538 for low producers; R-squared = 0.1575, p = 0.0925 for high producers, p = 0.9930 for the interaction term (slope*bacterial groups)). i, TH17 cell percentages do not affect C. rodentium-encoded espB levels. Citrobacter colonization was measured by qPCR analyses detecting espB and plotted against TH17 cell percentages in mice used for bacterial colonization experiments shown in Fig. 4g, Extended Data Fig. 5g, h were determined by qPCR and plotted against percentage of Th17 cells in individual mice. n = 31, R squared = 0.02928 for goodness of fit, F = 0.9352, p = 0.3414 for slope by simple linear regression. Dotted lines are 95% confidence bands of the best fit line.
Abundances of identifiable BAs in PRISM cohort. BA levels were not universally decreased in CD patients, indicating that decreased levels of LCA, 3-oxoLCA, and isoLCA were not due to lower levels of all BAs in these cohorts. Boxplots show median and lower/upper quartiles with outliers outside of boxplot ‘whiskers’ (indicating the inner fences of the data). n = 34 for CD, n = 52 for UC and n = 34 for non-IBD. The percentage of zeros in each condition are added as x-axis tick labels. See Table S6 for full results.
Abundances of identifiable BAs in HMP2 cohort. BA levels were not universally decreased in dysbiotic CD patients, indicating that decreased levels of LCA, 3-oxoLCA, and isoLCA were not due to lower levels of all BAs in these cohorts. Boxplots show median and lower/upper quartiles with outliers outside of boxplot ‘whiskers’ (indicating the inner fences of the data). n = 47 for dysbiotic CD, n = 169 for non-dysbiotic CD, n = 12 for dysbiotic UC, n = 110 for non-dysbiotic UC and n = 122 for non-IBD. The percentage of zeros in each condition are added as x-axis tick labels. See Table S6 for full results.
Extended Data Fig. 8 Correlation between TH17/IL-17-related features and LCA metabolite abundance in HMP2.
TH17/IL-17-related genes upregulated in IBD were significantly negatively correlated with 3-oxoLCA and isoLCA (FDR-adjusted p-value < 0.25) but not the other 3 control BAs (LCA, DCA, and CDCA). Differentially expressed TH17/IL-17-related genes with at least one significant association are shown. This analysis was based on a subset of n = 71 participant-unique samples with matched metagenomic, metabolomic, and host transcriptomic profiling in the HMP2 cohort (33 CD, 21 UC, and 17 non-IBD controls, Spearman correlation with FDR adjusted p-value < 0.25). Correlations were based on residual transcript and metabolite abundance after correcting for diagnosis, consent age, and antibiotic use. See Table S8 for full results.
Extended Data Fig. 9 Correlation between 3α,β-HSDH-related microbial features and LCA metabolite abundance in HMP2.
a–b, Relative abundance distributions of differentially abundant 3α-HSDH (a) and 3α-HSDH (b) homologues profiled from HMP2 metagenomes (n = 1,595 samples from 130 subjects: linear mixed-effects model coefficient for dysbiosis within diagnosis, FDR-adjusted p-values < 0.05). Boxplots show median and lower/upper quartiles with outliers outside of boxplot ‘whiskers’ (indicating the inner fences of the data). The percentage of zeros in each condition are added as x-axis tick labels. See Table S9 for full results. c–f, LCA metabolites show significant differential abundance after adjusting for variation in underlying taxonomic abundance. Accounting for underlying variation in the taxonomic abundance of the major producers of isoLCA (Actinobacteria and Firmicutes), we used the phyla abundances as additional covariates to normalize the abundance of LCA metabolites and enzymes. 3-OxoLCA (c) and isoLCA (d) as derived from metabolomic profiles of HMP2 cohorts are significantly depleted in HMP2 dysbiotic CD samples (n = 48) relative to non-dysbiotic controls (n = 169). Meanwhile, 3α-HSDH (e) and 3β-HSDH (f) homologues were also profiled from HMP2 metagenomes (n = 1,595 samples from 130 participants; linear mixed-effects model coefficient for dysbiosis within diagnosis, FDR-adjusted p-values < 0.05). The percentage of zeros in each condition are added as x-axis tick labels. Boxplot ‘boxes’ indicate the first, second (median), and third quartiles of the data. The points outside of boxplot whiskers are outliers. Statistical analysis was performed using a linear mixed-effect model and its coefficient and significance, FDR-adjusted p-values, are shown.
Extended Data Fig. 10 3α- and 3β-HSDH homologues and species with 3α-/ 3β-HSDH activity are likely to be positively correlated with 3-oxoLCA/ isoLCA in HMP2.
a, Differentially abundant 3α-/ 3β-HSDH homologues (FDR adjusted p-value < 0.05) with at least one significant metabolite association (Spearman correlation with FDR adjusted p-value < 0.25). Correlations were computed over a subset of paired metabolomes and metagenomes from the HMP2 cohort derived from 106 participants (CD, n = 50; UC, n = 30; Non-IBD, n = 26). b, Differentially abundant species with validated 3α-/ 3β-HSDH activity (FDR adjusted p-value < 0.05) with at least one significant metabolite association (Spearman correlation with FDR adjusted p-value < 0.25) with five metabolites are shown for the paired metabolome and metagenome samples from 106 participants (CD, n = 50; UC, n = 30; Non-IBD, n = 26) in HMP2.
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Paik, D., Yao, L., Zhang, Y. et al. Human gut bacteria produce ΤΗ17-modulating bile acid metabolites. Nature 603, 907–912 (2022). https://doi.org/10.1038/s41586-022-04480-z
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