Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Bacterial metabolism of bile acids promotes generation of peripheral regulatory T cells

Abstract

Intestinal health relies on the immunosuppressive activity of CD4+ regulatory T (Treg) cells1. Expression of the transcription factor Foxp3 defines this lineage, and can be induced extrathymically by dietary or commensal-derived antigens in a process assisted by a Foxp3 enhancer known as conserved non-coding sequence 1 (CNS1)2,3,4. Products of microbial fermentation including butyrate facilitate the generation of peripherally induced Treg (pTreg) cells5,6,7, indicating that metabolites shape the composition of the colonic immune cell population. In addition to dietary components, bacteria modify host-derived molecules, generating a number of biologically active substances. This is epitomized by the bacterial transformation of bile acids, which creates a complex pool of steroids8 with a range of physiological functions9. Here we screened the major species of deconjugated bile acids for their ability to potentiate the differentiation of pTreg cells. We found that the secondary bile acid 3β-hydroxydeoxycholic acid (isoDCA) increased Foxp3 induction by acting on dendritic cells (DCs) to diminish their immunostimulatory properties. Ablating one receptor, the farnesoid X receptor, in DCs enhanced the generation of Treg cells and imposed a transcriptional profile similar to that induced by isoDCA, suggesting an interaction between this bile acid and nuclear receptor. To investigate isoDCA in vivo, we took a synthetic biology approach and designed minimal microbial consortia containing engineered Bacteroides strains. IsoDCA-producing consortia increased the number of colonic RORγt-expressing Treg cells in a CNS1-dependent manner, suggesting enhanced extrathymic differentiation.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Bacterial epimerization of bile acids generates molecules with Treg cell-inducing activity.
Fig. 2: Potentiation of Treg cell generation by isoDCA requires FXR expression in DCs.
Fig. 3: Engineering an isoDCA-producing strain of Bacteroides thetaiotaomicron (B. theta).
Fig. 4: Defined bacterial consortia containing isoDCA-producing strains promote generation of pTreg cells in vivo.

Similar content being viewed by others

Data availability

RNA-sequencing and 16S amplicon sequencing data are available under BioProject (https://www.ncbi.nlm.nih.gov/bioproject/) identification codes PRJNA600898 and PRJNA600979. Source data for Figs. 14 and Extended Data Figs. 18 are available as .xsl tables with the paper. Other relevant data are available from the corresponding authors upon reasonable request.

References

  1. Josefowicz, S. Z. et al. Extrathymically generated regulatory T cells control mucosal TH2 inflammation. Nature 482, 395–399 (2012).

    Article  ADS  CAS  Google Scholar 

  2. Zheng, Y. et al. Role of conserved non-coding DNA elements in the Foxp3 gene in regulatory T-cell fate. Nature 463, 808–812 (2010).

    Article  ADS  CAS  Google Scholar 

  3. Nutsch, K. et al. Rapid and efficient generation of regulatory T cells to commensal antigens in the periphery. Cell Rep. 17, 206–220 (2016).

    Article  CAS  Google Scholar 

  4. Kim, K. S. et al. Dietary antigens limit mucosal immunity by inducing regulatory T cells in the small intestine. Science 351, 858–863 (2016).

    Article  ADS  CAS  Google Scholar 

  5. Arpaia, N. et al. Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation. Nature 504, 451–455 (2013).

    Article  ADS  CAS  Google Scholar 

  6. Furusawa, Y. et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature 504, 446–450 (2013).

    Article  ADS  CAS  Google Scholar 

  7. Smith, P. M. et al. The microbial metabolites, short-chain fatty acids, regulate colonic Treg cell homeostasis. Science 341, 569–573 (2013).

    Article  ADS  CAS  Google Scholar 

  8. Ridlon, J. M., Kang, D. J. & Hylemon, P. B. Bile salt biotransformations by human intestinal bacteria. J. Lipid Res. 47, 241–259 (2006).

    Article  CAS  Google Scholar 

  9. Ridlon, J. M., Harris, S. C., Bhowmik, S., Kang, D. J. & Hylemon, P. B. Consequences of bile salt biotransformations by intestinal bacteria. Gut Microbes 7, 22–39 (2016).

    Article  CAS  Google Scholar 

  10. Wahlström, A., Sayin, S. I., Marschall, H. U. & Bäckhed, F. Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metab. 24, 41–50 (2016).

    Article  Google Scholar 

  11. 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).

    Article  CAS  Google Scholar 

  12. Eyssen, H., De Pauw, G., Stragier, J. & Verhulst, A. Cooperative formation of omega-muricholic acid by intestinal microorganisms. Appl. Environ. Microbiol. 45, 141–147 (1983).

    Article  CAS  Google Scholar 

  13. Eyssen, H. J., De Pauw, G. & Van Eldere, J. Formation of hyodeoxycholic acid from muricholic acid and hyocholic acid by an unidentified gram-positive rod termed HDCA-1 isolated from rat intestinal microflora. Appl. Environ. Microbiol. 65, 3158–3163 (1999).

    Article  CAS  Google Scholar 

  14. Hamilton, J. P. et al. Human cecal bile acids: concentration and spectrum. Am. J. Physiol. Gastrointest. Liver Physiol. 293, G256–G263 (2007).

    Article  CAS  Google Scholar 

  15. Hofmann, A. F. & Small, D. M. Detergent properties of bile salts: correlation with physiological function. Annu. Rev. Med. 18, 333–376 (1967).

    Article  CAS  Google Scholar 

  16. Kretschmer, K. et al. Inducing and expanding regulatory T cell populations by foreign antigen. Nat. Immunol. 6, 1219–1227 (2005).

    Article  CAS  Google Scholar 

  17. Haxhinasto, S., Mathis, D. & Benoist, C. The AKT-mTOR axis regulates de novo differentiation of CD4+Foxp3+ cells. J. Exp. Med. 205, 565–574 (2008).

    Article  CAS  Google Scholar 

  18. Hang, S. et al. Bile acid metabolites control TH17 and Treg cell differentiation. Nature 576, 143–148 (2019).

    Article  ADS  CAS  Google Scholar 

  19. Song, X. et al. Microbial bile acid metabolites modulate gut RORγ+ regulatory T cell homeostasis. Nature 577, 410–415 (2020).

    Article  CAS  Google Scholar 

  20. Sefik, E. et al. Individual intestinal symbionts induce a distinct population of RORγ+ regulatory T cells. Science 349, 993–997 (2015).

    Article  ADS  CAS  Google Scholar 

  21. Ohnmacht, C. et al. The microbiota regulates type 2 immunity through RORγt+ T cells. Science 349, 989–993 (2015).

    Article  ADS  CAS  Google Scholar 

  22. Parks, D. J. et al. Bile acids: natural ligands for an orphan nuclear receptor. Science 284, 1365–1368 (1999).

    Article  ADS  CAS  Google Scholar 

  23. Campbell, C. et al. Extrathymically generated regulatory T cells establish a niche for intestinal border-dwelling bacteria and affect physiologic metabolite balance. Immunity 48, 1245–1257 (2018).

    Article  CAS  Google Scholar 

  24. White, J. et al. Two better cell lines for making hybridomas expressing specific T cell receptors. J. Immunol. 143, 1822–1825 (1989).

    CAS  PubMed  Google Scholar 

  25. Hooijberg, E., Bakker, A. Q., Ruizendaal, J. J. & Spits, H. NFAT-controlled expression of GFP permits visualization and isolation of antigen-stimulated primary human T cells. Blood 96, 459–466 (2000).

    Article  CAS  Google Scholar 

  26. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  Google Scholar 

  27. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  Google Scholar 

  28. 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).

    Article  Google Scholar 

  29. Schmidt, J. et al. NSAIDs ibuprofen, indometacin, and diclofenac do not interact with farnesoid X receptor. Sci. Rep. 5, 14782 (2015).

    Article  ADS  CAS  Google Scholar 

  30. Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108 (Suppl 1), 4516–4522 (2011).

    Article  ADS  CAS  Google Scholar 

  31. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    Article  CAS  Google Scholar 

  32. Edgar, R. C. SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. bioRxiv https://doi.org/10.1101/074161 (2016).

  33. Cole, J. R. et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).

    Article  CAS  Google Scholar 

  34. Whitaker, W. R., Shepherd, E. S. & Sonnenburg, J. L. Tunable expression tools enable single-cell strain distinction in the gut microbiome. Cell 169, 538–546 (2017).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank F. Gonzalez (National Institutes of Health, NIH) for providing Nr1h4fl mice and F. Geissmann (MSKCC) for Csf1rcre mice. We thank J. Sonnenburg (Stanford University) for providing genetic tools to engineer B. theta. We thank S.-E. Brown, M. Rosenthal, T. Nguyen and P. Gonzales (Boehringer Ingelheim) and R. Pinedo (Weill Cornell Medical College) for assistance with germ-free mice. We thank O. Ouerfelli and the staff at the Organic Synthesis core at MSKCC for producing 6-oxoMCA. We thank A. Pickard (MKSCC) for assistance with metabolomic data analyses. We thank E. D. D’Andrea (University of Arizona) for assistance with DSF experiments. We thank J. van der Veeken for discussion of gene-expression data and all other members of the Rudensky laboratory for suggestions and technical assistance. This study was supported by NIH grant R37, the Ludwig Institute for Cancer Research, the Hilton Foundation, and Research Beyond Borders at Boehringer Ingelheim. W.G. and C.J.G were supported by NIH grant 1DP2HD101401-01. A.Y.R is an investigator with the Howard Hughes Medical Institute.

Author information

Authors and Affiliations

Authors

Contributions

C.C., P.T.M and A.Y.R. conceived the study, designed experiments and wrote the manuscript; C.C. and P.T.M performed experiments and analysed data; D.K. designed and cloned the original B. theta constructs; K.K. and C.M. provided technical assistance with experiments; M.S. and O.I.I. analysed gene-expression data sets; J.V. maintained germ-free mouse strains; C.-J.G. and W.-B.J. analysed bacterial bile acid transformation by mass spectrometry; S.V., R.J.R. and J.R.C. quantified SCFAs by mass spectrometry; J.H. and A.Y.R. supervised the study.

Corresponding authors

Correspondence to Clarissa Campbell or Alexander Y. Rudensky.

Ethics declarations

Competing interests

An invention disclosure has been filed based on the data generated in this study. P.T.M. and A.Y.R. received funding from Boehringer Ingelheim. A.Y.R. is a co-founder and member of the scientific advisory board of, and holds stock options in, Vedanta Biosciences. P.T.M. receives licensing royalties from Seres Therapeutics and is a co-inventor on patent applications US20170087196A1, US20180256653A1 and WO2018195467A1.

Additional information

Peer review information Nature thanks Richard Steven Blumberg 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 Effects of iso- and oxo-bile acids on T cell differentiation and proliferation.

a, Effects of Treg cell-inducing bile acids on the in vitro generation of TH17 cells. Naive CD4+ T cells were activated by DCs in TH17-polarizing conditions (2 ng ml−1 TGF-β, 1 μg ml−1 CD3 antibody and 20 ng ml IL-6). On day 3, cocultures were restimulated with phorbol myristate (PMA) and ionomycin in the presence of brefeldin A and monensin for 3 h before FACS analysis of IL-17 production. b, The 6β-OH group of ω-MCA is required for its Treg cell-inducing activity. Naive CD4+ T cells were activated by DCs in suboptimal Treg cell-inducing conditions (1 ng ml−1 TGF-β, 1 μg ml−1 CD3 antibody and 100 U ml−1 IL-2) and exposed to ω-MCA or 6-oxoMCA at the indicated concentrations. Foxp3 induction was assessed by FACS on day 3. c, Assessment of cell division in the presence of isoDCA and 3-oxoDCA (100 μM). Naive CD4+ T cells were labelled with Cell Trace Violet and activated with CD3/CD28 antibody-coated beads in the presence of TGF-β and IL-2 for three days before FACS analysis. Data shown are means ± s.d. of replicates (ac, n = 3). Statistical significance determined by one-way (a, b) or two-way (c) ANOVA followed by a Dunnet’s (a) or Tukey’s (b, c) multiple comparison test. *P < 0.05, **P < 0.01, ***P < 0.001 versus vehicle; hash symbol, P < 0.05 vs ω-MCA (paired concentration); plus symbol, P < 0.05 versus isoDCA (paired concentration); ns, not significant. Data are representative of at least two independent experiments.

Source Data

Extended Data Fig. 2 Characterization of mice with FXR deficiency in the myeloid compartment.

ae, WT (Csf1rWT Nr1h4fl/fl) and DCΔFXR (Csf1rcre Nr1h4fl/fl) littermate mice were analysed between six to eight weeks of age. a, Gating strategy and b, quantification of conventional (c)DC1s (live CD45+ Lin (dump: CD90, CD3; CD64, Ly6C, Siglec-F) CD11c+ MHC class IIhi CD11b XCR1+) and cDC2 (live CD45+ Lin (dump: CD90, CD3; CD64, Ly6C, Siglec-F) CD11c+ MHC class IIhi CD11b+ XCR1) in the spleen (Spl), mesenteric lymph node (MLN) and LILP. c, Gating strategy for d, e. d, Number of total Foxp3+ Treg cells in the indicated organs. e, Quantification of RORγt+ Foxp3+ Treg cells in the LILP. Data shown are means ± s.d. (n = 5), representative of two independent cohorts of mice. Statistical significance determined by a two-tailed t-test. **P < 0.01.

Source Data

Extended Data Fig. 3 Anti-inflammatory effects of isoDCA treatment on DCs.

a, DCs (1 × 105) were stimulated for 18 h with various TLR agonists (x-axes) in the presence or absence of 50 μM isoDCA. Levels of the indicated cytokines (TNF-α and IL-6) in the culture supernatant were determined by ELISA. b, DCs (1 × 104) were pulsed with ovalbumin (OVA, 1 mg ml−1) in the presence of various concentrations of isoDCA for 1 h in serum-free medium and allowed to process antigen for 4 h in complete medium before addition of an NFAT–GFP reporter cell line expressing the MHC-II-restricted OT-II TCR recognizing the ISQAVHAAHAEINEAGR peptide of OVA. The frequency of GFP+ cells was determined by FACS analysis after 24 h. Cocultures treated with CD3 antibody (1 μg ml−1) served as controls for DC-dependent, antigen-processing-independent effects of isoDCA on the activation of reporter cells. Activation with CD3/CD28 antibody beads in the presence of isoDCA served as a control for DC-independent effects on reporter gene expression. Shown are means ± s.d. of replicates in a and fold-change relative to vehicle (0 μM isoDCA) within each condition (OVA, CD3 or CD3/CD28 antibody-coated beads) in b. Statistical significance in a was determined by multiple t-tests using the Holm–Sidak correction method with α = 0.05. ****P < 0.001 versus vehicle. Statistical significance in b was determined by a two-way ANOVA followed by Dunnet’s multiple comparison’s test. *P < 0.05; ****P < 0.001 versus vehicle in each condition. Data are representative of three independent experiments.

Source Data

Extended Data Fig. 4 Liquid chromatography–mass spectrometry (LC–MS)-based analysis of isoDCA production by engineered B. theta strains.

Bacteria were grown to exponential phase and transferred to media containing DCA. Following incubation for 24 h, media was extracted with methanol and supernatants were analysed by liquid chromatography-mass spectrometry (LC–MS). Shown are traces for spike-in controls with DCA and isoDCA standards, and for media conditioned by B. thetaeWT, B. thetaeCD or the parental, unmanipulated B. thetaVPI strain VPI-5482. Data are representative of two independent experiments carried out in triplicate.

Extended Data Fig. 5 Analyses of microbial community composition in gnotobiotic and conventionalized mice.

GF mice were gavaged with WT or CD engineered consortia (C. scindens plus B. thetaeWT or C. scindens plus B. thetaeCD). Recipients of an FMT or noncolonized mice (PBS) served as references. The OTU composition of the caecal microbiota on day 10 post-colonization was determined by 16S sequencing. Shown are total read counts (left) and relative abundances (right) of bacteria in individual experimental mice, with data pooled from two independent experiments (n = 10).

Extended Data Fig. 6 Effects of isoDCA-producing consortia on colonic lymphocytes.

GF mice were gavaged with engineered consortia (C. scindens plus B. thetaeWT or C. scindens plus B. thetaeCD), PBS or a complex microbial community (FMT) as in Fig. 4b. ag, Immune cell composition in the LILP was analysed by FACS on day 10 (D10; ae) or day 30 (f, g) post-colonization. a, b, Frequencies of total Foxp3+ (a) and RORγt+ Foxp3+ (b) Treg cells among CD45+ cells. c, d, Frequency of RORγt+ Foxp3+ cells in the MLN (c) and small intestine lamina propria (SILP, d). e, Frequency of RORγt+ cells among Foxp3 CD4+ T cells (e, f) and Foxp3+ CD4+ T cells (g). Data shown are means ± s.d. (n = 10), pooled from two independent experiments. Statistical significance determined by one-way ANOVA followed by Tukey’s multiple comparison’s test. **P < 0.01; ***P < 0.001; ****P < 0.0001; ns, not significant.

Source Data

Extended Data Fig. 7 IsoDCA production by engineered Bacteroides sp. strains.

a, Quantification of isoDCA production by engineered and reference strains in vitro. Bacteria were grown to exponential phase and transferred to medium containing DCA. Following incubation for 24 h, medium was extracted with methanol and supernatants were analysed by LC–MS. AUC, area under curve. b, c, GF mice were colonized with consortia containing either the engineered strain of B. frag capable of producing isoDCA or the catalytically dead mutant in combination with C. scindens (C. scindens plus B. frageWT and C. scindens plus B. frageCD, respectively). Recipients of an FMT and noncolonized mice (PBS) served as references. Immune-cell composition and isoDCA quantification were performed 10 days post-colonization. b, FACS analysis of the frequency of RORγt+ Foxp3+ CD4+ T cells in the LILP. c, Quantification of isoDCA in caecal contents. Faecal material was weighed, homogenized and extracted with methanol for LC–MS analysis. In a, c, the AUC is normalized by the weight of the input material. Shown are means ± s.d. (a, n = 3; b, n = 10; c, n = 5). Data in a, c are representative of two independent experiments. Data in b are pooled from two independent experiments. Statistical significance determined by a one-way ANOVA followed by Tukey’s multiple comparison’s test. *P < 0.05; ND, not detected; ns, not significant.

Source Data

Extended Data Fig. 8 SCFA production by minimal, defined microbial consortia.

GF mice were colonized with consortia containing either the engineered strain of B. frag capable of producing isoDCA or the catalytically dead mutant in combination with C. scindens (C. scindens plus B. frageWT and C. scindens plus B. frageCD, respectively). Recipients of an FMT and noncolonized mice (PBS) served as references. Caecal content material was weighed, homogenized and subjected to organic solvent extraction for GC–MS-based quantification of SCFA levels. Shown are means ± s.d. (n = 6), with data pooled from two independent experiments. Statistical significance determined by a one-way ANOVA followed by Tukey’s multiple comparison’s test. ****P < 0.0001; ns, not significant.

Source Data

Supplementary information

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Campbell, C., McKenney, P.T., Konstantinovsky, D. et al. Bacterial metabolism of bile acids promotes generation of peripheral regulatory T cells. Nature 581, 475–479 (2020). https://doi.org/10.1038/s41586-020-2193-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-020-2193-0

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing