Keto diet foods keto diet recipes keto pills keto diet menu for beginners keto diet for beginners keto diet explained A metabolic pathway for bile acid dehydroxylation by the gut microbiome

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Keto diet foods keto diet recipes keto pills keto diet menu for beginners keto diet for beginners keto diet explained

Abstract

The gut microbiota synthesize hundreds of molecules, many of which influence host physiology. Among the most abundant metabolites are the secondary bile acids deoxycholic acid (DCA) and lithocholic acid (LCA), which accumulate at concentrations of around 500 μM and are known to block the growth of Clostridium difficile1, promote hepatocellular carcinoma2 and modulate host metabolism via the G-protein-coupled receptor TGR5 (ref. 3). More broadly, DCA, LCA and their derivatives are major components of the recirculating pool of bile acids4; the size and composition of this pool are a target of therapies for primary biliary cholangitis and nonalcoholic steatohepatitis. Nonetheless, despite the clear impact of DCA and LCA on host physiology, an incomplete knowledge of their biosynthetic genes and a lack of genetic tools to enable modification of their native microbial producers limit our ability to modulate secondary bile acid levels in the host. Here we complete the pathway to DCA and LCA by assigning and characterizing enzymes for each of the steps in its reductive arm, revealing a strategy in which the A–B rings of the steroid core are transiently converted into an electron acceptor for two reductive steps carried out by Fe–S flavoenzymes. Using anaerobic in vitro reconstitution, we establish that a set of six enzymes is necessary and sufficient for the eight-step conversion of cholic acid to DCA. We then engineer the pathway into Clostridium sporogenes, conferring production of DCA and LCA on a nonproducing commensal and demonstrating that a microbiome-derived pathway can be expressed and controlled heterologously. These data establish a complete pathway to two central components of the bile acid pool.

Data availability

Mass spectrometry data that support our findings have been deposited in MassIVE (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp) under accession code MSV000085048. Source data are provided with this paper.

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Acknowledgements

We thank C. T. Walsh, D. Dodd, C. O’Loughlin and members of the Fischbach and Almo laboratories for helpful comments on the manuscript. This work was supported by National Institutes of Health (NIH) grants DP1 DK113598 (to M.A.F.), R01 DK110174 (to M.A.F.), P01 HL147823 (to M.A.F.), P01 GM118303-01 (to S.C.A.), U54 GM093342 (to S.C.A.), U54 GM094662 (to S.C.A.) and DP2 HD101401-01 (to C.G.); the Chan–Zuckerberg Biohub (to M.A.F.); a Howard Hughes Medical Institute (HHMI)–Simons Faculty Scholars Award (to M.A.F.); an Investigators in the Pathogenesis of Infectious Disease Award from the Burroughs Wellcome Foundation (to M.A.F.); and the Price Family Foundation (to S.C.A.).

Author information

Author notes

  1. Masanori Funabashi

    Present address: Translational Research Department, Daiichi Sankyo RD Novare Co. Ltd, Tokyo, Japan

  2. These authors contributed equally: Masanori Funabashi, Tyler L. Grove

Affiliations

  1. Department of Bioengineering and ChEM-H, Stanford University, Stanford, CA, USA

    Masanori Funabashi, Min Wang, Yug Varma, Chunjun Guo & Michael A. Fischbach

  2. Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA

    Tyler L. Grove & Steven C. Almo

  3. Department of Chemistry, Indiana University, Bloomington, IN, USA

    Molly E. McFadden & Laura C. Brown

  4. Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA

    Steven Higginbottom

  5. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Michael A. Fischbach

Contributions

M.F., T.L.G., S.C.A. and M.A.F. conceived and designed the experiments. M.F. developed the system for gene-cluster expression in Clostridium, and M.F., C.G. and Y.V. performed the bacterial genetics experiments. T.L.G. expressed and purified enzymes and set up biochemical reconstitution experiments. M.F. analysed the data from biochemical and microbiological experiments by LC–MS. M.E.M. and L.C.B. synthesized bile acid intermediates. M.W. and S.H. performed and analysed mouse experiments. M.F., T.L.G., M.W., S.C.A. and M.A.F. analysed data and wrote the manuscript. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to
Steven C. Almo or Michael A. Fischbach.

Ethics declarations

Competing interests

M.A.F. is a co-founder and director of Federation Bio.

Additional information

Peer review information Nature thanks Pieter Dorrestein 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. 2 Purification of recombinant Bai proteins.

a, SDS–PAGE analysis of purified Bai proteins after Ni-affinity and size-exclusion purification, visualized by Coomassie blue staining. The image was generated using a Bio-Rad Gel Doc Universal Hood II Molecular Imager. MWM, molecular weight marker; 1, BaiB from C. scindens; 2, BaiB from C. hylemonae; 3, BaiCD from C. hiranonis; 4, BaiE from C. scindens; 5, BaiE from C. hiranonis; 6, BaiA2 from C. scindens; 7, BaiF from C. hylemonae; 8, BaiH from C. scindens; 9, BaiI from C. scindens; 10, BaiI from C. hiranonis.b, Ultraviolet–visible spectra of BaiCD from C. hiranonis (24 μM, left) and BaiH from C. scindens (13 μM, right). Features at 370 nm and 450 nm are indicative of flavin bound to BaiCD and BaiH, and are partially obscured by the presence of a [4Fe–4S] cluster, which has broad absorbance between 300 nm and 700 nm. c, The presence of FMN and FAD is confirmed by mass spectrometry. Experiments in ac were repeated independent twice, with similar results.

Extended Data Fig. 3 Bile acid standards.

a, For each compound in the study for which we have an authentic standard, we show an EIC of the authentic standard and the experimentally observed compound. Because the data shown here were collected from samples run at different times, a drift in retention time may be responsible for the peak pairs that do not have identical retention times. b, We observed a drift in retention time in the LC–MS data collected for the experiment shown in Fig. 2c. For two representative compounds from that data set, we show an EIC of the experimentally observed compound and an authentic standard run contemporaneously, showing that the retention times remain consistent with our peak assignments.

Extended Data Fig. 4 Kinetic parameters for BaiCD and BaiH.

a, Michaelis–Menten analysis of the conversion of 3-oxo-4,5-dehydro-DCA to 3-oxo-DCA by BaiCD. Reaction mixtures contained 0.45 μM BaiCD and 1 mM NADH, with the substrate concentration varying between 15 μM and 500 μM. b, Michaelis–Menten analysis of the conversion of 3-oxo-4,5,6,7-didehydro-DCA to 3-oxo-4,5-dehydro-DCA by BaiH. Reaction mixtures contained 0.45 μM BaiH and 1 mM NADH, with the substrate concentration varying between 3 μM and 100 μM. Data indicate the average product level ± 1 s.d. (three biological replicates).

Extended Data Fig. 5 Biochemical analysis of 3-oxo-DCA reduction by BaiA2.

Combined EICs showing the conversion of 3-oxo-DCA to DCA by recombinant BaiA2. This experiment was performed once.

Extended Data Fig. 6 7α-dehydroxylation of CDCA in vivo.

Combined EICs showing the conversion of CDCA to LCA by a C. sporogenes strain harbouring the complete bai operon on three plasmids (MF001) versus a control strain of C. sporogenes harbouring the transporter baiG (MF012). The strains were cultivated with 1 μM cholic acid for 72 h; an acetone extract of the culture supernatant was analysed by high-performance LC (HPLC)/MS. The single asterisk indicates isoLCA; the peak indicated by the double asterisk is provisionally assigned as isoCDCA. This experiment was performed once.

Extended Data Fig. 7 Constructs for expressing the bai operon and portions thereof in C. sporogenes.

Each of the plasmids has replication origins (origin and repH) for E. coli and Clostridium, the traJ gene to enable conjugal plasmid transfer, and an antibiotic-resistance gene (catP, aad9 or ermB). The bai genes were introduced into these plasmids under the control of the fdx or spoIIE promoter. For the genetic analysis of baiCD and baiH function, pMTL83153-based plasmids were used.

Extended Data Fig. 8 Metabolic logic of the 7α-dehydroxylation pathway.

Highly oxidized metabolic intermediates as anaerobic electron acceptors. In the first half of the 7α-dehydroxylation pathway, two successive two-electron oxidations set up a vinylogous dehydration of the 7-hydroxyl, yielding the highly oxidized intermediate 3-oxo-4,5-6,7-didehydro-DCA. In the second half of the pathway, three successive two-electron reductions reduce this molecule to DCA, resulting in a net two-electron reduction. The first two of these reductions are carried out by Fe–S flavoenzymes, which comprise a suite of four cofactors that enable them to convert two-electron inputs to a one-electron manifold. The previously proposed pathway is shown in Extended Data Fig. 1.

Supplementary information

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Funabashi, M., Grove, T.L., Wang, M. et al. A metabolic pathway for bile acid dehydroxylation by the gut microbiome.
Nature (2020). https://doi.org/10.1038/s41586-020-2396-4

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Keto diet foods keto diet recipes keto pills keto diet menu for beginners keto diet for beginners keto diet explained

Abstract

The gut microbiota synthesize hundreds of molecules, many of which influence host physiology. Among the most abundant metabolites are the secondary bile acids deoxycholic acid (DCA) and lithocholic acid (LCA), which accumulate at concentrations of around 500 μM and are known to block the growth of Clostridium difficile1, promote hepatocellular carcinoma2 and modulate host metabolism via the G-protein-coupled receptor TGR5 (ref. 3). More broadly, DCA, LCA and their derivatives are major components of the recirculating pool of bile acids4; the size and composition of this pool are a target of therapies for primary biliary cholangitis and nonalcoholic steatohepatitis. Nonetheless, despite the clear impact of DCA and LCA on host physiology, an incomplete knowledge of their biosynthetic genes and a lack of genetic tools to enable modification of their native microbial producers limit our ability to modulate secondary bile acid levels in the host. Here we complete the pathway to DCA and LCA by assigning and characterizing enzymes for each of the steps in its reductive arm, revealing a strategy in which the A–B rings of the steroid core are transiently converted into an electron acceptor for two reductive steps carried out by Fe–S flavoenzymes. Using anaerobic in vitro reconstitution, we establish that a set of six enzymes is necessary and sufficient for the eight-step conversion of cholic acid to DCA. We then engineer the pathway into Clostridium sporogenes, conferring production of DCA and LCA on a nonproducing commensal and demonstrating that a microbiome-derived pathway can be expressed and controlled heterologously. These data establish a complete pathway to two central components of the bile acid pool.

Data availability

Mass spectrometry data that support our findings have been deposited in MassIVE (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp) under accession code MSV000085048. Source data are provided with this paper.

References

  1. 1.

    Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  2. 2.

    Yoshimoto, S. et al. Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome. Nature 499, 97–101 (2013); corrigendum 506, 396 (2014).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  3. 3.

    Duboc, H., Taché, Y. & Hofmann, A. F. The bile acid TGR5 membrane receptor: from basic research to clinical application. Dig. Liver Dis. 46, 302–312 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  4. 4.

    Arab, J. P., Karpen, S. J., Dawson, P. A., Arrese, M. & Trauner, M. Bile acids and nonalcoholic fatty liver disease: molecular insights and therapeutic perspectives. Hepatology 65, 350–362 (2017).

    PubMed 

    Google Scholar
     

  5. 5.

    Nicholson, J. K. et al. Host-gut microbiota metabolic interactions. Science 336, 1262–1267 (2012).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  6. 6.

    Lee, W.-J. & Hase, K. Gut microbiota-generated metabolites in animal health and disease. Nat. Chem. Biol. 10, 416–424 (2014).

    CAS 
    PubMed 

    Google Scholar
     

  7. 7.

    Koppel, N., Maini Rekdal, V. & Balskus, E. P. Chemical transformation of xenobiotics by the human gut microbiota. Science 356, eaag2770 (2017).

    PubMed 

    Google Scholar
     

  8. 8.

    Donia, M. S. & Fischbach, M. A. Small molecules from the human microbiota. Science 349, 1254766 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

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    Patel, K. P., Luo, F. J.-G., Plummer, N. S., Hostetter, T. H. & Meyer, T. W. The production of p-cresol sulfate and indoxyl sulfate in vegetarians versus omnivores. Clin. J. Am. Soc. Nephrol. 7, 982–988 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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    Bouatra, S. et al. The human urine metabolome. PLoS ONE 8, e73076 (2013).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  11. 11.

    Furusawa, Y. et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature 504, 446–450 (2013); erratum 506, 254 (2014).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  12. 12.

    Maslowski, K. M. et al. Regulation of inflammatory responses by gut microbiota and chemoattractant receptor GPR43. Nature 461, 1282–1286 (2009).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  13. 13.

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

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  14. 14.

    Wells, J. E., Berr, F., Thomas, L. A., Dowling, R. H. & Hylemon, P. B. Isolation and characterization of cholic acid 7α-dehydroxylating fecal bacteria from cholesterol gallstone patients. J. Hepatol. 32, 4–10 (2000).

    CAS 
    PubMed 

    Google Scholar
     

  15. 15.

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

    CAS 
    PubMed 

    Google Scholar
     

  16. 16.

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

    CAS 
    PubMed 

    Google Scholar
     

  17. 17.

    de Aguiar Vallim, T. Q., Tarling, E. J. & Edwards, P. A. Pleiotropic roles of bile acids in metabolism. Cell Metab. 17, 657–669 (2013).

    PubMed 
    PubMed Central 

    Google Scholar
     

  18. 18.

    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).

    PubMed 

    Google Scholar
     

  19. 19.

    Brestoff, J. R. & Artis, D. Commensal bacteria at the interface of host metabolism and the immune system. Nat. Immunol. 14, 676–684 (2013).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  20. 20.

    White, B. A., Lipsky, R. L., Fricke, R. J. & Hylemon, P. B. Bile acid induction specificity of 7α-dehydroxylase activity in an intestinal Eubacterium species. Steroids 35, 103–109 (1980).

    CAS 
    PubMed 

    Google Scholar
     

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Acknowledgements

We thank C. T. Walsh, D. Dodd, C. O’Loughlin and members of the Fischbach and Almo laboratories for helpful comments on the manuscript. This work was supported by National Institutes of Health (NIH) grants DP1 DK113598 (to M.A.F.), R01 DK110174 (to M.A.F.), P01 HL147823 (to M.A.F.), P01 GM118303-01 (to S.C.A.), U54 GM093342 (to S.C.A.), U54 GM094662 (to S.C.A.) and DP2 HD101401-01 (to C.G.); the Chan–Zuckerberg Biohub (to M.A.F.); a Howard Hughes Medical Institute (HHMI)–Simons Faculty Scholars Award (to M.A.F.); an Investigators in the Pathogenesis of Infectious Disease Award from the Burroughs Wellcome Foundation (to M.A.F.); and the Price Family Foundation (to S.C.A.).

Author information

Author notes

  1. Masanori Funabashi

    Present address: Translational Research Department, Daiichi Sankyo RD Novare Co. Ltd, Tokyo, Japan

  2. These authors contributed equally: Masanori Funabashi, Tyler L. Grove

Affiliations

  1. Department of Bioengineering and ChEM-H, Stanford University, Stanford, CA, USA

    Masanori Funabashi, Min Wang, Yug Varma, Chunjun Guo & Michael A. Fischbach

  2. Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA

    Tyler L. Grove & Steven C. Almo

  3. Department of Chemistry, Indiana University, Bloomington, IN, USA

    Molly E. McFadden & Laura C. Brown

  4. Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA

    Steven Higginbottom

  5. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Michael A. Fischbach

Contributions

M.F., T.L.G., S.C.A. and M.A.F. conceived and designed the experiments. M.F. developed the system for gene-cluster expression in Clostridium, and M.F., C.G. and Y.V. performed the bacterial genetics experiments. T.L.G. expressed and purified enzymes and set up biochemical reconstitution experiments. M.F. analysed the data from biochemical and microbiological experiments by LC–MS. M.E.M. and L.C.B. synthesized bile acid intermediates. M.W. and S.H. performed and analysed mouse experiments. M.F., T.L.G., M.W., S.C.A. and M.A.F. analysed data and wrote the manuscript. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to
Steven C. Almo or Michael A. Fischbach.

Ethics declarations

Competing interests

M.A.F. is a co-founder and director of Federation Bio.

Additional information

Peer review information Nature thanks Pieter Dorrestein 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. 2 Purification of recombinant Bai proteins.

a, SDS–PAGE analysis of purified Bai proteins after Ni-affinity and size-exclusion purification, visualized by Coomassie blue staining. The image was generated using a Bio-Rad Gel Doc Universal Hood II Molecular Imager. MWM, molecular weight marker; 1, BaiB from C. scindens; 2, BaiB from C. hylemonae; 3, BaiCD from C. hiranonis; 4, BaiE from C. scindens; 5, BaiE from C. hiranonis; 6, BaiA2 from C. scindens; 7, BaiF from C. hylemonae; 8, BaiH from C. scindens; 9, BaiI from C. scindens; 10, BaiI from C. hiranonis.b, Ultraviolet–visible spectra of BaiCD from C. hiranonis (24 μM, left) and BaiH from C. scindens (13 μM, right). Features at 370 nm and 450 nm are indicative of flavin bound to BaiCD and BaiH, and are partially obscured by the presence of a [4Fe–4S] cluster, which has broad absorbance between 300 nm and 700 nm. c, The presence of FMN and FAD is confirmed by mass spectrometry. Experiments in ac were repeated independent twice, with similar results.

Extended Data Fig. 3 Bile acid standards.

a, For each compound in the study for which we have an authentic standard, we show an EIC of the authentic standard and the experimentally observed compound. Because the data shown here were collected from samples run at different times, a drift in retention time may be responsible for the peak pairs that do not have identical retention times. b, We observed a drift in retention time in the LC–MS data collected for the experiment shown in Fig. 2c. For two representative compounds from that data set, we show an EIC of the experimentally observed compound and an authentic standard run contemporaneously, showing that the retention times remain consistent with our peak assignments.

Extended Data Fig. 4 Kinetic parameters for BaiCD and BaiH.

a, Michaelis–Menten analysis of the conversion of 3-oxo-4,5-dehydro-DCA to 3-oxo-DCA by BaiCD. Reaction mixtures contained 0.45 μM BaiCD and 1 mM NADH, with the substrate concentration varying between 15 μM and 500 μM. b, Michaelis–Menten analysis of the conversion of 3-oxo-4,5,6,7-didehydro-DCA to 3-oxo-4,5-dehydro-DCA by BaiH. Reaction mixtures contained 0.45 μM BaiH and 1 mM NADH, with the substrate concentration varying between 3 μM and 100 μM. Data indicate the average product level ± 1 s.d. (three biological replicates).

Extended Data Fig. 5 Biochemical analysis of 3-oxo-DCA reduction by BaiA2.

Combined EICs showing the conversion of 3-oxo-DCA to DCA by recombinant BaiA2. This experiment was performed once.

Extended Data Fig. 6 7α-dehydroxylation of CDCA in vivo.

Combined EICs showing the conversion of CDCA to LCA by a C. sporogenes strain harbouring the complete bai operon on three plasmids (MF001) versus a control strain of C. sporogenes harbouring the transporter baiG (MF012). The strains were cultivated with 1 μM cholic acid for 72 h; an acetone extract of the culture supernatant was analysed by high-performance LC (HPLC)/MS. The single asterisk indicates isoLCA; the peak indicated by the double asterisk is provisionally assigned as isoCDCA. This experiment was performed once.

Extended Data Fig. 7 Constructs for expressing the bai operon and portions thereof in C. sporogenes.

Each of the plasmids has replication origins (origin and repH) for E. coli and Clostridium, the traJ gene to enable conjugal plasmid transfer, and an antibiotic-resistance gene (catP, aad9 or ermB). The bai genes were introduced into these plasmids under the control of the fdx or spoIIE promoter. For the genetic analysis of baiCD and baiH function, pMTL83153-based plasmids were used.

Extended Data Fig. 8 Metabolic logic of the 7α-dehydroxylation pathway.

Highly oxidized metabolic intermediates as anaerobic electron acceptors. In the first half of the 7α-dehydroxylation pathway, two successive two-electron oxidations set up a vinylogous dehydration of the 7-hydroxyl, yielding the highly oxidized intermediate 3-oxo-4,5-6,7-didehydro-DCA. In the second half of the pathway, three successive two-electron reductions reduce this molecule to DCA, resulting in a net two-electron reduction. The first two of these reductions are carried out by Fe–S flavoenzymes, which comprise a suite of four cofactors that enable them to convert two-electron inputs to a one-electron manifold. The previously proposed pathway is shown in Extended Data Fig. 1.

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Funabashi, M., Grove, T.L., Wang, M. et al. A metabolic pathway for bile acid dehydroxylation by the gut microbiome.
Nature (2020). https://doi.org/10.1038/s41586-020-2396-4

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