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:

The microbial gbu gene cluster links cardiovascular disease risk associated with red meat consumption to microbiota l-carnitine catabolism

Nature Microbiology volume 7, pages 73–86 (2022)Cite this article

Subjects

Abstract

The heightened cardiovascular disease (CVD) risk observed among omnivores is thought to be linked, in part, to gut microbiota-dependent generation of trimethylamine-N-oxide (TMAO) from l-carnitine, a nutrient abundant in red meat. Gut microbial transformation of l-carnitine into trimethylamine (TMA), the precursor of TMAO, occurs via the intermediate γ-butyrobetaine (γBB). However, the interrelationship of γBB, red meat ingestion and CVD risks, as well as the gut microbial genes responsible for the transformation of γBB to TMA, are unclear. In the present study, we show that plasma γBB levels in individuals from a clinical cohort (n = 2,918) are strongly associated with incident CVD event risks. Culture of human faecal samples and microbial transplantation studies in gnotobiotic mice with defined synthetic communities showed that the introduction of Emergencia timonensis, a human gut microbe that can metabolize γBB into TMA, is sufficient to complete the carnitine → γBB → TMA transformation, elevate TMAO levels and enhance thrombosis potential in recipients after arterial injury. RNA-sequencing analyses of E. timonensis identified a six-gene cluster, herein named the γBB utilization (gbu) gene cluster, which is upregulated in response to γBB. Combinatorial cloning and functional studies identified four genes (gbuA, gbuB, gbuC and gbuE) that are necessary and sufficient to recapitulate the conversion of γBB to TMA when coexpressed in Escherichia coli. Finally, reanalysis of samples (n = 113) from a clinical, randomized diet, intervention study showed that the abundance of faecal gbuA correlates with plasma TMAO and a red meat-rich diet. Our findings reveal a microbial gene cluster that is critical to dietary carnitine → γBB → TMA → TMAO transformation in hosts and contributes to CVD risk.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Elevated levels of γBB are associated with increased incidence of MACE (MI, stroke or death) risk in human subjects and increased in vivo thrombosis potential in animal models via production of TMAO.
Fig. 2: E. timonensis is sufficient to provide vegan faecal polymicrobial communities with the anaerobic metabolic transformation capability to produce TMA from l-carnitine.
Fig. 3: In a synthetic community, E. timonensis is required to increase plasma TMAO levels and enhanced thrombotic potential in response to l-carnitine supplementation.
Fig. 4: Identification of the gbu gene cluster in E. timonensis.
Fig. 5: Microbial gbuA abundance in human faeces is both enriched by a red meat-rich diet and associated with plasma TMAO levels.
Fig. 6: Critical role of gut microbial γBB transformation into TMAO and CVD risk from a red meat-rich diet in host.

Similar content being viewed by others

Data availability

Sequencing datasets used for community composition analysis of gnotobiotic mice are publicly available through NCBI’s Sequence Read Archive (SRA) Database under BioProject accession no. PRJNA701645. Shotgun metagenomic datasets used for gbuA abundance analysis in subjects are publicly available through NCBI’s SRA Database under BioProject accession no. PRJEB44883. The 16S rRNA gene-sequencing files can be found under BioProject accession no. PRJNA498128. RNA-seq data are available under BioProject accession no. PRJNA769418. There are restrictions to the availability of some of the clinical data generated in the present study (Fig. 1), because we do not have permission in our informed consent from research subjects to share data outside our institution without their authorizations. Where permissible, the datasets generated and/or analysed during the present study are available from the corresponding author on request. Source data are provided with this paper.

Code availability

Customized R code is available at https://doi.org/10.5281/zenodo.5603090.52

References

  1. Kwok, C. S. et al. Dietary components and risk of cardiovascular disease and all-cause mortality: a review of evidence from meta-analyses. Eur. J. Prev. Cardiol. 26, 1415–1429 (2019).

    Article  PubMed  Google Scholar 

  2. Bechthold, A. et al. Food groups and risk of coronary heart disease, stroke and heart failure: a systematic review and dose-response meta-analysis of prospective studies. Crit. Rev. Food Sci. Nutr. 59, 1071–1090 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Pan, A. et al. Red meat consumption and mortality: results from 2 prospective cohort studies. Arch. Intern. Med. 172, 555 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wang, X. et al. Red and processed meat consumption and mortality: dose–response meta-analysis of prospective cohort studies. Public Health Nutr. 19, 893–905 (2016).

    Article  PubMed  Google Scholar 

  5. Abete, I., Romaguera, D., Vieira, A. R., Lopez de Munain, A. & Norat, T. Association between total, processed, red and white meat consumption and all-cause, CVD and IHD mortality: a meta-analysis of cohort studies. Br. J. Nutr. 112, 762–775 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. Koeth, R. A. et al. Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Bennett, B. J. et al. Trimethylamine-N-oxide, a metabolite associated with atherosclerosis, exhibits complex genetic and dietary regulation. Cell Metab. 17, 49–60 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Koeth, R. A. et al. l-Carnitine in omnivorous diets induces an atherogenic gut microbial pathway in humans. J. Clin. Invest. 129, 373–387 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Koeth, R. A. et al. γ-Butyrobetaine is a proatherogenic intermediate in gut microbial metabolism of l-carnitine to TMAO. Cell Metab. 20, 799–812 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zhu, Y. et al. Carnitine metabolism to trimethylamine by an unusual Rieske-type oxygenase from human microbiota. Proc. Natl Acad. Sci. USA 111, 4268–4273 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Craciun, S. & Balskus, E. P. Microbial conversion of choline to trimethylamine requires a glycyl radical enzyme. Proc. Natl Acad. Sci. USA 109, 21307–21312 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Thibodeaux, C. J. & van der Donk, W. A. Converging on a mechanism for choline degradation. Proc. Natl Acad. Sci. USA 109, 21184–21185 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Romano, K. A., Vivas, E. I., Amador-Noguez, D. & Rey, F. E. Intestinal microbiota composition modulates choline bioavailability from diet and accumulation of the proatherogenic metabolite trimethylamine-N-oxide. mBio 6, e02481–14 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Martínez-del Campo, A. et al. Characterization and detection of a widely distributed gene cluster that predicts anaerobic choline utilization by human gut bacteria. mBio 6, e00042–15 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Eichler, K., Bourgis, F., Buchet, A., Kleber, H.-P. & Mandrand-Berthelot, M.-A. Molecular characterization of the cai operon necessary for carnitine metabolism in Escherichia coli. Mol. Microbiol. 13, 775–786 (1994).

    Article  CAS  PubMed  Google Scholar 

  17. Eichler, K., Buchet, A., Lemke, R., Kleber, H. P. & Mandrand-Berthelot, M. A. Identification and characterization of the caiF gene encoding a potential transcriptional activator of carnitine metabolism in Escherichia coli. J. Bacteriol. 178, 1248–1257 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Roberts, A. B. et al. Development of a gut microbe–targeted nonlethal therapeutic to inhibit thrombosis potential. Nat. Med. 24, 1407–1417 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Skye, S. M. et al. Microbial transplantation with human gut commensals containing cutc is sufficient to transmit enhanced platelet reactivity and thrombosis potential. Circ. Res. 123, 1164–1176 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Micha, R., Michas, G., Lajous, M. & Mozaffarian, D. Processing of meats and cardiovascular risk: time to focus on preservatives. BMC Med. 11, 136 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wang, Z. et al. Impact of chronic dietary red meat, white meat, or non-meat protein on trimethylamine N-oxide metabolism and renal excretion in healthy men and women. Eur. Heart J. 40, 583–594 (2019).

    Article  CAS  PubMed  Google Scholar 

  22. Pagliai, G. et al. Influence of a 3-month low-calorie Mediterranean diet compared to the vegetarian diet on human gut microbiota and SCFA: the CARDIVEG study. Eur. J. Nutr. 59, 2011–2024 (2020).

    Article  CAS  PubMed  Google Scholar 

  23. Sakkas, H. et al. Nutritional status and the influence of the vegan diet on the gut microbiota and human health. Medicine 56, 88 (2020).

    Google Scholar 

  24. Djekic, D. et al. Effects of a vegetarian diet on cardiometabolic risk factors, gut microbiota, and plasma metabolome in subjects with ischemic heart disease: a randomized, crossover study. J. Am. Heart Assoc. 9, e016518 (2020).

  25. David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

    Article  CAS  PubMed  Google Scholar 

  26. Zhu, W. et al. Gut mcrobial metabolite TMAO enhances platelet hyperreactivity and thrombosis risk. Cell 165, 111–124 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Rajakovich, L. J., Fu, B., Bollenbach, M. & Balskus, E. P. Elucidation of an anaerobic pathway for metabolism of l-carnitine-derived γ-butyrobetaine to trimethylamine in human gut bacteria. Proc. Natl Acad. Sci. USA 118, e2101498118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Guasti, L. et al. TMAO as a biomarker of cardiovascular events: a systematic review and meta-analysis. Intern. Emerg. Med. 16, 201–207 (2021).

    Article  PubMed  Google Scholar 

  29. Zhuang, R. et al. Gut microbe-generated metabolite trimethylamine N‐oxide and the risk of diabetes: a systematic review and dose‐response meta‐analysis. Obes. Rev. 20, 883–894 (2019).

    Article  CAS  PubMed  Google Scholar 

  30. Xu, R., Wang, Q. & Li, L. A genome-wide systems analysis reveals strong link between colorectal cancer and trimethylamine N-oxide (TMAO), a gut microbial metabolite of dietary meat and fat. BMC Genom. 16, S4 (2015).

    Article  Google Scholar 

  31. Tang, W. H. W. et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N. Engl. J. Med. 368, 1575–1584 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Romano, K. A. et al. Metabolic, epigenetic, and transgenerational effects of gut bacterial choline consumption. Cell Host Microbe 22, 279–290.e7 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Goodman, A. L. et al. Identifying genetic determinants needed to establish a human gut symbiont in its habitat. Cell Host Microbe 6, 279–289 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kageyama, A., Sakamoto, M. & Benno, Y. Rapid identification and quantification of Collinsella aerofaciens using PCR. FEMS Microbiol. Lett. 183, 43–47 (2000).

    Article  CAS  PubMed  Google Scholar 

  35. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 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  PubMed  PubMed Central  Google Scholar 

  37. Finn, R. D. et al. HMMER web server: 2015 update. Nucleic Acids Res. 43, W30–W38 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Almeida, A. et al. A new genomic blueprint of the human gut microbiota. Nature 568, 499–504 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, K. S. & Kyrpides, N. C. New insights from uncultivated genomes of the global human gut microbiome. Nature 568, 505–510 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Zou, Y. et al. 1,520 reference genomes from cultivated human gut bacteria enable functional microbiome analyses. Nat. Biotechnol. 37, 179–185 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bergeron, N., Chiu, S., Williams, P. T., M King, S. & Krauss, R. M. Effects of red meat, white meat, and nonmeat protein sources on atherogenic lipoprotein measures in the context of low compared with high saturated fat intake: a randomized controlled trial. Am. J. Clin. Nutr. 110, 24–33 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Lang, J. M. et al. Impact of individual traits, saturated fat, and protein source on the gut microbiome. mBio https://doi.org/10.1128/mBio.01604-18 (2018).

  44. Bäckhed, F. et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe 17, 690–703 (2015).

    Article  PubMed  Google Scholar 

  45. Cox, M. P., Peterson, D. A. & Biggs, P. J. SolexaQA: at-a-glance quality assessment of Illumina second-generation sequencing data. BMC Bioinf. 11, 485 (2010).

    Article  Google Scholar 

  46. Li, R., Li, Y., Kristiansen, K. & Wang, J. SOAP: short oligonucleotide alignment program. Bioinformatics 24, 713–714 (2008).

    Article  CAS  PubMed  Google Scholar 

  47. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

  51. Benjamini, Y. Discovering the false discovery rate: false discovery rate. J. R. Stat. Soc. Ser. B Stat. Methodol. 72, 405–416 (2010).

    Article  Google Scholar 

  52. maf167. The-microbial-gbu-gene-cluster-cardiovascular-disease: the-microbial-gene-cluster-gbu-CVD-custom-code-102721 (gbu-v2.0). Zenodo https://doi.org/10.5281/zenodo.5603090 (2021).

Download references

Acknowledgements

We thank the University of Wisconsin Biotechnology Center DNA Sequencing Facility and K. Kasahara for assistance in sequencing caecal contents from mice with defined synthetic communities. This work was supported in part by the NIH including the NIH Office of Dietary Supplements (grant nos. R01HL103866 and P01 HL147823 to S.L.H., and R01HL126827 to W.H.W.T. and S.L.H.). S.L.H. and F.E.R. were also supported by an award from the Leducq Foundation (17CVD01). Funds for all metagenomic and 16S sequence data for the APPROACH study were provided by Procter & Gamble. The APPROACH study was supported by a grant (no. R01 HL106003 to R.M.K. and N.B.) and the NIH National Center for Advancing Translational Sciences through the University of California, San Francisco Clinical & Translational Science Institute (award no. UL1TR000004 to R.M.K. and N.B.). MS studies were performed on instrumentation housed in a facility supported in part through a Shimadzu Center of Excellence award.

Author information

Author notes

  1. Sarah Skye

    Present address: Abbott Structural Heart, Santa Clara, CA, USA

  2. Amy McMillan

    Present address: Dose Biosystems Inc., Toronto, Ontario, Canada

  3. These authors contributed equally: Jennifer A. Buffa, Kymberleigh A. Romano, Matthew F. Copeland.

Authors and Affiliations

  1. Department of Cardiovascular & Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA

    Jennifer A. Buffa, Kymberleigh A. Romano, Weifei Zhu, Xiaoming Fu, Marc Ferrell, Sarah Skye, Lin Li, Amy McMillan, Ina Nemet, Robert A. Koeth, Xinmin S. Li, Zeneng Wang, Naseer Sangwan, Adeline M. Hajjar, Mohammed Dwidar, Taylor L. Weeks, W. H. Wilson Tang, Joseph A. DiDonato & Stanley L. Hazen

  2. Center for Microbiome & Human Health, Cleveland Clinic, Cleveland, OH, USA

    Jennifer A. Buffa, Kymberleigh A. Romano, Weifei Zhu, Xiaoming Fu, Marc Ferrell, Sarah Skye, Lin Li, Amy McMillan, Ina Nemet, Robert A. Koeth, Xinmin S. Li, Zeneng Wang, Naseer Sangwan, Adeline M. Hajjar, Mohammed Dwidar, W. H. Wilson Tang, Joseph A. DiDonato & Stanley L. Hazen

  3. Life Sciences TPT and Global Biosciences, The Procter & Gamble Company, Cincinnati, OH, USA

    Matthew F. Copeland, David B. Cody, Rachel Galvez, Kathryn Ward, Hong J. Dai, Ping Hu, Mirjana Parlov, Xingtao Wei, G. Frank Gerberick & Jose Carlos Garcia-Garcia

  4. Department of Cardiovascular Medicine, Heart, Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA

    Robert A. Koeth, W. H. Wilson Tang & Stanley L. Hazen

  5. Department of Biological and Pharmaceutical Sciences, College of Pharmacy, Touro University California, Vallejo, CA, USA

    Nathalie Bergeron

  6. Departments of Pediatrics and Medicine, University of California, San Francisco, CA, USA

    Ronald M. Krauss

  7. Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA

    Federico E. Rey

  8. Department of Chemistry, Cleveland State University, Cleveland, OH, USA

    Valentin Gogonea

Authors
  1. Jennifer A. Buffa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kymberleigh A. Romano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthew F. Copeland
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David B. Cody
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weifei Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rachel Galvez
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaoming Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kathryn Ward
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Marc Ferrell
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hong J. Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sarah Skye
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ping Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Lin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Mirjana Parlov
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Amy McMillan
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Xingtao Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Ina Nemet
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Robert A. Koeth
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Xinmin S. Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Zeneng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Naseer Sangwan
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Adeline M. Hajjar
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Mohammed Dwidar
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Taylor L. Weeks
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Nathalie Bergeron
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Ronald M. Krauss
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. W. H. Wilson Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Federico E. Rey
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Joseph A. DiDonato
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Valentin Gogonea
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. G. Frank Gerberick
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Jose Carlos Garcia-Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Stanley L. Hazen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the planning, execution or interpretation of studies and/or samples utilized as part of these studies. J.A.B., K.A.R., M.F.C., T.L.W. and S.L.H. wrote the manuscript and supplementary files with input from all authors. J.A.B., K.A.R., M.F.C., D.B.C., W.Z., R.G., K.W., M.F., H.J.D., S.S., P.H., M.P., A.M., X.W., R.A.K. and N.B. worked on study design, and performed sample/data collection and dataset analysis. V.G. performed computational analysis and in silico modelling. L.L. was responsible for clinical database management and statistical analysis. I.N., X.F., X.L.S. and W.Z. performed MS analyses of samples and dataset analysis. N.S. performed microbial sequencing and bioinformatics. A.M.H. worked on study design and supervised gnotobiotic mouse studies. M.D. worked on study design and supervised bacterial culture. N.B., R.M.K. and W.H.W.T. coordinated sample and metadata collection. G.F.G., F.E.R., J.C.G.G. and S.L.H. worked on study conception and design. J.C.G.G. and S.L.H. oversaw funding acquisition. All authors contributed to critical review and editing of the manuscript.

Corresponding author

Correspondence to Stanley L. Hazen.

Ethics declarations

Competing interests

S.L.H. and Z.W. report being named as co-inventors on pending and issued patents held by the Cleveland Clinic relating to cardiovascular diagnostics and therapeutics. They also report having received royalty payments for inventions or discoveries related to cardiovascular diagnostics or therapeutics from Cleveland Heart Lab, a fully owned subsidiary of Quest Diagnostics, and Procter & Gamble. S.L.H. is a paid consultant for Procter & Gamble and Zehna Therapeutics, and has received research funds from Procter & Gamble, Pfizer Inc., Roche Diagnostics and Zehna Therapeutics. J.B. reports having received royalty payments from Procter & Gamble. G.F.G., M.F.C., D.B.C., K.L.W., B.R.L.G., H.J.D., P.H. and J.C.G.G. are employees of Procter & Gamble. W.H.W.T. reports being a consultant for Sequana Medical A.G., Owkin Inc, Relypsa Inc. and PreCardiac Inc., having received honoraria from Springer Nature for authorship/editorship and the American Board of Internal Medicine for exam-writing committee participation—all unrelated to the subject and contents of the present paper. The other authors declare that they have no relationships relevant to the contents of this paper to disclose.

Additional information

Peer review information Nature Microbiology thanks Rachel Carmody and the other, anonymous, reviewers 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

Extended Data Fig. 1 Emergencia timonensis enables anaerobic metabolism of L-carnitine to TMA, via the intermediate γBB, in co-culture with bacterial species containing the cai operon.

(a) Quantification of d9-γBB to TMA pathway metabolites from 22 hour anaerobic mono- or co-cultures of representative bacterial species encoding either the caiTABCDE operon (P. mirabilis), the cntAB operon (K. pneumoniae) or both operons (E. fergusonii) and the strict anaerobe E. timonensis. (b) Quantification of L-carnitine to TMA pathway metabolites (including γBB intermediate) in the same anaerobic mono- and co-cultures detailed in (a). All biological replicates (n = 3) are shown. Bar height represents the mean, error bars are ± one standard deviation from the mean.

Source data

Extended Data Fig. 2 Relative abundance and functional metabolism of Core community microbes, P. penneri, and E. timonensis utilized in gnotobiotic mouse model studies.

(a) Relative abundance of community members in each of the four synthetic communities used to colonize germ-free mice for tracer studies. n = 6 (Core, Core+P.penneri, and Core+P.penneri + E.timonensis), n = 3 (Core + E.timonensis). (b) Circulating plasma levels of L-carnitine in mice following in vivo thrombosis. For panels b-d: n = 8 (Core), n = 9 (Core + P.p.), n = 37 (Core + E.t.), n = 8 (Core + P.p.+E.t.). (c) Circulating plasma levels of γBB in mice following in vivo thrombosis. (d) Circulating plasma levels of TMAO in mice following in vivo thrombosis. All data points are shown. Bar height represents the mean, error bars are ± one standard deviation from the mean. For b-d, significance was determined by Kruskal-Walis one-way ANOVA with Dunn’s post-hoc test for multiple comparisons.

Source data

Extended Data Fig. 3 Schematic of the proposed function for each gbu gene cluster encoded enzyme and multiple sequence alignment of acyl-CoA dehydrogenases to GbuA.

(a) Scheme depicting functional components of the gbu gene cluster in the overall transformation of γBB into TMA and crotonate. Proposed functions of the indicated genes were based both on sequence homology and bioinformatics, as well as combinatorial cloning and functional studies where analytes with m/z ratios appropriate for the predicted intermediates shown were detected in cell lysate. See Supplemental Results. (b) Multiple Amino Acid Sequence Alignment of 18 biochemically characterized acyl-CoA dehydrogenases to GbuA from two isolates of E. timonensis (SN18 and isolate 71.3) and Agathobaculum desmolans. Amino acidsare highlighted by level of conservation with residues highlighted yellow least conserved and those in blue mostconserved. Amino acids highlighted in green, red, and magenta highlight conserved residues responsible forpotential flavin adenine nucleotide (FAD) binding sites, a key acyl-CoA dehydrogenase active site glutamate,and carboxylate binding, respectively. The multiple sequence alignment was created using the EMBL Clustal Omega program (https://www.ebi.ac.uk/Tools/msa/clustalo/) and the resulting alignment was visualized andcolored-coded using the Jalview Desktop application (http://www.jalview.org/taxonomy/term/6).

Extended Data Fig. 4 gbu gene cluster expression in E. coli and predicted presence in additional bacteria.

(a) Heterologous expression of one or more recombinant, codon-optimized E. timonensis gbu gene cluster open reading frames in the E. coli host strain BL21 Star (DE3). Cells were aerobically cultivated in Magic Media and levels of the metabolites d9-γBB and d9-TMA were determined by LC-MS/MS as described in the Methods. (+) symbols denote the presence of the recombinant ORF of interest on a plasmid. Bar height represents the average metabolite concentration from at least two biological replicates and each dot represents a biological replicate sample. (b) Phylogenetic tree of twelve bacteria and their associated in vitro anaerobic γBB → TMA activity (average TMA from three biological replicates error bars are ± one standard deviation from the mean). The tree was built based on SILVA alignment of full length 16S rRNA gene sequences and constructed by the Tamura-Nei maximum likelihood method. Numbers at nodes are bootstrap values derived from 1000 bootstrap replications. Evolutionary analyses were conducted in MEGA7. Organism names highlighted in green were bioinformatically predicted to contain gbu gene cluster. Indicated in green are the gene groups that when expressed allow for the conversion of γBB → TMA.

Source data

Extended Data Fig. 5 Fecal gbuA trends downward after transition from a red meat to non-meat or white meat diet.

Using samples from the APPROACH study we assessed if gbuA abundance is regulated by diet. Participants were randomly assigned to consume red meat (highest L-carnitine containing diet), white meat, and non-meat (lowest L-carnitine containing diet) diets in one of six orders, with a washout period of at least two weeks between each diet intervention, as described under Methods. The two-week washout periods are expected to reduce the effect of white meat that occur between red meat and non-meat. To test the effect of this intermediate white meat diet on gbuA abundance, we separated those participants with and without an intermediate white meat diet. Using shotgun metagenomic sequencing gbuA abundance was calculated. Fecal gbuA decreased after transitioning from red meat to white meat, while no such change was detected after transition from white meat to non-meat. (a) Fecal gbuA abundance (RPKM) in subjects while consuming a non-meat diet and after transition to a red meat diet without an intervening white meat diet. (b) Fecal gbuA abundance (RPKM) in subjects while consuming a red-meat diet and after transition to a non-meat diet without an intervening white meat diet. (c) Fecal gbuA abundance (RPKM) in subjects while consuming a non-meat diet and after transition to a red meat diet with an intervening white meat diet. (d) Fecal gbuA abundance (RPKM) in subjects while consuming a red-meat diet and after transition to a non-meat diet with an intervening white meat diet. (e) Fecal gbuA abundance (RPKM) in subjects consuming a white meat diet and after transition to a red meat diet regardless of intermediary diet consumption. (f) Fecal gbuA abundance (RPKM) in subjects while consuming a red meat diet and after transition to a white meat diet regardless of intermediary diet consumption. (g) Fecal gbuA abundance (RPKM) in subjects while consuming a non-meat diet and after transition to a white meat diet regardless of intermediary diet consumption. (h) Fecal gbuA abundance (RPKM) in subjects while consuming a white meat diet and after transition to a non-meat diet regardless of intermediary diet consumption. Median lines are shown and P values were determined by two-way Wilcox test.

Source data

Extended Data Fig. 6 Fecal gbuA abundance in all diet order groups.

Using samples from the APPROACH study we assessed if gbuA abundance is regulated by diet. Participants were randomly assigned to consume red meat (highest L-carnitine containing diet, white meat, and non-meat (lowest L-carnitine containing diet) diets in one of six orders, with a washout period of at least two weeks between each diet intervention, as described under Methods. Using shotgun metagenomic sequencing gbuA abundance was calculated. In the box-whisker plot, the upper and lower boundaries of the box represent the 25th and 75th percentiles, the median is marked by a horizontal line inside the box, and whiskers extend to the largest or smallest point within 1.5 times the interquartile range of the 25th or 75th percentile. Values of outliers are shown in parenthesis next to the point. P values are determined by Kruskal-Wallis test (KW) or post-hoc Wilcox test.

Source data

Extended Data Fig. 7 Changes in plasma TMAO and fecal gbuA across all diet transitions.

Using samples from the APPROACH study we assessed if gbuA abundance is regulated by diet. Participants were randomly assigned to consume red meat (highest L-carnitine containing diet), white meat, and non-meat (lowest L-carnitine containing diet) diets in one of six orders, with a washout period of at least two weeks between each diet intervention, as described under Methods. Using shotgun metagenomic sequencing gbuA abundance was calculated and correlated to corresponding plasma TMAO levels. (a) Changes in fecal gbuA abundance and paired changes observed in plasma TMAO levels in subjects consuming the red-meat diet followed by the direct transition to the non-meat diet without an intervening white meat diet (non-meat minus red meat). (b) Changes in fecal gbuA abundance and paired changes observed in plasma TMAO levels in subjects consuming the non-meat diet followed by the direct transition to the red-meat diet without an intervening white meat diet (red meat minus non-meat). (c) Changes in fecal gbuA abundance and paired changes observed in plasma TMAO levels in subjects consuming the white-meat diet followed by the direct transition to the non-meat diet without an intervening red meat diet (non-meat minus white meat). (d) Changes in fecal gbuA abundance and paired changes observed in plasma TMAO levels in subjects consuming the non-meat diet followed by the direct transition to the white-meat diet without an intervening red meat diet (white meat minus non-meat). (e) Changes in fecal gbuA abundance and paired changes observed in plasma TMAO levels in subjects consuming the red-meat diet followed by the direct transition to the white-meat diet without an intervening non meat diet (white meat minus red meat). (f) Changes in fecal gbuA abundance and paired changes observed in plasma TMAO levels in subjects consuming the white-meat diet followed by the direct transition to the red meat diet without an intervening non-meat diet (red meat minus white meat). Two-tailed Spearman rank correlation coefficient (rho) and p values are shown.

Source data

Extended Data Fig. 8 Emergencia timonensis abundance decreased after transitioning from a red meat to a non-meat diet.

The gbuA gene is necessary to confer the ability to convert γBB to TMA to E. coli. Since this conversion is required to convert L-carnitine to TMA is human feces, we tested whether gbuA abundance is regulated by diet in a dietary intervention study. Participants were randomly assigned to consume red meat, white meat, and non-meat diets in one of six orders, with a two-week washout period between each diet intervention. Fecal gbuA decreases after switching to a non-meat diet from a red meat diet. If E. timonensis is the primary carrier of gbuA in the human fecal microbiota, then E. timonensis should decrease with gbuA. The V4 region of the E. timonensis 16S rRNA gene may have the same sequence as closely related species, causing 16S analysis to conflate E. timonensis with other organisms. Publicly available microbial genome sequences assembled de novo from shotgun metagenomic data allow the use of any distinctive feature of the genome to quantify E. timonensis. High-quality reads generated in shotgun metagenomic sequencing were mapped to metagenome-assembled genomes identified as E. timonensis through sequence homology. This analysis shows that E. timonensis decreased after switching from red meat to non-meat. Participants were included if they were assigned to red meat before non-meat, regardless of intervening white meat diet. P value determined with White’s non-parametric t-test.

Source data

Extended Data Fig. 9 Emergencia timonensis decreased after switching from red meat to non-meat diet.

Using samples from the APPROACH study we assessed if E. timonensis abundance is regulated by diet. Participants were randomly assigned to consume red meat (highest L-carnitine containing diet, white meat, and non-meat (lowest L-carnitine containing diet) diets in one of six orders, with a washout period of at least two weeks between each diet intervention, as described under Methods. Using shotgun metagenomic sequencing E. timonensis abundance was calculated. In the box-whisker plot, the upper and lower boundaries of the box represent the 25th and 75th percentiles, the median is marked by a horizontal line inside the box, and whiskers extend to the largest or smallest point within 1.5 times the interquartile range of the 25th or 75th percentile. Values of outliers are shown in parenthesis next to the point. P values are determined by Kruskal-Wallis test (KW) or post-hoc Wilcox test.

Source data

Extended Data Fig. 10 Fecal caiA abundance in all diet order groups.

Using samples from the APPROACH study we assessed if caiA abundance is regulated by diet. Participants were randomly assigned to consume red meat (highest L-carnitine containing diet, white meat, and non-meat (lowest L-carnitine containing diet) diets in one of six orders, with a washout period of at least two weeks between each diet intervention, as described under Methods. Using shotgun metagenomic sequencing caiA abundance was calculated. In the box-whisker plot, the upper and lower boundaries of the box represent the 25th and 75th percentiles, the median is marked by a horizontal line inside the box, and whiskers extend to the largest or smallest point within 1.5 times the interquartile range of the 25th or 75th percentile. Values of outliers are shown in parenthesis next to the point. P values are determined by Kruskal-Wallis test (KW) or post-hoc Wilcox test.

Source data

Supplementary information

Supplementary Information

Supplementary Results and Tables 1–7.

Reporting Summary

Source data

Source Data Fig. 1

Numerical source data.

Source Data Fig. 1

Full set of unprocessed thrombosis images.

Source Data Fig. 2

Numerical source data.

Source Data Fig. 3

Numerical source data.

Source Data Fig. 3

Full set of unprocessed thrombosis images.

Source Data Fig. 4

Numerical source data.

Source Data Fig. 5

Numerical source data.

Source Data Extended Data Fig. 1

Numerical source data.

Source Data Extended Data Fig. 2

Numerical source data.

Source Data Extended Data Fig. 4

Numerical source data.

Source Data Extended Data Fig. 5

Numerical source data.

Source Data Extended Data Fig. 6

Numerical source data.

Source Data Extended Data Fig. 7

Numerical source data.

Source Data Extended Data Fig. 8

Numerical source data.

Source Data Extended Data Fig. 9

Numerical source data.

Source Data Extended Data Fig. 10

Numerical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buffa, J.A., Romano, K.A., Copeland, M.F. et al. The microbial gbu gene cluster links cardiovascular disease risk associated with red meat consumption to microbiota l-carnitine catabolism. Nat Microbiol 7, 73–86 (2022). https://doi.org/10.1038/s41564-021-01010-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-021-01010-x

This article is cited by

Search

Advanced 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