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Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease

Nature Medicine volume 29, pages 700–709 (2023)Cite this article

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Abstract

For decades, variability in clinical efficacy of the widely used inflammatory bowel disease (IBD) drug 5-aminosalicylic acid (5-ASA) has been attributed, in part, to its acetylation and inactivation by gut microbes. Identification of the responsible microbes and enzyme(s), however, has proved elusive. To uncover the source of this metabolism, we developed a multi-omics workflow combining gut microbiome metagenomics, metatranscriptomics and metabolomics from the longitudinal IBDMDB cohort of 132 controls and patients with IBD. This associated 12 previously uncharacterized microbial acetyltransferases with 5-ASA inactivation, belonging to two protein superfamilies: thiolases and acyl-CoA N-acyltransferases. In vitro characterization of representatives from both families confirmed the ability of these enzymes to acetylate 5-ASA. A cross-sectional analysis within the discovery cohort and subsequent prospective validation within the independent SPARC IBD cohort (n = 208) found three of these microbial thiolases and one acyl-CoA N-acyltransferase to be epidemiologically associated with an increased risk of treatment failure among 5-ASA users. Together, these data address a longstanding challenge in IBD management, outline a method for the discovery of previously uncharacterized gut microbial activities and advance the possibility of microbiome-based personalized medicine.

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Fig. 1: Identification of microbial 5-ASA-inactivating enzymes from IBD microbiome population multi-omics.
Fig. 2: 5-ASA directly impacts the fecal metabolome and undergoes biotransformation by the microbiome.
Fig. 3: Microbial genes metatranscriptomically implicated in generation of N-acetyl 5-ASA cluster into thiolase and acyl CoA NAT superfamilies.
Fig. 4: Heterologous expression and purification of a gut microbial acetyltransferase confirms 5-ASA acetylation activity in vitro.
Fig. 5: Gut microbial 5-ASA-inactivating acetyltransferases are associated with greater risk of treatment failure in 5-ASA users.

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Data availability

All multi-omics and participant data from the HMP2 used in this analysis are available at http://IBDMDB.org. Access to PRISM data may be available after contact of rxavier@molbio.harvard.edu. The SPARC IBD data are available upon approved application to Crohn’s & Colitis Foundation IBD Plexus (https://www.crohnscolitisfoundation.org/ibd-plexus).

Code availability

All analysis code can be accessed at https://github.com/drraajmehta/hmp2_5asa for purposes of reproducibility.

References

  1. Plichta, D. R., Graham, D. B., Subramanian, S. & Xavier, R. J. Therapeutic opportunities in inflammatory bowel disease: mechanistic dissection of host–microbiome relationships. Cell 178, 1041–1056 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ham, M. & Moss, A. C. Mesalamine in the treatment and maintenance of remission of ulcerative colitis. Expert Rev. Clin. Pharmacol. 5, 113–123 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Klag, T., Stange, E. F. & Wehkamp, J. Management of Crohn’s disease—are guidelines transferred to clinical practice?. United European Gastroenterol. J. 3, 371–380 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Ford, A. C. et al. Efficacy of 5-aminosalicylates in ulcerative colitis: systematic review and meta-analysis. Am. J. Gastroenterol. 106, 601–616 (2011).

    Article  CAS  PubMed  Google Scholar 

  5. Ford, A. C. et al. Efficacy of 5-aminosalicylates in Crohn’s disease: systematic review and meta-analysis. Am. J. Gastroenterol. 106, 617–629 (2011).

    Article  CAS  PubMed  Google Scholar 

  6. Javdan, B. et al. Personalized mapping of drug metabolism by the human gut microbiome. Cell 181, 1661–1679 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zimmermann, M., Zimmermann-Kogadeeva, M., Wegmann, R. & Goodman, A. L. Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature 570, 462–467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  9. Balaich, J. et al. The human microbiome encodes resistance to the antidiabetic drug acarbose. Nature 600, 110–115 (2021).

    Article  CAS  PubMed  Google Scholar 

  10. Haiser, H. J. et al. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 341, 295–298 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Rekdal, V. M., Bess, E. N., Bisanz, J. E., Turnbaugh, P. J. & Balskus, E. P. Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism. Science 364, eaau6323 (2019).

    Article  PubMed Central  Google Scholar 

  12. Haiser, H. J., Seim, K. L., Balskus, E. P. & Turnbaugh, P. J. Mechanistic insight into digoxin inactivation by Eggerthella lenta augments our understanding of its pharmacokinetics. Gut Microbes 5, 233–238 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Lee, J. W. J. et al. Multi-omics reveal microbial determinants impacting responses to biologic therapies in inflammatory bowel disease. Cell Host Microbe 29, 1294–1304.e4 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Forslund, S. K. et al. Combinatorial, additive and dose-dependent drug–microbiome associations. Nature 600, 500–505 (2021).

    Article  CAS  PubMed  Google Scholar 

  15. van Hogezand, R. A. et al. Bacterial acetylation of 5-aminosalicylic acid in faecal suspensions cultured under aerobic and anaerobic conditions. Eur. J. Clin. Pharmacol. 43, 189–192 (1992).

    Article  PubMed  Google Scholar 

  16. Dull, B. J., Salata, K. & Goldman, P. Role of the intestinal flora in the acetylation of sulfasalazine metabolites. Biochem. Pharmacol. 36, 3772–3774 (1987).

    Article  CAS  PubMed  Google Scholar 

  17. van Hogezand, R. A. et al. Double-blind comparison of 5-aminosalicylic acid and acetyl-5-aminosalicylic acid suppositories in patients with idiopathic proctitis. Aliment Pharmacol. Ther. 2, 33–40 (1988).

    Article  PubMed  Google Scholar 

  18. Sandborn, W. J. & Hanauer, S. B. Systematic review: the pharmacokinetic profiles of oral mesalazine formulations and mesalazine pro-drugs used in the management of ulcerative colitis. Aliment Pharmacol. Ther. 17, 29–42 (2003).

    Article  CAS  PubMed  Google Scholar 

  19. Ireland, A., Priddle, J. D. & Jewell, D. P. Comparison of 5-aminosalicylic acid and N-acetylaminosalicylic acid uptake by the isolated human colonic epithelial cell. Gut 33, 1343–1347 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Deloménie, C. et al. Identification and functional characterization of arylamine N-acetyltransferases in eubacteria: evidence for highly selective acetylation of 5-aminosalicylic acid. J. Bacteriol. 183, 3417–3427 (2001).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Westwood, I. M. et al. Expression, purification, characterization and structure of Pseudomonas aeruginosa arylamine N-acetyltransferase. Biochem. J 385, 605–612 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife 10, e65088 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hafferty, J. D. et al. Self-reported medication use validated through record linkage to national prescribing data. J. Clin. Epidemiol. 94, 132–142 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Akobeng, A. K., Zhang, D., Gordon, M. & MacDonald, J. K. Oral 5‐aminosalicylic acid for maintenance of medically‐induced remission in Crohn’s disease. Cochrane Database Syst. Rev. 9, CD003715 (2016).

    PubMed  Google Scholar 

  26. Neshich, I. A., Kiyota, E. & Arruda, P. Genome-wide analysis of lysine catabolism in bacteria reveals new connections with osmotic stress resistance. ISME J. 7, 2400–2410 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sell, D. R., Strauch, C. M., Shen, W. & Monnier, V. M. 2-aminoadipic acid is a marker of protein carbonyl oxidation in the aging human skin: effects of diabetes, renal failure and sepsis. Biochem. J. 404, 269–277 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Harary, I. Bacterial degradation of nicotinic acid. Nature 177, 328–329 (1956).

    Article  CAS  PubMed  Google Scholar 

  29. Li, J. et al. Niacin ameliorates ulcerative colitis via prostaglandin D2‐mediated D prostanoid receptor 1 activation. EMBO Mol. Med. 9, 571–588 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Franzosa, E. A. et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat. Microbiol. 4, 293 (2019).

    Article  CAS  PubMed  Google Scholar 

  31. Zhang, Y., Thompson, K. N., Huttenhower, C. & Franzosa, E. A. Statistical approaches for differential expression analysis in metatranscriptomics. Bioinformatics 37, i34–i41 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Devos, D. & Valencia, A. Practical limits of function prediction. Proteins 41, 98–107 (2000).

    Article  CAS  PubMed  Google Scholar 

  33. Fitzgerald, C. B. et al. Comparative analysis of Faecalibacterium prausnitzii genomes shows a high level of genome plasticity and warrants separation into new species-level taxa. BMC Genomics 19, 931 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Rousseaux, C. et al. Intestinal antiinflammatory effect of 5-aminosalicylic acid is dependent on peroxisome proliferator–activated receptor-γ. J. Exp. Med. 201, 1205–1215 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Modis, Y. & Wierenga, R. K. Crystallographic analysis of the reaction pathway of Zoogloea ramigera biosynthetic thiolase. J. Mol. Biol. 297, 1171–1182 (2000).

    Article  CAS  PubMed  Google Scholar 

  36. Kim, S. et al. Redox-switch regulatory mechanism of thiolase from Clostridium acetobutylicum. Nat. Commun. 6, 8410 (2015).

    Article  CAS  PubMed  Google Scholar 

  37. Mathieu, M. et al. The 1.8 Å crystal structure of the dimeric peroxisomal 3-ketoacyl-CoA thiolase of Saccharomyces cerevisiae: implications for substrate binding and reaction mechanism. J. Mol. Biol. 273, 714–728 (1997).

  38. Hyams, J. S. et al. Clinical and biological predictors of response to standardised paediatric colitis therapy (PROTECT): a multicentre inception cohort study. Lancet 393, 1708–1720 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Ananthakrishnan, A. N. Environmental triggers for inflammatory bowel disease. Curr. Gastroenterol. Rep. 15, 302 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Ricart, E. et al. N-acetyltransferase 1 and 2 genotypes do not predict response or toxicity to treatment with mesalamine and sulfasalazine in patients with ulcerative colitis. Am. J. Gastroenterol. 97, 1763–1768 (2002).

    Article  CAS  PubMed  Google Scholar 

  41. Yee, J. et al. The association between NAT2 acetylator status and adverse drug reactions of sulfasalazine: a systematic review and meta-analysis. Sci. Rep. 10, 3658 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lück, H., Kinzig, M., Jetter, A., Fuhr, U. & Sörgel, F. Mesalazine pharmacokinetics and NAT2 phenotype. Eur. J. Clin. Pharmacol. 65, 47–54 (2009).

    Article  PubMed  Google Scholar 

  43. Ha, C. Y., Newberry, R. D., Stone, C. D. & Ciorba, M. A. Patients with late adult onset ulcerative colitis have better outcomes than those with early onset disease. Clin. Gastroenterol. Hepatol. 8, 682–687 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Huberts, D. H. E. W. & van der Klei, I. J. Moonlighting proteins: an intriguing mode of multitasking. Biochim. Biophys. Acta 1803, 520–525 (2010).

    Article  CAS  PubMed  Google Scholar 

  45. Hong, J., Park, W., Seo, H., Kim, I.-K. & Kim, K.-J. Crystal structure of an acetyl-CoA acetyltransferase from PHB producing bacterium Bacillus cereus ATCC 14579. Biochem. Biophys. Res. Commun. 533, 442–448 (2020).

    Article  CAS  PubMed  Google Scholar 

  46. Maier, L. et al. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555, 623–628 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wallace, B. D. et al. Alleviating cancer drug toxicity by inhibiting a bacterial enzyme. Science 330, 831–835 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. De Vos, M. et al. Concentrations of 5-ASA and Ac-5-ASA in human ileocolonic biopsy homogenates after oral 5-ASA preparations. Gut 33, 1338–1342 (1992).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Lohman, B. K., Weber, J. N. & Bolnick, D. I. Evaluation of TagSeq, a reliable low-cost alternative for RNAseq. Mol. Ecol. Resour. 16, 1315–1321 (2016).

    Article  CAS  PubMed  Google Scholar 

  50. Suzek, B. E. et al. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Oksanen, J. et al. The vegan package. Community ecology package 10, 719 (2007).

    Google Scholar 

  52. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  53. Wang, M. et al. Mass spectrometry searches using MASST. Nat. Biotechnol. 38, 23–26 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Aron, A. T. et al. Reproducible molecular networking of untargeted mass spectrometry data using GNPS. Nat. Protoc. 15, 1954–1991 (2020).

    Article  CAS  PubMed  Google Scholar 

  55. Mehta, R. S. et al. Dietary patterns and risk of colorectal cancer: analysis by tumor location and molecular subtypes. Gastroenterology 152, 1944–1953 (2017).

    Article  CAS  PubMed  Google Scholar 

  56. Bar, N. et al. A reference map of potential determinants for the human serum metabolome. Nature 588, 135–140 (2020).

    Article  PubMed  Google Scholar 

  57. Groemping, U. & Matthias, L. Package ‘relaimpo’. Relative Importance of Regressors in Linear Models. https://cran.r-project.org/web/packages/relaimpo/relaimpo.pdf (R Foundation for Statstical Computing, 2021).

  58. Bustion, A., Agrawal, A., Turnbaugh, P. J. & Pollard, K. S. A novel in silico method employs chemical and protein similarity algorithms to accurately identify chemical transformations in the human gut microbiome. Preprint at https://www.biorxiv.org/content/10.1101/2022.08.02.502504v1 (2022).

  59. Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, E2329–E2338 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Pinheiro, J. et al. Package ‘nlme’. Linear and Nonlinear Mixed Effects Models, version 3 (2017).

  61. Kenny, D. J. et al. Cholesterol metabolism by uncultured human gut bacteria influences host cholesterol level. Cell Host Microbe 28, 245–257 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. The UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480–D489 (2021).

    Article  Google Scholar 

  63. Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Henikoff, S. & Henikoff, J. G. Amino acid substitution matrices from protein blocks. Proc. Natl Acad. Sci. USA 89, 10915–10919 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Hunter, S. et al. InterPro: the integrative protein signature database. Nucleic Acids Res. 37, D211–D215 (2009).

    Article  CAS  PubMed  Google Scholar 

  66. Kitts, P. A. et al. Assembly: a resource for assembled genomes at NCBI. Nucleic Acids Res. 44, D73–D80 (2016).

    Article  CAS  PubMed  Google Scholar 

  67. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    Article  CAS  PubMed  Google Scholar 

  68. Dawwas, G. K. et al. Prevalence and factors associated with fecal urgency among patients with ulcerative colitis and Crohn’s disease in the study of a prospective adult research cohort with inflammatory bowel disease. Crohns Colitis 360, otab046 (2021).

    Article  Google Scholar 

  69. Raffals, L. E. et al. The development and initial findings of a Study of a Prospective Adult Research Cohort with Inflammatory Bowel Disease (SPARC IBD). Inflamm. Bowel Dis. 28, 192–199 (2021).

    Article  PubMed Central  Google Scholar 

  70. Carey, V. J., Lumley, T. S., Moler, C. & Ripley, B. Package ‘gee’. Generalized Estimation Equation Solver. https://cran.r-project.org/web/packages/gee/gee.pdf (2022).

  71. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Soft. 36, 1–48 (2010).

    Article  Google Scholar 

  72. García-Closas, M. et al. NAT2 slow acetylation and GSTM1 null genotypes increase bladder cancer risk: results from the Spanish Bladder Cancer Study and meta-analyses. Lancet 366, 649–659 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Chan, S. L. et al. Association and clinical utility of NAT2 in the prediction of isoniazid-induced liver injury in Singaporean patients. PLoS ONE 12, e0186200 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Selinski, S. et al. Genotyping NAT2 with only two SNPs (rs1041983 and rs1801280) outperforms the tagging SNP rs1495741 and is equivalent to the conventional 7-SNP NAT2 genotype. Pharmacogenet. Genomics 21, 673–678 (2011).

    Article  CAS  PubMed  Google Scholar 

  75. XDS Program Package. https://xds.mr.mpg.de/

  76. Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr. D Struct. Biol. 75, 861–877 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Coot. https://www2.mrc-lmb.cam.ac.uk/personal/pemsley/coot/

  78. Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article  CAS  PubMed  Google Scholar 

  79. Ding, R. W. et al. Pharmacokinetics of nicotinic acid—salicylic acid interaction. Clin. Pharmacol. Ther. 46, 642–647 (1989).

    Article  CAS  PubMed  Google Scholar 

  80. Santoru, M. L. et al. Cross sectional evaluation of the gut–microbiome metabolome axis in an Italian cohort of IBD patients. Sci. Rep. 7, 9523 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We express our sincere thanks to the members of the Huttenhower laboratory for their editorial assistance. The computations in this paper were run on the FASRC Cannon cluster supported by the FAS Division of Science Research Computing Group at Harvard University or on the O2 High Performance Compute Cluster, supported by the Research Computing Group, at Harvard Medical School. See https://it.hms.harvard.edu/our-services/research-computing for more information. Funding: National Institutes of Health grant R35 CA253185 (A.T.C.); National Institutes of Health grant R24 DK110499-01A1 (C.H.); National Institutes of Health grant T32HL008633-36 (J.R.M.); National Institutes of Health grant 1K23DK125838 (L.H.N.); American College of Gastroenterology (R.S.M.); Crohn’s & Colitis Foundation (R.S.M. and L.H.N.); American Gastroenterological Association (L.H.N. and W.M.); Pfizer Ulcerative Colitis Grant (A.T.C.); Stuart and Suzanne Steele MGH Research Scholarship (A.T.C.); Howard Hughes Medical Institute (E.P.B.); National Science Foundation Postdoctoral Research Fellowship in Biology grant 1907240 (N.R.G.); National Science Foundation Alan T. Waterman Award 2038059 (J.R.M.); Linde Family Foundation (S.D.P.); Novartis Institute for Biomedical Research (S.D.P.); Doris Duke Charitable Foundation (S.D.P.); National Institutes of Health grant P30 GM124165 (S.D.P.); and National Institutes of Health-ORIP HEI grant S10OD021527 (S.D.P.).

Author information

Author notes

  1. These authors contributed equally: Raaj S. Mehta, Jared R. Mayers.

Authors and Affiliations

  1. Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Raaj S. Mehta, Long H. Nguyen, Wenjie Ma, Ashwin N. Ananthakrishnan & Andrew T. Chan

  2. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Raaj S. Mehta, Yancong Zhang, Amrisha Bhosle, Tobyn Branck, Julian Avila Pacheco, Hyuk-Soo Seo, Clary Clish, Eric A. Franzosa, Emily P. Balskus, Andrew T. Chan & Curtis Huttenhower

  3. Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Raaj S. Mehta, Long H. Nguyen, Wenjie Ma, Ashwin N. Ananthakrishnan & Andrew T. Chan

  4. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    Raaj S. Mehta, Jared R. Mayers & Emily P. Balskus

  5. Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Jared R. Mayers

  6. Department of Biostatistics, T. H. Chan School of Public Health, Harvard University, Boston, MA, USA

    Yancong Zhang, Amrisha Bhosle, Tobyn Branck, Eric A. Franzosa & Curtis Huttenhower

  7. Harvard Chan Microbiome in Public Health Center, T. H. Chan School of Public Health, Harvard University, Boston, MA, USA

    Yancong Zhang, Amrisha Bhosle & Curtis Huttenhower

  8. Resnick Sustainability Institute, California Institute of Technology, Pasadena, CA, USA

    Nathaniel R. Glasser

  9. Department of Immunology & Infectious Disease, T. H. Chan School of Public Health, Harvard University, Boston, MA, USA

    Sena Bae, Andrew T. Chan & Curtis Huttenhower

  10. Department of Cancer Biology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA

    Kijun Song, Luke Sebastian & Sirano Dhe-Paganon

  11. Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA

    Emily P. Balskus

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Contributions

Conceptualization: R.S.M., E.P.B., A.T.C. and C.H. Methodology: R.S.M., J.R.M., Y.Z., N.G., J.A.P., H.S., C.C., S.D.P., E.F., E.P.B. and C.H. Investigation: R.S.M., J.R.M., Y.Z., A.B., N.R.G., L.H.N., W.M., S.B., T.B., K.S., L.B., A.N.A. and E.F. Visualization: R.S.M. and J.R.M. Funding acquisition: R.S.M., E.P.B., A.T.C. and C.H. Project administration: R.S.M., E.P.B., A.T.C. and C.H. Supervision: E.P.B., A.T.C. and C.H. contributed equally. Writing—original draft: R.S.M., J.R.M., A.T.C. and C.H. Writing—review and editing: R.S.M., J.R.M., Y.Z., N.R.G., A.B., L.H.N., W.M., S.B., T.B., A.N.A., J.A.P., H.S., S.D.P., E.F., E.P.B., A.T.C. and C.H.

Corresponding author

Correspondence to Curtis Huttenhower.

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Competing interests

C.H. is a consultant to Zoe, Ltd. and is on the scientific advisory boards of Seres Therapeutics and Empress Therapeutics. A.T.C. has been an investigator on a clinical study supported by Zoe, Ltd. Other authors declare that they have no competing interests. R.S.M., J.R.M., E.P.B., A.T.C. and C.H. filed a provisional patent application (63/383,269) on 11 November 2022.

Peer review

Peer review information

Nature Medicine thanks Andrew Macpherson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary handling editor: João Monteiro, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Fecal 5-ASA levels according to self-reported 5-ASA use in the IBDMDB.

We determined 5-ASA use according to detection of drug levels in stool using LC-MS, defined as detection of fecal 5-ASA levels > 10e7. Concordance, determined according to statistical accuracy (Methods), between self-reported use of 5-ASA and detection of fecal 5-ASA was 80.3%. Users (light blue) had 5-ASA levels which were ~10,000x greater than non-users (navy blue). Each individual is represented by multiple points on this graph given that participants provided multiple samples across the year-long cohort. Boxplots show median and lower/upper quartiles; whiskers show inner fences. N = 345 independent biological samples.

Extended Data Fig. 2 Validity of 5-ASA and N-acetyl 5-ASA annotation by metabolomics methods.

Two separate standards for 5-ASA (SIGMA catalog PHR1060 and 18858) confirmed the identity of the 5-ASA peak in the IBDMDB (m/z: 154.0502, RT: 3.83 min) through retention time (panel A) and spectral matching (panel B). A standard for N-acetyl 5-ASA (Cayman Catalogue 27618) produced two peaks which matched two peaks in the IBDMDB (QI3818 and QI3816) (panel C). The later eluting peak (QI3816), was more abundant in the IBDMBD stool, correlated better with 5-ASA (Pearson’s correlation r = 0.89 vs r = 0.60), and had excellent MS/MS spectral matching (panel D). Further still, levels of QI3816 perfectly discriminate 5-ASA users from non-users (c-statistic 0.99).

Extended Data Fig. 3 Representative indirect effects of 5-ASA on the fecal metabolome include shifts in Vitamin B3 and its metabolism towards glycine conjugation and formation of nicotinuric acid.

A) We considered that 5-ASA may lead to highly specific markers of medication use mediated through indirect microbial pathways, such as promotion of growth of certain bacteria which, in turn, generate compounds. Here, we find that the abundance of nicotinic acid (NA) dropped as nicotinuric acid (NUA) dramatically increased; there was no consistent change in the third niacin metabolite detected on our platform, N1-methyl-nicotinamide. While the precise role of 5-ASA in determining the fate of NA is unknown, for >70 years, anaerobic bacteria, including Clostridia species have been known to metabolize NA24. Furthermore, medications, such as aspirin, are known to affect the balance of NA and NUA in the blood79. Intriguingly, fecal NA levels have previously been detected at lower levels in IBD patients compared to healthy controls80, but confounding by 5-ASA use was not explored. Given the role of anaerobic gut bacteria in metabolism of NA, it is conceivable that gut bacteria promoted by 5-ASA – or 5-ASA itself – shunts NA towards the glycine conjugation pathway without profound impact on amidation. This phenomenon was not seen in initiators of steroids or biological drugs in the cohort; NUA levels were undetectable in non-5-ASA users. N = 283 independent biological samples. B) These effects were highly specific to 5-ASA use and could not be attributed simply to suppression of inflammation: in a subset of 9 participants in the IBDMDB who started biologic drugs, there was no change in nicotinic acid levels, and nicotinuric acid levels were undetectable in non-users of 5-ASA. In both panels, boxplots show median and lower/upper quartiles; whiskers show inner fences.

Extended Data Fig. 4 Impact of 5-ASA on the fecal metabolome is influenced by the gut microbiome.

A) Drug levels, followed by gut microbiome taxonomic profiles, independently explain variation in most 5-ASA-derived metabolite levels. These were analyzed systematically by linear models constructed for each metabolite with independent variables being: fecal 5-ASA levels; microbiome data; host data, including diet, disease type, age, other medications, and sex; and other/unexplained. We quantified variance explained (EV) by each model and partitioned EV by each term for each 5-ASA-shifted metabolite (shown along the x-axis, as a stacked proportional bar plot). As an example, N-acetyl 5-ASA had a moderate correlation with 5-ASA levels in stool (Spearman rho 0.50, p = 4e-9), with 35% of variance explained by drug levels, 15% explained by the microbiome, and 7% explained by other host features. B) Associations between differentially abundant (DA) metabolites and DA species using HAllA (Methods). Block associations are numbered in descending order of significance (max FDR < 0.25), with each numbered block corresponding to a group of co-occurring metabolites with a species. A white dot indicates marginal significance of a particular pair of features (p < 0.05). C) Abundance of R. inulinivorans was inversely correlated with the metabolite peak 242.0458 (represented by Block 17 in (B)), which was 37,661-fold greater on average among users than non-users (FDR 0.001). Boxplots show median and lower/upper quartiles; whiskers show inner fences. D) F. prausnitzii was positively associated with the peak 373.1254 (represented by Block 20 in (B)), which was 73-fold greater among users than non-users (FDR 0.001). Boxplots show median and lower/upper quartiles; whiskers show inner fences. In (C) and (C), N = 283 independent biological samples.

Extended Data Fig. 5 Microbial species previously characterized to acetylate 5-ASA have low or absent representation in the gut microbiomes of patients with IBD.

A) Phylogenetic analysis shows the taxonomic contributions to the MTX (purple, n = 301 species) and MGX (gold, n = 322 species) data in the IBDMDB. Also shown are bacteria (green, n = 20 species) previously found to acetylate 5-ASA in vitro18, all of which belong to the Proteobacteria phylum. Notably, only six of these had detectable MGX and MTX levels in the HMP2, which are labeled and listed. B) The abundance of Proteobacteria is low across the vast majority of participants, relative to Firmicutes and Bacteroidetes species. C) The prevalence of the six species with potential for 5-ASA acetylation activity is low, except E. coli. Most importantly, none of these six bacterial species have detectable arylamine N-acetyltransferase enzymes at the metatranscriptomic level in the IBDMDB.

Extended Data Fig. 6 Human gut microbiome genes were prioritized for experimental characterization via a three-pronged multi-omics approach.

We took three-pronged approach to identifying 5-ASA metabolizing enzymes; each part was independent of the other. In Part 1, we used two arylamine N-acetyltransferase (NAT) sequences from Salmonella enterica serovar typhimurium LT2 (nhoA) (UniProt accession, Q00267)20 and Pseudomonas aeruginosa21 (UniProt accession, Q9HUY3), previously shown to metabolize 5-ASA, as well as 105 NAT or N-hydroxyarylamine O-acetyltransferase microbial protein sequences predicted to metabolize 5-ASA (Methods, Extended Table 4) as the query for a BLASTP search (Diamond v0.9.24.125) of the Human Microbiome Project (HMP) reference isolate genomes with an e-value of 10 down to the 25% identity. Given that there were no hits at the metatranscriptomic level, in Part 2, we then used differential abundance testing to identify significantly overexpressed acetyltransferases, fit with a per-feature linear mixed-effects model, which adjusted for DNA copy number, which allows for biological and technical zero values while also controlling for underlying DNA levels (q < 0.25). These two hits were expanded at 80% homology to find similar functional proteins. In Part 3 of our approach to identifying 5-ASA metabolizing enzymes, we compared metatranscriptomic abundance of individual gene families (present/absent) with the metabolomics readout of dichotomized N-acetyl 5-ASA (high/low) in each stool sample, and then derived measures of sensitivity (true positives / (true positives + false negatives)) and specificity (true negatives / (true negatives + false positives)) for each gene cluster to estimate how well a given cluster correlates with the drug metabolite. All of these were pooled to arrive at 12 candidates.

Extended Data Fig. 7 Heterologous expression of thiolase and acyl-CoA N-acyltransferase enzymes in E. coli BL21.

(A) Coomassie-stain gel shows purity of indicated enzymes after overexpression and cobalt purification. N = 1 protein purification per enzyme; repeated independently n = 2–4 for each enzyme. Abbreviations: FcTHL = a predicted thiolase (R6CZ24) from an uncultured Firmicutes; FpGNAT = a predicted acyl CoA N-acyltransferase (C7H1G6) from F. prausnitzii; StNAT = a known arylamine N-acetyltransferase from Salmonella enterica serovar typhimurium LT2. (B) Confirmation of in vitro acetylation of 5-ASA by known S. typhimurium enzyme using 1 mM of each substrate and 5 uM enzyme incubated for 1 hr at RT. Data are presented as mean values + /- SEM.

Extended Data Fig. 8 Extended biochemical data for the 5-ASA metabolizing thiolase enzymes.

(A) In vitro competition assay with 1 mM of 5-ASA, 4-ASA, procainamide, hydralazine and isoniazide demonstrates relative specificity of the Firmicutes CAG:176 thiolase (FcTHL) for 5-ASA. N = 3 biologically independent samples per enzyme/condition. (B) Shown is a representative Michaelis-Menten plot of n = 1 thiolase enzyme preparation, conducted in technical triplicates at each concentration of 5-ASA; summary data is from n = 5 biologically independent experiments each conducted in technical triplicate. (C) Live culture of Oscillibacter sp., strain KLE 1745 encoding another predicted thiolase gene (UniRef90 R6TIX3) was capable of acetylation of 5-ASA to N-acetyl 5-ASA. N = 3 biologically independent samples per condition, representative data from n = 2 independent experiments, unpaired, two-sided T-test, *** = p = 0.0015. Data are presented as mean values + /- SEM.

Extended Data Fig. 9 Comparison of canonical acetyltransferase reaction mechanisms for thiolase and arylamine NAT enzymes, now both shown to acetylate 5-ASA.

A) The thiolase two-step ‘ping pong’ mechanism begins after a cysteine is activated by a nearby histidine residue and then performs a nucleophilic attack on an acetyl CoA molecule to form a covalent acetyl-enzyme intermediate (shown in Fig. 4d). In the second step, the substrate (classicaly a second acetyl CoA molecule) nucleophilically attacks the acetyl-enzyme intermediate to yield the final acetyoacetyl-CoA and enzyme. The second nucleophilic attack is activated by a second cysteine residue in the active site, which deprotonates the substrate. B) In a similar two-step ‘ping pong’ mechanism in the arylamine NAT, a cysteine is also activated by a nearby histidine residue and then performs a nucleophilic attack on an acetyl CoA molecule to form a covalent acetyl-enzyme intermediate. In contrast, rather than abstracting a proton from another cysteine residue in the active site, the departing CoA molecule deprotonates the histidine residue, which allows an arylamine substrate to perform nucleophilic attack on the acetyl-enzyme intermediate to yield the final acetylated substrate and enzyme. C) We speculate that in some cases, an acetyl-thiolase enzyme intermediate is formed which may allow nucleophilic attack by an arylamine, such as 5-ASA.

Extended Data Fig. 10 Overview of the Study of a Prospective Adult Research Cohort with IBD (SPARC IBD) study.

A) Timeline shows the stool sampling scheme in relation to clinical assessments throughout the cohort. At study entry, all participants provided a single stool sample. The median time to event (use of steroids) was 229 days. Among a minority of participants (n = 33) who voluntarily provided additional stool samples throughout the cohort (denoted by empty blue circles, Methods), median interval sampling time was 133 days. B). Flow diagram illustrating inclusion and exclusion criteria for this analysis. C) In a sensitivity analysis, we limited our analysis to a single (baseline) stool sample per participant. With diminished power, the SPARC IBD-specific estimate was no longer significant (OR 2.22, 95% CI 0.74-6.69). Accordingly, age and CD were not significantly associated with risk of steroids despite being established risk factors for 5-ASA treatment failure39. Nevertheless, the pooled meta-analysis was not meaningfully different from the primary analysis (OR 2.78, 95% CI 1.19–6.50).

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Mehta, R.S., Mayers, J.R., Zhang, Y. et al. Gut microbial metabolism of 5-ASA diminishes its clinical efficacy in inflammatory bowel disease. Nat Med 29, 700–709 (2023). https://doi.org/10.1038/s41591-023-02217-7

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