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Gut microbiome structure and metabolic activity in inflammatory bowel disease

Abstract

The inflammatory bowel diseases (IBDs), which include Crohn’s disease (CD) and ulcerative colitis (UC), are multifactorial chronic conditions of the gastrointestinal tract. While IBD has been associated with dramatic changes in the gut microbiota, changes in the gut metabolome—the molecular interface between host and microbiota—are less well understood. To address this gap, we performed untargeted metabolomic and shotgun metagenomic profiling of cross-sectional stool samples from discovery (n = 155) and validation (n = 65) cohorts of CD, UC and non-IBD control patients. Metabolomic and metagenomic profiles were broadly correlated with faecal calprotectin levels (a measure of gut inflammation). Across >8,000 measured metabolite features, we identified chemicals and chemical classes that were differentially abundant in IBD, including enrichments for sphingolipids and bile acids, and depletions for triacylglycerols and tetrapyrroles. While > 50% of differentially abundant metabolite features were uncharacterized, many could be assigned putative roles through metabolomic ‘guilt by association’ (covariation with known metabolites). Differentially abundant species and functions from the metagenomic profiles reflected adaptation to oxidative stress in the IBD gut, and were individually consistent with previous findings. Integrating these data, however, we identified 122 robust associations between differentially abundant species and well-characterized differentially abundant metabolites, indicating possible mechanistic relationships that are perturbed in IBD. Finally, we found that metabolome- and metagenome-based classifiers of IBD status were highly accurate and, like the vast majority of individual trends, generalized well to the independent validation cohort. Our findings thus provide an improved understanding of perturbations of the microbiome–metabolome interface in IBD, including identification of many potential diagnostic and therapeutic targets.

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Data availability

Metagenomic sequences for the PRISM, LifeLines DEEP and NLIBD cohorts are available via SRA with BioProject number PRJNA400072. Metabolomics data (accession number PR000677) are available at the National Institutes of Health Common Fund’s Metabolomics Data Repository and Coordinating Center (supported by National Institutes of Health grant no. U01-DK097430): Metabolomics Workbench (http://www.metabolomicsworkbench.org). Tables of processed metabolite, microbial species and microbial enzyme abundance are available as Supplementary Datasets 2, 4 and 6.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Change history

  • 10 April 2019

    In the Supplementary Tables 2, 4 and 6 originally published with this Article, the authors mistakenly included sample identifiers in the form of UMCGs rather than UMCG IBDs in the validation cohort; this has now been amended.

References

  1. 1.

    Wlodarska, M., Kostic, A. D. & Xavier, R. J. An integrative view of microbiome-host interactions in inflammatory bowel diseases. Cell Host Microbe 17, 577–591 (2015).

  2. 2.

    Imhann, F. et al. Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease. Gut 67, 108–119 (2018).

  3. 3.

    Huttenhower, C., Kostic, A. D. & Xavier, R. J. Inflammatory bowel disease as a model for translating the microbiome. Immunity 40, 843–854 (2014).

  4. 4.

    Morgan, X. C. et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 13, R79 (2012).

  5. 5.

    Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).

  6. 6.

    Haberman, Y. et al. Pediatric Crohn disease patients exhibit specific ileal transcriptome and microbiome signature. J. Clin. Invest. 124, 3617–3633 (2014).

  7. 7.

    Lane, E. R., Zisman, T. L. & Suskind, D. L. The microbiota in inflammatory bowel disease: current and therapeutic insights. J. Inflamm. Res. 10, 63–73 (2017).

  8. 8.

    Blander, J. M., Longman, R. S., Iliev, I. D., Sonnenberg, G. F. & Artis, D. Regulation of inflammation by microbiota interactions with the host. Nat. Immunol. 18, 851–860 (2017).

  9. 9.

    Dorrestein, P. C., Mazmanian, S. K. & Knight, R. Finding the missing links among metabolites, microbes, and the host. Immunity 40, 824–832 (2014).

  10. 10.

    McHardy, I. H. et al. Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships. Microbiome 1, 17 (2013).

  11. 11.

    Wu, G. D. Diet, the gut microbiome and the metabolome in IBD. Nestle Nutr. Inst. Workshop Ser. 79, 73–82 (2014).

  12. 12.

    Kim, S., Kim, J.-H., Park, B. O. & Kwak, Y. S. Perspectives on the therapeutic potential of short-chain fatty acid receptors. BMB Rep. 47, 173–178 (2014).

  13. 13.

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

  14. 14.

    Fernando, M. R., Saxena, A., Reyes, J.-L. & McKay, D. M. Butyrate enhances antibacterial effects while suppressing other features of alternative activation in IL-4-induced macrophages. Am. J. Physiol. Gastrointest. Liver Physiol. 310, G822–G831 (2016).

  15. 15.

    Marchesi, J. R. et al. Rapid and noninvasive metabonomic characterization of inflammatory bowel disease. J. Proteome Res. 6, 546–551 (2007).

  16. 16.

    Wikoff, W. R. et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc. Natl Acad. Sci. USA 106, 3698–3703 (2009).

  17. 17.

    Williams, B. B. et al. Discovery and characterization of gut microbiota decarboxylases that can produce the neurotransmitter tryptamine. Cell Host Microbe 16, 495–503 (2014).

  18. 18.

    Zelante, T. et al. Tryptophan catabolites from microbiota engage aryl hydrocarbon receptor and balance mucosal reactivity via interleukin-22. Immunity 39, 372–385 (2013).

  19. 19.

    Lamas, B. et al. CARD9 impacts colitis by altering gut microbiota metabolism of tryptophan into aryl hydrocarbon receptor ligands. Nat. Med. 22, 598–605 (2016).

  20. 20.

    Le Gall, G. et al. Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in ulcerative colitis and irritable bowel syndrome. J. Proteome Res. 10, 4208–4218 (2011).

  21. 21.

    Bjerrum, J. T. et al. Metabonomics of human fecal extracts characterize ulcerative colitis, Crohn’s disease and healthy individuals. Metabolomics 11, 122–133 (2015).

  22. 22.

    De Preter, V. et al. Faecal metabolite profiling identifies medium-chain fatty acids as discriminating compounds in IBD. Gut 64, 447–458 (2015).

  23. 23.

    Jansson, J. et al. Metabolomics reveals metabolic biomarkers of Crohn’s disease. PLoS ONE 4, e6386 (2009).

  24. 24.

    Kolho, K.-L., Pessia, A., Jaakkola, T., de Vos, W. M. & Velagapudi, V. Faecal and serum metabolomics in paediatric inflammatory bowel disease. J. Crohns Colitis 11, 321–334 (2017).

  25. 25.

    Jacobs, J. P. et al. A disease-associated microbial and metabolomics state in relatives of pediatric inflammatory bowel disease patients. Cell. Mol. Gastroenterol. Hepatol. 2, 750–766 (2016).

  26. 26.

    Melnik, A. V. et al. Coupling targeted and untargeted mass spectrometry for metabolome-microbiome-wide association studies of human fecal samples. Anal. Chem. 89, 7549–7559 (2017).

  27. 27.

    Sokol, H. & Seksik, P. The intestinal microbiota in inflammatory bowel diseases: time to connect with the host. Curr. Opin. Gastroenterol. 26, 327–331 (2010).

  28. 28.

    Joossens, M. et al. Dysbiosis of the faecal microbiota in patients with Crohn’s disease and their unaffected relatives. Gut 60, 631–637 (2011).

  29. 29.

    Sokol, H. et al. Low counts of Faecalibacterium prausnitzii in colitis microbiota. Inflamm. Bowel Dis. 15, 1183–1189 (2009).

  30. 30.

    Wishart, D. S. et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 35, D521–D526 (2007).

  31. 31.

    Mosli, M. H. et al. C-reactive protein, fecal calprotectin, and stool lactoferrin for detection of endoscopic activity in symptomatic inflammatory bowel disease patients: a systematic review and meta-analysis. Am. J. Gastroenterol. 110, 802–819 (2015).

  32. 32.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

  33. 33.

    Duboc, H. et al. Connecting dysbiosis, bile-acid dysmetabolism and gut inflammation in inflammatory bowel diseases. Gut 62, 531–539 (2013).

  34. 34.

    Abdel Hadi, L., Di Vito, C. & Riboni, L. Fostering inflammatory bowel disease: sphingolipid strategies to join forces. Mediators Inflamm. 2016, 3827684 (2016).

  35. 35.

    An, D. et al. Sphingolipids from a symbiotic microbe regulate homeostasis of host intestinal natural killer T cells. Cell 156, 123–133 (2014).

  36. 36.

    Braun, A. et al. Alterations of phospholipid concentration and species composition of the intestinal mucus barrier in ulcerative colitis: a clue to pathogenesis. Inflamm. Bowel Dis. 15, 1705–1720 (2009).

  37. 37.

    Qi, Y. et al. PPARα-dependent exacerbation of experimental colitis by the hypolipidemic drug fenofibrate. Am. J. Physiol. Gastrointest. Liver Physiol. 307, G564–G573 (2014).

  38. 38.

    Fischbeck, A. et al. Sphingomyelin induces cathepsin D-mediated apoptosis in intestinal epithelial cells and increases inflammation in DSS colitis. Gut 60, 55–65 (2011).

  39. 39.

    Heimerl, S. et al. Alterations in intestinal fatty acid metabolism in inflammatory bowel disease. Biochim. Biophys. Acta 1762, 341–350 (2006).

  40. 40.

    Hove, H. & Mortensen, P. B. Influence of intestinal inflammation (IBD) and small and large bowel length on fecal short-chain fatty acids and lactate. Dig. Dis. Sci. 40, 1372–1380 (1995).

  41. 41.

    Stuart, J. M., Segal, E., Koller, D. & Kim, S. K. A gene-coexpression network for global discovery of conserved genetic modules. Science 302, 249–255 (2003).

  42. 42.

    Wolfe, C. J., Kohane, I. S. & Butte, A. J. Systematic survey reveals general applicability of” guilt-by-association” within gene coexpression networks. BMC Bioinformatics 6, 227 (2005).

  43. 43.

    Frank, D. N. et al. Molecular-phylogenetic characterization of microbial community imbalances in human inflammatory bowel diseases. Proc. Natl Acad. Sci. USA 104, 13780–13785 (2007).

  44. 44.

    Lewis, J. D. et al. Inflammation, antibiotics, and diet as environmental stressors of the gut microbiome in pediatric Crohn's disease. Cell Host Microbe 18, 489–500 (2015).

  45. 45.

    Desbois, A. P. & Smith, V. J. Antibacterial free fatty acids: activities, mechanisms of action and biotechnological potential. Appl. Microbiol. Biotechnol. 85, 1629–1642 (2010).

  46. 46.

    German, J. B. & Dillard, C. J. Saturated fats: a perspective from lactation and milk composition. Lipids 45, 915–923 (2010).

  47. 47.

    Galland, L. Magnesium and inflammatory bowel disease. Magnesium 7, 78–83 (1988).

  48. 48.

    Lih-Brody, L. et al. Increased oxidative stress and decreased antioxidant defenses in mucosa of inflammatory bowel disease. Dig. Dis. Sci. 41, 2078–2086 (1996).

  49. 49.

    Yang, J. Y. et al. Molecular networking as a dereplication strategy. J. Nat. Prod. 76, 1686–1699 (2013).

  50. 50.

    Jaskowski, T. D., Litwin, C. M. & Hill, H. R. Analysis of serum antibodies in patients suspected of having inflammatory bowel disease. Clin. Vaccine Immunol. 13, 655–660 (2006).

  51. 51.

    Tigchelaar, E. F. et al. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open 5, e006772 (2015).

  52. 52.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

  53. 53.

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

  54. 54.

    Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814 (2012).

  55. 55.

    Franzosa, E. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).

  56. 56.

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

  57. 57.

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

  58. 58.

    The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45632, D158–D169 (2017).

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Acknowledgements

The authors are grateful to the members of the PRISM, LifeLines DEEP and NLIBD cohorts for participating in the study and providing sample material. We thank T. Poon for project management and coordination of data generation, T. Reimels for editorial assistance and A. Garner for providing helpful feedback on the manuscript. The Dutch research team was funded by: CVON IN-CONTROL (CVON2012–03 to A.Z. and J.F.); the Dutch Digestive Foundation (D16–14 to R.K.W. and A.Z.); the Netherlands Organization for Scientific Research (NWO-VIDI 864.13.013 to J.F., NWO-VIDI 016.Vidi.178.056 to A.Z., NWOOW-VIDI 016.136.308 to R.K.W.); a Spinoza Prize (SPI 92–266 to C.W.); and the European Research Council (ERC-Starting no. 715772 to A.Z. and ERC-Advanced 2012–322698 to C.W.). The Boston research team was funded by: the National Science Foundation (NSF CAREER DBI-1053486 and NSF EAGER MCB-1453942 to C.H.); the National Institutes of Health (R01HG00596 to C.H., U54DK102557 to C.H. and R.J.X., R01DK92405 to R.J.X., R24DK110499 to C.H.); the Crohn’s and Colitis Foundation of America to R.J.X. and C.H.; and the Center for Microbiome Informatics and Therapeutics (6933665 PO no. 5710004058 to R.J.X.). A.B.H. is a Merck Fellow of the Helen Hay Whitney Foundation.

Author information

E.A.F., A.S-M., H.V., C.H. and R.J.X. designed the research. E.A.F., A.S-M., J.A-P., N.F., T.V., H.M. and L.J.M. performed the research. H.J.H., S.R., J.S.S., R.G.W., B.W.S., F.I., A.Z., J.F., R.K.W. and C.W. contributed materials. J.A-P., J.M.S., K.P., A.A.D., K.B. and C.B.C. generated the data. E.A.F., A.S-M., J.A-P., N.F., A.B.H. and H.V. analysed the data. E.A.F., J.A.P., R.K.W., C.W., H.V., C.H. and R.J.X. provided project oversight. E.A.F., A.S-M., H.V., C.H. and R.J.X. wrote the paper.

Competing interests

F.I. received a speaker’s fee from AbbVie.

Correspondence to Curtis Huttenhower or Ramnik J. Xavier.

Supplementary information

Supplementary Information

Supplementary Figures 1–13

Reporting Summary

Supplementary Dataset 1

Metabolite feature metadata

Supplementary Dataset 2

Per-subject metabolite relative abundance profiles

Supplementary Dataset 3

IBD-metabolite associations

Supplementary Dataset 4

Per-subject microbial species relative abundance profiles

Supplementary Dataset 5

IBD-microbial species associations

Supplementary Dataset 6

Per-subject microbial enzyme relative abundance profiles

Supplementary Dataset 7

IBD-microbial enzyme associations

Supplementary Dataset 8

Genedata Expressionist configuration

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Further reading

Fig. 1: IBD is associated with broad changes in the gut multi-omic profiles of individuals.
Fig. 2: Metabolic enrichments in IBD versus control phenotypes.
Fig. 3: Clusters of chemically related, IBD-perturbed metabolites revealed by abundance covariation.
Fig. 4: Potentially mechanistic associations between IBD-linked microbes and metabolites.
Fig. 5: IBD-associated changes in microbial function and their metabolic associations.
Fig. 6: Predicting IBD status and subtype from gut microbiome multi-omic features.