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Dynamics of the human gut microbiome in inflammatory bowel disease

Nature Microbiology volume 2, Article number: 17004 (2017) Cite this article

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Abstract

Inflammatory bowel disease (IBD) is characterized by flares of inflammation with a periodic need for increased medication and sometimes even surgery. The aetiology of IBD is partly attributed to a deregulated immune response to gut microbiome dysbiosis. Cross-sectional studies have revealed microbial signatures for different IBD subtypes, including ulcerative colitis, colonic Crohn's disease and ileal Crohn's disease. Although IBD is dynamic, microbiome studies have primarily focused on single time points or a few individuals. Here, we dissect the long-term dynamic behaviour of the gut microbiome in IBD and differentiate this from normal variation. Microbiomes of IBD subjects fluctuate more than those of healthy individuals, based on deviation from a newly defined healthy plane (HP). Ileal Crohn's disease subjects deviated most from the HP, especially subjects with surgical resection. Intriguingly, the microbiomes of some IBD subjects periodically visited the HP then deviated away from it. Inflammation was not directly correlated with distance to the healthy plane, but there was some correlation between observed dramatic fluctuations in the gut microbiome and intensified medication due to a flare of the disease. These results will help guide therapies that will redirect the gut microbiome towards a healthy state and maintain remission in IBD.

Both the state and the dynamics of the human gut microbiome in healthy individuals are highly personalized1–7. Although cross-sectional studies have revealed dysbiosis of the gut microbiome in inflammatory bowel disease (IBD)8–12, little is known about the individual nature of microbiome dynamics in IBD, beyond a study of three ulcerative colitis (UC) patients before and after ileostomy and two small studies of IBD patients in remission or during changes in disease activity13–15. Here, we study the long-term dynamics of the gut microbiome from an IBD cohort of 137 individuals (49 Crohn's disease (CD), 60 UC, 4 lymphocytic colitis (LC), 15 collagenous colitis (CC)) and 9 healthy controls (HC). We sampled at three-month intervals, collecting 1–10 samples per individual for a total of 683 samples (Supplementary Table 1). The microbiome composition in each sample was determined by sequencing the V4 region of the 16S rRNA gene for a total of 248 million 16S rRNA gene amplicons. To determine links between the gut microbiome and clinical factors, we collected clinical data, including faecal calprotectin (f-calprotectin) concentration and surgical resection status. To control sampling bias, we restricted our statistical analyses of volatility to a subset of the cohort that had sequence data from the first four time points and that had matching f-calprotectin concentrations. This yielded 276 samples from 69 patients (Supplementary Table 1 and Supplementary Section 1; results were similar when all subjects were considered). We also included patient genetic load scores (GLSs) based on 163 known IBD risk loci for 29 patients to assess potential links between the host genetics, IBD and the microbiome16.

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Figure 1: Defining a healthy plane.
Figure 2: The gut microbiomes of different IBD subtypes display different distributions relative to a healthy plane.
Figure 3: Correlation between faecal calprotectin concentrations and distance to a defined HP in three-dimensional ordination space.
Figure 4: Microbiome dynamics of selected individuals from each IBD subtype and a healthy control.

References

  1. Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308, 1635–1638 (2005).

    Article  Google Scholar 

  2. Costello, E. K. et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009).

    Article  CAS  Google Scholar 

  3. Human Microbiome Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

    Article  Google Scholar 

  4. Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).

    Article  CAS  Google Scholar 

  5. Martínez, I. et al. Gut microbiome composition is linked to whole grain-induced immunological improvements. ISME J. 7, 269–280 (2013).

    Article  Google Scholar 

  6. Flores, G. E. et al. Temporal variability is a personalized feature of the human microbiome. Genome Biol. 15, 531 (2014).

    Article  Google Scholar 

  7. Zaura, E. et al. Same exposure but two radically different responses to antibiotics: resilience of the salivary microbiome versus long-term microbial shifts in feces. mBio 6, e01693-15 (2015).

    Article  Google Scholar 

  8. Sokol, H. et al. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc. Natl Acad. Sci. USA 105, 16731–16736 (2008).

    Article  CAS  Google Scholar 

  9. Willing, B. et al. Twin studies reveal specific imbalances in the mucosa-associated microbiota of patients with ileal Crohn's disease. Inflamm. Bowel Dis. 15, 653–660 (2009).

    Article  Google Scholar 

  10. Willing, B. P. et al. A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology 139, 1844–1854 (2010).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  12. Rajca, S. et al. Alterations in the intestinal microbiome (dysbiosis) as a predictor of relapse after infliximab withdrawal in Crohn's disease. Inflamm. Bowel Dis. 20, 978–986 (2014).

    PubMed  Google Scholar 

  13. Wills, E. S. et al. Fecal microbial composition of ulcerative colitis and Crohn's disease patients in remission and subsequent exacerbation. PLoS ONE 9, e90981 (2014).

    Article  Google Scholar 

  14. Young, V. B. et al. Multiphasic analysis of the temporal development of the distal gut microbiota in patients following ileal pouch anal anastomosis. Microbiome 1, 9 (2013).

    Article  Google Scholar 

  15. Martinez, C. et al. Unstable composition of the fecal microbiota in ulcerative colitis during clinical remission. Am. J. Gastroenterol. 103, 643–648 (2008).

    Article  Google Scholar 

  16. Jostins, L. et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

    Article  CAS  Google Scholar 

  17. Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).

    Article  Google Scholar 

  18. Manichanh, C., Borruel, N., Casellas, F. & Guarner, F. The gut microbiota in IBD. Nat. Rev. Gastroenterol. Hepatol. 9, 599–608 (2012).

    Article  CAS  Google Scholar 

  19. Lewis, J. D. The utility of biomarkers in the diagnosis and therapy of inflammatory bowel disease. Gastroenterology 140, 1817–1826 (2011).

    Article  CAS  Google Scholar 

  20. Kolho, K.-L. et al. Fecal microbiota in pediatric inflammatory bowel disease and its relation to inflammation. Am. J. Gastroenterol. 110, 921–930 (2015).

    Article  Google Scholar 

  21. Rooks, M. G. et al. Gut microbiome composition and function in experimental colitis during active disease and treatment-induced remission. ISME J. 8, 1403–1417 (2014).

    Article  CAS  Google Scholar 

  22. Sipponen, T. et al. Correlation of faecal calprotectin and lactoferrin with an endoscopic score for Crohn's disease and histological findings. Aliment. Pharmacol. Ther. 28, 1221–1229 (2008).

    Article  CAS  Google Scholar 

  23. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  24. Love, M. I., Huber, W. & Anders, S . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  25. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    Article  CAS  Google Scholar 

  26. Knights, D., Parfrey, L. W., Zaneveld, J., Lozupone, C. & Knight, R. Human-associated microbial signatures: examining their predictive value. Cell Host Microbe 10, 292–296 (2011).

    Article  CAS  Google Scholar 

  27. Silverberg, M. S. et al. Toward an integrated clinical, molecular and serological classification of inflammatory bowel disease: report of a working party of the 2005 Montreal World Congress of Gastroenterology. Can. J. Gastroenterol. 19(Suppl. A), 5A–36A (2005).

    Article  Google Scholar 

  28. DNA Extraction Protocol (EMP, 2011); http://www.earthmicrobiome.org/emp-standard-protocols/dna-extraction-protocol/

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

    Article  CAS  Google Scholar 

  30. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    Article  CAS  Google Scholar 

  31. Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).

    Article  CAS  Google Scholar 

  32. McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).

    Article  CAS  Google Scholar 

  33. Lozupone, C. & Knight, R. Unifrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).

    Article  CAS  Google Scholar 

  34. Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013).

    Article  Google Scholar 

  35. Ihaka, R. & Gentleman, R. R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314 (1996).

    Google Scholar 

  36. 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  Google Scholar 

  37. Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).

    Google Scholar 

  38. Oksanen, J . et al. vegan: Community Ecology Package. R package v. 2.4 (2016); https://cran.r-project.org/web/packages/vegan

Download references

Acknowledgements

This research was partially supported by the United States National Institutes of Health (grant NIH IU54DE023798-01), the Pacific Northwest National Laboratory (contract DE-AC05-76RLO1830) and by the Crohn's and Colitis Foundation of America. Financial support was also provided by the Örebro University Hospital Research Foundation (grant OLL-507001), the Swedish Foundation For Strategic Research (grant RB13-0160) and the Swedish Research Council (521-2011-2764). Additional support was provided by a grant to Juniata College from the Howard Hughes Medical Institute through the Precollege and Undergraduate Science Education Program and the National Science Foundation (NSF award no. DBI-1248096). R.K. was a Howard Hughes Institute Early Career Scientist for part of the duration of this project.

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Authors and Affiliations

  1. Department of Gastroenterology, Faculty of Medicine and Health, Örebro University, Örebro, SE-701 82, Sweden

    Jonas Halfvarson

  2. Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, 99354, Washington, USA

    Colin J. Brislawn & Janet K. Jansson

  3. Juniata College, Huntingdon, 16652, Pennsylvania, USA

    Regina Lamendella, Erin E. McClure & Mitchell F. Dunklebarger

  4. Department of Computer Science and Engineering, University of California, San Diego, 92093, California, USA

    Yoshiki Vázquez-Baeza & Rob Knight

  5. Max Planck Institute, 72076 Tübingen, Germany

    William A. Walters

  6. National Security Directorate, Pacific Northwest National Laboratory, Richland, 99354, Washington, USA

    Lisa M. Bramer

  7. Department of Medicine Solna, Clinical Epidemiology Unit, Karolinska Institutet, SE-171 76 Stockholm, Sweden

    Mauro D'Amato

  8. BioDonostia Health Research Institute San Sebastian, and IKERBASQUE Basque Foundation for Science Bilbao 48011, Spain

    Mauro D'Amato

  9. Department of Biosciences and Nutrition, Karolinska Institutet, SE-171 77, Stockholm, Sweden

    Ferdinando Bonfiglio

  10. Department of Pediatrics, University of California, San Diego, 92093, California, USA

    Daniel McDonald & Rob Knight

  11. Center for Microbiome Innovation, University of California, San Diego, 92093, California, USA

    Antonio Gonzalez & Rob Knight

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  1. Jonas Halfvarson
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Contributions

J.H. and J.K.J. designed the study. J.H. carried out the clinical study and collected samples and clinical data. C.J.B., Y.V.-B., W.A.W., D.M., M.D'A., F.B., A.G., R.L., E.E.M., L.M.B. and M.F.D. performed bioinformatics and statistical analyses of the data. C.J.B., Y.V.-B., W.A.W. and A.G. produced the figures. J.H., C.J.B., Y.V.-B., J.K.J. and R.K. wrote the majority of the text with input from all co-authors.

Corresponding author

Correspondence to Janet K. Jansson.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Legend for Supplementary Video 1, Legend for Supplementary Dataset 1, Supplementary Tables 1–4 and Supplementary Figures 1–4. (PDF 698 kb)

Supplementary Dataset 1

Clinical metadata for patients and samples. (TXT 287 kb)

Supplementary Video 1

Dynamics of healthy controls and IBD subtypes within ordinated UniFrac space. (MOV 72737 kb)

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Halfvarson, J., Brislawn, C., Lamendella, R. et al. Dynamics of the human gut microbiome in inflammatory bowel disease. Nat Microbiol 2, 17004 (2017). https://doi.org/10.1038/nmicrobiol.2017.4

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