Research ArticleGUT MICROBIOTA

Gut microbiota composition and functional changes in inflammatory bowel disease and irritable bowel syndrome

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Science Translational Medicine  19 Dec 2018:
Vol. 10, Issue 472, eaap8914
DOI: 10.1126/scitranslmed.aap8914
  • Fig. 1 Principal coordinate analysis of Bray-Curtis dissimilarities showing the gut microbiome spectrum of 1792 human fecal metagenomes.

    Bray-Curtis dissimilarities were calculated from taxonomic end points. End points were defined as the lowest nonredundant taxonomic level. The first principal coordinate is represented by the x axis, and the second principal coordinate is represented by the y axis. The relative abundance of the three most abundant bacterial phyla—Actinobacteria (A), Bacteroidetes (B), and Firmicutes (C)—underlies the first two principal coordinates (PCos). The metagenomes of patients with IBS (D) or IBD (E) differed from those of the population controls (IBD versus control PCo1, P = 1.20 × 10−5; PCo2, P = 2.20 × 10−16; IBS versus control PCo1, P = 8.05 × 10−6; PCo2 P = 6.72 × 10−7; two-sided unpaired Wilcoxon rank-sum test) and from each other (PCo1, P = 2.22 × 10−7; PCo2, P = 5.06 × 10−12). On average, as schematically depicted (F), controls had more Actinobacteria in their stool than did patients with IBD or IBS. Patients with IBS had more Firmicutes and less Bacteroidetes than did controls. In contrast, patients with IBD had less Firmicutes and more Bacteroidetes than did controls.

  • Fig. 2 Gut microbiota species associated with CD, UC, and IBS-GE compared with controls.

    Statistically significant results (FDR < 0.01) of the case-control multivariate model analyses are depicted. Per microbial family, the number of species that were increased (orange) or decreased (blue) is shown including 134 species in CD belonging to 24 families, 58 species in UC belonging to 21 families, and 37 species in IBS-GE belonging to 15 families.

  • Fig. 3 Differences in bacterial abundance, bacterial strain diversity, and bacterial growth rates of key species in patients with IBD and IBS and controls.

    (A) Bar plots representing the heterozygosity values within bacterial species are shown; SEs are indicated. Heterozygosity is used as an estimation of the strain diversity within a species. Higher heterozygosity values indicate the presence of multiple strains of the same species. Each bar represents a cohort: Controls are depicted in purple, patients with CD in blue, patients with UC in gray, patients with IBS-GE in yellow, and patients with IBS-POP in red. Each asterisk indicates significant differences when comparing to controls (FDR < 0.01). (B) Heatmaps indicate significant changes in relative abundance and growth rates [peak-to-trough ratio algorithm (PTR)] of each bacterial species in disease cohorts compared with controls. Red boxes indicate a significant increase, and blue boxes a significant decrease (FDR < 0.01).

  • Fig. 4 Prediction model to distinguish IBD from IBS.

    Shown is a receiver operating characteristic curve (ROC) describing the prediction accuracy of three different models calculated using a 10-fold cross-validation. The black line represents the prediction accuracy when using age, sex, and body mass index of each participant to discriminate between patients with IBD or IBS-GE. When adding fecal calprotectin measurements to the model (blue line), the AUC achieved a mean value of 0.80. Adding the relative abundance of the top 20 most discriminating bacterial taxa (red line) improved the classification accuracy power (AUC = 0.90).

  • Fig. 5 Associated phenotypes for microbial richness and gut microbiota composition.

    Shown are associated phenotypes for microbial richness and gut microbiota composition in four disease cohorts: (A) CD, (B) UC, (C) IBS-GE, (D) IBS-POP. In the bar plots, the x axis represents the explained variance of each phenotype on gut microbiota composition expressed as Bray-Curtis (BC) dissimilarities. Black bars indicate statistical significance (FDR < 0.1). The heatmap indicates significant positive correlations (red) or negative correlations (blue) between phenotypes and microbial richness (Shannon index) and bacterial gene richness (the number of different microbial gene families per sample). PPI, proton pump inhibitors; SSCAI, Simple Clinical Colitis Activity Index; SSRI, Selective Serotonin Reuptake Inhibitor.

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/10/472/eaap8914/DC1

    Materials and Methods

    Fig. S1. Comparison of microbial richness between cohorts.

    Fig. S2. Venn diagram of overlapping taxa between IBD and clinical IBS.

    Fig. S3. Cohorts, sample collection, and sample processing algorithm.

    Fig. S4. Principal coordinate analysis plot on Bray-Curtis dissimilarities of controls.

    Fig. S5. Phenotype data processing algorithm.

    Fig. S6. Metagenomic sequencing data pipeline.

    Fig. S7. Overview of statistical analyses.

    Fig. S8. Prediction model to distinguish cohort of origin in disease.

    Fig. S9. Prediction model to distinguish cohort of origin in controls.

    Table S1. Summary statistics of phenotypes.

    Table S2. Summary statistics of gut microbiome taxonomy.

    Table S3. Variables included in linear models case-control analyses.

    Table S4. Taxonomy results of CD versus controls.

    Table S5. Taxonomy results of UC versus controls.

    Table S6. Taxonomy results of IBS-GE versus controls.

    Table S7. Taxonomy results in the overlap of IBD and IBS-GE.

    Table S8. Taxonomy results of IBS-POP versus controls.

    Table S9. Taxonomy results of all diseases versus controls.

    Table S10. Strain diversity results of all diseases versus controls.

    Table S11. Bacterial growth rate results of all diseases versus controls.

    Table S12. Prediction accuracy of all prediction models.

    Table S13. Top 20 gut microbiome features in the prediction model.

    Table S14. Summary statistics of gut microbiome MetaCyc function.

    Table S15. Pathway results of all diseases versus controls.

    Table S16. Virulence factor results of all diseases versus controls.

    Table S17. Antibiotic resistance gene results of all diseases versus controls.

    Table S18. Associated phenotypes on gene richness, gut microbiome composition, and Shannon index in CD.

    Table S19. Associated phenotypes on gene richness, gut microbiome composition, and Shannon index in UC.

    Table S20. Associated phenotypes on gene richness, gut microbiome composition, and Shannon index in IBS-GE.

    Table S21. Associated phenotypes on gene richness, gut microbiome composition, and Shannon index in IBS-POP.

    Table S22. Correlation phenotypic factors with an FDR <0.1 from the Adonis analysis in CD.

    Table S23. Correlation phenotypic factors with an FDR <0.1 from the Adonis analysis in UC.

    Table S24. Correlation phenotypic factors with an FDR <0.1 from the Adonis analysis in IBS-GE.

    Table S25. Correlation phenotypic factors with an FDR <0.1 from the Adonis analysis in IBS-POP.

    Table S26. Variables included in multivariate linear models within disease cohorts.

    Table S27. Taxonomy results within the CD univariate model.

    Table S28. Taxonomy results within the CD multivariate model.

    Table S29. Taxonomy results within the UC univariate model.

    Table S30. Taxonomy results within the UC multivariate model.

    Table S31. Taxonomy results within the IBS-GE univariate model.

    Table S32. Taxonomy results within the IBS-GE multivariate model.

    Table S33. Taxonomy results within the IBS-POP univariate model.

    Table S34. Taxonomy results within the IBS-POP multivariate model.

    Table S35. Pathway results within the CD univariate model.

    Table S36. Pathway results within the CD multivariate model.

    Table S37. Pathway results within the UC univariate model.

    Table S38. Pathway results within the UC multivariate model.

    Table S39. Pathway results within the IBS-GE univariate model.

    Table S40. Pathway results within the IBS-GE multivariate model.

    Table S41. Pathway results within the IBS-POP univariate model.

    Table S42. Pathway results within the IBS-POP multivariate model.

    Table S43. Cohort-associated taxa and IBD versus IBS taxonomical associations.

    References (3751)

  • The PDF file includes:

    • Materials and Methods
    • Fig. S1. Comparison of microbial richness between cohorts.
    • Fig. S2. Venn diagram of overlapping taxa between IBD and clinical IBS.
    • Fig. S3. Cohorts, sample collection, and sample processing algorithm.
    • Fig. S4. Principal coordinate analysis plot on Bray-Curtis dissimilarities of controls.
    • Fig. S5. Phenotype data processing algorithm.
    • Fig. S6. Metagenomic sequencing data pipeline.
    • Fig. S7. Overview of statistical analyses.
    • Fig. S8. Prediction model to distinguish cohort of origin in disease.
    • Fig. S9. Prediction model to distinguish cohort of origin in controls.
    • References (3751)

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Table S1 (Microsoft Excel format). Summary statistics of phenotypes.
    • Table S2 (Microsoft Excel format). Summary statistics of gut microbiome taxonomy.
    • Table S3 (Microsoft Excel format). Variables included in linear models case-control analyses.
    • Table S4 (Microsoft Excel format). Taxonomy results of CD versus controls.
    • Table S5 (Microsoft Excel format). Taxonomy results of UC versus controls.
    • Table S6 (Microsoft Excel format). Taxonomy results of IBS-GE versus controls.
    • Table S7 (Microsoft Excel format). Taxonomy results in the overlap of IBD and IBS-GE.
    • Table S8 (Microsoft Excel format). Taxonomy results of IBS-POP versus controls.
    • Table S9 (Microsoft Excel format). Taxonomy results of all diseases versus controls.
    • Table S10 (Microsoft Excel format). Strain diversity results of all diseases versus controls.
    • Table S11 (Microsoft Excel format). Bacterial growth rate results of all diseases versus controls.
    • Table S12 (Microsoft Excel format). Prediction accuracy of all prediction models.
    • Table S13 (Microsoft Excel format). Top 20 gut microbiome features in the prediction model.
    • Table S14 (Microsoft Excel format). Summary statistics of gut microbiome MetaCyc function.
    • Table S15 (Microsoft Excel format). Pathway results of all diseases versus controls.
    • Table S16 (Microsoft Excel format). Virulence factor results of all diseases versus controls.
    • Table S17 (Microsoft Excel format). Antibiotic resistance gene results of all diseases versus controls.
    • Table S18 (Microsoft Excel format). Associated phenotypes on gene richness, gut microbiome composition, and Shannon index in CD.
    • Table S19 (Microsoft Excel format). Associated phenotypes on gene richness, gut microbiome composition, and Shannon index in UC.
    • Table S20 (Microsoft Excel format). Associated phenotypes on gene richness, gut microbiome composition, and Shannon index in IBS-GE.
    • Table S21 (Microsoft Excel format). Associated phenotypes on gene richness, gut microbiome composition, and Shannon index in IBS-POP.
    • Table S22 (Microsoft Excel format). Correlation phenotypic factors with an FDR <0.1 from the Adonis analysis in CD.
    • Table S23 (Microsoft Excel format). Correlation phenotypic factors with an FDR <0.1 from the Adonis analysis in UC.
    • Table S24 (Microsoft Excel format). Correlation phenotypic factors with an FDR <0.1 from the Adonis analysis in IBS-GE.
    • Table S25 (Microsoft Excel format). Correlation phenotypic factors with an FDR <0.1 from the Adonis analysis in IBS-POP.
    • Table S26 (Microsoft Excel format). Variables included in multivariate linear models within disease cohorts.
    • Table S27 (Microsoft Excel format). Taxonomy results within the CD univariate model.
    • Table S28 (Microsoft Excel format). Taxonomy results within the CD multivariate model.
    • Table S29 (Microsoft Excel format). Taxonomy results within the UC univariate model.
    • Table S30 (Microsoft Excel format). Taxonomy results within the UC multivariate model.
    • Table S31 (Microsoft Excel format). Taxonomy results within the IBS-GE univariate model.
    • Table S32 (Microsoft Excel format). Taxonomy results within the IBS-GE multivariate model.
    • Table S33 (Microsoft Excel format). Taxonomy results within the IBS-POP univariate model.
    • Table S34 (Microsoft Excel format). Taxonomy results within the IBS-POP multivariate model.
    • Table S35 (Microsoft Excel format). Pathway results within the CD univariate model.
    • Table S36 (Microsoft Excel format). Pathway results within the CD multivariate model.
    • Table S37 (Microsoft Excel format). Pathway results within the UC univariate model.
    • Table S38 (Microsoft Excel format). Pathway results within the UC multivariate model.
    • Table S39 (Microsoft Excel format). Pathway results within the IBS-GE univariate model.
    • Table S40 (Microsoft Excel format). Pathway results within the IBS-GE multivariate model.
    • Table S41 (Microsoft Excel format). Pathway results within the IBS-POP univariate model.
    • Table S42 (Microsoft Excel format). Pathway results within the IBS-POP multivariate model.
    • Table S43 (Microsoft Excel format). Cohort-associated taxa and IBD versus IBS taxonomical associations.

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