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.

Conflict of interest statement

Competing interests

Figures

Figure 1.. IBD is associated with broad changes in subjects’ gut multi’omic profiles.

(A) We collected and profiled stool metagenomic and metabolomic data from two IBD cohorts: a 155-member discovery cohort (PRISM) and a 65-member validation cohort (NLIBD/LLDeep). (B) Principal coordinates analysis (PCoA) of PRISM cohort subjects based on gut metabolomic profiles (Bray-Curtis distance). (C) The same subjects ordinated on Bray-Curtis distances between gut metagenomic species profiles. (D, E) Subject fecal calprotectin (FC) levels (μg/g) plotted against the first PCoA axes from panels B and C, respectively. Note that FC measurements were not available for all subjects.

Figure 2.. Metabolic enrichments in IBD versus control phenotypes.

We applied Wilcoxon rank-sum tests to metabolites’ individual differential abundance trends (t-statistics from the linear models) to identify classes of molecules that were broadly enriched in IBD. Focusing on classes of molecules with at least 10 putative members (see the “n=“ column), (A) eight were significantly (FDR q<0.05) positively enriched in CD, meaning that their members tended to be more abundant in CD, and 17 classes were significantly negatively enriched, meaning that their members tended to be more abundant in controls (nominal p-values were two-tailed). (B) A subset of these trends were similarly significant in comparisons between UC and controls, with the remainder (gray) tending to trend in the same direction as CD vs. control comparisons. The dotted line indicates the significance threshold for an individual metabolic feature [abs(t)>2.61]. Panels C through H highlight examples of individually differentially abundant standards measured across 68 CD, 53 UC, and 34 non-IBD control subjects. Metabolites highlighted in panels C, D, E, and F are representatives of broader classes analyzed in A and B. Abundances are in units of parts per million (PPM) after separately sum-normalizing within each LC-MS method; values are square-root scaled for visualization. Boxplot “boxes” indicate the first, second, and third quartiles of the data. Boxplot “whiskers” indicate the inner fences of the data, with points outside the inner fences plotted as outliers.

Figure 3.. Clusters of chemically related, IBD-perturbed metabolites revealed by abundance covariation.

We clustered differentially abundant (DA) metabolites after regressing out the effects of diagnosis, subject age, and medication use (Methods). A small number of (large) clusters explained many of the DA metabolites. (A) The second-largest cluster contained 39 metabolite features, all of them significantly elevated among CD patients (and one in UC patients as well). This cluster was enriched for putative bile acids and derivatives. Multiple variants of the standards cholate (light green triangles) and chenodeoxycholate (dark green triangles) occur in this cluster. (B) The largest cluster contained 62 metabolite features, all of them significantly elevated in non-IBD controls. This cluster was enriched for putative tetrapyrroles and derivatives. The 155 samples (columns) are ordered the same way in both panels according to Bray-Curtis similarity (and phenotype) of overall metabolic profile (as established in Supplementary Fig. 1). Note the control-like versus CD-like substructure among UC subjects.

Figure 4.. Potentially mechanistic associations between IBD-linked microbes and metabolites.

(A) Covariation between microbes and small molecules DA in IBD, specifically those linking FDR-significant, confirmed-in-controls metagenomic species and metabolites matched against standards (Spearman correlation with two-tailed nominal p-values). When multiple metabolomic features matched the same standard, the feature with the highest mean absolute correlation was selected for plotting. Starred (*) metabolites indicate a match to a standard with isomeric forms that could not be differentiated. The standard L-1,2,3,4-Tetrahydro-beta-carboline-3-carboxylic acid is listed as “cyclomethyltryptophan.” (B), (C), and (D) highlight examples of individual correlations across 68 CD, 53 UC, and 34 non-IBD control subjects (see text). Metabolites and species in these examples are colored in panel A. Values plotted are raw measurements (not residuals) normalized to parts per million (PPM) units and then log10-transformed. Values <1 PPM (including 0s) were set to 1 PPM for plotting; corresponding points are shown without fill and jittered (all other points have solid fill).

Figure 5.. IBD-associated changes in microbial function and their metabolic associations.

(A) - (E) highlight examples of metagenomically contributed enzymes that were differentially abundant in IBD, annotated by their taxonomic contributors (A - C are enriched in IBD; D and E are depleted). In each case, the enzyme was contributed by a mixture of species across the cohort, and not dominated by a single species. Each set of stacked bars represents one of the 155 PRISM metagenomes (arrayed on horizontal axes). Community enzyme abundance (log10-transformed parts per million) is represented by the top of each stack of bars; contributions from major species are linearly scaled within the total bar height. Samples are first sorted according to the dominant contributor to a function and then grouped by phenotype (sample ordering differs between panels). (F) and (G) illustrate correlations between community-total enzyme abundance and IBD-associated metabolites across 68 CD, 53 UC, and 34 non-IBD control subjects. Values plotted are raw measurements (not residuals) normalized to parts per million (PPM) units and then log10-transformed. Values <1 PPM (including 0s) were set to 1 PPM for plotting; corresponding points are shown without fill and jittered (all other points have solid fill). The given r values indicate Spearman correlation.

Figure 6.. Predicting IBD status and subtype from gut microbiome multi’omic features.

We trained random forest classifiers on metabolites, microbial species, and their combination to identify IBD patients and IBD subtypes. Training/testing was carried out within the PRISM cohort using five-fold cross-validation, in addition to models trained on the full PRISM cohort and then tested (validated) on the independent Netherlands cohorts. (A) ROC curves depict trade-offs between classifiers’ true positive rates (TPRs) and false positive rates (FPRs) as classification stringency varies. The area under the curve (AUC) statistic is a summary measure of classifier performance: AUC values close to 1 indicate that a high TPR was achieved with low FPR (ideal performance), while AUC values close to 0.5 indicate random performance. (B) “Confusion matrix” evaluations of IBD subtype classifiers within the Boston PRISM cohort. The number in row i and column j indicates how many samples were labeled as subtype i but assigned to subtype j. A perfect subtype classifier (100% accuracy) would have 0 counts for all non-diagonal entries (i.e. no misclassified samples). Matrix cells are shaded within-row in proportion to their value (red for CD, orange for UC, and blue for non-IBD control). (C) Confusion matrix evaluations of IBD subtype classifiers trained on the Boston PRISM cohort and tested on the independent Netherlands cohorts. Accuracy values in B and C indicate the fraction of correctly classified instances; error values reflect the standard error of a proportion.