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. 2023 Jan 10;11(1):5.
doi: 10.1186/s40168-022-01450-5.

Multi-omics profiles of the intestinal microbiome in irritable bowel syndrome and its bowel habit subtypes

Jonathan P Jacobs  1   2   3 , Venu Lagishetty  4 , Megan C Hauer  4 , Jennifer S Labus  4   5 , Tien S Dong  4   6 , Ryan Toma  7 , Momchilo Vuyisich  7 , Bruce D Naliboff  4   5 , Jeffrey M Lackner  8 , Arpana Gupta  4   5 , Kirsten Tillisch  4   5   9 , Emeran A Mayer  10   11
Affiliations

Multi-omics profiles of the intestinal microbiome in irritable bowel syndrome and its bowel habit subtypes

Jonathan P Jacobs et al. Microbiome. .

Abstract

Background: Irritable bowel syndrome (IBS) is a common gastrointestinal disorder that is thought to involve alterations in the gut microbiome, but robust microbial signatures have been challenging to identify. As prior studies have primarily focused on composition, we hypothesized that multi-omics assessment of microbial function incorporating both metatranscriptomics and metabolomics would further delineate microbial profiles of IBS and its subtypes.

Methods: Fecal samples were collected from a racially/ethnically diverse cohort of 495 subjects, including 318 IBS patients and 177 healthy controls, for analysis by 16S rRNA gene sequencing (n = 486), metatranscriptomics (n = 327), and untargeted metabolomics (n = 368). Differentially abundant microbes, predicted genes, transcripts, and metabolites in IBS were identified by multivariate models incorporating age, sex, race/ethnicity, BMI, diet, and HAD-Anxiety. Inter-omic functional relationships were assessed by transcript/gene ratios and microbial metabolic modeling. Differential features were used to construct random forests classifiers.

Results: IBS was associated with global alterations in microbiome composition by 16S rRNA sequencing and metatranscriptomics, and in microbiome function by predicted metagenomics, metatranscriptomics, and metabolomics. After adjusting for age, sex, race/ethnicity, BMI, diet, and anxiety, IBS was associated with differential abundance of bacterial taxa such as Bacteroides dorei; metabolites including increased tyramine and decreased gentisate and hydrocinnamate; and transcripts related to fructooligosaccharide and polyol utilization. IBS further showed transcriptional upregulation of enzymes involved in fructose and glucan metabolism as well as the succinate pathway of carbohydrate fermentation. A multi-omics classifier for IBS had significantly higher accuracy (AUC 0.82) than classifiers using individual datasets. Diarrhea-predominant IBS (IBS-D) demonstrated shifts in the metatranscriptome and metabolome including increased bile acids, polyamines, succinate pathway intermediates (malate, fumarate), and transcripts involved in fructose, mannose, and polyol metabolism compared to constipation-predominant IBS (IBS-C). A classifier incorporating metabolites and gene-normalized transcripts differentiated IBS-D from IBS-C with high accuracy (AUC 0.86).

Conclusions: IBS is characterized by a multi-omics microbial signature indicating increased capacity to utilize fermentable carbohydrates-consistent with the clinical benefit of diets restricting this energy source-that also includes multiple previously unrecognized metabolites and metabolic pathways. These findings support the need for integrative assessment of microbial function to investigate the microbiome in IBS and identify novel microbiome-related therapeutic targets. Video Abstract.

Keywords: Biomarkers; Bowel habit subtypes; Irritable bowel syndrome; Metabolomics; Metatranscriptomics; Microbiome; Multi-omics.

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Conflict of interest statement

EAM is a scientific advisory board member of Danone, Axial Biotherapeutics, Amare, Mahana Therapeutics, Pendulum, Bloom Biosciences, Seed, and APC Microbiome Ireland. RT and MV are employees of Viome Life Sciences. JPJ, VL, MH, JSL, TSD, BDN, JML, AG, and KT do not have any disclosures.

Figures

Fig. 1
Fig. 1
IBS is associated with global alterations in microbiome composition and function. A The Shannon index of microbial richness and evenness in fecal samples from IBS subjects and healthy controls (HC) is shown for 16S rRNA sequencing and metatranscriptomics data. B Beta diversity was assessed by Bray-Curtis dissimilarity for 16S rRNA sequence data, predicted metagenomics (PM), metatranscriptomics taxonomy (MT-T), and KEGG orthology transcript annotations (MT-KO). Euclidean distance was used for normalized metabolomics data (MET). Contribution of clinical and demographic traits to variation in these five datasets was determined by R2 calculated from univariate (left) and multivariate (right) PERMANOVA. R2 in the multivariate model reflects the remaining explained variance after accounting for the other variables. All PERMANOVA analyses included batch (sequencing or metabolomics) as a covariate. Significance of differences was calculated by permutation and is denoted by color. (C) Distance-based redundancy analysis (dbRDA) was performed to visualize variation in beta diversity related to IBS status, age, sex, race/ethnicity, BMI, dietary category, and HAD-Anxiety (HAD-A). IBS group and statistically significant categorical variables are denoted by letters or symbols indicating the centroid for each category. Statistically significant continuous variables are shown as arrows originating from the centroid of all samples, with length proportional to strength of association. F = female, M = male, A = Asian, B = African-American, H = Hispanic, W = non-Hispanic white, R = multiracial
Fig. 2
Fig. 2
IBS is characterized by altered abundances of bacterial taxa, metabolites, and transcripts, including for genes involved in fructooligosaccharide utilization. A, B Differentially abundant bacterial taxa (q < 0.25) between IBS subjects and HC were identified in multivariate models adjusting for batch, age, sex, race/ethnicity, BMI, dietary category, and HAD-A. Results are shown for A 16S rRNA sequence data (n = 486) and B metatranscriptomics data (n = 327), with bold indicating the single overlapping taxon (B. dorei). Effect size is represented as log2 of the fold change (FC). Color indicates phylum and dot size is proportional to taxon abundance. Bars indicate standard error of log2 fold change estimates. C Differentially abundant fecal metabolites (q < 0.25 in multivariate models) detected by global untargeted metabolomics (n = 368) are shown, with color representing functional category. Bold indicates metabolites that were associated with microbial community metabolic potential by MIMOSA2. D Differentially abundant bacterial transcripts, annotated by KEGG KO number, gene symbol, and gene name. Transcripts for genes that were also differentially abundant in the predicted metagenome are shown in bold. Dot size is proportional to transcript relative abundance and color represents KEGG pathway annotation (legend above the plot)
Fig. 3
Fig. 3
Inter-omic comparison of bacterial taxonomy and function demonstrated upregulation in IBS of transcripts involved in the succinate pathway of carbohydrate fermentation. A Scatterplots depicting median relative abundances of genera in 16S rRNA sequence data and in the metatranscriptome for the 322 subjects with both data types available. B Median ratios of genus abundances in the metatranscriptome (RNA) vs. 16S data (DNA) are plotted for IBS subjects and HC. C Scatterplots of median gene abundances in the predicted metagenome compared to median transcript abundances. D Median transcript/gene abundance ratios are plotted for IBS subjects and HC. Transcripts that were significantly upregulated or downregulated in IBS compared to HC (adjusting for batch, age, sex, race/ethnicity, BMI, dietary category, and HAD-A) are colored. E Pathways that were significantly enriched in differentially regulated transcripts were identified. Significant transcripts within these pathways (represented by dot color) are shown, with dot size proportional to RNA/DNA ratio. Bars indicate standard error of log2 fold change estimates. F Succinate pathway of carbohydrate fermentation. Enzymes transcriptionally upregulated in IBS are colored blue
Fig. 4
Fig. 4
Multi-omics microbiome classifier for IBS showed increased accuracy compared to classifiers using single datasets. A ROC curves for random forest classifiers constructed from differentially abundant features in each of the five datasets (colored in red) are compared to the ROC curve for a multi-omics classifier (colored in blue). The multi-omics classifier was constructed from transcripts that were differentially abundant in both the metatranscriptome and predicted metagenome, significantly upregulated transcripts in enriched pathways, and differentially abundant metabolites associated with microbial community metabolic potential. All classifiers were trained on 60% of the dataset and tested on the remaining 40% of samples (n = 230 with all three data types). Colored areas indicate the 95% confidence intervals of the ROC curves. P values for the AUC of single dataset classifiers compared to the multi-omics classifier were calculated by bootstrapping. B Importance scores are shown for features included in the multi-omics classifier, colored by feature type. Bar color indicates whether each feature shown was enriched or depleted in IBS subjects compared to HC
Fig. 5
Fig. 5
IBS bowel habit (BH) subtypes have distinct functional profiles by metatranscriptomics and metabolomics. A Multivariate PERMONOVA models were used to assess the association of phenotypes within IBS including BH subtype, visceral sensitivity (VSI), general physical symptom perception (PILL), and IBS severity (IBS-SSS) with the five datasets, adjusting for batch, age, sex, race/ethnicity, BMI, dietary category, and HAD-A. B DbRDA plots for each of the five datasets visualizing differences in beta diversity related to BH subtype and significant categorical or continuous covariates. F = female, M = male, A = Asian, B = African-American, H = Hispanic, W = non-Hispanic white, R = multiracial
Fig. 6
Fig. 6
IBS-D is characterized by transcriptional upregulation relative to IBS-C of genes involved in proponoate metabolism, terpenoid biosynthesis, fructose and mannose metabolism, and glycolysis. A Pathways that were significantly enriched in transcripts differentially regulated in IBS-D vs. IBS-C were identified. Significant transcripts within these pathways (q < 0.25 adjusting for batch, age, sex, race/ethnicity, BMI, dietary category, and HAD-A) are shown, with color denoting pathway and dot size proportional to RNA/DNA ratio. Bars indicate standard error of log2 fold change estimates. B Transcript/gene abundance ratios are plotted for the 205 IBS-D and IBS-C subjects with both data types available. Transcripts that were significantly upregulated or downregulated in IBS-D compared to IBS-C are colored
Fig. 7
Fig. 7
Metabolites, transcripts, and transcript/gene ratios can differentiate IBS-D from IBS-C with high accuracy. A ROC curves for random forest classifiers constructed from differentially abundant predicted genes, transcripts, and metabolites (colored in red) are compared to the ROC curve for a multi-omics classifier (colored in blue). The multi-omics classifier was constructed from significantly upregulated transcripts (by RNA/DNA ratio) in enriched pathways and differential metabolites associated with microbial community metabolic potential. All classifiers were trained on 60% of the dataset and tested on the remaining 40% of samples (n = 126 with all three data types). Colored areas indicate the 95% confidence intervals of the ROC curves. P values were calculated by bootstrapping. B–D Importance scores are shown for features included in the B metatranscriptomics, C metabolomics, and D multi-omics classifiers, with bar color denoting features that were enriched or depleted in IBS-D
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