A comparative study of the gut microbiota in immune-mediated inflammatory diseases-does a common dysbiosis exist?

Abstract

Background: Immune-mediated inflammatory disease (IMID) represents a substantial health concern. It is widely recognized that IMID patients are at a higher risk for developing secondary inflammation-related conditions. While an ambiguous etiology is common to all IMIDs, in recent years, considerable knowledge has emerged regarding the plausible role of the gut microbiome in IMIDs. This study used 16S rRNA gene amplicon sequencing to compare the gut microbiota of patients with Crohn's disease (CD; N = 20), ulcerative colitis (UC; N = 19), multiple sclerosis (MS; N = 19), and rheumatoid arthritis (RA; N = 21) versus healthy controls (HC; N = 23). Biological replicates were collected from participants within a 2-month interval. This study aimed to identify common (or unique) taxonomic biomarkers of IMIDs using both differential abundance testing and a machine learning approach.

Results: Significant microbial community differences between cohorts were observed (pseudo F = 4.56; p = 0.01). Richness and diversity were significantly different between cohorts (pFDR < 0.001) and were lowest in CD while highest in HC. Abundances of Actinomyces, Eggerthella, Clostridium III, Faecalicoccus, and Streptococcus (pFDR < 0.001) were significantly higher in all disease cohorts relative to HC, whereas significantly lower abundances were observed for Gemmiger, Lachnospira, and Sporobacter (pFDR < 0.001). Several taxa were found to be differentially abundant in IMIDs versus HC including significantly higher abundances of Intestinibacter in CD, Bifidobacterium in UC, and unclassified Erysipelotrichaceae in MS and significantly lower abundances of Coprococcus in CD, Dialister in MS, and Roseburia in RA. A machine learning approach to classify disease versus HC was highest for CD (AUC = 0.93 and AUC = 0.95 for OTU and genus features, respectively) followed by MS, RA, and UC. Gemmiger and Faecalicoccus were identified as important features for classification of subjects to CD and HC. In general, features identified by differential abundance testing were consistent with machine learning feature importance.

Conclusions: This study identified several gut microbial taxa with differential abundance patterns common to IMIDs. We also found differentially abundant taxa between IMIDs. These taxa may serve as biomarkers for the detection and diagnosis of IMIDs and suggest there may be a common component to IMID etiology.

Keywords: 16S rRNA gene amplicon sequencing; Bacteria; Gut microbiota; Immune-mediated inflammatory disease; Inflammatory bowel disease; Machine learning classifiers; Multiple sclerosis; Rheumatoid arthritis; Taxonomic biomarkers.

Fig. 1

Principal coordinate analysis (PCoA) based on the overall structure of the stool microbiota in all samples. Each data point represents an individual sample. PCoA was calculated using Bray-Curtis distances with a multivariate t-distribution. Ellipses represent an 80% confidence level. Color/shape is indicative of cohort

Fig. 2

Alpha-diversity assessed by richness (Chao1, ACE) and diversity (Shannon, Simpson). Median estimates compared across cohorts using the Kruskal-Wallis test and Dunn’s post hoc tests for multiple comparisons. Boxes represent the interquartile range, lines indicate medians, and whiskers indicate the range. p values represent the overall FDR-corrected p values. ^aCD/UC; ^bCD/MS; ^cCD/RA; ^dCD/HC; ^eUC/MS; ^fUC/RA; ^gUC/HC; ^hMS/RA; ⁱMS/HC; ^jRA/HC

Fig. 3

Abundance of Gram-positive phyla. Median estimates compared across cohorts using the Kruskal-Wallis test and Dunn’s post hoc tests for multiple comparisons. Boxes represent the interquartile range, lines indicate medians, diamond indicates the mean, and whiskers indicate the range. p values represent the overall FDR-corrected p values. ^aCD/UC; ^bCD/MS; ^cCD/RA; ^dCD/HC; ^eUC/MS; ^fUC/RA; ^gUC/HC; ^hMS/RA; ⁱMS/HC; ^jRA/HC

Fig. 4

Feature importance from random forest classifiers for CD versus HCs in addition to feature abundance. Results from OTU and genus classifiers are shown in figures a and b, respectively. The corresponding genera of OTU features were labeled for the ease of interpretation. Each heatmap displays the abundance of the top ten features (rows) in samples (columns) according to the machine learning classifiers. The column bar colors represent the categories of the samples. Feature importance is shown on the right, and features are ordered in decreasing importance from top to bottom according to the mean decrease in Gini index