Gut microbiota analyses of inflammatory bowel diseases from a representative Saudi population

Abstract

Background: Crohn's diseases and ulcerative colitis, both of which are chronic immune-mediated disorders of the gastrointestinal tract are major contributors to the overarching Inflammatory bowel diseases. It has become increasingly evident that the pathological processes of IBDs results from interactions between genetic and environmental factors, which can skew immune responses against normal intestinal flora.

Methods: The aim of this study is to assess and analyze the taxa diversity and relative abundances in CD and UC in the Saudi population. We utilized a sequencing strategy that targets all variable regions in the 16 S rRNA gene using the Swift Amplicon 16 S rRNA Panel on Illumina NovaSeq 6000.

Results: The composition of stool 16 S rRNA was analyzed from 219 patients with inflammatory bowel disease and from 124 healthy controls. We quantified the abundance of microbial communities to examine any significant differences between subpopulations of samples. At the genus level, two genera in particular, Veillonella and Lachnoclostridium showed significant association with CD versus controls. There were significant differences between subjects with CD versus UC, with the top differential genera spanning Akkermansia, Harryflintia, Maegamonas and Phascolarctobacterium. Furthermore, statistically significant taxa diversity in microbiome composition was observed within the UC and CD groups.

Conclusions: In conclusion we have shown that there are significant differences in gut microbiota between UC, CD and controls in a Saudi Arabian inflammatory bowel disease cohort. This reinforces the need for further studies in large populations that are ethnically and geographically diverse. In addition, our results show the potential to develop classifiers that may have add additional richness of context to clinical diagnosis of UC and CD with larger inflammatory bowel disease cohorts.

Keywords: Crohn’s disease; IBD; Inflammation; Microbiota; Saudi; Ulcerative colitis.

Fig. 1

Heatmap of Bray-Curtis dissimilarity. Each sample is represented as a cell in the heatmap matrix. Color scale describes the Bray-Curtis dissimilarity, with 0 meaning the two microbial communities are the same, and 1 meaning they are completely different. Horizontal bar plots above the dendrogram are annotated with categorical fields from the metadata

Fig. 2

Bray-Curtis PCoA scatterplots of dissimilarity on principal coordinates axes 1 and 2. Each dot represents a sample, colored by 6 age bins with remaining samples shown in grey. Percent of variance explained by each principal coordinate is displayed on associated axis

Fig. 3

Volcano plots faceted by each comparison in DESeq2 model with log2 fold change in abundance between groups (x-axis) and -log10 raw p-value (y-axis). Dashed vertical black lines represent − 1 and + 1 log2 fold change; the dashed horizontal black line represents the raw p-value threshold of 0.05. Each OTU is represented as a dot, with red dots representing differentially abundant OTUs with adjusted p-values < 0.05 and absolute value of log2 fold change estimates > 1

Fig. 4

Barplot of statistically significant differentially abundant OTUs for disease diagnosis comparisons (adjusted p-value < 0.05). Genus associated with each OTU (x-axis) and the shrunken log2 fold change estimate (y-axis_. Negative log2 fold change estimates correspond to reduced abundance of each OTU in the Healthy controls (left plots) and CD samples (right plots)

Fig. 5

Phylum-level OTU abundance across CD, UC, Healthy Controls and unknown/unclassified samples