Structural and functional alterations in the colonic microbiome of the rat in a model of stress induced irritable bowel syndrome

Abstract

Stress is known to perturb the microbiome and exacerbate irritable bowel syndrome (IBS) associated symptoms. Characterizing structural and functional changes in the microbiome is necessary to understand how alterations affect the biomolecular environment of the gut in IBS. Repeated water avoidance (WA) stress was used to induce IBS-like symptoms in rats. The colon-mucosa associated microbiome was characterized in 13 stressed and control animals by 16S sequencing. In silico analysis of the functional domains of microbial communities was done by inferring metagenomic profiles from 16S data. Microbial communities and functional profiles were compared between conditions. WA animals exhibited higher α-diversity and moderate divergence in community structure (β-diversity) compared with controls. Specific clades and taxa were consistently and significantly modified in the WA animals. The WA microbiome was particularly enriched in Proteobacteria and depleted in several beneficial taxa. A decreased capacity in metabolic domains, including energy- and lipid-metabolism, and an increased capacity for fatty acid and sulfur metabolism was inferred for the WA microbiome. The stressed condition favored the proliferation of a greater diversity of microbes that appear to be functionally similar, resulting in a functionally poorer microbiome with implications for epithelial health. Taxa, with known beneficial effects, were found to be depleted, which supports their relevance as therapeutic agents to restore microbial health. Microbial sulfur metabolism may form a key component of visceral nerve sensitization pathways and is therefore of interest as a target metabolic domain in microbial ecological restoration.

Keywords: colon; microbiome; mucosa; stress; visceral hypersensitivity.

Figure 1.

a) Observed Richness and the b) ACE diversity indices indicate significantly (*p < 0.05) increased diversity in the microbiome of WA animals, the c) Chao1 index tended to significance (p = 0.05), whereas the d) Shannon's index and e) Species Accumulation curve did not show differences in diversity in the microbiome of Control and WA animals.

Figure 2.

a) PCoA of abundance based Bray-Curtis distances of the colonic microbiome from control (orange) and WA (blue) rats plotted on the first 2 principal coordinates (PC) which account for approximately 48% of the observed variation b) Hierarchical clustering using Ward's method on Bray-Curtis distances reflect the clustering of microbial communities by treatment condition (Control and WA stress) observed in the PCoA analysis.

Figure 3.

PCoA of presence-absence based a) Jaccard and b) Sorenson distances of the colonic microbiome from control (orange) and WA (blue) rats plotted on the first 2 principal coordinates (PC) which account for approximately 48% of the observed variation, showing little separation between conditions c) Cladogram constructed using individual rats as taxa, microbial OTUs and clades as characters, and their presence absence as binary character states. Maximum parsimony was used as the optimality criterion to select the tree that best described the descendant relationships between animals. Uniquely shared-derived traits (synapomorphies) in the form of microbes which define the terminal clade (i.e., the most derived clade) on the tree are indicated in the blue triangle. This clade is primarily represented by WA animals.

Figure 4.

Circular cladogram of statistically and biologically consistent differences in colonic microbial clades between WA and control animals. Red indicates higher abundance in control animals, green indicates higher abundance in WA animals and yellow is non-significant. Each circle's diameter is proportional to the taxon's abundance. The cladogram simultaneously highlights high-level trends and specific genera/species. Abbreviations: Sphing: Sphongobacteriia, Flav: Flavobacteriia, Ver: Verrucomicrobiae, Opi: Opitutae.

Figure 5.

Histogram of the LDA scores computed for taxa that were found to be differentially abundant at between experimental conditions (WA vs. controls). The magnitude of the LEfSe score indicates the degree of consistency of the difference in relative abundance of the significant feature between the 2 conditions.

Figure 6.

Differentially abundant features. Each point represents an OTU belonging to each Genus. Features were considered significant if their FDR-corrected p-value was less than or equal to 0.05, and the absolute value of the Log-2-fold change was greater than or equal to 1.

Figure 7.

Characterization of statistically and biologically consistent differences in capacity functional domains of the colonic microbiome WA and control animals organized as a functional taxonomy using KEGG BRITE functional hierarchies. Red indicates higher abundance in control animals, green indicates higher abundance in WA animals. Abbreviations: CellProces – CellularProcesses, EnvironInfoProces - Environmental Information Processing, GeneticInfoProces – Genetic Information Processing, XenobioticBioDegr – Xenobiotics Biodegradation, AminoAcidMet – Amino Acid Metabolism, Biosynth2ndryMetabs –Biosynthesis of Other Secondary Metabolites, CarbohydMet – Carbohydrate Metabolism, CoFactVitaminMet – Metabolism of Cofactors and Vitamins, DrugMet – Drug Metabolism, EnergyMet – Energy Metabolism, FldSortDgr – Folding Sorting Degradation, GlycanBiosynthMet – Glycan Biosynthesis and Metabolism, InfectDisease – Infectious Diseases, LipidMet - Lipid Metabolism, MemTransp – Membrane Transport, NeucleotideMet – Nucleotide Metabolism, PolykTerpMet – Metabolism of Terpenoids and Polyketides, ReplRepr – Replication Repair, SigTransDct – Signal Transduction, Translat – Translation, TranspCatab – Transport Catabolism.