Stressor Exposure Disrupts Commensal Microbial Populations in the Intestines and Leads to Increased Colonization by Citrobacter rodentium

Abstract

The gastrointestinal tract is colonized by an enormous array of microbes that are known to have many beneficial effects on the host. Previous studies have indicated that stressor exposure can disrupt the stability of the intestinal microbiota, but the extent of these changes, as well as the effects on enteric infection, has not been well characterized. In order to examine the ability of stressors to induce changes in the gut microbiota, we exposed mice to a prolonged restraint stressor and then characterized microbial populations in the intestines using both traditional culture techniques and bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP). Exposure to the stressor led to an overgrowth of facultatively anaerobic microbiota while at the same time significantly reducing microbial richness and diversity in the ceca of stressed mice. Some of these effects could be explained by a stressor-induced reduction in the relative abundance of bacteria in the family Porphyromonadaceae. To determine whether these alterations would lead to increased pathogen colonization, stressed mice, as well as nonstressed controls, were challenged orally with the enteric murine pathogen Citrobacter rodentium. Exposure to the restraint stressor led to a significant increase in C. rodentium colonization over that in nonstressed control mice. The increased colonization was associated with increased tumor necrosis factor alpha (TNF-␣) gene expression in colonic tissue. Together, these data demonstrate that a prolonged stressor can significantly change the composition of the intestinal microbiota and suggest that this disruption of the microbiota increases susceptibility to an enteric pathogen.

Stress and microbial endocrinology: prospects for ruminant nutrition

“…A very recent report may shed some light into this question. Work from Bailey et al (2010) demonstrated that such stressinduced changes can indeed alter the microbial diversity of the gut to such a degree that provides a greater opportunity for a pathogen to infect a host. Bailey et al stressed mice and showed through the use of a metagenomics-based 454 pyrosequencing techniqure that the microbial diversity of the colon was altered to such a degree as to provide a more favourable environment for infection with the gut pathogen Citrobacter rodentium.…”

“…Bailey et al stressed mice and showed through the use of a metagenomics-based 454 pyrosequencing techniqure that the microbial diversity of the colon was altered to such a degree as to provide a more favourable environment for infection with the gut pathogen Citrobacter rodentium. Critically, Bailey et al showed that these changes in microbiota occurred in the absence of any demonstrable change in a number of immune-related parameters (Bailey et al, 2010).…”

Molecular Identification and Characterization of Ileal and Cecal Fungus Communities in Broilers Given Probiotics, Specific Essential Oil Blends, and Under MixedEimeriaInfection

“…After resolving based upon these parameters, the percentage of each fungal ID was individually analyzed for each sample providing relative abundance information within and among the individual samples based upon relative numbers of reads within each. Evaluations presented at each taxonomic level, including percentage compilations, represent all sequences resolved to their primary identification or their closest relative (Bailey et al, 2010).…”

“…Database sequences were characterized as high quality based upon similar criteria utilized by RDP ver 9 (Cole et al, 2009). Using a .NET and C# analysis pipeline, the resulting BLASTn outputs were compiled and were validated using taxonomic distance methods, and data reduction analysis was performed as described previously (Bailey et al, 2010).…”

“…Following sequencing, all failed sequence reads, lowquality sequence ends and tags and primers were removed, and sequence collections were depleted of non-fungal ribosome sequences and chimeras using B2C2 (Gontcharova, 2010), as described previously (Bailey et al, 2010). To identify fungi in the remaining sequences, sequences were denoised, assembled into clusters, and queried using a distributed BLASTn .NEt algorithm (Dowd et al, 2005) against a database of high-quality 28s fungal sequences derived and curated monthly from National Center for Biotechnology Information (NCBI).…”