Metaproteogenomics Reveals Taxonomic and Functional Changes between Cecal and Fecal Microbiota in Mouse

Abstract

Previous studies on mouse models report that cecal and fecal microbial communities may differ in the taxonomic structure, but little is known about their respective functional activities. Here, we employed a metaproteogenomic approach, including 16S rRNA gene sequencing, shotgun metagenomics and shotgun metaproteomics, to analyze the microbiota of paired mouse cecal contents (CCs) and feces, with the aim of identifying changes in taxon-specific functions. As a result, Gram-positive anaerobes were observed as considerably higher in CCs, while several key enzymes, involved in oxalate degradation, glutamate/glutamine metabolism, and redox homeostasis, and most actively expressed by Bacteroidetes, were clearly more represented in feces. On the whole, taxon and function abundance appeared to vary consistently with environmental changes expected to occur throughout the transit from the cecum to outside the intestine, especially when considering metaproteomic data. The results of this study indicate that functional and metabolic differences exist between CC and stool samples, paving the way to further metaproteogenomic investigations aimed at elucidating the functional dynamics of the intestinal microbiota.

Gut microbial diversity in two insectivorous bats: Insights into the effect of different sampling sources

“…Observed differences across the sampling sources may be caused by environmental heterogeneity in different intestinal compartments and niches, such as different oxygen exposure, pH, and substrate availability (Hao & Lee, ). In addition, functional changes have also been reported between cecal and fecal microbiota in mouse (Tanca et al., ). Our study further confirms that fecal samples, although easily accessible, cannot be used as a proxy of the microbiota in other gut regions (Li, Li, et al., b).…”

“…Gut microbiota is often called the "forgotten organ" in its symbiotic host (O'Hara & Shanahan, 2006) and plays essential roles in food digestion, energy harvest, metabolism, and immune training of its host (Donaldson, Lee, & Mazmanian, 2016;Hooper, Littman, & Macpherson, 2012;Hooper, Midtvedt, & Gordon, 2002;Qin et al, 2010;Turnbaugh et al, 2006;Velagapudi et al, 2010). In mammals, large-scale studies of gut microbiota have been conducted in humans (Huttenhower et al, 2012;Lozupone, Stombaugh, Gordon, Jansson, & Knight, 2012;Saraswati & Sitaraman, 2015;Yatsunenko et al, 2012), mice (Gu et al, 2013;Tanca et al, 2017), other domestic animals such as pigs (Mu, Yang, Su, Zoetendal, & Zhu, 2017) and sheep (Zeng et al, 2017), and to a lesser extent in wild animals, such as bats (Chiroptera). Bats have been largely overlooked in terms of their gut microbiota although they represent the second largest mammalian order and over 20% of mammal species (Simmons, 2005).…”

Prebiotic, Immunomodulating, and Antifatigue Effects of Konjac Oligosaccharide

“…SCFAs have been shown to exert multiple beneficial effects on mammalian energy metabolism and play a key role in the prevention and treatment of the metabolic syndrome, bowel disorders and certain types of cancer (den Besten et al., ). Considering that cecum was the major fermentation site for oligosaccharides in rodent (Tanca et al., ), the cecal SCFA in the mice fed with KOS was measured in present study. The result showed that compared to the CG, the mice fed with KOS had a higher SCFAs concentration ( P < 0.05), and acetic acid was identified as the chief SCFAs, accounting for more than 50% of the total SCFAs (Figure B).…”

Caloric restriction promotes functional changes involving short-chain fatty acid biosynthesis in the rat gut microbiota

“…Only Illumina reads with a length >200 bp were retained for further analysis. OTUs generation was done using a pipeline based on USEARCH’s OTU clustering recommendations ( http://www.drive5.com/usearch/manual/otu_clustering.html ) using the closed-reference OTU picking to allow clustering of 16S sequences, as previously described 63 . Reads were clustered at 97% identity using UCLUST to produce OTUs 64 .…”