Metaproteomics of colonic microbiota unveils discrete protein functions among colitic mice and control groups

Abstract

Metaproteomics can greatly assist established high-throughput sequencing methodologies to provide systems biological insights into the alterations of microbial protein functionalities correlated with disease-associated dysbiosis of the intestinal microbiota. Here, we utilized the well-characterized murine T cell transfer model of colitis to find specific changes within the intestinal luminal proteome associated with inflammation. MS proteomic analysis of colonic samples permitted the identification of ~10,000-12,000 unique peptides that corresponded to 5,610 protein clusters identified across three groups, including the colitic Rag1 -/-T cell recipients, isogenic Rag1 -/-controls, and wild-type mice. We demonstrate that the colitic mice exhibited a significant increase in Proteobacteria and Verrucomicrobia and show that such alterations in the microbial communities contributed to the enrichment of specific proteins with transcription and translation gene ontology terms. In combination with 16S sequencing, our metaproteomics-based microbiome studies provide a foundation for assessing alterations in intestinal luminal protein functionalities in a robust and well-characterized mouse model of colitis, and set the stage for future studies to further explore the functional mechanisms of altered protein functionalities associated with dysbiosis and inflammation.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/219782 doi: bioRxiv preprint first posted online Nov. 15, 2017; Page 3 of 25 Statement of significance of the studyThe commensal gut microbiota is essential to maintaining health and has a primary role in digestion/metabolism, homeostasis, and protection from pathogenic bacteria. High-throughput sequencing has established Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria as the four major bacterial phyla that comprise the ecological makeup of the intestinal microbiota.However, the tremendous inter-/intra-variability in microbial composition across individuals, as well as along the length of the intestinal tract has made it difficult to definitively ascertain specific bacterial species associated with health or as drivers of disease states, including inflammatory bowel disease. In this study, we expanded upon the current metaproteomics techniques and use the robust and highly reproducible murine T cell transfer model of colitis as well as a comprehensive database of mouse, human, plant, and all microbial genomes sequenced to date to elucidate alterations in both host and gut microbial proteins associated with intestinal inflammation. Our results show that host genetics, gut microbiota, and inflammation have tremendous influences on the intestinal luminal proteomic landscape.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted ...

Metabolomics activity screening of T cell–induced colitis reveals anti-inflammatory metabolites

“…The relative abundance of IBD-dysregulated metabolic features for each different GI tract region comprised a broad and chemically diverse metabolome, including (but not limited to) amino acids, carbohydrates, peptides, nucleotides, lipids, vitamins, and sterols. Dysbiosis of the metabolome was rather limited relative to the well-documented large alterations in taxonomic composition found in IBD-associated distal gut microbiomes ( 35 – 39 ). These observations offer some support for the neutral theory of composition, which posits that the fitness of neutral species that share the same general niche changes identically (or similarly) along an environmental axis.…”

Triflic Acid Treatment Enables LC-MS/MS Analysis of Insoluble Bacterial Biomass

“…Protein clustering, cluster taxonomy, and gene ontology (GO) term annotations were performed as previously described. [50][51][52] Protein loci were mapped to protein clusters with ComPIL using a sequence identity threshold of 70%. A protein cluster was annotated with all GO terms associated with any domain for all possible proteins in that cluster.…”

“…53 This program has previously been applied to proteomic studies. 51,52,54 Briefly, count data is modeled using the negative binomial distribution, and the mean-variance relationship is estimated. Variance is estimated using an information sharing approach, whereby a single feature's (or protein cluster's) variance is estimated by taking into account variances of other protein clusters measured in the same experiment.…”

“…■ RESULTS AND DISCUSSION P. aeruginosa, B. subtilis, Jurkat cells, and a healthy human microbiome sample were partitioned into two sets. Samples in the first set were lysed by sonication 52 (A-supernatant), clarified by centrifugation, and prepared for LC-MS/MS analysis (Figure 1). While Jurkat cells fully dissolved, bacterial samples yielded yellow supernatants and insoluble pellets.…”

ComPIL 2.0: An Updated Comprehensive Metaproteomics Database

“…1,9 We thoroughly evaluated the sensitivity of the databases against public proteomics data as well as newly collected MS/MS shotgun proteomics data collected from human microbiome samples. We have since implemented ComPIL for all our subsequent metaproteomics studies on colitic mice 10,11 and healthy human samples. 12 The combination of ComPIL with Blazmass allowed for proteomic searches to be performed with database sizes much larger than previously possible.…”