Quantitative Metaproteomics and Activity-Based Probe Enrichment Reveals Significant Alterations in Protein Expression from a Mouse Model of Inflammatory Bowel Disease

Abstract

Tandem mass spectrometry based shotgun proteomics of distal gut microbiomes is exceedingly difficult due to the inherent complexity and taxonomic diversity of the samples. We introduce two new methodologies to improve metaproteomic studies of microbiome samples. These methods include the stable isotope labeling in mammals to permit protein quantitation across two mouse cohorts, as well as the application of activity-based probes to enrich and analyze both host and microbial proteins with specific functionalities. We used these technologies to study the microbiota from the adoptive T cell transfer mouse model of inflammatory bowel disease (IBD) and compare these samples to an isogenic control; thereby, limiting genetic and environmental variables that influence microbiome composition. The data and results generated highlight quantitative alterations in both host and microbial proteins due to intestinal inflammation and corroborates the observed phylogenetic changes in bacteria that accompany IBD in humans and mouse models. The combination of isotope labeling with shotgun proteomics resulted in the total identification of 4434 protein clusters expressed in the microbial proteomic environment, 276 of which demonstrated differential abundance between control and IBD mice. Notably, application of a novel cysteine-reactive probe uncovered several microbial proteases and hydrolases overrepresented in the IBD mice. Implementation of these methods demonstrates that substantial insights into the identity and dysregulation of host and microbial proteins altered in IBD can be accomplished and can be used in the interrogation of other microbiome-related diseases.

Activity-Based Protein Profiling—Enabling Multimodal Functional Studies of Microbial Communities

“…The wide dynamic range of peptide abundances, co-elution limiting detection of low abundance peptides, and difficulties developing sequence databases are even more pronounced in these highly complex systems where hundreds to thousands of different taxa may be present. To address this problem enrichment strategies, sample fractionation, and two-dimensional chromatography have all been employed (Biteen et al 2016; Moon et al 2018; Mayers et al 2017; Gilbert et al 2016, 2018; Xiao et al 2017; Jansson and Hofmockel 2018; Jansson and Baker 2016). However, these steps can dramatically limit the number of samples that can be analyzed.…”

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

“…Dynamic exclusion was enabled with a repeat count of 1, repeat duration of 30.00 sec, exclusion list size of 100, and exclusion duration set to 60.00 sec. peptides per protein and a false discovery rate (FDR) of 1% with respect to peptides [23,24].…”

“…The source code for this analysis is available online (https://github.com/stuppie/CM7_CM1E2d56col_unenr123_rawextract_2017/). Protein clustering, cluster taxonomy, and gene ontology (GO) term annotations were performed as previously described [20,23]. Briefly, protein loci were mapped to protein clusters using a preclustered version of ComPIL using a sequence identity threshold of 70% [20].…”

“…Briefly, protein loci were mapped to protein clusters using a preclustered version of ComPIL using a sequence identity threshold of 70% [20]. A protein cluster was annotated with all GO terms associated with any domain for all possible proteins within that cluster, while removing any GO terms that were parents ("is a" or "part of" relationships) of other GO terms in that protein cluster, using annotations generated from InterProScan v5 (version 5.8-49.0) [23]. Protein cluster differential analysis was performed using DESeq2 1.14.1.…”

“…across all samples by a normalization factor of the total number of counts for one experiment divided by the median across all LC-MS/MS experiments, as previously described [23]. Briefly, Page 9 of 25 the peptide taxonomy search space was restricted to the proteins identifiable in a given sample and analysis was performed at the phylum level.…”