Biology subjects

Stupp, G. S.

Publications and source records attributed to Stupp, G. S..

4 recordsLinked to original sources

bioRxiv2020

Quantitative metaproteomics of patient fecal microbiota identifies host and microbial proteins associated with ulcerative colitis

Mass spectrometry-based metaproteomics technologies enable the direct observation of proteins within complex multi-organism environments. A major hurdle in mapping metaproteomic fragmentation spectra to their corresponding peptides is the need for large peptide databases encompassing all anticipated species contained within a biological sample. As we cannot predict the taxonomic composition of microbiomes a priori, we developed the ComPIL database which contains a comprehensive collection of 4.8 billion unique peptides from public sequencing repositories to enable our proteomics analyses. We analyzed fecal samples from ulcerative colitis (UC) patients using a tandem mass spectrometry (LC-MS/MS) workflow coupled to ComPIL in search of aberrant UC-associated proteins. We found 176 host and microbial protein groups differentially enriched between the healthy (control) or UC volunteer groups. Notably, gene ontology (GO) enrichment analysis revealed that serine-type endopeptidases are overrepresented in UC compared to healthy volunteers. Additionally, we demonstrate the feasibility of serine hydrolase chemical enrichment from fecal samples using a biotinylated fluorophosphate (FP) probe. Our findings illustrate that probe-susceptible hydrolases from hosts and microbes are likely active in the distal gut. Finally, we applied de novo peptide sequencing methods to our metaproteomics data to estimate the size of the "dark peptidome," the complement of peptides unidentified using ComPIL. We posit that our metaproteomics methods are generally applicable to future microbiota analyses and that our list of FP probe-enriched hydrolases may represent an important functionality to understanding the etiology of UC.

microbiology↗

bioRxiv2019

Structured Reviews for Data and Knowledge Driven Research

MotivationHypothesis generation is a critical step in research and a cornerstone in the rare disease field. Research is most efficient when those hypotheses are based on the entirety of knowledge known to date. Systematic review articles are commonly used in biomedicine to summarize existing knowledge and contextualize experimental data. But the information contained within review articles is typically only expressed as free-text, which is difficult to use computationally. Researchers struggle to navigate, collect and remix prior knowledge as it is scattered in several silos without seamless integration and access. This lack of a structured information framework hinders research by both experimental and computational scientists.\n\nResultsTo better organize knowledge and data, we built a structured review article that is specifically focused on NGLY1 Deficiency, an ultra-rare genetic disease first reported in 2012. We represented this structured review as a knowledge graph, and then stored this knowledge graph in a Neo4j database to simplify dissemination, querying, and visualization of the network. Relative to free-text, this structured review better promotes the principles of findability, accessibility, interoperability, and reusability (FAIR). In collaboration with domain experts in NGLY1 Deficiency, we demonstrate how this resource can improve the efficiency and comprehensiveness of hypothesis generation. We also developed a read-write interface that allows domain experts to contribute FAIR structured knowledge to this community resource. In contrast to traditional free-text review articles, this structured review exists as a living knowledge graph that is curated by humans and accessible to computational analyses. Finally, we have generalized this workflow into modular and repurposable components that can be applied to other domain areas. This NGLY1 Deficiency-focused network is publicly available at http://ngly1graph.org/.\n\nAvailability and implementationSource code and network data files are at: https://github.com/SuLab/ngly1-graph and https://github.com/SuLab/bioknowledge-reviewer.\n\nContactasu@scripps.edu

bioinformatics↗

bioRxiv2018

Triflic acid treatment enables LC-MS/MS analysis of insoluble bacterial biomass

The lysis and extraction of soluble bacterial proteins from cells is a common practice for proteomics analyses, but insoluble bacterial biomasses are often left behind. Here, we show that with triflic acid treatment, the insoluble bacterial biomass of Gram- and Gram+ bacteria can be rendered soluble. We use LC-MS/MS shotgun proteomics to show that bacterial proteins in the soluble and insoluble post-lysis fractions differ significantly. Additionally, in the case of Gram- Pseudomonas aeruginosa, triflic acid treatment enables the enrichment of cell envelope-associated proteins. Finally, we apply triflic acid to a human microbiome sample to show that this treatment is robust and enables the identification of a new, complementary subset of proteins from a complex microbial mixture.

systems biology↗

bioRxiv2017

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

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 {bsim}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.\n\nStatement 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.

bioinformatics↗