Bridging the Chromosome-centric and Biology/Disease-driven Human Proteome Projects: Accessible and Automated Tools for Interpreting the Biological and Pathological Impact of Protein Sequence Variants Detected via Proteogenomics.

The Chromosome-centric Human Proteome Project (C-HPP) seeks to comprehensively characterize all protein products coded by the genome, including those expressed sequence variants confirmed via proteogenomics methods. The closely related Biology/Disease-driven Human Proteome Project (B/D-HPP) seeks to understand the biological and pathological associations of expressed protein products, especially those carrying sequence variants that may be drivers of disease. To achieve these objectives, informatics tools are required that interpret potential functional or disease implications of variant protein sequence detected via proteogenomics. Toward this end, we have developed an automated workflow within the Galaxy for Proteomics (Galaxy-P) platform, which leverages the Cancer-Related Analysis of Variants Toolkit (CRAVAT) and makes it interoperable with proteogenomic results. Protein sequence variants confirmed by proteogenomics are assessed for potential structure-function effects as well as associations with cancer using CRAVAT's rich suite of functionalities, including visualization of results directly within the Galaxy user interface. We demonstrate the effectiveness of this workflow on proteogenomic results generated from an MCF7 breast cancer cell line. Our free and open software should enable improved interpretation of the functional and pathological effects of protein sequence variants detected via proteogenomics, acting as a bridge between the C-HPP and B/D-HPP.

Proteomics in non-human primates: utilizing RNA-Seq data to improve protein identification by mass spectrometry in vervet monkeys.

BACKGROUND: Shotgun proteomics utilizes a database search strategy to compare detected mass spectra to a library of theoretical spectra derived from reference genome information. As such, the robustness of proteomics results is contingent upon the completeness and accuracy of the gene annotation in the reference genome. For animal models of disease where genomic annotation is incomplete, such as non-human primates, proteogenomic methods can improve the detection of proteins by incorporating transcriptional data from RNA-Seq to improve proteomics search databases used for peptide spectral matching. Customized search databases derived from RNA-Seq data are capable of identifying unannotated genetic and splice variants while simultaneously reducing the number of comparisons to only those transcripts actively expressed in the tissue. RESULTS: We collected RNA-Seq and proteomic data from 10 vervet monkey liver samples and used the RNA-Seq data to curate sample-specific search databases which were analyzed in the program Morpheus. We compared these results against those from a search database generated from the reference vervet genome. A total of 284 previously unannotated splice junctions were predicted by the RNA-Seq data, 92 of which were confirmed by peptide spectral matches. More than half (53/92) of these unannotated splice variants had orthologs in other non-human primates, suggesting that failure to match these peptides in the reference analyses likely arose from incomplete gene model information. The sample-specific databases also identified 101 unique peptides containing single amino acid substitutions which were missed by the reference database. Because the sample-specific searches were restricted to actively expressed transcripts, the search databases were smaller, more computationally efficient, and identified more peptides at the empirically derived 1 % false discovery rate. CONCLUSION: Proteogenomic approaches are ideally suited to facilitate the discovery and annotation of proteins in less widely studies animal models such as non-human primates. We expect that these approaches will help to improve existing genome annotations of non-human primate species such as vervet.

Proteogenomic Analysis of a Hibernating Mammal Indicates Contribution of Skeletal Muscle Physiology to the Hibernation Phenotype.

Mammalian hibernation is a strategy employed by many species to survive fluctuations in resource availability and environmental conditions. Hibernating mammals endure conditions of dramatically depressed heart rate, body temperature, and oxygen consumption yet do not show the typical pathological response. Because of the high abundance and metabolic cost of skeletal muscle, not only must it adjust to the constraints of hibernation, but also it is positioned to play a more active role in the initiation and maintenance of the hibernation phenotype. In this study, MS/MS proteomic data from thirteen-lined ground squirrel skeletal muscles were searched against a custom database of transcriptomic and genomic protein predictions built using the platform Galaxy-P. This proteogenomic approach allows for a thorough investigation of skeletal muscle protein abundance throughout their circannual cycle. Of the 1563 proteins identified by these methods, 232 were differentially expressed. These data support previously reported physiological transitions, while also offering new insight into specific mechanisms of how their muscles might be reducing nitrogenous waste, preserving mass and function, and signaling to other tissues. Additionally, the combination of proteomic and transcriptomic data provides unique opportunities for estimating post-transcriptional regulation in skeletal muscle throughout the year and improving genomic annotation for this nonmodel organism.

Characterizing Cardiac Molecular Mechanisms of Mammalian Hibernation via Quantitative Proteogenomics.

This study uses advanced proteogenomic approaches in a nonmodel organism to elucidate cardioprotective mechanisms used during mammalian hibernation. Mammalian hibernation is characterized by drastic reductions in body temperature, heart rate, metabolism, and oxygen consumption. These changes pose significant challenges to the physiology of hibernators, especially for the heart, which maintains function throughout the extreme conditions, resembling ischemia and reperfusion. To identify novel cardioadaptive strategies, we merged large-scale RNA-seq data with large-scale iTRAQ-based proteomic data in heart tissue from 13-lined ground squirrels (Ictidomys tridecemlineatus) throughout the circannual cycle. Protein identification and data analysis were run through Galaxy-P, a new multiomic data analysis platform enabling effective integration of RNA-seq and MS/MS proteomic data. Galaxy-P uses flexible, modular workflows that combine customized sequence database searching and iTRAQ quantification to identify novel ground squirrel-specific protein sequences and provide insight into molecular mechanisms of hibernation. This study allowed for the quantification of 2007 identified cardiac proteins, including over 350 peptide sequences derived from previously uncharacterized protein products. Identification of these peptides allows for improved genomic annotation of this nonmodel organism, as well as identification of potential splice variants, mutations, and genome reorganizations that provides insights into novel cardioprotective mechanisms used during hibernation.

The Galaxy Platform for Reproducible Affinity Proteomic Mass Spectrometry Data Analysis.

Affinity proteomics (AP-MS) is growing in importance for characterizing protein-protein interactions (PPIs) in the form of protein complexes and signaling networks. The AP-MS approach necessitates several different software tools, integrated into reproducible and accessible workflows. However, if the scientist (e.g., a bench biologist) lacks a computational background, then managing large AP-MS datasets can be challenging, manually formatting AP-MS data for input into analysis software can be error-prone, and data visualization involving dozens of variables can be laborious. One solution to address these issues is Galaxy, an open source and web-based platform for developing and deploying user-friendly computational pipelines or workflows. Here, we describe a Galaxy-based platform enabling AP-MS analysis. This platform enables researchers with no prior computational experience to begin with data from a mass spectrometer (e.g., peaklists in mzML format) and perform peak processing, database searching, assignment of interaction confidence scores, and data visualization with a few clicks of a mouse. We provide sample data and a sample workflow with step-by-step instructions to quickly acquaint users with the process.