PAnalyzer: A software tool for protein inference in shotgun proteomics

<p>Abstract</p> <p>Background</p> <p>Protein inference from peptide identifications in shotgun proteomics must deal with ambiguities that arise due to the presence of peptides shared between different proteins, which is common in higher eukaryotes. Recently data independent acquisition (DIA) approaches have emerged as an alternative to the traditional data dependent acquisition (DDA) in shotgun proteomics experiments. MS^{<it>E </it>}is the term used to name one of the DIA approaches used in QTOF instruments. MS^{<it>E </it>}data require specialized software to process acquired spectra and to perform peptide and protein identifications. However the software available at the moment does not group the identified proteins in a transparent way by taking into account peptide evidence categories. Furthermore the inspection, comparison and report of the obtained results require tedious manual intervention. Here we report a software tool to address these limitations for MS^{<it>E </it>}data.</p> <p>Results</p> <p>In this paper we present PAnalyzer, a software tool focused on the protein inference process of shotgun proteomics. Our approach considers all the identified proteins and groups them when necessary indicating their confidence using different evidence categories. PAnalyzer can read protein identification files in the XML output format of the ProteinLynx Global Server (PLGS) software provided by Waters Corporation for their MS^{<it>E </it>}data, and also in the mzIdentML format recently standardized by HUPO-PSI. Multiple files can also be read simultaneously and are considered as technical replicates. Results are saved to CSV, HTML and mzIdentML (in the case of a single mzIdentML input file) files. An MS^{<it>E </it>}analysis of a real sample is presented to compare the results of PAnalyzer and ProteinLynx Global Server.</p> <p>Conclusions</p> <p>We present a software tool to deal with the ambiguities that arise in the protein inference process. Key contributions are support for MS^{<it>E </it>}data analysis by ProteinLynx Global Server and technical replicates integration. PAnalyzer is an easy to use multiplatform and free software tool.</p

In-depth proteomic characterization of classical and non-classical monocyte subsets

Monocytes are bone marrow-derived leukocytes that are part of the innate immune system. Monocytes are divided into three subsets: classical, intermediate and non-classical, which can be differentiated by their expression of some surface antigens, mainly CD14 and CD16. These cells are key players in the inflammation process underlying the mechanism of many diseases. Thus, the molecular characterization of these cells may provide very useful information for understanding their biology in health and disease. We performed a multicentric proteomic study with pure classical and non-classical populations derived from 12 healthy donors. The robust workflow used provided reproducible results among the five participating laboratories. Over 5000 proteins were identified, and about half of them were quantified using a spectral counting approach. The results represent the protein abundance catalogue of pure classical and enriched non-classical blood peripheral monocytes, and could serve as a reference dataset of the healthy population. The functional analysis of the differences between cell subsets supports the consensus roles assigned to human monocytes

In-depth proteomic characterization of classical and non-classical monocyte subsets

Monocytes are bone marrow-derived leukocytes that are part of the innate immune system. Monocytes are divided into three subsets: classical, intermediate and non-classical, which can be differentiated by their expression of some surface antigens, mainly CD14 and CD16. These cells are key players in the inflammation process underlying the mechanism of many diseases. Thus, the molecular characterization of these cells may provide very useful information for understanding their biology in health and disease. We performed a multicentric proteomic study with pure classical and non-classical populations derived from 12 healthy donors. The robust workflow used provided reproducible results among the five participating laboratories. Over 5000 proteins were identified, and about half of them were quantified using a spectral counting approach. The results represent the protein abundance catalogue of pure classical and enriched non-classical blood peripheral monocytes, and could serve as a reference dataset of the healthy population. The functional analysis of the differences between cell subsets supports the consensus roles assigned to human monocytes

Multi-Omics Integration Highlights the Role of Ubiquitination in CCl4-Induced Liver Fibrosis

Liver fibrosis is the excessive accumulation of extracellular matrix proteins that occurs in chronic liver disease. Ubiquitination is a post-translational modification that is crucial for a plethora of physiological processes. Even though the ubiquitin system has been implicated in several human diseases, the role of ubiquitination in liver fibrosis remains poorly understood. Here, multi-omics approaches were used to address this. Untargeted metabolomics showed that carbon tetrachloride (CCl4)-induced liver fibrosis promotes changes in the hepatic metabolome, specifically in glycerophospholipids and sphingolipids. Gene ontology analysis of public deposited gene array-based data and validation in our mouse model showed that the biological process “protein polyubiquitination” is enriched after CCl4-induced liver fibrosis. Finally, by using transgenic mice expressing biotinylated ubiquitin (bioUb mice), the ubiquitinated proteome was isolated and characterized by mass spectrometry in order to unravel the hepatic ubiquitinated proteome fingerprint in CCl4-induced liver fibrosis. Under these conditions, ubiquitination appears to be involved in the regulation of cell death and survival, cell function, lipid metabolism, and DNA repair. Finally, ubiquitination of proliferating cell nuclear antigen (PCNA) is induced during CCl4-induced liver fibrosis and associated with the DNA damage response (DDR). Overall, hepatic ubiquitome profiling can highlight new therapeutic targets for the clinical management of liver fibrosis

Multi-Omics Integration Highlights the Role of Ubiquitination in CCl4-Induced Liver Fibrosis

Liver fibrosis is the excessive accumulation of extracellular matrix proteins that occurs in chronic liver disease. Ubiquitination is a post-translational modification that is crucial for a plethora of physiological processes. Even though the ubiquitin system has been implicated in several human diseases, the role of ubiquitination in liver fibrosis remains poorly understood. Here, multi-omics approaches were used to address this. Untargeted metabolomics showed that carbon tetrachloride (CCl4)-induced liver fibrosis promotes changes in the hepatic metabolome, specifically in glycerophospholipids and sphingolipids. Gene ontology analysis of public deposited gene array-based data and validation in our mouse model showed that the biological process “protein polyubiquitination” is enriched after CCl4-induced liver fibrosis. Finally, by using transgenic mice expressing biotinylated ubiquitin (bioUb mice), the ubiquitinated proteome was isolated and characterized by mass spectrometry in order to unravel the hepatic ubiquitinated proteome fingerprint in CCl4-induced liver fibrosis. Under these conditions, ubiquitination appears to be involved in the regulation of cell death and survival, cell function, lipid metabolism, and DNA repair. Finally, ubiquitination of proliferating cell nuclear antigen (PCNA) is induced during CCl4-induced liver fibrosis and associated with the DNA damage response (DDR). Overall, hepatic ubiquitome profiling can highlight new therapeutic targets for the clinical management of liver fibrosis