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Oral cancer is common worldwide but lacks robust diagnostics and therapeutics. Lifestyle factors, such as tobacco chewing and smoking, are significantly associated with oral cancers. Mapping the changes in the global proteome, secretome and post-translational modifications (PTMs) during tobacco exposure of oral keratinocytes hold great potential for understanding the mechanisms of oral carcinogenesis, not to mention for innovation toward clinical interventions in the future. On the other hand, although advances in mass spectrometry (MS)-based techniques have enabled the deep mining of complex proteomes, a large portion of the mass spectrometric data remains unassigned. These unassigned spectral data can be researched for multiple post-translational modifications (multiPTMs). Using data mining of publicly available proteomics data, we report, in this study, a multiPTM analysis of high-resolution MS-derived datasets on cellular proteome and secretome of chronic tobacco-treated oral keratinocytes. We identified 800 PTM sites in 496 proteins. Among them, 43 PTM sites in 37 proteins were found to be differentially expressed, accounting for their protein-level expression. Enrichment analysis of the proteins with altered phosphosite expression and the known kinases of these phosphosites discovered the overrepresentation of certain biological processes such as splicing and hemidesmosome assembly. These findings contribute to a deeper understanding of omics level changes in chronic tobacco-treated oral keratinocytes, and by extension, pathophysiology of oral cancers.

Metabolomics is a leading frontier of systems science and biomedical innovation. However, metabolite identification in mass spectrometry (MS)-based global metabolomics investigations remains a formidable challenge. Moreover, lack of comprehensive spectral databases hinders accurate identification of compounds in global MS-based metabolomics. Creating experiment-derived metabolite spectral libraries tailored to each experiment is labor-intensive. Therefore, predicted spectral libraries could serve as a better alternative. User-friendly tools are much needed, as the currently available metabolomic analysis tools do not offer adequate provision for users to create or choose context-specific databases. Here, we introduce the MS2Compound, a metabolite identification tool, which can be used to generate a custom database of predicted spectra using the Competitive Fragmentation Modeling-ID (CFM-ID) algorithm, and identify metabolites or compounds from the generated database. The database generator can create databases of the model/context/species used in the metabolomics study. The MS2Compound is also powered with mS-score, a scoring function for matching raw fragment spectra to a predicted spectra database. We demonstrated that mS-score is robust in par with dot product and hypergeometric score in identifying metabolites using benchmarking datasets. We evaluated and highlight here the unique features of the MS2Compound by a re-analysis of a publicly available metabolomic dataset (MassIVE id: MSV000086784) for a complex traditional drug formulation called Triphala. In conclusion, we believe that the omics systems science and biomedical research and innovation community in the field of metabolomics will find the MS2Compound as a user-friendly analysis tool of choice to accelerate future metabolomic analyses.

Glioblastoma multiforme (GBM) is one of the most lethal brain tumors with an inadequately understood pathophysiology. Biomarkers that guide accurate diagnosis and treatment decisions would greatly support precision medicine for GBM. Previous studies of GBM have focused on signaling pathways such as epidermal growth factor receptor (EGFR), platelet-derived growth factor receptors (PDGFRs), notch, wnt, and others, identified with single omics technology platforms (genomics, transcriptomics, or proteomics), but not with their integrated use. In this context, we report here a multi-omics pathway view, expanded through integration of the expression data at transcriptomic and proteomic levels, followed by selection of a functionally related group of proteins such as kinases deregulated in GBM. By using this strategy, we observed a highly significant enrichment of the gonadotropin-releasing hormone (GnRH) signaling pathway that was not deciphered with single omics datasets. The curation of the GnRH pathway with extensive literature analysis brought about a comprehensive annotation of the pathway, which included several additional pathway members that were not previously annotated. A targeted search resulted in identification of additional nonkinase members of the pathway in the GBM multi-omics datasets. We found evidence of GnRH receptor expression in GBM and other cancers. We offer here an updated generic pathway map of GnRH signaling, show its enrichment in the context of GBM, and discuss its plausible cross-connectivity with EGFR, wnt, calcium, and focal adhesion kinase signaling pathways that were earlier shown to be the top deregulated pathways in GBM. In conclusion, this study demonstrates the promise of multi-omics research and analyses to better understand complex cancers and suggests continued efforts and research in this direction in the field of integrative biology.

Among the neglected tropical diseases, leishmaniasis is one of the most devastating, resulting in significant mortality and contributing to nearly 2 million disability-adjusted life years. Cutaneous leishmaniasis is a debilitating disorder caused by the kinetoplastid protozoan parasite Leishmania major, which results in disfiguration and scars. L. major genome was the first to be sequenced within the genus Leishmania. Use of proteomic data for annotating genomes is a complementary approach to conventional genome annotation approaches and is referred to as proteogenomics. We have used a proteogenomics-based approach to map the proteome of L. major and also annotate its genome. In this study, we searched L. major promastigote proteomic data against the annotated L. major protein database. Additionally, we searched the proteomic data against six-frame translated L. major genome. In all, we identified 3613 proteins in L. major promastigotes, which covered 43% of its proteome. We also identified 26 genome search-specific peptides, which led to the identification of three novel genes previously not identified in L. major. We also corrected the annotation of N-termini of 15 genes, which resulted in extension of their protein products. We have validated our proteogenomics findings by RT-PCR and sequencing. In addition, our study resulted in identification of 266 N-terminally acetylated peptides in L. major, one of the largest acetylated peptide datasets thus far in Leishmania. This dataset should be a valuable resource to researchers focusing on neglected tropical diseases.