| Summary: | Nonsense-mediated mRNA decay (NMD) is a surveillance pathway that recognizes and selectively degrades mRNAs carrying premature translation-termination codons (PTCs). The physiological importance of NMD is manifested by the fact that about one third of all genetic diseases and some forms of cancer are caused by nonsense or frameshift mutations that introduce PTCs, and NMD can modulate the clinical phenotype of these diseases. Noteworthy, in total, genetic diseases attributable to PTCs affect millions of patients worldwide. Recent studies have shown that NMD also targets mRNAs transcribed from a large subset of wild-type genes, shaping their levels. NMD is a complex process where several proteins interact with each other and cooperate to induce degradation of a given transcript. Although this pathway has been extensively studied, the interactions and connectivity among these components is only partly elucidated. Aiming to expand the knowledge about the NMD pathway, we are combining bioinformatics, network analysis and experimental work to identify new proteins involved in NMD or its regulation. Our work, begins with a network analysis approach that integrates publicly available data regarding different types of interactions: 1) protein-protein, 2) kinase-target, 3) phosphatase-target, 4) miRNA-target, 5) transcription factor-target, 6) gene co-expression and 7) ubiquitination-target. Additionally, our network include data regarding known NMD-targets and NMD-triggering features. The generated network will be used to find novel NMD-associated proteins, prioritizing candidates with simultaneous interactions with different mRNA processing pathways (mRNA splicing, mRNA transport, mRNA translation and mRNA decay). Following data integration, we will develop a scoring algorithm to select the most central proteins in the generated network, which can be essential to further understand NMD and its regulation. The predicted candidates will be experimentally validated and their role in NMD will be tested. Due to the diversity of regulatory links integrated in this workflow, we propose it can be applied to find molecular bridges between related biological processes and generate novel hypotheses about the molecular mechanisms co-regulating these phenomena.
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