NMD-associated proteins predicted by an integrative network approach

The mechanism of nonsense-mediated decay (NMD) selectively degrades mRNAs carrying a premature translation-termination codon and regulates the abundance of a large number of physiological mRNAs that encode full-length proteins. Although this complex process has been extensively studied along the yea...

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Bibliographic Details
Main Author: Nogueira, Gonçalo (author)
Other Authors: Pinto, Francisco (author), Romão, Luísa (author)
Format: conferenceObject
Language:eng
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10400.18/6819
Country:Portugal
Oai:oai:repositorio.insa.pt:10400.18/6819
Description
Summary:The mechanism of nonsense-mediated decay (NMD) selectively degrades mRNAs carrying a premature translation-termination codon and regulates the abundance of a large number of physiological mRNAs that encode full-length proteins. Although this complex process has been extensively studied along the years, the interactions and connectivity among NMD players is not completely understood. Additionally, some NMD mechanistical aspects suggest missing roles that can be played by proteins still not reported as involved in this pathway. To tackle this hypothesis, we developed a network-based approach to predict new proteins involved in NMD. Our approach explores the ability of proteins to bridge related processes, while integrating data regarding protein-protein interactions, co-expression and co-regulation by miRNAs and transcription factors. We found that known NMD-factors have physical, regulatory and co-expression interaction signatures with related processes (mRNA translation, mRNA splicing, mRNA degradation and mRNA transport), which can be used to distinguish them from other proteins. We computed a scoring algorithm to rank NMD-neighbors according to the similarity to these signatures, generating a list of NMD candidates, that we aim to validate experimentally. Interestingly, some candidates were recently studied in NMD context and showed promising results. Furthermore, a cross-validation analysis indicated the robustness of the predictions provided by our method. We propose that such approach can be applied to find molecular bridges between related biological processes and generate novel hypotheses about the molecular mechanisms co-regulating these phenomena.