An integrative methodology based on protein-protein interaction networks for identification and functional annotation of disease-relevant genes applied to channelopathies
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AuteurMarín, Milagros; Esteban, Francisco; Ramírez Rodrigo, Hilario; Ros Vidal, Eduardo; Sáez Lara, María José
ChannelopathiesProtein-protein interaction networksBehavioural diagnosisGenetic diseasesSystems medicine
Marín, M., Esteban, F. J., Ramírez-Rodrigo, H., Ros, E., & Sáez-Lara, M. J. (2019). An integrative methodology based on protein-protein interaction networks for identification and functional annotation of disease-relevant genes applied to channelopathies. BMC bioinformatics, 20(1), 1-13.
PatrocinadorThis work was supported by funds from MINECO-FEDER (TIN2016–81041-R to E.R.), European Human Brain Project SGA2 (H2020-RIA 785907 to M.J.S.), Junta de Andalucía (BIO-302 to F.J.E.) and MEIC (Systems Medicine Excellence Network, SAF2015–70270-REDT to F.J.E.).
Biologically data-driven networks have become powerful analytical tools that handle massive, heterogeneous datasets generated from biomedical fields. Protein-protein interaction networks can identify the most relevant structures directly tied to biological functions. Functional enrichments can then be performed based on these structural aspects of gene relationships for the study of channelopathies. Channelopathies refer to a complex group of disorders resulting from dysfunctional ion channels with distinct polygenic manifestations. This study presents a semi-automatic workflow using protein-protein interaction networks that can identify the most relevant genes and their biological processes and pathways in channelopathies to better understand their etiopathogenesis. In addition, the clinical manifestations that are strongly associated with these genes are also identified as the most characteristic in this complex group of diseases. This research provides a systems biology approach to extract information from interaction networks of gene expression. We show how large-scale computational integration of heterogeneous datasets, PPI network analyses, functional databases and published literature may support the detection and assessment of possible potential therapeutic targets in the disease. Applying our workflow makes it feasible to spot the most relevant genes and unknown relationships in channelopathies and shows its potential as a first-step approach to identify both genes and functional interactions in clinical-knowledge scenarios of target diseases.