Introducing APOA1 as a key protein in COVID-19 infection: a bioinformatics approach

Introducing possible diagnostic and therapeutic biomarker candidates via the identification of chief dysregulated proteins in COVID-19 patients is the aim of this study. Background: Molecular studies, especially proteomics, can be considered as suitable approaches for discovering the hidden aspect of the disease. Methods: Differentially expressed proteins (DEPs) of three patients with demonstrated severe condition (S-COVID-19) were compared to healthy cases by a proteomics study. Cytoscape software and STRING database were used to construct the proteinprotein interaction (PPI) network. The central DEPs were identified through topological analysis of the network. ClueGO+CluePedia were applied to find the biological processes related to the central nodes. MCODE molecular complex detection (MCODE) was used to discover protein complexes. Results: A total of 242 DEPs from among 256 query ones were included in the network. Centrality analysis of the network assigned 16 hub-bottlenecks, nine of which were presented in the highest-scored protein complex. Ten protein complexes were determined. APOA1 was identified as the protein complex seed, and APP, EGF, and C3 were the top hub-bottlenecks of the network. The results specify that up-regulation of C3 and down-regulation of APOA1 in urine play a role in the stiffness in respiration and, accordingly, the severity of COVID-19. Moreover, dysregulation of APP and APOA1 could both contribute to the possible adverse effects of COVID-19 on the nervous system. Conclusion: The introduced central proteins of the S-COVID-19 interaction network, particularly APOA1, can be considered as diagnostic and therapeutic targets related to the coronavirus disease after being approved with complementary studies.