ACE2 interaction networks in COVID-19: a physiological framework for prediction of outcome in patients with cardiovascular risk factors

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease 2019; COVID-19) is associated with adverse outcome in patients with cardiovascular disease (CVD). Aim: To characterize the interaction between SARS-CoV-2 and Angiotensin Converting Enzyme 2 (ACE2) functional networks with focus on CVD. Methods: Using bioinformatic tools, network medicine approaches and publicly available datasets, we investigated ACE2 tissue expression and described ACE2 interaction network which could be affected by SARS-CoV-2 infection. We identified top ACE2 interactors, including miRNAs which are shared regulators between the ACE2, virus-infection related proteins and heart interaction networks, using lung and nervous system networks as a reference. We also identified main SARS-CoV-2 risk groups and performed drug predictions for them. Results: We found the same range of ACE2 expression confidence in respiratory and cardiovascular systems (averaging 4.48 and 4.64, respectively). Analysing the complete ACE2 interaction network, we identified 11 genes (ACE2, DPP4, ANPEP, CCL2, TFRC, MEP1A, ADAM17, FABP2, NPC1, CLEC4M, TMPRSS2) associated with virus-infection related processes. Previously described genes associated with cardiovascular risk factors DPP4, CCL2 and ANPEP were extensively connected with top regulators of ACE2 network, including ACE, INS and KNG1. Enrichment analysis revealed several disease phenotypes associated with interaction networks of ACE2, heart tissue, and virus-infection related protein, with the strongest associations with the following diseases (in decreasing rank order): obesity, hypertensive disease, non-insulin dependent diabetes mellitus, congestive heart failure, and coronary artery disease. We described for the first time microRNAs-miR (miR-302c-5p, miR-1305, miR-587, miR-26b-5p, and mir-27a-3p), which were common regulators of the three networks: ACE2, heart tissue and virus-infection related proteins. Conclusion: Our study provides novel information regarding the complexity of signaling pathways affected by SARS-CoV-2 and proposes predictive tools as miR towards personalized diagnosis and therapy in COVID-19. Additionally, our study provides a list of miRNAs with biomarker potential in prediction of adverse outcome in patients with COVID-19 and CVD.