ABSTRACT
In the present study, we applied the software “Genome Enhancer” to several datasets that contain transcriptomics data on COVID-19 disease. The goal of this pipeline is to identify potential drug targets in the molecular network that governs the studied pathological process. In the first step of analysis, pipeline discovers transcription factors (TFs) that regulate gene activities in the pathological state. The activities of these TFs are controlled by so-called master regulators, which are identified in the second step of analysis. After a subsequent druggability checkup, the most promising master regulators are chosen as potential drug targets for the analyzed pathology. At the end, the pipeline comes up with a prioritized list of drugs with the potential to interact with selected drug targets.
From the datasets analyzed in this study, we found the following TFs to be potentially involved in the regulation of the differentially expressed genes: Interferon Regulatory Factors (1–8), STAT factors (3 and 5), GR and AR nuclear receptors, TCF7, LEF1, and few other factors. The subsequent network analysis suggested IL-6, TNF-alpha, IL-1, ACE2, JAK3, EGF signaling receptors, ERK as well as cell-cycle regulators, such as ANAPC1 as master regulators, and the most promising molecular targets for further research, drug development, and drug repurposing initiatives on the basis of identified molecular mechanism of the studied pathology. Having checked the actual druggability potential of the full list of identified targets, both, via information available in medical literature and via cheminformatics analysis of drug compounds, we have identified several drugs as the most promising treatment candidates for the studied pathology in different stages of disease.
Application of the approach of finding master regulators in the gene regulatory networks helps to decipher the details of the pathological mechanisms on various stages of the COVID-19 disease, identify promising drug targets, and come close to the possibility of personalized medicine by selecting and prioritizing drugs for each individual patient.
