Abstract
The coronavirus disease 2019 (COVID-19) epidemic is currently raging around the world at a rapid speed. Among COVID-19 patients, SARS-CoV-2-associated acute respiratory distress syndrome (ARDS) is the main contribution to the high ratio of morbidity and mortality. However, clinical manifestations between SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS are quite common, and their therapeutic treatments are limited because the intricated pathophysiology having been not fully understood. In this study, to investigate the pathogenic mechanism of SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS, first, we constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via database mining. With the help of host-pathogen RNA sequencing (RNA-Seq) data, real HPI-GWGEN of COVID-19-associated ARDS and non-viral ARDS were obtained by system modeling, system identification, and Akaike information criterion (AIC) model order selection method to delete the false positives in candidate HPI-GWGEN. For the convenience of mitigation, the principal network projection (PNP) approach is utilized to extract core HPI-GWGEN, and then the corresponding core signaling pathways of COVID-19-associated ARDS and non-viral ARDS are annotated via their core HPI-GWGEN by KEGG pathways. In order to design multiple-molecule drugs of COVID-19-associated ARDS and non-viral ARDS, we identified essential biomarkers as drug targets of pathogenesis by comparing the core signal pathways between COVID-19-associated ARDS and non-viral ARDS. The deep neural network of the drug-target interaction (DNN-DTI) model could be trained by drug-target interaction databases in advance to predict candidate drugs for the identified biomarkers. We further narrowed down these predicted drug candidates to repurpose potential multiple-molecule drugs by the filters of drug design specifications, including regulation ability, sensitivity, excretion, toxicity, and drug-likeness. Taken together, we not only enlighten the etiologic mechanisms under COVID-19-associated ARDS and non-viral ARDS but also provide novel therapeutic options for COVID-19-associated ARDS and non-viral ARDS.
Keywords: COVID-19; DTI model; HPI-GWGEN; SARS-CoV-2; biomarkers; deep neural network; etiologic mechanism; host-pathogen RNA-Seq data; non-viral ARDS; systems biology.
【저자키워드】 COVID-19, SARS-CoV-2, Biomarkers, Systems biology, deep neural network, DTI model, HPI-GWGEN, etiologic mechanism, host-pathogen RNA-Seq data, non-viral ARDS, 【초록키워드】 coronavirus disease, Coronavirus disease 2019, ARDS, Biomarker, Biomarkers, Pathogenesis, acute respiratory distress syndrome, drug design, Genetic, Toxicity, Systems biology, drug, database, drug-likeness, clinical manifestations, sensitivity, Epidemic, pathophysiology, RNA sequencing, morbidity, pathway, Mitigation, drug target, morbidity and mortality, signaling pathway, information, False positive, Epigenetic, predict, mechanism, COVID-19 patients, acute respiratory distress, Interaction, drug-target, Pathways, False positives, therapeutic option, respiratory distress, clinical manifestation, Regulation, syndrome, help, criterion, pathogenic, candidate drugs, convenience, advance, excretion, drug candidate, KEGG, Therapeutic treatment, Akaike information criterion, approach, AIC, COVID-19-associated ARDS, predicted, candidate drug, PNP, 【제목키워드】 repurposing, drug, Biology, Model, respiratory, specification, System,