Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2

Abstract

Background The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling pathways of host cells play critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells. Drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment. Methods In this study, we developed a novel integrative analysis approach to identify the related molecular signaling pathways within host cells, and repurposed drugs as potentially effective treatments for COVID-19, based on the transcriptional response of host cells. Results We identified activated signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through integrative analysis. Then, the activated gene ontologies (GOs) and super GOs were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly activated. Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. In addition to many drugs being evaluated in clinical trials, the dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support. Conclusions The integrative genomics data analysis and results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment.

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

“…As an example, consider the following studies: Al-Waisy et al [ 46 ] achieved 99.99% accuracy, Dhiman et al [ 47 ] achieved 98.54% accuracy, Ozturk et al [ 48 ] achieved 98.08% accuracy, and Ahuja et al [ 49 ] achieved 99.4% accuracy. The effectiveness of the research is because lung involvement is the most common symptom of COVID-19 disease [ 50 ], and the symptoms may be seen clearly on radiographic lung imaging [ 51 ].…”

Gene Signature-Based Drug Repositioning

“…It empowered simultaneous, gene set knowledgebase-driven drug repositioning analysis and illustrated the mode of action of the predicted drug and disease pairs. Cogena has been widely used in drug repositioning for several diseases, including psoriasis, Coronavirus Disease 2019 (COVID-19) [56,57], Crohn's disease [58], periodontitis [59].…”

Endoplasmic reticulum stress and NF‐kB activation in SARS‐CoV‐2 infected cells and their response to antiviral therapy

“… 6 Again, this transcription factor has been speculated to represent one of the therapeutic targets in this infection along with other cellular stress and pro‐inflammatory pathways. 7 …”