Weighted Gene Co-Expression Network Analysis Combined with Machine Learning Validation to Identify Key Modules and Hub Genes Associated with SARS-CoV-2 Infection

The coronavirus disease-2019 (COVID-19) pandemic has caused an enormous loss of lives. Various clinical trials of vaccines and drugs are being conducted worldwide; nevertheless, as of today, no effective drug exists for COVID-19. The identification of key genes and pathways in this disease may lead to finding potential drug targets and biomarkers. Here, we applied weighted gene co-expression network analysis and LIME as an explainable artificial intelligence algorithm to comprehensively characterize transcriptional changes in bronchial epithelium cells (primary human lung epithelium (NHBE) and transformed lung alveolar (A549) cells) during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our study detected a network that significantly correlated to the pathogenicity of COVID-19 infection based on identified hub genes in each cell line separately. The novel hub gene signature that was detected in our study, including PGLYRP4 and HEPHL1, may shed light on the pathogenesis of COVID-19, holding promise for future prognostic and therapeutic approaches. The enrichment analysis of hub genes showed that the most relevant biological process and KEGG pathways were the type I interferon signaling pathway, IL-17 signaling pathway, cytokine-mediated signaling pathway, and defense response to virus categories, all of which play significant roles in restricting viral infection. Moreover, according to the drug-target network, we identified 17 novel FDA-approved candidate drugs, which could potentially be used to treat COVID-19 patients through the regulation of four hub genes of the co-expression network. In conclusion, the aforementioned hub genes might play potential roles in translational medicine and might become promising therapeutic targets. Further in vitro and in vivo experimental studies are needed to evaluate the role of these hub genes in COVID-19.

PM2.5 induces autophagy-mediated cell apoptosis via PI3K/AKT/mTOR signaling pathway in mice bronchial epithelium cells

Air pollution can highly impact the respiratory system in healthy individuals. Studies have indicated that particles with an aerodynamic diameter of ≤2.5 µm (PM2.5) can be considered to be harmful for lung alveoli and bronchial epithelium cells. PM2.5 can be directly inhaled and can deeply penetrate into the lung alveoli, causing lung dysfunction. However, the toxicological mechanism mediated by PM2.5 for respiratory disease has still not been clearly determined. The purpose of the current study was to investigate the effects of PM2.5 on mouse bronchial epithelium cells (MBECs) and explored the possible mechanism mediated by PM2.5 in MBECs. The results of the current study indicated that PM2.5 markedly decreased lung function, including total lung capacity, residual volume, vital capacity and airway resistance in experimental mice. The results demonstrated that PM2.5 markedly induced inflammatory responses, oxidative injury and MBEC apoptosis. PM2.5 increased interleukin (IL)-1β and IL-6 expression, and reactive oxygen species production in MBECs. Furthermore, PM2.5 specifically induced PI3K, AKT and mTOR expression in MBECs. Disruption of PI3K/AKT/mTOR signaling was also indicated to effectively inhibit apoptosis of MBECs. In conclusion, the results of the current study systematically demonstrated the role of apoptosis-mediated MBEC apoptosis in PM2.5-treated mice, and provides a potential strategy for preclinical intervention in patients with PM2.5-induced lung diseases.