BackgroundThe recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.MethodsRNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.ResultsBased on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19’s main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.ConclusionThis study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

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Background and objective: Cisplatin treats some cancers, but its side effects have questioned its use. It seems that exercise activity can reduce its side effects. The present study aimed to investigate the impact of two types of exercise training on some inflammatory markers and dyslipidemia induced by Cisplatin in rats. Methods: 24 Sprague Dawley rats were divided into four groups: Healthy control (HC), Cisplatin control (CC), Cisplatin moderate-intensity continuous training (C-MICT), and Cisplatin high-intensity interval training (C-HIIT). Intraperitoneal injection induced irradiation at a 5‏mg/kg dose dissolved in normal saline. The exercise training lasted ten weeks. Results: Cisplatin significantly increased Interleukin-6 (IL-6; p<0.05) and tumor necrosis factor-alpha (TNF-α; p<0.05) in the cisplatin control group. However, there was no difference between IL-6 in the HC, C-MICT, and C-HIIT groups. The TNF-α in the two training groups was higher than the healthy control group (p<0.05) but lower than the Cisplatin control group (p<0.05). Also, the two training groups observed no significant difference between serum levels of IL-6 and TNF-α (p>0.05). Total cholesterol (TC), Triglycerides (TG), and Low-density lipoprotein cholesterol (LDL-C) were significantly higher in CC than in other groups (p<0.05) but High-density lipoprotein cholesterol (HDL-C) was lower(p<0.05). Both types of exercise training caused a significant decrease in LDL, TC, and TG(p<0.05), and a significant increase in HDL-C (p>0.05). Finally, there was no difference between the two types of exercise training on lipoproteins(p>0.05). Conclusion: It seems that MICT and HIIT can reduce inflammatory responses and improve blood lipids profile in rats induced by Cisplatin.