Exploring Potential Biomarkers and Molecular Mechanisms of Ischemic Cardiomyopathy and COVID-19 Comorbidity Based on Bioinformatics and Systems Biology.

Cardiovascular complications combined with COVID-19 (SARS-CoV-2) lead to a poor prognosis in patients. The common pathogenesis of ischemic cardiomyopathy (ICM) and COVID-19 is still unclear. Here, we explored potential molecular mechanisms and biomarkers for ICM and COVID-19. Common differentially expressed genes (DEGs) of ICM (GSE5406) and COVID-19 (GSE164805) were identified using GEO2R. We performed enrichment and protein-protein interaction analyses and screened key genes. To confirm the diagnostic performance for these hub genes, we used external datasets (GSE116250 and GSE211979) and plotted ROC curves. Transcription factor and microRNA regulatory networks were constructed for the validated hub genes. Finally, drug prediction and molecular docking validation were performed using cMAP. We identified 81 common DEGs, many of which were enriched in terms of their relation to angiogenesis. Three DEGs were identified as key hub genes (HSP90AA1, HSPA9, and SRSF1) in the protein-protein interaction analysis. These hub genes had high diagnostic performance in the four datasets (AUC > 0.7). Mir-16-5p and KLF9 transcription factor co-regulated these hub genes. The drugs vindesine and ON-01910 showed good binding performance to the hub genes. We identified HSP90AA1, HSPA9, and SRSF1 as markers for the co-pathogenesis of ICM and COVID-19, and showed that co-pathogenesis of ICM and COVID-19 may be related to angiogenesis. Vindesine and ON-01910 were predicted as potential therapeutic agents. Our findings will contribute to a deeper understanding of the comorbidity of ICM with COVID-19.

Socio-Economic Factors Affecting the Regional Spread and Outbreak of COVID-19 in China.

BACKGROUND: This study investigated the impact of socio-economic factors on the spread and outbreak of COVID-19 based on Chinese data. METHODS: Cumulative confirmed cases were collected and divided into the First-stage cases cluster dominated by imported cases, and the Second-stage cases cluster dominated by secondary cases, according to the time of emergency state and Wuhan city lockdown. The linear regression was used for data analysis. RESULTS: A total of 12,877 cases in 30 provinces were analyzed in the study. The First-stage cases cluster included 675 cases and Second-stage cases cluster included 12,202 cases. The socio-economic factors were significantly associated with the cases (P<0.05). The GDP and proportion of population moving out of Wuhan were associate with the First-stage dominated by imported cases (ß>0, P<0.05). The First-stage cases cluster, proportion of population moving out of Wuhan and urban population were associate with the Second-stage dominated by secondary cases (ß>0, P<0.05). CONCLUSION: Socio-economic factors had impacts on the spread and outbreak of COVID-19. The combination of different socio-economic indicators at different stages of the epidemic may help control the epidemic.

SARS-CoV-2 Spike protein enhances ACE2 expression via facilitating Interferon effects in bronchial epithelium.

OBJECTIVE: In this study, we focused on the interaction between SARS-CoV-2 and host Type I Interferon (IFN) response, so as to identify whether IFN effects could be influenced by the products of SARS-CoV-2. METHODS: All the structural and non-structural proteins of SARS-CoV-2 were transfected and overexpressed in the bronchial epithelial cell line BEAS-2B respectively, and typical antiviral IFN-stimulated gene (ISG) ISG15 expression was detected by qRT-PCR. RNA-seq based transcriptome analysis was performed between control and Spike (S) protein-overexpressed BEAS-2B cells. The expression of ACE2 and IFN effector JAK-STAT signaling activation were detected in control and S protein-overexpressed BEAS-2B cells by qRT-PCR or/and Western blot respectively. The interaction between S protein with STAT1 and STAT2, and the association between JAK1 with downstream STAT1 and STAT2 were measured in BEAS-2B cells by co-immunoprecipitation (co-IP). RESULTS: S protein could activate IFN effects and downstream ISGs expression. By transcriptome analysis, overexpression of S protein induced a set of genes expression, including series of ISGs and the SARS-CoV-2 receptor ACE2. Mechanistically, S protein enhanced the association between the upstream JAK1 and downstream STAT1 and STAT2, so as to promote STAT1 and STAT2 phosphorylation and ACE2 expression. CONCLUSION: SARS-CoV-2 S protein enhances ACE2 expression via facilitating IFN effects, which may help its infection.