Bioinformatic analysis and preliminary validation of potential therapeutic targets for COVID-19 infection in asthma patients.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 causes coronavirus disease 19 (COVID-19). The number of confirmed cases of COVID-19 is also rapidly increasing worldwide, posing a significant challenge to human safety. Asthma is a risk factor for COVID-19, but the underlying molecular mechanisms of the asthma-COVID-19 interaction remain unclear. METHODS: We used transcriptome analysis to discover molecular biomarkers common to asthma and COVID-19. Gene Expression Omnibus database RNA-seq datasets (GSE195599 and GSE196822) were used to identify differentially expressed genes (DEGs) in asthma and COVID-19 patients. After intersecting the differentially expressed mRNAs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify the common pathogenic molecular mechanism. Bioinformatic methods were used to construct protein-protein interaction (PPI) networks and identify key genes from the networks. An online database was used to predict interactions between transcription factors and key genes. The differentially expressed long noncoding RNAs (lncRNAs) in the GSE195599 and GSE196822 datasets were intersected to construct a competing endogenous RNA (ceRNA) regulatory network. Interaction networks were constructed for key genes with RNA-binding proteins (RBPs) and oxidative stress-related proteins. The diagnostic efficacy of key genes in COVID-19 was verified with the GSE171110 dataset. The differential expression of key genes in asthma was verified with the GSE69683 dataset. An asthma cell model was established with interleukins (IL-4, IL-13 and IL-17A) and transfected with siRNA-CXCR1. The role of CXCR1 in asthma development was preliminarily confirmed. RESULTS: By intersecting the differentially expressed genes for COVID-19 and asthma, 393 common DEGs were obtained. GO and KEGG enrichment analyses of the DEGs showed that they mainly affected inflammation-, cytokine- and immune-related functions and inflammation-related signaling pathways. By analyzing the PPI network, we obtained 10 key genes: TLR4, TLR2, MMP9, EGF, HCK, FCGR2A, SELP, NFKBIA, CXCR1, and SELL. By intersecting the differentially expressed lncRNAs for COVID-19 and asthma, 13 common differentially expressed lncRNAs were obtained. LncRNAs that regulated microRNAs (miRNAs) were mainly concentrated in intercellular signal transduction, apoptosis, immunity and other related functional pathways. The ceRNA network suggested that there were a variety of regulatory miRNAs and lncRNAs upstream of the key genes. The key genes could also bind a variety of RBPs and oxidative stress-related genes. The key genes also had good diagnostic value in the verification set. In the validation set, the expression of key genes was statistically significant in both the COVID-19 group and the asthma group compared with the healthy control group. CXCR1 expression was upregulated in asthma cell models, and interference with CXCR1 expression significantly reduced cell viability. CONCLUSIONS: Key genes may become diagnostic and predictive biomarkers of outcomes in COVID-19 and asthma. Video Abstract.

Anxiety and depressive symptoms among COVID-19 patients in Jianghan Fangcang Shelter Hospital in Wuhan, China.

Fangcang shelter hospitals were established in China during the coronavirus disease 2019 (COVID-19) pandemic as a countermeasure to stop the spread of the disease. To our knowledge, no research has been conducted on mental health problems among patients in Fangcang shelter hospitals. This study aimed to determine the prevalence and major influencing factors of anxiety and depressive symptoms among COVID-19 patients admitted to Fangcang shelter hospitals. From February 23, 2020, to February 26, 2020, we obtained sociodemographic and clinical characteristics information of COVID-19 patients in Jianghan Fangcang Shelter Hospital (Wuhan, China) and assessed their mental health status and sleep quality. Data were obtained with an online questionnaire. The questionnaire consisted of a set of items on demographic characteristics, a set of items on clinical characteristics, the Self-Rating Anxiety Scale, Self-Rating Depression Scale, and Pittsburgh Sleep Quality Index. Three hundred seven COVID-19 patients who were admitted to Jianghan Fangcang Shelter Hospital participated in this study. The prevalence of anxiety and depressive symptoms were 18.6% and 13.4%, respectively. Poor sleep quality and having ≥ two current physical symptoms were independent risk factors for anxiety symptoms. Female sex, having a family member with confirmed COVID-19, and having ≥ two current physical symptoms were independent risk factors for depressive symptoms. Anxiety and depressive symptoms were found to be common among COVID-19 patients in Fangcang Shelter Hospital, with some patients being at high risk.