The World Health Organization recently announced that pandemic status has been achieved for coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Exponential increases in patient numbers have been reported around the world, along with proportional increases in the number of COVID-19-related deaths. The SARS-CoV-2 infection rate in a population is expected to be influenced by social practices, availability of vaccines or prophylactics, and the prevalence of susceptibility genes in the population. Previous work revealed that cellular uptake of SARS-CoV-2 requires Angiotensin Converting Enzyme 2 (ACE-2) and a cellular protease. The spike (S) protein on SARS-CoV-2 binds ACE-2, which functions as an entry receptor. Following receptor binding, transmembrane protease serine 2 (encoded by TMPRSS2 ) primes the S protein to allow cellular uptake. Therefore, individual expression of TMPRSS2 may be a crucial determinant of SARS-CoV-2 infection susceptibility. Here, we utilized multiple large genome databases, including the GTEx portal, SNP nexus, and Ensembl genome project, to identify gene expression profiles for TMPRSS2 and its important expression quantitative trait loci. Our results show that four variants (rs464397, rs469390, rs2070788 and rs383510) affect expression of TMPRSS2 in lung tissue. The allele frequency of each variant was then assessed in regional populations, including African, American, European, and three Asian cohorts (China, Japan and Taiwan). Interestingly, our data shows that TMPRSS2 -upregulating variants are at higher frequencies in European and American populations than in the Asian populations, which implies that these populations might be relatively susceptible to SARS-CoV-2 infection.

Atopic Dermatitis (AD) is a chronic and relapsing skin disease. The medications for treating AD are still limited, most of them are topical corticosteroid creams or antibiotics. The current study attempted to discover potential AD treatments by integrating a gene network and genomic analytic approaches. Herein, the Single Nucleotide Polymorphism (SNPs) associated with AD were extracted from the GWAS catalog. We identified 70 AD-associated loci, and then 94 AD risk genes were found by extending to proximal SNPs based on r2 > 0.8 in Asian populations using HaploReg v4.1. Next, we prioritized the AD risk genes using in silico pipelines of bioinformatic analysis based on six functional annotations to identify biological AD risk genes. Finally, we expanded them according to the molecular interactions using the STRING database to find the drug target genes. Our analysis showed 27 biological AD risk genes, and they were mapped to 76 drug target genes. According to DrugBank and Therapeutic Target Database, 25 drug target genes overlapping with 53 drugs were identified. Importantly, dupilumab, which is approved for AD, was successfully identified in this bioinformatic analysis. Furthermore, ten drugs were found to be potentially useful for AD with clinical or preclinical evidence. In particular, we identified filgotinub and fedratinib, targeting gene JAK1, as potential drugs for AD. Furthermore, four monoclonal antibody drugs (lebrikizumab, tralokinumab, tocilizumab, and canakinumab) were successfully identified as promising for AD repurposing. In sum, the results showed the feasibility of gene networking and genomic information as a potential drug discovery resource.

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