Microbial and human transcriptional profiling of coronavirus disease 2019 patients: Potential predictors of disease severity.

The high morbidity of patients with coronavirus disease 2019 (COVID-19) brings on a panic around the world. COVID-19 is associated with sex bias, immune system, and preexisting chronic diseases. We analyzed the gene expression in patients with COVID-19 and in their microbiota in order to identify potential biomarkers to aid in disease management. A total of 129 RNA samples from nasopharyngeal, oropharyngeal, and anal swabs were collected and sequenced in a high-throughput manner. Several microbial strains differed in abundance between patients with mild or severe COVID-19. Microbial genera were more abundant in oropharyngeal swabs than in nasopharyngeal or anal swabs. Oropharyngeal swabs allowed more sensitive detection of the causative SARS-CoV-2. Microbial and human transcriptomes in swabs from patients with mild disease showed enrichment of genes involved in amino acid metabolism, or protein modification via small protein removal, and antibacterial defense responses, respectively, whereas swabs from patients with severe disease showed enrichment of genes involved in drug metabolism, or negative regulation of apoptosis execution, spermatogenesis, and immune system, respectively. Microbial abundance and diversity did not differ significantly between males and females. The expression of several host genes on the X chromosome correlated negatively with disease severity. In this way, our analyses identify host genes whose differential expression could aid in the diagnosis of COVID-19 and prediction of its severity via non-invasive assay.
Competing Interests: JMMi, ZZ, XYHe, LC, JS, ZS, and JMMa were employed by the company BGI-Shenzhen. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2022 Gan, Min, Long, Li, Hu, Zhu, Li, Wang, He, Cai, Zhang, He, Chen, Wang, Su, Zhao, Huang, Zhang, Su, Guo, Hu, Mao, Ma and Pang.)