期刊
SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41598-021-95698-w
关键词
-
资金
- Okinawa Prefecture's Project to Promote the Use of Information Technology in the Health and Medical Industries [20G1000012]
COVID-19, caused by the SARS-CoV-2 virus, is a global pandemic. A study using a machine learning method identified 123 genes critical for COVID-19 progression, with immune-related genes being enriched. These genes are influenced by NF-κB activity and histone modification, suggesting a potential mechanism of immune suppression in COVID-19 patients.
Coronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-19 patients to identify disease-related genes through an innovative machine learning method that enables a data-driven strategy for gene selection from a data set with a small number of samples and many candidates. Principal-component-analysis-based unsupervised feature extraction (PCAUFE) was applied to the RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects. The results identified 123 genes as critical for COVID-19 progression from 60,683 candidate probes, including immune-related genes. The 123 genes were enriched in binding sites for transcription factors NFKB1 and RELA, which are involved in various biological phenomena such as immune response and cell survival: the primary mediator of canonical nuclear factor-kappa B (NF-kappa B) activity is the heterodimer RelA-p50. The genes were also enriched in histone modification H3K36me3, and they largely overlapped the target genes of NFKB1 and RELA. We found that the overlapping genes were downregulated in COVID-19 patients. These results suggest that canonical NF-kappa B activity was suppressed by H3K36me3 in COVID-19 patient blood.
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