Transcriptomic analysis of asymptomatic and symptomatic severe Turkish patients in SARS-CoV-2 infection

OBJECTIVE: Coronavirus disease 2019 (COVID-19), leading to mild infection (MI), acute respiratory distress syndrome or death in different persons. Although the basis of these variabilities has not been fully elucidated, some possible findings have been encountered. In the present study, we aimed to reveal genes with different expression profiles by next-generation sequencing of RNA isolated from blood taken from infected patients to reveal molecular causes of different response. METHODS: Two healthy, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-negative control individuals (NCI), two SARS-CoV-2-positive patients who have MI, and two patients who have critical infection (CI) were included in the study. Total RNA was extracted from blood samples and sequenced. Raw RNA-Seq data were analyzed on Galaxy platform for the identification of differentially expressed genes and their pathway involvements. RESULTS: We found that 199 and 521 genes were downregulated in whole blood of COVID-19-positive CI patients compared to NCI and MI patients, respectively. We identified 21 gene ontology pathways commonly downregulated in CI patients compared to both NCI and MI, mostly associated with innate and adaptive immune responses. Three hundred and fifty-four and 600 genes were found to be upregulated compared to NCI and MI, respectively. Upregulated six pathways included genes that function in inflammatory response and inflammatory cytokine release. CONCLUSION: The transcriptional profile of CI patients deviates more significantly from that of MI in terms of the number of differentially expressed genes, implying that genotypic differences may account for the severity of SARS-CoV-2 infection and inflammatory responses through differential regulation of gene expression. Therefore, further studies that involve whole genome analysis coupled with differential expression analysis are required in order to determine the dynamics of genotype – gene expression profile associations.