Identification of severity related mutation hotspots in SARS-CoV-2 using a density-based clustering approach

Sohyun Youn, Dabin Jeong, Hwijun Kwon, Eonyong Han, Sun Kim, Inuk Jung

Abstract: The immune response to SARS-CoV-2 varies greatly among individuals yielding highly varying severity levels among the patients. While there are various methods to spot severity associated biomarkers in COVID-19 patients, we investigated highly mutated regions, or mutation hotspots, within the SARS-CoV-2 genome that correlate with patient severity levels. SARS-CoV-2 mutation hotspots were searched in the GISAID database using a density based clustering algorithm, Mutclust, that searches for loci with high mutation density and diversity. Using Mutclust, 477 mutation hotspots were searched in the SARS-CoV-2 genome, of which 28 showed significant association with severity levels in a multi-omics COVID-19 cohort comprised of 387 infected patients. The patients were further stratified into moderate and severe patient groups based on the 28 severity related mutation hotspots that showed distinctive cytokine and gene expression levels in both cytokine profile and single-cell RNA-seq samples. The effect of the SARS-CoV-2 mutation hotspots on human genes was further investigated by network propagation analysis, where two mutation hotspots specific to the severe group showed association with NK cell activity. One of them showed to decrease the affinity between the viral epitope of the hotspot region and its binding HLA when compared to the non-mutated epitope. Genes related to the immunological function of NK cells, especially the NK cell receptor and co-activating receptor genes, were significantly dysregulated in the severe patient group in both cytokine and single-cell levels. Collectively, mutation hotspots associated with severity and their related NK cell associated gene expression regulation were identified.

External IDs:dblp:journals/biodatamining/YounJKHKJ25