Youn Sohyun, Jeong Dabin, Kwon Hwijun, Han Eonyong, Kim Sun, Jung Inuk
School of Computer Science and Engineering, Kyungpook National University, Buk-gu, Daegu, 41566, Republic of Korea.
Wellcome Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK.
BioData Min. 2025 Sep 1;18(1):61. doi: 10.1186/s13040-025-00476-3.
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.
个体对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的免疫反应差异很大，导致患者之间的严重程度差异很大。虽然有多种方法可以在2019冠状病毒病（COVID-19）患者中发现与严重程度相关的生物标志物，但我们研究了SARS-CoV-2基因组中与患者严重程度水平相关的高度突变区域，即突变热点。使用基于密度的聚类算法Mutclust在全球共享流感数据倡议组织（GISAID）数据库中搜索SARS-CoV-2突变热点，该算法用于搜索具有高突变密度和多样性的基因座。
使用Mutclust在SARS-CoV-2基因组中搜索到477个突变热点，其中28个在由387名感染患者组成的多组学COVID-19队列中与严重程度水平显著相关。根据这28个与严重程度相关的突变热点，患者被进一步分为中度和重度患者组，这些热点在细胞因子谱和单细胞RNA测序样本中显示出独特的细胞因子和基因表达水平。通过网络传播分析进一步研究了SARS-CoV-2突变热点对人类基因的影响，其中重度组特有的两个突变热点与自然杀伤（NK）细胞活性相关。与未突变的表位相比，其中一个显示出降低了热点区域病毒表位与其结合的人类白细胞抗原（HLA）之间的亲和力。
在重度患者组中，无论是在细胞因子水平还是单细胞水平，与NK细胞免疫功能相关的基因，尤其是NK细胞受体和共激活受体基因，均出现显著失调。总体而言，鉴定出了与严重程度相关的突变热点及其相关的NK细胞相关基因表达调控。
