A Density Based Clustering Algorithm for Identyfing Mutation Hotspots in Genomes : 유전체 돌연변이 핫스팟 탐지를 위한 밀도 기반 클러스터링 알고리즘

다국어 초록 (Multilingual 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 be- tween 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.

목차 (Table of Contents)

• 1 Introduction 1
• 1.1 Background 1
• 1.2 Overview of thesis 3
• 2 Related Research 5
• 2.1 Shannon Entropy for Understanding Genomic Variability 5
• 2.2 Computational Methods for Viral Mutation Hotspot Detection 6
• 2.3 SARS-CoV-2 Mutations on HLA–Peptide Interactions 7
• 3 Materials and Method 10
• 3.1 Materials 10
• 3.1.1 Dataset for mutation hotspot detection 10
• 3.1.2 The multi-omics COVID-19 cohort dataset 11
• 3.2 SARS-CoV-2 mutation hotspots 13
• 3.2.1 Computing the H-score 13
• 3.2.2 Density based clustering algorithm 15
• 3.3 COVID-19 severity related hotspots 29
• 3.3.1 Selection of severity related hotspots 29
• 3.3.2 Network propagation analysis 30
• 3.3.3 Differentially expressed gene analysis 36
• 3.3.4 Validation on Influenza genome 36
• 4 Result 38
• 4.1 Mutation Hotspots of SARS-CoV-2 Virus 38
• 4.2 Mutation hotspots of moderate and severe patients 40
• 4.3 Network Propagation Analysis on the Mutation Hotspots 54
• 5 Discussion 60
• References 65
• Abstract (Korean) 75