Single cell sequencing analysis uncovers genetics-influenced CD16+monocytes and memory CD8+T cells involved in severe COVID-19

Abstract

BackgroundUnderstanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown.MethodsWe constructed a powerful computational framework to characterize the host genetics-influenced immune cell subpopulations for severe COVID-19 by integrating GWAS summary statistics (N = 969,689 samples) with four independent scRNA-seq datasets (N = 606,534 cells).ResultsWe found that 34 risk genes were significantly associated with severe COVID-19, and the number of highly-expressed genetics-risk genes increased with the severity of COVID-19. Three cell-subtypes that are CD16+monocytes, megakaryocytes, and memory CD8+T cells were significantly enriched by COVID-19-related genetic association signals. Notably, three causal risk genes of CCR1, CXCR6, and ABO were specifically expressed in these three cell types, respectively. CCR1+CD16+monocytes and ABO+ megakaryocytes with significant up-regulated genes including S100A12, S100A8, S100A9, and IFITM1 confer higher risk to the cytokine storms among severe patients. CXCR6+ memory CD8+ T cells exhibit a notable polyfunctionality of multiple immunologic features, including elevation of proliferation, migration, and chemotaxis. Moreover, we observed a prominent increase in cell-cell interactions of both CCR1+ CD16+monocytes and CXCR6+ memory CD8+T cells in severe patients compared to normal controls among both PBMCs and lung tissues, and elevated interactions with epithelial cells could contribute to enhance the resident to lung airway for against COVID-19 infection.ConclusionsWe uncover a major genetics-modulated immunological shift between mild and severe infection, including an increase in up-regulated genetic-risk genes, excessive secreted inflammatory cytokines, and functional immune cell subsets contributing high risk to severity, which provides novel insights in parsing the host genetics-influenced immune cells for severe COVID-19.

Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19

“…To cluster these identified KEGG pathways, we performed a multidimensional scaling (MDS) analysis based on the Jaccard distance method [ 38 , 39 ] and constructed a pathway-pathway interaction network for these significantly enriched pathways setting the Jaccard distance > 0.1. For the analyzed codes, please refer to the GitHub repository ( https://github.com/mayunlong89/COVID19_scRNA [ 40 ]).…”

“…The Wilcoxon sum-rank test was used to assess DEGs in mild, moderate, and severe COVID-19 groups compared with normal control [ 40 ]. Both the Wilcoxon sum-rank test and scCODA were used for composition data analysis to examine the different proportions of peripheral immune cells across different COVID-19 severity.…”

“…Pathway- and disease-based enrichment analyses used the hypergeometric test to identify remarkable biological pathways and disease terms [ 36 ]. The Pearson correlation analysis was used to calculate the correlation coefficient of highly expressed genes in CCR1 + CD16+monocytes between moderate and severe patients [ 40 ]. The paired Student’s t test was used to calculate the significance of ligand/receptor interactions of CCR1 + CD16+monocytes and CXCR6 + memory CD8+T cells with other immune cells between normal control and severe COVID-19.…”

“…Four scRNA-seq datasets were downloaded from the GEO database ( https://www.ncbi.nlm.nih.gov/gds/?term=GSE149689 [ 18 ], https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158055 [ 12 ], and https://www.ncbi.nlm.nih.gov/gds/?term=GSE150861 [ 11 ]) and the ArrayExpress database ( https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9357 ) [ 10 ]. All analyzed codes for hypergenometric analysis [ 95 ], hierarchical clustering analysis [ 18 ], in silico permutation analysis [ 25 ], S-PrediXcan [ 41 ], S-MultiXcan [ 43 ], MDS [ 39 ], MAGMA [ 35 ], RolyPoly [ 49 ], scCODA [ 54 ], Cell-ID [ 64 ], and CellChat [ 53 ] analysis in the “ Methods ” section are available in an online GitHub repository at https://github.com/mayunlong89/COVID19_scRNA [ 40 ].…”