Common and rare variant analyses combined with single-cell multiomics reveal cell-type-specific molecular mechanisms of COVID-19 severity

Abstract

The determinants of severe COVID-19 in non-elderly adults are poorly understood, which limits opportunities for early intervention and treatment. Here we present novel machine learning frameworks for identifying common and rare disease-associated genetic variation, which outperform conventional approaches. By integrating single-cell multiomics profiling of human lungs to link genetic signals to cell-type-specific functions, we have discovered and validated over 1,000 risk genes underlying severe COVID-19 across 19 cell types. Identified risk genes are overexpressed in healthy lungs but relatively downregulated in severely diseased lungs. Genetic risk for severe COVID-19, within both common and rare variants, is particularly enriched in natural killer (NK) cells, which places these immune cells upstream in the pathogenesis of severe disease. Mendelian randomization indicates that failed NKG2D-mediated activation of NK cells leads to critical illness. Network analysis further links multiple pathways associated with NK cell activation, including type-I-interferon-mediated signalling, to severe COVID-19. Our rare variant model, PULSE, enables sensitive prediction of severe disease in non-elderly patients based on whole-exome sequencing; individualized predictions are accurate independent of age and sex, and are consistent across multiple populations and cohorts. Risk stratification based on exome sequencing has the potential to facilitate post-exposure prophylaxis in at-risk individuals, potentially based around augmentation of NK cell function. Overall, our study characterizes a comprehensive genetic landscape of COVID-19 severity and provides novel insights into the molecular mechanisms of severe disease, leading to new therapeutic targets and sensitive detection of at-risk individuals.

Delineating COVID-19 immunological features using single-cell RNA sequencing

“…To understand the genetic basis of severe COVID-19 in non-elderly adults, Zhang et al performed whole-exome sequencing and identified common and rare disease-associated genetic variants within over 1,000 risk genes. 78 By integrating single-cell multiomics profilings of human lung data, they identified particularly enriched risk genes in NK cells. The Mendelian randomization indicates the proportion of NK cells have a causal relationship with critical illness of COVD-19.…”

“…While scRNA-seq has improved our understanding of the infection and immune response of COVID-19, integrating it with other technology to generate multi-omics data would provide valuable insights from other aspects. There are many promising technologies and methods to study COVID-19 in combination with scRNA-seq, such as GWAS for identifying risk genes or alleles for COVID-19, 78 single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) for profiling chromatin accessibility, 45,86,98 cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) for profiling surface proteome, 11 and spatial transcriptomics for acquiring spatial information. 2 The multi-omics data would facilitate characterizing an integrated framework of COVID-19 pathogenesis and immune response.…”

Low expression of EXOSC2 protects against clinical COVID-19 and impedes SARS-CoV-2 replication

“…As a result current therapeutic approaches seek to either boost host immunity (e.g. vaccines) (Zhang et al 2021; Olliaro et al 2021), reduce viral replication (e.g. molpunavir (Jayk Bernal et al 2021)) or reduce hyperinflammation (e.g.…”

COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 prediction

“…Hence, accurate prediction at the state and county level is crucial for informed decisions. Indeed, the county-level analysis could provide insightful information at finer granularity for policymakers 16 , 20 – 22 . Policymakers can maximize resource allocation efficiency and react promptly in the legislative areas that require urgent attention.…”