Integrated multi-omics analyses identify key anti-viral host factors and pathways controlling SARS-CoV-2 infection

Abstract

Host anti-viral factors are essential for controlling SARS-CoV-2 infection but remain largely unknown due to the biases of previous large-scale studies toward pro-viral host factors. To fill in this knowledge gap, we performed a genome-wide CRISPR dropout screen and integrated analyses of the multi-omics data of the CRISPR screen, genome-wide association studies, single-cell RNA-seq, and host-virus proteins or protein/RNA interactome. This study has uncovered many host factors that were missed by previous studies, including the components of V-ATPases, ESCRT, and N-glycosylation pathways that modulated viral entry and/or replication. The cohesin complex was also identified as a novel anti-viral pathway, suggesting an important role of three-dimensional chromatin organization in mediating host-viral interaction. Furthermore, we discovered an anti-viral regulator KLF5, a transcriptional factor involved in sphingolipid metabolism, which was up-regulated and harbored genetic variations linked to the COVID-19 patients with severe symptoms. Our results provide a resource for understanding the host anti-viral network during SARS-CoV-2 infection and may help develop new countermeasure strategies.

Keywords: Genome-wide CRISPR Screen; SARS-CoV-2; host factors.

Figure 1. Discovery of host factors controlling SARS-CoV-2 infection.

(a) A schematic diagram of the functional CRISPR/Cas9 dropout screen based on virus-induced cytopathic effect (CPE). A549-AC cells were transduced with a genome-wide human gRNA library (five sgRNAs per gene) and followed by puromycin selection. After 3-day puromycin selection, 30 million pooled cells were collected as the reference sample. On day 7 after selection, pooled A549-AC cells were infected with recombinant SARS-CoV-2 at MOI=5 for 48 hours. Pooled A549-AC cells without viral treatment were severed as the controls. The changes in gRNA distribution between the viral infected samples and controls were determined. (b) A volcano plot showing top candidates for pro-viral and anti-viral host factors. The gene-level MAGeCK scores and the changes of gRNA distribution between A549-AC cells with and without viral infection were calculated. The log2 fold change of the second-best gRNA for each gene was selected for data representation. Genes whose gRNAs were significantly enriched or depleted in the infected group (p value <0.05 and |log₂FC| ≥0.5) were labeled as red and green dots, respectively. The top ten enriched/depleted (pro-viral/anti-viral) genes based on MAGeCK scores were indicated. (c) Ingenuity pathway analysis of identified host factors for SARS-CoV-2 infection. Enriched pathways for pro-viral factors (enriched, left panel) and anti-viral factors (depleted, right panel) with statistical significance (p value< 0.05) were illustrated.

Figure 2. Integrative analysis revealing virus-host interactome networks and potential clinical relevance of identified host factors.

(a) Protein-protein interactome (PPI) networks between viral proteins and host factors identified by the dropout screen. 229 interactions between 26 SARS-CoV-2 proteins (red diamonds) and 147 human proteins (circles; depleted hits: blue; enriched hits: yellow) were found. The color of edge indicates the type of interaction (purple: host-host PPI; orange: viral-viral PPI; grey: host-viral PPI) and the thickness of edge indicates the count number of published datasets. (b) RNA-protein interactome networks between the viral RNA and host factors identified by the dropout screen. SARS-CoV-2 viral RNA was indicated as the red diamond; identified host factors were represented as circles (enriched hits: yellow; depleted hits: blue). The interaction between RNA and identified host factors were indicated as different edge types (colors: literature; thickness: count number of published manuscripts). (c) Gene variations in multiple identified host factors are associated with disease severity in COVID-19 patients. The Genome-Wide Association Study (GWAS) between variants of identified host factors and clinical features was performed by using the COVID19-hgGWAS meta-analyses. “Hospitalized” indicates that single nucleotide polymorphisms (SNPs) of the identified host factors were related to the hospitalized COVID-19 patients, which were labeled with blue dots. “Critically-ill” indicates SNPs of the identified host factors were related to COVID-19 patients with particularly severe respiratory symptoms, which were labeled with red dots. The names of enriched genes and depleted genes were labeled in red and green, respectively. (d) A volcano plot showing the changes of mRNA expression of identified host factors in epithelial cells from COVID-19 patients with and without severe illness. Multiple scRNA-seq datasets obtained from cells in bronchoalveolar lavage fluid of COVID-19 patients were extracted. COVID-19 patients with mild symptoms or hospitalized in the ward were stratified in the mild group, whereas COVID-19 patients with severe symptoms or hospitalized in the intensive care unit (ICU) were stratified in the severe group. The fold change of gene expression and p value were calculated. Identified pro-viral factors and anti-viral factors which are differentially expressed (p<0.05) in these two groups were highlighted with red and green dots, respectively.

Figure 3. Validation of host factors identified from the CRISPR dropout screen.

(a) Performance of identified host factors in previously reported SARS-CoV-2 screens. For each dataset, enriched and depleted hits that meet the listed criteria were marked as red and green squares, respectively. Others that fail to be identified in the listed datasets were marked as gray squares. (b) Effects of perturbation of top 30 hits on the CPE caused by SARS-CoV-2 infection. Four pro-viral and twenty-six anti-viral factors were selected for validation. A series of A549-AC cell lines expressing gene-specific gRNAs were infected with recombinant SARS-CoV-2 at MOI= 2.5. The percentages of viable cells were measured at 48 hours post-infection. Data were normalized using the viability of corresponding cells at mock condition. Statistical significance between cells expressing gene-specific gRNAs and non-targeting gRNA (NC) was determined by one-way ANOVA with repeated measurements. At least two independent experiments were performed, and samples were triplicated in each independent experiment. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. n.s., not significant.

Figure 4. Impacts of top-ranking host factors on virion entry and replication pathways.

(a-b) Effects of perturbation of top-ranking host factors on CPE caused by SARS-CoV-2 infection at different infection conditions. For gene-specific knockdown (KD) effect (a), two pro-viral factors (ATP6V0D1, DPAGTT) and three anti-viral factors (DAZAP2, VTA1, KLF5) were selected. For gene-specific overexpression effect (b), two anti-viral factors (DAZAP2, VTA1) were selected. Genetically modified A549-AC cells were infected with recombinant SARS-CoV-2 at MOI=0.5, 2.5, and 5 for 48 hours. A549-AC cells expressing a non-targeting gRNA (NC) or the GFP vector were served as control cells for the KD and overexpression experiments, respectively. Data were normalized using the viability of corresponding cells at mock condition. (c-f) Effects of perturbation of top-ranking host factors on SARS-CoV-2 attachment and entry. Genetically modified A549-AC cells were infected with recombinant SARS-CoV-2 at MOI =1. To evaluate the changes in the viral attachment (c and d), the infection was performed at 4°C for 1 h; whereas to evaluate the changes in viral entry (e and f), the infection was performed at 37°C for 1 h. The levels of RNAs encoding the viral N protein and ACTB mRNAs were determined by RT-PCR. Viral RNA levels were normalized using the expression of ACTB mRNA. (g-h) Effects of perturbation of top-ranking host factors on SARS-CoV-2 replication. A549-AC cells with gene-specific KD (g) or overexpression (h) were infected with SARS-CoV-2-Nluc at MOI= 0.02, 0.1, and 0.5. The luciferase signals were measured at 24 hours post-infection. Statistical significances between cells with gene-specific overexpression and control cells at each infection condition were determined by one-way ANOVA with repeated measurements. At least two independent experiments were performed, and samples were triplicated in each independent experiment. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. n.s., not significant.