Genome-Wide CRISPR Screen Identifies Novel Host Dependency Factors for Influenza a Virus Infection
CitationLi, Bo. 2019. Genome-Wide CRISPR Screen Identifies Novel Host Dependency Factors for Influenza a Virus Infection. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractInfluenza A Virus (IAV) causes annual epidemics and recurring pandemics and remains a global health problem. As an obligatory pathogen, IAV relies on host cellular machinery to replicate and complete its life cycle. These host dependency factors (HDFs) serve as ideal therapeutic targets since they are less likely to mutate under drug-mediated selection pressure compared to viral proteins. However, previous attempts to identify these host factors via RNAi screens have produced largely divergent results, with few overlapping hits across different studies. Here, we performed multiple rounds of genome-wide CRISPR/Cas9 screens as well as a meta-analysis of all prior data to more reliably identify IAV host factors.
From our CRISPR/Cas9 screens, we identified 121 genes that are required for IAV infection in A549 cells. Amongst these, we selected 4 novel genes - WDR7, CCDC115, TMEM199 and CMTR1, to further investigate their roles in the IAV life cycle. WDR7, CCDC115 and TMEM199 function as co-factors of V-type ATPases and regulate V-type ATPase assembly. The absence of any of these factors leads to compensatory expansion and over-acidification of the endo-lysosomal compartments, which hampers IAV entry. CMTR1, the human mRNA cap methyltransferase, is responsible for methylating the first ribose nucleotide of the mRNA cap to produce the cap1 structure, which is required for efficient IAV cap-snatching and elongation of viral transcripts. The absence of CMTR1 blocks viral replication and increases cell autonomous innate immune response against IAV infection.
To incorporate our new findings into the existing evidence base for IAV host factors, we developed a data-driven meta-analysis method (MAIC) to integrate ranked and unranked data from all prior studies. MAIC performs better than other cross-validation algorithms in both synthetic data and in an experimental test, and provides a comprehensive ranked list of IAV HDFs that will serve as useful resource for future studies.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:42013157
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