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A genome-wide CRISPR/Cas9 gene knockout screen identifies immunoglobulin superfamily DCC subclass member 4 as a key host factor that promotes influenza virus endocytosis.

ABSTRACT: Influenza virus infection is dependent on host cellular factors, and identification of these factors and their underlying mechanisms can provide important information for the development of strategies to inhibit viral infection. Here, we used a highly pathogenic H5N1 influenza virus to perform a genome-wide CRISPR/Cas9 gene knockout screen in human lung epithelial cells (A549 cells), and found that knockout of transmembrane protein immunoglobulin superfamily DCC subclass member 4 (IGDCC4) significantly reduced the replication of the virus in A549 cells. Further studies showed that IGDCC4 interacted with the viral hemagglutinin protein and facilitated virus internalization into host cells. Animal infection studies showed that replication of H5N1 virus in the nasal turbinates, lungs, and kidneys of IGDCC4-knockout mice was significantly lower than that in the corresponding organs of wild-type mice. Half of the IGDCC4-knockout mice survived a lethal H5N1 virus challenge, whereas all of the wild-type mice died within 11 days of infection. Our study identifies a novel host factor that promotes influenza virus infection by facilitating internalization and provides insights that will support the development of antiviral therapies.

SUBMITTER: Song Y

PROVIDER: S-EPMC8675923 | biostudies-literature |

REPOSITORIES: biostudies-literature

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CRISPR-Cas9 knockout screen informs efficient reduction of the Komagataella phaffii secretome.

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2023-03-04 | E-MTAB-12724 | biostudies-arrayexpress

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Project description:Genome-wide CRISPR/Cas9 knockout screens are revolutionizing mammalian functional genomics. However, their range of applications remains limited by signal variability from different guide RNAs that target the same gene, which confounds gene effect estimation and dictates large experiment sizes. To address this problem, we report JACKS, a Bayesian method that jointly analyzes screens performed with the same guide RNA library. Modeling the variable guide efficacies greatly improves hit identification over processing a single screen at a time and outperforms existing methods. This more efficient analysis gives additional hits and allows designing libraries with a 2.5-fold reduction in required cell numbers without sacrificing performance compared to current analysis standards.

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