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      Several cell-intrinsic effectors drive type I interferon-mediated restriction of HIV-1 in primary CD4 + T cells

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      1 , 2 , 2 , 3 , 2 , 4 , *
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          SUMMARY

          Type I interferon (IFN) upregulates proteins that inhibit HIV within infected cells. Prior studies have identified IFN-stimulated genes (ISGs) that impede lab-adapted HIV in cell lines, yet the ISG(s) that mediate IFN restriction in HIV target cells, primary CD4 + T cells, are unknown. Here, we interrogate ISG restriction of primary HIV in CD4 + T cells by performing CRISPR-knockout screens with a custom library that specifically targets ISGs expressed in CD4 + T cells. Our investigation identifies previously undescribed HIV-restricting ISGs (HM13, IGFBP2, LAP3) and finds that two factors characterized in other HIV infection models (IFI16 and UBE2L6) mediate IFN restriction in T cells. Inactivation of these five ISGs in combination further diminishes IFN’s protective effect against diverse HIV strains. This work demonstrates that IFN restriction of HIV is multifaceted, resulting from several effectors functioning collectively, and establishes a primary cell ISG screening model to identify both single and combinations of HIV-restricting ISGs.

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          In brief

          Itell et al. interrogate mediators of IFN restriction of HIV directly in primary HIV target cells, CD4 + T cells, utilizing customized CRISPR-KO screens. This unbiased strategy identifies ISGs not previously known to inhibit HIV. Inactivating several ISGs together reveals cumulative effects, underscoring the multifaceted nature of IFN restriction of HIV.

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          Most cited references86

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            Interferon-stimulated genes: a complex web of host defenses.

            Interferon-stimulated gene (ISG) products take on a number of diverse roles. Collectively, they are highly effective at resisting and controlling pathogens. In this review, we begin by introducing interferon (IFN) and the JAK-STAT signaling pathway to highlight features that impact ISG production. Next, we describe ways in which ISGs both enhance innate pathogen-sensing capabilities and negatively regulate signaling through the JAK-STAT pathway. Several ISGs that directly inhibit virus infection are described with an emphasis on those that impact early and late stages of the virus life cycle. Finally, we describe ongoing efforts to identify and characterize antiviral ISGs, and we provide a forward-looking perspective on the ISG landscape.
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              CHOPCHOP v3: expanding the CRISPR web toolbox beyond genome editing

              Abstract The CRISPR–Cas system is a powerful genome editing tool that functions in a diverse array of organisms and cell types. The technology was initially developed to induce targeted mutations in DNA, but CRISPR–Cas has now been adapted to target nucleic acids for a range of purposes. CHOPCHOP is a web tool for identifying CRISPR–Cas single guide RNA (sgRNA) targets. In this major update of CHOPCHOP, we expand our toolbox beyond knockouts. We introduce functionality for targeting RNA with Cas13, which includes support for alternative transcript isoforms and RNA accessibility predictions. We incorporate new DNA targeting modes, including CRISPR activation/repression, targeted enrichment of loci for long-read sequencing, and prediction of Cas9 repair outcomes. Finally, we expand our results page visualization to reveal alternative isoforms and downstream ATG sites, which will aid users in avoiding the expression of truncated proteins. The CHOPCHOP web tool now supports over 200 genomes and we have released a command-line script for running larger jobs and handling unsupported genomes. CHOPCHOP v3 can be found at https://chopchop.cbu.uib.no
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                28 July 2023
                27 June 2023
                23 May 2023
                23 October 2023
                : 42
                : 6
                : 112556
                Affiliations
                [1 ]Molecular and Cellular Biology PhD Program, University of Washington, Seattle, WA 98195, USA
                [2 ]Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
                [3 ]Present address: Tr1X, Inc., La Jolla, CA 92037, USA
                [4 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                J.O. conceived the project, and H.L.I., D.H., and J.O. designed the methodology of the study. H.L.I. and D.H. performed experiments. H.L.I. and D.H. conducted the data analysis with the supervision of J.O. H.L.I. generated data visualizations. J.O. and H.L.I. wrote the paper with input from D.H.

                [* ]Correspondence: joverbau@ 123456fredhutch.org
                Article
                NIHMS1912770
                10.1016/j.celrep.2023.112556
                10592456
                37227817
                03dbe334-f625-4c14-bb77-02c4aff6c5f4

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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