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      High-Throughput CRISPR Screening Identifies Genes Involved in Macrophage Viability and Inflammatory Pathways

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          SUMMARY

          Macrophages are critical effector cells of the immune system, and understanding genes involved in their viability and function is essential for gaining insights into immune system dysregulation during disease. We use a high-throughput, pooled-based CRISPR-Cas screening approach to identify essential genes required for macrophage viability. In addition, we target 3′ UTRs to gain insights into previously unidentified cis-regulatory regions that control these essential genes. Next, using our recently generated nuclear factor κB (NF-κB) reporter line, we perform a fluorescence-activated cell sorting (FACS)-based high-throughput genetic screen and discover a number of previously unidentified positive and negative regulators of the NF-κB pathway. We unravel complexities of the TNF signaling cascade, showing that it can function in an autocrine manner in macrophages to negatively regulate the pathway. Utilizing a single complex library design, we are capable of interrogating various aspects of macrophage biology, thus generating a resource for future studies.

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          In Brief

          Covarrubias et al. screen ~21,000 targets, generating a resource guide of genes required for macrophage viability as well as previously unidentified positive and negative regulators of NF-κB signaling. They identify regulatory elements within essential genes and show that membrane-bound TNF primarily functions in macrophages in an autocrine fashion to negatively regulate inflammation.

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          Most cited references48

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

            TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.
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              DrugBank 5.0: a major update to the DrugBank database for 2018

              Abstract DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year’s update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                1 February 2021
                29 December 2020
                23 February 2021
                : 33
                : 13
                : 108541
                Affiliations
                [1 ]Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, USA
                [2 ]Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
                [3 ]Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA 94143, USA
                [4 ]Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
                [5 ]W.M. Keck Center for Noncoding RNAs, University of California, San Francisco, San Francisco, CA, USA
                [6 ]Institute for Molecular Medicine, Martin Luther University Halle-Wittenberg, Halle, Germany
                [7 ]Center for Biomolecular Science and Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
                [8 ]These authors contributed equally
                [9 ]Lead Contact
                Author notes

                AUTHOR CONTRIBUTIONS

                S. Covarrubias and S. Carpenter conceptualized and designed the research study. S. Covarrubias and A.C.V. performed the screen. M.B., J.B., and M.T.M. provided the custom whole-genome sgRNA library. A.C., H.H., E.K.R., A.C.V., M.R.C., A.S., and L.O. performed candidate validation experiments. C.V. developed data analysis tools. S.K. performed MAGeCK analysis. S. Covarrubias, A.C.V., and S. Carpenter analyzed and interpreted data and wrote the paper.

                [* ]Correspondence: sucarpen@ 123456ucsc.edu
                Article
                NIHMS1658621
                10.1016/j.celrep.2020.108541
                7901356
                33378675
                9cbeb994-b2c0-408c-b690-86c7ad3e4730

                This is an open access article under the CC BY-NC-ND license.

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                Cell biology
                Cell biology

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