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      A functional map of HIV-host interactions in primary human T cells

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          Abstract

          Human Immunodeficiency Virus (HIV) relies on host molecular machinery for replication. Systematic attempts to genetically or biochemically define these host factors have yielded hundreds of candidates, but few have been functionally validated in primary cells. Here, we target 426 genes previously implicated in the HIV lifecycle through protein interaction studies for CRISPR-Cas9-mediated knock-out in primary human CD4+ T cells in order to systematically assess their functional roles in HIV replication. We achieve efficient knockout (>50% of alleles) in 364 of the targeted genes and identify 86 candidate host factors that alter HIV infection. 47 of these factors validate by multiplex gene editing in independent donors, including 23 factors with restrictive activity. Both gene editing efficiencies and HIV-1 phenotypes are highly concordant among independent donors. Importantly, over half of these factors have not been previously described to play a functional role in HIV replication, providing numerous novel avenues for understanding HIV biology. These data further suggest that host-pathogen protein-protein interaction datasets offer an enriched source of candidates for functional host factor discovery and provide an improved understanding of the mechanics of HIV replication in primary T cells.

          Abstract

          Here, Hiatt et al. report the knock-out of over 400 genes in primary CD4+ T cells to assess their functional role in HIV replication, finding 86 initial candidates of which 47 are validated as HIV host factors, including 23 with restrictive activity.

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          Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

          S Altschul (1997)
          The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
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            New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

            PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
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              PAML 4: phylogenetic analysis by maximum likelihood.

              PAML, currently in version 4, is a package of programs for phylogenetic analyses of DNA and protein sequences using maximum likelihood (ML). The programs may be used to compare and test phylogenetic trees, but their main strengths lie in the rich repertoire of evolutionary models implemented, which can be used to estimate parameters in models of sequence evolution and to test interesting biological hypotheses. Uses of the programs include estimation of synonymous and nonsynonymous rates (d(N) and d(S)) between two protein-coding DNA sequences, inference of positive Darwinian selection through phylogenetic comparison of protein-coding genes, reconstruction of ancestral genes and proteins for molecular restoration studies of extinct life forms, combined analysis of heterogeneous data sets from multiple gene loci, and estimation of species divergence times incorporating uncertainties in fossil calibrations. This note discusses some of the major applications of the package, which includes example data sets to demonstrate their use. The package is written in ANSI C, and runs under Windows, Mac OSX, and UNIX systems. It is available at -- (http://abacus.gene.ucl.ac.uk/software/paml.html).
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                Author and article information

                Contributors
                judd.hultquist@northwestern.edu
                alexander.marson@ucsf.edu
                nevan.krogan@ucsf.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                1 April 2022
                1 April 2022
                2022
                : 13
                : 1752
                Affiliations
                [1 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Microbiology and Immunology, , University of California, ; San Francisco, CA 94143 USA
                [2 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Diabetes Center, , University of California, ; San Francisco, CA 94143 USA
                [3 ]GRID grid.249878.8, ISNI 0000 0004 0572 7110, Gladstone Institutes, ; San Francisco, CA 94158 USA
                [4 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Innovative Genomics Institute, , University of California, ; Berkeley, CA 94720 USA
                [5 ]GRID grid.16753.36, ISNI 0000 0001 2299 3507, Division of Infectious Diseases, , Northwestern University Feinberg School of Medicine, ; Chicago, IL 60611 USA
                [6 ]GRID grid.16753.36, ISNI 0000 0001 2299 3507, Center for Pathogen Genomics and Microbial Evolution, Institute for Global Health, , Northwestern University Feinberg School of Medicine, ; Chicago, IL 60611 USA
                [7 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Cellular and Molecular Pharmacology, , University of California, ; San Francisco, CA 94158 USA
                [8 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Quantitative Biosciences Institute, QBI, , University of California, ; San Francisco, CA 94158 USA
                [9 ]GRID grid.499295.a, ISNI 0000 0004 9234 0175, Chan Zuckerberg BioHub, ; San Francisco, CA 94158 USA
                [10 ]GRID grid.270240.3, ISNI 0000 0001 2180 1622, Howard Hughes Medical Institute, , Fred Hutchinson Cancer Research Center, ; Seattle, WA 98109 USA
                [11 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Howard Hughes Medical Institute, , University of California, ; Berkeley, CA 94720 USA
                [12 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Department of Molecular and Cell Biology, , University of California, ; Berkeley, CA 94720 USA
                [13 ]GRID grid.184769.5, ISNI 0000 0001 2231 4551, MBIB Division, , Lawrence Berkeley National Laboratory, ; Berkeley, CA 94720 USA
                [14 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, Department of Chemistry, , University of California, ; Berkeley, CA 94720 USA
                [15 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Medicine, , University of California, ; San Francisco, CA 94143 USA
                [16 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, UCSF Helen Diller Family Comprehensive Cancer Center, , University of California, ; San Francisco, CA 94158 USA
                Author information
                http://orcid.org/0000-0002-8015-9614
                http://orcid.org/0000-0001-6424-4280
                http://orcid.org/0000-0003-4041-6775
                http://orcid.org/0000-0001-8220-8427
                http://orcid.org/0000-0002-3970-9573
                http://orcid.org/0000-0002-2195-639X
                http://orcid.org/0000-0002-1463-6726
                http://orcid.org/0000-0001-9161-999X
                http://orcid.org/0000-0003-3567-3435
                http://orcid.org/0000-0002-2734-5776
                http://orcid.org/0000-0003-4902-337X
                Article
                29346
                10.1038/s41467-022-29346-w
                8976027
                35365639
                f0c5f67f-6784-4342-8e94-931722ad963e
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 November 2021
                : 8 March 2022
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                © The Author(s) 2022

                Uncategorized
                virus-host interactions,systems virology
                Uncategorized
                virus-host interactions, systems virology

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