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      High-throughput mapping of the phage resistance landscape in E. coli

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          Abstract

          Bacteriophages (phages) are critical players in the dynamics and function of microbial communities and drive processes as diverse as global biogeochemical cycles and human health. Phages tend to be predators finely tuned to attack specific hosts, even down to the strain level, which in turn defend themselves using an array of mechanisms. However, to date, efforts to rapidly and comprehensively identify bacterial host factors important in phage infection and resistance have yet to be fully realized. Here, we globally map the host genetic determinants involved in resistance to 14 phylogenetically diverse double-stranded DNA phages using two model Escherichia coli strains (K-12 and BL21) with known sequence divergence to demonstrate strain-specific differences. Using genome-wide loss-of-function and gain-of-function genetic technologies, we are able to confirm previously described phage receptors as well as uncover a number of previously unknown host factors that confer resistance to one or more of these phages. We uncover differences in resistance factors that strongly align with the susceptibility of K-12 and BL21 to specific phage. We also identify both phage-specific mechanisms, such as the unexpected role of cyclic-di-GMP in host sensitivity to phage N4, and more generic defenses, such as the overproduction of colanic acid capsular polysaccharide that defends against a wide array of phages. Our results indicate that host responses to phages can occur via diverse cellular mechanisms. Our systematic and high-throughput genetic workflow to characterize phage-host interaction determinants can be extended to diverse bacteria to generate datasets that allow predictive models of how phage-mediated selection will shape bacterial phenotype and evolution. The results of this study and future efforts to map the phage resistance landscape will lead to new insights into the coevolution of hosts and their phage, which can ultimately be used to design better phage therapeutic treatments and tools for precision microbiome engineering.

          Abstract

          To map the landscape of genetic determinants important in host-phage interactions, this study applies unbiased high-throughput loss-of-function and gain-of-function screening methods to two different strains of E. coli. These methods rapidly identify phage receptors and other host factors (both novel and previously described) involved in resistance across a wide panel of 13 dsDNA phages.

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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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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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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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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: ValidationRole: Writing – review & editing
                Role: Investigation
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: MethodologyRole: Resources
                Role: ResourcesRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Software
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: VisualizationRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                13 October 2020
                October 2020
                13 October 2020
                : 18
                : 10
                : e3000877
                Affiliations
                [1 ] Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
                [2 ] Innovative Genomics Institute, Berkeley, California, United States of America
                [3 ] Department of Bioengineering, University of California – Berkeley, Berkeley, California, United States of America
                [4 ] Biophysics Graduate Group, University of California – Berkeley, Berkeley, California, United States of America
                [5 ] Designated Emphasis Program in Computational and Genomic Biology, University of California – Berkeley, Berkeley, California, United States of America
                [6 ] Department of Integrative Biology, University of California – Berkeley, Berkeley, California, United States of America
                [7 ] The Evergreen State College, Olympia, Washington, United States of America
                [8 ] Department of Molecular and Cell Biology, University of California – Berkeley, Berkeley, California, United States of America
                [9 ] Department of Plant and Microbial Biology, University of California – Berkeley, Berkeley, California, United States of America
                Wageningen University, NETHERLANDS
                Author notes

                I have read the journal’s policy and the authors of this manuscript have the following competing interests: VKM, AMD, and APA consult for and hold equity in Felix Biotechnology, Inc.

                Author information
                https://orcid.org/0000-0001-7934-0400
                https://orcid.org/0000-0002-7488-3040
                https://orcid.org/0000-0001-8495-4511
                https://orcid.org/0000-0003-1760-8496
                https://orcid.org/0000-0002-4251-0362
                Article
                PBIOLOGY-D-20-00974
                10.1371/journal.pbio.3000877
                7553319
                33048924
                d708de57-3573-4ef8-9436-dbb11836701e
                © 2020 Mutalik et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 12 April 2020
                : 8 September 2020
                Page count
                Figures: 7, Tables: 0, Pages: 46
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100014220, Innovative Genomics Institute;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000015, U.S. Department of Energy;
                Award ID: DE-AC02-05CH11231
                Award Recipient :
                This project was funded by the Microbiology Program of the Innovative Genomics Institute, Berkeley (to VKM, AMD, and APA). The initial concepts for this project were funded by ENIGMA, a Scientific Focus Area Program at the Lawrence Berkeley National Laboratory, supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research under contract DE-AC02-05CH11231 (to VKM, AMD, and APA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Viruses
                Bacteriophages
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Cloning
                DNA cloning
                Shotgun Sequencing
                Research and analysis methods
                Molecular biology techniques
                Cloning
                DNA cloning
                Shotgun Sequencing
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Sequencing Techniques
                Shotgun Sequencing
                Research and Analysis Methods
                Molecular Biology Techniques
                Sequencing Techniques
                Shotgun Sequencing
                Biology and Life Sciences
                Genetics
                Gene Identification and Analysis
                Genetic Screens
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Molecular Biology Assays and Analysis Techniques
                Gene Expression and Vector Techniques
                Hyperexpression Techniques
                Research and Analysis Methods
                Molecular Biology Techniques
                Molecular Biology Assays and Analysis Techniques
                Gene Expression and Vector Techniques
                Hyperexpression Techniques
                Biology and Life Sciences
                Genetics
                Genomics
                Biology and Life Sciences
                Biochemistry
                Biosynthesis
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Research and analysis methods
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Genomic Libraries
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Genomic Libraries
                Custom metadata
                Sequencing data have been uploaded to the Sequence Read Archive under BioProject accession number PRJNA645443 [ https://www.ncbi.nlm.nih.gov/bioproject/PRJNA645443/]. Supplementary Tables with complete CRISPRi data are deposited here: https://doi.org/10.6084/m9.figshare.11859216.v1. In addition, the complete data from RB-TnSeq experiments are deposited here: https://doi.org/10.6084/m9.figshare.11413128. And Dub-seq experiments are deposited here: https://doi.org/10.6084/m9.figshare.11838879.v2. The underlying data for all figures are provided in supporting information file S1 Data.

                Life sciences
                Life sciences

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