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      Genome mapping coupled with CRISPR gene editing reveals a P450 gene confers avermectin resistance in the beet armyworm

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          Abstract

          The evolution of insecticide resistance represents a global constraint to agricultural production. Because of the extreme genetic diversity found in insects and the large numbers of genes involved in insecticide detoxification, better tools are needed to quickly identify and validate the involvement of putative resistance genes for improved monitoring, management, and countering of field-evolved insecticide resistance. The avermectins, emamectin benzoate (EB) and abamectin are relatively new pesticides with reduced environmental risk that target a wide number of insect pests, including the beet armyworm, Spodoptera exigua, an important global pest of many crops. Unfortunately, field resistance to avermectins recently evolved in the beet armyworm, threatening the sustainable use of this class of insecticides. Here, we report a high-quality chromosome-level assembly of the beet armyworm genome and use bulked segregant analysis (BSA) to identify the locus of avermectin resistance, which mapped on 15–16 Mbp of chromosome 17. Knockout of the CYP9A186 gene that maps within this region by CRISPR/Cas9 gene editing fully restored EB susceptibility, implicating this gene in avermectin resistance. Heterologous expression and in vitro functional assays further confirm that a natural substitution (F116V) found in the substrate recognition site 1 (SRS1) of the CYP9A186 protein results in enhanced metabolism of EB and abamectin. Hence, the combined approach of coupling gene editing with BSA allows for the rapid identification of metabolic resistance genes responsible for insecticide resistance, which is critical for effective monitoring and adaptive management of insecticide resistance.

          Author summary

          Insecticide resistance is a global constraint to agricultural production, and rapid identification of resistance genes is critical for better monitoring and management of resistant insect pests. Identification of metabolic resistance genes has always been a challenging task due to the high diversity of insect detoxification enzyme genes. Here, we report a high-quality chromosome-level assembly of the beet armyworm genome and use bulked segregant analysis (BSA) to identify the locus of avermectin resistance, which mapped on 15–16 Mbp of chromosome 17. Knockout of the CYP9A186 gene that maps within this region by CRISPR/Cas9 gene editing fully restored avermectin susceptibility. Heterologous expression and in vitro functional assays further confirm that a natural substitution (F116V) found in the substrate recognition site 1 (SRS1) of the CYP9A186 protein results in enhanced metabolism of avermectin. Hence, the combined approach of coupling gene editing with BSA allows for the rapid identification of metabolic resistance genes responsible for insecticide resistance, which is critical for field monitoring of such mutations, for making improved decisions on appropriate use of effective chemistries, as well for improvements in the design of future compounds that target S. exigua.

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          Most cited references86

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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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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              The Sequence Alignment/Map format and SAMtools

              Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                Role: Data curationRole: InvestigationRole: Writing – original draft
                Role: Data curationRole: InvestigationRole: Writing – original draft
                Role: Data curationRole: InvestigationRole: Writing – original draft
                Role: Data curationRole: Investigation
                Role: Data curationRole: Investigation
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: ResourcesRole: Supervision
                Role: ConceptualizationRole: Data curationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                12 July 2021
                July 2021
                : 17
                : 7
                : e1009680
                Affiliations
                [1 ] The Key Laboratory of Plant Immunity and College of Plant Protection, Nanjing Agricultural University, Nanjing, China
                [2 ] Institute of Pesticide Science, College of Plant Protection, Northwest A&F University, Yangling, Shaanxi, China
                [3 ] Department of Plant and Environmental Sciences, University of Copenhagen, Copenhagen, Denmark
                [4 ] USDA ARS, U.S. Arid Land Agricultural Research Center, Maricopa, Arizona, United States of America
                University of Kentucky, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-9198-4057
                https://orcid.org/0000-0002-1371-266X
                https://orcid.org/0000-0003-0758-1672
                https://orcid.org/0000-0002-1904-5124
                https://orcid.org/0000-0002-9560-571X
                https://orcid.org/0000-0002-3893-9545
                https://orcid.org/0000-0003-3456-3373
                Article
                PGENETICS-D-21-00568
                10.1371/journal.pgen.1009680
                8297932
                34252082
                327263ac-a3cd-43ee-ba50-9ea9a7c839e9

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 26 April 2021
                : 23 June 2021
                Page count
                Figures: 5, Tables: 3, Pages: 25
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, national natural science foundation of china;
                Award ID: 31572030
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100002855, Ministry of Science and Technology of the People's Republic of China;
                Award ID: 2018FY101103
                Award Recipient :
                This work was funded by grants to YY from the National Natural Science Foundation of China (Grant No. 31572030) and National Science & Technology Fundamental Resources Investigation Program of China (Grant No. 2018FY101103). 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
                Agriculture
                Agrochemicals
                Insecticides
                Biology and Life Sciences
                Genetics
                Genomics
                Animal Genomics
                Invertebrate Genomics
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Artificial Gene Amplification and Extension
                Polymerase Chain Reaction
                Research and Analysis Methods
                Molecular Biology Techniques
                Artificial Gene Amplification and Extension
                Polymerase Chain Reaction
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolites
                Physical Sciences
                Chemistry
                Chemical Compounds
                Salts
                Benzoates
                Biology and Life Sciences
                Genetics
                Genomics
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Gene Mapping
                Research and Analysis Methods
                Molecular Biology Techniques
                Gene Mapping
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Protein Metabolism
                Custom metadata
                vor-update-to-uncorrected-proof
                2021-07-22
                The project and raw sequencing data have been deposited at the NCBI under the accessions PRJNA588360 and SRR10441878-SRR10441889, and at the FigShare ( https://figshare.com/projects/Genome_assembly_and_annotations_of_the_Spodoptera_exigua_beet_armyworm_/100319).

                Genetics
                Genetics

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