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      De Novo Transcriptomic Analyses Revealed Some Detoxification Genes and Related Pathways Responsive to Noposion Yihaogong ® 5% EC (Lambda-Cyhalothrin 5%) Exposure in Spodoptera frugiperda Third-Instar Larvae

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          Insect pest resistance to synthetic insecticides is a major problem that limits efficient management and thus decreases productivity for farmers and increases the use of harmful materials that pollute the environment and endanger humans and beneficial organisms. A major approach for resistance management is understanding how insect pest field populations develop resistance at molecular levels. To provide a comprehensive insight into the resistance mechanisms of Spodoptera frugiperda larvae to lambda-cyhalothrin 5%, we investigated the molecular basis of resistance mechanism in field collected population of fall armyworm ( Spodoptera frugiperda) to lambda-cyhalothrin 5% insecticide, a pyrethroid insecticide by using de novo transcriptomics analysis. We found that resistance to lambda-cyhalothrin 5% can be metabolic by increasing the levels of detoxifying enzymes such as P450, GST and UGT and related genes to insecticide resistance in the field population. The obtained transcriptome information provides large gene resources available for further studying the resistance development of Spodoptera frugiperda to pesticides. The DGE data provide comprehensive insights into the gene expression profiles of fall armyworm ( Spodoptera frugiperda) to lambda-cyhalothrin 5% and will facilitate the study of the role of each gene in lambda-cyhalothrin resistance development.

          Abstract

          The fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), is a polyphagous, invasive insect pest which causes significant losses in important crops wherever it has spread. The use of pesticides in agriculture is a key tool in the management of many important crop pests, including S. frugiperda, but continued use of insecticides has selected for various types of resistance, including enzyme systems that provide enhanced mechanisms of detoxification. In the present study, we analyzed the de novo transcriptome of S. frugiperda larvae exposed to Noposion Yihaogong ® 5% emulsifiable concentrate (EC) insecticide focusing on detoxification genes and related pathways. Results showed that a total of 1819 differentially expressed genes (DEGs) were identified in larvae after being treated with Noposion Yihaogong ® 5% EC insecticide, of which 863 were up- and 956 down-regulated. Majority of these differentially expressed genes were identified in numerous Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, including metabolism of xenobiotics and drug metabolism. Furthermore, many of S. frugiperda genes involved in detoxification pathways influenced by lambda-cyhalothrin stress support their predicted role by further co-expression network analysis. Our RT-qPCR results were consistent with the DEG’s data of transcriptome analysis. The comprehensive transcriptome sequence resource attained through this study enriches the genomic platform of S. frugiperda, and the identified DEGs may enable greater molecular underpinnings behind the insecticide-resistance mechanism caused by lambda-cyhalothrin.

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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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            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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              Cutadapt removes adapter sequences from high-throughput sequencing reads

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                Author and article information

                Journal
                Insects
                Insects
                insects
                Insects
                MDPI
                2075-4450
                03 February 2021
                February 2021
                : 12
                : 2
                : 132
                Affiliations
                [1 ]State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Institute of Plant Protection and Microbiology, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; hafeez_203@ 123456yahoo.com (M.H.); lixiaowei1005@ 123456163.com (X.L.); zhijunzhanglw@ 123456hotmail.com (Z.Z.); junhuang1981@ 123456aliyun.com (J.H.); wanglikun1314@ 123456sina.cn (L.W.); zhanginsect@ 123456163.com (J.Z.)
                [2 ]Hubei Insect Resources Utilization and Sustainable Pest Management Key Laboratory, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; shahentomology@ 123456webmail.hzau.edu.cn
                [3 ]Key Laboratory of Bio-Pesticide Innovation and Application, South China Agricultural University, Guangzhou 510642, China; drmusakhan@ 123456outlook.com
                [4 ]Central Laboratory of Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China; fxu@ 123456zaas.ac.cn
                [5 ]Natural Resources Institute, University of Greenwich, Chatham Maritime, Kent ME4 4TB, UK; m.fernandez-grandon@ 123456greenwich.ac.uk
                [6 ]School of Biological Sciences, The University of Queensland, Brisbane, QLD 4072, Australia; m.zalucki@ 123456uq.edu.au
                Author notes
                [* ]Correspondence: luybcn@ 123456163.com
                Author information
                https://orcid.org/0000-0003-0275-5450
                https://orcid.org/0000-0003-1273-6800
                https://orcid.org/0000-0002-2993-390X
                https://orcid.org/0000-0001-9603-7577
                https://orcid.org/0000-0002-1723-8432
                Article
                insects-12-00132
                10.3390/insects12020132
                7913311
                33546242
                d34a9e30-49d4-4547-a8f3-148d345e1432
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 December 2020
                : 30 January 2021
                Categories
                Article

                transcriptome analysis,s. frugiperda,noposion yihaogong® 5% ec,lambda-cyhalothrin,detoxification genes,pathways

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