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      Knockdown of an ATP-binding cassette transporter in resistant western corn rootworm larvae partially reverses resistance to eCry3.1Ab protein

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          Abstract

          The western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte, has evolved resistance to nearly every management tactic utilized in the field. This study investigated the resistance mechanisms in a WCR strain resistant to the Bacillus thuringiensis (Bt) protein eCry3.1Ab using dsRNA to knockdown WCR midgut genes previously documented to be associated with the resistance. ATP-binding cassette transporter (ABCC4), aminopeptidase-N, cadherin, and cathepsin-B were previously found to be differentially expressed in eCry3.1Ab-resistant WCR larvae when compared to susceptible larvae after feeding on maize expressing eCry3.1Ab and its near-isoline. Here we compared the susceptibility of resistant and susceptible WCR larvae to eCry3.1Ab protein in presence or absence of dsRNA targeting the above genes using 10-day diet overlay toxicity assays. Combining ABCC4 dsRNA with eCry3.1Ab protein increased susceptibility to Bt protein in WCR-resistant larvae, but the other three genes had no such effect. Among 65 ABC transport genes identified, several were expressed differently in resistant or susceptible WCR larvae, fed on eCry3.1Ab-expressing maize versus its isoline, that may be involved in Bt resistance. Our findings provide strong evidence that ABCC4 is indirectly involved in WCR resistance to eCry3.1Ab protein by enhancing the effects of Bt-induced toxicity.

          Supplementary Information

          The online version contains supplementary material available at 10.1038/s41598-024-83135-7.

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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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            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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              RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

              Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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                Author and article information

                Contributors
                manhuynh@missouri.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                28 December 2024
                28 December 2024
                2024
                : 14
                : 31508
                Affiliations
                [1 ]Division of Plant Science and Technology, University of Missouri, ( https://ror.org/02ymw8z06) Columbia, MO 65211 USA
                [2 ]Present Address: RNAiSSANCE AG, St. Louis, MO 63132 USA
                [3 ]Biological Control of Insects Research Laboratory, USDA-ARS, ( https://ror.org/05xqthq76) Columbia, MO 65203 USA
                [4 ]Present Address: Agricultural Research and Development Program, Central State University, ( https://ror.org/03xhrps63) Wilberforce, OH 45384 USA
                [5 ]Plant Genetics Research Unit, USDA-ARS, University of Missouri, ( https://ror.org/02ymw8z06) Columbia, MO 65211 USA
                Article
                83135
                10.1038/s41598-024-83135-7
                11682398
                39733129
                3ce18d1b-2d97-4b1e-9a54-cd6bc20abfcf
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 22 August 2024
                : 11 December 2024
                Funding
                Funded by: Syngenta Biotechnology
                Award ID: 58-3K95-4-1697
                Categories
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                Custom metadata
                © Springer Nature Limited 2024

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                diabrotica virgifera virgifera,bacillus thuringiensis,corn rootworm,resistance mechanisms,rnai,insect resistance management,entomology,assay systems

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