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      Transcriptomic analysis reveals pathogenicity mechanisms of Phytophthora capsici in black pepper

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          Abstract

          The devastating disease “quick wilt” or “foot rot” is caused by the oomycete Phytophthora capsici Leonian and is affecting the economically significant spice crop black pepper ( Piper nigrum L.). The details on the mechanism of interaction of P. capsici with its host black pepper remain poorly understood, hindering efforts to enhance disease resistance. To address this knowledge gap, we conducted an RNA-seq analysis to investigate the gene expression profile of P. capsici infecting black pepper. Comparative transcriptome analysis between axenic culture, and early and late infection stages of P. capsici revealed a substantial number of differentially expressed genes. Our findings demonstrate the induction of metabolic pathways, signaling cascades, and crucial pathogenicity-related processes during infection of black pepper by P. capsici. Specifically, we observed orchestrated expression of cell wall-degrading enzymes, effectors, and, detoxifying transporters at different infection time points, implicating their roles in pathogenicity. The expression patterns of key pathogenicity-associated genes, including effectors, were validated using reverse transcription quantitative real-time PCR. The effectiveness of agroinfiltration-mediated transient expression in black pepper for functional studies of effectors is also demonstrated in this study. Overall, this study establishes a strong foundation for further studies elucidating the pathogenic mechanisms employed by P. capsici infecting black pepper and for developing effective disease management strategies. Future investigations building upon these findings are essential for advancing our understanding of this pathosystem and for implementing targeted approaches to mitigate black pepper foot rot.

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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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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              clusterProfiler: an R package for comparing biological themes among gene clusters.

              Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/2658065/overviewRole: Role: Role: Role:
                Role: Role: Role:
                URI : https://loop.frontiersin.org/people/321496/overviewRole: Role: Role: Role: Role: Role: Role:
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                18 November 2024
                2024
                : 15
                : 1418816
                Affiliations
                [1] 1Plant Disease Biology Lab, Rajiv Gandhi Centre for Biotechnology , Thiruvananthapuram, India
                [2] 2Research Centre, University of Kerala , Thiruvananthapuram, India
                Author notes

                Edited by: Akhtar Ali, University of Tulsa, United States

                Reviewed by: U. M. Aruna Kumara, University of Colombo, Sri Lanka

                Natarajan Rameshkumar, National Institute for Interdisciplinary Science and Technology (CSIR), India

                *Correspondence: Manjula Sakuntala, smanjula@ 123456rgcb.res.in
                Article
                10.3389/fmicb.2024.1418816
                11609936
                39624727
                81ef310e-eea6-4d2e-9847-a514c58ce0da
                Copyright © 2024 Vijayakumar, Saraswathy and Sakuntala.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 17 April 2024
                : 21 October 2024
                Page count
                Figures: 9, Tables: 2, Equations: 0, References: 91, Pages: 14, Words: 10399
                Funding
                The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Department of Biotechnology, Government of India through the project (Grant no. BT/PR23599/BPA/118/319/2017) granted to MS.
                Categories
                Microbiology
                Original Research
                Custom metadata
                Microbe and Virus Interactions with Plants

                Microbiology & Virology
                phytophthora,black pepper,transcriptome,pathogenicity,quick wilt,effectors
                Microbiology & Virology
                phytophthora, black pepper, transcriptome, pathogenicity, quick wilt, effectors

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