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      Dual RNA-Seq Analysis of the Interaction Between Edible Fungus Morchella sextelata and Its Pathogenic Fungus Paecilomyces penicillatus Uncovers the Candidate Defense and Pathogenic Factors

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          Abstract

          Morels ( Morchella spp.) are economically important mushrooms cultivated in many countries. However, their production and quality are hindered by white mold disease because of Paecilomyces penicillatus infection. In this study, we aimed to understand the genetic mechanisms of interactions between P. penicillatus and Morchella. M. sextelata, the most prevalent species of Morchella in China, was inoculated with P. penicillatus; then, the expression profiles of both fungi were determined simultaneously at 3 and 6 days post-inoculation (dpi) using a dual RNA-Seq approach. A total of 460 and 313 differentially expressed genes (DEGs) were identified in P. penicillatus and M. sextelata, respectively. The CAZymes of β-glucanases and mannanases, as well as subtilase family, were upregulated in P. penicillatus, which might be involved in the degradation of M. sextelata cell walls. Chitin recognition protein, caffeine-induced death protein, and putative apoptosis-inducing protein were upregulated, while cyclin was downregulated in infected M. sextelata. This indicates that P. penicillatus could trigger programmed cell death in M. sextelata after infection. Laccase-2, tyrosinases, and cytochrome P450s were also upregulated in M. sextelata. The increased expression levels of these genes suggest that M. sextelata could detoxify the P. penicillatus toxins and also form a melanin barrier against P. penicillatus invasion. The potential pathogenic mechanisms of P. penicillatus on M. sextelata and the defense mechanisms of M. sextelata against P. penicillatus were well described.

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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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            Gene Ontology: tool for the unification of biology

            Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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              Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

              Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                02 December 2021
                2021
                : 12
                : 760444
                Affiliations
                [1] 1National-Local Joint Engineering Laboratory of Breeding and Cultivation of Edible and Medicinal Fungi, Institute of Agricultural Resources and Environment, Sichuan Academy of Agricultural Sciences , Chengdu, China
                [2] 2National Observing and Experimental Station of Agricultural Microbiology, Ministry of Agriculture and Rural Affairs , Chengdu, China
                [3] 3School of Bioengineering, Jiangnan University , Wuxi, China
                Author notes

                Edited by: Anhuai Lu, Peking University, China

                Reviewed by: José Ascención Martínez-Álvarez, University of Guanajuato, Mexico; Chenyang Huang, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences (CAAS), China

                *Correspondence: Yang Yu, yangyu0221@ 123456139.com

                This article was submitted to Microbiological Chemistry and Geomicrobiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2021.760444
                8675245
                34925269
                e9598e7a-dc6b-4f8d-a457-dd1c08f71281
                Copyright © 2021 Yu, Tan, Liu, Liu, Tang and Peng.

                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
                : 18 August 2021
                : 05 November 2021
                Page count
                Figures: 7, Tables: 0, Equations: 0, References: 54, Pages: 12, Words: 7239
                Funding
                Funded by: National Science Foundation of China , doi 10.13039/501100001809;
                Award ID: NSFC31901119
                Award ID: 2021YFYZ0026
                Award ID: 2021ZSSFGH04
                Funded by: Edible Fungus Innovation Team of Sichuan Province
                Award ID: SCCXTD-2021-7
                Funded by: talent introduction and training of SAAS
                Award ID: 510000-01-114852
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                morchella sextelata,paecilomyces penicillatus,pathogenic factors,response,transcriptomics,cazymes,laccase,tyrosinase

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