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      Comparative genomics of plant pathogenic Diaporthe species and transcriptomics of Diaporthe caulivora during host infection reveal insights into pathogenic strategies of the genus

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          Abstract

          Background

          Diaporthe caulivora is a fungal pathogen causing stem canker in soybean worldwide. The generation of genomic and transcriptomic information of this ascomycete, together with a comparative genomic approach with other pathogens of this genus, will contribute to get insights into the molecular basis of pathogenicity strategies used by D. caulivora and other Diaporthe species.

          Results

          In the present work, the nuclear genome of D. caulivora isolate (D57) was resolved, and a comprehensive annotation based on gene expression and genomic analysis is provided. Diaporthe caulivora D57 has an estimated size of 57,86 Mb and contains 18,385 predicted protein-coding genes, from which 1501 encode predicted secreted proteins. A large array of D. caulivora genes encoding secreted pathogenicity-related proteins was identified, including carbohydrate-active enzymes (CAZymes), necrosis-inducing proteins, oxidoreductases, proteases and effector candidates. Comparative genomics with other plant pathogenic Diaporthe species revealed a core secretome present in all Diaporthe species as well as Diaporthe-specific and D. caulivora-specific secreted proteins. Transcriptional profiling during early soybean infection stages showed differential expression of 2659 D. caulivora genes. Expression patterns of upregulated genes and gene ontology enrichment analysis revealed that host infection strategies depends on plant cell wall degradation and modification, detoxification of compounds, transporter activities and toxin production. Increased expression of effectors candidates suggests that D. caulivora pathogenicity also rely on plant defense evasion. A high proportion of the upregulated genes correspond to the core secretome and are represented in the pathogen-host interaction (PHI) database, which is consistent with their potential roles in pathogenic strategies of the genus Diaporthe.

          Conclusions

          Our findings give novel and relevant insights into the molecular traits involved in pathogenicity of D. caulivora towards soybean plants. Some of these traits are in common with other Diaporthe pathogens with different host specificity, while others are species-specific. Our analyses also highlight the importance to have a deeper understanding of pathogenicity functions among Diaporthe pathogens and their interference with plant defense activation.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-022-08413-y.

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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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              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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                Author and article information

                Contributors
                iponce@iibce.edu.uy
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                3 March 2022
                3 March 2022
                2022
                : 23
                : 175
                Affiliations
                [1 ]GRID grid.482688.8, ISNI 0000 0001 2323 2857, Departamento de Biología Molecular, , Instituto de Investigaciones Biológicas Clemente Estable, ; Avenida Italia 3318, CP 11600 Montevideo, Uruguay
                [2 ]GRID grid.473327.6, ISNI 0000 0004 0604 4346, Instituto Nacional de Investigación Agropecuaria (INIA), Estación Experimental INIA Las Brujas, ; Ruta 48 Km 10, Canelones, Uruguay
                [3 ]GRID grid.473327.6, ISNI 0000 0004 0604 4346, Instituto Nacional de Investigación Agropecuaria (INIA), Programa Cultivos de Secano, Estación Experimental La Estanzuela, ; Ruta 50 km 11, 70000 Colonia, Uruguay
                [4 ]GRID grid.11630.35, ISNI 0000000121657640, Laboratorio de Fisiología Vegetal, Centro de Investigaciones Nucleares, Facultad de Ciencias, , Universidad de la República, ; Mataojo 2055, CP 11400 Montevideo, Uruguay
                Article
                8413
                10.1186/s12864-022-08413-y
                8896106
                35240994
                4de4e76e-e438-46ab-94b1-2344b9202c60
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 21 October 2021
                : 23 February 2022
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Genetics
                diaporthe pathogens,soybean,genomes,rnaseq,pathogenicity factors,secretome,effectors
                Genetics
                diaporthe pathogens, soybean, genomes, rnaseq, pathogenicity factors, secretome, effectors

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