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      Gene regulatory network inference in soybean upon infection by Phytophthora sojae

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          Abstract

          Phytophthora sojae is a soil-borne oomycete and the causal agent of Phytophthora root and stem rot (PRR) in soybean ( Glycine max [L.] Merrill). Yield losses attributed to P. sojae are devastating in disease-conducive environments, with global estimates surpassing 1.1 million tonnes annually. Historically, management of PRR has entailed host genetic resistance (both vertical and horizontal) complemented by disease-suppressive cultural practices (e.g., oomicide application). However, the vast expansion of complex and/or diverse P. sojae pathotypes necessitates developing novel technologies to attenuate PRR in field environments. Therefore, the objective of the present study was to couple high-throughput sequencing data and deep learning to elucidate molecular features in soybean following infection by P. sojae. In doing so, we generated transcriptomes to identify differentially expressed genes (DEGs) during compatible and incompatible interactions with P. sojae and a mock inoculation. The expression data were then used to select two defense-related transcription factors (TFs) belonging to WRKY and RAV families. DNA Affinity Purification and sequencing (DAP-seq) data were obtained for each TF, providing putative DNA binding sites in the soybean genome. These bound sites were used to train Deep Neural Networks with convolutional and recurrent layers to predict new target sites of WRKY and RAV family members in the DEG set. Moreover, we leveraged publicly available Arabidopsis ( Arabidopsis thaliana) DAP-seq data for five TF families enriched in our transcriptome analysis to train similar models. These Arabidopsis data-based models were used for cross-species TF binding site prediction on soybean. Finally, we created a gene regulatory network depicting TF-target gene interactions that orchestrate an immune response against P. sojae. Information herein provides novel insight into molecular plant-pathogen interaction and may prove useful in developing soybean cultivars with more durable resistance to P. sojae.

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          Most cited references131

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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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              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
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Writing – original draft
                Role: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                7 July 2023
                2023
                : 18
                : 7
                : e0287590
                Affiliations
                [1 ] Molecular Biosciences Graduate Program, Arkansas State University, State University, AR, United States of America
                [2 ] Arkansas Biosciences Institute, Arkansas State University, State University, AR, United States of America
                [3 ] College of Science and Mathematics, Arkansas State University, State University, AR, United States of America
                [4 ] Houston High School, Germantown, TN, United States of America
                [5 ] Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, United States of America
                National Taiwan University, TAIWAN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                [¤a]

                Current address: Donald Danforth Plant Science Center, St. Louis, MO, United States of America

                [¤b]

                Current address: St. Jude Children’s Research Hospital, Memphis, TN, United States of America

                Author information
                https://orcid.org/0000-0002-7817-6351
                Article
                PONE-D-22-29762
                10.1371/journal.pone.0287590
                10328377
                37418376
                c242d986-6eb5-408d-990a-c588814a50fa
                © 2023 Hale et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 28 October 2022
                : 7 June 2023
                Page count
                Figures: 6, Tables: 4, Pages: 28
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100008231, Arkansas Biosciences Institute;
                Award Recipient :
                Funded by: IDeA Networks of Biomedical Research Excellence
                Award Recipient :
                Funded by: Startup fund
                Funded by: funder-id http://dx.doi.org/10.13039/100008231, Arkansas Biosciences Institute;
                Award Recipient :
                This research was funded by a Startup fund and grants from Arkansas BioSciences Institute to AJW and AER, and from The Arkansas IDeA Network of Biomedical Research Excellence (Arkansas INBRE) to AJW. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Agriculture
                Crop Science
                Crops
                Soybeans
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Model Organisms
                Arabidopsis Thaliana
                Research and Analysis Methods
                Model Organisms
                Arabidopsis Thaliana
                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Brassica
                Arabidopsis Thaliana
                Research and Analysis Methods
                Animal Studies
                Experimental Organism Systems
                Plant and Algal Models
                Arabidopsis Thaliana
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
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                Molecular biology
                Molecular biology techniques
                Sequencing techniques
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                Bioengineering
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                Custom metadata
                All codes needed for the full reproduction of this study is on GitHub: https://github.com/ajwije/Hale_etal2022. The raw sequencing data are available in the NCBI’s Sequence Read Archive (SRA) under the accession number PRJNA915414.

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