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      Molecular mechanisms underpinning quantitative resistance to Phytophthora sojae in Glycine max using a systems genomics approach

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          Abstract

          Expression of quantitative disease resistance in many host–pathogen systems is controlled by genes at multiple loci, each contributing a small effect to the overall response. We used a systems genomics approach to study the molecular underpinnings of quantitative disease resistance in the soybean- Phytophthora sojae pathosystem, incorporating expression quantitative trait loci (eQTL) mapping and gene co-expression network analysis to identify the genes putatively regulating transcriptional changes in response to inoculation. These findings were compared to previously mapped phenotypic (phQTL) to identify the molecular mechanisms contributing to the expression of this resistance. A subset of 93 recombinant inbred lines (RILs) from a Conrad × Sloan population were inoculated with P. sojae isolate 1.S.1.1 using the tray-test method; RNA was extracted, sequenced, and the normalized read counts were genetically mapped from tissue collected at the inoculation site 24 h after inoculation from both mock and inoculated samples. In total, more than 100,000 eQTLs were mapped. There was a switch from predominantly cis-eQTLs in the mock treatment to an almost entirely nonoverlapping set of predominantly trans-eQTLs in the inoculated treatment, where greater than 100-fold more eQTLs were mapped relative to mock, indicating vast transcriptional reprogramming due to P. sojae infection occurred. The eQTLs were organized into 36 hotspots, with the four largest hotspots from the inoculated treatment corresponding to more than 70% of the eQTLs, each enriched for genes within plant–pathogen interaction pathways. Genetic regulation of trans-eQTLs in response to the pathogen was predicted to occur through transcription factors and signaling molecules involved in plant–pathogen interactions, plant hormone signal transduction, and MAPK pathways. Network analysis identified three co-expression modules that were correlated with susceptibility to P. sojae and associated with three eQTL hotspots. Among the eQTLs co-localized with phQTLs, two cis-eQTLs with putative functions in the regulation of root architecture or jasmonic acid, as well as the putative master regulators of an eQTL hotspot nearby a phQTL, represent candidates potentially underpinning the molecular control of these phQTLs for resistance.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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              KEGG: kyoto encyclopedia of genes and genomes.

              M Kanehisa (2000)
              KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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                Author and article information

                Contributors
                Role: Role: Role: Role: Role: Role: Role:
                Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/1764332Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/1793476Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/412668Role: Role: Role: Role: Role:
                URI : https://loop.frontiersin.org/people/541139Role: Role: Role: Role: Role:
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                07 November 2023
                2023
                : 14
                : 1277585
                Affiliations
                [1] 1 Department of Plant Pathology, The Ohio State University , Wooster, OH, United States
                [2] 2 Center for Soybean Research and Center for Applied Plant Sciences, The Ohio State University , Columbus, OH, United States
                [3] 3 Molecular and Cellular Imaging Center, The Ohio State University , Wooster, OH, United States
                [4] 4 Translational Plant Sciences Graduate Program, The Ohio State University , Columbus, OH, United States
                [5] 5 Department of Biology, Brandon University , Brandon, Manitoba, MB, Canada
                [6] 6 Department of Horticulture and Crop Science, The Ohio State University , Columbus, OH, United States
                Author notes

                Edited by: Laura Medina-Puche, University of Tübingen, Germany

                Reviewed by: Brett Hale, Arkansas State University, United States; Jose Sebastian Rufian, Universidad de Málaga, Spain

                *Correspondence: Leah K. McHale, mchale.21@ 123456osu.edu ; Anne E. Dorrance, dorrance.1@ 123456osu.edu

                †Present addresses: Cassidy R. Million, Ag Science, Heliae Agriculture, Gilbert, AZ, United States; Saranga Wijeratne, Abigail Wexner Research Institute Nationwide Children’s Hospital, Columbus, OH, United States; Stephanie Karhoff, Department of Extension, The Ohio State University, Ottawa, OH, United States

                ‡These authors have contributed equally to this work and share senior authorship

                Article
                10.3389/fpls.2023.1277585
                10662313
                566c0cbf-4e47-4361-bab7-12469de3905d
                Copyright © 2023 Million, Wijeratne, Karhoff, Cassone, McHale and Dorrance

                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
                : 14 August 2023
                : 16 October 2023
                Page count
                Figures: 2, Tables: 5, Equations: 0, References: 183, Pages: 22, Words: 12435
                Funding
                Funded by: Ohio Soybean Council , doi 10.13039/100013437;
                Award ID: 14-2-18, 17-2-03, 16-R06, 17-R-03, 18-R-05
                Funded by: United Soybean Board , doi 10.13039/100012009;
                Award ID: 1720-172-0125
                Funded by: National Institute of Food and Agriculture , doi 10.13039/100005825;
                Award ID: OHO01303, OHO01279
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Funding for this project was provided by the Ohio Soybean Council (projects Nos. 14-2-18, 17-2-03, 16-R-06, 17-R-03, and 18-R-05); the United Soybean Board (project No. 1720-172-0125); The Ohio State University Center for Applied Plant Sciences and Molecular and Cellular Imaging Center; State and Federal funds appropriated to The Ohio State University, College of Food, Agricultural, and Environmental Sciences; the National Institute of Food and Agriculture, U.S. Department of Agriculture Hatch projects for Development of Disease Management Strategies for Soybean Pathogens in Ohio OHO01303; and the Genetic Analysis of Soybean Added-Value Traits and Soybean Variety Development for Ohio OHO01279.
                Categories
                Plant Science
                Original Research
                Custom metadata
                Plant Pathogen Interactions

                Plant science & Botany
                glycine max,soybean,phytophthora sojae,eqtl,systems genomics,master regulators,weighted gene co-expression network analysis

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