9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Heritable Variation in Pea for Resistance Against a Root Rot Complex and Its Characterization by Amplicon Sequencing

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Soil-borne pathogens cause severe root rot of pea ( Pisum sativum L.) and are a major constraint to pea cultivation worldwide. Resistance against individual pathogen species is often ineffective in the field where multiple pathogens form a pea root rot complex (PRRC) and conjointly infect pea plants. On the other hand, various beneficial plant-microbe interactions are known that offer opportunities to strengthen plant health. To account for the whole rhizosphere microbiome in the assessment of root rot resistance in pea, an infested soil-based resistance screening assay was established. The infested soil originated from a field that showed severe pea root rot in the past. Initially, amplicon sequencing was employed to characterize the fungal microbiome of diseased pea roots grown in the infested soil. The amplicon sequencing evidenced a diverse fungal community in the roots including pea pathogens Fusarium oxysporum, F. solani, Didymella sp., and Rhizoctonia solani and antagonists such as Clonostachys rosea and several mycorrhizal species. The screening system allowed for a reproducible assessment of disease parameters among 261 pea cultivars, breeding lines, and landraces grown for 21 days under controlled conditions. A sterile soil control treatment was used to calculate relative shoot and root biomass in order to compare growth performance of pea lines with highly different growth morphologies. Broad sense heritability was calculated from linear mixed model estimated variance components for all traits. Emergence on the infested soil showed high ( H 2 = 0.89), root rot index ( H 2 = 0.43), and relative shoot dry weight ( H 2 = 0.51) medium heritability. The resistance screening allowed for a reproducible distinction between PRRC susceptible and resistant pea lines. The combined assessment of root rot index and relative shoot dry weight allowed to identify resistant (low root rot index) and tolerant pea lines (low relative shoot dry weight at moderate to high root rot index). We conclude that relative shoot dry weight is a valuable trait to select disease tolerant pea lines. Subsequently, the resistance ranking was verified in an on-farm experiment with a subset of pea lines. We found a significant correlation ( r s = 0.73, p = 0.03) between the controlled conditions and the resistance ranking in a field with high PRRC infestation. The screening system allows to predict PRRC resistance for a given field site and offers a tool for selection at the seedling stage in breeding nurseries. Using the complexity of the infested field soil, the screening system provides opportunities to study plant resistance in the light of diverse plant-microbe interactions occurring in the rhizosphere.

          Related collections

          Most cited references83

          • Record: found
          • Abstract: not found
          • Article: not found

          Fitting Linear Mixed-Effects Models Usinglme4

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            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
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Cutadapt removes adapter sequences from high-throughput sequencing reads

                Bookmark

                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                03 November 2020
                2020
                : 11
                : 542153
                Affiliations
                [1] 1Department of Crop Sciences, Research Institute of Organic Agriculture (FiBL) , Frick, Switzerland
                [2] 2Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich , Zurich, Switzerland
                Author notes

                Edited by: Sukhjiwan Kaur, Department of Jobs, Precincts and Regions, Agriculture Victoria, Australia

                Reviewed by: Shimna Sudheesh, Department of Economic Development Jobs Transport and Resources, Australia; Patricia Ann Okubara, Agricultural Research Service, United States Department of Agriculture, United States

                *Correspondence: Bruno Studer, bruno.studer@ 123456usys.ethz.ch

                This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2020.542153
                7669989
                33224157
                5f0129b1-1e07-42de-b4b4-ac53b683c813
                Copyright © 2020 Wille, Messmer, Bodenhausen, Studer and Hohmann.

                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
                : 11 March 2020
                : 08 October 2020
                Page count
                Figures: 4, Tables: 3, Equations: 0, References: 84, Pages: 15, Words: 0
                Funding
                Funded by: Stiftung Mercator Schweiz 10.13039/501100002331
                Funded by: European Commission 10.13039/501100000780
                Funded by: Staatssekretariat für Bildung, Forschung und Innovation 10.13039/501100007352
                Funded by: Bundesamt für Landwirtschaft 10.13039/501100010473
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                damping-off,disease tolerance,foot rot,legumes,plant-microbe interactions,resistance breeding,root rot

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content23

                Cited by9

                Most referenced authors1,757