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      The leucine-rich repeats in allelic barley MLA immune receptors define specificity towards sequence-unrelated powdery mildew avirulence effectors with a predicted common RNase-like fold

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          Abstract

          Nucleotide-binding domain leucine-rich repeat-containing receptors (NLRs) in plants can detect avirulence (AVR) effectors of pathogenic microbes. The Mildew locus a ( Mla) NLR gene has been shown to confer resistance against diverse fungal pathogens in cereal crops. In barley, Mla has undergone allelic diversification in the host population and confers isolate-specific immunity against the powdery mildew-causing fungal pathogen Blumeria graminis forma specialis hordei ( Bgh). We previously isolated the Bgh effectors AVR A1, AVR A7, AVR A9, AVR A13, and allelic AVR A10/AVR A22, which are recognized by matching MLA1, MLA7, MLA9, MLA13, MLA10 and MLA22, respectively. Here, we extend our knowledge of the Bgh effector repertoire by isolating the AVR A6 effector, which belongs to the family of catalytically inactive RNase-Like Proteins expressed in Haustoria (RALPHs). Using structural prediction, we also identified RNase-like folds in AVR A1, AVR A7, AVR A10/AVR A22, and AVR A13, suggesting that allelic MLA recognition specificities could detect structurally related avirulence effectors. To better understand the mechanism underlying the recognition of effectors by MLAs, we deployed chimeric MLA1 and MLA6, as well as chimeric MLA10 and MLA22 receptors in plant co-expression assays, which showed that the recognition specificity for AVR A1 and AVR A6 as well as allelic AVR A10 and AVR A22 is largely determined by the receptors’ C-terminal leucine-rich repeats (LRRs). The design of avirulence effector hybrids allowed us to identify four specific AVR A10 and five specific AVR A22 aa residues that are necessary to confer MLA10- and MLA22-specific recognition, respectively. This suggests that the MLA LRR mediates isolate-specific recognition of structurally related AVR A effectors. Thus, functional diversification of multi-allelic MLA receptors may be driven by a common structural effector scaffold, which could be facilitated by proliferation of the RALPH effector family in the pathogen genome.

          Author summary

          Barley powdery mildew caused by the fungus Blumeria graminis forma specialis hordei ( Bgh) can result in annual yield losses of 15% of this cereal crop. Bgh promotes virulence in plants through the secretion of diverse effector molecules, small proteins of which a subset enters into and modifies the immune status and physiology of the host leaf. In response, the host has evolved a multitude of disease resistance genes. The Mildew locus a ( Mla) resistance gene stands out because diversification in the host population has generated numerous Mla variants encoding multi-domain receptors, each of which can directly recognize an isolate-specific Bgh effector, designated as avirulence (AVR A) effectors. Recognition of AVR A effectors by MLA triggers plant immune responses, a phenomenon known as isolate-specific resistance, which invariably results in localized host cell death. Here, we identify the powdery mildew effector AVR A6 and validate its specific interaction with its matching receptor MLA6. Furthermore, through the use of hybrid receptors constructed from MLA1 and MLA6 as well as MLA10 and MLA22 receptors, we provide insights into the specific domains and amino acid residues generally important for AVR A recognition by MLA receptors. We find that sequence variation in the leucine-rich repeats (LRRs) of multi-allelic MLA receptors determines specific recognition of AVR A effectors. These effectors are sequence-unrelated, but our analysis indicates that they may be structurally related. This data may assist in the future generation of synthetic immune receptors with pre-defined recognition specificities.

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees

              Interactive Tree Of Life (http://itol.embl.de) is a web-based tool for the display, manipulation and annotation of phylogenetic trees. It is freely available and open to everyone. The current version was completely redesigned and rewritten, utilizing current web technologies for speedy and streamlined processing. Numerous new features were introduced and several new data types are now supported. Trees with up to 100,000 leaves can now be efficiently displayed. Full interactive control over precise positioning of various annotation features and an unlimited number of datasets allow the easy creation of complex tree visualizations. iTOL 3 is the first tool which supports direct visualization of the recently proposed phylogenetic placements format. Finally, iTOL's account system has been redesigned to simplify the management of trees in user-defined workspaces and projects, as it is heavily used and currently handles already more than 500,000 trees from more than 10,000 individual users.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: Methodology
                Role: Formal analysisRole: Investigation
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: VisualizationRole: Writing – review & editing
                Role: Data curationRole: SoftwareRole: VisualizationRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: Methodology
                Role: SupervisionRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Resources
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                plospath
                PLoS Pathogens
                Public Library of Science (San Francisco, CA USA )
                1553-7366
                1553-7374
                3 February 2021
                February 2021
                : 17
                : 2
                : e1009223
                Affiliations
                [1 ] Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany
                [2 ] Institute of Biochemistry, University of Cologne at Max Planck Institute for Plant Breeding Research, Cologne, Germany
                [3 ] DOE Joint Genome Institute, Berkeley, California, United States of America
                [4 ] Cluster of Excellence on Plant Sciences, Düsseldorf, Germany
                University of Queensland, AUSTRALIA
                Author notes

                The authors have declared that no competing interests exist.

                [¤a]

                Current address: Bayer Crop Science, West Sacramento, California, United States of America

                [¤b]

                Current address: Institute for Plant Science, University of Cologne, Cologne, Germany, Cluster of Excellence on Plant Sciences, Düsseldorf, Germany

                Author information
                https://orcid.org/0000-0003-4559-5063
                https://orcid.org/0000-0002-5610-1260
                https://orcid.org/0000-0001-8947-6934
                https://orcid.org/0000-0002-0478-8072
                Article
                PPATHOGENS-D-20-01364
                10.1371/journal.ppat.1009223
                7857584
                33534797
                4fc46818-2f11-4074-99b7-9c4e026bc560
                © 2021 Bauer 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
                : 23 June 2020
                : 7 December 2020
                Page count
                Figures: 11, Tables: 0, Pages: 36
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100004189, Max-Planck-Gesellschaft;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100004189, Max-Planck-Gesellschaft;
                Award Recipient :
                Funded by: Deutsche Forschungsgemeinschaft
                Award ID: SFB-1403–414786233
                Award Recipient :
                Funded by: Deutsche Forschungsgemeinschaft
                Award ID: SFB-1403–414786233
                Award Recipient :
                Funded by: Deutsche Forschungsgemeinschaft
                Award ID: SFB-1403–414786233
                Award Recipient :
                Funded by: Deutsche Forschungsgemeinschaft
                Award ID: CEPLAS 2048/1 – project 390686111
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100007601, Horizon 2020;
                Award ID: 742263
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100002974, Daimler und Benz Stiftung;
                Award Recipient :
                Funded by: Office of Science of the U.S. Department of Energy
                Award ID: DE-AC02-05CH11231
                Award Recipient :
                This work was supported by the Max Planck Society (SB and PSL), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in the Collaborative Research Centre Grant (SFB-1403 – 414786233 to JC, TM, PSL) and under Germany’s Excellence Strategy – EXC-Number 2048/1 – project 390686111 (PSL), the Horizon 2020 Framework Programme (742263) and the Daimler and Benz Foundation (both to IMLS). Work conducted by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 (to LF). 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
                Organisms
                Eukaryota
                Plants
                Grasses
                Barley
                Biology and Life Sciences
                Cell Biology
                Cell Processes
                Cell Death
                Biology and life sciences
                Biochemistry
                Proteins
                DNA-binding proteins
                Nucleases
                Ribonucleases
                Biology and Life Sciences
                Biochemistry
                Enzymology
                Enzymes
                Hydrolases
                Nucleases
                Ribonucleases
                Biology and Life Sciences
                Biochemistry
                Proteins
                Enzymes
                Hydrolases
                Nucleases
                Ribonucleases
                Biology and Life Sciences
                Immunology
                Immune System Proteins
                Immune Receptors
                Pattern Recognition Receptors
                Medicine and Health Sciences
                Immunology
                Immune System Proteins
                Immune Receptors
                Pattern Recognition Receptors
                Biology and Life Sciences
                Biochemistry
                Proteins
                Immune System Proteins
                Immune Receptors
                Pattern Recognition Receptors
                Biology and Life Sciences
                Cell Biology
                Signal Transduction
                Immune Receptors
                Pattern Recognition Receptors
                Biology and Life Sciences
                Computational Biology
                Genome Complexity
                Introns
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Complexity
                Introns
                Biology and Life Sciences
                Plant Science
                Plant Anatomy
                Leaves
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Protein Structure
                Protein Structure Prediction
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Protein Structure Prediction
                Biology and Life Sciences
                Biochemistry
                Enzymology
                Enzymes
                Ribozymes
                Biology and Life Sciences
                Biochemistry
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                Biology and Life Sciences
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                Custom metadata
                RNA-seq data can be found in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database (accession no. GSE110266, GSE106282 and GSE83237). Primary data and intermediate files of the phylogenetic analysis can be found at https://github.com/lambros-f/avra6_2019 All other relevant datasets are shown within the manuscript and its Supporting Information files.

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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