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Li Z, Velásquez‐Zapata V, Elmore JM, Li X, Xie W, Deb S, Tian X, Banerjee S, Jørgensen HJL, Pedersen C, Wise RP, Thordal‐Christensen H. Powdery mildew effectors AVR A1 and BEC1016 target the ER J-domain protein HvERdj3B required for immunity in barley. MOLECULAR PLANT PATHOLOGY 2024; 25:e13463. [PMID: 38695677 PMCID: PMC11064805 DOI: 10.1111/mpp.13463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/06/2024] [Accepted: 04/11/2024] [Indexed: 05/05/2024]
Abstract
The barley powdery mildew fungus, Blumeria hordei (Bh), secretes hundreds of candidate secreted effector proteins (CSEPs) to facilitate pathogen infection and colonization. One of these, CSEP0008, is directly recognized by the barley nucleotide-binding leucine-rich-repeat (NLR) receptor MLA1 and therefore is designated AVRA1. Here, we show that AVRA1 and the sequence-unrelated Bh effector BEC1016 (CSEP0491) suppress immunity in barley. We used yeast two-hybrid next-generation interaction screens (Y2H-NGIS), followed by binary Y2H and in planta protein-protein interactions studies, and identified a common barley target of AVRA1 and BEC1016, the endoplasmic reticulum (ER)-localized J-domain protein HvERdj3B. Silencing of this ER quality control (ERQC) protein increased Bh penetration. HvERdj3B is ER luminal, and we showed using split GFP that AVRA1 and BEC1016 translocate into the ER signal peptide-independently. Overexpression of the two effectors impeded trafficking of a vacuolar marker through the ER; silencing of HvERdj3B also exhibited this same cellular phenotype, coinciding with the effectors targeting this ERQC component. Together, these results suggest that the barley innate immunity, preventing Bh entry into epidermal cells, requires ERQC. Here, the J-domain protein HvERdj3B appears to be essential and can be regulated by AVRA1 and BEC1016. Plant disease resistance often occurs upon direct or indirect recognition of pathogen effectors by host NLR receptors. Previous work has shown that AVRA1 is directly recognized in the cytosol by the immune receptor MLA1. We speculate that the AVRA1 J-domain target being inside the ER, where it is inapproachable by NLRs, has forced the plant to evolve this challenging direct recognition.
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Affiliation(s)
- Zizhang Li
- Department of Plant and Environmental SciencesUniversity of CopenhagenFrederiksberg CDenmark
- Present address:
Institute for Bioscience and Biotechnology Research & Department of Plant Sciences and Landscape ArchitectureUniversity of MarylandRockvilleMarylandUSA
| | - Valeria Velásquez‐Zapata
- Program in Bioinformatics & Computational BiologyIowa State UniversityAmesIowaUSA
- Department of Plant Pathology, Entomology and MicrobiologyIowa State UniversityAmesIowaUSA
- Present address:
GreenLight Biosciences, IncResearch Triangle ParkNorth CarolinaUSA
| | - J. Mitch Elmore
- Department of Plant Pathology, Entomology and MicrobiologyIowa State UniversityAmesIowaUSA
- USDA‐Agricultural Research Service, Corn Insects and Crop Genetics Research UnitAmesIowaUSA
- Present address:
USDA‐Agricultural Research Service, Cereal Disease LaboratorySt. PaulMinnesotaUSA
| | - Xuan Li
- Department of Plant and Environmental SciencesUniversity of CopenhagenFrederiksberg CDenmark
| | - Wenjun Xie
- Department of Plant and Environmental SciencesUniversity of CopenhagenFrederiksberg CDenmark
| | - Sohini Deb
- Department of Plant and Environmental SciencesUniversity of CopenhagenFrederiksberg CDenmark
| | - Xiao Tian
- Department of Plant and Environmental SciencesUniversity of CopenhagenFrederiksberg CDenmark
| | - Sagnik Banerjee
- Program in Bioinformatics & Computational BiologyIowa State UniversityAmesIowaUSA
- Department of StatisticsIowa State UniversityAmesIowaUSA
- Present address:
Bristol Myers SquibbSan DiegoCaliforniaUSA
| | - Hans J. L. Jørgensen
- Department of Plant and Environmental SciencesUniversity of CopenhagenFrederiksberg CDenmark
| | - Carsten Pedersen
- Department of Plant and Environmental SciencesUniversity of CopenhagenFrederiksberg CDenmark
| | - Roger P. Wise
- Program in Bioinformatics & Computational BiologyIowa State UniversityAmesIowaUSA
- Department of Plant Pathology, Entomology and MicrobiologyIowa State UniversityAmesIowaUSA
- USDA‐Agricultural Research Service, Corn Insects and Crop Genetics Research UnitAmesIowaUSA
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Exome-wide variation in a diverse barley panel reveals genetic associations with ten agronomic traits in Eastern landraces. J Genet Genomics 2022; 50:241-252. [PMID: 36566016 DOI: 10.1016/j.jgg.2022.12.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022]
Abstract
Barley (Hordeum vulgare ssp. vulgare) was one of the first crops to be domesticated and is adapted to a wide range of environments. Worldwide barley germplasm collections possess valuable allelic variations that could further improve barley productivity. Although barley genomics has offered a global picture of allelic variation among varieties and its association with various agronomic traits, polymorphisms from East Asian varieties remain scarce. In this study, we analyzed exome polymorphisms in a panel of 274 barley varieties collected worldwide, including 137 varieties from East Asian countries and Ethiopia. We revealed the underlying population structure and conducted genome-wide association studies for ten agronomic traits. Moreover, we examined genome-wide associations for traits related to grain size such as awn length and glume length. Our results demonstrate the value of diverse barley germplasm panels containing Eastern varieties, highlighting their distinct genomic signatures relative to Western subpopulations.
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Velásquez-Zapata V, Elmore JM, Fuerst G, Wise RP. An interolog-based barley interactome as an integration framework for immune signaling. Genetics 2022; 221:iyac056. [PMID: 35435213 PMCID: PMC9157089 DOI: 10.1093/genetics/iyac056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/04/2022] [Indexed: 12/12/2022] Open
Abstract
The barley MLA nucleotide-binding leucine-rich-repeat (NLR) receptor and its orthologs confer recognition specificity to many fungal diseases, including powdery mildew, stem-, and stripe rust. We used interolog inference to construct a barley protein interactome (Hordeum vulgare predicted interactome, HvInt) comprising 66,133 edges and 7,181 nodes, as a foundation to explore signaling networks associated with MLA. HvInt was compared with the experimentally validated Arabidopsis interactome of 11,253 proteins and 73,960 interactions, verifying that the 2 networks share scale-free properties, including a power-law distribution and small-world network. Then, by successive layering of defense-specific "omics" datasets, HvInt was customized to model cellular response to powdery mildew infection. Integration of HvInt with expression quantitative trait loci (eQTL) enabled us to infer disease modules and responses associated with fungal penetration and haustorial development. Next, using HvInt and infection-time-course RNA sequencing of immune signaling mutants, we assembled resistant and susceptible subnetworks. The resulting differentially coexpressed (resistant - susceptible) interactome is essential to barley immunity, facilitates the flow of signaling pathways and is linked to mildew resistance locus a (Mla) through trans eQTL associations. Lastly, we anchored HvInt with new and previously identified interactors of the MLA coiled coli + nucleotide-binding domains and extended these to additional MLA alleles, orthologs, and NLR outgroups to predict receptor localization and conservation of signaling response. These results link genomic, transcriptomic, and physical interactions during MLA-specified immunity.
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Affiliation(s)
- Valeria Velásquez-Zapata
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA 50011, USA
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, USA
| | - James Mitch Elmore
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, USA
- Corn Insects and Crop Genetics Research, USDA-Agricultural Research Service, Ames, IA 50011, USA
| | - Gregory Fuerst
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, USA
- Corn Insects and Crop Genetics Research, USDA-Agricultural Research Service, Ames, IA 50011, USA
| | - Roger P Wise
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, IA 50011, USA
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, IA 50011, USA
- Corn Insects and Crop Genetics Research, USDA-Agricultural Research Service, Ames, IA 50011, USA
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Velásquez-Zapata V, Elmore JM, Banerjee S, Dorman KS, Wise RP. Next-generation yeast-two-hybrid analysis with Y2H-SCORES identifies novel interactors of the MLA immune receptor. PLoS Comput Biol 2021; 17:e1008890. [PMID: 33798202 PMCID: PMC8046355 DOI: 10.1371/journal.pcbi.1008890] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 04/14/2021] [Accepted: 03/17/2021] [Indexed: 12/21/2022] Open
Abstract
Protein-protein interaction networks are one of the most effective representations of cellular behavior. In order to build these models, high-throughput techniques are required. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate interactome networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we present a computational framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens. Y2H-SCORES considers key aspects of NGIS experimental design and important characteristics of the resulting data that distinguish it from RNA-seq expression datasets. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS enabled us to identify conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, with an equivalent or superior performance than existing methods. Proof-of-concept was demonstrated by discovery and validation of novel interactions between the barley nucleotide-binding leucine-rich repeat (NLR) immune receptor MLA6, and fourteen proteins, including those that function in signaling, transcriptional regulation, and intracellular trafficking.
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Affiliation(s)
- Valeria Velásquez-Zapata
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, Iowa, United States of America
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, Iowa, United States of America
| | - J. Mitch Elmore
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, Iowa, United States of America
- Corn Insects and Crop Genetics Research, USDA-Agricultural Research Service, Ames, Iowa, United States of America
| | - Sagnik Banerjee
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, Iowa, United States of America
- Department of Statistics, Iowa State University, Ames, Iowa, United States of America
| | - Karin S. Dorman
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, Iowa, United States of America
- Department of Statistics, Iowa State University, Ames, Iowa, United States of America
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa, United States of America
| | - Roger P. Wise
- Program in Bioinformatics & Computational Biology, Iowa State University, Ames, Iowa, United States of America
- Department of Plant Pathology & Microbiology, Iowa State University, Ames, Iowa, United States of America
- Corn Insects and Crop Genetics Research, USDA-Agricultural Research Service, Ames, Iowa, United States of America
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