Biology subjects

Elmore, J. M.

Publications and source records attributed to Elmore, J. M..

6 recordsLinked to original sources

bioRxiv2022

Powdery mildew effectors AVRA1 and BEC1016 target the ER J-domain protein HvERdj3B required for immunity in barley

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 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 the Bh penetration. HvERdj3B is ER luminal, and we showed using split GFP that AVRA1 and BEC1016 translocate into the ER - signal peptide-independently. Silencing of HvERdj3B and expression the two effectors hampered trafficking of a vacuolar marker through the ER as a shared cellular phenotype, agreeing with the effectors targeting this ERQC component. Together, these results suggest that the barley innate immunity, preventing Bh entry into epidermal cells, is dependent on ERQC, which in turn requires the J-domain protein, HvERdj3B, 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. SIGNIFICANCEThe complex plant immune system is highly dependent on fundamental cellular machineries, such as the endomembrane system and the ER quality control (ERQC), essential for delivery of immunity-associated membrane-bound and endomembrane soluble proteins to their destinations. We now find that pathogen effectors can interact with an ERQC component and suppress immunity, thereby adding to the molecular insight in plant-pathogen interactions.

plant biology↗

bioRxiv2022

Integration of multi-omics data reveals interplay between brassinosteroid and TORC signaling in Arabidopsis

Brassinosteroids (BR) and Target of Rapamycin Complex (TORC) are two major actors coordinating plant growth and stress responses. BRs function through a signaling pathway to extensively regulate gene expression and TORC is known to regulate translation and autophagy. Recent studies revealed that these two pathways crosstalk, but a system-wide view of their interplay is still missing. Thus, we quantified the level of 23,975 transcripts, 11,183 proteins, and 27,887 phosphorylation sites in wild-type Arabidopsis and in mutants with altered levels of either BRASSINOSTEROID INSENSITIVE 2 (BIN2) or REGULATORY ASSOCIATED PROTEIN OF TOR 1B (RAPTOR1B), two key players in BR and TORC signaling, respectively. We found that perturbation of BIN2 or RAPTOR1B levels affects a common set of gene-products involved in growth and stress responses. Furthermore, we used the multi-omic data to reconstruct an integrated signaling network. We screened 41 candidate genes identified from the reconstructed network and found that loss of function mutants of many of these proteins led to an altered BR response and/or modulated autophagy activity. Altogether, these results establish a predictive network that defines different layers of molecular interactions between BR-or TORC-regulated growth and autophagy.

plant biology↗

bioRxiv2021

An interolog-based barley interactome as an integration framework for immune signaling

The barley MLA nucleotide-binding, leucine-rich-repeat (NLR) receptor and its orthologs confer recognition specificity to many cereal diseases, including powdery mildew, stem and stripe rust, Victoria blight, and rice blast. We used interolog inference to construct a barley protein interactome (HvInt) comprising 66133 edges and 7181 nodes, as a foundation to explore signaling networks associated with MLA. HvInt was compared to the experimentally validated Arabidopsis interactome of 11253 proteins and 73960 interactions, verifying that the two 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 an infection-time-course transcriptome, we assembled resistant (R) and susceptible (S) subnetworks. The resulting differentially co-expressed (R-S) interactome is essential to barley immunity, facilitates the flow of signaling pathways and is linked to Mla through trans eQTL associations. Lastly, next-generation, yeast-two-hybrid screens identified fifteen novel MLA interactors, which were incorporated into HvInt, to predict receptor localization, and signaling response. These results link genomic, transcriptomic, and physical interactions during MLA-specified immunity. AUTHOR SUMMARYPowdery mildew fungi infect more than 9,500 agronomic and horticultural plant species. In order to prevent economic loss due to diseases caused by pathogens, plant breeders incorporate resistance genes into varieties that are grown for food, feed, fuel and fiber. One of these resistance genes encodes the barley MLA immune receptor, an ancestral cereal protein that confers recognition to powdery mildew, stem and stripe rust, rice blast and Victoria blight. However, in order to function properly, these immune receptors must interact with additional proteins and protein complexes during the different stages of fungal infection and plant defense. We used a combination of computational- and laboratory-based methods to predict over 66,000 possible protein-protein interactions in barley. This network of proteins was then integrated with various defense-specific datasets to assemble the molecular building blocks associated with resistance to the powdery mildew pathogen, in addition to those proteins that interact with the MLA immune receptor. Our application of genome-scale, protein-protein interaction data provides a foundation to decipher the complex molecular components that control immune responses in crops.

systems biology↗

bioRxiv2020

ER bodies are induced by Pseudomonas syringae and negatively regulate immunity

ER bodies are endoplasmic reticulum-derived organelles present in plants belonging to the Brassicales order. In Arabidopsis thaliana, ER bodies are ubiquitous in cotyledons and roots, and present only in certain cell types in rosette leaves. However, both wounding and jasmonic acid treatment induce the formation of ER bodies in leaves. Formation of this structure is dependent on the transcription factor NAI1. The main components of the ER bodies are {beta}-glucosidases (BGLUs), enzymes that hydrolyze specialized compounds. In Arabidopsis, PYK10 (BGLU23) and BGLU18 are the most abundant ER body proteins. In this work, we found that ER bodies are downregulated as a consequence of the immune responses induced by bacterial flagellin perception. Arabidopsis mutants defective in ER body formation show enhanced responses upon flagellin perception and enhanced resistance to bacterial infections. Furthermore, the bacterial toxin coronatine induces the formation of de novo ER bodies in leaves and its virulence function is partially dependent on this structure. Finally, we show that performance of the polyphagous beet armyworm herbivore, Spodoptera exigua, increases in plants lacking ER bodies. Altogether, we provide new evidence for the role of the ER bodies in plant immune responses.

plant biology↗

bioRxiv2020

NGPINT: A Next-generation protein-protein interaction software

Mapping protein-protein interactions at a proteome scale is critical to understanding how cellular signaling networks respond to stimuli. Since eukaryotic genomes encode thousands of proteins, testing their interactions one-by-one is a challenging prospect. High-throughput yeast-two hybrid (Y2H) assays that employ next-generation sequencing to interrogate cDNA libraries represent an alternative approach that optimizes scale, cost, and effort. We present NGPINT, a robust and scalable software to identify all putative interactors of a protein using Y2H in batch culture. NGPINT combines diverse tools to align sequence reads to target genomes, reconstruct prey fragments and compute gene enrichment under reporter selection. Central to this pipeline is the identification of fusion reads containing sequences derived from both the Y2H expression plasmid and the cDNA of interest. To reduce false positives, these fusion reads are evaluated as to whether the cDNA fragment forms an in-frame translational fusion with the Y2H transcription factor. NGPINT successfully recognized 95% of interactions in simulated test runs. As proof of concept, NGPINT was tested using published data sets and recognized all validated interactions. NGPINT can be used in any organism with an available reference, thus facilitating the discovery of protein-protein interactions in non-model organisms.

bioinformatics↗

bioRxiv2020

Y2H-SCORES: A statistical framework to infer protein-protein interactions from next-generation yeast-two-hybrid sequence data

Interactomes embody one of the most effective representations of cellular behavior by revealing function through protein associations. In order to build these models at the organism scale, high-throughput techniques are required to identify interacting pairs of proteins. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate protein-protein interaction networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we describe a statistical framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens that considers key aspects of experimental design, normalization, and controls. 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 identified 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, accelerating the biological interpretation of experimental results. Proof-of-concept was demonstrated by discovery and validation of a novel interaction between the barley powdery mildew effector, AVRA13, with the vesicle-mediated thylakoid membrane biogenesis protein, HvTHF1. Author SummaryOrganisms respond to their environment through networks of interacting proteins and other biomolecules. In order to investigate these interacting proteins, many in vitro and in vivo techniques have been used. Among these, yeast two-hybrid (Y2H) has been integrated with next generation sequencing (NGS) to approach protein-protein interactions on a genome-wide scale. The fusion of these two methods has been termed next-generation-interaction screening, abbreviated as Y2H-NGIS. However, the massive and diverse data sets resulting from this technology have presented unique challenges to analysis. To address these challenges, we optimized the computational and statistical evaluation of Y2H-NGIS to provide metrics to identify high-confidence interacting proteins under a variety of dataset scenarios. Our proposed framework can be extended to different yeast-based interaction settings, utilizing the general principles of enrichment, specificity, and in-frame prey selection to accurately assemble protein-protein interaction networks. Lastly, we showed how the pipeline works experimentally, by identifying and validating a novel interaction between the barley powdery mildew effector AVRA13 and the barley vesicle-mediated thylakoid membrane biogenesis protein, HvTHF1. Y2H-SCORES software is available at GitHub repository https://github.com/Wiselab2/Y2H-SCORES.

systems biology↗