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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Isoform-specific functions of synaptopodin-2 variants in cytoskeleton stabilization and autophagy regulation in muscle under mechanical stress

Highlights: • Muscle cells express different synaptopodin-2 isoforms with individual functions. • Reduced SYNPO2 expression results in enhanced vulnerability of myofibrils. • SYNPO2e recruites BAG3 to mechanically stressed myofibrillar areas. • SYNPO2 is an organizer and regulator of BAG3-containing proteostasis systems. • BAG3 forms functionally distinct protein complexes with different SYNPO2 isoforms. Protein homeostasis (proteostasis) in multicellular organisms depends on the maintenance of force-bearing and force-generating cellular structures. Within myofibrillar Z-discs of striated muscle, isoforms of synaptopodin-2 (SYNPO2/myopodin) act as adapter proteins that are engaged in proteostasis of the actin-crosslinking protein filamin C (FLNc) under mechanical stress. SYNPO2 directly binds F-actin, FLNc and α-actinin and thus contributes to the architectural features of the actin cytoskeleton. By its association with autophagy mediating proteins, i.e. BAG3 and VPS18, SYNPO2 is also engaged in protein quality control and helps to target mechanical unfolded and damaged FLNc for degradation. Here we show that deficiency of all SYNPO2-isoforms in myotubes leads to decreased myofibrillar stability and deregulated autophagy under mechanical stress. In addition, isoform-specific proteostasis functions were revealed. The PDZ-domain containing variant SYNPO2b and the shorter, PDZ-less isoform SYNPO2e both localize to Z-discs. Yet, SYNPO2e is less stably associated with the Z-disc than SYNPO2b, and is dynamically transferred into FLNc-containing myofibrillar lesions under mechanical stress. SYNPO2e also recruits BAG3 into these lesions via interaction with the WW domain of BAG3. Our data provide evidence for a role of myofibrillar lesions as a transient quality control compartment essential to prevent and repair contraction-induced myofibril damage in muscle and indicate an important coordinating activity for SYNPO2 therein.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Cryo-EM structure of transmembrane AAA+ protease FtsH in the ADP state

Abstract AAA+ proteases regulate numerous physiological and cellular processes through tightly regulated proteolytic cleavage of protein substrates driven by ATP hydrolysis. FtsH is the only known family of membrane-anchored AAA+ proteases essential for membrane protein quality control. Although a spiral staircase rotation mechanism for substrate translocation across the FtsH pore has been proposed, the detailed conformational changes among various states have not been clear due to absence of FtsH structures in these states. We report here the cryo-EM structure for Thermotoga maritima FtsH ( Tm FtsH) in a fully ADP-bound symmetric state. Comparisons of the ADP-state structure with its apo-state and a substrate-engaged yeast YME1 structure show conformational changes in the ATPase domains, rather than the protease domains. A reconstruction of the full-length Tm FtsH provides structural insights for the dynamic transmembrane and the periplasmic domains. Our structural analyses expand the understanding of conformational switches between different nucleotide states in ATP hydrolysis by FtsH.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Assembly and Repair of the Photosystem II Reaction Center

This project investigated the biochemical and biophysical mechanisms governing assembly and repair of Photosystem II (PSII), the membrane protein complex responsible for solar-driven water oxidation in oxygenic photosynthesis. The work focused on three integrated areas: (1) protein–protein interactions that facilitate PSII assembly in cyanobacterial biogenesis centers, (2) the chemical mechanism of photo-assembly of the Mn 4 CaO 5 oxygen-evolving complex (OEC), and (3) mechanisms that target PSII reaction centers for degradation and repair in photosynthetic organisms. Using electron paramagnetic resonance spectroscopy, protein biochemistry, molecular genetics, quantitative mass spectrometry, and computational modeling, the project demonstrated that proton release events limit early steps of OEC assembly and that chloride and calcium ions facilitate Mn oxidation and intermediate stabilization. Complementary studies identified chaperone recruitment mechanisms in cyanobacterial PSII biogenesis centers and translation and protease factors involved in PSII turnover in Chlamydomonas. Together, these results establish proton management and coordinated protein quality control as central design principles in PSII assembly and repair and provide mechanistic insight relevant to biological and artificial photosynthetic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network

Abstract Background Estimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality of protein models. Inter-residue distances are key information for predicting protein’s tertiary structures and therefore have good potentials to predict the quality of protein structural models. However, few methods have been developed to fully take advantage of predicted inter-residue distance maps to estimate the accuracy of a single protein structural model. Result We developed an attentive 2D convolutional neural network (CNN) with channel-wise attention to take only a raw difference map between the inter-residue distance map calculated from a single protein model and the distance map predicted from the protein sequence as input to predict the quality of the model. The network comprises multiple convolutional layers, batch normalization layers, dense layers, and Squeeze-and-Excitation blocks with attention to automatically extract features relevant to protein model quality from the raw input without using any expert-curated features. We evaluated DISTEMA’s capability of selecting the best models for CASP13 targets in terms of ranking loss of GDT-TS score. The ranking loss of DISTEMA is 0.079, lower than several state-of-the-art single-model quality assessment methods. Conclusion This work demonstrates that using raw inter-residue distance information with deep learning can predict the quality of protein structural models reasonably well. DISTEMA is freely at https://github.com/jianlin-cheng/DISTEMA

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Impacts of feeding three strains of microalgae alone or in combination on growth performance, protein metabolism, and meat quality of broiler chickens

Variations in nutrient compositions, especially amino acid (AA) profiles, among microalgal species may enable a superior feeding outcome from a combined than singular supplementation in poultry diets. Therefore, a feeding trial was conducted to compare the effects of three strains of microalgal biomass supplemented alone or in combination to replace 5 % (starter) and 10 % (grower) soybean meal (on weight-to-weight basis) on growth performance, protein metabolism, and meat quality of broiler chickens. Day-old Cornish Cross male chicks (total = 180) were divided into 5 groups (6 cages/treatment, 6 birds/cage) and fed a corn-soybean meal basal diet (BD), BD + H117 (Chlorella sp., H117), BD + C985 (Tetraselmis sp., C985), BD + Nannochloropsis oceanica (NO), and BD + H117 + C985 + NO (Combination). Feeding any of the microalgae diets did not alter growth performance nor meat quality including texture, pH, color, and water holding capacity of breast and thigh meats. However, the breast weight percentages were decreased (P < 0.05) by feeding the C985, NO, and Combination diets. Compared with the BD, the 4 microalgal diets led to higher (P < 0.05) plasma uric acid and protein concentrations at weeks 3 and (or) 6. The mRNA levels of MAFbx, MURF1, FOXO1, and calpastatin in the breast and thigh muscles were altered by the microalgal diets but not those of genes associated with other quality traits. In conclusion, replacing 5 % or 10 % soybean meal with three sources of microalgae in broiler diets decreased breast weights percentage but not absolute weight. Furthermore, feeding chickens with the combination of three microalgae did not restore the breast loss and induced different expressions of genes related to muscle hypertrophy or atrophy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Scalable production of recombinant three-finger proteins: from inclusion bodies to high quality molecular probes

The three-finger proteins are a collection of disulfide bond rich proteins of great biomedical interests. Scalable recombinant expression and purification of bioactive three-finger proteins is quite difficult. We introduce a working pipeline for expression, purification and validation of disulfide-bond rich three-finger proteins using E. coli as the expression host. With this pipeline, we have successfully obtained highly purified and bioactive recombinant α-Βungarotoxin, k-Bungarotoxin, Hannalgesin, Mambalgin-1, α-Cobratoxin, MTα, Slurp1, Pate B etc. Milligrams to hundreds of milligrams of recombinant three finger proteins were obtained within weeks in the lab. The recombinant proteins showed specificity in binding assay and six of them were crystallized and structurally validated using X-ray diffraction protein crystallography. Our pipeline allows refolding and purifying recombinant three finger proteins under optimized conditions and can be scaled up for massive production of three finger proteins. As many three finger proteins have attractive therapeutic or research interests and due to the extremely high quality of the recombinant three finger proteins we obtained, our method provides a competitive alternative to either their native counterparts or chemically synthetic ones and should facilitate related research and applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14

Abstract The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-residue distance predictions to enhance EMA, during the 2020 CASP14 experiment, we integrated several new inter-residue distance features with the existing model quality assessment features in several deep learning methods to predict the quality of protein structural models. According to the evaluation of performance in selecting the best model from the models of CASP14 targets, our three multi-model predictors of estimating model accuracy (MULTICOM-CONSTRUCT, MULTICOM-AI, and MULTICOM-CLUSTER) achieve the averaged loss of 0.073, 0.079, and 0.081, respectively, in terms of the global distance test score (GDT-TS). The three methods are ranked first, second, and third out of all 68 CASP14 predictors. MULTICOM-DEEP, the single-model predictor of estimating model accuracy (EMA), is ranked within top 10 among all the single-model EMA methods according to GDT-TS score loss. The results demonstrate that inter-residue distance features are valuable inputs for deep learning to predict the quality of protein structural models. However, larger training datasets and better ways of leveraging inter-residue distance information are needed to fully explore its potentials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15

Abstract Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter‐chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and performed very well in estimating the global structure accuracy of assembly models. The average per‐target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per‐target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analyzed. The results demonstrate that combining the multi‐model method (PSS) with the complementary single‐model method (ICPS) is a promising approach to EMA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Smart culture medium optimization for recombinant protein production: Experimental, modeling, and AI/ML-driven strategies

Recombinant protein production (RPP) is central to biotechnology, where recombinant proteins are used as either end products or catalysts in the synthesis of chemicals, fuels, and materials. Among the major cost drivers, culture medium plays a pivotal role in determining protein yield and quality. This review presents a comprehensive perspective on the critical stages of “smart” culture medium optimization: planning, screening, modeling, optimization, and validation. In the planning stage, we examine the nutritional and energetic roles of medium components, including carbon, nitrogen, amino acids, salts, and trace metals, and their impacts on culture parameters such as pH, oxidative state, and osmolality. We highlight the variability in trace metal content due to water sources, culture vessels, and raw materials, which can substantially influence RPP. The screening stage covers Design of Experiments (DoE) approaches, assessing their theoretical basis, implementation, and limitations. For modeling, we describe methods that integrate experimental data to develop predictive models for smart medium formulation. Model-based optimization strategies can then be employed to select optimal media compositions for a given application. The validation stage aims to evaluate model predictions and provide feedback for model training and refinement. Finally, we survey mechanistic and artificial intelligence/machine learning (AI/ML)-driven models as integrated, transformational tools for predictive modeling of bioprocess conditions, nutrient availability, cellular metabolism, and protein quality, with the goal of optimizing culture media to enhance protein yields while reducing costs and environmental impact. We conclude by addressing the challenges of translating laboratory-scale medium optimization to industrial-scale settings and exploring future AI/ML-driven approaches that may overcome current bottlenecks and accelerate medium design for RPP. Overall, this review provides a unified framework for advancing smart medium design in RPP.

Artificial Intelligence/Machine Learning (AI/ML)↗
DOE OSTI2022

Excessive Aurantiochytrium acetophilum docosahexaenoic acid supplementation decreases growth performance and breast muscle mass of broiler chickens

We report Docosahexaenoic acid (DHA) is an n-3 polyunsaturated fatty acid with health-promoting potential. This study was to investigate effects of supplemental DHA from Aurantiochytrium acetophilum on growth performance, health status, meat quality, and protein synthesis signaling of broiler chickens. Day-old male chicks were housed in an environmental control room (6 cages/treatment, 8 chicks/cage), and fed a corn-soybean meal basal diet supplemented with the DHA-rich A. acetophilum biomass (Heliae, Gilbert, AZ) at 0, 1, 2, and 4% (0, 1.7, 3.4 and 6.8 g DHA/kg diet) for 6 weeks. Growth performance was measured weekly. Blood samples were collected at weeks 3 and 6 (2 chicks/cage). Four tissues were sampled (2 chicks/cage) for biochemical and meat quality analyses. Data were analyzed by one-way ANOVA and regression. Compared with the control, the 4% A. acetophilum diet decreased (p < 0.05) body weight gain (19%) and gain to feed ratio (19%) during weeks 4–6. The A. acetophilum supplementation dose-dependently decreased (p < 0.05, R 2 = 0.21–0.54) plasma alanine amino transferase activity and glucose concentrations, but had little effect on plasma activity of alkaline phosphatase or concentrations of inorganic phosphorus and uric acid at weeks 3 and 6. Compared with the control, the 4% A. acetophilum diet decreased (p < 0.05) breast muscle weight by 21%, and down-regulated (p < 0.05) mRNA levels of mammalian target of rapamycin and ribosomal s6 protein (S6), and protein levels of phosphorylated S6 to S6 and phosphorylated S6 kinase beta 1 to S6 kinase beta 1. The A. acetophilum supplementation linearly increased (p < 0.01) lipid peroxidation (R 2 = 0.62–0.90) and hardness and chewiness (R 2 = 0.34–0.44) of breast and thigh muscles. In conclusion, supplemental 4% (6.8 g DHA/kg), but not 1 or 2%, of A. acetophilum impaired growth performance, breast muscle mass accumulation, and(or) protein synthesis signaling of broilers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Effect of High-Protein Diets on Integrated Myofibrillar Protein Synthesis before Anterior Cruciate Ligament Reconstruction: A Randomized Controlled Pilot Study

Increasing dietary protein intake during periods of muscle disuse may mitigate the resulting decline in muscle protein synthesis (MPS). The purpose of this randomized pilot study was to determine the effect of increased protein intake during periods of disuse before anterior cruciate ligament (ACL) reconstruction on myofibrillar protein synthesis (MyoPS), and proteolytic and myogenic gene expression. Six healthy, young males (30 ± 9 y) were randomized to consume a high-quality, optimal protein diet (OP; 1.9 g·kg−1·d−1) or adequate protein diet (AP; 1.2 g·kg−1·d−1) for two weeks before ACL reconstruction. Muscle biopsies collected during surgery were used to measure integrated MyoPS during the intervention (via daily deuterium oxide ingestion) and gene expression at the time of surgery. MyoPS tended to be higher, with a large effect size in OP compared to AP (0.71 ± 0.1 and 0.54 ± 0.1%·d−1; p = 0.076; g = 1.56). Markers of proteolysis and myogenesis were not different between groups (p > 0.05); however, participants with greater MyoPS exhibited lower levels of MuRF1 gene expression compared to those with lower MyoPS (r = −0.82, p = 0.047). The data from this pilot study reveal a potential stimulatory effect of increased daily protein intake on MyoPS during injury-mediated disuse conditions that warrants further investigation.

Howard, Emily E. (ORCID:0000000179791236)↗
DOE OSTI2022

Proteome-scale Structure Prediction Data - Pseudodesulfovibrio mercurii

The number of proteins predicted for Pseudodesulfovibrio mercurii is 3,446, each of which have five predicted structures from an AlphaFold run, as well as structural alignment results using the TMscore-based structural alignment method within the APoc program. Specifically, AlphaFold outputs the atoms and coordinates of the protein model in human-readable PDB files and quantitative prediction metrics in Python PICKLE files. The 5 models have been ranked based on the predicted TM-score (pTMS), a quantitative confidence metric output by AlphaFold that reports on protein model quality. The top ranked model has undergone an energy minimization calculation to relax and remove any potential clashes in the atomic coordinates. Structural alignment results are stored in two files for each protein; the top ranked model (as discussed above) is used for all alignment analyses. Both are compressed gzip files that, once unpacked, are human readable. The first file is the TMalign score results and contains the quantitative metrics for the top alignments between the predicted structure and experimental structures from the PDB70, a curated non-redundant database of about 80,000 experimental structures developed by the Soding lab. Each data point in this file is directly associated with one experimental structure; PDB ID and brief meta-data about the protein taken from the PDB70 file are reported alongside the quantitative metrics. The second results file contains the raw results associated with each alignment reported in the score results file. Specifically, the translation and rotation arrays for each alignment are provided so that the structural alignment can be recreated. Additionally, residue-level scores are reported to quantify the closeness of the aligned residues between the predicted and experimental models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Transforming the Future of Marine Aquaculture: A Circular Economy Approach

By mid-century, society will need to significantly intensify the output of its food production system while simultaneously reducing that system’s detrimental impacts on climate, land use, freshwater resources, and biodiversity. This will require finding alternatives to carbon emissions-intensive agriculture, which provides the backbone of today’s global food production system. Here, we explore the hypothesis that marine algae-based aquaculture can help close the projected gap in society’s future nutritional demands while simultaneously improving environmental sustainability. Food production from marine algae-based aquaculture has the potential to contribute more than the total global protein demand projected for 2050, which ranges from 263.8 Mt/yr to 286.5 Mt/yr. It also offers important nutritional and environmental sustainability advantages relative to terrestrial agriculture. Marine algae can provide a better source of high-quality nutritional protein, essential amino acids, and other micronutrients relative to terrestrial plants. In addition, because marine algae do not require soil, irrigation, and the open application of fertilizer, their cultivation does not need to compete with agriculture for arable land and freshwater nor does it lead to fertilizer runoff and downstream eutrophication. Furthermore, by reducing agriculture’s demand for arable land and freshwater, marine algae-based aquaculture can reduce the pressure for deforestation, potentially leading to globally significant reductions in carbon emissions and biodiversity loss.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Improvement of eukaryotic protein predictions from soil metagenomes

During the last decades, metagenomics has highlighted the diversity of microorganisms from environmental or host-associated samples. Most metagenomics public repositories use annotation pipelines tailored for prokaryotes regardless of the taxonomic origin of contigs. Consequently, eukaryotic contigs with intrinsically different gene features, are not optimally annotated. Using a bioinformatics pipeline, we have filtered 7.9 billion contigs from 6,872 soil metagenomes in the JGI’s IMG/M database to identify eukaryotic contigs. We have re-annotated genes using eukaryote-tailored methods, yielding 8 million eukaryotic proteins and over 300,000 orphan proteins lacking homology in public databases. Comparing the gene predictions we made with initial JGI ones on the same contigs, we confirmed our pipeline improves eukaryotic proteins completeness and contiguity in soil metagenomes. The improved quality of eukaryotic proteins combined with a more comprehensive assignment method yielded more reliable taxonomic annotation. This dataset of eukaryotic soil proteins with improved completeness, quality and taxonomic annotation reliability is of interest for any scientist aiming at studying the composition, biological functions and gene flux in soil communities involving eukaryotes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Kinetic Growth of Multicomponent Microcompartment Shells

An important goal of systems and synthetic biology is to produce high value chemical species in large quantities. Microcompartments, which are protein nanoshells encapsulating catalytic enzyme cargo, could potentially function as tunable nanobioreactors inside and outside cells to generate these high value species. Modifying the morphology of microcompartments through genetic engineering of shell proteins is one viable strategy to tune cofactor and metabolite access to encapsulated enzymes. However, this is a difficult task without understanding how changing interactions between the many different types of shell proteins and enzymes affect microcompartment assembly and shape. Here, we use multiscale molecular dynamics and experimental data to describe assembly pathways available to microcompartments composed of multiple types of shell proteins with varied interactions. As the average interaction between the enzyme cargo and the multiple types of shell proteins is weakened, the shell assembly pathway transitions from (i) nucleating on the enzyme cargo to (ii) nucleating in the bulk and then binding the cargo as it grows to (iii) an empty shell. Atomistic simulations and experiments using the 1,2-propanediol utilization microcompartment system demonstrate that shell protein interactions are highly varied and consistent with our multicomponent, coarse-grained model. Furthermore, our results suggest that intrinsic bending angles control the size of these microcompartments. Altogether, our simulations and experiments provide guidance to control microcomparmtent size and assembly by modulating the interactions between shell proteins.

defects↗
DOE OSTI2021

The cardiac molecular setting of metabolic syndrome in pigs reveals disease susceptibility and suggests mechanisms that exacerbate COVID-19 outcomes in patients

Abstract Although metabolic syndrome (MetS) is linked to an elevated risk of cardiovascular disease (CVD), the cardiac-specific risk mechanism is unknown. Obesity, hypertension, and diabetes (all MetS components) are the most common form of CVD and represent risk factors for worse COVID-19 outcomes compared to their non MetS peers. Here, we use obese Yorkshire pigs as a highly relevant animal model of human MetS, where pigs develop the hallmarks of human MetS and reproducibly mimics the myocardial pathophysiology in patients. Myocardium-specific mass spectroscopy-derived metabolomics, proteomics, and transcriptomics enabled the identity and quality of proteins and metabolites to be investigated in the myocardium to greater depth. Myocardium-specific deregulation of pro-inflammatory markers, propensity for arterial thrombosis, and platelet aggregation was revealed by computational analysis of differentially enriched pathways between MetS and control animals. While key components of the complement pathway and the immune response to viruses are under expressed, key N6-methyladenosin RNA methylation enzymes are largely overexpressed in MetS. Blood tests do not capture the entirety of metabolic changes that the myocardium undergoes, making this analysis of greater value than blood component analysis alone. Our findings create data associations to further characterize the MetS myocardium and disease vulnerability, emphasize the need for a multimodal therapeutic approach, and suggests a mechanism for observed worse outcomes in MetS patients with COVID-19 comorbidity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Powdery mildew effectors AVR A1 and BEC1016 target the ER J‐domain protein Hv ERdj3B required for immunity in barley

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 AVR A1 . Here, we show that AVR A1 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 AVR A1 and BEC1016, the endoplasmic reticulum (ER)‐localized J‐domain protein Hv ERdj3B. Silencing of this ER quality control (ERQC) protein increased Bh penetration. Hv ERdj3B is ER luminal, and we showed using split GFP that AVR A1 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 Hv ERdj3B 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 Hv ERdj3B appears to be essential and can be regulated by AVR A1 and BEC1016. Plant disease resistance often occurs upon direct or indirect recognition of pathogen effectors by host NLR receptors. Previous work has shown that AVR A1 is directly recognized in the cytosol by the immune receptor MLA1. We speculate that the AVR A1 J‐domain target being inside the ER, where it is inapproachable by NLRs, has forced the plant to evolve this challenging direct recognition.

54 ENVIRONMENTAL SCIENCES↗