Light cues induce protective anticipation of environmental water loss in terrestrial bacteria.

The ecological significance of light perception in nonphotosynthetic bacteria remains largely elusive. In terrestrial environments, diurnal oscillations in light are often temporally coupled to other environmental changes, including increased temperature and evaporation. Here, we report that light functions as an anticipatory cue that triggers protective adaptations to tolerate a future rapid loss of environmental water. We demonstrate this photo-anticipatory stress tolerance in leaf-associated Pseudomonas syringae pv. syringae (Pss) and other plant- and soil-associated pseudomonads. We found that light influences the expression of 30% of the Pss genome, indicating that light is a global regulatory signal, and this signaling occurs almost entirely via a bacteriophytochrome photoreceptor that senses red, far-red, and blue wavelengths. Bacteriophytochrome-mediated light control disproportionally up-regulates water-stress adaptation functions and confers enhanced fitness when cells encounter light prior to water limitation. Given the rapid speed at which water can evaporate from leaf surfaces, such anticipatory activation of a protective response enhances fitness beyond that of a reactive stress response alone, with recurring diurnal wet-dry cycles likely further amplifying the fitness advantage over time. These findings demonstrate that nonphotosynthetic bacteria can use light as a cue to mount an adaptive anticipatory response against a physiologically unrelated but ecologically coupled stress.

Adjusting for gene-specific covariates to improve RNA-seq analysis.

SUMMARY: This article suggests a novel positive false discovery rate (pFDR) controlling method for testing gene-specific hypotheses using a gene-specific covariate variable, such as gene length. We suppose the null probability depends on the covariate variable. In this context, we propose a rejection rule that accounts for heterogeneity among tests by using two distinct types of null probabilities. We establish a pFDR estimator for a given rejection rule by following Storey's q-value framework. A condition on a type 1 error posterior probability is provided that equivalently characterizes our rejection rule. We also present a suitable procedure for selecting a tuning parameter through cross-validation that maximizes the expected number of hypotheses declared significant. A simulation study demonstrates that our method is comparable to or better than existing methods across realistic scenarios. In data analysis, we find support for our method's premise that the null probability varies with a gene-specific covariate variable. AVAILABILITY AND IMPLEMENTATION: The source code repository is publicly available at https://github.com/hsjeon1217/conditional_method.

High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants.

High-throughput plant phenotyping-the use of imaging and remote sensing to record plant growth dynamics-is becoming more widely used. The first step in this process is typically plant segmentation, which requires a well-labeled training dataset to enable accurate segmentation of overlapping plants. However, preparing such training data is both time and labor intensive. To solve this problem, we propose a plant image processing pipeline using a self-supervised sequential convolutional neural network method for in-field phenotyping systems. This first step uses plant pixels from greenhouse images to segment nonoverlapping in-field plants in an early growth stage and then applies the segmentation results from those early-stage images as training data for the separation of plants at later growth stages. The proposed pipeline is efficient and self-supervising in the sense that no human-labeled data are needed. We then combine this approach with functional principal components analysis to reveal the relationship between the growth dynamics of plants and genotypes. We show that the proposed pipeline can accurately separate the pixels of foreground plants and estimate their heights when foreground and background plants overlap and can thus be used to efficiently assess the impact of treatments and genotypes on plant growth in a field environment by computer vision techniques. This approach should be useful for answering important scientific questions in the area of high-throughput phenotyping.

"Just right" combinations of adjuvants with nanoscale carriers activate aged dendritic cells without overt inflammation.

BACKGROUND: The loss in age-related immunological markers, known as immunosenescence, is caused by a combination of factors, one of which is inflammaging. Inflammaging is associated with the continuous basal generation of proinflammatory cytokines. Studies have demonstrated that inflammaging reduces the effectiveness of vaccines. Strategies aimed at modifying baseline inflammation are being developed to improve vaccination responses in older adults. Dendritic cells have attracted attention as an age-specific target because of their significance in immunization as antigen presenting cells that stimulate T lymphocytes. RESULTS: In this study, bone marrow derived dendritic cells (BMDCs) were generated from aged mice and used to investigate the effects of combinations of adjuvants, including Toll-like receptor, NOD2, and STING agonists with polyanhydride nanoparticles and pentablock copolymer micelles under in vitro conditions. Cellular stimulation was characterized via expression of costimulatory molecules, T cell-activating cytokines, proinflammatory cytokines, and chemokines. Our results indicate that multiple TLR agonists substantially increase costimulatory molecule expression and cytokines associated with T cell activation and inflammation in culture. In contrast, NOD2 and STING agonists had only a moderate effect on BMDC activation, while nanoparticles and micelles had no effect by themselves. However, when nanoparticles and micelles were combined with a TLR9 agonist, a reduction in the production of proinflammatory cytokines was observed while maintaining increased production of T cell activating cytokines and enhancing cell surface marker expression. Additionally, combining nanoparticles and micelles with a STING agonist resulted in a synergistic impact on the upregulation of costimulatory molecules and an increase in cytokine secretion from BMDCs linked with T cell activation without excessive secretion of proinflammatory cytokines. CONCLUSIONS: These studies provide new insights into rational adjuvant selection for vaccines for older adults. Combining appropriate adjuvants with nanoparticles and micelles may lead to balanced immune activation characterized by low inflammation, setting the stage for designing next generation vaccines that can induce mucosal immunity in older adults.

Single-parent expression complementation contributes to phenotypic heterosis in maize hybrids.

The dominance model of heterosis explains the superior performance of F1-hybrids via the complementation of deleterious alleles by beneficial alleles in many genes. Genes active in one parent but inactive in the second lead to single-parent expression (SPE) complementation in maize (Zea mays L.) hybrids. In this study, SPE complementation resulted in approximately 700 additionally active genes in different tissues of genetically diverse maize hybrids on average. We established that the number of SPE genes is significantly associated with mid-parent heterosis (MPH) for all surveyed phenotypic traits. In addition, we highlighted that maternally (SPE_B) and paternally (SPE_X) active SPE genes enriched in gene co-expression modules are highly correlated within each SPE type but separated between these two SPE types. While SPE_B-enriched co-expression modules are positively correlated with phenotypic traits, SPE_X-enriched modules displayed a negative correlation. Gene ontology term enrichment analyses indicated that SPE_B patterns are associated with growth and development, whereas SPE_X patterns are enriched in defense and stress response. In summary, these results link the degree of phenotypic MPH to the prevalence of gene expression complementation observed by SPE, supporting the notion that hybrids benefit from SPE complementation via its role in coordinating maize development in fluctuating environments.

KAT4IA: K-Means Assisted Training for Image Analysis of Field-Grown Plant Phenotypes.

High-throughput phenotyping enables the efficient collection of plant trait data at scale. One example involves using imaging systems over key phases of a crop growing season. Although the resulting images provide rich data for statistical analyses of plant phenotypes, image processing for trait extraction is required as a prerequisite. Current methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised data. Unfortunately, preparing a sufficiently large training data is both time and labor-intensive. We describe a self-supervised pipeline (KAT4IA) that uses K-means clustering on greenhouse images to construct training data for extracting and analyzing plant traits from an image-based field phenotyping system. The KAT4IA pipeline includes these main steps: self-supervised training set construction, plant segmentation from images of field-grown plants, automatic separation of target plants, calculation of plant traits, and functional curve fitting of the extracted traits. To deal with the challenge of separating target plants from noisy backgrounds in field images, we describe a novel approach using row-cuts and column-cuts on images segmented by transform domain neural network learning, which utilizes plant pixels identified from greenhouse images to train a segmentation model for field images. This approach is efficient and does not require human intervention. Our results show that KAT4IA is able to accurately extract plant pixels and estimate plant heights.

rmRNAseq: differential expression analysis for repeated-measures RNA-seq data.

MOTIVATION: With the reduction in price of next-generation sequencing technologies, gene expression profiling using RNA-seq has increased the scope of sequencing experiments to include more complex designs, such as designs involving repeated measures. In such designs, RNA samples are extracted from each experimental unit at multiple time points. The read counts that result from RNA sequencing of the samples extracted from the same experimental unit tend to be temporally correlated. Although there are many methods for RNA-seq differential expression analysis, existing methods do not properly account for within-unit correlations that arise in repeated-measures designs. RESULTS: We address this shortcoming by using normalized log-transformed counts and associated precision weights in a general linear model pipeline with continuous autoregressive structure to account for the correlation among observations within each experimental unit. We then utilize parametric bootstrap to conduct differential expression inference. Simulation studies show the advantages of our method over alternatives that do not account for the correlation among observations within experimental units. AVAILABILITY AND IMPLEMENTATION: We provide an R package rmRNAseq implementing our proposed method (function TC_CAR1) at https://cran.r-project.org/web/packages/rmRNAseq/index.html. Reproducible R codes for data analysis and simulation are available at https://github.com/ntyet/rmRNAseq/tree/master/simulation.

Assessing plant performance in the Enviratron.

BACKGROUND: Assessing the impact of the environment on plant performance requires growing plants under controlled environmental conditions. Plant phenotypes are a product of genotype × environment (G × E), and the Enviratron at Iowa State University is a facility for testing under controlled conditions the effects of the environment on plant growth and development. Crop plants (including maize) can be grown to maturity in the Enviratron, and the performance of plants under different environmental conditions can be monitored 24 h per day, 7 days per week throughout the growth cycle. RESULTS: The Enviratron is an array of custom-designed plant growth chambers that simulate different environmental conditions coupled with precise sensor-based phenotypic measurements carried out by a robotic rover. The rover has workflow instructions to periodically visit plants growing in the different chambers where it measures various growth and physiological parameters. The rover consists of an unmanned ground vehicle, an industrial robotic arm and an array of sensors including RGB, visible and near infrared (VNIR) hyperspectral, thermal, and time-of-flight (ToF) cameras, laser profilometer and pulse-amplitude modulated (PAM) fluorometer. The sensors are autonomously positioned for detecting leaves in the plant canopy, collecting various physiological measurements based on computer vision algorithms and planning motion via "eye-in-hand" movement control of the robotic arm. In particular, the automated leaf probing function that allows the precise placement of sensor probes on leaf surfaces presents a unique advantage of the Enviratron system over other types of plant phenotyping systems. CONCLUSIONS: The Enviratron offers a new level of control over plant growth parameters and optimizes positioning and timing of sensor-based phenotypic measurements. Plant phenotypes in the Enviratron are measured in situ-in that the rover takes sensors to the plants rather than moving plants to the sensors.

Correction to: small RNA discovery in the interaction between barley and the powdery mildew pathogen.

Following the publication of the original article [1], the authors noted several typesetting errors which are noted in this Correction article.

Small RNA discovery in the interaction between barley and the powdery mildew pathogen.

BACKGROUND: Plants encounter pathogenic and non-pathogenic microorganisms on a nearly constant basis. Small RNAs such as siRNAs and miRNAs/milRNAs influence pathogen virulence and host defense responses. We exploited the biotrophic interaction between the powdery mildew fungus, Blumeria graminis f. sp. hordei (Bgh), and its diploid host plant, barley (Hordeum vulgare) to explore fungal and plant sRNAs expressed during Bgh infection of barley leaf epidermal cells. RESULTS: RNA was isolated from four fast-neutron immune-signaling mutants and their progenitor over a time course representing key stages of Bgh infection, including appressorium formation, penetration of epidermal cells, and development of haustorial feeding structures. The Cereal Introduction (CI) 16151 progenitor carries the resistance allele Mla6, while Bgh isolate 5874 harbors the AVRa6 avirulence effector, resulting in an incompatible interaction. Parallel Analysis of RNA Ends (PARE) was used to verify sRNAs with likely transcript targets in both barley and Bgh. Bgh sRNAs are predicted to regulate effectors, metabolic genes, and translation-related genes. Barley sRNAs are predicted to influence the accumulation of transcripts that encode auxin response factors, NAC transcription factors, homeodomain transcription factors, and several splicing factors. We also identified phasing small interfering RNAs (phasiRNAs) in barley that overlap transcripts that encode receptor-like kinases (RLKs) and nucleotide-binding, leucine-rich domain proteins (NLRs). CONCLUSIONS: These data suggest that Bgh sRNAs regulate gene expression in metabolism, translation-related, and pathogen effectors. PARE-validated targets of predicted Bgh milRNAs include both EKA (effectors homologous to AVRk1 and AVRa10) and CSEP (candidate secreted effector protein) families. We also identified barley phasiRNAs and miRNAs in response to Bgh infection. These include phasiRNA loci that overlap with a significant proportion of receptor-like kinases, suggesting an additional sRNA control mechanism may be active in barley leaves as opposed to predominant R-gene phasiRNA overlap in many eudicots. In addition, we identified conserved miRNAs, novel miRNA candidates, and barley genome mapped sRNAs that have PARE validated transcript targets in barley. The miRNA target transcripts are enriched in transcription factors, signaling-related proteins, and photosynthesis-related proteins. Together these results suggest both barley and Bgh control metabolism and infection-related responses via the specific accumulation and targeting of genes via sRNAs.

Fully Bayesian analysis of RNA-seq counts for the detection of gene expression heterosis.

Heterosis, or hybrid vigor, is the enhancement of the phenotype of hybrid progeny relative to their inbred parents. Heterosis is extensively used in agriculture, and the underlying mechanisms are unclear. To investigate the molecular basis of phenotypic heterosis, researchers search tens of thousands of genes for heterosis with respect to expression in the transcriptome. Difficulty arises in the assessment of heterosis due to composite null hypotheses and non-uniform distributions for p-values under these null hypotheses. Thus, we develop a general hierarchical model for count data and a fully Bayesian analysis in which an efficient parallelized Markov chain Monte Carlo algorithm ameliorates the computational burden. We use our method to detect gene expression heterosis in a two-hybrid plant-breeding scenario, both in a real RNA-seq maize dataset and in simulation studies. In the simulation studies, we show our method has well-calibrated posterior probabilities and credible intervals when the model assumed in analysis matches the model used to simulate the data. Although model misspecification can adversely affect calibration, the methodology is still able to accurately rank genes. Finally, we show that hyperparameter posteriors are extremely narrow and an empirical Bayes (eBayes) approach based on posterior means from the fully Bayesian analysis provides virtually equivalent posterior probabilities, credible intervals, and gene rankings relative to the fully Bayesian solution. This evidence of equivalence provides support for the use of eBayes procedures in RNA-seq data analysis if accurate hyperparameter estimates can be obtained.

Stress response to CO2 deprivation by Arabidopsis thaliana in plant cultures.

After being the standard plant propagation protocol for decades, cultures of Arabidopsis thaliana sealed with Parafilm remain common today out of practicality, habit, or necessity (as in co-cultures with microorganisms). Regardless of concerns over the aeration of these cultures, no investigation has explored the CO2 transport inside these cultures and its effect on the plants. Thereby, it was impossible to assess whether Parafilm-seals used today or in thousands of older papers in the literature constitute a treatment, and whether this treatment could potentially affect the study of other treatments.For the first time we report the CO2 concentrations in Parafilm-sealed cultures of A. thaliana with a 1 minute temporal resolution, and the transcriptome comparison with aerated cultures. The data show significant CO2 deprivation to the plants, a drastic suppression of photosynthesis, respiration, starch accumulation, chlorophyll biosynthesis, and an increased accumulation of reactive oxygen species. Most importantly, CO2 deprivation occurs as soon as the cotyledons emerge. Gene expression analysis indicates a significant alteration of 35% of the pathways when compared to aerated cultures, especially in stress response and secondary metabolism processes. On the other hand, the observed increase in the production of glucosinolates and flavonoids suggests intriguing possibilities for CO2 deprivation as an organic biofortification treatment in high-value crops.

QQS orphan gene and its interactor NF-YC4 reduce susceptibility to pathogens and pests.

Enhancing the nutritional quality and disease resistance of crops without sacrificing productivity is a key issue for developing varieties that are valuable to farmers and for simultaneously improving food security and sustainability. Expression of the Arabidopsis thaliana species-specific AtQQS (Qua-Quine Starch) orphan gene or its interactor, NF-YC4 (Nuclear Factor Y, subunit C4), has been shown to increase levels of leaf/seed protein without affecting the growth and yield of agronomic species. Here, we demonstrate that overexpression of AtQQS and NF-YC4 in Arabidopsis and soybean enhances resistance/reduces susceptibility to viruses, bacteria, fungi, aphids and soybean cyst nematodes. A series of Arabidopsis mutants in starch metabolism were used to explore the relationships between QQS expression, carbon and nitrogen partitioning, and defense. The enhanced basal defenses mediated by QQS were independent of changes in protein/carbohydrate composition of the plants. We demonstrate that either AtQQS or NF-YC4 overexpression in Arabidopsis and in soybean reduces susceptibility of these plants to pathogens/pests. Transgenic soybean lines overexpressing NF-YC4 produce seeds with increased protein while maintaining healthy growth. Pull-down studies reveal that QQS interacts with human NF-YC, as well as with Arabidopsis NF-YC4, and indicate two QQS binding sites near the NF-YC-histone-binding domain. A new model for QQS interaction with NF-YC is speculated. Our findings illustrate the potential of QQS and NF-YC4 to increase protein and improve defensive traits in crops, overcoming the normal growth-defense trade-offs.

Empirical Comparisons of Different Statistical Models To Identify and Validate Kernel Row Number-Associated Variants from Structured Multi-parent Mapping Populations of Maize.

Advances in next generation sequencing technologies and statistical approaches enable genome-wide dissection of phenotypic traits via genome-wide association studies (GWAS). Although multiple statistical approaches for conducting GWAS are available, the power and cross-validation rates of many approaches have been mostly tested using simulated data. Empirical comparisons of single variant (SV) and multi-variant (MV) GWAS approaches have not been conducted to test if a single approach or a combination of SV and MV is effective, through identification and cross-validation of trait-associated loci. In this study, kernel row number (KRN) data were collected from a set of 6,230 entries derived from the Nested Association Mapping (NAM) population and related populations. Three different types of GWAS analyses were performed: 1) single-variant (SV), 2) stepwise regression (STR) and 3) a Bayesian-based multi-variant (BMV) model. Using SV, STR, and BMV models, 257, 300, and 442 KRN-associated variants (KAVs) were identified in the initial GWAS analyses. Of these, 231 KAVs were subjected to genetic validation using three unrelated populations that were not included in the initial GWAS. Genetic validation results suggest that the three GWAS approaches are complementary. Interestingly, KAVs in low recombination regions were more likely to exhibit associations in independent populations than KAVs in recombinationally active regions, probably as a consequence of linkage disequilibrium. The KAVs identified in this study have the potential to enhance our understanding of the genetic basis of ear development.

Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning.

The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets.

A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets.

BACKGROUND: Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The relationships among gene categories induce logical restrictions among the corresponding null hypotheses. An existing fully Bayesian method is powerful but computationally demanding. RESULTS: We develop a computationally efficient method based on a hidden Markov tree model (HMTM). Our method is several orders of magnitude faster than the existing fully Bayesian method. Through simulation and an expression quantitative trait loci study, we show that the HMTM method provides more powerful results than other existing methods that honor the logical restrictions. CONCLUSIONS: The HMTM method provides an individual estimate of posterior probability of being differentially expressed for each gene set, which can be useful for result interpretation. The R package can be found on https://github.com/k22liang/HMTGO .

Single-Parent Expression Is a General Mechanism Driving Extensive Complementation of Non-syntenic Genes in Maize Hybrids.

Maize (Zea mays L.) displays an exceptional degree of structural genomic diversity [1, 2]. In addition, variation in gene expression further contributes to the extraordinary phenotypic diversity and plasticity of maize. This study provides a systematic investigation on how distantly related homozygous maize inbred lines affect the transcriptomic plasticity of their highly heterozygous F1 hybrids. The classical dominance model of heterosis explains the superiority of hybrid plants by the complementation of deleterious parental alleles by superior alleles of the second parent at many loci [3]. Genes active in one inbred line but inactive in another represent an extreme instance of allelic diversity defined as single-parent expression [4]. We observed on average ∼1,000 such genes in all inbred line combinations during primary root development. These genes consistently displayed expression complementation (i.e., activity) in their hybrid progeny. Consequently, extreme expression complementation is a general mechanism that results on average in ∼600 additionally active genes and their encoded biological functions in hybrids. The modern maize genome is complemented by a set of non-syntenic genes, which emerged after the separation of the maize and sorghum lineages and lack syntenic orthologs in any other grass species [5]. We demonstrated that non-syntenic genes are the driving force of gene expression complementation in hybrids. Among those, the highly diversified families of bZIP and bHLH transcription factors [6] are systematically overrepresented. In summary, extreme gene expression complementation extensively shapes the transcriptomic plasticity of maize hybrids and might therefore be one factor controlling the developmental plasticity of hybrids.

Distinct genetic architectures for phenotype means and plasticities in Zea mays.

Phenotypic plasticity describes the phenotypic variation of a trait when a genotype is exposed to different environments. Understanding the genetic control of phenotypic plasticity in crops such as maize is of paramount importance for maintaining and increasing yields in a world experiencing climate change. Here, we report the results of genome-wide association analyses of multiple phenotypes and two measures of phenotypic plasticity in a maize nested association mapping (US-NAM) population grown in multiple environments and genotyped with ~2.5 million single-nucleotide polymorphisms. We show that across all traits the candidate genes for mean phenotype values and plasticity measures form structurally and functionally distinct groups. Such independent genetic control suggests that breeders will be able to select semi-independently for mean phenotype values and plasticity, thereby generating varieties with both high mean phenotype values and levels of plasticity that are appropriate for the target performance environments.

Substantial contribution of genetic variation in the expression of transcription factors to phenotypic variation revealed by eRD-GWAS.

BACKGROUND: There are significant limitations in existing methods for the genome-wide identification of genes whose expression patterns affect traits. RESULTS: The transcriptomes of five tissues from 27 genetically diverse maize inbred lines were deeply sequenced to identify genes exhibiting high and low levels of expression variation across tissues or genotypes. Transcription factors are enriched among genes with the most variation in expression across tissues, as well as among genes with higher-than-median levels of variation in expression across genotypes. In contrast, transcription factors are depleted among genes whose expression is either highly stable or highly variable across genotypes. We developed a Bayesian-based method for genome-wide association studies (GWAS) in which RNA-seq-based measures of transcript accumulation are used as explanatory variables (eRD-GWAS). The ability of eRD-GWAS to identify true associations between gene expression variation and phenotypic diversity is supported by analyses of RNA co-expression networks, protein-protein interaction networks, and gene regulatory networks. Genes associated with 13 traits were identified using eRD-GWAS on a panel of 369 maize inbred lines. Predicted functions of many of the resulting trait-associated genes are consistent with the analyzed traits. Importantly, transcription factors are significantly enriched among trait-associated genes identified with eRD-GWAS. CONCLUSIONS: eRD-GWAS is a powerful tool for associating genes with traits and is complementary to SNP-based GWAS. Our eRD-GWAS results are consistent with the hypothesis that genetic variation in transcription factor expression contributes substantially to phenotypic diversity.

Interchromosomal Transfer of Immune Regulation During Infection of Barley with the Powdery Mildew Pathogen.

Powdery mildew pathogens colonize over 9500 plant species, causing critical yield loss. The Ascomycete fungus, Blumeria graminis f. sp. hordei (Bgh), causes powdery mildew disease in barley (Hordeum vulgare L.). Successful infection begins with penetration of host epidermal cells, culminating in haustorial feeding structures, facilitating delivery of fungal effectors to the plant and exchange of nutrients from host to pathogen. We used expression Quantitative Trait Locus (eQTL) analysis to dissect the temporal control of immunity-associated gene expression in a doubled haploid barley population challenged with Bgh Two highly significant regions possessing trans eQTL were identified near the telomeric ends of chromosomes (Chr) 2HL and 1HS. Within these regions reside diverse resistance loci derived from barley landrace H. laevigatum (MlLa) and H. vulgare cv. Algerian (Mla1), which associate with the altered expression of 961 and 3296 genes during fungal penetration of the host and haustorial development, respectively. Regulatory control of transcript levels for 299 of the 961 genes is reprioritized from MlLa on 2HL to Mla1 on 1HS as infection progresses, with 292 of the 299 alternating the allele responsible for higher expression, including Adaptin Protein-2 subunit µ AP2M and Vesicle Associated Membrane Protein VAMP72 subfamily members VAMP721/722. AP2M mediates effector-triggered immunity (ETI) via endocytosis of plasma membrane receptor components. VAMP721/722 and SNAP33 form a Soluble N-ethylmaleimide-sensitive factor Attachment Protein REceptor (SNARE) complex with SYP121 (PEN1), which is engaged in pathogen associated molecular pattern (PAMP)-triggered immunity via exocytosis. We postulate that genes regulated by alternate chromosomal positions are repurposed as part of a conserved immune complex to respond to different pathogen attack scenarios.