Natural variation in LONELY GUY-Like 1 regulates rice grain weight under warmer night conditions.

Global nighttime temperatures are rising at twice the rate of daytime temperatures and pose a challenge for rice (Oryza sativa) production. High nighttime temperature (HNT) stress affects rice yield by reducing grain weight, size, and fertility. Although the genes associated with these yield parameters have been identified and characterized under normal temperatures, the genetic basis of grain weight regulation under HNT stress remains less explored. We examined the natural variation for rice single grain weight (SGW) under HNT stress imposed during grain development. A genome-wide association analysis identified several loci associated with grain weight under HNT stress. A locus, single grain weight 1 (SGW1), specific to HNT conditions resolved to LONELY GUY-Like 1 (LOGL1), which encodes a putative cytokinin activation enzyme. We demonstrated that LOGL1 contributes to allelic variation at SGW1. Accessions with lower LOGL1 transcript abundance had higher grain weight under HNT. This was supported by higher grain weight of logl1 mutants relative to the wild type under HNT. Compared to logl1 mutants, LOGL1 over-expressers showed increased sensitivity to HNT. We showed that LOGL1 regulates the thiamine biosynthesis pathway, which is under circadian regulation, which in turn is likely perturbed by HNT stress. These findings provide a genetic source to enhance rice adaptation to warming night temperatures and improve our mechanistic understanding of HNT stress tolerance pathways.

Tissue- and time-dependent metabolite profiles during early grain development under normal and high night-time temperature conditions.

BACKGROUND: Wheat grain development in the first few days after pollination determines the number of endosperm cells that influence grain yield potential and is susceptible to various environmental conditions, including high night temperatures (HNTs). Flag leaves and seed-associated bracts (glumes, awn, palea, and lemma) provide nutrients to the developing seed. However, the specific metabolic roles of these tissues are uncertain, especially their dynamics at different developmental stages and the time in a day. Tissue- and time-dependent metabolite profiling may hint at the metabolic roles of tissues and the mechanisms of how HNTs affect daytime metabolic status in early grain development. RESULTS: The metabolite profiles of flag leaf, bract, seed (embryo and endosperm), and entire spike were analyzed at 12:00 (day) and 23:00 (night) on 2, 4, and 6 days after fertilization under control and HNT conditions. The metabolite levels in flag leaves and bracts showed day/night oscillations, while their behaviors were distinct between the tissues. Some metabolites, such as sucrose, cellobiose, and succinic acid, showed contrasting oscillations in the two photosynthetic tissues. In contrast, seed metabolite levels differed due to the days after fertilization rather than the time in a day. The seed metabolite profile altered earlier in the HNT than in the control condition, likely associated with accelerated grain development caused by HNT. HNT also disrupted the day/night oscillation of sugar accumulation in flag leaves and bracts. CONCLUSIONS: These results highlight distinct metabolic roles of flag leaves and bracts during wheat early seed development. The seed metabolite levels are related to the developmental stages. The early metabolic events in the seeds and the disruption of the day/night metabolic cycle in photosynthetic tissues may partly explain the adverse effects of HNT on grain yield.

Stress-induced deeper rooting introgression enhances wheat yield under terminal drought.

Water scarcity is the primary environmental constraint affecting wheat growth and production and is increasingly exacerbated due to climatic fluctuation, which jeopardizes future food security. Most breeding efforts to improve wheat yields under drought have focused on above-ground traits. Root traits are closely associated with various drought adaptability mechanisms, but the genetic variation underlying these traits remains untapped, even though it holds tremendous potential for improving crop resilience. Here, we examined this potential by re-introducing ancestral alleles from wild emmer wheat (Triticum turgidum ssp. dicoccoides) and studied their impact on root architecture diversity under terminal drought stress. We applied an active sensing electrical resistivity tomography approach to compare a wild emmer introgression line (IL20) and its drought-sensitive recurrent parent (Svevo) under field conditions. IL20 exhibited greater root elongation under drought, which resulted in higher root water uptake from deeper soil layers. This advantage initiated at the pseudo-stem stage and increased during the transition to the reproductive stage. The increased water uptake promoted higher gas exchange rates and enhanced grain yield under drought. Overall, we show that this presumably 'lost' drought-induced mechanism of deeper rooting profile can serve as a breeding target to improve wheat productiveness under changing climate.

Leaf-Counting in Monocot Plants Using Deep Regression Models.

Leaf numbers are vital in estimating the yield of crops. Traditional manual leaf-counting is tedious, costly, and an enormous job. Recent convolutional neural network-based approaches achieve promising results for rosette plants. However, there is a lack of effective solutions to tackle leaf counting for monocot plants, such as sorghum and maize. The existing approaches often require substantial training datasets and annotations, thus incurring significant overheads for labeling. Moreover, these approaches can easily fail when leaf structures are occluded in images. To address these issues, we present a new deep neural network-based method that does not require any effort to label leaf structures explicitly and achieves superior performance even with severe leaf occlusions in images. Our method extracts leaf skeletons to gain more topological information and applies augmentation to enhance structural variety in the original images. Then, we feed the combination of original images, derived skeletons, and augmentations into a regression model, transferred from Inception-Resnet-V2, for leaf-counting. We find that leaf tips are important in our regression model through an input modification method and a Grad-CAM method. The superiority of the proposed method is validated via comparison with the existing approaches conducted on a similar dataset. The results show that our method does not only improve the accuracy of leaf-counting, with overlaps and occlusions, but also lower the training cost, with fewer annotations compared to the previous state-of-the-art approaches.The robustness of the proposed method against the noise effect is also verified by removing the environmental noises during the image preprocessing and reducing the effect of the noises introduced by skeletonization, with satisfactory outcomes.

Graph Convolutional Network Using Adaptive Neighborhood Laplacian Matrix for Hyperspectral Images with Application to Rice Seed Image Classification.

Graph convolutional neural network architectures combine feature extraction and convolutional layers for hyperspectral image classification. An adaptive neighborhood aggregation method based on statistical variance integrating the spatial information along with the spectral signature of the pixels is proposed for improving graph convolutional network classification of hyperspectral images. The spatial-spectral information is integrated into the adjacency matrix and processed by a single-layer graph convolutional network. The algorithm employs an adaptive neighborhood selection criteria conditioned by the class it belongs to. Compared to fixed window-based feature extraction, this method proves effective in capturing the spectral and spatial features with variable pixel neighborhood sizes. The experimental results from the Indian Pines, Houston University, and Botswana Hyperion hyperspectral image datasets show that the proposed AN-GCN can significantly improve classification accuracy. For example, the overall accuracy for Houston University data increases from 81.71% (MiniGCN) to 97.88% (AN-GCN). Furthermore, the AN-GCN can classify hyperspectral images of rice seeds exposed to high day and night temperatures, proving its efficacy in discriminating the seeds under increased ambient temperature treatments.

A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures.

A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature exposure durations during day, night, and both day and night. A stand-alone application with Graphical User Interfaces (GUI) for calibrating, preprocessing, and classification of hyperspectral rice seed images is presented. The software application can be used for training two deep learning architectures for the classification of any type of hyperspectral seed images. The average overall classification accuracy of 91.33% and 89.50% is obtained for seed-based classification using 3D-CNN for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The DNN gives an average accuracy of 94.83% and 91% for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The accuracies obtained are higher than those presented in the literature for hyperspectral rice seed image classification. The HSI analysis presented here is on the Kitaake cultivar, which can be extended to study the temperature tolerance of other rice cultivars.

Wild emmer introgression alters root-to-shoot growth dynamics in durum wheat in response to water stress.

Water deficit during the early vegetative growth stages of wheat (Triticum) can limit shoot growth and ultimately impact grain productivity. Introducing diversity in wheat cultivars to enhance the range of phenotypic responses to water limitations during vegetative growth can provide potential avenues for mitigating subsequent yield losses. We tested this hypothesis in an elite durum wheat background by introducing a series of introgressions from a wild emmer (Triticum turgidum ssp. dicoccoides) wheat. Wild emmer populations harbor rich phenotypic diversity for drought-adaptive traits. To determine the effect of these introgressions on vegetative growth under water-limited conditions, we used image-based phenotyping to catalog divergent growth responses to water stress ranging from high plasticity to high stability. One of the introgression lines exhibited a significant shift in root-to-shoot ratio in response to water stress. We characterized this shift by combining genetic analysis and root transcriptome profiling to identify candidate genes (including a root-specific kinase) that may be linked to the root-to-shoot carbon reallocation under water stress. Our results highlight the potential of introducing functional diversity into elite durum wheat for enhancing the range of water stress adaptation.

Modifying root-to-shoot ratio improves root water influxes in wheat under drought stress.

Drought intensity as experienced by plants depends upon soil moisture status and atmospheric variables such as temperature, radiation, and air vapour pressure deficit. Although the role of shoot architecture with these edaphic and atmospheric factors is well characterized, the extent to which shoot and root dynamic interactions as a continuum are controlled by genotypic variation is less well known. Here, we targeted these interactions using a wild emmer wheat introgression line (IL20) with a distinct drought-induced shift in the shoot-to-root ratio and its drought-sensitive recurrent parent Svevo. Using a gravimetric platform, we show that IL20 maintained higher root water influx and gas exchange under drought stress, which supported a greater growth. Interestingly, the advantage of IL20 in root water influx and transpiration was expressed earlier during the daily diurnal cycle under lower vapour pressure deficit and therefore supported higher transpiration efficiency. Application of a structural equation model indicates that under drought, vapour pressure deficit and radiation are antagonistic to transpiration rate, whereas the root water influx operates as a feedback for the higher atmospheric responsiveness of leaves. Collectively, our results suggest that a drought-induced shift in root-to-shoot ratio can improve plant water uptake potential in a short preferable time window during early morning when vapour pressure deficit is low and the light intensity is not a limiting factor for assimilation.

Genome-wide discovery of natural variation in pre-mRNA splicing and prioritising causal alternative splicing to salt stress response in rice.

Pre-mRNA splicing is an essential step for the regulation of gene expression. In order to specifically capture splicing variants in plants for genome-wide association studies (GWAS), we developed a software tool to quantify and visualise Variations of Splicing in Population (VaSP). VaSP can quantify splicing variants from short-read RNA-seq datasets and discover genotype-specific splicing (GSS) events, which can be used to prioritise causal pre-mRNA splicing events in GWAS. We applied our method to an RNA-seq dataset with 328 samples from 82 genotypes from a rice diversity panel exposed to optimal and saline growing conditions. In total, 764 significant GSS events were identified in salt stress conditions. GSS events were used as markers for a GWAS with the shoot Na+ accumulation, which identified six GSS events in five genes significantly associated with the shoot Na+ content. Two of these genes, OsNUC1 and OsRAD23 emerged as top candidate genes with splice variants that exhibited significant divergence between the variants for shoot growth under salt stress conditions. VaSP is a versatile tool for alternative splicing analysis in plants and a powerful tool for prioritising candidate causal pre-mRNA splicing and corresponding genomic variations in GWAS.

Loss of COMT activity reduces lateral root formation and alters the response to water limitation in sorghum brown midrib (bmr) 12 mutant.

Lignin is a key target for modifying lignocellulosic biomass for efficient biofuel production. Brown midrib 12 (bmr12) encodes the sorghum caffeic acid O-methyltransferase (COMT) and is one of the key enzymes in monolignol biosynthesis. Loss of function mutations in COMT reduces syringyl (S) lignin subunits and improves biofuel conversion rate. Although lignin plays an important role in maintaining cell wall integrity of xylem vessels, physiological and molecular consequences due to loss of COMT on root growth and adaptation to water deficit remain unexplored. We addressed this gap by evaluating the root morphology, anatomy and transcriptome of bmr12 mutant. The mutant had reduced lateral root density (LRD) and altered root anatomy and response to water limitation. The wild-type exhibits similar phenotypes under water stress, suggesting that bmr12 may be in a water deficit responsive state even in well-watered conditions. bmr12 had increased transcript abundance of genes involved in (a)biotic stress response, gibberellic acid (GA) biosynthesis and signaling. We show that bmr12 is more sensitive to exogenous GA application and present evidence for the role of GA in regulating reduced LRD in bmr12. These findings elucidate the phenotypic and molecular consequences of COMT deficiency under optimal and water stress environments in grasses.

Allelic variation in rice Fertilization Independent Endosperm 1 contributes to grain width under high night temperature stress.

A higher minimum (night-time) temperature is considered a greater limiting factor for reduced rice yield than a similar increase in maximum (daytime) temperature. While the physiological impact of high night temperature (HNT) has been studied, the genetic and molecular basis of HNT stress response remains unexplored. We examined the phenotypic variation for mature grain size (length and width) in a diverse set of rice accessions under HNT stress. Genome-wide association analysis identified several HNT-specific loci regulating grain size as well as loci that are common for optimal and HNT stress conditions. A novel locus contributing to grain width under HNT conditions colocalized with Fie1, a component of the FIS-PRC2 complex. Our results suggest that the allelic difference controlling grain width under HNT is a result of differential transcript-level response of Fie1 in grains developing under HNT stress. We present evidence to support the role of Fie1 in grain size regulation by testing overexpression (OE) and knockout mutants under heat stress. The OE mutants were either unaltered or had a positive impact on mature grain size under HNT, while the knockouts exhibited significant grain size reduction under these conditions.

MADS78 and MADS79 Are Essential Regulators of Early Seed Development in Rice.

MADS box transcription factors (TFs) are subdivided into type I and II based on phylogenetic analysis. The type II TFs regulate floral organ identity and flowering time, but type I TFs are relatively less characterized. Here, we report the functional characterization of two type I MADS box TFs in rice (Oryza sativa), MADS78 and MADS79 Transcript abundance of both these genes in developing seed peaked at 48 h after fertilization and was suppressed by 96 h after fertilization, corresponding to syncytial and cellularized stages of endosperm development, respectively. Seeds overexpressing MADS78 and MADS 79 exhibited delayed endosperm cellularization, while CRISPR-Cas9-mediated single knockout mutants showed precocious endosperm cellularization. MADS78 and MADS 79 were indispensable for seed development, as a double knockout mutant failed to make viable seeds. Both MADS78 and 79 interacted with MADS89, another type I MADS box, which enhances nuclear localization. The expression analysis of Fie1, a rice FERTILIZATION-INDEPENDENT SEED-POLYCOMB REPRESSOR COMPLEX2 component, in MADS78 and 79 mutants and vice versa established an antithetical relation, suggesting that Fie1 could be involved in negative regulation of MADS78 and MADS 79 Misregulation of MADS78 and MADS 79 perturbed auxin homeostasis and carbon metabolism, as evident by misregulation of genes involved in auxin transport and signaling as well as starch biosynthesis genes causing structural abnormalities in starch granules at maturity. Collectively, we show that MADS78 and MADS 79 are essential regulators of early seed developmental transition and impact both seed size and quality in rice.

Deciphering the genetic basis of wheat seminal root anatomy uncovers ancestral axial conductance alleles.

Root axial conductance, which describes the ability of water to move through the xylem, contributes to the rate of water uptake from the soil throughout the whole plant lifecycle. Under the rainfed wheat agro-system, grain-filling is typically occurring during declining water availability (i.e., terminal drought). Therefore, preserving soil water moisture during grain filling could serve as a key adaptive trait. We hypothesized that lower wheat root axial conductance can promote higher yields under terminal drought. A segregating population derived from a cross between durum wheat and its direct progenitor wild emmer wheat was used to underpin the genetic basis of seminal root architectural and functional traits. We detected 75 QTL associated with seminal roots morphological, anatomical and physiological traits, with several hotspots harbouring co-localized QTL. We further validated the axial conductance and central metaxylem QTL using wild introgression lines. Field-based characterization of genotypes with contrasting axial conductance suggested the contribution of low axial conductance as a mechanism for water conservation during grain filling and consequent increase in grain size and yield. Our findings underscore the potential of harnessing wild alleles to reshape the wheat root system architecture and associated hydraulic properties for greater adaptability under changing climate.

Endoplasmic reticulum stress pathway mediates the early heat stress response of developing rice seeds.

A transient heat stress occurring during early seed development in rice (Oryza sativa) reduces seed size by altering endosperm development. However, the relationship between the timing of the stress and specific developmental stage on heat sensitivity is not well-understood. To address this, we imposed a series of non-overlapping heat stress treatments and found that young seeds are most sensitive during the first two days after flowering. Temporal transcriptome analysis of developing, heat stressed (35°C) seeds during this window shows that Inositol-requiring enzyme 1 (IRE1)-mediated endoplasmic reticulum (ER) stress response and jasmonic acid (JA) pathways are the early (1-3 h) drivers of heat stress response. We propose that increased JA levels under heat stress may precede ER stress response as JA application promotes the spliced form of OsbZIP50, an ER response marker gene linked to IRE1-specific pathway. This study presents temporal and mechanistic insights into the role of JA and ER stress signalling during early heat stress response of rice seeds that impact both grain size and quality. Modulating the heat sensitivity of the early sensing pathways and downstream endosperm development genes can enhance rice resilience to transient heat stress events.

High night temperature effects on wheat and rice: Current status and way forward.

Rapid increases in minimum night temperature than in maximum day temperature is predicted to continue, posing significant challenges to crop productivity. Rice and wheat are two major staples that are sensitive to high night-temperature (HNT) stress. This review aims to (i) systematically compare the grain yield responses of rice and wheat exposed to HNT stress across scales, and (ii) understand the physiological and biochemical responses that affect grain yield and quality. To achieve this, we combined a synthesis of current literature on HNT effects on rice and wheat with information from a series of independent experiments we conducted across scales, using a common set of genetic materials to avoid confounding our findings with differences in genetic background. In addition, we explored HNT-induced alterations in physiological mechanisms including carbon balance, source-sink metabolite changes and reactive oxygen species. Impacts of HNT on grain developmental dynamics focused on grain-filling duration, post-flowering senescence, changes in grain starch and protein composition, starch metabolism enzymes and chalk formation in rice grains are summarized. Finally, we highlight the need for high-throughput field-based phenotyping facilities for improved assessment of large-diversity panels and mapping populations to aid breeding for increased resilience to HNT in crops.

Transmission Routes of the Microbiome and Resistome from Manure to Soil and Lettuce.

The land application of animal manure can introduce manure microbiome and resistome to croplands where food crops are grown. The objective of this study was to characterize the microbiome and resistome on and in the leaves of lettuce grown in manured soil and identify the main transmission routes of microbes and antibiotic resistance genes (ARGs) from soil to the episphere and endosphere of lettuce. Shotgun metagenomic results show that manure application significantly altered the composition of the microbiome and resistome of surface soil. SourceTracker analyses indicate that manure and original soil were the main source of the microbiome and resistome of the surface soil and rhizosphere soil, respectively. Manure application altered the microbiome and resistome in the episphere of lettuce (ADONIS p < 0.05), and surface soil accounted for ∼81% of the microbes and ∼62% of the ARGs in episphere. Manure application had limited impacts on the microbiome and resistome in the endosphere (ADONIS p > 0.05). Our results show that manure-borne microbes and ARGs reached the episphere primarily through surface soil and some epiphytic microbes and ARGs further entered the endosphere. Our findings can inform the development of pre- and postharvest practices to minimize the transmission of manure-borne resistome from food crops to consumers.

HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds.

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest.

Characterization of the transcriptional divergence between the subspecies of cultivated rice (Oryza sativa).

BACKGROUND: Cultivated rice consists of two subspecies, Indica and Japonica, that exhibit well-characterized differences at the morphological and genetic levels. However, the differences between these subspecies at the transcriptome level remains largely unexamined. Here, we provide a comprehensive characterization of transcriptome divergence and cis-regulatory variation within rice using transcriptome data from 91 accessions from a rice diversity panel (RDP1). RESULTS: The transcriptomes of the two subspecies of rice are highly divergent. Japonica have significantly lower expression and genetic diversity relative to Indica, which is likely a consequence of a population bottleneck during Japonica domestication. We leveraged high-density genotypic data and transcript levels to identify cis-regulatory variants that may explain the genetic divergence between the subspecies. We identified significantly more eQTL that were specific to the Indica subspecies compared to Japonica, suggesting that the observed differences in expression and genetic variability also extends to cis-regulatory variation. CONCLUSIONS: Using RNA sequencing data for 91diverse rice accessions and high-density genotypic data, we show that the two species are highly divergent with respect to gene expression levels, as well as the genetic regulation of expression. The data generated by this study provide, to date, the largest collection of genome-wide transcriptional levels for rice, and provides a community resource to accelerate functional genomic studies in rice.

The LATERAL ROOT DENSITY gene regulates root growth during water stress in wheat.

Drought stress is the major limiting factor in agriculture. Wheat, which is the most widely grown crop in the world, is predominantly cultivated in drought-prone rainfed environments. Since roots play a critical role in water uptake, root response to water limitations is an important component for enhancing wheat adaptation. In an effort to discover novel genetic sources for improving wheat adaptation, we characterized a wheat translocation line with a chromosomal segment from Agropyron elongatum, a wild relative of wheat, which unlike common wheat maintains root growth under limited-water conditions. By exploring the root transcriptome data, we found that reduced transcript level of LATERAL ROOT DENSITY (LRD) gene under limited water in the Agropyron translocation line confers it the ability to maintain root growth. The Agropyron allele of LRD is down-regulated in response to water limitation in contrast with the wheat LRD allele, which is up-regulated by water deficit stress. Suppression of LRD expression in wheat RNAi plants confers the ability to maintain root growth under water limitation. We show that exogenous gibberellic acid (GA) promotes lateral root growth and present evidence for the role of GA in mediating the differential regulation of LRD between the common wheat and the Agropyron alleles under water stress. Suppression of LRD also had a positive pleiotropic effect on grain size and number under optimal growth conditions. Collectively, our findings suggest that LRD can be potentially useful for improving wheat response to water stress and altering yield components.

High night temperature induced changes in grain starch metabolism alters starch, protein, and lipid accumulation in winter wheat.

Unlike sporadic daytime heat spikes, a consistent increase in night-time temperatures can potentially derail the genetic gains being achieved. Ten winter wheat genotypes were exposed to six different night-time temperatures (15-27°C) during flowering and grain-filling stages in controlled environment chambers. We identified the night-time temperature of 23o C as the critical threshold beyond which a consistent decline in yields and quality was observed. Confocal laser scanning micrographs of central endosperm, bran, and germ tissue displayed differential accumulation of protein, lipid, and starch with increasing night-time temperatures. KS07077M-1 recorded a decrease in starch and an increase in protein and lipid in central endosperm with increasing night-time temperatures, whereas the same was significantly lower in the tolerant SY Monument. Expression analysis of genes encoding 21 enzymes (including isoforms) involved in grain-starch metabolism in developing grains revealed a high night-time temperature (HNT)-induced reduction in transcript levels of adenosine diphosphate glucose pyrophosphorylase small subunit involved in starch synthesis and a ≥2-fold increase in starch degrading enzymes isoamylase III, alpha-, and beta-amylase. The identified critical threshold, grain compositional changes, and the key enzymes in grain starch metabolism that lead to poor starch accumulation in grains establish the foundational knowledge for enhancing HNT tolerance in wheat.