Wheat genetic resources have avoided disease pandemics, improved food security, and reduced environmental footprints: A review of historical impacts and future opportunities.

The use of plant genetic resources (PGR)-wild relatives, landraces, and isolated breeding gene pools-has had substantial impacts on wheat breeding for resistance to biotic and abiotic stresses, while increasing nutritional value, end-use quality, and grain yield. In the Global South, post-Green Revolution genetic yield gains are generally achieved with minimal additional inputs. As a result, production has increased, and millions of hectares of natural ecosystems have been spared. Without PGR-derived disease resistance, fungicide use would have easily doubled, massively increasing selection pressure for fungicide resistance. It is estimated that in wheat, a billion liters of fungicide application have been avoided just since 2000. This review presents examples of successful use of PGR including the relentless battle against wheat rust epidemics/pandemics, defending against diseases that jump species barriers like blast, biofortification giving nutrient-dense varieties and the use of novel genetic variation for improving polygenic traits like climate resilience. Crop breeding genepools urgently need to be diversified to increase yields across a range of environments (>200 Mha globally), under less predictable weather and biotic stress pressure, while increasing input use efficiency. Given that the ~0.8 m PGR in wheat collections worldwide are relatively untapped and massive impacts of the tiny fraction studied, larger scale screenings and introgression promise solutions to emerging challenges, facilitated by advanced phenomic and genomic tools. The first translocations in wheat to modify rhizosphere microbiome interaction (reducing biological nitrification, reducing greenhouse gases, and increasing nitrogen use efficiency) is a landmark proof of concept. Phenomics and next-generation sequencing have already elucidated exotic haplotypes associated with biotic and complex abiotic traits now mainstreamed in breeding. Big data from decades of global yield trials can elucidate the benefits of PGR across environments. This kind of impact cannot be achieved without widescale sharing of germplasm and other breeding technologies through networks and public-private partnerships in a pre-competitive space.

Improving wheat grain composition for human health by constructing a QTL atlas for essential minerals.

Wheat is an important source of minerals for human nutrition and increasing grain mineral content can contribute to reducing mineral deficiencies. Here, we identify QTLs for mineral micronutrients in grain of wheat by determining the contents of six minerals in a total of eleven sample sets of three biparental populations from crosses between A.E. Watkins landraces and cv. Paragon. Twenty-three of the QTLs are mapped in two or more sample sets, with LOD scores above five in at least one set with the increasing alleles for sixteen of the QTLs being present in the landraces and seven in Paragon. Of these QTLs, the number for each mineral varies between three and five and they are located on 14 of the 21 chromosomes, with clusters on chromosomes 5A (four), 6A (three), and 7A (three). The gene content within 5 megabases of DNA on either side of the marker for the QTL with the highest LOD score is determined and the gene responsible for the strongest QTL (chromosome 5A for Ca) identified as an ATPase transporter gene (TraesCS5A02G543300) using mutagenesis. The identification of these QTLs, together with associated SNP markers and candidate genes, will facilitate the improvement of grain nutritional quality.

The 4T and 7T introgressions from Amblyopyrum muticum and the 5Au introgression from Triticum urartu increases grain zinc and iron concentrations in Malawian wheat backgrounds.

Micronutrient deficiencies (MNDs) particularly zinc (Zn) and iron (Fe) remain widespread in sub-Saharan Africa (SSA) due to low dietary intake. Wheat is an important source of energy globally, although cultivated wheat is inherently low in grain micronutrient concentrations. Malawian wheat/Am. muticum and Malawian wheat/T. urartu BC1F3 introgression lines, developed by crossing three Malawian wheat varieties (Kenya nyati, Nduna and Kadzibonga) with DH-348 (wheat/Am. muticum) and DH-254 (wheat/T. urartu), were phenotyped for grain Zn and Fe, and associated agronomic traits in Zn-deficient soils, in Malawi. 98% (47) of the BC1F3 introgression lines showed higher Zn above the checks Paragon, Chinese Spring, Kadzibonga, Kenya Nyati and Nduna. 23% (11) of the introgression lines showed a combination of high yields and an increase in grain Zn by 16-30 mg kg -1 above Nduna and Kadzibonga, and 11-25 mg kg -1 above Kenya nyati, Paragon and Chinese Spring. Among the 23%, 64% (7) also showed 8-12 mg kg -1 improvement in grain Fe compared to Nduna and Kenya nyati. Grain Zn concentrations showed a significant positive correlation with grain Fe, whilst grain Zn and Fe negatively and significantly correlated with TKW and grain yield. This work will contribute to the efforts of increasing mineral nutrient density in wheat, specifically targeting countries in the SSA.

At the crossroads: strigolactones mediate changes in cytokinin synthesis and signalling in response to nitrogen limitation.

Strigolactones (SLs) are key regulators of shoot growth and responses to environmental stimuli. Numerous studies have indicated that nitrogen (N) limitation induces SL biosynthesis, suggesting that SLs may play a pivotal role in coordinating systemic responses to N availability, but this idea has not been clearly demonstrated. Here, we generated triple knockout mutants in the SL synthesis gene TaDWARF17 (TaD17) in bread wheat and investigated their phenotypic and transcriptional responses under N limitation, aiming to elucidate the role of SLs in the adaptation to N limitation. Tad17 mutants display typical SL mutant phenotypes, and fail to adapt their shoot growth appropriately to N. Despite exhibiting an increased tillering phenotype, Tad17 mutants continued to respond to N limitation by reducing tiller number, suggesting that SLs are not the sole regulators of tillering in response to N availability. RNA-seq analysis of basal nodes revealed that the loss of D17 significantly altered the transcriptional response of N-responsive genes, including changes in the expression profiles of key N response master regulators. Crucially, our findings suggest that SLs are required for the transcriptional downregulation of cytokinin (CK) synthesis and signalling in response to N limitation. Collectively, our results suggest that SLs are essential for the appropriate morphological and transcriptional adaptation to N limitation in wheat, and that the repressive effect of SLs on shoot growth is partly mediated by their repression of CK synthesis.

Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods.

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.

Field phenotyping for African crops: overview and perspectives.

Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.

Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping.

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green-red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.

Identification of traits underpinning good breadmaking performance of wheat grown with reduced nitrogen fertilisation.

BACKGROUND: Nitrogen fertiliser is the major input and cost for wheat production, being required to support the development of the canopy to maximise yield and for the synthesis of the gluten proteins that are necessary for breadmaking. Consequently, current high-yielding cultivars require the use of nitrogen fertilisation levels above the yield optimum to achieve the grain protein content needed for breadmaking. This study aimed to reduce this requirement by identifying traits that allow the use of lower levels of nitrogen fertiliser to produce wheat for breadmaking. RESULTS: A range of commercial wheat genotypes (cultivars) were grown in multiple field trials (six sites over 3 years) in the UK with optimal (200 kg Ha-1 ) and suboptimal (150 kg Ha-1 ) application of nitrogen. Bulked grain samples from four sites per year were milled and white flours were baked using three types of breadmaking process. This identified five cultivars that consistently exhibited good breadmaking quality when grown with the lower nitrogen application. Chemical and biochemical analyses showed that the five cultivars were characterised by exhibiting grain protein deviation (GPD) and high dough elasticity. CONCLUSIONS: It is possible to develop novel types of wheat that exhibit good breadmaking quality by selecting for GPD and high dough strength. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Feeding the world sustainably: efficient nitrogen use.

Globally, overuse of nitrogen (N) fertilizers in croplands is causing severe environmental pollution. In this context, Gu et al. suggest environmentally friendly and cost-effective N management practices and Hamani et al. highlight the use of microbial inoculants to improve crop yields, while reducing N-associated environmental pollution and N-fertilizer use.

Nutritional and tissue-specific regulation of cytochrome P450 CYP711A MAX1 homologues and strigolactone biosynthesis in wheat.

Strigolactones (SLs) are a class of phytohormones regulating branching/tillering, and their biosynthesis has been associated with nutritional signals and plant adaptation to nutrient-limiting conditions. The enzymes in the SL biosynthetic pathway downstream of carlactone are of interest as they are responsible for structural diversity in SLs, particularly cytochrome P450 CYP711A subfamily members, such as MORE AXILLARY GROWTH1 (MAX1) in Arabidopsis. We identified 13 MAX1 homologues in wheat, clustering in four clades and five homoeologous subgroups. The utilization of RNA-sequencing data revealed a distinct expression pattern of MAX1 homologues in above- and below-ground tissues, providing insights into the distinct roles of MAX1 homologues in wheat. In addition, a transcriptional analysis showed that SL biosynthetic genes were systematically regulated by nitrogen supply. Nitrogen limitation led to larger transcriptional changes in the basal nodes than phosphorus limitation, which was consistent with the observed tillering suppression, as wheat showed higher sensitivity to nitrogen. The opposite was observed in roots, with phosphorus limitation leading to stronger induction of most SL biosynthetic genes compared with nitrogen limitation. The observed tissue-specific regulation of SL biosynthetic genes in response to nutritional signals is likely to reflect the dual role of SLs as rhizosphere signals and branching inhibitors.

Time series canopy phenotyping enables the identification of genetic variants controlling dynamic phenotypes in soybean.

Advances in plant phenotyping technologies are dramatically reducing the marginal costs of collecting multiple phenotypic measurements across several time points. Yet, most current approaches and best statistical practices implemented to link genetic and phenotypic variation in plants have been developed in an era of single-time-point data. Here, we used time-series phenotypic data collected with an unmanned aircraft system for a large panel of soybean (Glycine max (L.) Merr.) varieties to identify previously uncharacterized loci. Specifically, we focused on the dissection of canopy coverage (CC) variation from this rich data set. We also inferred the speed of canopy closure, an additional dimension of CC, from the time-series data, as it may represent an important trait for weed control. Genome-wide association studies (GWASs) identified 35 loci exhibiting dynamic associations with CC across developmental stages. The time-series data enabled the identification of 10 known flowering time and plant height quantitative trait loci (QTLs) detected in previous studies of adult plants and the identification of novel QTLs influencing CC. These novel QTLs were disproportionately likely to act earlier in development, which may explain why they were missed in previous single-time-point studies. Moreover, this time-series data set contributed to the high accuracy of the GWASs, which we evaluated by permutation tests, as evidenced by the repeated identification of loci across multiple time points. Two novel loci showed evidence of adaptive selection during domestication, with different genotypes/haplotypes favored in different geographic regions. In summary, the time-series data, with soybean CC as an example, improved the accuracy and statistical power to dissect the genetic basis of traits and offered a promising opportunity for crop breeding with quantitative growth curves.

The Functional Diversity of the High-Affinity Nitrate Transporter Gene Family in Hexaploid Wheat: Insights from Distinct Expression Profiles.

High-affinity nitrate transporters (NRT) are key components for nitrogen (N) acquisition and distribution within plants. However, insights on these transporters in wheat are scarce. This study presents a comprehensive analysis of the NRT2 and NRT3 gene families, where the aim is to shed light on their functionality and to evaluate their responses to N availability. A total of 53 NRT2s and 11 NRT3s were identified in the bread wheat genome, and these were grouped into different clades and homoeologous subgroups. The transcriptional dynamics of the identified NRT2 and NRT3 genes, in response to N starvation and nitrate resupply, were examined by RT-qPCR in the roots and shoots of hydroponically grown wheat plants through a time course experiment. Additionally, the spatial expression patterns of these genes were explored within the plant. The NRT2s of clade 1, TaNRT2.1-2.6, showed a root-specific expression and significant upregulation in response to N starvation, thus emphasizing a role in N acquisition. However, most of the clade 2 NRT2s displayed reduced expression under N-starved conditions. Nitrate resupply after N starvation revealed rapid responsiveness in TaNRT2.1-2.6, while clade 2 genes exhibited gradual induction, primarily in the roots. TaNRT2.18 was highly expressed in above-ground tissues and exhibited distinct nitrate-related response patterns for roots and shoots. The TaNRT3 gene expression closely paralleled the profiles of TaNRT2.1-2.6 in response to nitrate induction. These findings enhance the understanding of NRT2 and NRT3 involvement in nitrogen uptake and utilization, and they could have practical implications for improving nitrogen use efficiency. The study also recommends a standardized nomenclature for wheat NRT2 genes, thereby addressing prior naming inconsistencies.

Nitrogen uptake and remobilization from pre- and post-anthesis stages contribute towards grain yield and grain protein concentration in wheat grown in limited nitrogen conditions.

Background: In wheat, nitrogen (N) remobilization from vegetative tissues to developing grains largely depends on genetic and environmental factors. The evaluation of genetic potential of crops under limited resource inputs such as limited N supply would provide an opportunity to identify N-efficient lines with improved N utilisation efficiency and yield potential. We assessed the genetic variation in wheat recombinant inbred lines (RILs) for uptake, partitioning, and remobilization of N towards grain, its association with grain protein concentration (GPC) and grain yield. Methods: We used the nested association mapping (NAM) population (195 lines) derived by crossing Paragon (P) with CIMMYT core germplasm (P × Cim), Baj (P × Baj), Watkins (P × Wat), and Wyalkatchem (P × Wya). These lines were evaluated in the field for two seasons under limited N supply. The plant sampling was done at anthesis and physiological maturity stages. Various physiological traits were recorded and total N uptake and other N related indices were calculated. The grain protein deviation (GPD) was calculated from the regression of grain yield on GPC. These lines were grouped into different clusters by hierarchical cluster analysis based on grain yield and N-remobilization efficiency (NRE). Results: The genetic variation in accumulation of biomass at both pre- and post-anthesis stages were correlated with grain-yield. The NRE significantly correlated with aboveground N uptake at anthesis (AGNa) and grain yield but negatively associated with AGN at post-anthesis (AGNpa) suggesting higher N uptake till anthesis favours high N remobilization during grain filling. Hierarchical cluster analysis of these RILs based on NRE and yield resulted in four clusters, efficient (31), moderately efficient (59), moderately inefficient (58), and inefficient (47). In the N-efficient lines, AGNa contributed to 77% of total N accumulated in grains, while it was 63% in N-inefficient lines. Several N-efficient lines also exhibited positive grain protein deviation (GPD), combining high grain yield and GPC. Among crosses, the P × Cim were superior and N-efficient, while P × Wya responded poorly to low N input. Conclusions: We propose that traits favouring pre- or post-anthesis biomass accumulation and pre-anthesis N uptake may be targeted for breeding to improve grain-yield under limited N. The lines with positive GPD, a first report of genotype-dependent GPD associated with both AGNpa and AGNa in wheat, may be used as varieties or genetic resources to improve grain yield with high GPC for sustainable development under limited N conditions. Supplementary Information: The online version contains supplementary material available at 10.1186/s43170-023-00153-7.

Global wheat production could benefit from closing the genetic yield gap.

Global food security requires food production to be increased in the coming decades. The closure of any existing genetic yield gap (Yig) by genetic improvement could increase crop yield potential and global production. Here we estimated present global wheat Yig, covering all wheat-growing environments and major producers, by optimizing local wheat cultivars using the wheat model Sirius. The estimated mean global Yig was 51%, implying that global wheat production could benefit greatly from exploiting the untapped global Yig through the use of optimal cultivar designs, utilization of the vast variation available in wheat genetic resources, application of modern advanced breeding tools, and continuous improvements of crop and soil management.

Next Generation dsRNA-Based Insect Control: Success So Far and Challenges.

RNA interference (RNAi) is a method of gene silencing where dsRNA is digested into small interfering RNA (siRNA) in the presence of enzymes. These siRNAs then target homologous mRNA sequences aided by the RNA-induced silencing complex (RISC). The mechanism of dsRNA uptake has been well studied and established across many living organisms including insects. In insects, RNAi is a novel and potential tool to develop future pest management means targeting various classes of insects including dipterans, coleopterans, hemipterans, lepidopterans, hymenopterans and isopterans. However, the extent of RNAi in individual class varies due to underlying mechanisms. The present review focuses on three major insect classes viz hemipterans, lepidopterans and coleopterans and the rationale behind this lies in the fact that studies pertaining to RNAi has been extensively performed in these groups. Additionally, these classes harbour major agriculturally important pest species which require due attention. Interestingly, all the three classes exhibit varying levels of RNAi efficiencies with the coleopterans exhibiting maximum response, while hemipterans are relatively inefficient. Lepidopterans on the other hand, show minimum response to RNAi. This has been attributed to many facts and few important being endosomal escape, high activity dsRNA-specific nucleases, and highly alkaline gut environment which renders the dsRNA unstable. Various methods have been established to ensure safe delivery of dsRNA into the biological system of the insect. The most common method for dsRNA administration is supplementing the diet of insects via spraying onto leaves and other commonly eaten parts of the plant. This method is environment-friendly and superior to the hazardous effects of pesticides. Another method involves submergence of root systems in dsRNA solutions and subsequent uptake by the phloem. Additionally, more recent techniques are nanoparticle- and Agrobacterium-mediated delivery systems. However, due to the novelty of these biotechnological methods and recalcitrant nature of certain crops, further optimization is required. This review emphasizes on RNAi developments in agriculturally important insect species and the major hurdles for efficient RNAi in these groups. The review also discusses in detail the development of new techniques to enhance RNAi efficiency using liposomes and nanoparticles, transplastomics, microbial-mediated delivery and chemical methods.

Determination of wheat spike and spikelet architecture and grain traits using X-ray Computed Tomography imaging.

BACKGROUND: Wheat spike architecture is a key determinant of multiple grain yield components and detailed examination of spike morphometric traits is beneficial to explain wheat grain yield and the effects of differing agronomy and genetics. However, quantification of spike morphometric traits has been very limited because it relies on time-consuming manual measurements. RESULTS: In this study, using X-ray Computed Tomography imaging, we proposed a method to efficiently detect the 3D architecture of wheat spikes and component spikelets by clustering grains based on their Euclidean distance and relative positions. Morphometric characteristics of wheat spikelets and grains, e.g., number, size and spatial distribution along the spike can be determined. Two commercial wheat cultivars, one old, Maris Widgeon, and one modern, Siskin, were studied as examples. The average grain volume of Maris Widgeon and Siskin did not differ, but Siskin had more grains per spike and therefore greater total grain volume per spike. The spike length and spikelet number were not statistically different between the two cultivars. However, Siskin had a higher spikelet density (number of spikelets per unit spike length), with more grains and greater grain volume per spikelet than Maris Widgeon. Spatial distribution analysis revealed the number of grains, the average grain volume and the total grain volume of individual spikelets varied along the spike. Siskin had more grains and greater grain volumes per spikelet from spikelet 6, but not spikelet 1-5, compared with Maris Widgeon. The distribution of average grain volume along the spike was similar for the two wheat cultivars. CONCLUSION: The proposed method can efficiently extract spike, spikelet and grain morphometric traits of different wheat cultivars, which can contribute to a more detailed understanding of the sink of wheat grain yield.

Wheat amino acid transporters highly expressed in grain cells regulate amino acid accumulation in grain.

Amino acids are delivered into developing wheat grains to support the accumulation of storage proteins in the starchy endosperm, and transporters play important roles in regulating this process. RNA-seq, RT-qPCR, and promoter-GUS assays showed that three amino acid transporters are differentially expressed in the endosperm transfer cells (TaAAP2), starchy endosperm cells (TaAAP13), and aleurone cells and embryo of the developing grain (TaAAP21), respectively. Yeast complementation revealed that all three transporters can transport a broad spectrum of amino acids. RNAi-mediated suppression of TaAAP13 expression in the starchy endosperm did not reduce the total nitrogen content of the whole grain, but significantly altered the composition and distribution of metabolites in the starchy endosperm, with increasing concentrations of some amino acids (notably glutamine and glycine) from the outer to inner starchy endosperm cells compared with wild type. Overexpression of TaAAP13 under the endosperm-specific HMW-GS (high molecular weight glutenin subunit) promoter significantly increased grain size, grain nitrogen concentration, and thousand grain weight, indicating that the sink strength for nitrogen transport was increased by manipulation of amino acid transporters. However, the total grain number was reduced, suggesting that source nitrogen remobilized from leaves is a limiting factor for productivity. Therefore, simultaneously increasing loading of amino acids into the phloem and delivery to the spike would be required to increase protein content while maintaining grain yield.

Post-translational cleavage of HMW-GS Dy10 allele improves the cookie-making quality in common wheat (Triticum aestivum).

Wheat is a major staple food crop worldwide because of the unique properties of wheat flour. High molecular weight glutenin subunits (HMW-GSs), which are among the most critical determinants of wheat flour quality, are responsible for the formation of glutenin polymeric structures via interchain disulfide bonds. We herein describe the identification of a new HMW-GS Dy10 allele (Dy10-m619SN). The amino acid substitution (serine-to-asparagine) encoded in this allele resulted in a partial post-translational cleavage that produced two new peptides. These new peptides disrupted the interactions among gluten proteins because of the associated changes to the number of available cysteine residues for interchain disulfide bonds. Consequently, Dy10-m619SN expression decreased the size of glutenin polymers and weakened glutens, which resulted in wheat dough with improved cookie-making quality, without changes to the glutenin-to-gliadin ratio. In this study, we clarified the post-translational processing of HMW-GSs and revealed a new genetic resource useful for wheat breeding. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01238-9.

Disruption of the Nα-Acetyltransferase NatB Causes Sensitivity to Reductive Stress in Arabidopsis thaliana.

In Arabidopsis thaliana, the evolutionary conserved N-terminal acetyltransferase (Nat) complexes NatA and NatB co-translationally acetylate 60% of the proteome. Both have recently been implicated in the regulation of plant stress responses. While NatA mediates drought tolerance, NatB is required for pathogen resistance and the adaptation to high salinity and high osmolarity. Salt and osmotic stress impair protein folding and result in the accumulation of misfolded proteins in the endoplasmic reticulum (ER). The ER-membrane resident E3 ubiquitin ligase DOA10 targets misfolded proteins for degradation during ER stress and is conserved among eukaryotes. In yeast, DOA10 recognizes conditional degradation signals (Ac/N-degrons) created by NatA and NatB. Assuming that this mechanism is preserved in plants, the lack of Ac/N-degrons required for efficient removal of misfolded proteins might explain the sensitivity of NatB mutants to protein harming conditions. In this study, we investigate the response of NatB mutants to dithiothreitol (DTT) and tunicamycin (TM)-induced ER stress. We report that NatB mutants are hypersensitive to DTT but not TM, suggesting that the DTT hypersensitivity is caused by an over-reduction of the cytosol rather than an accumulation of unfolded proteins in the ER. In line with this hypothesis, the cytosol of NatB depleted plants is constitutively over-reduced and a global transcriptome analysis reveals that their reductive stress response is permanently activated. Moreover, we demonstrate that doa10 mutants are susceptible to neither DTT nor TM, ruling out a substantial role of DOA10 in ER-associated protein degradation (ERAD) in plants. Contrary to previous findings in yeast, our data indicate that N-terminal acetylation (NTA) does not inhibit ER targeting of a substantial amount of proteins in plants. In summary, we provide further evidence that NatB-mediated imprinting of the proteome is vital for the response to protein harming stress and rule out DOA10 as the sole recognin for substrates in the plant ERAD pathway, leaving the role of DOA10 in plants ambiguous.