Characterization of flag leaf morphology identifies a major genomic region controlling flag leaf angle in the US winter wheat (Triticum aestivum L.).

KEY MESSAGE: Multi-environmental characterization of flag leaf morphology traits in the US winter wheat revealed nine stable genomic regions for different flag leaf-related traits including a major region governing flag leaf angle. Flag leaf in wheat is the primary contributor to accumulating photosynthetic assimilates. Flag leaf morphology (FLM) traits determine the overall canopy structure and capacity to intercept the light, thus influencing photosynthetic efficiency. Hence, understanding the genetic control of these traits could be useful for breeding desirable ideotypes in wheat. We used a panel of 272 accessions from the hard winter wheat (HWW) region of the USA to investigate the genetic architecture of five FLM traits including flag leaf length (FLL), width (FLW), angle (FLANG), length-width ratio, and area using multilocation field experiments. Multi-environment GWAS using 14,537 single-nucleotide polymorphisms identified 36 marker-trait associations for different traits, with nine being stable across environments. A novel and major stable region for FLANG (qFLANG.1A) was identified on chromosome 1A accounting for 9-13% variation. Analysis of spatial distribution for qFLANG.1A in a set of 2354 breeding lines from the HWW region showed a higher frequency of allele associated with narrow leaf angle. A KASP assay was developed for allelic discrimination of qFLANG.1A and was used for its independent validation in a diverse set of spring wheat accessions. Furthermore, candidate gene analysis for two regions associated with FLANG identified seven putative genes of interest for each of the two regions. The present study enhances our understanding of the genetic control of FLM in wheat, particularly FLANG, and these results will be useful for dissecting the genes underlying canopy architecture in wheat facilitating the development of climate-resilient wheat varieties.

An unprecedented fall drought drives Dust Bowl-like losses associated with La Niña events in US wheat production.

Unprecedented precipitation deficits in the 2022-2023 growing season across the primary wheat-producing region in the United States caused delays in winter wheat emergence and poor crop growth. Using an integrated approach, we quantitatively unraveled a 37% reduction in wheat production as being attributable to both per-harvested acre yield loss and severe crop abandonment, reminiscent of the Dust Bowl years in the 1930s. We used random forest machine learning and game theory analytics to show that the main driver of yield loss was spring drought, whereas fall drought dominated abandonment rates. Furthermore, results revealed, across the US winter wheat belt, the La Niña phase of the El Niño Southern Oscillation (ENSO), increased abandonment rates compared to the El Niño phase. These findings underscore the necessity of simultaneously addressing crop abandonment and yield decline to stabilize wheat production amid extreme climatic conditions and provide a holistic understanding of global-scale ENSO dynamics on wheat production.

Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.

Integrating high-throughput phenotyping (HTP) based traits into phenomic and genomic selection (GS) can accelerate the breeding of high-yielding and climate-resilient wheat cultivars. In this study, we explored the applicability of Unmanned Aerial Vehicles (UAV)-assisted HTP combined with deep learning (DL) for the phenomic or multi-trait (MT) genomic prediction of grain yield (GY), test weight (TW), and grain protein content (GPC) in winter wheat. Significant correlations were observed between agronomic traits and HTP-based traits across different growth stages of winter wheat. Using a deep neural network (DNN) model, HTP-based phenomic predictions showed robust prediction accuracies for GY, TW, and GPC for a single location with R2 of 0.71, 0.62, and 0.49, respectively. Further prediction accuracies increased (R2 of 0.76, 0.64, and 0.75) for GY, TW, and GPC, respectively when advanced breeding lines from multi-locations were used in the DNN model. Prediction accuracies for GY varied across growth stages, with the highest accuracy at the Feekes 11 (Milky ripe) stage. Furthermore, forward prediction of GY in preliminary breeding lines using DNN trained on multi-location data from advanced breeding lines improved the prediction accuracy by 32% compared to single-location data. Next, we evaluated the potential of incorporating HTP-based traits in multi-trait genomic selection (MT-GS) models in the prediction of GY, TW, and GPC. MT-GS, models including UAV data-based anthocyanin reflectance index (ARI), green chlorophyll index (GCI), and ratio vegetation index 2 (RVI_2) as covariates demonstrated higher predictive ability (0.40, 0.40, and 0.37, respectively) as compared to single-trait model (0.23) for GY. Overall, this study demonstrates the potential of integrating HTP traits into DL-based phenomic or MT-GS models for enhancing breeding efficiency.

Evaluating a worldwide wheat collection for resistance to Hessian fly biotype 'Great Plains'.

Hessian fly (HF), Mayetiola destructor, is a major insect pest that causes severe losses in grain yield and quality of wheat (Triticum aestivum). Growing resistant cultivars is the most cost-effective approach to minimize wheat yield losses caused by HF. In this study, 2,496 wheat accessions were screened for resistance to the HF biotype 'Great Plains' (GP) in the greenhouse experiments. To purify seeds from heterogeneous resistant accessions, we recovered single resistant plants from 331 accessions that had at least one resistant plant after HF infestation of a global collection of 1,595 accessions and confirmed 27 accessions with high resistance (HR), and 91 accessions with moderate resistance (MR) to the GP biotype using purified seeds. Screening of 203 U.S. winter wheat accessions in three experiments identified 63 HR and 28 MR accessions; and screening of three additional Asian panels identified 4 HR and 25 MR accessions. Together, this study identified 96 HR accessions and 144 MR accessions. Analysis of the geographic distribution of these HR and MR accessions revealed that these countries with HF as a major wheat pest usually showed higher frequencies of resistant accessions, with the highest frequency of HR (81.3%) and MR (30.6%) accessions identified from the U.S. In addition, phenotyping of 39 wheat accessions that carry known HF resistance genes showed that all the accessions except H1H2 remain effective against GP biotype. Some of these newly identified resistant accessions may contain new HF resistance genes and can be valuable sources for developing HF resistant wheat cultivars.

Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat.

Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivalenol (DON), is either prone to human biases or resource expensive, hindering the progress in breeding for FHB-resistant cultivars. Though genomic selection (GS) can be an effective way to select these traits, inaccurate phenotyping remains a hurdle in exploiting this approach. Here, we used an artificial intelligence (AI)-based precise FDK estimation that exhibits high heritability and correlation with DON. Further, GS using AI-based FDK (FDK_QVIS/FDK_QNIR) showed a two-fold increase in predictive ability (PA) compared to GS for traditionally estimated FDK (FDK_V). Next, the AI-based FDK was evaluated along with other traits in multi-trait (MT) GS models to predict DON. The inclusion of FDK_QNIR and FDK_QVIS with days to heading as covariates improved the PA for DON by 58% over the baseline single-trait GS model. We next used hyperspectral imaging of FHB-infected wheat kernels as a novel avenue to improve the MT GS for DON. The PA for DON using selected wavebands derived from hyperspectral imaging in MT GS models surpassed the single-trait GS model by around 40%. Finally, we evaluated phenomic prediction for DON by integrating hyperspectral imaging with deep learning to directly predict DON in FHB-infected wheat kernels and observed an accuracy (R2 = 0.45) comparable to best-performing MT GS models. This study demonstrates the potential application of AI and vision-based platforms to improve PA for FHB-related traits using genomic and phenomic selection.

Characterization of Quantitative Trait Loci for Leaf Rust Resistance from CI 13227 in Three Winter Wheat Populations.

Leaf rust is a widespread foliar wheat disease causing substantial yield losses worldwide. Slow rusting is "adult plant" resistance that significantly slows epidemic development and thereby reduces yield loss. Wheat accession CI 13227 was previously characterized as having slow-rusting resistance. To validate the quantitative trait loci (QTLs) and develop diagnostic markers for slow rusting resistance in CI 13227, a new population of recombinant inbred lines of CI 13227 × Everest was evaluated for latent period, final severity, area under the disease progress curve, and infection type in greenhouses and genotyped using genotyping-by-sequencing. Four QTLs were identified on chromosome arms 2BL, 2DS, 3BS, and 7BL, explaining 6.82 to 28.45% of the phenotypic variance for these traits. Seven kompetitive allele-specific polymorphism markers previously reported to be linked to the QTLs in two other CI 13227 populations were validated. In addition, the previously reported QLr.hwwg-7AL was remapped to 2BL (renamed QLr.hwwg-2BL) after adding new markers in this study. Phenotypic data showed that the recombinant inbred lines harboring two or three of the QTLs had a significantly longer latent period. QLr.hwwg-2DS on 2DS showed a major effect on all rust resistance traits and was finely mapped to a 2.7-Mb interval by two newly developed flanking markers from exome capture. Three disease-resistance genes and two transporter genes were identified as the putative candidates for QLr.hwwg-2DS. The validated QTLs can be used as slow-rusting resistance resources, and the markers developed in this study will be useful for marker-assisted selection.

Characterization of a new greenbug resistance gene Gb9 in a synthetic hexaploid wheat.

Greenbug [Schizaphis graminum (Rondani)] is a serious insect pest that not only damages cereal crops, but also transmits several destructive viruses. The emergence of new greenbug biotypes in the field makes it urgent to identify novel greenbug resistance genes in wheat. CWI 76364 (PI 703397), a synthetic hexaploid wheat (SHW) line, exhibits greenbug resistance. Evaluation of an F2:3 population from cross OK 14319 × CWI 76364 indicated that a dominant gene, designated Gb9, conditions greenbug resistance in CWI 76364. Selective genotyping of a subset of F2 plants with contrasting phenotypes by genotyping-by-sequencing identified 25 SNPs closely linked to Gb9 on chromosome arm 7DL. Ten of these SNPs were converted to Kompetitive allele-specific polymerase chain reaction (KASP) markers for genotyping the entire F2 population. Genetic analysis delimited Gb9 to a 0.6-Mb interval flanked by KASP markers located at 599,835,668 bp (Stars-KASP872) and 600,471,081 bp (Stars-KASP881) on 7DL. Gb9 was 0.5 cM distal to Stars-KASP872 and 0.5 cM proximal to Stars-KASP881. Allelism tests indicated that Gb9 is a new greenbug resistance gene which confers resistance to greenbug biotypes C, E, H, I, and TX1. TX1 is one of the most widely virulent biotypes and has overcome most known wheat greenbug resistance genes. The introgression of Gb9 into locally adapted wheat cultivars is of economic importance, and the KASP markers developed in this study can be used to tag Gb9 in cultivar development.

A Glutathione S-Transferase from Thinopyrum ponticum Confers Fhb7 Resistance to Fusarium Head Blight in Wheat.

Fusarium head blight (FHB), mainly incited by Fusarium graminearum, has caused great losses in grain yield and quality of wheat globally. Fhb7, a major gene from 7E chromosome of Thinopyrum ponticum, confers broad resistance to multiple Fusarium species in wheat and has recently been cloned and identified as encoding a glutathione S-transferase (GST). However, some recent reports raised doubt about whether GST is the causal gene of Fhb7. To resolve the discrepancy and validate the gene function of GST in wheat, we phenotyped Fhb7 near-isogenic lines (Jimai22-Fhb7 versus Jimai22) and GST overexpressed lines for FHB resistance. Jimai22-Fhb7 showed significantly higher FHB resistance with a lower percentage of symptomatic spikelets, Fusarium-damaged kernels, and deoxynivalenol content than susceptible Jimai22 in three experiments. All the positive GST transgenic lines driven by either the maize ubiquitin promoter or its native promoter with high gene expression in the wheat cultivar 'Fielder' showed high FHB resistance. Only one maize ubiquitin promoter-driven transgenic line showed low GST expression and similar susceptibility to Fielder, suggesting that high GST expression confers Fhb7 resistance to FHB. Knockout of GST in the Jimai22-Fhb7 line using CRISPR-Cas9-based gene editing showed significantly higher FHB susceptibility compared with the nonedited control plants. Therefore, we confirmed GST as the causal gene of Fhb7 for FHB resistance. Considering its major effect on FHB resistance, pyramiding Fhb7 with other quantitative trait loci has a great potential to create highly FHB-resistant wheat cultivars.

Reduction of Fusarium head blight and trichothecene contamination in transgenic wheat expressing Fusarium graminearum trichothecene 3-O-acetyltransferase.

Fusarium graminearum, the causal agent of Fusarium head blight (FHB), produces various mycotoxins that contaminate wheat grains and cause profound health problems in humans and animals. Deoxynivalenol (DON) is the most common trichothecene found in contaminated grains. Our previous study showed that Arabidopsis-expressing F. graminearum trichothecene 3-O-acetyltransferase (FgTRI101) converted DON to 3-acetyldeoxynivalenol (3-ADON) and excreted it outside of Arabidopsis cells. To determine if wheat can convert and excrete 3-ADON and reduce FHB and DON contamination, FgTRI101 was cloned and introduced into wheat cv Bobwhite. Four independent transgenic lines containing FgTRI101 were identified. Gene expression studies showed that FgTRI101 was highly expressed in wheat leaf and spike tissues in the transgenic line FgTri101-1606. The seedlings of two FgTri101 transgenic wheat lines (FgTri101-1606 and 1651) grew significantly longer roots than the controls on media containing 5 µg/mL DON; however, the 3-ADON conversion and excretion was detected inconsistently in the seedlings of FgTri101-1606. Further analyses did not detect 3-ADON or other possible DON-related products in FgTri101-1606 seedlings after adding deuterium-labeled DON into the growth media. FgTri101-transgenic wheat plants showed significantly enhanced FHB resistance and lower DON content after they were infected with F. graminearum, but 3-ADON was not detected. Our study suggests that it is promising to utilize FgTRI101, a gene that the fungus uses for self-protection, for managing FHB and mycotoxin in wheat production.

Characterization of Quantitative Trait Loci for Leaf Rust Resistance in the Uzbekistani Wheat Landrace Teremai Bugdai.

Leaf rust, caused by Puccinia triticina, is a major cause of wheat yield losses globally, and novel leaf rust resistance genes are needed to enhance wheat leaf rust resistance. Teremai Bugdai is a landrace from Uzebekistan that is highly resistant to many races of P. triticina in the United States. To unravel leaf rust resistance loci in Teremai Bugdai, a recombinant inbred line (RIL) population of Teremai Bugdai × TAM 110 was evaluated for response to P. triticina race Pt54-1 (TNBGJ) and genotyped using single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS). Quantitative trait loci (QTL) analysis using 5,130 high-quality GBS-SNPs revealed three QTLs, QLr-Stars-2DS, QLr-Stars-6BL, and QLr.Stars-7BL, for leaf rust resistance in two experiments. QLr-Stars-2DS, which is either a new Lr2 allele or a new resistance locus, was delimited to an ∼19.47-Mb interval between 46.4 and 65.9 Mb on 2DS and explained 31.3 and 33.2% of the phenotypic variance in the two experiments. QLr-Stars-6BL was mapped in an ∼84.0-kb interval between 719.48 and 719.56 Mb on 6BL, accounting for 33 to 36.8% of the phenotypic variance in two experiments. QLr.Stars-7BL was placed in a 350-kb interval between 762.41 and 762.76 Mb on 7BL and explained 4.4 to 5.3% of the phenotypic variance. Nine GBS-SNPs flanking these QTLs were converted to kompetitive allele specific PCR (KASP) markers, and these markers can be used to facilitate their introgression into locally adapted wheat lines.

HaploCatcher: An R package for prediction of haplotypes.

Wheat (Triticum aestivum L.) is crucial to global food security but is often threatened by diseases, pests, and environmental stresses. Wheat-stem sawfly (Cephus cinctus Norton) poses a major threat to food security in the United States, and solid-stem varieties, which carry the stem-solidness locus (Sst1), are the main source of genetic resistance against sawfly. Marker-assisted selection uses molecular markers to identify lines possessing beneficial haplotypes, like that of the Sst1 locus. In this study, an R package titled "HaploCatcher" was developed to predict specific haplotypes of interest in genome-wide genotyped lines. A training population of 1056 lines genotyped for the Sst1 locus, known to confer stem solidness, and genome-wide markers was curated to make predictions of the Sst1 haplotypes for 292 lines from the Colorado State University wheat breeding program. Predicted Sst1 haplotypes were compared to marker-derived haplotypes. Our results indicated that the training set was substantially predictive, with kappa scores of 0.83 for k-nearest neighbors and 0.88 for random forest models. Forward validation on newly developed breeding lines demonstrated that a random forest model, trained on the total available training data, had comparable accuracy between forward and cross-validation. Estimated group means of lines classified by haplotypes from PCR-derived markers and predictive modeling did not significantly differ. The HaploCatcher package is freely available and may be utilized by breeding programs, using their own training populations, to predict haplotypes for whole-genome sequenced early generation material.

Identification of a new Rsg1 allele conferring resistance to multiple greenbug biotypes from barley accessions PI 499276 and PI 566459.

Greenbug [Schizaphis graminum (Rondani)] is a major insect pest that significantly affects barley production worldwide. The identification of novel greenbug resistance genes is crucial for sustainable barley production and global food security. To identify greenbug resistance genes from a US breeding line PI 499276 and a Chinese cultivar PI 566459, two F6:7 recombinant inbred line (RIL) populations developed from crosses Weskan × PI 499276 and Weskan × PI 566459 were phenotyped for responses to greenbug biotype E and genotyped using genotyping-by-sequencing (GBS). Linkage analysis using single nucleotide polymorphism and kompetitive allele-specific polymorphism (KASP) markers delimited the greenbug resistance genes from PI 499276 and PI 566459 to a 1.2 Mb genomic region between 666.5 and 667.7 Mb on the long arm of chromosome 3H in the Morex Hordeum vulgare r1 reference sequence. Allelism tests based on responses of four F2 populations to greenbug biotype E indicated that the greenbug resistance gene in PI 499276 and PI 566459 is either allelic or very close to Rsg1. Given that PI 499276 and PI 566459 shared the same unique resistance pattern to a set of 14 greenbug biotypes, which is different from those of other Rsg1 alleles, they carry a new Rsg1 allele. The greenbug resistance genes in Post 90, PI 499276/PI 566459, and WBDC 336 were designated as Rsg1.a1, Rsg1.a2, and Rsg1.a3, respectively. KASP markers KASP-Rsg1a3-1, KASP-Rsg1a3-2, and KASP160 can be used to tag Rsg1.a2 in barley breeding.

Genetic characterization of root architectural traits in barley (Hordeum vulgare L.) using SNP markers.

Increasing attention is paid to providing new tools to breeders for targeted breeding for specific root traits that are beneficial in low-fertility, drying soils; however, such information is not available for barley (Hordeum vulgare L.). A panel of 191 barley accessions (originating from Australia, Europe, and Africa) was phenotyped for 26 root and shoot traits using the semi-hydroponic system and genotyped using 21 062 high-quality single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS). The population structure analysis of the barley panel identified six distinct groups. We detected 1199 significant (P<0.001) marker-trait associations (MTAs) with r2 values up to 0.41. The strongest MTAs were found for root diameter in the top 20 cm and the longest root length. Based on the physical locations of these MTAs in the barley reference genome, we identified 37 putative QTLs for the root traits, and three QTLs for shoot traits, with nine QTLs located in the same physical regions. The genomic region 640-653 Mb on chromosome 7H was significant for five root length-related traits, where 440 annotated genes were located. The putative QTLs for various root traits identified in this study may be useful for genetic improvement regarding the adaptation of new barley cultivars to suboptimal environments and abiotic stresses.

Skim exome capture genotyping in wheat.

Next-generation sequencing (NGS) technology advancements continue to reduce the cost of high-throughput genome-wide genotyping for breeding and genetics research. Skim sequencing, which surveys the entire genome at low coverage, has become feasible for quantitative trait locus (QTL) mapping and genomic selection in various crops. However, the genome complexity of allopolyploid crops such as wheat (Triticum aestivum L.) still poses a significant challenge for genome-wide genotyping. Targeted sequencing of the protein-coding regions (i.e., exome) reduces sequencing costs compared to whole genome re-sequencing and can be used for marker discovery and genotyping. We developed a method called skim exome capture (SEC) that combines the strengths of these existing technologies and produces targeted genotyping data while decreasing the cost on a per-sample basis compared to traditional exome capture. Specifically, we fragmented genomic DNA using a tagmentation approach, then enriched those fragments for the low-copy genic portion of the genome using commercial wheat exome baits and multiplexed the sequencing at different levels to achieve desired coverage. We demonstrated that for a library of 48 samples, ∼7-8× target coverage was sufficient for high-quality variant detection. For higher multiplexing levels of 528 and 1056 samples per library, we achieved an average coverage of 0.76× and 0.32×, respectively. Combining these lower coverage SEC sequencing data with genotype imputation using a customized wheat practical haplotype graph database that we developed, we identified hundreds of thousands of high-quality genic variants across the genome. The SEC method can be used for high-resolution QTL mapping, genome-wide association studies, genomic selection, and other downstream applications.

Multi-trait genomic selection improves the prediction accuracy of end-use quality traits in hard winter wheat.

Improvement of end-use quality remains one of the most important goals in hard winter wheat (HWW) breeding. Nevertheless, the evaluation of end-use quality traits is confined to later development generations owing to resource-intensive phenotyping. Genomic selection (GS) has shown promise in facilitating selection for end-use quality; however, lower prediction accuracy (PA) for complex traits remains a challenge in GS implementation. Multi-trait genomic prediction (MTGP) models can improve PA for complex traits by incorporating information on correlated secondary traits, but these models remain to be optimized in HWW. A set of advanced breeding lines from 2015 to 2021 were genotyped with 8725 single-nucleotide polymorphisms and was used to evaluate MTGP to predict various end-use quality traits that are otherwise difficult to phenotype in earlier generations. The MTGP model outperformed the ST model with up to a twofold increase in PA. For instance, PA was improved from 0.38 to 0.75 for bake absorption and from 0.32 to 0.52 for loaf volume. Further, we compared MTGP models by including different combinations of easy-to-score traits as covariates to predict end-use quality traits. Incorporation of simple traits, such as flour protein (FLRPRO) and sedimentation weight value (FLRSDS), substantially improved the PA of MT models. Thus, the rapid low-cost measurement of traits like FLRPRO and FLRSDS can facilitate the use of GP to predict mixograph and baking traits in earlier generations and provide breeders an opportunity for selection on end-use quality traits by culling inferior lines to increase selection accuracy and genetic gains.

Identification of a Novel Pm65 Allele Conferring a Wide Spectrum of Resistance to Powdery Mildew in Wheat Accession PI 351817.

Powdery mildew is caused by the highly adaptive biotrophic fungus Blumeria graminis f. sp. tritici infecting wheat worldwide. Novel powdery mildew resistance genes are urgently needed that can be used rapidly in wheat cultivar development with minimal disruption of trait advances elsewhere. PI 351817 is a German cultivar exhibiting a wide spectrum of resistance to B. graminis f. sp. tritici isolates collected from different wheat-growing regions of the United States. Evaluation of an F2 population and 237 F2:3 lines derived from OK1059060-2C14 × PI 351817 for responses to B. graminis f. sp. tritici isolate OKS(14)-B-3-1 identified a single dominant gene, designated Pm351817, for powdery mildew resistance in PI 351817. Using bulked segregant analysis (BSA) and simple sequence repeat (SSR) markers, Pm351817 was mapped in the terminal region of the long arm of chromosome 2A. Deep sequencing of the genotyping-by-sequencing libraries of the two parental lines identified a set of single-nucleotide polymorphism (SNP) markers in the 2AL candidate gene region. Those SNP markers was subsequently converted to Kompetitive allele-specific PCR (KASP) markers for genotyping the mapping population. Linkage analysis delimited Pm351817 to a 634-kb interval between Stars-KASP656 (771,207,512 bp) and Stars-KASP662 (771,841,609 bp) on 2AL, based on the Chinese Spring reference sequence IWGSC RefSeq v 2.1. Tests of allelism indicated that Pm351817 is located at the Pm65 locus. Pm351817 shows resistance to all B. graminis f. sp. tritici isolates used in this study and can be used to enhance powdery mildew resistance in the United States. KASP markers flanking Pm351817 can be used to select Pm351817 in wheat breeding programs after further tests for polymorphism.

Reducing brassinosteroid signalling enhances grain yield in semi-dwarf wheat.

Modern green revolution varieties of wheat (Triticum aestivum L.) confer semi-dwarf and lodging-resistant plant architecture owing to the Reduced height-B1b (Rht-B1b) and Rht-D1b alleles1. However, both Rht-B1b and Rht-D1b are gain-of-function mutant alleles encoding gibberellin signalling repressors that stably repress plant growth and negatively affect nitrogen-use efficiency and grain filling2-5. Therefore, the green revolution varieties of wheat harbouring Rht-B1b or Rht-D1b usually produce smaller grain and require higher nitrogen fertilizer inputs to maintain their grain yields. Here we describe a strategy to design semi-dwarf wheat varieties without the need for Rht-B1b or Rht-D1b alleles. We discovered that absence of Rht-B1 and ZnF-B (encoding a RING-type E3 ligase) through a natural deletion of a haploblock of about 500 kilobases shaped semi-dwarf plants with more compact plant architecture and substantially improved grain yield (up to 15.2%) in field trials. Further genetic analysis confirmed that the deletion of ZnF-B induced the semi-dwarf trait in the absence of the Rht-B1b and Rht-D1b alleles through attenuating brassinosteroid (BR) perception. ZnF acts as a BR signalling activator to facilitate proteasomal destruction of the BR signalling repressor BRI1 kinase inhibitor 1 (TaBKI1), and loss of ZnF stabilizes TaBKI1 to block BR signalling transduction. Our findings not only identified a pivotal BR signalling modulator but also provided a creative strategy to design high-yield semi-dwarf wheat varieties by manipulating the BR signal pathway to sustain wheat production.

Quantitative trait loci for rolled leaf in a wheat EMS mutant from Jagger.

KEY MESSAGE: Two QTLs with major effects on rolled leaf trait were consistently detected on chromosomes 1A (QRl.hwwg-1AS) and 5A (QRl.hwwg-5AL) in the field experiments. Rolled leaf (RL) is a morphological strategy to protect plants from dehydration under stressed field conditions. Identification of quantitative trait loci (QTLs) underlining RL is essential to breed drought-tolerant wheat cultivars. A mapping population of 154 recombinant inbred lines was developed from the cross between JagMut1095, a mutant of Jagger, and Jagger to identify quantitative trait loci (QTLs) for the RL trait. A linkage map of 3106 cM was constructed with 1003 unique SNPs from 21 wheat chromosomes. Two consistent QTLs were identified for RL on chromosomes 1A (QRl.hwwg-1AS) and 5A (QRl.hwwg-5AL) in all field experiments. QRl.hwwg-1AS explained 24-56% of the phenotypic variation and QRl.hwwg-5AL explained up to 20% of the phenotypic variation. The combined percent phenotypic variation associated with the two QTLs was up to 61%. Analyses of phenotypic and genotypic data of recombinants generated from heterogeneous inbred families of JagMut1095 × Jagger delimited QRl.hwwg-1AS to a 6.04 Mb physical interval. This work lays solid foundation for further fine mapping and map-based cloning of QRl.hwwg-1AS.

Fusarium graminearum Effector FgNls1 Targets Plant Nuclei to Induce Wheat Head Blight.

Fusarium head blight (FHB) caused by Fusarium graminearum is one of the most devastating diseases of wheat and barley worldwide. Effectors suppress host immunity and promote disease development. The genome of F. graminearum contains hundreds of effectors with unknown function. Therefore, investigations of the functions of these effectors will facilitate developing novel strategies to enhance wheat resistance to FHB. We characterized a F. graminearum effector, FgNls1, containing a signal peptide and multiple eukaryotic nuclear localization signals. A fusion protein of green fluorescent protein and FgNls1 accumulated in plant cell nuclei when transiently expressed in Nicotiana benthamiana. FgNls1 suppressed Bax-induced cell death when co-expressed in N. benthamiana. We revealed that the expression of FgNLS1 was induced in wheat spikes infected with F. graminearum. The Fgnls1 mutants significantly reduced initial infection and FHB spread within a spike. The function of FgNLS1 was restored in the Fgnls1-complemented strains. Wheat histone 2B was identified as an interacting protein by FgNls1-affinity chromatography. Furthermore, transgenic wheat plants that silence FgNLS1 expression had significantly lower FHB severity than control plants. This study demonstrates a critical role of FgNls1 in F. graminearum pathogenesis and indicates that host-induced gene silencing targeting F. graminearum effectors is a promising approach to enhance FHB resistance. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.