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.

Enlisting wild grass genes to combat nitrification in wheat farming: A nature-based solution.

Active nitrifiers and rapid nitrification are major contributing factors to nitrogen losses in global wheat production. Suppressing nitrifier activity is an effective strategy to limit N losses from agriculture. Production and release of nitrification inhibitors from plant roots is termed "biological nitrification inhibition" (BNI). Here, we report the discovery of a chromosome region that controls BNI production in "wheat grass" Leymus racemosus (Lam.) Tzvelev, located on the short arm of the "Lr#3Nsb" (Lr#n), which can be transferred to wheat as T3BL.3NsbS (denoted Lr#n-SA), where 3BS arm of chromosome 3B of wheat was replaced by 3NsbS of L. racemosus We successfully introduced T3BL.3NsbS into the wheat cultivar "Chinese Spring" (CS-Lr#n-SA, referred to as "BNI-CS"), which resulted in the doubling of its BNI capacity. T3BL.3NsbS from BNI-CS was then transferred to several elite high-yielding hexaploid wheat cultivars, leading to near doubling of BNI production in "BNI-MUNAL" and "BNI-ROELFS." Laboratory incubation studies with root-zone soil from field-grown BNI-MUNAL confirmed BNI trait expression, evident from suppression of soil nitrifier activity, reduced nitrification potential, and N2O emissions. Changes in N metabolism included reductions in both leaf nitrate, nitrate reductase activity, and enhanced glutamine synthetase activity, indicating a shift toward ammonium nutrition. Nitrogen uptake from soil organic matter mineralization improved under low N conditions. Biomass production, grain yields, and N uptake were significantly higher in BNI-MUNAL across N treatments. Grain protein levels and breadmaking attributes were not negatively impacted. Wide use of BNI functions in wheat breeding may combat nitrification in high N input-intensive farming but also can improve adaptation to low N input marginal areas.

On-farm assessment of yield and quality traits in durum wheat.

BACKGROUND: Durum wheat is key source of calories and nutrients for many regions of the world. Demand for it is predicted to increase. Further efforts are therefore needed to develop new cultivars adapted to different future scenarios. Developing a novel cultivar takes, on average, 10 years and advanced lines are tested during the process, in general, under standardized conditions. Although evaluating candidate genotypes for commercial release under different on-farm conditions is a strategy that is strongly recommended, its application for durum wheat and particularly for quality traits has been limited. This study evaluated the grain yield and quality performance of eight different genotypes across five contrasting farmers' fields over two seasons. Combining different analysis strategies, the most outstanding and stable genotypes were identified. RESULTS: The analyses revealed that some traits were mainly explained by the genotype effect (thousand kernel weight, flour sodium dodecyl sulfate sedimentation volume, and flour yellowness), others by the management practices (yield and grain protein content), and others (test weight) by the year effect. In general, yield showed the highest range of variation across genotypes, management practices, and years and test weight the narrowest range. Flour yellowness was the most stable trait across management conditions, while yield-related traits were the most unstable. We also determined the most representative and discriminative field conditions, which is a beneficial strategy when breeders are constrained in their ability to develop multi-environment experiments. CONCLUSIONS: We concluded that assessing genotypes in different farming systems is a valid and complementary strategy for on-station trials for determining the performance of future commercial cultivars in heterogeneous environments to improve the breeding process and resources. © 2023 Society of Chemical Industry.

Impact of different on-farm management practices on bread wheat quality: a case study in the Yaqui Valley.

BACKGROUND: Continuous development of new wheat varieties is necessary to satisfy the demands of farmers, industry, and consumers. The evaluation of candidate genotypes for commercial release under different on-farm conditions is a strategy that has been strongly recommended to assess the performance and stability of new cultivars in heterogeneous environments and under different farming systems. The main objectives of this study were to evaluate the grain yield and quality performance of ten different genotypes across six contrasting farmers' field conditions with different irrigation and nitrogen fertilization levels, and to develop suggestions to aid breeding programs and farmers to use resources more efficiently. Genotype and genotype by environment (GGE) interaction biplot analyses were used to identify the genotypes with the strongest performance and greatest stability in the Yaqui Valley. RESULTS: Analyses showed that some traits were mainly explained by the genotype effect, others by the field management conditions, and the rest by combined effects. The most representative and diverse field conditions in the Yaqui Valley were also identified, a useful strategy when breeders have limited resources. The independent effects of irrigation and nitrogen levels and their interaction were analyzed for each trait. The results showed that full irrigation was not always necessary to maximize grain yield in the Yaqui Valley. Other suggestions for more efficient use of resources are proposed. CONCLUSIONS: The combination of on-farm trials with GGE interaction analyses is an effective strategy to include in breeding programs to improve processes and resources. Identifying the most outstanding and stable genotypes under real on-farm systems is key to the development of novel cultivars adapted to different management and environmental conditions. © 2023 Society of Chemical Industry.

Identification of Novel Genomic Regions and Superior Alleles Associated with Zn Accumulation in Wheat Using a Genome-Wide Association Analysis Method.

Micronutrient deficiencies, and especially zinc (Zn) deficiency, pose serious health problems to people who mainly depend on cereal-based diets. Here, we performed a genome-wide association study (GWAS) to detect the genetic basis of the Zn accumulation in wheat (Triticum aestivum L.) grains with a diversity panel of 207 bread wheat varieties. To uncover authentic quantitative trait loci (QTL) controlling Zn accumulation, the varieties were planted in three locations. In total, 29 unique loci associated with Zn grain accumulation were identified. Notably, seven non-redundant loci located on chromosomes 1B, 3B, 3D, 4A, 5A, 5B, and 7A, were detected at least in two environments. Of these quantitative trait loci (QTL), six coincided with known QTL or genes, whereas the highest effect QTL on chromosome 3D identified in this study was not reported previously. Searches of public databases revealed that the seven identified QTL coincided with seven putative candidate genes linked to Zn accumulation. Among these seven genes, NAC domain-containing protein gene (TraesCS3D02G078500) linked with the most significant single nucleotide polymorphism (SNP) AX-94729264 on chromosome 3D was relevant to metal accumulation in wheat grains. Results of this study provide new insights into the genetic architecture of Zn accumulation in wheat grains.

Efficient arabinoxylan assay for wheat: Exploring variability and molecular marker associations in Wholemeal and refined flour.

In this study, we present a modified high throughput phloroglucinol colorimetric assay for the quantification of arabinoxylans (AX) in wheat named PentoQuant. The method was downscaled from a 10 ml glass tube to 2 ml microcentrifuge tube format, resulting in a fivefold increase in throughput while concurrently reducing the overall cost and manual labor required for the analysis. Comparison with established colorimetric assays and gas chromatography validates the modified protocol, demonstrating its superior repeatability, rapidity, and simplicity. The effectiveness of the protocol was tested on 606 unique whole meal (WM) and refined flour (RF) bread wheat samples which revealed the presence of more than a twofold variation in both the soluble (WE-AX) and total (TOT-AX) AX fractions in WM (TOT-AX = 31.9-76.1 mg/g; WE-AX = 4.4-12.6 mg/g) and RF (TOT-AX = 7.7-22.4 mg/g; WE-AX = 3.9-11.4 mg/g). Results obtained from the AX quantification were used to test the effectiveness of four molecular markers associated with AX variation and targeting two major genomic regions on the 1BL and 6BS chromosomes. These markers appeared to be particularly relevant for the WE-AX fraction, providing insights to enable marker-assisted breeding.

Genomic insights of a native bacterial consortium for wheat production sustainability.

The use of plant growth-promoting bacteria as bioinoculants is a powerful tool to increase crop yield and quality and to improve nitrogen use efficiency (NUE) from fertilizers in plants. This study aimed to bioprospecting a native bacterial consortium (Bacillus cabrialesii subsp. cabrialesii TE3T, Priestia megaterium TRQ8, and Bacillus paralicheniformis TRQ65), through bioinformatic analysis, and to quantify the impact of its inoculation on NUE (measured through 15N-isotopic techniques), grain yield, and grain quality of durum wheat variety CIRNO C2008 grown under three doses of urea (0, 120, and 240 kg N ha-1) during two consecutive agricultural cycles in the Yaqui Valley, Mexico. The inoculation of the bacterial consortium (BC) to the wheat crop, at a total N concentration of 123-225 kg N ha-1 increased crop productivity and maintained grain quality, resulting in a yield increase of 1.1 ton ha-1 (6.0 vs. 7.1 ton ha-1, 0 kg N ha-1 added, 123 kg N ha-1 in the soil) and of 2.0 ton ha-1 (5.9 vs. 7.9 ton ha-1, 120 kg N ha-1 added, 104 kg N ha-1 in the soil) compared to the uninoculated controls at the same doses of N. The genomic bioinformatic analysis of the studied strains showed a great number of biofertilization-related genes regarding N and Fe acquisition, P assimilation, CO2 fixation, Fe, P, and K solubilization, with important roles in agroecosystems, as well as genes related to the production of siderophores and stress response. A positive effect of the BC on NUE at the studied initial N content (123 and 104 kg N ha-1) was not observed. Nevertheless, increases of 14 % and 12.5 % on NUE (whole plant) were observed when 120 kg N ha-1 was applied compared to when wheat was fully fertilized (240 kg N ha-1). This work represents a link between bioinformatic approaches of a native bacterial inoculant and the quantification of its impact on durum wheat.

Processing and Bread-Making Quality Profile of Spanish Spelt Wheat.

Spelt wheat (Triticum aestivum L. ssp. spelta Thell.) is an ancient wheat that has been widely cultivated for hundreds of years. Recently, this species has been neglected in most of Europe; however, the desire for more natural and traditional foods has driven a revival of the crop. In the current study, eighty-eight traditional spelt genotypes from Spain, together with nine common wheat cultivars and one modern spelt (cv. Anna Maria) were grown during a period of two years in Andalucia (southern Spain). In each, several traits were measured in to evaluate their milling, processing, and end-use quality (bread-making). The comparison between species suggested that, in general, spelt and common wheat showed differences for most of the measured traits; on average, spelt genotypes had softer grains, higher protein content (14.3 vs. 11.9%) and gluten extensibility (alveograph P/L 0.5 vs. 1.8), and lower gluten strength (alveograph W 187 vs. 438 × 10-4 J). In the baking test, both species showed similar values. Nevertheless, the analysis of this set of spelt genotypes showed a wide range for all measured traits, with higher values than common wheat in some spelt genotypes for some traits. This opens up the possibility of using these materials in future breeding programs, to develop either new spelt or common wheat cultivars.

New flavors from old wheats: exploring the aroma profiles and sensory attributes of local Mediterranean wheat landraces.

Introduction: During the 20th century, the worldwide genetic diversity of wheat was sharply eroded by continual selection for high yields and industry demands for particular standardized qualities. A collection of Israeli and Palestinian landraces (IPLR) was established to represent genetic diversity, accumulated for ten millennia under diverse environments, which was mostly lost in this transition. As our long-term goal is to study this pre- Green Revolution genetic reservoir, herein we focus on its flour and bread quality and sensorial attributes. Methods: Initially, a database was built for the entire IPLR collection (n=901) holding both Triticum durum (durum wheat) and T. aestivum (bread wheat) which included genetic and phenotypic characterization of agronomic traits, grain and flour quality. Then, a representative subset of the IPLR was selected and compared to modern varieties for dough quality, rheology, aroma and taste using both whole and refined flours and breads. The sensory panel used 40 subjects who evaluated common protocol or sourdough breads made by four artisan bakers. Results: Results show modern durum cultivar C-9 had superior rheological properties (gluten index, elasticity, dough development time) as compared with landraces, while bread landrace 'Diar Alla' was markedly preferable for baking in relation to the modern cultivar Gadish. Baking tests and subsequent sensory evaluation clearly demonstrated a preference toward refined breads, apart from whole breads prepared using sourdough starters. In bread wheat, loaves baked using landrace flour were scored higher in several quality parameters, whereas in durum lines, the opposite trend was evident. Loaves baked from landraces 'Diar Alla' and to a lesser extent 'Hittia Soada' presented a markedly different aroma from the control loaves prepared from modern flours, both in terms of overall compositions and individual compounds, including classes such as pyranones, pyrazines, furans and pyrroles (maltol). Modern lines, on the other hand, were consistently richer in terpenes and phenylpropanoids. Further analysis demonstrated a significant association between specific aroma classes and sensory attributes scored by panelists. Discussion: The findings of the study may help advance new niches in the local wheat market aimed at health and nutrition including adapting durum varieties to the bread market and developing flavor-enhanced wholemeal breads.

Boosting the antioxidant potential of pasta by a premature stop mutation in wheat keto-acythiolase-2.

Phenolics are a class of chemical compounds possessing antioxidant activity, which are mainly located in the wheat (Triticum aestivum) bran. Different approaches have been used in food industry to increase the availability of phenolics. Compared to these methods, however, genetic improvement of the wheat antioxidant potential, is a cost-effective, easier and safer approach. Here, we showed a single premature stop mutation in the keto-acythiolase-2 (kat-2b) gene, which significantly improved the antioxidant potential of pasta by a 60 ± 16% increase in its antioxidant potential by increasing the accumulation of ferulic acid. These changes are likely determined by the increased transcription (46% higher) and activity (120% higher) of the phenylalanine lyase genes observed in the mutated line compared to the control. Even if more studies will need to be done, overall, this study suggested that the kat-2b mutant could represent an excellent genetic resource to improve wheat's antioxidant and health-promoting potential.

High molecular weight glutenin gene diversity in Aegilops tauschii demonstrates unique origin of superior wheat quality.

Central to the diversity of wheat products was the origin of hexaploid bread wheat, which added the D-genome of Aegilops tauschii to tetraploid wheat giving rise to superior dough properties in leavened breads. The polyploidization, however, imposed a genetic bottleneck, with only limited diversity introduced in the wheat D-subgenome. To understand genetic variants for quality, we sequenced 273 accessions spanning the known diversity of Ae. tauschii. We discovered 45 haplotypes in Glu-D1, a major determinant of quality, relative to the two predominant haplotypes in wheat. The wheat allele 2 + 12 was found in Ae. tauschii Lineage 2, the donor of the wheat D-subgenome. Conversely, the superior quality wheat allele 5 + 10 allele originated in Lineage 3, a recently characterized lineage of Ae. tauschii, showing a unique origin of this important allele. These two wheat alleles were also quite similar relative to the total observed molecular diversity in Ae. tauschii at Glu-D1. Ae. tauschii is thus a reservoir for unique Glu-D1 alleles and provides the genomic resource to begin utilizing new alleles for end-use quality improvement in wheat breeding programs.

Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials.

Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson's correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson's correlation calculated by fitting a bivariate model was higher than the division of the Pearson's correlation by the squared root of the heritability across traits, by 2.53-11.46%. Across the datasets, the corrected Pearson's correlation was higher than the uncorrected by 5.80-14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits.

Genome-based prediction of multiple wheat quality traits in multiple years.

Wheat quality improvement is an important objective in all wheat breeding programs. However, due to the cost, time and quantity of seed required, wheat quality is typically analyzed only in the last stages of the breeding cycle on a limited number of samples. The use of genomic prediction could greatly help to select for wheat quality more efficiently by reducing the cost and time required for this analysis. Here were evaluated the prediction performances of 13 wheat quality traits under two multi-trait models (Bayesian multi-trait multi-environment [BMTME] and multi-trait ridge regression [MTR]) using five data sets of wheat lines evaluated in the field during two consecutive years. Lines in the second year (testing) were predicted using the quality information obtained in the first year (training). For most quality traits were found moderate to high prediction accuracies, suggesting that the use of genomic selection could be feasible. The best predictions were obtained with the BMTME model in all traits and the worst with the MTR model. The best predictions with the BMTME model under the mean arctangent absolute percentage error (MAAPE) were for test weight across the five data sets, whereas the worst predictions were for the alveograph trait ALVPL. In contrast, under Pearson's correlation, the best predictions depended on the data set. The results obtained suggest that the BMTME model should be preferred for multi-trait prediction analyses. This model allows to obtain not only the correlation among traits, but also the correlation among environments, helping to increase the prediction accuracy.