Wheat Microbiome: Structure, Dynamics, and Role in Improving Performance Under Stress Environments

Wheat is an important cereal crop species consumed globally. The growing global population demands a rapid and sustainable growth of agricultural systems. The development of genetically efficient wheat varieties has solved the global demand for wheat to a greater extent. The use of chemical substances for pathogen control and chemical fertilizers for enhanced agronomic traits also proved advantageous but at the cost of environmental health. An efficient alternative environment-friendly strategy would be the use of beneficial microorganisms growing on plants, which have the potential of controlling plant pathogens as well as enhancing the host plant’s water and mineral availability and absorption along with conferring tolerance to different stresses. Therefore, a thorough understanding of plant-microbe interaction, identification of beneficial microbes and their roles, and finally harnessing their beneficial functions to enhance sustainable agriculture without altering the environmental quality is appealing. The wheat microbiome shows prominent variations with the developmental stage, tissue type, environmental conditions, genotype, and age of the plant. A diverse array of bacterial and fungal classes, genera, and species was found to be associated with stems, leaves, roots, seeds, spikes, and rhizospheres, etc., which play a beneficial role in wheat. Harnessing the beneficial aspect of these microbes is a promising method for enhancing the performance of wheat under different environmental stresses. This review focuses on the microbiomes associated with wheat, their spatio-temporal dynamics, and their involvement in mitigating biotic and abiotic stresses.

WAPO-A1 is the causal gene of the 7AL QTL for spikelet number per spike in wheat

Improving our understanding of the genes regulating grain yield can contribute to the development of more productive wheat varieties. Previously, a highly significant QTL affecting spikelet number per spike (SNS), grain number per spike (GNS) and grain yield was detected on chromosome arm 7AL in multiple genome-wide association studies. Using a high-resolution genetic map, we established that the A-genome homeolog of WHEAT ORTHOLOG OF APO1 (WAPO-A1) was a leading candidate gene for this QTL. Using mutants and transgenic plants, we demonstrate in this study that WAPO-A1 is the causal gene underpinning this QTL. Loss-of-function mutants wapo-A1 and wapo-B1 showed reduced SNS in tetraploid wheat, and the effect was exacerbated in wapo1 combining both mutations. By contrast, spikes of transgenic wheat plants carrying extra copies of WAPO-A1 driven by its native promoter had higher SNS, a more compact spike apical region and a smaller terminal spikelet than the wild type. Taken together, these results indicate that WAPO1 affects SNS by regulating the timing of terminal spikelet formation. Both transgenic and wapo1 mutant plants showed a wide range of floral abnormalities, indicating additional roles of WAPO1 on wheat floral development. Previously, we found three widespread haplotypes in the QTL region (H1, H2 and H3), each associated with particular WAPO-A1 alleles. Results from this and our previous study, show that the WAPO-A1 allele in the H1 haplotype (115-bp deletion in the promoter) is expressed at significantly lower levels in the developing spikes than the alleles in the H2 and H3 haplotypes, resulting in reduced SNS. Field experiments also showed that the H2 haplotype is associated with the strongest effects in increasing SNS and GNS (H2>H3>H1). The H2 haplotype is already present in most modern common wheat varieties but is rare in durum wheat, where it might be particularly useful to improve grain yield.

Technological Potential of Saratov Spring Durum Wheat Grain

Introduction. Durum wheat is vital for high-quality pasta production. The present research tested the high technological potential of durum wheat varieties developed in the Saratov region. The research objective was to study the effect of the quality of durum wheat on the quality of pasta. Study objects and methods. The study featured durum wheat of the following varieties: Saratovskaya Zolotistaya, Valentina, Nik, Krasnokutka 13, Luch 25, Pamyati Vasilchuka, Bezenchukskaya 182 and Annushka. The experiment involved an original PSL-13 press for standard spaghetti with a diameter of 1.8 mm. The content of protein, raw gluten, and their quality were determined by standard methods. The cooking properties of the pasta were evaluated according to the method developed in the South-Eastern Federal Agricultural Research Center. Results and discussion. The indicators of raw gluten and protein are known to correlate. The samples of Saratovskaya Zolotistaya and Luch 25 had a high protein content of 15.3 and 15.6%, respectively, as well as a high content of raw gluten (33.2 and 35.1%, respectively). The raw gluten of Saratov varieties proved to be much better than in the control samples. The indicator of microSDs sedimentation was 30–36 mm. The strength of spaghetti followed the increase in crude gluten (33–35%) and protein (15.3–15.6%), which is typical of this type of pasta. The strength, coefficient of determination (R2 = 0.98), and sharing force (R2 = 0.92) depended on the protein content. Conclusion. The study established the following optimal selection criteria for durum wheat varieties to be used in strong spaghetti production: virtuosity – 80%, raw gluten – 33–35%, protein content – 5–7% higher than normal, raw gluten – 72–80 units.

A Major Quantitative Trait Loci Cluster Controlling Three Components of Yield and Plant Height Identified on Chromosome 4B of Common Wheat

Wheat yield is not only affected by three components of yield, but also affected by plant height (PH). Identification and utilization of the quantitative trait loci (QTL) controlling these four traits is vitally important for breeding high-yielding wheat varieties. In this work, we conducted a QTL analysis using the recombinant inbred lines (RILs) derived from a cross between two winter wheat varieties of China, “Nongda981” (ND981) and “Nongda3097” (ND3097), exhibiting significant differences in spike number per unit area (SN), grain number per spike (GNS), thousand grain weight (TGW), and PH. A total of 11 environmentally stable QTL for these four traits were detected. Among them, four major and stable QTLs (QSn.cau-4B-1.1, QGns.cau-4B-1, QTgw.cau-4B-1.1, and QPh.cau-4B-1.2) explaining the highest phenotypic variance for SN, GNS, TGW, and PH, respectively, were mapped on the same genomic region of chromosome 4B and were considered a QTL cluster. The QTL cluster spanned a genetic distance of about 12.3 cM, corresponding to a physical distance of about 8.7 Mb. Then, the residual heterozygous line (RHL) was used for fine mapping of the QTL cluster. Finally, QSn.cau-4B-1.1, QGns.cau-4B-1, and QPh.cau-4B-1.2 were colocated to the physical interval of about 1.4 Mb containing 31 annotated high confidence genes. QTgw.cau-4B-1.1 was divided into two linked QTL with opposite effects. The elite NILs of the QTL cluster increased SN and PH by 55.71–74.82% and 14.73–23.54%, respectively, and increased GNS and TGW by 29.72–37.26% and 5.81–11.24% in two environments. Collectively, the QTL cluster for SN, GNS, TGW, and PH provides a theoretical basis for improving wheat yield, and the fine-mapping result will be beneficial for marker-assisted selection and candidate genes cloning.

Mineral composition of wheat species as influenced by different fertilizer sources and different weed control practices

This research was carried out to determine the effects of agronomic practices on the mineral composition of organically-grown wheat species. In terms of all nutrients evaluated, the mineral content of wheat showed significant differences according to crop years, varieties, weed control methods and fertilizer sources. As the average of all factors, the Cu, Fe, Mn, Se, Zn, Cd, Co, Cr, Ni and Pb contents of the ground wheat grain were; 3.93, 42.8, 79.6, 0.549, 11.34, 0.012, 0.140, 0.194, 3.71 and 0.269 mg / kg, respectively. According to the wheat varieties, the Kırik was superior in terms of Cu, Fe, Se, Zn, Co and Cr, and the Dogu-88 was superior in terms of Mn, Cd, and Ni. The effect of weed control methods on mineral content was variable. According to fertilizer sources, the highest mineral content was obtained from the control plots without fertilizer treatments. The lowest mineral contents were obtained from chemical fertilization, cattle manure and organic fertilizer applications. There was no significant increase in the mineral content of wheat with organic fertilization, however, organic agriculture still preserves its place in terms of healthy food. As a result, it has been determined that the values obtained for all mineral elements were not at a level that pose a risk on the environment, human and animal health according to WHO.

Analysis of historical selection in winter wheat

Winter wheat is a major crop with a rich selection history in the modern era of crop breeding. Genetic gains across economically important traits like yield have been well characterized and are the major force driving its production. Winter wheat is also an excellent model for analyzing historical genetic selection. As a proof of concept, we analyze two major collections of winter wheat varieties that were bred in western Europe from 1916 to 2010, namely the Triticeae Genome (TG) and WAGTAIL panels, which include 333 and 403 varieties respectively. We develop and apply a selection mapping approach, Regression of Alleles on Years (RALLY), in these panels, as well as in simulated populations. RALLY maps loci under sustained historical selection by using a simple logistic model to regress allele counts on years of variety release. To control for drift-induced allele frequency change, we develop a hybrid approach of genomic control and delta control. Within the TG panel, we identify 22 significant RALLY quantitative selection loci (QSLs) and estimate the local heritabilities for 12 traits across these QSLs. By correlating predicted marker effects with RALLY regression estimates, we show that alleles whose frequencies have increased over time are heavily biased towards conferring positive yield effect, but negative effects in flowering time, lodging, plant height and grain protein content. Altogether, our results (1) demonstrate the use of RALLY to identify selected genomic regions while controlling for drift, and (2) reveal key patterns in the historical selection in winter wheat and guide its future breeding.