Metabolomics is an effective biotechnological tool that can be used to attain comprehensive information on metabolites. In this study, the profiles of metabolites produced by wheat seedlings in response to drought stress were investigated using an untargeted approach with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) to determine various physiological processes related to drought tolerance from the cross between drought-tolerant genotype (HX10) and drought-sensitive genotype (YN211). The current study results showed that under drought stress, HX10 exhibited higher growth indices than YN211. After drought stress treatment, a series of phenolics accumulated higher in HX10 than in YN211, whereas the amount of thymine, a pyrimidine, is almost 13 folds of that in YN211. These metabolites, as well as high levels of different amino acids, alkaloids, organic acids, and flavonoids in the drought treated HX10 could help to explain its strong drought-tolerant capacity. The current study explored the understanding of the mechanisms involved in the drought response of wheat seedling; these metabolome data could also be used for potential QTL or GWAS studies to identify locus (loci) or gene(s) associated with these metabolic traits for the crop improvement.

Wheat is one of the most important crops in the world. Mapping QTLs for root traits is essential for the selection of wheat roots desirable for the efficient acquisition of nutrients. Here, a QTL analysis for wheat root traits was performed using 142 recombinant inbred lines derived from two wheat varieties Xiaoyan 54 and Jing 411 in a soil column culture trial. The genetic map used in this study contained 470 SSR markers and covered 3438.4 cM of wheat genome. A total of 25 QTLs for root and shoot traits were detected, located at 16 marker intervals of 13 chromosomes. The percentage of phenotypic variation explained by individual QTLs varied from 6.1% to 22.0%. The QTLs regulating RDW and root distribution on chromosomes 1A, 3A, 4A, and 5B are important for root growth in both the top- and subsoils. For qRDW-1A, qRDW-3A, and qRDW-5B, the nearest markers to the QTLs were much closer than that of qRDW-4A, with the genetic distances ranging from 0.01 to 1.18 cM. Combining these three QTLs not only increased RDW and nutrient uptake, but also increased GW, SDW, and BDW under low nitrogen conditions in the field trial. Therefore, these QTLs are valuable for marker-assisted selection of wheat root traits.

Excessive input of nitrogen fertilizer not only causes a great waste of resources but brings about a series of ecological and environmental problems. Although Small Auxin Up-regulated RNAs (SAURs) participate in diverse biological processes, the function of SAURs in the nitrogen starvation response has not been well-studied. Here, we identified 308 TaSAURs in wheat and divided them into 10 subfamilies. The promoter regions of most TaSAURs contain hormone responsive elements, and their expression levels change under the treatment of different hormones, such as IAA, MeJA, and ABA. Interestingly, overexpression of one of the TaSAUR family members, a nitrogen starvation responsive gene, TaSAUR66-5B, can promote the growth of Arabidopsis and wheat roots. In addition, overexpression of TaSAUR66-5B in Arabidopsis up-regulates the expression levels of auxin biosynthesis related genes, suggesting that overexpression TaSAUR66-5B may promote root growth by increasing the biosynthesis of auxin. Furthermore, overexpression of TaSAUR66-5B in wheat can increase the biomass and grain yields of transgenic plants, as well as the nitrogen concentration and accumulation of both shoots and grains, especially under low nitrogen conditions. This study provides important genomic information of the TaSAUR gene family and lays a foundation for elucidating the functions of TaSAURs in improving nitrogen utilization efficiency in wheat.

Plants have evolved intricate signaling pathways, which operate as networks governed by feedback to deal with stressors. Nevertheless, the sophisticated molecular mechanisms underlying these routes still need to be comprehended, and experimental validation poses significant challenges and expenses. Consequently, computational hypothesis evaluation gains prominence in understanding plant signaling dynamics. Biosensors are genetically modified to emit light when exposed to a particular hormone, such as abscisic acid (ABA), enabling quantification. We developed computational models to simulate the relationship between ABA concentrations and bioluminescent sensors utilizing the Hill equation and ordinary differential equations (ODEs), aiding better hypothesis development regarding plant signaling. Based on simulation results, the luminescence intensity was recorded for a concentration of 47.646 RLUs for 1.5 μmol, given the specified parameters and model assumptions. This method enhances our understanding of plant signaling pathways at the cellular level, offering significant benefits to the scientific community in a cost-effective manner. The alignment of these computational predictions with experimental results emphasizes the robustness of our approach, providing a cost-effective means to validate mathematical models empirically. The research intended to correlate the bioluminescence of biosensors with plant signaling and its mathematical models for quantified detection of specific plant hormone ABA.