Exploring the genetic architecture of powdery mildew resistance in wheat through QTL meta-analysis.

Powdery mildew (PM), caused by Blumeria graminis f. sp. tritici, poses a significant threat to wheat production, necessitating the development of genetically resistant varieties for long-term control. Therefore, exploring genetic architecture of PM in wheat to uncover important genomic regions is an important area of wheat research. In recent years, the utilization of meta-QTL (MQTL) analysis has gained prominence as an essential tool for unraveling the complex genetic architecture underlying complex quantitative traits. The aim of this research was to conduct a QTL meta-analysis to pinpoint the specific genomic regions in wheat responsible for governing PM resistance. This study integrated 222 QTLs from 33 linkage-based studies using a consensus map with 54,672 markers. The analysis revealed 39 MQTLs, refined to 9 high-confidence MQTLs (hcMQTLs) with confidence intervals of 0.49 to 12.94 cM. The MQTLs had an average physical interval of 41.00 Mb, ranging from 0.000048 Mb to 380.71 Mb per MQTL. Importantly, 18 MQTLs co-localized with known resistance genes like Pm2, Pm3, Pm8, Pm21, Pm38, and Pm41. The study identified 256 gene models within hcMQTLs, providing potential targets for marker-assisted breeding and genomic prediction programs to enhance PM resistance. These MQTLs would serve as a foundation for fine mapping, gene isolation, and functional genomics studies, facilitating a deeper understanding of molecular mechanisms. The identification of candidate genes opens up exciting possibilities for the development of PM-resistant wheat varieties after validation.

Aryl selenonium vs. aryl sulfonium counterions in polyoxometalate chemistry: the impact of Se+ cationic centers on the photocatalytic reduction of dichromate.

A selenonium organic counter ion has been used in polyoxometalate chemistry to develop a new aryl selenonium polyoxometalate (POM) hybrid, and its photocatalytic properties have been explored in comparison with an aryl sulfonium POM-hybrid counterpart for the first time. The chalcogenonium counterions, namely, methyldiphenylsulfonium trifluoromethane sulfonate (MDPST) and methyldiphenylselenonium trifluoromethane sulfonate (MDPSeT), and their octamolybdate ([Mo8O26]4-) hybrids, 1 and 2, with the general formula (C13H13X)4[Mo8O26] (where X = S for 1 and Se for 2) were synthesized and characterized. Hybrids 1 and 2 vary in their chalcogenonium cationic center (S+vs. Se+), which enabled a direct comparison of their photocatalytic properties as a function of the cationic center. The photocatalytic activities of hybrids 1 and 2 were tested using the reduction of dichromate (Cr2O72-) as a model reaction under UV irradiation. A 99% photocatalytic reduction of Cr2O72- with a rate constant of 0.0305 min-1 was achieved with hybrid 2, while only a 67% reduction with a rate constant of 0.0062 min-1 was observed with hybrid 1 in 180 minutes. The better catalytic performance of hybrid 2 may be correlated to the larger atomic radii of Se than S, which helps in better stabilizing the photogenerated electron-hole (e--h+) pair on the POM cluster by polarizing its lone pair more efficiently compared to S. The catalytic recyclability was tested for up to 4 cycles using hybrid 2, and up to 98% reduction was obtained even after the 4th cycle. Recyclability tests and control experiments also indicated the generation of some elemental Se through possible cleavage of some C-Se bonds of MDPSe under prolonged UV exposure during catalysis, and the Se thus generated was found to contribute to the catalytic reduction of dichromate. This study, therefore, opens new avenues for aryl selenonium moieties and their POM hybrids for potential catalytic applications.

Meta-QTL analysis in wheat: progress, challenges and opportunities.

Wheat, an important cereal crop globally, faces major challenges due to increasing global population and changing climates. The production and productivity are challenged by several biotic and abiotic stresses. There is also a pressing demand to enhance grain yield and quality/nutrition to ensure global food and nutritional security. To address these multifaceted concerns, researchers have conducted numerous meta-QTL (MQTL) studies in wheat, resulting in the identification of candidate genes that govern these complex quantitative traits. MQTL analysis has successfully unraveled the complex genetic architecture of polygenic quantitative traits in wheat. Candidate genes associated with stress adaptation have been pinpointed for abiotic and biotic traits, facilitating targeted breeding efforts to enhance stress tolerance. Furthermore, high-confidence candidate genes (CGs) and flanking markers to MQTLs will help in marker-assisted breeding programs aimed at enhancing stress tolerance, yield, quality and nutrition. Functional analysis of these CGs can enhance our understanding of intricate trait-related genetics. The discovery of orthologous MQTLs shared between wheat and other crops sheds light on common evolutionary pathways governing these traits. Breeders can leverage the most promising MQTLs and CGs associated with multiple traits to develop superior next-generation wheat cultivars with improved trait performance. This review provides a comprehensive overview of MQTL analysis in wheat, highlighting progress, challenges, validation methods and future opportunities in wheat genetics and breeding, contributing to global food security and sustainable agriculture.

Classification of properties and their relation to chemical bonding: Essential steps toward the inverse design of functional materials.

To design advanced functional materials, different concepts are currently pursued, including machine learning and high-throughput calculations. Here, a different approach is presented, which uses the innate structure of the multidimensional property space. Clustering algorithms confirm the intricate structure of property space and relate the different property classes to different chemical bonding mechanisms. For the inorganic compounds studied here, four different property classes are identified and related to ionic, metallic, covalent, and recently identified metavalent bonding. These different bonding mechanisms can be quantified by two quantum chemical bonding descriptors, the number of electrons transferred and the number of electrons shared between adjacent atoms. Hence, we can link these bonding descriptors to the corresponding property portfolio, turning bonding descriptors into property predictors. The close relationship between material properties and quantum chemical bonding descriptors can be used for an inverse material design, identifying particularly promising materials based on a set of target functionalities.

The potential of chemical bonding to design crystallization and vitrification kinetics.

Controlling a state of material between its crystalline and glassy phase has fostered many real-world applications. Nevertheless, design rules for crystallization and vitrification kinetics still lack predictive power. Here, we identify stoichiometry trends for these processes in phase change materials, i.e. along the GeTe-GeSe, GeTe-SnTe, and GeTe-Sb2Te3 pseudo-binary lines employing a pump-probe laser setup and calorimetry. We discover a clear stoichiometry dependence of crystallization speed along a line connecting regions characterized by two fundamental bonding types, metallic and covalent bonding. Increasing covalency slows down crystallization by six orders of magnitude and promotes vitrification. The stoichiometry dependence is correlated with material properties, such as the optical properties of the crystalline phase and a bond indicator, the number of electrons shared between adjacent atoms. A quantum-chemical map explains these trends and provides a blueprint to design crystallization kinetics.