Dynamic metabolite QTL analyses provide novel biochemical insights into kernel development and nutritional quality improvement in common wheat

Transcriptome analysis reveals the key roles of TaSMP1 and ABA signaling pathway in wheat seed dormancy and germination

MAIN CONCLUSION

This study analyzed dynamic transcriptome profiles to reveal differential expression patterns of ABA related and LEA protein family genes and verified that TaSMP1 affects seed germination by interacting with TaABI5. Seed dormancy is a crucial survival strategy for plants to cope with environmental stresses. High levels of seed dormancy result in uneven germination, while low levels of seed dormancy increase the risk of pre-harvest sprouting (PHS), which threatens crop yield and quality. Therefore, achieving the optimal balance between seed dormancy and germination is vital for maximum potential crop yield and quality. This study constructed dynamic transcriptome expression profiles of the germination process for the weakly dormant wheat variety Jing 411 (J411) and the strongly dormant landrace variety Hongsuibai (HSB), revealing the temporal expression of differentially expressed genes. Plant hormone-related genes played a crucial role in the early germination response, particularly the abscisic acid (ABA) signaling gene TaABI5 and the ABA catabolism gene TaCYP707A1. The late embryogenesis abundant (LEA) protein family genes exhibited differential expression patterns during the germination of seeds with varying levels of dormancy. The TaSMP1 gene, a member of the LEA protein family, was identified as a negative regulator of seed dormancy, interacting directly with the key transcription factor TaABI5 in the ABA signaling pathway and influencing the expression of the seed germination gene TaDOG1L1. This study provides essential insights into the molecular mechanisms balancing seed dormancy and germination, offering potential targets for enhancing wheat resistance to PHS.

Metabolic marker-assisted genomic prediction improves hybrid breeding

Hybrid breeding is widely acknowledged as the most effective method for increasing crop yield, particularly in maize and rice. However, a major challenge in hybrid breeding is the selection of desirable combinations from the vast pool of potential crosses. Genomic selection (GS) has emerged as a powerful tool to tackle this challenge, but its success in practical breeding depends on prediction accuracy. Several strategies have been explored to enhance prediction accuracy for complex traits, such as the incorporation of functional markers and multi-omics data. Metabolome-wide association studies (MWAS) help to identify metabolites that are closely linked to phenotypes, known as metabolic markers. However, the use of preselected metabolic markers from parental lines to predict hybrid performance has not yet been explored. In this study, we developed a novel approach called metabolic marker-assisted genomic prediction (MM_GP), which incorporates significant metabolites identified from MWAS into GS models to improve the accuracy of genomic hybrid prediction. In maize and rice hybrid populations, MM_GP outperformed genomic prediction (GP) for all traits, regardless of the method used (genomic best linear unbiased prediction or eXtreme gradient boosting). On average, MM_GP demonstrated 4.6% and 13.6% higher predictive abilities than GP for maize and rice, respectively. MM_GP could also match or even surpass the predictive ability of M_GP (integrated genomic-metabolomic prediction) for most traits. In maize, the integration of only six metabolic markers significantly associated with multiple traits resulted in 5.0% and 3.1% higher average predictive ability compared with GP and M_GP, respectively. With advances in high-throughput metabolomics technologies and prediction models, this approach holds great promise for revolutionizing genomic hybrid breeding by enhancing its accuracy and efficiency.

Wheat2035: Integrating pan-omics and advanced biotechnology for future wheat design

Wheat (Triticum aestivum) production is vital for global food security, providing energy and protein to millions of people worldwide. Recent advancements in wheat research have led to significant increases in production, fueled by technological and scientific innovation. Here, we summarize the major advancements in wheat research, particularly the integration of biotechnologies and a deeper understanding of wheat biology. The shift from multi-omics to pan-omics approaches in wheat research has greatly enhanced our understanding of the complex genome, genomic variations, and regulatory networks to decode complex traits. We also outline key scientific questions, potential research directions, and technological strategies for improving wheat over the next decade. Since global wheat production is expected to increase by 60% in 2050, continued innovation and collaboration are crucial. Integrating biotechnologies and a deeper understanding of wheat biology will be essential for addressing future challenges in wheat production, ensuring sustainable practices and improved productivity.

Constructing the metabolic network of wheat kernels based on structure-guided chemical modification and multi-omics data

Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities, although it often proves to be challenging and labor-intensive, particularly with non-model organisms. In this study, we develop a computational approach that employs reaction models based on the structure-guided chemical modification and related compounds to construct a metabolic network in wheat. This construction results in a comprehensive structure-guided network, including 625 identified metabolites and additional 333 putative reactions compared with the Kyoto Encyclopedia of Genes and Genomes database. Using a combination of gene annotation, reaction classification, structure similarity, and correlations from transcriptome and metabolome analysis, a total of 229 potential genes related to these reactions are identified within this network. To validate the network, the functionality of a hydroxycinnamoyltransferase (TraesCS3D01G314900) for the synthesis of polyphenols and a rhamnosyltransferase (TraesCS2D01G078700) for the modification of flavonoids are verified through in vitro enzymatic studies and wheat mutant tests, respectively. Our research thus supports the utility of structure-guided chemical modification as an effective tool in identifying causal candidate genes for constructing metabolic networks and further in metabolomic genetic studies.