Molecular Plant最新文献

Volatilomics is essential for understanding the biological functions and fragrance contributions of plant volatiles. However, the annotation coverage achieved using current untargeted and widely targeted volatomics (WTV) methods has been limited by low sensitivity and/or low acquisition coverage. Here, we introduce WTV 2.0, which enabled the construction of a high-coverage library containing 2111 plant volatiles, and report the development of a comprehensive selective ion monitoring (cSIM) acquisition method, including the selection of characteristic qualitative ions with the minimal ion number for each compound and an optimized segmentation method, that can acquire the smallest but sufficient number of ions for most plant volatiles, as well as the automatic qualitative and semi-quantitative analysis of cSIM data. Importantly, the library and acquisition method we developed can be self-expanded by incorporating compounds not present in the library, utilizing the obtained cSIM data. We showed that WTV 2.0 increases the median signal-to-noise ratio by 7.6-fold compared with the untargeted method, doubled the annotation coverage compared with the untargeted and WTV 1.0 methods in tomato fruit, and led to the discovery of menthofuran as a novel flavor compound in passion fruit. WTV 2.0 is a Python library with a user-friendly interface and is applicable to profiling of volatiles and primary metabolites in any species.

Understanding the underlying mechanisms and links between genome evolution and adaptive innovations stands as a key goal in evolutionary studies. Poplars, among the world's most widely distributed and cultivated trees, exhibit extensive phenotypic diversity and environmental adaptability. In this study, we present a genus-level super-pangenome comprising 19 Populus genomes, revealing the likely pivotal role of private genes in facilitating local environmental and climate adaptation. Through the integration of pangenomes with transcriptomes, methylomes, and chromatin accessibility mapping, we unveil that the evolutionary trajectories of pangenes and duplicated genes are closely linked to local genomic landscapes of regulatory and epigenetic architectures, notably CG methylation in gene-body regions. Further comparative genomic analyses have enabled the identification of 142 202 structural variants across species that intersect with a significant number of genes and contribute substantially to both phenotypic and adaptive divergence. We have experimentally validated a ∼180-bp presence/absence variant affecting the expression of the CUC2 gene, crucial for leaf serration formation. Finally, we developed a user-friendly web-based tool encompassing the multi-omics resources associated with the Populus super-pangenome (http://www.populus-superpangenome.com). Together, the present pioneering super-pangenome resource in forest trees not only aids in the advancement of breeding efforts of this globally important tree genus but also offers valuable insights into potential avenues for comprehending tree biology.

In plants and mammals, non-homologous end-joining is the dominant pathway to repair DNA double-strand breaks, making it challenging to generate knock-in events. In this study, we identified two groups of exonucleases from the herpes virus and the bacteriophage T7 families that conferred an up to 38-fold increase in homology-directed repair frequencies when fused to Cas9/Cas12a in a tobacco mosaic virus-based transient assay in Nicotiana benthamiana. We achieved precise and scar-free insertion of several kilobases of DNA both in transient and stable transformation systems. In Arabidopsis thaliana, fusion of Cas9 to a herpes virus family exonuclease led to 10-fold higher frequencies of knock-ins in the first generation of transformants. In addition, we demonstrated stable and heritable knock-ins in wheat in 1% of the primary transformants. Taken together, our results open perspectives for the routine production of heritable knock-in and gene replacement events in plants.

RNA-binding proteins (RBPs) accompany RNA from synthesis to decay, mediating every aspect of RNA metabolism and impacting diverse cellular and developmental processes in eukaryotes. Many RBPs undergo phase separation along with their bound RNA to form and function in dynamic membraneless biomolecular condensates for spatiotemporal coordination or regulation of RNA metabolism. Increasing evidence suggests that phase-separating RBPs with RNA-binding domains and intrinsically disordered regions play important roles in plant development and stress adaptation. Here, we summarize the current knowledge about how dynamic partitioning of RBPs into condensates controls plant development and enables sensing of experimental changes to confer growth plasticity under stress conditions, with a focus on the dynamics and functional mechanisms of RBP-rich nuclear condensates and cytoplasmic granules in mediating RNA metabolism. We also discuss roles of multiple factors, such as environmental signals, protein modifications, and N⁶-methyladenosine RNA methylation, in modulating the phase separation behaviors of RBPs, and highlight the prospects and challenges for future research on phase-separating RBPs in crops.

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.