Plant Communications最新文献

The formation of symbiotic associations with rhizospheric microbes is an important strategy for sessile plants to acquire nitrogen and phosphorus from the soil. Root exudate plays a key role in shaping the rhizosphere microbiome. Depending on their needs for nitrogen or phosphorus, plants can adjust the composition of root exudate to attract the appropriate microbes. Flavonoids, a group of secondary metabolites, have been well studied for their role in shaping the root microbiome, particularly in mediating root nodule symbiosis in legumes. However, the mechanism by which plants regulate the absorption of microbe-mediated nitrogen and phosphorus remains unclear. Here, we show that the Medicago truncatula phosphate starvation response regulatory network SPX1/3-PHR2 controls flavonoid biosynthesis to recruit nitrogen-fixing microbes for nitrogen acquisition. Nitrogen-fixing microbes, including rhizobia, were fewer recruited in the rhizosphere of the spx1spx3 double mutant. This was caused by lower flavonoid levels in the root exudate compared to wild-type plants R108. Further results indicate that the control of flavonoid biosynthesis is exerted via PHR2, the interacting transcription factor of SPX1/3. Under phosphate-limiting conditions, PHR2 suppresses the expression of flavonoid biosynthetic genes to reduce root nodule symbiosis levels. Under phosphate-sufficient conditions, the interaction between SPX1/3 and PHR2 releases this suppression, thereby promoting root nodule symbiosis. We further showed that PHR2 can bind to the promoter regions of flavonoid biosynthetic genes in yeast. We propose that the SPX1/3-PHR2 network can modulate root nodule-dependent nitrogen acquisition in response to phosphate levels. Thus, the SPX1/3-PHR2 module contributes to maintaining a balance in microbe-mediated nitrogen and phosphorus acquisition for optimal plant growth.

Heterosis has significantly improved crop yields, yet hybrid seed production remains hindered by labor-intensive manual emasculation. Although current male-sterility systems, such as cytoplasmic male sterility and environment-sensitive genic male sterility, have improved the efficiency of hybrid seed production, their limited genetic adaptability and high environmental dependence remain major challenges. Here, we report an all-in-one seed production technology (ASPT) that integrates CRISPR-Cas9, RUBY, and key seed production technology (SPT) components into a single vector, enabling efficient generation and propagation of male-sterile lines in both maize and rice. The engineered RUBY marker enables visual identification of male-sterile and maintainer lines, with an accuracy of 99.81% in automated seed sorting and 100% in secondary field screening. Notably, ASPT was successfully introduced into 21 genetically diverse elite maize inbred lines, demonstrating broad compatibility. ASPT enables scalable and precise propagation of male-sterile lines in both maize and rice, providing a broadly applicable strategy to advance hybrid seed production in crops.

RNA splicing removes non-coding introns from pre-mRNA to produce mature mRNA in eukaryotes. Accurate identification of splice sites is essential for the understanding of gene structures. Previous gene annotation and prediction heavily rely on the availability of high-quality genome assemblies, intensive functional studies and massive amount of resources, which restrict the analysis and application of the genomic sequences in various non-model species. Here, we present a deep learning-based model training framework that is able to develop accurate intron splice site prediction models for diverse species with relatively limited transcriptomic data. The UniSplicer-based models (http://www.unisplicer.com) outperform existing prediction models in various species, from plants to fungi and metazoans. UniSplicer-based models prediction scores could serve as reliable indicators of the effects of mutations in various types of splice mutants. Moreover, UniSplicer A. thaliana model identified genes in Arabidopsis ecotypes that exhibit abnormal splicing due to sequence variations near splice sites, which may be under environmental selection. Overall, UniSplicer-based models achieved high prediction accuracy and provided insights into how sequence variations result in splicing alteration of genes in large genomic data sets.

Excessive cadmium (Cd) in rice grains seriously threatens food security. Cd uptake by roots and transport to grains are mediated by the transporters for mineral elements in rice. Therefore, the reduction of Cd accumulation is often accompanied by the decrease of mineral elements. How to substantially reduce grain Cd concentrations and maintain proper concentrations of mineral elements is the bottleneck in low-Cd rice breeding. Here, we report that the combination of elite alleles of OsNRAMP5 and OsHMA3, two key Cd transporter-encoding genes, conferred low-Cd accumulation in grains without causing sensitivity to Mn-deficiency in Layandabu (LAA), a tropical japonica rice cultivar. The amino acid substitution at position 313 from serine (S) to phenylalanine (F) in OsNRAMP5^LAA weakens its binding to OsVAP1-3, which is a vesicle-associated membrane protein (VAMP)-associated protein and facilitates OsNRAMP5 export from the endoplasmic reticulum (ER) to the plasma membrane. This results in partial retention of OsNRAMP5^LAA in the ER, consequently diminishing Cd and Mn uptake in rice. Introgression of the linked OsNRAMP5^LAA and OsHMA3^LAA into a commercial rice cultivar WSSM dramatically reduced brown rice Cd concentrations in Cd-contaminated fields without yield penalty, even when exposed to heat and low-Mn stress. Thus, our findings provide mechanistic insights into the balance between low-Cd accumulation and stress resilience and a novel strategy for developing rice cultivars with low-Cd grains and broad adaptability.

Centromeres are essential for accurate chromosome segregation and genome stability; with the advent of telomere-to-telomere genome assemblies, they have become central targets of genome-wide studies. Here, we present CentriVision, a modular bioinformatics platform that integrates candidate centromere identification, structural similarity assessment, DNA repeat unit decomposition, and a framework for exploring potential relationships between single-nucleotide conservation and functional features. CentriVision provides a comprehensive suite of analytical tools, including edit-distance dot plots, intra-segment heatmaps, kilobase-scale mini-dot plots, repeat monomer scanning with conserved-site visualization, and satellite DNA expansion-divergence estimation, all of which can be seamlessly integrated with CENH3 chromatin immunoprecipitation sequencing (ChIP-seq) data. When applied to representative plant species, CentriVision achieved high predictive accuracy and revealed diverse organizational patterns. Arabidopsis thaliana centromeres are primarily composed of 178-188-bp repeats interspersed with rarer ∼502-bp variants that exhibit pronounced sequence conservation but only background CENH3-ChIP signal, suggesting that these elements represent pre-centromeric sequences overlooked in earlier studies. Oryza sativa contains two dominant classes of centromeric repeats rather than the single class previously reported. In contrast, Zea mays exhibits strongly biased expansion toward the evolution of a single dominant repeat unit, reflecting a distinct evolutionary strategy of centromere reconstruction, whereas Papaver setigerum displays a notable three-layered nested repeat structure. Integration of repeat sequence divergence with CENH3 binding further revealed lineage-specific evolutionary trajectories of centromere specification. Collectively, these findings advance our understanding of centromere structure and function. CentriVision offers a reproducible, scalable, and user-friendly framework that quantitatively links repeat evolution, structural variation, and functional epigenomics, providing new insights into the architecture and diversification of plant centromeres.

Crop traits are the integrated outcome of genetic variation, environmental conditions, and their complex interactions, rendering accurate prediction from genetic markers alone a persistent challenge. Here, we present KineticGP, a computational framework that combines genomic prediction with genotype-specific kinetic models of C₄ photosynthesis to make predictions of leaf photosynthetic traits across genotypes from a multiple-parent advanced generation intercross maize population. Using genetic markers and gas exchange measurements from three field seasons, we show that KineticGP outperforms a baseline genomic prediction model in predicting the photosynthetic rate at saturating light by 86% for unseen genotypes across two seen seasons. In addition, KineticGP enabled us to survey genetic variability in enzyme kinetic parameters, which can be used to identify targets for the improvement of photosynthesis. This approach paves the way for interrogating and integrating the dynamic interactions between genotype and environment to improve the accuracy of photosynthetic trait predictions.