New Crops最新文献

Microbes accompany plants throughout their entire lifecycles, from seeds to ripe fruits. Plant–microbe interactions have long been a focus of research in many subdisciplines, leading to thousands of articles that demonstrate the importance of these interactions in agriculture. Here, we review previous findings and discuss future directions and prospects for the application of plant–microbe interactions. These interactions are delineated from multiple perspectives: community composition, interaction pathways, influencing external and endogenous factors, methods and techniques for analysis, and potential targeted applications in agriculture. We propose that exploitation and utilization of core beneficial microbes, artificial microbial community assembly, and in situ regulation of microbiome function will become essential components of agricultural production in the future.

Throughout their life cycle, plants encounter a myriad of challenges arising from both abiotic and biotic stresses, which significantly impact crop yield and nutritional content. In natural ecological settings, plants often experience simultaneous exposure to multiple stresses, prompting intricate crosstalk interactions between different stress types. While current research predominantly addresses individual stress responses, the nuanced interplay among plants facing multiple stresses remains a subject requiring extensive exploration. Plants exposed to one type of stress have demonstrated the capacity to influence their responses to other stressors, indicating the presence of complex stress response networks shaped by their enduring coexistence with diverse environmental pressures. Within these networks, transcription factors emerge as pivotal regulators of stress-responsive genes, positioned as promising candidates for enhancing crop resilience. Notably, certain transcription factors have exhibited the ability to modulate plant tolerance to a spectrum of stresses, suggesting their potential role as convergence points within regulation networks responding to diverse stresses. Extensively studied transcription factors, including NAC, MYB, WRKY, bHLH, and ERF/DREB, are recognized for their crucial involvement in both abiotic and biotic stress responses. Beyond transcription factors, phytohormone signaling pathways governed by abscisic acid, salicylic acid, jasmonic acid, ethylene, and ROS are pivotal in orchestrating the crosstalk between biotic and abiotic stress signaling. This comprehensive review aims to encapsulate the current progress in understanding the intricate crosstalk dynamics underlying plant responses to abiotic and biotic stresses. Furthermore, it delves into the molecular mechanisms orchestrated by transcription factors to navigate the challenges posed by both abiotic and biotic stressors. The review also explores the involvement of transcription factors in regulating phytohormone signaling pathways, providing a holistic perspective on the multifaceted responses of plants to the complexities of their environmental stresses.

Hybrid rice breeding has significantly increased the yield and stability of rice production. However, due to trait segregation in F2 progeny seeds, the F1 hybrid seeds must be renewed annually. Apomixis is a form of reproduction leading to clonal seeds, which has a revolutionary potential in fixing heterosis and realizing selfretention of hybrid rice seeds. Previously, we successfully engineered synthetic apomixis in rice by combining Mitosis instead of Meiosis (MiMe) with haploid induction (mutation of MATRILINEAL), a strategy known as Fix for Fixation of hybrids. However, only one hybrid rice variety has been tested for the Fix synthetic apomixis system. In this study, we expanded the application of the Fix strategy and achieved synthetic apomixis in multiple hybrid rice varieties. We observed significant variations in seed setting rate across diverse genetic backgrounds and witnessed a remarkable tenfold increase in clonal seed efficiency. Our findings will provide valuable insights into the application of the Fix strategy for synthetic apomixis in hybrid rice.

Tomato (Solanum lycopersicum), belonging to the Solanaceae family, holds the distinction of being the second most important vegetable crop on a global scale. As a model plant renowned for its insights into fruit ripening and disease resistance, the collaborative analysis of multi-omics data takes on an indispensable role in advancing the flavor and genetic traits of this vital crop. In our endeavor, we have seamlessly integrated a staggering 343 transcriptome datasets to create a co-expression network, including global network and conditional network, offering a expression view for multi-dimensional insight into gene expression patterns. Simultaneously, we harnessed the power of 136 epigenomic datasets to define 35 distinct chromatin states, employing the sophisticated ChromHMM algorithm. Our pursuit of holistic understanding culminated in the fusion of multi-omics data, encompassing the genome, transcriptome, and epigenome. This comprehensive approach extends to functional identification, offering invaluable insights into the intricate web of biological interactions. Our offering goes beyond mere data analysis; it presents a platform for comparative network exploration, enabling users to draw meaningful comparisons between two networks. Additionally, we have thoughtfully included extensive annotation for gene sets, encompassing GO terms, KEGG pathways, plantCyc, gene families, literature references, miRNA targets, and functional modules. The culmination of our efforts is the Tomato multi-omics data Analysis Platform (TomAP, http://bioinformatics.cau.edu.cn/TomAP/). The co-expression network and the defined chromatin states open up a realm of possibilities, not only for investigating the commonalities and variations among co-expressed genes in the context of chromatin states but also for comparative functional assessments of orthologs across species. Our aspiration is that TomAP will become avaluable resource for the research community, enabling the identification of functional genes or modules that underpin critical tomato agronomic traits.