Plant Communications最新文献

Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been developed for predicting gene expression. However, existing methods do not take into consideration the effect of chromatin interactions on target gene expression, thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements. In this study, we developed a highly accurate deep learning-based gene expression prediction model (DeepCBA) based on maize chromatin interaction data. Compared with existing models, DeepCBA exhibits higher accuracy in expression classification and expression value prediction. The average Pearson correlation coefficients (PCCs) for predicting gene expression using gene promoter proximal interactions, proximal-distal interactions, and both proximal and distal interactions were 0.818, 0.625, and 0.929, respectively, representing an increase of 0.357, 0.16, and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences. Some important motifs were identified through DeepCBA; they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity. Importantly, experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression. Moreover, promoter editing and verification of two reported genes (ZmCLE7 and ZmVTE4) demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding. DeepCBA is available at http://www.deepcba.com/ or http://124.220.197.196/.

Plant jasmonoyl-L-isoleucine (JA-Ile) is a major defense signal against insect feeding, but whether or how insect salivary effectors suppress JA-Ile synthesis and thus facilitate viral transmission in the plant phloem remains elusive. Insect carboxylesterases (CarEs) are the third major family of detoxification enzymes. Here, we identify a new leafhopper CarE, CarE10, that is specifically expressed in salivary glands and is secreted into the rice phloem as a saliva component. Leafhopper CarE10 directly binds to rice jasmonate resistant 1 (JAR1) and promotes its degradation by the proteasome system. Moreover, the direct association of CarE10 with JAR1 clearly impairs JAR1 enzyme activity for conversion of JA to JA-Ile in an in vitro JA-Ile synthesis system. A devastating rice reovirus activates and promotes the co-secretion of virions and CarE10 via virus-induced vesicles into the saliva-storing salivary cavities of the leafhopper vector and ultimately into the rice phloem to establish initial infection. Furthermore, a virus-mediated increase in CarE10 secretion or overexpression of CarE10 in transgenic rice plants causes reduced levels of JAR1 and thus suppresses JA-Ile synthesis, promoting host attractiveness to insect vectors and facilitating initial viral transmission. Our findings provide insight into how the insect salivary protein CarE10 suppresses host JA-Ile synthesis to promote initial virus transmission in the rice phloem.

Despite considerable advances in extracting crucial insights from bio-omics data to unravel the intricate mechanisms underlying complex traits, the absence of a universal multi-modal computational tool with robust interpretability for accurate phenotype prediction and identification of trait-associated genes remains a challenge. This study introduces the dual-extraction modeling (DEM) approach, a multi-modal deep-learning architecture designed to extract representative features from heterogeneous omics datasets, enabling the prediction of complex trait phenotypes. Through comprehensive benchmarking experiments, we demonstrate the efficacy of DEM in classification and regression prediction of complex traits. DEM consistently exhibits superior accuracy, robustness, generalizability, and flexibility. Notably, we establish its effectiveness in predicting pleiotropic genes that influence both flowering time and rosette leaf number, underscoring its commendable interpretability. In addition, we have developed user-friendly software to facilitate seamless utilization of DEM's functions. In summary, this study presents a state-of-the-art approach with the ability to effectively predict qualitative and quantitative traits and identify functional genes, confirming its potential as a valuable tool for exploring the genetic basis of complex traits.

Whole-genome genotyping (WGG) stands as a pivotal element in genomic-assisted plant breeding. Nevertheless, sequencing-based approaches for WGG continue to be costly, primarily owing to the high expenses associated with library preparation and the laborious protocol. During prior development of foreground and background integrated genotyping by sequencing (FBI-seq), we discovered that any sequence-specific primer (SP) inherently possesses the capability to amplify a massive array of stable and reproducible non-specific PCR products across the genome. Here, we further improved FBI-seq by replacing the adapter ligated by Tn5 transposase with an arbitrary degenerate (AD) primer. The protocol for the enhanced FBI-seq unexpectedly mirrors a simplified thermal asymmetric interlaced (TAIL)-PCR, a technique that is widely used for isolation of flanking sequences. However, the improved TAIL-PCR maximizes the primer-template mismatched annealing capabilities of both SP and AD primers. In addition, leveraging of next-generation sequencing enhances the ability of this technique to assay tens of thousands of genome-wide loci for any species. This cost-effective, user-friendly, and powerful WGG tool, which we have named TAIL-PCR by sequencing (TAIL-peq), holds great potential for widespread application in breeding programs, thereby facilitating genome-assisted crop improvement.

To combat pathogen attacks, plants have developed a highly advanced immune system, which requires tight regulation to initiate robust defense responses while simultaneously preventing autoimmunity. The ubiquitin-proteasome system (UPS), which is responsible for degrading excess or misfolded proteins, has vital roles in ensuring strong and effective immune responses. E3 ligases, as key UPS components, play extensively documented roles in rice immunity by modulating the ubiquitination and degradation of downstream substrates involved in various immune signaling pathways. Here, we summarize the crucial roles of rice E3 ligases in both pathogen/microbe/damage-associated molecular pattern-triggered immunity and effector-triggered immunity, highlight the molecular mechanisms by which E3 ligases function in rice immune signaling, and emphasize the functions of E3 ligases as targets of pathogen effectors for pathogenesis. We also discuss potential strategies for application of immunity-associated E3 ligases in breeding of disease-resistant rice varieties without growth penalty. This review provides a comprehensive and updated understanding of the sophisticated and interconnected regulatory functions of E3 ligases in rice immunity and in balancing immunity with growth and development.

The future of agriculture is uncertain under the current climate change scenario. Climate change directly and indirectly affects the biotic and abiotic elements that control agroecosystems, jeopardizing the safety of the world's food supply. A new area that focuses on characterizing the phytobiome is emerging. The phytobiome comprises plants and their immediate surroundings, involving numerous interdependent microscopic and macroscopic organisms that affect the health and productivity of plants. Phytobiome studies primarily focus on the microbial communities associated with plants, which are referred to as the plant microbiome. The development of high-throughput sequencing technologies over the past 10 years has dramatically advanced our understanding of the structure, functionality, and dynamics of the phytobiome; however, comprehensive methods for using this knowledge are lacking, particularly for major crops such as rice. Considering the impact of rice production on world food security, gaining fresh perspectives on the interdependent and interrelated components of the rice phytobiome could enhance rice production and crop health, sustain rice ecosystem function, and combat the effects of climate change. Our review re-conceptualizes the complex dynamics of the microscopic and macroscopic components in the rice phytobiome as influenced by human interventions and changing environmental conditions driven by climate change. We also discuss interdisciplinary and systematic approaches to decipher and reprogram the sophisticated interactions in the rice phytobiome using novel strategies and cutting-edge technology. Merging the gigantic datasets and complex information on the rice phytobiome and their application in the context of regenerative agriculture could lead to sustainable rice farming practices that are resilient to the impacts of climate change.