The Plant Journal最新文献

Tenderness of tea leaves tightly associates with the quality and commercial value of tea beverage. Cellulose synthesis and secondary cell wall (SCW) formation negatively correlate with the tenderness of tea shoots. Light is one of the core environmental factors affecting cellulose synthesis and SCW formation; however, how light quality regulating cellulose accumulation remains unclear in tea [Camellia sinensis (L.) Kuntze]. In this study, we identified a transcription factor CsMYB192, which was repressed by red/blue light at both transcription and protein levels, could promote cellulose biosynthesis and SCW formation by targeting cellulose synthase-encoding genes CsCesAs in tea plants. Specifically, treatment with red/blue light individually or simultaneously significantly reduced cellulose contents, thickness of SCW, as well as transcription of CsCesAs to a certain extent in new shoots. Further analysis showed that CsCesA4, 7, 8A, and 8B, four CsCesAs involved in SCW cellulose biosynthesis, interacted with each other and contributed to cellulose accumulation and SCW formation in tea leaves. Moreover, by performing yeast one-hybrid screening of the tea cDNA library using promoter of the four CsCesAs as baits, respectively, we identified a common R2R3-MYB transcription factor CsMYB192 shared by the four targets. Further analysis revealed that CsMYB192 directly bound to promoter of the four targets and activated their transcriptions. Moreover, silencing CsMYB192 using the tobacco rattle virus (TRV)-mediated gene silencing system resulted in compromised cellulose biosynthesis and SCW formation in tea plants, while ectopic expression of CsMYB192 in Arabidopsis mutant myb46, which is a homolog of CsMYB192, rescued the growth defect, as well as decreased cellulose contents, of the mutant plants. Therefore, our data dissect a possible pathway by which red/blue light regulates cellulose biosynthesis and SCW formation through the CsMYB192-CsCesAs module in tea plants, which might be involved in manipulating the tenderness of tea shoots.

Salweenia species are evergreen shrubs capable of preventing desertification and maintaining the health of alpine dry–warm ecosystems in the Hengduan Mountains. However, both the narrowly distributed S. bouffordiana and its more widespread close relative S. wardii are endemic and endangered. Furthermore, their small population sizes render each of these species at risk of extinction. To infer how past climate changes have shaped the evolutionary history of these species, we developed a chromosome-level S. bouffordiana genome (788 Mb) and compared the two species' evolutionary histories, genetic loads and the genomic adaptions to local environmental conditions using whole-genome resequencing data. Our findings reveal a sharp population decline from the Pliocene to the Quaternary. However, populations of S. bouffordiana then started to recover before declining further, while S. wardii populations continued to decline until recently. Abundant homozygous-derived variants accumulated in the two species, particularly in S. bouffordiana, while the species with the most heterozygous variants was S. wardii. Accumulated extensive inbreeding effects but possessed few LOF mutations and few highly deleterious variants in the S. bouffordiana that have experienced the most severe demographic bottlenecks, most likely because of purging effects. This accelerating decline cascade will likely be detrimental to the consequences for the species' future viability and adaptive potential. Overall, this study improves our understanding of the evolutionary history of Salweenia shrubs tolerant to extreme environments and offers a genetic resource for future breeding and conservation efforts.

Long non-coding RNAs (lncRNAs), once overlooked as transcriptional byproducts, are now recognized for their crucial roles in plant growth, development, and stress responses, with increasing focus on their epigenetic regulation. However, studies investigating epigenomic signals to explore the functions of lncRNAs in plants remain relatively limited. This study collected a comprehensive dataset of over 160 000 high-quality lncRNAs from 19 representative plant species and integrated 6715 ChIP-seq, BS-seq, and RNA-seq datasets to analyze epigenomic patterns at lncRNA loci. Results showed elevated DNA methylation in lncRNA regions. The highest levels occurred in transposable element-associated lncRNAs. Additionally, activating histone modifications at lncRNA loci showed tissue specificity, with epigenetic preferences differed from those at protein-coding gene (PCG) loci. Differential site analysis in epigenetic mutants further highlighted the selective regulation of lncRNA loci by specific epigenetic factors. To facilitate research, we developed PERlncDB, a platform that provides species-specific lncRNA browsing, epigenetic annotation, cross-species conservation analysis, and visualization of epigenomic landscapes. Case studies on MARS and LINC-AP2 emphasized the platform's utility. Conserved epigenetic mechanisms regulating lncRNAs across species, exemplified by a syntenic conserved MET1-regulated lncRNA pair in Arabidopsis and tomato, suggested the stability of regulatory mechanisms underlying lncRNA functions. This work provides critical insights and resources for understanding plant lncRNA epigenetic regulation.

Saline-alkali stress, as a widely existing abiotic stress, severely restricts plant growth and development. NAC transcription factors play a key role in plants' adaptation to abiotic stresses such as low temperature, drought, and salt damage. This study found that SlNAC35, a member of the NAC transcription factor family, negatively affects saline-alkali tolerance in tomato (Solanum lycopersicum L.) by directly targeting and regulating the allene oxide cyclase (AOC) SlAOC, thereby inhibiting jasmonic acid (JA) biosynthesis. Overexpression of SlAOC significantly increased JA accumulation and reduced the excessive accumulation of reactive oxygen species under saline-alkali stress, indicating that SlAOC plays a positive role in JA synthesis and saline-alkali tolerance in tomato. Further studies confirmed that SlNAC35 interacts with the E3 ubiquitin ligase SlMIEL1, which mediates the ubiquitin-dependent degradation of SlNAC35. This reduces its inhibitory effect on SlAOC, promoting JA accumulation and enhancing the saline-alkali tolerance of tomato. This study reveals that the SlNAC35-SlMIEL1 module enhances tomato tolerance to saline-alkali stress by regulating JA synthesis and accumulation. The findings link E3 ubiquitin ligase-mediated post-translational modifications to JA biosynthesis in the tomato's response to saline-alkali stress, filling a gap in the research field of ubiquitination-regulated JA synthesis to combat saline-alkali stress.

Plant pathogens, such as fungi and oomycetes, primarily rely on the secretion of effector proteins to successfully colonize host plants and suppress immune responses, ultimately leading to disease symptoms. Therefore, accurately identifying these effector proteins is crucial for understanding the pathogenic mechanisms and developing strategies for disease resistance. However, the current effector protein identification mainly depends on experimental screening methods, which are labor-intensive and costly. In recent years, the rapid development of deep learning approaches and the emergence of protein language models (PLMs) have presented unprecedented opportunities for developing new bioinformatics methods to identify effector proteins. In this context, we propose Plant Pathogen Effector Protein Finder (PPEPFinder), an integrated deep learning framework designed to predict effector proteins of fungi and oomycetes. PPEPFinder consists of three individual predictive models: (i) a sequence-based transformer model that utilizes rich semantic embeddings generated by a pre-trained PLM, Evolutionary Scale Modeling (ESM), as input; (ii) two structure-based Graph Attention Network models that represent protein structures as residue contact graphs using the ESM-generated embeddings or the structure pre-trained model SaProt embeddings as node features. To maximize the predictive performance, PPEPFinder employs a logistic regression model to aggregate the three predictors' outputs into a final prediction score. Compared to existing state-of-the-art effector prediction tools, PPEPFinder demonstrates superior performance by jointly leveraging sequence and structural information. We have made PPEPFinder and its associated datasets freely accessible to the community through an online prediction server (http://zzdlab.com/PPEPFinder/) and GitHub (https://github.com/mdiyuan/PPEPFinder).