Current Plant Biology最新文献

{"title":"Phenotypic evaluation of worldwide germplasm of arugula (Eruca sativa Mill.) and identification of underlying latent factors contributing to phenotypic variation under indoor farming conditions","authors":"Seam Choon Law ,&nbsp;Ting Xiang Neik ,&nbsp;Ethan Tze Cherng Lim ,&nbsp;Adrian Ming Jern Lee ,&nbsp;Yi Lin Lim ,&nbsp;Wan Zu Tang ,&nbsp;Shuang Song ,&nbsp;Pei-Wen Ong ,&nbsp;Sin Joe Ng ,&nbsp;Fook Tim Chew","doi":"10.1016/j.cpb.2025.100528","DOIUrl":"10.1016/j.cpb.2025.100528","url":null,"abstract":"<div><div><em>Eruca sativa</em> (arugula) is often consumed fresh in regions where raw salads are a dietary staple. Studies investigating the phenotypic diversity of <em>E. sativa</em> have been reported in the past differentiating them by gene pools according to geographical origins. We expanded the scope of analysis to include deep phenotypes, and the diversity of germplasm. Furthermore, there is no report of such crop being evaluated in a large scale under indoor farming conditions. In this study, 185 accessions were subjected to phenotypic evaluation across 68 phenotypic traits. High-throughput phenotyping machines and image processing platforms employed were efficient to measure vegetative yield-, hyperspectral-, and plant architecture-related traits of <em>E. sativa</em>. Wide phenotypic variations were evidenced in the collection and significant differences were observed between accessions in majority of the traits evaluated. The population genetic structure divided the germplasm collection into three major continental clusters (Asia, Africa, and Europe). In addition, the three major continental clusters also showed significant differences in the tendency to flower early, vegetative leafy plant yield, plant height, vegetative index, hairiness and leaf blade color. Factor analysis revealed nine underlying latent factors contributing approximately 70 % of the total phenotypic variations, with each potentially enhancing crop’s productivity and quality. Based on desirable agronomic traits that are suitable for controlled environment agriculture (CEA), bivariate analysis was conducted using four latent factors (Total yield-, plant height-, post-harvest-, and flowering-related). Subsequently, three ideal accessions (ERU12, PI 178901, and PI 251491) were highlighted as high-yielding, short, long shelf-life crops for potential future plant breeding and genetic improvement.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"43 ","pages":"Article 100528"},"PeriodicalIF":4.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding stress specific transcriptional regulation by causality aware Graph-Transformer deep learning","authors":"Umesh Bhati ,&nbsp;Akanksha Sharma ,&nbsp;Sagar Gupta ,&nbsp;Anchit Kumar ,&nbsp;Upendra Kumar Pradhan ,&nbsp;Ravi Shankar","doi":"10.1016/j.cpb.2025.100521","DOIUrl":"10.1016/j.cpb.2025.100521","url":null,"abstract":"<div><div>Cells respond to environmental stimuli through transcriptional reprogramming orchestrated by transcription factors (TFs) which interpret cis-regulatory DNA sequences to determine the timing and locations of gene expression. The diversification of TFs and their interactions with cis-regulatory elements (CREs) underpins plant adaptation to stress through the formation of gene regulatory networks (GRNs). However, deciphering condition-specific GRNs through selective TF bindings for spatio-temporal gene expression remains major challenge in plant biology. To decipher that the present study brings forward a novel computational framework designed to reason about the spatio-temporal dynamics of TF interaction. Leveraging over ∼23TB of multi-omics data (ChIP-seq, RNA-seq, and protein-protein interaction), a system of Bayesian causal networks was raised. It is capable of explaining TF’s conditional bindings across diverse conditions for <em>Arabidopsis</em>. These networks, validated against extensive experimental data, became input to a Graph Transformer deep learning system. Models were developed for 110 abiotic stress-related TFs, enabling accurate condition-specific detection of TF binding directly from RNA-seq data, bypassing the need for separate ChIP-seq experiments. The approach, CTF-BIND achieved a high average accuracy of ∼93 % when tested against a large volume of experimentally established data from various conditions. It is implemented as an interactive, open-access web server and database which captures dynamic shifts in regulatory pathways. CTF-BIND revolutionizes TF condition-specific binding identification with deep-learning, offering a cost-effective alternative to ChIP-seq. It is expected to accelerate the research towards crop improvement strategies. CTF-BIND is freely available as a web server at <span><span>https://hichicob.ihbt.res.in/ctfbind/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"43 ","pages":"Article 100521"},"PeriodicalIF":4.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcription factors participate in methyl jasmonate-induced diterpenoid biosynthesis in Andrographis paniculata","authors":"Yuan Li ,&nbsp;Yue Shi ,&nbsp;Yong Fan ,&nbsp;Guangxi Ren ,&nbsp;Dan Jiang ,&nbsp;Kuangwei Cao ,&nbsp;Yaogong Zhang ,&nbsp;Zhengyan Li ,&nbsp;Da Li ,&nbsp;Chunsheng Liu","doi":"10.1016/j.cpb.2025.100530","DOIUrl":"10.1016/j.cpb.2025.100530","url":null,"abstract":"<div><div><em>Andrographis paniculata</em> is renowned for its wide range of pharmaceutical properties, largely owing to the presence of bioactive diterpenoids. However, the mechanism of methyl jasmonate (MeJA) -induced diterpenoid biosynthesis in <em>A. paniculata</em> remains poorly understood. In this study, we found that the MeJA-induced accumulation of diterpenoids was attributed to the increased expression of genes involved in diterpenoid biosynthetic pathways. Transient overexpression and Y1H assays revealed that <em>ApMYC2</em>, <em>ApbZIP46</em>, and <em>ApWRKY33</em> were positive regulators that promoted the accumulation of diterpenoids by directly binding to the promoters of the downstream target gene <em>ApUGT76E1</em>. Thus, <em>ApMYC2</em>, <em>ApbZIP46</em>, and <em>ApWRKY33</em> may be involved in the regulation of the diterpenoid biosynthesis pathway in <em>A. paniculata</em>. Overall, this research lays the groundwork for elucidating the molecular mechanism by which <em>MYC</em>s, <em>bZIP</em>s and <em>WRKY</em>s regulate the accumulation of diterpenoids in <em>A. paniculata</em> under MeJA induction. Our results provide a theoretical basis for the molecular breeding and quality improvement of <em>A. paniculata</em> in the future.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"43 ","pages":"Article 100530"},"PeriodicalIF":4.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined transcriptome and metabolome analysis reveal the chemical composition diversity and ferulate 5-hydroxylase mediated metabolite regulatory mechanism in Polygonatum","authors":"Rong Liu,&nbsp;Pingtao Wang,&nbsp;Pijian Lu,&nbsp;Ya Dai,&nbsp;Wang Wu,&nbsp;Yurui Chen,&nbsp;Xin Chen","doi":"10.1016/j.cpb.2025.100527","DOIUrl":"10.1016/j.cpb.2025.100527","url":null,"abstract":"<div><div>The active ingredients in different <em>Polygonatum</em> species (P-HJ) differ greatly, which causes confusion regarding their use. This study was to systematically compare the contents of the main active ingredients of different P-HJ (pharmacopoeia), as well as the types and contents of other metabolic compounds. Analyzed the mechanisms of the main active component synthesis in P-HJ and the related disease regulatory network. The microstructure, physicochemical indices, LC-MS/MS, RNA-Seq, and pharmacological network analysis were performed on <em>Polygonatum cyrtonema</em> Hua (CM), <em>Polygonatum sibiricum</em> Red. (SM), and <em>Polygonatum kingianum</em> Coll. et Hemsl (KM). The phenotypes and microstructures are sufficiently different to distinguish the authenticity of various species of <em>Polygonatum</em>. A total of 672 metabolites were identified including flavonoids, phenolic acids, and saccharides, etc. These metabolic compounds have different characteristics and accumulation patterns in the CM, SM, and KM. The active components in different germplasms had significant differences to affected the medicinal quality. Key metabolites and regulated genes were identified in flavonoid, lignin, and saccharide biosynthesis by association network analysis, including <em>ferulate 5-hydroxylase</em> (<em>F5H</em>). These key genes were verified using RT-qPCR. The subcellular localization and transgenic (gene overexpression) verification was conducted for <em>F5H</em>. In <em>Polygonatum</em>, 28 differentially accumulated metabolites (DAMs) have 156 targets and 134 related diseases by pharmacological network analysis. This study provides an important basis for the high-quality breeding of P-HJ.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"43 ","pages":"Article 100527"},"PeriodicalIF":4.5,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Soursop leaf disease classification with a lightweight ensemble model and explainable AI","authors":"Sumaya Mustofa,&nbsp;Shahrin Khan,&nbsp;Shahriar Ahmed Shovo,&nbsp;Yousuf Rayhan Emon,&nbsp;Md. Sadekur Rahman","doi":"10.1016/j.cpb.2025.100526","DOIUrl":"10.1016/j.cpb.2025.100526","url":null,"abstract":"<div><div>Traditional deep-learning methods to detect plant leaf disease can be complex and time-consuming if image numbers and size increase. Moreover, complex deep learning networks take longer and require larger memory to produce results. However, feature extraction methods provide some advantages in such a scenario. Using heavy-weighted models to enhance accuracy without considering the long execution time is a drawback of research. A weighted model increases the time and space complexity of an experiment. Considering the mentioned limitations, this study proposes a lightweight model experimenting with six deep feature extraction models, five feature selection models, and four machine learning classifiers. During the experiment, a soft voting ensemble classifier was developed to remove a single classifier's limitations and the unstable performance of the standalone classifiers. After a rigorous experiment, the (ResNet101 – RFE – Ensemble Classifier) together formed the best performer Soursop Ensemble (S-Ensemble) model that obtained a test accuracy of 99.6 % with an execution time of 648.05 s, outperforming other models. The whole experimental analysis was performed on a primary Soursop leaf disease dataset with six classes containing 3838 images. Finally, the Explainable AI (XAI) model Local Interpretable Model-agnostic Explanations (LIME) is used to interpret the reasons behind the best-performer and lowest-performer models' performance. LIME visually highlights which leaf regions influence each prediction, helping users understand model behaviour and enhancing its practical usability in real-world agricultural settings. This research aims to assist farmers with detecting Soursop leaf disease with less execution time and offer researchers an in-depth preview of deep feature-based detection and classification technology to detect and classify diseases within a short training time.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"43 ","pages":"Article 100526"},"PeriodicalIF":4.5,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144723443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Caleosin expression enhances plant insect resistance","authors":"Sakihito Kitajima ,&nbsp;Toshiharu Akino ,&nbsp;Hideki Yoshida ,&nbsp;Kenji Miura ,&nbsp;Toki Taira ,&nbsp;Eric Hyrmeya Savadogo ,&nbsp;Naoki Tani","doi":"10.1016/j.cpb.2025.100525","DOIUrl":"10.1016/j.cpb.2025.100525","url":null,"abstract":"<div><div>This study investigated the anti-insect activity of the caleosin homolog CLO3, which accumulates in the latex of <em>Euphorbia tirucalli</em> (Euphorbiaceae). <em>Nicotiana benthamiana</em> leaves transiently producing EtCLO3 were fed to <em>Spodoptera litura</em> (Lepidoptera) larvae, and their body weights were recorded. The production of EtCLO3 significantly retarded larval growth. Similar effects were observed with other plants’ caleosin homologs that share unique N-terminal motifs located upstream of the Ca^2 + -binding EF-hand, including <em>Arabidopsis thaliana</em> CLO3 (AT2G33380) and homologs from lower plants (liverworts Mapoly0027s0099 and <em>Chlamydomonas</em> Cre06.g273650_4532). In contrast, <em>A. thaliana</em> CLO5 (AT5G19530), which belongs to a different class of caleosins, did not exhibit this growth retardation effect. Notably, the anti-insect activity of EtCLO3 persisted even when mutated in its peroxygenase catalytic site or EF-hand. A transcriptome analysis revealed that EtCLO3 up-regulated endogenous defense-related gene expression levels and altered sugar metabolism pathways. These findings suggest that EtCLO3 may, at least in part, exert its anti-insect effects by activating the host plant’s endogenous defense system. This research provides insights into how EtCLO3 and some other homologs influence larval development and suggests potential applications for these proteins in pest management.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"43 ","pages":"Article 100525"},"PeriodicalIF":4.5,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strong elicitation of plant defense pathways by foliar and collar inoculations of wheat with the Bacillus pumilus strain JM79","authors":"Aline Ballot ,&nbsp;Matthieu Gaucher ,&nbsp;Marjolaine Rey ,&nbsp;Marie-Noelle Brisset ,&nbsp;Pierre Joly ,&nbsp;Assia Dreux-Zigha ,&nbsp;Ahmed Taïbi ,&nbsp;Thierry Langin ,&nbsp;Claire Prigent-Combaret","doi":"10.1016/j.cpb.2025.100524","DOIUrl":"10.1016/j.cpb.2025.100524","url":null,"abstract":"<div><div>The ability of the <em>Bacillus pumilus</em> JM79 strain to induce systemic resistance in wheat against <em>Fusarium graminearum</em> (<em>Fg</em>), a major wheat pathogen, was investigated using the Fusarium crown rot (FCR) pathosystem. The <em>B. pumilus</em> strain JM79 exhibited the ability to colonize both root and leaf surfaces while secreting surfactin-like pumilacidin in the root zone of wheat plantlets. Experiments involving foliar inoculation with JM79 revealed its ability to induce a strong local defense response in wheat, characterized by the selective overexpression of genes associated with phenylpropanoid metabolism and cell wall reinforcement pathways. Moreover, pre-inoculation of the wheat collar, at the soil surface interface, with the JM79 strain prior to <em>Fg</em> inoculation led to the overexpression of wheat genes linked to both jasmonic acid/ethylene (JA/ET) and salicylic acid (SA)-dependent defense pathways. This direct induction occurred during the asymptomatic phase of <em>Fg</em> infection, compensating for the lack or absence of an early immune response triggered by <em>Fg</em> infection. Collectively, these findings reveal for the first time that the <em>B. pumilus</em> strain JM79 produces a higher proportion of long-chain pumilacidins under <em>in planta</em> conditions than under <em>in vitro</em> conditions<em>,</em> and is capable of activating both local and systemic resistance in wheat plants, underscoring its potential as a biocontrol agent against major wheat fungal diseases.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"43 ","pages":"Article 100524"},"PeriodicalIF":4.5,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unifying RNA-seq data using meta-analysis: Bioinformatics frameworks and application for plant genomics","authors":"Bahman Panahi ,&nbsp;Rasmieh Hamid ,&nbsp;Feba Jacob ,&nbsp;Hossein Mohammadzadeh Jalaly","doi":"10.1016/j.cpb.2025.100523","DOIUrl":"10.1016/j.cpb.2025.100523","url":null,"abstract":"<div><div>RNA sequencing (RNA-Seq) has transformed plant genomics by enabling high-resolution profiling of gene expression across various conditions. However, integrating RNA-Seq data from different studies is challenging due to variability in experimental designs, sequencing platforms, and data processing workflows, which limits the comparability and applicability of transcriptomic datasets. This review provides an overview of current meta-analysis approaches that address these challenges and enhance the consistency, accuracy, and interpretability of RNA-Seq data integration. We discuss methodologies such as data normalization techniques, statistical frameworks for aggregating results, and computational tools that reduce inter-study variability. We also highlight preprocessing strategies, including batch effect correction and standardized gene annotation pipelines, which facilitate reliable cross-study comparisons. We emphasize the practical significance of RNA-Seq meta-analysis in plant genomics. Meta-analysis improves the identification of consistent differentially expressed genes (DEGs), enhances functional annotation, and uncovers conserved regulatory mechanisms across plant species. These insights have applications in precision breeding, stress-response studies, and trait improvement programs. For researchers implementing meta-analysis, this review outlines key considerations, recommended practices, and available resources. We conclude by highlighting the need for standardized protocols and promoting multi-omics integration to unlock deeper insights. As transcriptomic datasets expand, meta-analysis will play a crucial role in advancing our understanding of plant biology and its application in agriculture.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"43 ","pages":"Article 100523"},"PeriodicalIF":5.4,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Open cotton boll detection using LiDAR point clouds and RGB images from unmanned aerial systems","authors":"Zhe Lin ,&nbsp;Wenxuan Guo ,&nbsp;Nathan S. Gill ,&nbsp;Glen Ritchie ,&nbsp;Brendan Kelly ,&nbsp;Xiao-Peng Song","doi":"10.1016/j.cpb.2025.100519","DOIUrl":"10.1016/j.cpb.2025.100519","url":null,"abstract":"<div><h3>Background</h3><div>Accurate quantification of open bolls and their distribution is crucial for understanding cotton growth, development, and yield in optimized crop management and enhanced plant breeding. Manual boll counting methods are time-consuming, labor-intensive, and subjective. Leveraging the potential of high-resolution images for high-throughput phenotyping offers a promising avenue for efficient trait quantification. The objectives of this study were to develop methods to detect and count open cotton bolls using LiDAR point cloud and RGB images and to compare the effectiveness of these two data sources.</div></div><div><h3>Methods</h3><div>A DJI Phantom 4 RTK Unmanned Aerial System (UAS) equipped with a 4 K RGB camera was used to acquire high-resolution RGB images, and a DJI Matrice 300 RTK with a Zenmuse L1 sensor was used to acquire LiDAR point cloud data. The RGB images were converted to point cloud using photogrammetry by measuring multiple points of overlapping images. The boll detection workflow involved data filtering and clustering using the density-based spatial clustering of applications with noise (DBSCAN) method. Evaluation of the methods involved 48 plots representing small, medium, and large plant sizes using metrics including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (r²).</div></div><div><h3>Results</h3><div>The methods using both data sources performed well in estimating open bolls, with LiDAR point cloud data slightly outperforming those derived from RGB images. Generally, the performance of the DBSCAN method in boll detection improved with decreasing plant sizes. Specifically, LiDAR data yielded MAPE values of 5.03 %, 8.05 %, and 13.46 %, RMSE values of 7.26, 14.33, and 23.40 bolls per m², and r² values of 0.93, 0.84, and 0.84 for small, medium, and large plant sizes, respectively. RGB image-based data exhibited MAPE values of 7.21 %, 6.49 %, and 16.41 %, RMSE values of 11.05, 13.66, and 26.49 bolls per m², and r² values of 0.82, 0.74, and 0.83 for small, medium, and large plant sizes, respectively.</div></div><div><h3>Conclusions</h3><div>The method demonstrates the potential of RGB imagery and LiDAR data for estimating boll counts, offering valuable tools for enhanced plant phenotyping in plant breeding and site-specific crop management. Both data sources underestimated boll counts, with smaller plants showing less undercounting, likely due to improved light penetration and separation of bolls. These findings highlight the influence of plant structure on boll detection accuracy and the need to address challenges posed by dense canopies to enhance detection reliability.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"43 ","pages":"Article 100519"},"PeriodicalIF":5.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Target of Rapamycin (TOR) signaling coordinates a balance between wheat photosynthetic performance and transpirational water conservation for improved water use efficiency and performance under drought","authors":"Huajin Sheng ,&nbsp;Peng Gao ,&nbsp;Li Liu ,&nbsp;Sheng Wang ,&nbsp;Achala Bakshi ,&nbsp;Zhigang Liu ,&nbsp;Hanh Nguyen ,&nbsp;Li Xi ,&nbsp;Tongfei Qin ,&nbsp;Daoquan Xiang ,&nbsp;Vivijan Babic ,&nbsp;Rui Wen ,&nbsp;Teagen D. Quilichini ,&nbsp;Maozhi Ren ,&nbsp;Raju Datla ,&nbsp;Leon Kochian","doi":"10.1016/j.cpb.2025.100520","DOIUrl":"10.1016/j.cpb.2025.100520","url":null,"abstract":"<div><div>Drought is an important abiotic stress limiting wheat production worldwide. Hence there is a critical need to develop wheat varieties with improved performance under drought. Target of Rapamycin (TOR) kinase is a central regulator that integrates diverse nutrient, energy, hormone, and environmental stress response signals to coordinate plant growth and development. Recent studies have demonstrated that TOR is also involved in plant responses to abiotic stress. In this current study, in order to address TOR functions in response to wheat drought stress, we generated transgenic wheat lines expressing <em>TaTOR</em> under the control of constitutive and drought-inducible promoters. Inhibition of plant growth in response to drought was discovered to be closely associated with the expression and activity of the wheat TOR protein. Enhancing <em>TaTOR</em> expression driven by a constitutive promoter (<em>UBQ</em>) or drought-inducible promoters (<em>DREB/DEH</em>), significantly improved drought resistance and greatly reduced yield losses caused by drought stress in wheat. Examination of plant water relations, other related physiological parameters, and genome-wide transcriptomic comparisons demonstrated that enhancing <em>TaTOR</em> expression under drought helps wheat minimize transpirational water loss without compromising photosynthetic performance, thus improving water-use efficiency. This is achieved through efficient regulation of stomatal closure, along with enhanced photosynthetic efficiency, upregulation of ABA-mediated stress signaling, increased antioxidant capacity, and more robust recovery from drought. Our findings highlight the functional roles of TaTOR in wheat drought resistance, providing a valuable new molecular tool for developing wheat cultivars with improved drought resistance needed to address the drought and climate change challenges threatening wheat productivity worldwide.</div></div>","PeriodicalId":38090,"journal":{"name":"Current Plant Biology","volume":"43 ","pages":"Article 100520"},"PeriodicalIF":5.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}