Crop Design最新文献

{"title":"NLRs in plant immunity: Structural insights and molecular mechanisms","authors":"Qingshuo Gu ,&nbsp;Shasha Liu ,&nbsp;Zuhua He ,&nbsp;Xiangzong Meng ,&nbsp;Yiwen Deng","doi":"10.1016/j.cropd.2025.100103","DOIUrl":"10.1016/j.cropd.2025.100103","url":null,"abstract":"<div><div>Plants defend against pathogens by employing intracellular NLR (nucleotide-binding leucine-rich repeat) receptors to detect pathogen effectors and initiate immune responses. While some NLRs function independently, increasing evidence reveals that many NLRs act in single, pairs or within immune networks, involving cooperative or antagonistic interactions mediated by domains such as TIR, CC, or integrated decoy domains. Recent structural breakthroughs have shown how NLRs assemble into oligomeric resistosomes, such as ZAR1 and Sr35 forming Ca²⁺-permeable channels, and TNL resistosomes acting as NADases to generate signaling molecules. These molecules are sensed by EDS1–PAD4 or EDS1–SAG101 complexes, which subsequently activate helper NLRs like ADR1s and NRG1s to mediate defense signaling and cell death. Moreover, novel regulatory mechanisms and negative regulators are being uncovered. These advances offer mechanistic insights into the NLR immune network and provide valuable insight into novel <em>R</em> gene design and molecular breeding for crop disease resistance.</div></div>","PeriodicalId":100341,"journal":{"name":"Crop Design","volume":"4 2","pages":"Article 100103"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855206","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":"Control of seed weight by a DNA demethylase in soybean","authors":"Mengzhu Zhang ,&nbsp;Wu Jiao ,&nbsp;Xinyu Jiang ,&nbsp;Jinhui Wang ,&nbsp;Longfei Wang ,&nbsp;Wenxue Ye ,&nbsp;Yue Wang ,&nbsp;Qingshan Chen ,&nbsp;Dawei Xin ,&nbsp;Qingxin Song","doi":"10.1016/j.cropd.2025.100102","DOIUrl":"10.1016/j.cropd.2025.100102","url":null,"abstract":"<div><div>Soybean seeds are a major source of protein and oil for human and animal nutrition. The molecular mechanisms underlying seed weight regulation, especially through epigenetic processes, are still poorly understood in soybean. Here, we reveal that a DNA demethylase gene, <em>GmDMEa</em>, underlies a genetic locus controlling seed weight through genome-wide association studies of 316 soybean accessions. Disruption of <em>GmDMEa</em> by CRISPR/Cas9 significantly increases seed weight and yield per plant accompanied with increased DNA methylation levels in the specific genomic regions which are demethylated in endosperm relative to embryo. <em>GmDMEa</em> is involved in activation of the endosperm-preferred genes that are negatively correlated with seed weight. Furthermore, DNA methylation variations induce significant changes of chromatin accessibility in endosperm. Notably, allelic variations of <em>GmDMEa</em> were artificially selected during soybean domestication. These findings reveal the role of dynamic DNA methylation in regulation of seed weight and provide a valuable gene resource for soybean breeding.</div></div>","PeriodicalId":100341,"journal":{"name":"Crop Design","volume":"4 2","pages":"Article 100102"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859235","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":"Heterologous expression of a chloride transporter gene AoCLCf from Avicennia officinalis enhances salt tolerance of Arabidopsis plants","authors":"Sivamathini Rajappa ,&nbsp;Prakash Kumar","doi":"10.1016/j.cropd.2025.100101","DOIUrl":"10.1016/j.cropd.2025.100101","url":null,"abstract":"<div><div>Plant chloride transporters are pivotal for preserving turgor pressure, pH, and cellular ion balance while adapting to salinity stress. We identified a salt-responsive gene, <em>AoCLCf</em> from <em>Avicennia officinalis</em>, which belongs to the chloride channel (CLC) family, and it shares significant sequence similarity with its <em>Arabidopsis</em> counterpart, <em>AtCLCf</em>. Through functional characterization in yeast mutants and <em>Arabidopsis</em> plants, we found that <em>AoCLCf</em> expression was induced primarily in roots under salt stress. Subcellular localization revealed a salt-induced translocation of GFP-AoCLCf from the Golgi apparatus to the plasma membrane. Expression of <em>AoCLCf</em> in the <em>Saccharomyces cerevisiae</em> mutant strain <em>Δgef1</em> helped to rescue the growth of the mutant at high NaCl concentrations (up to 1.25M). Moreover, constitutive expression of <em>AoCLCf</em> in wild-type <em>Arabidopsis</em> significantly enhanced salt tolerance, as evidenced by increased seed germination rates, and improved seedling growth (greater root and shoot length) under 150 ​mM NaCl treatment. Spectrofluorimetric assays using liposomes embedded with recombinant AoCLCf protein showed that it functions as a chloride channel. These findings underscore the pivotal role of AoCLCf in improving salt stress tolerance through the maintenance of cellular ion homeostasis.</div></div>","PeriodicalId":100341,"journal":{"name":"Crop Design","volume":"4 2","pages":"Article 100101"},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859234","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":"Environmental stress induced biosynthesis of plant secondary metabolites- transcriptional regulation as a key","authors":"Aishwarya Ashok Gaude,&nbsp;Siddhi Kashinath Jalmi","doi":"10.1016/j.cropd.2025.100100","DOIUrl":"10.1016/j.cropd.2025.100100","url":null,"abstract":"<div><div>Secondary metabolites represnt are diverse array of plant-synthesized compounds that, while not essential for growth and development, play crucial roles in plant defense against biotic and abiotic stresses, attracting pollinators and seed dispersers, and facilating adaptation to environmental challenges The biosynthesis of these secondary metabolites, incuding alkaloids, terpenoids, phenolics, and flavonoids is tightly regulated through multiple pathways, particularly under stress conditions which enables the plant to tolerate the stressful environment. Understanding how environmental stresses modulate secondary metabolite biosynthesis can be harnessed to develop stress-resistant crops and enhance the production of commercially and pharmaceutically valuable compounds by utilizing stress as an elicitor. This review provides a comprehensive overview of the current understanding of the transcriptional regulation of secondary metabolite pathways, with a focus on key classes such as flavonoids, terpenoids, and terpenoid indole alkaloids in response to abiotic stresses (e.g. salinity, drought, light, and temperature) and biotic stress. We highlight the ​critical ​roles of transcription factors like MYB, bHLH, and WRKY in regulating these pathways and their contribution to plant stress tolerance. This comprehensive analysis offers insights into improving crop resilience and enabling the sustainable production of high-value phytochemicals through advanced understanding of secondary metabolite regulation.</div></div>","PeriodicalId":100341,"journal":{"name":"Crop Design","volume":"4 2","pages":"Article 100100"},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873895","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":"Effect of the B chromosome-located long non-coding RNAs on gene expression in maize","authors":"Xin Liu ,&nbsp;Wenjie Yue ,&nbsp;Shiqi Lin,&nbsp;Yuxian Yang,&nbsp;Tong Chen,&nbsp;Xiaowen Shi","doi":"10.1016/j.cropd.2024.100091","DOIUrl":"10.1016/j.cropd.2024.100091","url":null,"abstract":"<div><div>Using artificial chromosomes in maize breeding allows for site-specific integration of multigene stacks, effectively overcoming the limitations of conventional transgenic approaches. The maize B chromosome, which is dispensable and highly heterochromatic, has minimal impact on phenotypes at low copy numbers, making it a promising platform for engineering artificial chromosomes. However, recent studies have demonstrated that the maize B chromosome can impact gene expression and recombination on the A chromosome. Understanding the genetic characteristics of the B chromosomes and their impact on gene expression is essential for their application in artificial chromosome construction. Despite advancements in elucidating how the B chromosome affects A chromosome expression, the role of long non-coding RNAs (lncRNAs) in this context remains unclear. In this study, we analyzed the RNA-seq data from leaf tissue of plants with 0–7 ​B chromosomes, identifying a total of 1614 lncRNAs, including 1516 A chromosome-located and 98 ​B chromosome-located lncRNAs, 72 of which are specific to the B chromosome. While A-located lncRNAs show greater dependence on the mere presence of the B chromosome, the expression of B-located lncRNAs is significantly affected by the number of B chromosomes present. Regulatory networks constructed in this study suggest that B-located lncRNAs may drive the differential expression of A chromosome-located transcription factors and genes associated with circadian rhythm regulation, indicating their regulatory role in A chromosome gene expression.</div></div>","PeriodicalId":100341,"journal":{"name":"Crop Design","volume":"4 1","pages":"Article 100091"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143095653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic dissection and genomic prediction of drought indices in bread wheat (Triticum aestivum L.) genotypes","authors":"Zakaria El Gataa ,&nbsp;Alemu Admas ,&nbsp;Samira El Hanafi ,&nbsp;Zakaria Kehel ,&nbsp;Fatima Ezzahra Rachdad ,&nbsp;Wuletaw Tadesse","doi":"10.1016/j.cropd.2024.100084","DOIUrl":"10.1016/j.cropd.2024.100084","url":null,"abstract":"<div><div>Drought constitutes the main obstacle to agricultural productivity in the Central and West Asia and North Africa (CWANA) region, notably leading to substantial reductions in wheat yields due to terminal water stress. The adoption of drought-resistant wheat varieties appears to be a vital strategy to maintain wheat production in the face of climatic challenges. In this context, a study was conducted utilizing a set of 198 elite bread wheat genotypes developed at the International Center for Agricultural Research in the Dry Areas (ICARDA). This set of elite genotypes was evaluated at the Sidi Al-Aidi station in Morocco over two years (2021–2022), under rain-fed and irrigated conditions. Phenotypic assessments for grain yield and drought indices were performed, alongside genotyping the population using 15k SNP markers. These preparatory steps facilitated a genome-wide association study (GWAS) and genomic prediction, leveraging the Mixed Linear Model (MLM) to pinpoint marker-trait associations (MTAs) and candidate genes pertinent to grain yield and drought indices. The results manifested substantial variations in both grain yield and drought indices among the genotypes tested. Grain yield performance ranged from 0.34 to 2.57 ​t/ha under rain-fed conditions and 1.12 to 4.57 ​t/ha under irrigated scenarios. The comprehensive analysis identified 39 significant MTAs (p ​&lt; ​0.001) and 14 putative genes associated with drought indices and grain yield. Noteworthy is the marker “<em>wsnp_Ex_c12127_19394952”</em> on chromosome 5B, which displayed a significant correlation with grain yield in rain-fed environments. Furthermore, the most prominent marker linked to tolerance index (TOL) was “BobWhite<em>_c42349_99”,</em> situated on chromosome 5A and associated with the <em>TraesCS5A02G498000</em> gene. This gene plays a critical role, encoding for catalase protein crucial for response to hydrogen peroxide. These markers could be used for marker-assisted selection in wheat breeding programs targeting drought tolerance.</div></div>","PeriodicalId":100341,"journal":{"name":"Crop Design","volume":"4 1","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthetic genomics in crop breeding: Evidence, opportunities and challenges","authors":"Yuhan Zhou,&nbsp;Ziqi Zhou,&nbsp;Qingyao Shu","doi":"10.1016/j.cropd.2024.100090","DOIUrl":"10.1016/j.cropd.2024.100090","url":null,"abstract":"<div><div>Synthetic genomics represents a formidable domain, encompassing the intentional design, construction, and manipulation of artificial genetic material to generate novel organisms or modify existing ones. In the context of crop breeding, molecular design breeding has emerged as a transformative force, ushering in notable progress. Nevertheless, the field faces unprecedented challenges, with climate change, population growth, and the scarcity of superior genetic resources exerting significant pressures. Recent strides in DNA synthesis methodologies, exemplified by innovative techniques like SCRaMbLE, have empowered the assembly and engineering of viral and microbial genomes. These advancements open promising avenues for the application of synthetic genomics in multicellular eukaryotic organisms, particularly in the realm of crop improvement. Synthetic genomics, with its capacity to manipulate gene sequences and regulatory elements, holds immense promise for the breeding of crops that meet diverse needs. Despite these advancements, the integration of synthetic genomics into crop breeding encounters hurdles, including the intricacies of complex crop genomes, the unpredictability introduced by epigenetic modification, and the limitations in achieving robust transformation processes. Addressing these challenges is pivotal to unlock the full potential of synthetic genomics in revolutionizing crop breeding. Looking ahead, we envision synthetic genomics in crop breeding not only as a scientific frontier but also as a burgeoning industry.</div></div>","PeriodicalId":100341,"journal":{"name":"Crop Design","volume":"4 1","pages":"Article 100090"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-based tools for next-generation seed quality analysis","authors":"Sumeet Kumar Singh ,&nbsp;Rashmi Jha ,&nbsp;Saurabh Pandey ,&nbsp;Chander Mohan ,&nbsp;Chetna ,&nbsp;Saipayan Ghosh ,&nbsp;Satish Kumar Singh ,&nbsp;Sarita Kumari ,&nbsp;Ashutosh Singh","doi":"10.1016/j.cropd.2024.100094","DOIUrl":"10.1016/j.cropd.2024.100094","url":null,"abstract":"<div><div>Innovation in agrotechnologies is urgently needed to fulfill the demand burden on food and agriculture industries. The key challenge in producing a high-quality, high-yielding crop is using quality seed and its identification. Seed quality identification in the seed industry often uses traditional methods based on manual observations, which are cumbersome and time-consuming. Still, there is always the risk of faulty reporting and non-uniformity in test results among different testing agencies. Because of the changing requirements of the seed industry, Artificial Intelligence (AI)-based tools and various methods have been developed to test the quality of seeds. AI-based tools have been extensively applied in different farming applications. This review explores these tools and strategies, including traditional, semi-automatic, or automated ones developed using machine learning. These include non-destructive techniques such as x-ray imaging, remote sensing, multispectral imaging, hyperspectral imaging, and near-infrared (NIR) spectroscopy, which are less expensive and time and/or labor-savings. Furthermore, we discuss the characteristics of AI-based techniques for depth analysis and their application in various aspects of seed quality, including seed vigor, seed health, seed germination, and seed viability. Lastly, we furhter evaluate the challenges of these methods and how they will provide healthy seeds to each farmer in the future and increase the overall production of crops. We propose to leverage AI-based tools to bridge the knowledge gap between traditional screening methods and integration of advanced technologies for better screening of crop seeds.</div></div>","PeriodicalId":100341,"journal":{"name":"Crop Design","volume":"4 1","pages":"Article 100094"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A real time monitoring system for accurate plant leaves disease detection using deep learning","authors":"Kazi Naimur Rahman,&nbsp;Sajal Chandra Banik,&nbsp;Raihan Islam,&nbsp;Arafath Al Fahim","doi":"10.1016/j.cropd.2024.100092","DOIUrl":"10.1016/j.cropd.2024.100092","url":null,"abstract":"<div><div>Accurate and timely detection of plant diseases is crucial for sustainable agriculture and food security. This research presents a real-time monitoring system utilizing deep learning techniques to detect diseases in plant leaves with high accuracy. We combined several plant datasets, including the PlantVillage Dataset, resulting in a comprehensive dataset of 30,945 images across eight plant types (potato, tomato, pepper bell, apple, corn, grape, peach, and rice) and 35 disease classes. Initially, a custom Convolutional Neural Network (CNN) model was developed, achieving a leaf classification accuracy of 95.62 ​%. Subsequently, the dataset was partitioned for individual plant disease detection, applying nine different CNN models (custom CNN, VGG16, VGG19, InceptionV3, MobileNet, DenseNet121, Xception, and two hybrid models) to each plant type. The highest accuracy rates for disease detection were: 100 ​% for potato (custom CNN), 98 ​% for tomato (InceptionV3, custom CNN, VGG16), 100 ​% for pepper bell (MobileNet, custom CNN), 100 ​% for apple (MobileNet, Xception), 98 ​% for corn (custom CNN), 99 ​% for grape (custom CNN, VGG19, DenseNet121), 100 ​% for peach (VGG16, custom CNN), and 98 ​% for rice (DenseNet121). A web and mobile application were developed based on the best-performing models, allowing users to insert or capture images of plant leaves, detect diseases, and receive treatment suggestions with high confidence levels. The results demonstrate the effectiveness of deep learning models in accurately identifying plant diseases, offering a valuable tool for enhancing disease management and crop yields.</div></div>","PeriodicalId":100341,"journal":{"name":"Crop Design","volume":"4 1","pages":"Article 100092"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting cold-stress responsive genes in cotton with machine learning models","authors":"Mengke Zhang ,&nbsp;Yayuan Deng ,&nbsp;Wanghong Shi ,&nbsp;Luyao Wang ,&nbsp;Na Zhou ,&nbsp;Heng Wang ,&nbsp;Zhiyuan Zhang ,&nbsp;Xueying Guan ,&nbsp;Ting Zhao","doi":"10.1016/j.cropd.2024.100085","DOIUrl":"10.1016/j.cropd.2024.100085","url":null,"abstract":"<div><div>Machine Learning (ML) serves as a potent tool for data mining and predictive analytics in genomic research. However, its application in identifying stress-responsive genes remains underexplored. This study identified distinct variations in the expression patterns of one-to-one homologous genes responding to cold stress in three cotton species: <em>Gossypium hirsutum</em>, <em>Gossypium barbadense</em>, and <em>Gossypium arboreum</em>. To better understand cold-responsive genes, we developed ML predictive models (LightGBM, XGBoost, and Random Forest) utilizing 121 biochemical features. The incorporating of these features significantly enhanced model accuracy. Moreover, incorporating evolutionary information further refined the models, achieving an impressive 80.80 ​% accuracy in predicting cold-stress responsive genes. Notably, models trained on sequence features from <em>G. hirsutum</em> showed transferability to the closely related species of <em>G. barbadense</em>, with accuracies ranging from 78.65 ​% to 83.04 ​%. This research presents a promising workflow for identifying candidate genes for experimental exploration of cold stress responses and establishes a systematic framework for predicting cold-stress related genes using ML methodologies.</div></div>","PeriodicalId":100341,"journal":{"name":"Crop Design","volume":"4 1","pages":"Article 100085"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}