Crop Design最新文献

Effect of the B chromosome-located long non-coding RNAs on gene expression in maize

Crop Design

Pub Date : 2025-02-01 DOI: 10.1016/j.cropd.2024.100091

Xin Liu , Wenjie Yue , Shiqi Lin, Yuxian Yang, Tong Chen, Xiaowen Shi

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.

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引用次数: 0

Genetic dissection and genomic prediction of drought indices in bread wheat (Triticum aestivum L.) genotypes

Crop Design

Pub Date : 2025-02-01 DOI: 10.1016/j.cropd.2024.100084

Zakaria El Gataa , Alemu Admas , Samira El Hanafi , Zakaria Kehel , Fatima Ezzahra Rachdad , Wuletaw Tadesse

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 < 0.001) and 14 putative genes associated with drought indices and grain yield. Noteworthy is the marker “wsnp_Ex_c12127_19394952” 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_c42349_99”, situated on chromosome 5A and associated with the TraesCS5A02G498000 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.

{"title":"Genetic dissection and genomic prediction of drought indices in bread wheat (Triticum aestivum L.) genotypes","authors":"Zakaria El Gataa , Alemu Admas , Samira El Hanafi , Zakaria Kehel , Fatima Ezzahra Rachdad , 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 < 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}

引用次数: 0

Artificial intelligence-based tools for next-generation seed quality analysis

Crop Design

Pub Date : 2025-02-01 DOI: 10.1016/j.cropd.2024.100094

Sumeet Kumar Singh , Rashmi Jha , Saurabh Pandey , Chander Mohan , Chetna , Saipayan Ghosh , Satish Kumar Singh , Sarita Kumari , Ashutosh Singh

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.

{"title":"Artificial intelligence-based tools for next-generation seed quality analysis","authors":"Sumeet Kumar Singh , Rashmi Jha , Saurabh Pandey , Chander Mohan , Chetna , Saipayan Ghosh , Satish Kumar Singh , Sarita Kumari , 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}

引用次数: 0

A real time monitoring system for accurate plant leaves disease detection using deep learning

Crop Design

Pub Date : 2025-02-01 DOI: 10.1016/j.cropd.2024.100092

Kazi Naimur Rahman, Sajal Chandra Banik, Raihan Islam, Arafath Al Fahim

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.

{"title":"A real time monitoring system for accurate plant leaves disease detection using deep learning","authors":"Kazi Naimur Rahman, Sajal Chandra Banik, Raihan Islam, 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}

引用次数: 0

Synthetic genomics in crop breeding: Evidence, opportunities and challenges

Crop Design

Pub Date : 2025-02-01 DOI: 10.1016/j.cropd.2024.100090

Yuhan Zhou, Ziqi Zhou, Qingyao Shu

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.

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引用次数: 0

Predicting cold-stress responsive genes in cotton with machine learning models

Crop Design

Pub Date : 2025-02-01 DOI: 10.1016/j.cropd.2024.100085

Mengke Zhang , Yayuan Deng , Wanghong Shi , Luyao Wang , Na Zhou , Heng Wang , Zhiyuan Zhang , Xueying Guan , Ting Zhao

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: Gossypium hirsutum, Gossypium barbadense, and Gossypium arboreum. 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 G. hirsutum showed transferability to the closely related species of G. barbadense, 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.

{"title":"Predicting cold-stress responsive genes in cotton with machine learning models","authors":"Mengke Zhang , Yayuan Deng , Wanghong Shi , Luyao Wang , Na Zhou , Heng Wang , Zhiyuan Zhang , Xueying Guan , 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}

引用次数: 0

Advance technologies for DNA-protein interactions and future research prospect

Crop Design

Pub Date : 2025-02-01 DOI: 10.1016/j.cropd.2024.100082

Chengyi Qu , Hao Du

DNA-protein interactions (DPIs) are essential for genome functioning, with billions of years of evolution shaping specific patterns of protein-DNA interactions to regulate gene networks in response to various stimuli. Over the years, scientists have developed numerous techniques to study these interactions. This review provides a historical overview of these methods, highlighting their advantages and disadvantages and offering the examples of recent applications. Our aim is to help researchers select the most appropriate technique on the basis of their working goals and capabilities. For the experimental design of DPIs assays, several kinds of techniques are relatively quicker or/and simpler, the precision and accuracy of these methods must be carefully considered to verify the DNA-protein interaction. The review also discusses the recent advances in the computational approaches for predicting DNA-protein bindings and diverse reporting systems.

引用次数: 0

Evaluation of different sesame varieties cultivated under saline conditions in the southwestern coastal region of Bangladesh

Crop Design

Pub Date : 2025-02-01 DOI: 10.1016/j.cropd.2024.100093

Md Shihab Uddine Khan , Md Moshiur Rahman , Arup Ratan Basak , Prodipto Bishnu Angon , Sadia Afroz Ritu , Milon Kobir , Md Riazul Islam

Sesame (Sesamum indicum L.) is widely used in many cooking techniques worldwide, and it is known as the "queen of oilseeds" because it contains polyunsaturated lipids that prevent oxidative rancidity and carry oil content up to 60%. The salty portions of the country have much lower agricultural yields, cropping intensities, and productivity than the rest of the country. In this paper, we compared the variations of yield performance and salinity torelance between modern and local sesame varieties to select the best-performing varieties under saline conditions in the southwestern coastal region of Bangladesh. The field experiment had been performed during the Kharif-1 season (mid-March to mid-July) of 2022 at the BINA (Bangladesh Institute of Nuclear Agriculture) substation farm, Satkhira. Four BINA varieties and two BARI (Bangladesh Agricultural Research Institute) varieties developed sesame varieties, and one local variety, viz. Binatil-1, Binatil-2, Binatil-3, Binatil-4, BARI Til-3, BARI Til-4 and Lal Til (Batiaghata local) were tested under saline conditions. A randomized complete block design was utilized, with three replications of each variety across the experimental field. The greatest plant height (92.00 cm) was found in BARI Til-3, whereas the lowest was observed in Binatil-3. BARI Til-4 had the highest number of branches per plant (4.55), whereas the lowest was found in Binatil-1. The highest number of capsules (22.22) was shown in Binatil-3, and the lowest was found in Binatil-1. The maximum number of seeds in capsule^-1 (72.55) was demonstrated by Lal Til, and the minimum was observed in BARI Til-4. The Lal Til variety presented the highest seed yield (1.25 ha^-1), whereas the Binatil-1 variety presented the lowest seed yield. These results indicate that the Lal Til variety performed better in yield. It may be cultivated in the Satkhira region under saline circumstances and used as breeding material for future breeding programs. These findings are highly important for the future of sesame cultivation in Bangladesh and other saline-prone areas.

{"title":"Evaluation of different sesame varieties cultivated under saline conditions in the southwestern coastal region of Bangladesh","authors":"Md Shihab Uddine Khan , Md Moshiur Rahman , Arup Ratan Basak , Prodipto Bishnu Angon , Sadia Afroz Ritu , Milon Kobir , Md Riazul Islam","doi":"10.1016/j.cropd.2024.100093","DOIUrl":"10.1016/j.cropd.2024.100093","url":null,"abstract":"<div><div>Sesame (<em>Sesamum indicum</em> L.) is widely used in many cooking techniques worldwide, and it is known as the \"queen of oilseeds\" because it contains polyunsaturated lipids that prevent oxidative rancidity and carry oil content up to 60%. The salty portions of the country have much lower agricultural yields, cropping intensities, and productivity than the rest of the country. In this paper, we compared the variations of yield performance and salinity torelance between modern and local sesame varieties to select the best-performing varieties under saline conditions in the southwestern coastal region of Bangladesh. The field experiment had been performed during the Kharif-1 season (mid-March to mid-July) of 2022 at the BINA (Bangladesh Institute of Nuclear Agriculture) substation farm, Satkhira. Four BINA varieties and two BARI (Bangladesh Agricultural Research Institute) varieties developed sesame varieties, and one local variety, viz. Binatil-1, Binatil-2, Binatil-3, Binatil-4, BARI Til-3, BARI Til-4 and Lal Til (Batiaghata local) were tested under saline conditions. A randomized complete block design was utilized, with three replications of each variety across the experimental field. The greatest plant height (92.00 cm) was found in BARI Til-3, whereas the lowest was observed in Binatil-3. BARI Til-4 had the highest number of branches per plant (4.55), whereas the lowest was found in Binatil-1. The highest number of capsules (22.22) was shown in Binatil-3, and the lowest was found in Binatil-1. The maximum number of seeds in capsule<sup>-1</sup> (72.55) was demonstrated by Lal Til, and the minimum was observed in BARI Til-4. The Lal Til variety presented the highest seed yield (1.25 ha<sup>-1</sup>), whereas the Binatil-1 variety presented the lowest seed yield. These results indicate that the Lal Til variety performed better in yield. It may be cultivated in the Satkhira region under saline circumstances and used as breeding material for future breeding programs. These findings are highly important for the future of sesame cultivation in Bangladesh and other saline-prone areas.</div></div>","PeriodicalId":100341,"journal":{"name":"Crop Design","volume":"4 1","pages":"Article 100093"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105204","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}

引用次数: 0

Analysis of CYP701A1 genes in gossypium species and functional characterization through gene silencing

Crop Design

Pub Date : 2025-02-01 DOI: 10.1016/j.cropd.2024.100081

Zhao Liang , Di Jiachun , Guo Qi , Xu Zhenzhen , Zhao Jun , Xu Peng , Xu Jianwen , Liu Jianguang , Shen Xinlian , Chen Xusheng

Gibberellins (GA) are known to play crucial roles in various aspects of plant growth and development. The cytochrome P450 enzyme family is recognized for its significance in plant metabolic processes. Specifically, CYP701s, a subgroup of CYP71, encode ent-kaurene oxidase in the gibberellin synthesis pathway. In this study, we analyzed genomic data from 30 Gossypium species, including nine allotetraploid genomes (AD1-AD7, with two each for AD1 and AD2), 21 diploid genomes (A-G, K, with two A-genomes and 12 D-genomes), and Gossypioides kirkii genome as an outgroup for evolutionary analysis, totaling 31 genomes. Subsequently, 40 CYP701A1 genes were identified from various genomes and conducted a comprehensive analysis of their structure and evolution. Virus-induced gene silencing (VIGS) technology was utilized to knock out the GhCYP701A1 gene in Gossypium hirsutum ac TM-1. Subsequent analysis revealed changes in hormone content, with decreased gibberellin levels and notable increases in auxin, cytokinin, and jasmonic acid contents. Conversely, salicylic acid content decreased, while the precursor for ethylene synthesis, 1-aminocyclopropane-1-carboxylic acid (ACC), remained relatively stable. Transcriptome analysis of the gene silencing plants identified 15,962 differentially expressed genes, including 8376 upregulated and 7586 downregulated genes. Enrichment analysis through KEGG pathway highlighted ‘Plant hormone signal transduction’ as a prominent pathway with 234 differentially expressed genes. The study provided insights into the function and regulatory network of the gene.

{"title":"Analysis of CYP701A1 genes in gossypium species and functional characterization through gene silencing","authors":"Zhao Liang , Di Jiachun , Guo Qi , Xu Zhenzhen , Zhao Jun , Xu Peng , Xu Jianwen , Liu Jianguang , Shen Xinlian , Chen Xusheng","doi":"10.1016/j.cropd.2024.100081","DOIUrl":"10.1016/j.cropd.2024.100081","url":null,"abstract":"<div><div>Gibberellins (GA) are known to play crucial roles in various aspects of plant growth and development. The cytochrome P450 enzyme family is recognized for its significance in plant metabolic processes. Specifically, CYP701s, a subgroup of CYP71, encode <em>ent</em>-kaurene oxidase in the gibberellin synthesis pathway. In this study, we analyzed genomic data from 30 <em>Gossypium</em> species, including nine allotetraploid genomes (AD1-AD7, with two each for AD1 and AD2), 21 diploid genomes (A-G, K, with two A-genomes and 12 D-genomes), and <em>Gossypioides kirkii</em> genome as an outgroup for evolutionary analysis, totaling 31 genomes. Subsequently, 40 <em>CYP701A1</em> genes were identified from various genomes and conducted a comprehensive analysis of their structure and evolution. Virus-induced gene silencing (VIGS) technology was utilized to knock out the <em>GhCYP701A1</em> gene in <em>Gossypium hirsutum</em> ac TM-1. Subsequent analysis revealed changes in hormone content, with decreased gibberellin levels and notable increases in auxin, cytokinin, and jasmonic acid contents. Conversely, salicylic acid content decreased, while the precursor for ethylene synthesis, 1-aminocyclopropane-1-carboxylic acid (ACC), remained relatively stable. Transcriptome analysis of the gene silencing plants identified 15,962 differentially expressed genes, including 8376 upregulated and 7586 downregulated genes. Enrichment analysis through KEGG pathway highlighted ‘Plant hormone signal transduction’ as a prominent pathway with 234 differentially expressed genes. The study provided insights into the function and regulatory network of the gene.</div></div>","PeriodicalId":100341,"journal":{"name":"Crop Design","volume":"4 1","pages":"Article 100081"},"PeriodicalIF":0.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105207","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}

引用次数: 0

Genome-wide association study and candidate gene identification for the cold tolerance at the seedling stage of rapeseed (Brassica napus L.)

Crop Design

Pub Date : 2025-02-01 DOI: 10.1016/j.cropd.2024.100083

Guangyu Wu, Yanda Zhou, Jingyi Zhang, Mengjie Gong, Lixi Jiang, Yang Zhu

Rapeseed (Brassica napus L.) is an important overwintering oilseed crop and suffers from severe cold stress during the seedling stage, due to the increasingly delayed sowing in the Yangtze river basin. However, the genetic basis underlying cold tolerance in rapeseed seedlings is not well understood. In this study, we observed the cold tolerance of 217 rapeseed accessions in the field and found a significant negative correlation between cold tolerance grades and malondialdehyde (MDA) content. A genome-wide association study (GWAS) of cold tolerance grades identified four significant loci in the genomic region of one MYB transcription factor BnaA8.MYB60. Furthermore, field accessions with BnaA8.MYB60 ^Hap1 exhibited significantly higher cold tolerance and lower expression of BnaA8.MYB60 compared to the majority of accessions with BnaA8.MYB60 ^Hap2. These results suggested that variation in the genomic sequences of BnaA8.MYB60 caused the divergence of gene expression levels and functions on cold tolerance in rapeseed seedlings. This study could provide the theoretical guidance for the breeding of cold-tolerant rapeseed varieties.

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引用次数: 0