农学最新文献

{"title":"GenoBaits Cassava35K: high-resolution multi-SNP arrays for genetic analysis and molecular breeding using targeted sequencing and liquid chip technology.","authors":"Chaochao Li, Xiaoxue Ye, Zhongxin Jin, Kaisen Huo, Jiangxiang Ma, Weiwei Tie, Zehong Ding, Yongfeng Zhou, Wei Hu","doi":"10.1093/hr/uhae305","DOIUrl":"10.1093/hr/uhae305","url":null,"abstract":"","PeriodicalId":57479,"journal":{"name":"园艺研究(英文)","volume":"12 2","pages":"uhae305"},"PeriodicalIF":7.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11817887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143411780","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":"Unlocking epigenetic breeding potential in tomato and potato","authors":"Pingxian Zhang,&nbsp;Yuehui He,&nbsp;Sanwen Huang","doi":"10.1007/s42994-024-00184-2","DOIUrl":"10.1007/s42994-024-00184-2","url":null,"abstract":"<div><p>Tomato (<i>Solanum lycopersicum</i>) and potato (<i>Solanum tuberosum</i>), two integral crops within the nightshade family, are crucial sources of nutrients and serve as staple foods worldwide. Molecular genetic studies have significantly advanced our understanding of their domestication, evolution, and the establishment of key agronomic traits. Recent studies have revealed that epigenetic modifications act as “molecular switches”, crucially regulating phenotypic variations essential for traits such as fruit ripening in tomatoes and tuberization in potatoes. This review summarizes the latest findings on the regulatory mechanisms of epigenetic modifications in these crops and discusses the integration of biotechnology and epigenomics to enhance breeding strategies. By highlighting the role of epigenetic control in augmenting crop yield and adaptation, we underscores its potential to address the challenges posed by a growing global population as well as changing climate.</p></div>","PeriodicalId":53135,"journal":{"name":"aBIOTECH","volume":"5 4","pages":"507 - 518"},"PeriodicalIF":4.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s42994-024-00184-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thiophanate-methyl and its major metabolite carbendazim weaken rhizobacteria-mediated defense responses in cucumbers against Fusarium wilt","authors":"Kai Cui,&nbsp;Xiaoming Xia,&nbsp;Youwei Wang,&nbsp;Yueli Zhang,&nbsp;Ying Zhang,&nbsp;Junli Cao,&nbsp;Jun Xu,&nbsp;Fengshou Dong,&nbsp;Xingang Liu,&nbsp;Xinglu Pan,&nbsp;Yongquan Zheng,&nbsp;Xiaohu Wu","doi":"10.1007/s42994-024-00181-5","DOIUrl":"10.1007/s42994-024-00181-5","url":null,"abstract":"<div><p>The effect of fungicides on the plant-rhizosphere microbiome is a subject of ongoing debate, but whether any alteration in the rhizosphere microbiome could affect plant health is an issue that has not been thoroughly investigated. To address this deficiency, we analyzed the rhizosphere microbiome of wilt disease—resistant and disease-susceptible cucumber cultivars to determine whether (and which) plant-associated microorganisms have a role in disease resistance. We further assessed whether the fungicides thiophanate-methyl and carbendazim affect the rhizosphere microbiome, which may contribute to the plant’s immune response. Based on results acquired with both radicle-inoculation and soil-inoculation methods, cultivars Longyuanxiuchun (LYXC) and Shuyan2 (SY2) were identified as being disease resistant, whereas Zhongnong6 (ZN6) and Zhongnong38 (ZN38) were susceptible. The microbiome structure differed substantially between the resistant and susceptible plants, with LYXC and SY2 each having a significantly greater Shannon index than Zhongnong38. These results revealed that the disease-resistant cucumber cultivars recruited more beneficial bacteria, i.e., <i>Bacillus</i>, in their rhizosphere soil; as such, <i>Bacillus</i> was identified as a keystone genus in the microbial co-occurrence network. Thus, the presence of <i>Bacillus</i> may help cucumbers defend against fungal pathogens within the rhizosphere. <i>Bacillus subtilis</i> strain LD15, which was isolated from LYXC rhizosphere soil, could suppress pathogen growth, in vitro, and reduce disease severity in pot assays. Moreover, evidence also confirmed the accumulation of LD1 in the rhizosphere soil of resistant cucumber cultivars. For LYXC, application of thiophanate-methyl or carbendazim altered the microbiome structure, decreased bacterial diversity, and reduced the abundance of <i>Bacillus</i> species. Finally, pot assays verified that fungicide application decreased the proportion of LD15 in rhizosphere soil. From a microbial perspective, thiophanate-methyl and carbendazim may weaken the rhizobacteria-mediated defense response of cucumbers against cucumber Fusarium wilt disease. Our findings reveal a role for the rhizosphere microbiome in protecting plants from pathogens and constitute a reference for assessing the ecotoxicological risk of pesticides to non-target soil microorganisms.</p></div>","PeriodicalId":53135,"journal":{"name":"aBIOTECH","volume":"5 4","pages":"417 - 430"},"PeriodicalIF":4.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s42994-024-00181-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disruption of <i>ClOSD1</i> leads to both somatic and gametic ploidy doubling in watermelon.","authors":"Wenyu Pang, Wenbing He, Jing Liang, Qiaran Wang, Shengcan Hou, Xiaodan Luo, Junhua Li, Jiafa Wang, Shujuan Tian, Li Yuan","doi":"10.1093/hr/uhae288","DOIUrl":"https://doi.org/10.1093/hr/uhae288","url":null,"abstract":"","PeriodicalId":57479,"journal":{"name":"园艺研究(英文)","volume":"12 1","pages":"uhae288"},"PeriodicalIF":7.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11775614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143070001","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":"Correction: The RUBY reporter for visual selection in soybean genome editing","authors":"Li Chen,&nbsp;Yupeng Cai,&nbsp;Xiaoqian Liu,&nbsp;Weiwei Yao,&nbsp;Shuiqing Wu,&nbsp;Wensheng Hou","doi":"10.1007/s42994-024-00156-6","DOIUrl":"10.1007/s42994-024-00156-6","url":null,"abstract":"","PeriodicalId":53135,"journal":{"name":"aBIOTECH","volume":"5 4","pages":"519 - 519"},"PeriodicalIF":4.6,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s42994-024-00156-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Processing pipelines and analytical methods for single-cell DNA methylation sequencing data.","authors":"Yan-Ni Wang, Jia Li","doi":"10.16288/j.yczz.24-154","DOIUrl":"https://doi.org/10.16288/j.yczz.24-154","url":null,"abstract":"<p><p>Single-cell DNA methylation sequencing technology has seen rapid advancements in recent years, playing a crucial role in uncovering cellular heterogeneity and the mechanisms of epigenetic regulation. As sequencing technologies have progressed, the quality and quantity of single-cell methylation data have also increased, making standardized preprocessing workflows and appropriate analysis methods essential for ensuring data comparability and result reliability. However, a comprehensive data analysis pipeline to guide researchers in mining existing data has yet to be established. This review systematically summarizes the preprocessing steps and analysis methods for single-cell methylation data, introduces relevant algorithms and tools, and explores the application prospects of single-cell methylation technology in neuroscience, hematopoietic differentiation, and cancer research. The aim is to provide guidance for researchers in data analysis and to promote the development and application of single-cell methylation sequencing technology.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"46 10","pages":"807-819"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509517","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":"Computational dissection of the regulatory mechanisms of aberrant metabolism in remodeling the microenvironment of breast cancer.","authors":"Yu-Xin Wan, Xin-Yu Zhu, Yu Zhao, Na Sun, Tian-Tong-Fei Jiang, Juan Xu","doi":"10.16288/j.yczz.24-167","DOIUrl":"https://doi.org/10.16288/j.yczz.24-167","url":null,"abstract":"<p><p>The composition of T cell subsets and tumor-specific T cell interactions within the tumor microenvironment (TME) contribute to the heterogeneity observed in breast cancer. Moreover, aberrant tumor metabolism is often intimately linked to dysregulated anti-tumor immune function of T cells. Identifying key metabolic genes that affect immune cell interactions thus holds promise for uncovering potential therapeutic targets in the treatment of breast cancer. This study leverages single-cell transcriptomic data from breast cancer to investigate tumor-specific T-cell subsets and their interacting subnetworks in the TME during cancer progression. We further assess the metabolic pathway activities of tumor-specifically activated T-cell subsets. The results reveal that metabolic pathways involved in insulin synthesis, secretion, degradation, as well as fructose catabolism, significantly influence multiple T cell interactions. By integrating the metabolic pathways that significantly up-regulate T cells in tumors and influence their interactions, we identify key abnormal metabolic genes associated with T-cell collaboration and further develop a breast cancer risk assessment model. Additionally, using gene expression profiles of prognosis-related genes significantly associated with aberrant metabolism and drug IC50 values, we predict targeted drugs, yielding potential candidates like GSK-J4 and PX-12. This study integrate the analysis of abnormal T-cell interactions and metabolic pathway abnormalities in the breast cancer TME, elucidating their roles in cancer progression and providing leads for novel breast cancer therapeutic strategies.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"46 10","pages":"871-885"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509514","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":"Machine learning applications in breast cancer survival and therapeutic outcome prediction based on multi-omic analysis.","authors":"Zi-Yi Zhang, Qi-Lin Wang, Jun-You Zhang, Ying-Ying Duan, Jia-Xin Liu, Zhao-Shuo Liu, Chun-Yan Li","doi":"10.16288/j.yczz.24-156","DOIUrl":"https://doi.org/10.16288/j.yczz.24-156","url":null,"abstract":"<p><p>The high heterogeneity within and between breast cancer patients complicates treatment determination and prognosis assessment. Treatment decision-making is influenced by various factors, such as tumor subtype, histological grade, and genotype, necessitating personalized treatment strategies. Prognostic outcomes vary significantly depending on patient-specific conditions. As a critical branch of artificial intelligence, machine learning efficiently handles large datasets and automates decision-making processes. The introduction of machine learning offers new solutions for breast cancer treatment selection and prognosis assessment. In the field of cancer therapy, traditional methods for predicting treatment and survival outcomes often rely on single or few biomarkers, limiting their ability to capture the complexity of biological processes comprehensively. Machine learning analyzes patients' multi-omic data and the intricate patterns of variations during cancer initiation and progression to predict patients' survival and treatment outcomes. Consequently, it facilitates the selection of appropriate therapeutic interventions to implement early intervention and improve treatment efficacy for patients. Here, we first introduce common machine learning methods, and then elaborate on the application of machine learning in the field of survival prediction and prognosis from two aspects: evaluating survival and predicting treatment outcomes for breast cancer patients. The aim is to provide breast cancer patients with precise treatment strategies to improve therapeutic outcomes and quality of life.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"46 10","pages":"820-832"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509516","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":"Advances in high throughput sequencing methods for DNA damage and repair.","authors":"Yu Liang, Wei Wu","doi":"10.16288/j.yczz.24-203","DOIUrl":"https://doi.org/10.16288/j.yczz.24-203","url":null,"abstract":"<p><p>With the rapid development of high-throughput sequencing technology in the past decade, an increasing number of sequencing methods targeting different types of DNA damage have been developed and widely used in the field. These technologies not only help to elucidate the dynamic processes of repair pathways corresponding to different types of lesions, understand the underlying mechanisms of key factors and identify new hotspots prone to damage, but also greatly advanced our knowledge of crucial physiological processes such as meiotic homologous recombination, antibody generation and cytosine demethylation. These advancements hold significant potential for broader applications in exploring disease initiation and drug development. However, understanding and selecting the appropriate techniques have become difficult. This article reviews the main sequencing detection methods for the most common DNA lesions and introduce their principles, thereby providing valuable insights for the selection, application, further development and optimization of these technologies.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"46 10","pages":"779-794"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509512","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":"Application of Mendelian randomization analysis in investigating the genetic background of blood biomarkers for colorectal cancer.","authors":"Xin-Kun Wan, Shi-Cheng Yu, Song-Qing Mei, Wen Zhong","doi":"10.16288/j.yczz.24-179","DOIUrl":"https://doi.org/10.16288/j.yczz.24-179","url":null,"abstract":"<p><p>Colorectal cancer (CRC), a malignancy affecting the colon and rectum, ranks as the third most common cancer worldwide and the second leading cause of cancer-related deaths. Early detection of CRC is crucial for preventing metastasis, reducing mortality, improving prognosis, and enhancing patients' quality of life. Genetic factors play a significant role in CRC development, accounting for up to 35% of the disease risk. Genome-wide association studies have identified several genetic loci associated with CRC risk. However, these studies often lack direct evidence of causality. While traditional blood biomarkers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) are widely used for CRC diagnosis and monitoring, their sensitivity and accuracy in early diagnosis are limited. Thus, there is a pressing need to develop new biomarkers that reflect the genetic background of CRC to improve early detection and diagnostic accuracy. In addition, understanding the genetic mechanisms underlying these biomarkers is essential for elucidating CRC pathogenesis and developing precise personalized treatment strategies. Mendelian randomization (MR) analysis, as an emerging epidemiological tool, can accurately assess the causal relationship between genetic variations and diseases by reducing confounding biases in observational studies. MR analysis has been applied in evaluating the causal impact of various blood biomarkers on CRC risk, shedding lights on the potential causal relationships between these biomarkers and CRC pathogenesis in the context of genetic background. In this review, we summarize the applications of MR analysis in studies of blood biomarkers for CRC, aiming to enhance the early diagnosis and personalized treatment of CRC.</p>","PeriodicalId":35536,"journal":{"name":"遗传","volume":"46 10","pages":"833-848"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509513","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}