Pub Date : 2024-08-24DOI: 10.1007/s00344-024-11444-2
Pooja Moni Baruah, Niraj Agarwala, Kuntala Sarma Bordoloi, Preetom Regon, Bhaben Tanti
Tea plants exposed to temperature stress conditions exhibit reduced quality and yield. Long non-coding RNAs (lncRNAs) are key regulators in temperature stress responses. A genome-wide lncRNA analysis using RNA sequencing data from tea plants under varying temperature stresses was carried out in this study. The analysis identified a total of 23589 putative lncRNAs, with 2483 being differentially expressed (DE). Weighted gene co-expression network analysis (WGCNA) showed 445 DE lncRNAs co-expressed with 544 genes associated to temperature stress responses. Functional annotation indicated that these genes are involved in processes like protein folding, cellular response to decreased oxygen level, response to hypoxia, unfolded protein binding, and response to heat during high temperature stresses; and response to cold, water transport, and water channel activity during low temperature stresses. Additionally, competing endogenous RNA (ceRNA) network analysis revealed 230 temperature-responsive lncRNAs regulating 400 DE genes via 106 microRNAs (miRNAs). To validate high-throughput sequencing data, primers were designed for eight DE lncRNAs, and their expression levels were confirmed. This study enhances understanding of lncRNAs in temperature stress responses, providing a foundation for further research in tea plants.
{"title":"Long Non-Coding RNAs Responsive to Temperature Stress Conditions in Tea Plants","authors":"Pooja Moni Baruah, Niraj Agarwala, Kuntala Sarma Bordoloi, Preetom Regon, Bhaben Tanti","doi":"10.1007/s00344-024-11444-2","DOIUrl":"https://doi.org/10.1007/s00344-024-11444-2","url":null,"abstract":"<p>Tea plants exposed to temperature stress conditions exhibit reduced quality and yield. Long non-coding RNAs (lncRNAs) are key regulators in temperature stress responses. A genome-wide lncRNA analysis using RNA sequencing data from tea plants under varying temperature stresses was carried out in this study. The analysis identified a total of 23589 putative lncRNAs, with 2483 being differentially expressed (DE). Weighted gene co-expression network analysis (WGCNA) showed 445 DE lncRNAs co-expressed with 544 genes associated to temperature stress responses. Functional annotation indicated that these genes are involved in processes like protein folding, cellular response to decreased oxygen level, response to hypoxia, unfolded protein binding, and response to heat during high temperature stresses; and response to cold, water transport, and water channel activity during low temperature stresses. Additionally, competing endogenous RNA (ceRNA) network analysis revealed 230 temperature-responsive lncRNAs regulating 400 DE genes via 106 microRNAs (miRNAs). To validate high-throughput sequencing data, primers were designed for eight DE lncRNAs, and their expression levels were confirmed. This study enhances understanding of lncRNAs in temperature stress responses, providing a foundation for further research in tea plants.</p>","PeriodicalId":16842,"journal":{"name":"Journal of Plant Growth Regulation","volume":"430 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Acquisition of large-scale phenotyping data are the major bottleneck in associating phenotypic and genotypic data for accurate breeding decisions. High-throughput phenotyping platforms have been developed globally to hasten the next-generation breeding and more sustainable crop production. Phenomics involves collecting non-destructive, extensive, reliable, robust and multi-dimensional data on an organism's phenotype on a large-scale. The success of phenomics is driven by different imaging cameras and techniques like visible light imaging, infrared sensing, fluorescence imaging, 3D imaging, multi and hyperspectral imaging, etc. By utilizing advanced phenotyping platforms and technologies, it is possible to collect vast amounts of data on various aspects of plant growth and development, along with the response to environmental stresses. The phenomics approaches are more efficient based on maximising a plant’s phenotypic expression and differentiation. Throughout the globe, different HTP tools and platforms have been developed to help realize the true potential of breeding programs by bridging the gap between genotype and phenotype, and enhancing the efficiency of selection for maximizing the genetic gain. This review article discusses various platforms and their use in precision phenotyping to accelerate genetic improvement and provides insights into the optimal selection and utilization of HTPs.
{"title":"Plant Phenomics: The Force Behind Tomorrow’s Crop Phenotyping Tools","authors":"Pooja Kumari, Ashish Bhatt, Vijay Kamal Meena, Sneha Adhikari, Narain Dhar, Hitesh Chawda, Subhash Chand, Pushpesh Joshi, Vikas Mangal, Salej Sood","doi":"10.1007/s00344-024-11450-4","DOIUrl":"https://doi.org/10.1007/s00344-024-11450-4","url":null,"abstract":"<p>Acquisition of large-scale phenotyping data are the major bottleneck in associating phenotypic and genotypic data for accurate breeding decisions. High-throughput phenotyping platforms have been developed globally to hasten the next-generation breeding and more sustainable crop production. Phenomics involves collecting non-destructive, extensive, reliable, robust and multi-dimensional data on an organism's phenotype on a large-scale. The success of phenomics is driven by different imaging cameras and techniques like visible light imaging, infrared sensing, fluorescence imaging, 3D imaging, multi and hyperspectral imaging, etc. By utilizing advanced phenotyping platforms and technologies, it is possible to collect vast amounts of data on various aspects of plant growth and development, along with the response to environmental stresses. The phenomics approaches are more efficient based on maximising a plant’s phenotypic expression and differentiation. Throughout the globe, different HTP tools and platforms have been developed to help realize the true potential of breeding programs by bridging the gap between genotype and phenotype, and enhancing the efficiency of selection for maximizing the genetic gain. This review article discusses various platforms and their use in precision phenotyping to accelerate genetic improvement and provides insights into the optimal selection and utilization of HTPs.</p>","PeriodicalId":16842,"journal":{"name":"Journal of Plant Growth Regulation","volume":"123 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-24DOI: 10.1007/s00344-024-11463-z
Guannan Liu, Yunyun Zhao, Mian Wang, Muhammad Bilal, Pei Wang, Chong Xie, Hansong Yu, Runqiang Yang
UV-B treatment can promote the accumulation of isoflavones in soybeans. This study investigated the possible medium-wave ultraviolet (UV-B) photoreceptor genes in soybeans and the relationship between the UVR8 signaling system and isoflavones synthesis by designing three germination modes. All predicted GmUVR8 were classified into 10 classes (A–J) based on phylogenetic affinities. Isoflavone biosynthesis genes comprising GmCHS, GmCHR1, GmCHI1A, GmCHI1B, GmCHI4A, GmIFS1, and GmIFS2 were activated in response to UV-B treatment, while the corresponding enzyme activities were stably maintained at high levels. The accumulation of total isoflavones was proportional to the time, regardless of the germination pattern. UV-B treatment promoted the accumulation of isoflavones more than white light treatment and the accumulation of all isoflavones except glycitin was promoted by UV-B. The total isoflavones content of soybean sprouts reached a peak under the irradiation intensity of 45 μW/cm2 under different irradiation intensities, which increased by 72.65% compared with that in the dark group. Furthermore, the correlation analysis showed that GmUVR8-E, GmUVR8-F1, GmUVR8-J1~3, GmUVR8-J4, and GmUVR8-J5,6 were highly correlated with isoflavones synthesis and might regulate the transcription of isoflavones synthesis gene, presumed as photoreceptor of UV-B in soybean. The results will provide a scientific basis for developing soybean foods rich in isoflavones.