Rice drought treatment
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA476146 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Transcriptional profiling of rice shootunder drought condition. Goal was to determine the effects of drought on changes in gene expression.
2021-03-23 | GSE115826 | GEO
Project description:Background Drought is a global challenge that severely restricts crop yields and threatens food security. Plants respond to drought stress by modulating gene expression before visible phenotypic changes occur. However, most studies of drought resistance have examined phenotypes after drought treatment, with little emphasis on how severely the plants were perceiving drought-stress conditions before the appearance of stress symptoms. We therefore developed drought-stress biomarkers (DSBMs) to detect drought-stress perception levels based on gene expression profiles by performing time-series transcriptome analysis and phenotypic analysis of rice (Oryza sativa) under drought conditions in the growth chamber. Results Time-series RNA-seq of the drought-susceptible rice cultivar IR64 revealed drastic changes in the transcriptome after 4–6 days of drought treatment in plants grown in pot culture mimicking drought conditions in the field, particularly for genes related to photosynthesis. Among the differentially expressed genes, we selected 23 DSBM genes that consistently responded to drought stress. Rehydration immediately reset the changes in expression of these DSBM genes, indicating that their expression changes reflect current drought-stress perception levels, but not stress memories. Responses of DSBM genes tended to be conserved among rice accessions, irrespective of the rice subpopulation (such as indica, aus, and japonica). We developed a machine learning model using the expression levels of DSBM genes trained by the time-series RNA-seq data for IR64. This model successfully predicted the drought-stress perception levels of various rice accessions, representing the probability of exposure to drought treatment, with an accuracy of 75%. Extreme root architecture traits, such as the largest root surface area, narrowest crown root diameter, and largest ratio of deep rooting, influenced the predicted drought-stress perception levels. Conclusion We identified DSBM genes and developed a machine learning model as a robust tool for assessing drought-stress perception levels in rice. Monitoring and predicting drought-stress perception levels should contribute to more efficient crop management and breeding schemes. Furthermore, our dataset would serve as a resource for further understanding the mechanisms of drought resistance in rice.
2025-07-30 | GSE288412 | GEO
Project description:Background Drought is a global challenge that severely restricts crop yields and threatens food security. Plants respond to drought stress by modulating gene expression before visible phenotypic changes occur. However, most studies of drought resistance have examined phenotypes after drought treatment, with little emphasis on how severely the plants were perceiving drought-stress conditions before the appearance of stress symptoms. We therefore developed drought-stress biomarkers (DSBMs) to detect drought-stress perception levels based on gene expression profiles by performing time-series transcriptome analysis and phenotypic analysis of rice (Oryza sativa) under drought conditions in the growth chamber. Results Time-series RNA-seq of the drought-susceptible rice cultivar IR64 revealed drastic changes in the transcriptome after 4–6 days of drought treatment in plants grown in pot culture mimicking drought conditions in the field, particularly for genes related to photosynthesis. Among the differentially expressed genes, we selected 23 DSBM genes that consistently responded to drought stress. Rehydration immediately reset the changes in expression of these DSBM genes, indicating that their expression changes reflect current drought-stress perception levels, but not stress memories. Responses of DSBM genes tended to be conserved among rice accessions, irrespective of the rice subpopulation (such as indica, aus, and japonica). We developed a machine learning model using the expression levels of DSBM genes trained by the time-series RNA-seq data for IR64. This model successfully predicted the drought-stress perception levels of various rice accessions, representing the probability of exposure to drought treatment, with an accuracy of 75%. Extreme root architecture traits, such as the largest root surface area, narrowest crown root diameter, and largest ratio of deep rooting, influenced the predicted drought-stress perception levels. Conclusion We identified DSBM genes and developed a machine learning model as a robust tool for assessing drought-stress perception levels in rice. Monitoring and predicting drought-stress perception levels should contribute to more efficient crop management and breeding schemes. Furthermore, our dataset would serve as a resource for further understanding the mechanisms of drought resistance in rice.
2025-07-30 | GSE288713 | GEO
Project description:Background Drought is a global challenge that severely restricts crop yields and threatens food security. Plants respond to drought stress by modulating gene expression before visible phenotypic changes occur. However, most studies of drought resistance have examined phenotypes after drought treatment, with little emphasis on how severely the plants were perceiving drought-stress conditions before the appearance of stress symptoms. We therefore developed drought-stress biomarkers (DSBMs) to detect drought-stress perception levels based on gene expression profiles by performing time-series transcriptome analysis and phenotypic analysis of rice (Oryza sativa) under drought conditions in the growth chamber. Results Time-series RNA-seq of the drought-susceptible rice cultivar IR64 revealed drastic changes in the transcriptome after 4–6 days of drought treatment in plants grown in pot culture mimicking drought conditions in the field, particularly for genes related to photosynthesis. Among the differentially expressed genes, we selected 23 DSBM genes that consistently responded to drought stress. Rehydration immediately reset the changes in expression of these DSBM genes, indicating that their expression changes reflect current drought-stress perception levels, but not stress memories. Responses of DSBM genes tended to be conserved among rice accessions, irrespective of the rice subpopulation (such as indica, aus, and japonica). We developed a machine learning model using the expression levels of DSBM genes trained by the time-series RNA-seq data for IR64. This model successfully predicted the drought-stress perception levels of various rice accessions, representing the probability of exposure to drought treatment, with an accuracy of 75%. Extreme root architecture traits, such as the largest root surface area, narrowest crown root diameter, and largest ratio of deep rooting, influenced the predicted drought-stress perception levels. Conclusion We identified DSBM genes and developed a machine learning model as a robust tool for assessing drought-stress perception levels in rice. Monitoring and predicting drought-stress perception levels should contribute to more efficient crop management and breeding schemes. Furthermore, our dataset would serve as a resource for further understanding the mechanisms of drought resistance in rice.
2025-07-30 | GSE288615 | GEO
Project description:Comparative transcriptional profiling of two contrasting rice genotypes,IRAT109 (drought-resistant japonica cultivar) and ZS97 (drought-sensitive indica cultivar), under drought stress during the reproductive stage
2011-11-05 | GSE25176 | GEO
Project description:The young panicles 2 cm length were used for expression analysis in well watered control and drought stressed treatment. The panicle samples from biological replicates of six rice varieties were obtained in three independent experiments. The expression profiles were generated using Affymetrix rice genome arrays.
2017-06-14 | GSE83378 | GEO
Project description:Rice plants were exogenously sprayed with synthetic phenyl-urea cytokinin under drought stress. Leaf proteome was analyzed for the differential expression of proteins.
2020-09-08 | PXD021005 | Pride
Project description:Transcription factors play a crucial regulatory role in plant drought stress responses. In this study, a novel drought stress related bZIP transcription factor, OsbZIP62, was identified in rice. This gene was selected from transcriptome analysis of several typical rice varieties with different drought tolerance. The expression of OsbZIP62 was obviously induced by drought, hydrogen peroxide, and abscisic acid (ABA) treatment. Overexpression of OsbZIP62-VP64 (OsbZIP62V) enhanced the drought tolerance and oxidative stress tolerance of transgenic rice, while the osbzip62 mutants showed the opposite phenotype. RNA-seq analysis showed that many stress-related genes (e.g. OsGL1, OsNAC10, and DSM2) were up-regulated in OsbZIP62V plants. OsbZIP62 could bind to the abscisic acid–responsive element (ABRE) and promoters of several putative target genes. Taken together, OsbZIP62 positively regulated rice drought tolerance through regulated the expression of genes associated with stress.
2018-11-28 | GSE122887 | GEO
Project description:In this research, an array of 27,448 rice genes was used to elucidate gene expression in air-dried rice seedlings (lead and root) at various periods of treatment times. The analyses show that rice responds to drought stress mainly by down-regulating many biological processes including gene expression and regulation, protein phosphorylation, and cellular metabolism. Among strategies to actively adapt to drought, most significant are inducing protective molecules, which may be differentially regulated based on plant organs.
2012-09-21 | GSE38130 | GEO
Project description:OsNAC6 is a stress responsive NAC transcription factor in rice known as a regulator for the transcriptional networks of the drought tolerance mechanisms. However, little is known about the associated molecular mechanisms for drought tolerance. Here, we identified OsNAC6-mediated root structural adaptation such as increased root number and root diameter that was sufficient to confer drought tolerance. Multiyear (5 years) drought field tests clearly demonstrated that OsNAC6 overexpression in roots produced higher grain yield under drought conditions. Genome-wide analyses revealed that OsNAC6 directly up-regulated 13 genes. Taken together, OsNAC6 is a valuable candidate for genetic engineering of drought-tolerant high-yielding crops.
2017-04-29 | GSE80986 | GEO