Ontology highlight
ABSTRACT:
PROVIDER: PRJNA725109 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR14322728.fastq.gz | Fastqsanger.gz | |||
| SRR14322729.fastq.gz | Fastqsanger.gz | |||
| SRR14322730.fastq.gz | Fastqsanger.gz | |||
| SRR14322731.fastq.gz | Fastqsanger.gz | |||
| SRR14322732.fastq.gz | Fastqsanger.gz |
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Project description:To dissect the molecular mechanisms underlying drought tolerance (DT) in rice, transcriptome differences of a DT introgression line H471, the DT donor P28 and the drought sensitive recurrent parent HHZ under drought stress were investigated using deep transcriptome sequencing. Results revealed a differential constitutive gene expression prior to stress and distinct global transcriptome reprogramming among three genotypes under time-series drought stress, consistent with their differential genotypes and DT phenotypes.
2014-12-30 | GSE57950 | GEO
Project description:In this study, genome-wide transcriptome profiling was used to understand molecular genetic mechanism of drought tolerance in rice. Illumina High-Seq 2000 platform was used for sequencing RNA from leaf tissue of rice plants exposed to controlled drought stress and well-watered conditions. The differentially expressed genes were used to identify biological process and cis-regulatory elements enriched under drought stress compared to well-watered conditions.
2015-01-16 | GSE65022 | GEO
Project description:To dissect the molecular mechanisms underlying drought tolerance (DT) in rice, transcriptome differences of a DT introgression line H471, the DT donor P28 and the drought sensitive recurrent parent HHZ under drought stress were investigated using deep transcriptome sequencing. Results revealed a differential constitutive gene expression prior to stress and distinct global transcriptome reprogramming among three genotypes under time-series drought stress, consistent with their differential genotypes and DT phenotypes. DT introgression line H471, the DT donor P28 and the drought sensitive recurrent parent HHZ under drought stress were investigated using deep transcriptome sequencing.The drought stress treatment was started by withholding water at the tillering stage. The days were counted after the AWC in the soil reached 20% to allow drought measurements at precisely determined intervals, and the soil water content reached 15%, 10% and 7.5% after 1d, 3d and 4d drought treatment, respectively.Three top leaves for each sample were harvested for each genotype under 1d and 3d drought stress and control conditions. All samples were immediately frozen in liquid nitrogen and stored at -80C and then for transcriptome sequencing.
2014-12-30 | E-GEOD-57950 | biostudies-arrayexpress
Project description:The members of bHLH transcription factor superfamily are known to play key role in plant development and abiotic stress response. Loss-of-function of OsbHLH148 gene resulted in increased sensitivity of rice plants to drought stress. To identify the targets of OsbHLH148 and dissect the drought stress response pathway regulated by it, we performed transcriptome profiling of Osbhlh148 mutant plants under drought stress as well as well-watered conditions by RNA-sequencing.
2015-01-16 | GSE65024 | GEO
Project description:Heat shock factors (Hsfs) are known to regulate heat and drought stress response by controlling the expression of heat shock proteins and oxidative stress responsive genes. Loss-of-function of OsHSFA2e gene resulted in increased sensitivity of rice plants to drought and heat stress. To identify the targets of OsHSFA2e and dissect the stress response pathway regulated by it, we performed transcriptome profiling of Oshsfa2e mutant plants under drought stress as well as well-watered conditions by RNA-sequencing.
2015-01-16 | GSE65025 | 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:Drought stress response involves vigorous reprogramming of transcriptome while the mechanism modulating this process remains elusive. The role of 3D-genome in the regulation of rice development has recently been unveiled in rice, which exhibited characteristics distinct from that in mammals and other plants. However, the relationship between spatial chromosome organization and drought responsive gene reprogramming is still poorly understood. In this study, we identified H3K9ac as an efficient histone mark that response dynamically to drought stress in rice and re-constructed high-resolution 3D genome contact maps based on these sites under the normal, drought, and recovery conditions. We discovered significant decondensation of chromatin contacts with over 10000 chromatin loops lost upon drought stress treatment while the recovery of 3D genome was limited after 4-day’s re-watering. We identified dominate promoter-promoter interacting (PPI) loops under each condition and identified their significant correlations to altered gene expressions in response to the corresponding condition. Based on the relative contact intensities of the PPI connections, we characterized super-promoter regions that integrate closer connections of genes with more vigorous inductions to condition shifts. Especially, about 75% of the drought stress-dominate PPI loops were associated to the binding by a well-defined drought stress-responsive transcription factor, OsbZIP23. The mutation of OsbZIP23 led to the diffuse of over 80% DS-dominate PPI loops and impaired the expression of the connected genes. As a case study, we showed how OsbZIP23 binding to a super-promoter region can integrate the PPI loop formation and transcriptional activation of four function-vital dehydrin genes upon drought stress. Our results shed light on the mechanisms of 3D genome dynamic in response to water supply variations in rice and highlight the role of OsbZIP23 in the regulation of chromatin loop formation under drought stress.
2024-09-19 | GSE242453 | GEO
Project description:The current knowledge of drought stress transcriptomes in rice are mostly relying on comparative studies of diverse genetic background under drought. A more reliable approach is to use near-isogenic lines (NILs) with a common genetic background (Donar parent: Aday Sel, Recurrent parent: IR64), but contrasting levels of resistance to drought stress under initial exposure to water deficit. Here, we examined two pairs of NILs in IR64 background with contrasting drought tolerance. We obtained gene expression profile in shoots of rice NILs under different levels of drought stress help to identify genes and mechanisms involved in drought stress.
2012-07-01 | GSE30449 | GEO