Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1230809 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR32551220_1.fastq.gz | Fastqsanger.gz | |||
| SRR32551220_2.fastq.gz | Fastqsanger.gz | |||
| SRR32551221_1.fastq.gz | Fastqsanger.gz | |||
| SRR32551221_2.fastq.gz | Fastqsanger.gz | |||
| SRR32551222_1.fastq.gz | Fastqsanger.gz |
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Project description:Drought priming is a promising approach to improve drought tolerance, and root apex plays important role in coping with drought adversities in plants. It has been observed that less inhibition of root growth under drought stress was found in paralleling with the enhanced drought tolerance induced by drought priming in wheat. However, the contribution and mechanisms of root apex to the improved tolerance induced by drought priming remains unknown. Here, the transcriptome and proteome of three different zones along the root axis under drought stress were investigated. The two zones distal from the root apex showed more sensitive to drought priming and the later drought stress than the zone proximal to the root apex, as exemplified by the principal component analysis of the different expressed genes (DEGs) among treatments. The DEGs which decreased by drought stress, while higher expressed in primed plants than non-primed plants, might play critical roles in enhanced root growth in primed plants. These genes were mainly involved in the pathways related to cell membrane sensing, plant hormone signaling, stress defense and cell more modification. Moreover, the plant hormone pathway showed positive correlated between transcriptome and proteome analysis, and genes network analysis hinted that ABA receptor and aquaporin could be potential markers in regulation of priming induced root growth and drought stress tolerance.
2025-07-01 | GSE133921 | GEO
Project description:Chickpea (Cicer arietinum) is the third largest legume grown worldwide and are prone to drought and various pathogen infections. These stresses often occur concurrently in the field conditions. Previous studies in other plant species indicated that plant senses concurrently occurring stresses as new state of stress however, the molecular events in response to that is largely unknown. In the present study, we studied the transcriptome changes in chickpea plants exposed to combination of drought stress and a potential wilt pathogen, Ralstonia solanacearum by microarray analysis. Chickpea plants were exposed to short duration individual drought (SD-drought, soil field capacity, FC-35%), long duration individual drought (LD-drought, FC-30%), short duration individual pathogen stress (SD-pathogen = 2 days pathogen infection), long duration individual pathogen stress (LD-pathogen = 4 days of infection) and short duration and long duration combined stress, SD-combined = 2 days of pathogen infection with progressive drought (FC-40% to FC- 35%), LD combined = 4 days of pathogen infection with progressive drought (FC-35% to 30%).Transcriptome analysis for the leaf samples from above treatment were done by microarray analysis using Agilent ChickpeaGXP_8X60K chip. Result indicated presence of specific molecular events and also some common but tailored events in response to combined stress. Global transcriptional analysis in chickpea leaves exposed to individual and combined drought stress and Ralstonia solanacearum infection.
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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.
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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.
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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.
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