Protein phosphorylation in response to drought stress in rice
Ontology highlight
ABSTRACT:
ORGANISM(S): Oryza Sativa
SUBMITTER: 
Lijun Luo
PROVIDER: PXD031505 | iProX | Wed Feb 09 00:00:00 GMT 2022
REPOSITORIES: iProX
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Project description:Drought stress significantly reduces rice productivity, necessitating efforts to understand resistance mechanisms. This study utilizes quantitative proteomic and lysine acetylomic analyses to elucidate the complex drought responses in three rice cultivars with distinct drought resistance strategies. Proteomic analysis reveals that Hanyou73, an integrated drought avoidance and drought tolerance cultivar, exhibits exceptional resistance to water deprivation compared to other cultivars. Critically, lysine acetylomic analysis underscores the pivotal role of histone acetylation, particularly H4K5ac, in modulating water management efficiency, which underpins Hanyou73's drought resilience. Leveraging this enhanced water management, Hanyou73 exhibits superior photosynthetic efficiency under drought conditions, primarily attributed to the deacetylation of ATP synthase beta subunit (AtpB) at K100, thereby augmenting ATPase catalytic activity. Additionally, deacetylation of phosphoglucomutase (PGM) at K155 indicated an adaptive metabolic response across all drought-resistant cultivars, ensuring sustained energy and metabolic intermediate supply crucial for broad adaptation mechanisms. Collectively, these findings elucidate the intricate regulatory networks in Hanyou73, highlighting its sophisticated balance between drought avoidance and tolerance strategies. This research offers valuable insights into the proteomic and lysine acetylomic dynamics underlying rice drought resistance, providing potential biomarkers for breeding towards genetic enhancement.
2025-09-04 | GSE276295 | 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
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 roots of rice NILs under different levels of drought stress help to identify genes and mechanisms involved in drought stress.
2012-07-01 | GSE30463 | 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 panicles of rice NILs under different levels of drought stress help to identify genes and mechanisms involved in drought stress.
2012-07-01 | GSE30462 | GEO
Project description:We investigated the physiological and gene expression response of drought-tolerant (IR57311 and LC-93-4; subgroup indica) and drought-sensitive (Nipponbare and Taipai 309; subgroup japonica) rice (Oryza sativa) cultivars to 18 days of drought stress in climate chamber experiments. Rice plants were grown under water sufficient and water limiting conditions in three independent experiments in a controlled climate chamber with 12 h day length at 600 µE m-2 s-1; temperature was 26°C in the light and 22°C at night, with a relative humidity of 75% in the light and 70% at night. Leaf material for expression profiling analysis was harvested five hours after the beginning of the light period after 18 days of stress or control treatment. Normalization and statistical testing was performed using the R package limma (R 2.3.1, limma version 2.7.3; (Smyth, 2005)). The methods Robustspline for within array normalization and Quantile for between array normalization were applied. A linear model with the effects genotype, drought treatment, genotype x drought, dye was fitted to normalized data in limma that models the systemic variation in the data. Afterwards, for the comparisons of interest, moderated t-statistics that use an empirical Bayes method were calculated. Differentially expressed genes were identified using the decideTests function (method global, Benjamini & Hochberg fdr corrected p-value <0.05 in limma) Keywords: stress reponse
2008-10-29 | GSE7766 | 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: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: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. Oryza sativa ssp. japonica cv. Nipponbare plants were exposed to controlled drought stress and well-watered conditions at the vegetative stage. Controlled drought (DR) stress was applied on 45 day old plants following gravimetric approach. The soil water content was brought down to 40% field capacity over a period of 3-4 days and plants were maintained at that level for 10 days by weighing the pots daily at a fixed time of the day and replenishing the water lost through evapotranspiration. Another set of plants were maintained at 100% FC as well-watered (WW) condition. Total RNA isolated from leaf tissue was used for RNA-sequencing. Two biological replicates per sample were sequenced. cDNA library was constructed using TruSeq Stranded Total RNA with Ribo-Zero Plant kit (Illumina). Sequencing was carried out on each library to generate 50 bp SE reads using Illumina High-Seq 2000 platform. The transcriptome reads were mapped to the rice reference genome sequence (MSU 7.0) with tophat1.3.1 using the program’s default parameters (http://tophat.cbcb.umd.edu). Mapped RNA-Seq reads were assembled into transcripts by Cufflinks (http://cufflinks.cbcb.umd.edu/) and differentially expressed genes were identified by using Cuffdiff.
2015-01-16 | E-GEOD-65022 | biostudies-arrayexpress
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