Meta-Analysis of Microarray Data and Their Utility in Dissecting the Mapped QTLs for Heat Acclimation in Rice
Abstract
:1. Introduction
2. Materials and Methods
2.1. Plant Material, Heat Stress Treatment, and Sampling
2.2. Datasets for the Identification of Heat-Responsive Genes in Rice
2.3. Meta-Analysis and Database Construction
2.4. Construction of Protein–Protein Interaction (Epistasis) Network of Genes in the Known Major QTLs for Heat Stress Tolerance
2.5. Identification of Allelic Variants in the Key Heat Stress-Responsive Genes between IR64 and N22 in the Major Heat Stress QTL Regions
2.6. Expression Analysis of the Identified Heat Stress-Responsive Genes
3. Results
3.1. Part I: Identification of Heat Stress-Responsive Genes in N22 and IR64 under Heat Acclimatisation
3.1.1. Transcription Factors (TFs) and Genes of Transcriptional Regulation under Heat Stress
3.1.2. HSPs and Other Chaperones
3.1.3. Hormone Metabolism and Signal Transduction-Related Genes
3.1.4. ROS-Related Genes
3.1.5. Cell Division and Cell Cycle
3.1.6. Ubiquitin-Dependent Degradation
3.1.7. Sucrose-Starch Pathways
3.1.8. Expression Pattern of Microspore/Pollen and Tapetum-Specific Genes
3.2. Part II: Meta-Analysis and Database Creation for Heat Stress-Responsive Genes in Rice
HRGs Identified across Developmental Stages, Tissues, and Their Pathway Analysis
3.3. Part III: Utilisation and Validation of the Database following the Identification of HRGs from QTL Regions and Their Interacting Partners
3.3.1. Selection of Genes and Genomic Regions for Validation
3.3.2. Identification of Allelic Variants in the Key Heat Stress-Responsive Genes between IR64 and N22 in the Major Heat Stress QTL Regions and Network Genes
3.3.3. Expression Analysis of the Heat Stress-Responsive Genes Identified
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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S. No. | GEO ID | Variety | Tissue | Stage | Stress | No. of DEGs |
---|---|---|---|---|---|---|
1 | GSE19983 | Pusa Basmati | Seedlings | Seedling Stage | 10 min | 2024 |
30 min | 1313 | |||||
2 | GSE38665 | Cultivar 996 | Panicle | Reproductive Stage | 20 min | 2937 |
1 h | 3533 | |||||
2 h | 3463 | |||||
4 h | 4077 | |||||
8 h | 3948 | |||||
3 | GSE55341 | M202 | Leaf | Vegetative Stage | 2 | |
4 | GSE51426 | Cultivar 996 | Panicle | Reproductive Stage | 2 h | 1746 |
5 | GSE45259 | Flag Leaf | 20 min | 2951 | ||
1 h | 3651 | |||||
2 h | 0 | |||||
4 h | 2079 | |||||
8 h | 4198 | |||||
6 | GSE1367464 | Nagina 22 and IR64 | Panicle | 8 days | 1104 | |
3 days heat | 256 | |||||
1 day heat | 1126 | |||||
Anther | 3 days heat | 126 | ||||
7 | GSE57154 | Moroberekan | Pistil | 1 day heat | 435 | |
Panicle | 8 days | 1163 |
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Singh, B.K.; Venkadesan, S.; Ramkumar, M.K.; Shanmugavadivel, P.S.; Dutta, B.; Prakash, C.; Pal, M.; Solanke, A.U.; Rai, A.; Singh, N.K.; et al. Meta-Analysis of Microarray Data and Their Utility in Dissecting the Mapped QTLs for Heat Acclimation in Rice. Plants 2023, 12, 1697. https://doi.org/10.3390/plants12081697
Singh BK, Venkadesan S, Ramkumar MK, Shanmugavadivel PS, Dutta B, Prakash C, Pal M, Solanke AU, Rai A, Singh NK, et al. Meta-Analysis of Microarray Data and Their Utility in Dissecting the Mapped QTLs for Heat Acclimation in Rice. Plants. 2023; 12(8):1697. https://doi.org/10.3390/plants12081697
Chicago/Turabian StyleSingh, Bablee Kumari, Sureshkumar Venkadesan, M. K. Ramkumar, P. S. Shanmugavadivel, Bipratip Dutta, Chandra Prakash, Madan Pal, Amolkumar U. Solanke, Anil Rai, Nagendra Kumar Singh, and et al. 2023. "Meta-Analysis of Microarray Data and Their Utility in Dissecting the Mapped QTLs for Heat Acclimation in Rice" Plants 12, no. 8: 1697. https://doi.org/10.3390/plants12081697