Molecular Plant Breeding 2025, Vol.16, No.2, 105-118 http://genbreedpublisher.com/index.php/mpb 115 shortages. EGRINs have been shown to coordinate gene expression in response to water deficit, involving 113 TFs and 4052 target genes. Additionally, the DEGs under drought stress has been found to be highly tissue- and stage- specific, with significant roles played by TFs and unique cis-elements. These findings underscore the complexity and specificity of the genetic responses to water deficit in rice. The integration of various genomic and transcriptomic approaches has been pivotal in advancing our understanding of water deficit responses in rice. By combining GWAS with transcriptional analyses, researchers have been able to identify key genetic loci and regulatory networks that confer drought resistance. The use of EGRINs has further elucidated the dynamic regulatory mechanisms that control gene expression under stress conditions. Such integrated approaches enable a holistic view of the plant's response, facilitating the identification of candidate genes and regulatory elements that can be targeted for breeding programs aimed at improving drought tolerance. The study of transcriptional regulation and gene networks in rice under water deficit conditions has provided valuable insights into the genetic and molecular mechanisms underlying drought resistance. The identification of specific MTAs, TFs, and regulatory networks highlights the potential for developing more resilient rice varieties through targeted breeding and genetic engineering. Future research should continue to leverage integrated genomic and transcriptomic approaches to further unravel the complex interactions between genes and environmental stressors, ultimately contributing to sustainable rice production in the face of increasing water scarcity. Acknowledgments We extend our sincere thanks to two anonymous peer reviewers for their invaluable feedback on the initial draft of this paper, whose critical evaluations and constructive suggestions have greatly contributed to the improvement of our manuscript. Funding This work was supported by the grants from the Central Leading Local Science and Technology Development Project (grant no. 202207AA110010) and the Key and Major Science and Technology Projects of Yunnan (grant nos. 202202AE09002102, 202402AE090026-04). Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Ahn H., Jung I., Shin S., Park J., Rhee S., Kim J., Jung W., Kwon H., and Kim S., 2017, Transcriptional network analysis reveals drought resistance mechanisms of ap2/erf transgenic rice, Frontiers in Plant Science, 8: 1044. https://doi.org/10.3389/fpls.2017.01044 Baldoni E., Bagnaresi P., Locatelli F., Mattana M., and Genga A., 2016, Comparative leaf and root transcriptomic analysis of two rice japonica cultivars reveals major differences in the root early response to osmotic stress, Rice, 9: 25. https://doi.org/10.1186/s12284-016-0098-1 Basu P., Pandey A., and Dwivedi S, 2016, Physiological and biochemical responses of plants to water deficit stress, Plant Signaling & Behavior, 11(12): e1189142. Basu S., and Roychoudhury A., 2021, Transcript profiling of stress-responsive genes and metabolic changes during salinity in indica and japonica rice exhibit distinct varietal difference, Physiologia Plantarum, 173(4): 1434-1447. https://doi.org/10.1111/ppl.13440 Cui M., Zhang W., Zhang Q., Xu Z., Zhu Z., Duan F., and Wu R., 2011, Induced over-expression of the transcription factor OsDREB2A improves drought tolerance in rice, Plant Physiology and Biochemistry, 49(12): 1384-1391. https://doi.org/10.1016/j.plaphy.2011.09.012 Cutler S., Rodriguez L., Finkelstein R., and Abrams S., 2010, Regulation of plant responses to drought stress, Current Opinion in Plant Biology, 13(3): 296-302. Chen J., Zhong Y., and Qi X., 2021, LncRNA TCONS_00021861 is functionally associated with drought tolerance in rice (Oryza sativa L.) via competing endogenous RNA regulation, BMC Plant Biology, 21: 410. https://doi.org/10.1186/s12870-021-03195-z Eren A., Esen Ö., Quince C., Vineis J., Morrison H., Sogin M., and Delmont T., 2015, Anvi’o: an advanced analysis and visualization platform for omics data, PeerJ, 3: e1319. https://doi.org/10.7717/peerj.1319
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