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  • 标题:Transcriptomic data-driven discovery of global regulatory features of rice seeds developing under heat stress
  • 本地全文:下载
  • 作者:Mohammad Mazharul Islam ; Jaspreet Sandhu ; Harkamal Walia
  • 期刊名称:Computational and Structural Biotechnology Journal
  • 印刷版ISSN:2001-0370
  • 出版年度:2020
  • 卷号:18
  • 页码:2556-2567
  • DOI:10.1016/j.csbj.2020.09.022
  • 出版社:Computational and Structural Biotechnology Journal
  • 摘要:Plants respond to abiotic stressors through a suite of strategies including differential regulation of stress-responsive genes. Hence, characterizing the influences of the relevant global regulators or on stress-related transcription factors is critical to understand plant stress response. Rice seed development is highly sensitive to elevated temperatures. To elucidate the extent and directional hierarchy of gene regulation in rice seeds under heat stress, we developed and implemented a robust multi-level optimization-based algorithm called Minimal Regulatory Network identifier (MiReN). MiReN could predict the minimal regulatory relationship between a gene and its potential regulators from our temporal transcriptomic dataset. MiReN predictions for global regulators including stress-responsive gene Slender Rice 1 ( SLR1 ) and disease resistance gene XA21 were validated with published literature. It also predicted novel regulatory influences of other major regulators such as Kinesin-like proteins KIN12C and STD1, and WD repeat-containing protein WD40. Out of the 228 stress-responsive transcription factors identified, we predicted de novo regulatory influences on three major groups (MADS-box M-type, MYB, and bZIP) and investigated their physiological impacts during stress. Overall, MiReN results can facilitate new experimental studies to enhance our understanding of global regulatory mechanisms triggered during heat stress, which can potentially accelerate the development of stress-tolerant cultivars.
  • 关键词:Minimal regulatory network ; Heat stress ; Rice seed development ; Global regulation ; Optimization ; Transcriptomics
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