Srivastava Gyan Prakash, Li Ping, Liu Jingdong, Xu Dong
Computer Science Department, University of Missouri, Columbia, Missouri, USA.
BMC Syst Biol. 2010 Sep 13;4 Suppl 2(Suppl 2):S2. doi: 10.1186/1752-0509-4-S2-S2.
Transcription factors (TFs) regulate downstream genes in response to environmental stresses in plants. Identification of TF target genes can provide insight on molecular mechanisms of stress response systems, which can lead to practical applications such as engineering crops that thrive in challenging environments. Despite various computational techniques that have been developed for identifying TF targets, it remains a challenge to make best use of available experimental data, especially from time-series transcriptome profiling data, for improving TF target identification.
In this study, we used a novel approach that combined kinetic modelling of gene expression with a statistical meta-analysis to predict targets of 757 TFs using expression data of 14,905 genes in Arabidopsis exposed to different durations and types of abiotic stresses. Using a kinetic model for the time delay between the expression of a TF gene and its potential targets, we shifted a TF's expression profile to make an interacting pair coherent. We found that partitioning the expression data by tissue and developmental stage improved correlation between TFs and their targets. We identified consensus pairs of correlated profiles between a TF and all other genes among partitioned datasets. We applied this approach to predict novel targets of known TFs. Some of these putative targets were validated from the literature, for E2F's targets in particular, while others provide explicit genes as hypotheses for future studies.
Our method provides a general framework for TF target prediction with consideration of the time lag between initiation of a TF and activation of its targets. The framework helps make significant inferences by reducing the effects of independent noises in different experiments and by identifying recurring regulatory relationships under various biological conditions. Our TF target predictions may shed some light on common regulatory networks in abiotic stress responses.
转录因子（TFs）可响应植物中的环境胁迫调控下游基因。鉴定TF靶基因有助于深入了解胁迫响应系统的分子机制，进而实现诸如培育能在恶劣环境中茁壮成长的作物等实际应用。尽管已开发出多种用于鉴定TF靶标的计算技术，但充分利用现有实验数据，尤其是来自时间序列转录组分析数据来改进TF靶标鉴定仍是一项挑战。
在本研究中，我们采用了一种将基因表达动力学建模与统计元分析相结合的新方法，利用拟南芥中14905个基因在不同持续时间和类型的非生物胁迫下的表达数据，预测了757个TF的靶标。利用TF基因与其潜在靶标表达之间的时间延迟动力学模型，我们对TF的表达谱进行了移位，以使相互作用对具有一致性。我们发现按组织和发育阶段对表达数据进行划分可提高TF与其靶标之间的相关性。我们在划分的数据集中鉴定了TF与所有其他基因之间的相关谱的共有对。我们应用此方法预测已知TF的新靶标。其中一些推定靶标已从文献中得到验证，特别是E2F的靶标，而其他靶标则为未来研究提供了明确的基因假设。
我们的方法提供了一个用于TF靶标预测的通用框架，考虑了TF启动与其靶标激活之间的时间滞后。该框架通过减少不同实验中独立噪声的影响以及识别各种生物学条件下反复出现的调控关系，有助于做出重要推断。我们对TF靶标的预测可能为非生物胁迫响应中的常见调控网络提供一些线索。
