Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom.
Department of Biochemistry, Faculty of Science, and Integrative Computational BioScience Center, Mahidol University, Bangkok 10400, Thailand.
Plant Physiol. 2017 Oct;175(2):628-640. doi: 10.1104/pp.17.01086. Epub 2017 Sep 1.
Plants have significantly more transcription factor (TF) families than animals and fungi, and plant TF families tend to contain more genes; these expansions are linked to adaptation to environmental stressors. Many TF family members bind to similar or identical sequence motifs, such as G-boxes (CACGTG), so it is difficult to predict regulatory relationships. We determined that the flanking sequences near G-boxes help determine in vitro specificity but that this is insufficient to predict the transcription pattern of genes near G-boxes. Therefore, we constructed a gene regulatory network that identifies the set of bZIPs and bHLHs that are most predictive of the expression of genes downstream of perfect G-boxes. This network accurately predicts transcriptional patterns and reconstructs known regulatory subnetworks. Finally, we present Ara-BOX-cis (araboxcis.org), a Web site that provides interactive visualizations of the G-box regulatory network, a useful resource for generating predictions for gene regulatory relations.
植物的转录因子 (TF) 家族数量明显多于动物和真菌，且植物 TF 家族往往包含更多的基因；这些扩张与适应环境胁迫有关。许多 TF 家族成员与相似或相同的序列基序结合，例如 G 框 (CACGTG)，因此很难预测调控关系。我们确定了 G 框附近的侧翼序列有助于确定体外特异性，但这不足以预测 G 框附近基因的转录模式。因此，我们构建了一个基因调控网络，该网络确定了一组 bZIP 和 bHLH，它们最能预测完美 G 框下游基因的表达。该网络准确地预测了转录模式，并重建了已知的调控子网络。最后，我们展示了 Ara-BOX-cis（araboxcis.org），这是一个提供 G 框调控网络交互式可视化的网站，是生成基因调控关系预测的有用资源。
