Fadda Abeer, Fierro Ana Carolina, Lemmens Karen, Monsieurs Pieter, Engelen Kristof, Marchal Kathleen
Department of Microbial and Molecular Systems, KULeuven, Kasteelpark Arenberg 20, 3001 Heverlee, Belgium.
Mol Biosyst. 2009 Dec;5(12):1840-52. doi: 10.1039/b907310h. Epub 2009 Jul 28.
The adaptation of bacteria to the vigorous environmental changes they undergo is crucial to their survival. They achieve this adaptation partly via intricate regulation of the transcription of their genes. In this study, we infer the transcriptional network of the Gram-positive model organism, Bacillus subtilis. We use a data integration workflow, exploiting both motif and expression data, towards the generation of condition-dependent transcriptional modules. In building the motif data, we rely on both known and predicted information. Known motifs were derived from DBTBS, while predicted motifs were generated by a de novo motif detection method that utilizes comparative genomics. The expression data consists of a compendium of microarrays across different platforms. Our results indicate that a considerable part of the B. subtilis network is yet undiscovered; we could predict 417 new regulatory interactions for known regulators and 453 interactions for yet uncharacterized regulators. The regulators in our network showed a preference for regulating modules in certain environmental conditions. Also, substantial condition-dependent intra-operonic regulation seems to take place. Global regulators seem to require functional flexibility to attain their roles by acting as both activators and repressors.
细菌适应其所经历的剧烈环境变化对其生存至关重要。它们部分通过对基因转录进行复杂调控来实现这种适应。在本研究中，我们推断革兰氏阳性模式生物枯草芽孢杆菌的转录网络。我们使用一种数据整合工作流程，利用基序和表达数据来生成依赖于条件的转录模块。在构建基序数据时，我们依赖已知信息和预测信息。已知基序来自DBTBS，而预测基序则通过利用比较基因组学的从头基序检测方法生成。表达数据由不同平台的微阵列数据集组成。我们的结果表明，枯草芽孢杆菌网络的相当一部分尚未被发现；我们可以为已知调节因子预测417个新的调控相互作用，为尚未表征的调节因子预测453个相互作用。我们网络中的调节因子在某些环境条件下表现出对调控模块的偏好。此外，似乎发生了大量依赖于条件的操纵子内调控。全局调节因子似乎需要功能灵活性，通过同时充当激活剂和抑制剂来发挥其作用。
