Sanford-Burnham Medical Research Institute, La Jolla, California, USA.
BMC Genomics. 2011 Jun 15;12 Suppl 1(Suppl 1):S3. doi: 10.1186/1471-2164-12-S1-S3.
Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in bacteria is one of the critical tasks of modern genomics. The Shewanella genus is comprised of metabolically versatile gamma-proteobacteria, whose lifestyles and natural environments are substantially different from Escherichia coli and other model bacterial species. The comparative genomics approaches and computational identification of regulatory sites are useful for the in silico reconstruction of transcriptional regulatory networks in bacteria.
To explore conservation and variations in the Shewanella transcriptional networks we analyzed the repertoire of transcription factors and performed genomics-based reconstruction and comparative analysis of regulons in 16 Shewanella genomes. The inferred regulatory network includes 82 transcription factors and their DNA binding sites, 8 riboswitches and 6 translational attenuators. Forty five regulons were newly inferred from the genome context analysis, whereas others were propagated from previously characterized regulons in the Enterobacteria and Pseudomonas spp.. Multiple variations in regulatory strategies between the Shewanella spp. and E. coli include regulon contraction and expansion (as in the case of PdhR, HexR, FadR), numerous cases of recruiting non-orthologous regulators to control equivalent pathways (e.g. PsrA for fatty acid degradation) and, conversely, orthologous regulators to control distinct pathways (e.g. TyrR, ArgR, Crp).
We tentatively defined the first reference collection of ~100 transcriptional regulons in 16 Shewanella genomes. The resulting regulatory network contains ~600 regulated genes per genome that are mostly involved in metabolism of carbohydrates, amino acids, fatty acids, vitamins, metals, and stress responses. Several reconstructed regulons including NagR for N-acetylglucosamine catabolism were experimentally validated in S. oneidensis MR-1. Analysis of correlations in gene expression patterns helps to interpret the reconstructed regulatory network. The inferred regulatory interactions will provide an additional regulatory constrains for an integrated model of metabolism and regulation in S. oneidensis MR-1.
在细菌中进行全基因组规模的基因调控预测和转录调控网络重建是现代基因组学的关键任务之一。希瓦氏菌属由代谢多功能的γ-变形菌组成，其生活方式和自然环境与大肠杆菌和其他模式细菌物种有很大的不同。比较基因组学方法和调控位点的计算识别可用于细菌中转录调控网络的计算机重建。
为了探索希瓦氏菌转录网络的保守性和变异性，我们分析了转录因子的组成，并对 16 个希瓦氏菌基因组中的调控区进行了基于基因组学的重建和比较分析。推断的调控网络包括 82 个转录因子及其 DNA 结合位点、8 个核酶和 6 个翻译衰减子。从基因组背景分析中推断出 45 个新的调控区，而其他调控区则是从肠杆菌科和假单胞菌属的先前表征的调控区传播而来的。希瓦氏菌属和大肠杆菌之间的调控策略存在多种变化，包括调控区的收缩和扩张（如 PdhR、HexR、FadR）、许多情况下招募非同源调控因子来控制等效途径（例如 PsrA 用于脂肪酸降解），以及相反的情况下，同源调控因子控制不同的途径（例如 TyrR、ArgR、Crp）。
我们初步定义了 16 个希瓦氏菌基因组中约 100 个转录调控区的第一个参考集。由此产生的调控网络包含每个基因组约 600 个受调控的基因，这些基因主要参与碳水化合物、氨基酸、脂肪酸、维生素、金属和应激反应的代谢。包括 NagR 用于 N-乙酰葡萄糖胺代谢在内的几个重建调控区在 S. oneidensis MR-1 中进行了实验验证。对基因表达模式相关性的分析有助于解释重建的调控网络。推断的调控相互作用将为 S. oneidensis MR-1 的代谢和调控综合模型提供额外的调控约束。
