Laboratory of Microbial Genetics and Gene Expression, Institute of Microbiology CAS, v.v.i., Videnska 1083, 14220 Prague, Czech Republic; Department of Genetics and Microbiology, Faculty of Science, Charles University, Víničná 5, CZ-12843 Prague 2, Czech Republic.
Laboratory of Bioinformatics, Institute of Microbiology CAS, v.v.i., Videnska 1083, 14220 Prague, Czech Republic.
Biochim Biophys Acta Gene Regul Mech. 2017 Aug;1860(8):894-904. doi: 10.1016/j.bbagrm.2017.06.003. Epub 2017 Jun 22.
This study describes the meta-analysis and kinetic modelling of gene expression control by sigma factor SigA of Bacillus subtilis during germination and outgrowth based on microarray data from 14 time points. The analysis computationally models the direct interaction among SigA, SigA-controlled sigma factor genes (sigM, sigH, sigD, sigX), and their target genes. Of the >800 known genes in the SigA regulon, as extracted from databases, 311 genes were analysed, and 190 were confirmed by the kinetic model as being controlled by SigA. For the remaining genes, alternative regulators satisfying kinetic constraints were suggested. The kinetic analysis suggested another 214 genes as potential SigA targets. The modelling was able to (i) create a particular SigA-controlled gene expression network that is active under the conditions for which the expression time series was obtained, and where SigA is the dominant regulator, (ii) suggest new potential SigA target genes, and (iii) find other possible regulators of a given gene or suggest a new mechanism of its control by identifying a matching profile of unknown regulator(s). Selected predicted regulatory interactions were experimentally tested, thus validating the model.
本研究基于来自 14 个时间点的微阵列数据，描述了枯草芽孢杆菌 SigA 因子在发芽和生长过程中对基因表达的调控的荟萃分析和动力学建模。该分析通过计算模型直接模拟了 SigA、SigA 控制的 sigma 因子基因（sigM、sigH、sigD、sigX）及其靶基因之间的相互作用。在所提取的数据库中，从 SigA 调控子中提取了 800 多个已知基因，对其中的 311 个基因进行了分析，并通过动力学模型证实了其中 190 个基因受 SigA 调控。对于其余基因，建议采用符合动力学约束的替代调节剂。动力学分析还提示了另外 214 个基因可能是 SigA 的靶基因。该模型能够（i）创建一个特定的 SigA 控制的基因表达网络，该网络在获得表达时间序列的条件下是活跃的，并且 SigA 是主要的调节剂，（ii）提出新的潜在 SigA 靶基因，（iii）通过识别未知调节剂的匹配图谱，找到给定基因的其他可能调节剂或提出其控制的新机制，从而鉴定出匹配图谱。选择预测的调控相互作用进行了实验验证，从而验证了该模型。
