A computational procedure for identifying master regulator candidates: a case study on diabetes progression in Goto-Kakizaki rats.
A computational procedure for identifying master regulator candidates: a case study on diabetes progression in Goto-Kakizaki rats.
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识别主调节候选者的计算程序：Goto-Kakizaki 大鼠糖尿病进展的案例研究
DOI：
10.1186/1752-0509-6-s1-s2
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发表时间：
2012
影响因子：
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通讯作者：
Horimoto K
中科院分区：
文献类型：
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作者：
Piao G;Saito S;Sun Y;Liu ZP;Wang Y;Han X;Wu J;Zhou H;Chen L;Horimoto K
研究背景我们最近通过网络筛选的方法在Goto-Kakizaki（GK）大鼠中发现了一些参与糖尿病进展的主动调节网络。该网络与转录因子（TF）及其调控基因之间的调控关系的先前知识是相当一致的。为了研究与表型变化直接相关的潜在分子机制，例如疾病，我们以前还开发了一种计算程序，用于识别转录主调节因子（MR），结合网络筛选和网络推理，通过有效地扰动表型state.ResultsIn这项工作，我们进一步改进了我们以前的方法，用于识别MR候选人，通过以更可靠的方式列出它们，并应用该方法从主动网络中揭示GK大鼠糖尿病进展的MR候选者。具体而言，首先通过网络筛选从网络中提取GK大鼠中不同时间段的活性TF-基因对。通过考虑GK和Wistar-Kyoto（WKY）大鼠之间这些时期的基因表达特征，通过网络推理选择另一组活性TF-基因对。然后从GK大鼠TF的出现特异性和表达标签中TF的调控基因覆盖率的角度，进一步选择通过两种方法提取的TF-基因对。最后，我们缩小了所有的基因只有5个TF（Etv 4，Fus，Nr 2f 1，Sp2，和Tcfap 2b）作为候选人的MR，54个调节基因，通过合并选定的TF-基因pairs.ConclusionsThe本方法已成功地确定了生物学上合理的MR候选人，包括在以前的报告中与糖尿病相关的TF。虽然实验验证的候选人和本程序超出了本研究的范围，我们缩小了候选人的5个TF，这可以用来执行验证实验相对容易。数值结果表明，我们的计算方法是一种有效的方法来检测的关键分子负责的生物现象。
BackgroundWe have recently identified a number of active regulatory networks involved in diabetes progression in Goto-Kakizaki (GK) rats by network screening. The networks were quite consistent with the previous knowledge of the regulatory relationships between transcription factors (TFs) and their regulated genes. To study the underlying molecular mechanisms directly related to phenotype changes, such as diseases, we also previously developed a computational procedure for identifying transcriptional master regulators (MRs) in conjunction with network screening and network inference, by effectively perturbing the phenotype states.ResultsIn this work, we further improved our previous method for identifying MR candidates, by listing them in a more reliable manner, and applied the method to reveal the MR candidates for diabetes progression in GK rats from the active networks. Specifically, the active TF-gene pairs for different time periods in GK rats were first extracted from the networks by network screening. Another set of active TF-gene pairs was selected by network inference, by considering the gene expression signatures for those periods between GK and Wistar-Kyoto (WKY) rats. The TF-gene pairs extracted by the two methods were then further selected, from the viewpoints of the emergence specificity of TF in GK rats and the regulated-gene coverage of TF in the expression signature. Finally, we narrowed all of the genes down to only 5 TFs (Etv4, Fus, Nr2f1, Sp2, and Tcfap2b) as the candidates of MRs, with 54 regulated genes, by merging the selected TF-gene pairs.ConclusionsThe present method has successfully identified biologically plausible MR candidates, including the TFs related to diabetes in previous reports. Although the experimental verifications of the candidates and the present procedure are beyond the scope of this study, we narrowed down the candidates to 5 TFs, which can be used to perform the verification experiments relatively easily. The numerical results showed that our computational method is an efficient way to detect the key molecules responsible for biological phenomena.