Department of Physics and the Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, United States of America.
PLoS One. 2012;7(2):e31969. doi: 10.1371/journal.pone.0031969. Epub 2012 Feb 29.
Various methods of reconstructing transcriptional regulatory networks infer transcriptional regulatory interactions (TRIs) between strongly coexpressed gene pairs (as determined from microarray experiments measuring mRNA levels). Alternatively, however, the coexpression of two genes might imply that they are coregulated by one or more transcription factors (TFs), and do not necessarily share a direct regulatory interaction. We explore whether and under what circumstances gene pairs with a high degree of coexpression are more likely to indicate TRIs, coregulation or both. Here we use established TRIs in combination with microarray expression data from both Escherichia coli (a prokaryote) and Saccharomyces cerevisiae (a eukaryote) to assess the accuracy of predictions of coregulated gene pairs and TRIs from coexpressed gene pairs. We find that coexpressed gene pairs are more likely to indicate coregulation than TRIs for Saccharomyces cerevisiae, but the incidence of TRIs in highly coexpressed gene pairs is higher for Escherichia coli. The data processing inequality (DPI) has previously been applied for the inference of TRIs. We consider the case where a transcription factor gene is known to regulate two genes (one of which is a transcription factor gene) that are known not to regulate one another. According to the DPI, the non-interacting gene pairs should have the smallest mutual information among all pairs in the triplets. While this is sometimes the case for Escherichia coli, we find that it is almost always not the case for Saccharomyces cerevisiae. This brings into question the usefulness of the DPI sometimes employed to infer TRIs from expression data. Finally, we observe that when a TF gene is known to regulate two other genes, it is rarely the case that one regulatory interaction is positively correlated and the other interaction is negatively correlated. Typically both are either positively or negatively correlated.
各种重建转录调控网络的方法推断出强共表达基因对（根据测量 mRNA 水平的微阵列实验确定）之间的转录调控相互作用（TRIs）。然而，两个基因的共表达可能意味着它们受到一个或多个转录因子（TFs）的共同调控，并且不一定具有直接的调控相互作用。我们探讨了具有高度共表达的基因对是否以及在什么情况下更有可能指示 TRI、共同调控或两者兼而有之。在这里，我们使用已建立的 TRI 结合来自大肠杆菌（原核生物）和酿酒酵母（真核生物）的微阵列表达数据，评估从共表达基因对预测共同调控基因对和 TRI 的准确性。我们发现，对于酿酒酵母，共表达基因对更有可能指示共同调控，而不是 TRI，但在高度共表达基因对中，TRIs 的发生率更高。数据处理不等式（DPI）以前曾用于推断 TRI。我们考虑这样一种情况，即已知一个转录因子基因调节两个基因（其中一个是转录因子基因），而这两个基因彼此不调节。根据 DPI，非相互作用的基因对应该在三联体的所有对之间具有最小的互信息。虽然对于大肠杆菌来说，情况有时如此，但我们发现对于酿酒酵母来说，几乎总是如此。这使得有时从表达数据推断 TRI 所采用的 DPI 的有效性受到质疑。最后，我们观察到，当一个 TF 基因已知调节另外两个基因时，一个调节相互作用呈正相关而另一个相互作用呈负相关的情况很少见。通常两者都是正相关或负相关。
