Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA.
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Cell Syst. 2017 May 24;4(5):543-558.e8. doi: 10.1016/j.cels.2017.04.010.
Changes in transcriptional regulatory networks can significantly contribute to species evolution and adaptation. However, identification of genome-scale regulatory networks is an open challenge, especially in non-model organisms. Here, we introduce multi-species regulatory network learning (MRTLE), a computational approach that uses phylogenetic structure, sequence-specific motifs, and transcriptomic data, to infer the regulatory networks in different species. Using simulated data from known networks and transcriptomic data from six divergent yeasts, we demonstrate that MRTLE predicts networks with greater accuracy than existing methods because it incorporates phylogenetic information. We used MRTLE to infer the structure of the transcriptional networks that control the osmotic stress responses of divergent, non-model yeast species and then validated our predictions experimentally. Interrogating these networks reveals that gene duplication promotes network divergence across evolution. Taken together, our approach facilitates study of regulatory network evolutionary dynamics across multiple poorly studied species.
转录调控网络的变化可以显著促进物种的进化和适应。然而，在非模式生物中，大规模调控网络的鉴定仍然是一个开放的挑战。在这里，我们引入了多物种调控网络学习（MRTLE）方法，这是一种利用系统发育结构、序列特异性基序和转录组数据来推断不同物种调控网络的计算方法。使用来自已知网络的模拟数据和来自六种不同酵母的转录组数据，我们证明了 MRTLE 比现有方法更准确地预测网络，因为它整合了系统发育信息。我们使用 MRTLE 推断了控制不同非模式酵母物种渗透胁迫反应的转录网络的结构，然后通过实验验证了我们的预测。对这些网络的研究表明，基因复制促进了网络在进化过程中的分歧。总之，我们的方法促进了对多个研究较少的物种的调控网络进化动态的研究。
