Department of Genetics, Rosetta Inpharmatics, LLC, a wholly owned subsidiary of Merck & Co., Inc., Seattle, Washington, USA.
PLoS Comput Biol. 2010 Feb 12;6(2):e1000671. doi: 10.1371/journal.pcbi.1000671.
Gene expression data generated systematically in a given system over multiple time points provides a source of perturbation that can be leveraged to infer causal relationships among genes explaining network changes. Previously, we showed that food intake has a large impact on blood gene expression patterns and that these responses, either in terms of gene expression level or gene-gene connectivity, are strongly associated with metabolic diseases. In this study, we explored which genes drive the changes of gene expression patterns in response to time and food intake. We applied the Granger causality test and the dynamic Bayesian network to gene expression data generated from blood samples collected at multiple time points during the course of a day. The simulation result shows that combining many short time series together is as powerful to infer Granger causality as using a single long time series. Using the Granger causality test, we identified genes that were supported as the most likely causal candidates for the coordinated temporal changes in the network. These results show that PER1 is a key regulator of the blood transcriptional network, in which multiple biological processes are under circadian rhythm regulation. The fasted and fed dynamic Bayesian networks showed that over 72% of dynamic connections are self links. Finally, we show that different processes such as inflammation and lipid metabolism, which are disconnected in the static network, become dynamically linked in response to food intake, which would suggest that increasing nutritional load leads to coordinate regulation of these biological processes. In conclusion, our results suggest that food intake has a profound impact on the dynamic co-regulation of multiple biological processes, such as metabolism, immune response, apoptosis and circadian rhythm. The results could have broader implications for the design of studies of disease association and drug response in clinical trials.
在给定系统中多个时间点系统生成的基因表达数据提供了一种可以利用的扰动源，可以用来推断解释网络变化的基因之间的因果关系。以前，我们表明，食物摄入对血液基因表达模式有很大影响，并且这些反应（无论是在基因表达水平还是基因-基因连接性方面）都与代谢疾病密切相关。在这项研究中，我们探讨了哪些基因驱动基因表达模式随时间和食物摄入的变化。我们应用格兰杰因果检验和动态贝叶斯网络来分析从一天中多个时间点采集的血液样本中生成的基因表达数据。模拟结果表明，将许多短时间序列组合在一起推断格兰杰因果关系的能力与使用单个长时间序列一样强大。使用格兰杰因果检验，我们确定了作为网络中协调的时间变化的最可能因果候选基因。这些结果表明，PER1 是血液转录网络的关键调节剂，其中多个生物学过程受昼夜节律调节。禁食和进食的动态贝叶斯网络表明，超过 72%的动态连接是自连接。最后，我们表明，不同的过程，如炎症和脂质代谢，在静态网络中是不相关的，在进食后变得动态相关，这表明增加营养负荷会导致这些生物学过程的协调调节。总之，我们的结果表明，食物摄入对代谢、免疫反应、细胞凋亡和昼夜节律等多个生物学过程的动态协同调节有深远影响。这些结果可能对临床试验中疾病关联和药物反应研究的设计具有更广泛的意义。
