Swimming upstream: identifying proteomic signals that drive transcriptional changes using the interactome and multiple "-omics" datasets.
Swimming upstream: identifying proteomic signals that drive transcriptional changes using the interactome and multiple "-omics" datasets.
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DOI：
10.1016/b978-0-12-388403-9.00003-5
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发表时间：
2012
影响因子：
--
通讯作者：
Fraenkel, Ernest
中科院分区：
文献类型：
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作者：
Huang, Shao-shan Carol;Fraenkel, Ernest
信号和转录是紧密结合的过程，是许多细胞对环境反应的基础。通常由蛋白质翻译后修饰介导的信号事件网络，可以通过改变特定转录调节因子的活性，从而改变其下游靶标的表达水平，从而导致细胞行为的长期变化。由于许多高通量的“组学”方法现在可以同时测量数百种蛋白质和数千种转录物的变化，因此应该有可能系统地重建细胞对扰动的反应，以便发现以前未被识别的信号通路。本章描述了一种用于发现这些路径的计算方法，该方法旨在补偿这些不同数据源中存在的不同水平的噪声。基于网络约束优化的概念，该方法寻求实现两个相互冲突的目标：(1)使用先前报道的蛋白质-蛋白质和蛋白质- dna相互作用将实验中确定的许多信号蛋白和差异表达转录物连接在一起（“约束”），同时(2)保持结果网络较小并确保它由最高可信度的相互作用组成（“优化”）。这种方法的另一个显著特征是使用转录数据作为驱动基因表达变化的上游信号事件的证据，而不是作为编码蛋白水平下游变化的代理。我们最近证明，通过将这种方法应用于酵母信息素反应的磷酸化蛋白质组学和转录数据，我们能够恢复功能一致的途径，并揭示许多细胞反应的成分，这些成分在原始数据中并不明显。在这里，我们对该方法进行了更详细的描述，探讨了解决方案对输入数据噪声水平的鲁棒性，并讨论了参数值的影响。
Signaling and transcription are tightly integrated processes that underlie many cellular responses to the environment. A network of signaling events, often mediated by post-translational modification on proteins, can lead to long-term changes in cellular behavior by altering the activity of specific transcriptional regulators and consequently the expression level of their downstream targets. As many high-throughput, “-omics” methods are now available that can simultaneously measure changes in hundreds of proteins and thousands of transcripts, it should be possible to systematically reconstruct cellular responses to perturbations in order to discover previously unrecognized signaling pathways. This chapter describes a computational method for discovering such pathways that aims to compensate for the varying levels of noise present in these diverse data sources. Based on the concept of constraint optimization on networks, the method seeks to achieve two conflicting aims: (1) to link together many of the signaling proteins and differential expressed transcripts identified in the experiments (“constraints”) using previously reported protein-protein and protein-DNA interactions, while (2) keeping the resulting network small and ensuring it is composed of the highest confidence interactions (“optimization”). A further distinctive feature of this approach is the use of transcriptional data as evidence of upstream signaling events that drive changes in gene expression, rather than as proxies for downstream changes in the levels of the encoded proteins. We recently demonstrated that by applying this method to phosphoproteomic and transcriptional data from the pheromone response in yeast, we were able to recover functionally coherent pathways and to reveal many components of the cellular response that are not readily apparent in the original data. Here we provide a more detailed description of the method, explore the robustness of the solution to the noise level of input data and discuss the effect of parameter values.