Department of Mathematics, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA.
Genome Res. 2010 Mar;20(3):372-80. doi: 10.1101/gr.100248.109. Epub 2010 Feb 9.
Biological networks are highly complex systems, consisting largely of enzymes that act as molecular switches to activate/inhibit downstream targets via post-translational modification. Computational techniques have been developed to perform signaling network inference using some high-throughput data sources, such as those generated from transcriptional and proteomic studies, but comparable methods have not been developed to use high-content morphological data, which are emerging principally from large-scale RNAi screens, to these ends. Here, we describe a systematic computational framework based on a classification model for identifying genetic interactions using high-dimensional single-cell morphological data from genetic screens, apply it to RhoGAP/GTPase regulation in Drosophila, and evaluate its efficacy. Augmented by knowledge of the basic structure of RhoGAP/GTPase signaling, namely, that GAPs act directly upstream of GTPases, we apply our framework for identifying genetic interactions to predict signaling relationships between these proteins. We find that our method makes mediocre predictions using only RhoGAP single-knockdown morphological data, yet achieves vastly improved accuracy by including original data from a double-knockdown RhoGAP genetic screen, which likely reflects the redundant network structure of RhoGAP/GTPase signaling. We consider other possible methods for inference and show that our primary model outperforms the alternatives. This work demonstrates the fundamental fact that high-throughput morphological data can be used in a systematic, successful fashion to identify genetic interactions and, using additional elementary knowledge of network structure, to infer signaling relations.
生物网络是高度复杂的系统，主要由酶组成，这些酶作为分子开关，通过翻译后修饰来激活/抑制下游靶标。已经开发了计算技术来使用一些高通量数据源（例如转录组学和蛋白质组学研究产生的数据源）进行信号网络推断，但是尚未开发出可比的方法来利用高内涵形态学数据（主要来自大规模 RNAi 筛选）来达到这些目的。在这里，我们描述了一种基于分类模型的系统计算框架，该框架用于使用遗传筛选中的高维单细胞形态学数据识别遗传相互作用，将其应用于果蝇中的 RhoGAP/GTPase 调节，并评估其功效。通过了解 RhoGAP/GTPase 信号的基本结构（即 GAP 直接在上游作用于 GTPases），我们将我们的框架用于识别遗传相互作用以预测这些蛋白质之间的信号关系。我们发现，仅使用 RhoGAP 单敲低形态学数据，我们的方法做出了中等预测，但通过包含来自 RhoGAP 双敲低遗传筛选的原始数据，我们的方法大大提高了准确性，这可能反映了 RhoGAP/GTPase 信号的冗余网络结构。我们考虑了其他可能的推断方法，并表明我们的主要模型优于替代方法。这项工作证明了一个基本事实，即高通量形态学数据可以以系统和成功的方式用于识别遗传相互作用，并使用网络结构的其他基本知识来推断信号关系。
