Systems modeling of oncogenic G-protein and oncogenic GPCR signaling reveals unexpected differences in downstream signaling pathway activation [FAKi_experiment]

Mathematical models of biochemical reaction networks are an important and emerging tool for the study of cell signaling networks involved in disease processes. One promising potential application of such mathematical models is the study of how disease causing mutations to one or more proteins in the network promote the signaling phenotype that contributes to the disease. It is commonly assumed that one must have a thorough characterization of the network readily available for mathematical modeling to be useful, but we hypothesized that mathematical modeling could be useful when there is incomplete knowledge and that it could be a tool for discovery that opens new areas for further exploration. In the present study, we first develop a mechanistic mathematical model of a G-protein coupled receptor signaling network that is mutated in almost all cases of uveal melanoma and use model-driven explorations to uncover and explore multiple new areas for investigating this disease. Modeling the two major, mutually-exclusive, oncogenic mutations (Gαq/11 and CysLT2R) revealed the potential for previously unknown qualitative differences between seemingly interchangeable disease-promoting mutations, and our experiments confirmed oncogenic CysLT2R was impaired at activating the FAK/YAP/TAZ pathway relative to Gαq/11. This led us to hypothesize that CYSLT2R mutations in UM must co-occur with other mutations to activate FAK/YAP/TAZ signaling, and our bioinformatic analysis uncovers a role for co-occurring mutations involving the plexin/semaphorin pathway, which has been shown capable of activating this pathway. Overall, this work highlights the power of mechanism-based computational systems biology as a discovery tool that can leverage available information to open new research areas. To experimentally explore the effect of FAK inhibition over time, cells were treated with either FAKi or DMSO, and collected for RNA sequencing at 0, 3, 7, 14, and 21 days.

生化反应网络的数学模型是研究疾病相关细胞信号转导网络的重要新兴研究工具。这类数学模型的一项极具潜力的应用方向，便是探究网络中一种或多种蛋白的致病突变如何促进促成疾病发生的信号转导表型。学界通常认为，要让数学建模发挥作用，必须预先掌握该网络的完整特征解析结果；但我们提出假说：即便仅掌握不完全的认知，数学建模仍可发挥效用，甚至可作为探索工具，为后续研究开辟全新方向。本研究首先构建了一种几乎在所有葡萄膜黑色素瘤（uveal melanoma）病例中均发生突变的G蛋白偶联受体（G-protein coupled receptor, GPCR）信号转导网络的机制性数学模型，并借助模型驱动的探索，挖掘并拓展了研究该疾病的多个全新方向。
针对两种主要的互斥致癌突变（Gαq/11与CysLT2R）的建模分析，揭示了看似可互换的致病突变间此前未被发现的定性差异；我们的实验证实，相较于Gαq/11，致癌型CysLT2R在激活FAK/YAP/TAZ通路方面存在功能缺陷。这一结果促使我们提出假说：葡萄膜黑色素瘤（UM）中的CYSLT2R突变必须与其他突变协同发生，才能激活FAK/YAP/TAZ信号通路；我们的生物信息学分析揭示了涉及丛蛋白（plexin）/信号素（semaphorin）通路的协同突变的作用，而该通路已被证实可激活上述信号通路。
综上，本研究彰显了基于机制的计算系统生物学作为探索工具的强大潜力，其可利用现有信息为研究领域开辟全新方向。
为从实验层面探究FAK抑制作用随时间的变化效应，我们将细胞分别用FAK抑制剂（FAKi）与二甲基亚砜（DMSO）处理，并在第0、3、7、14及21天收集细胞进行RNA测序。