Computational model of brain endothelial cell signaling pathways predicts therapeutic targets for cerebral pathologies.
Computational model of brain endothelial cell signaling pathways predicts therapeutic targets for cerebral pathologies.
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DOI：
10.1016/j.yjmcc.2021.11.005
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发表时间：
2022-03
影响因子：
5
通讯作者：
Price RJ
中科院分区：
文献类型：
--
作者：
Gorick CM;Saucerman JJ;Price RJ
脑内皮细胞具有许多重要的稳态功能。除了感知和调节血流外，它们还维持血脑屏障功能，包括精确控制营养交换和外源性物质的流出。脑内皮细胞中的许多信号通路与健康和疾病有关;然而，我们对这些信号通路如何在功能上整合的了解有限。一个能够整合这些信号通路的模型既可以促进我们对脑内皮细胞信号网络的理解，也可以为基于内皮细胞的药物或基因治疗确定有前途的分子靶点。为此，我们开发了一个大规模的计算模型，其中脑内皮细胞信号通路从文献中重建，并转换为基于逻辑的微分方程网络。该模型集成了63个节点（包括蛋白质、mRNA、小分子和细胞表型）和连接这些节点的82个反应。具体而言，我们的模型结合了与VEGF-A，BDNF，NGF和Wnt信号相关的信号传导途径，此外还结合了与聚焦超声相关的途径作为治疗递送工具。为了验证该模型，独立建立的选定的输入和输出之间的关系进行了模拟，该模型产生正确的预测73%的时间。我们在不同的生理或病理背景下确定了有影响力和敏感的节点，包括胶质瘤，阿尔茨海默病和缺血性中风期间改变的脑内皮细胞条件。对所需模型输出的组合具有最大影响的节点被确定为这些疾病状况的潜在药物靶标。例如，该模型预测了在神经胶质瘤的背景下抑制AKT，Hif-1α或组织蛋白酶D的治疗益处-目前正在临床或临床前试验中对每种进行研究。值得注意的是，该模型还允许测试节点改变的多种组合对网络和所需输出的影响（例如在神经胶质瘤的背景下同时抑制AKT和过表达P75神经营养因子受体），从而预测最佳组合疗法。总之，我们的方法将过去100多项研究的结果整合到一个连贯而强大的模型中，既能够揭示孤立地研究任何一种途径所不明显的网络相互作用，又能够预测治疗破坏性脑病变的治疗靶点。
Brain endothelial cells serve many critical homeostatic functions. In addition to sensing and regulating blood flow, they maintain blood-brain barrier function, including precise control of nutrient exchange and efflux of xenobiotics. Many signaling pathways in brain endothelial cells have been implicated in both health and disease; however, our understanding of how these signaling pathways functionally integrate is limited. A model capable of integrating these signaling pathways could both advance our understanding of brain endothelial cell signaling networks and potentially identify promising molecular targets for endothelial cell-based drug or gene therapies. To this end, we developed a large-scale computational model, wherein brain endothelial cell signaling pathways were reconstructed from the literature and converted into a network of logic-based differential equations. The model integrates 63 nodes (including proteins, mRNA, small molecules, and cell phenotypes) and 82 reactions connecting these nodes. Specifically, our model combines signaling pathways relating to VEGF-A, BDNF, NGF, and Wnt signaling, in addition to incorporating pathways relating to focused ultrasound as a therapeutic delivery tool. To validate the model, independently established relationships between selected inputs and outputs were simulated, with the model yielding correct predictions 73% of the time. We identified influential and sensitive nodes under different physiological or pathological contexts, including altered brain endothelial cell conditions during glioma, Alzheimer's disease, and ischemic stroke. Nodes with the greatest influence over combinations of desired model outputs were identified as potential druggable targets for these disease conditions. For example, the model predicts therapeutic benefits from inhibiting AKT, Hif-1α, or cathepsin D in the context of glioma – each of which are currently being studied in clinical or pre-clinical trials. Notably, the model also permits testing multiple combinations of node alterations for their effects on the network and the desired outputs (such as inhibiting AKT and overexpressing the P75 neurotrophin receptor simultaneously in the context of glioma), allowing for the prediction of optimal combination therapies. In all, our approach integrates results from over 100 past studies into a coherent and powerful model, capable of both revealing network interactions unapparent from studying any one pathway in isolation and predicting therapeutic targets for treating devastating brain pathologies.
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影响因子：
--
作者：
Kraeutler, Matthew J.;Soltis, Anthony R.;Saucerman, Jeffrey J.
通讯作者：
Saucerman, Jeffrey J.
影响因子：
6.1
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通讯作者：
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影响因子：
20.1
作者：
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通讯作者：
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