Effective Reversal of Macrophage Polarization by Inhibitory Combinations Predicted by a Boolean Protein-Protein Interaction Model.
Effective Reversal of Macrophage Polarization by Inhibitory Combinations Predicted by a Boolean Protein-Protein Interaction Model.
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作者：
了解肿瘤微环境的影响是推进癌症治疗的重要一步，但这也是体外复制或体内研究存在问题的一步。当这些方法难以实施时，计算机方法通常能够帮助克服这些障碍。生物系统的网络模型侧重于组件（例如蛋白质或基因）的相互作用，并使用有关系统各部分的可用数据来预测涌现的质量。我们的研究重点是巨噬细胞以及它们的环境如何影响它们的极化。因此，我们通过手动搜索解释细胞内部状态变化的文献来整理信息，建立了巨噬细胞中发生的早期反应事件的布尔控制网络模型。我们使用该模型来模拟同时针对多个目标并对巨噬细胞极化具有协同作用的组合治疗方案。背景：巨噬细胞的功能和极化对许多疾病的结果具有重大影响。由于异质肿瘤微环境 (TME) 的体外重复性较低，靶向肿瘤相关巨噬细胞 (TAM) 是需要解决的最大挑战之一。为了创建更全面的模型并了解巨噬细胞的内部运作及其对驱动极化的细胞外信号的依赖性，我们提出了一种计算机方法。方法：基于科学文献的系统手动管理构建布尔控制网络，通过连接细胞外信号（输入）与基因转录（输出）来模拟巨噬细胞的早期反应事件。该网络由 106 个节点组成，分为 9 个输入节点、75 个内部节点和 22 个输出节点，由 217 条边连接。边缘的方向和极性是手动验证的，并且仅在文献明确支持这些参数的情况下才包含在模型中。模拟治疗干预，模拟单一或组合抑制，并分析输出模式以解释极化和细胞功能的变化。结果：我们表明，抑制单个靶标不足以改变已建立的极化，并且在联合治疗中，经常需要抑制多个单独作用较小的靶标。我们的研究结果表明，JAK1、JAK3 和 STAT6 以及较小程度上的 STK4、Sp1 和 Tyk2 在建立 M1 样促炎极化中的重要性，以及 NFAT5 在创建抗炎 M2 样表型中的重要性。结论：在这里，我们展示了一个蛋白质-蛋白质相互作用（PPI）网络，该网络模拟了驱动巨噬细胞极化的细胞内信号，提供了治疗复极化的可能性，并为多靶点方法提供了证据。
Understanding the effects of the tumor microenvironment is an essential step to advance treatments for cancer, but it is also one that is problematic to reproduce in vitro or study in vivo. When such approaches are difficult to implement, in silico methods are often able to help surmount these barriers. Network models of biological systems focus on the interactions of components (such as proteins or genes) and use available data about the parts of a system to predict emergent qualities. We focused our study on macrophages and how their environment affects their polarization. Thus, we built a Boolean control network model of the early response events going on in macrophages by collating information from a manual search of the literature that interprets the changes in the inner state of the cell. We used this model to simulate combinatorial treatment options that target multiple targets at the same time and have synergistic effects on macrophage polarization. Background: The function and polarization of macrophages has a significant impact on the outcome of many diseases. Targeting tumor-associated macrophages (TAMs) is among the greatest challenges to solve because of the low in vitro reproducibility of the heterogeneous tumor microenvironment (TME). To create a more comprehensive model and to understand the inner workings of the macrophage and its dependence on extracellular signals driving polarization, we propose an in silico approach. Methods: A Boolean control network was built based on systematic manual curation of the scientific literature to model the early response events of macrophages by connecting extracellular signals (input) with gene transcription (output). The network consists of 106 nodes, classified as 9 input, 75 inner and 22 output nodes, that are connected by 217 edges. The direction and polarity of edges were manually verified and only included in the model if the literature plainly supported these parameters. Single or combinatory inhibitions were simulated mimicking therapeutic interventions, and output patterns were analyzed to interpret changes in polarization and cell function. Results: We show that inhibiting a single target is inadequate to modify an established polarization, and that in combination therapy, inhibiting numerous targets with individually small effects is frequently required. Our findings show the importance of JAK1, JAK3 and STAT6, and to a lesser extent STK4, Sp1 and Tyk2, in establishing an M1-like pro-inflammatory polarization, and NFAT5 in creating an anti-inflammatory M2-like phenotype. Conclusions: Here, we demonstrate a protein–protein interaction (PPI) network modeling the intracellular signalization driving macrophage polarization, offering the possibility of therapeutic repolarization and demonstrating evidence for multi-target methods.
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影响因子：
14.9
作者：
Kanehisa M;Furumichi M;Sato Y;Ishiguro-Watanabe M;Tanabe M
通讯作者：
Tanabe M
DOI：
10.1016/j.mattod.2015.05.002
发表时间：
2015-12
期刊：
Materials today (Kidlington, England)
影响因子：
--
作者：
Asghar W;El Assal R;Shafiee H;Pitteri S;Paulmurugan R;Demirci U
通讯作者：
Demirci U
影响因子：
--
作者：
Fazekas D;Koltai M;Türei D;Módos D;Pálfy M;Dúl Z;Zsákai L;Szalay-Bekő M;Lenti K;Farkas IJ;Vellai T;Csermely P;Korcsmáros T
通讯作者：
Korcsmáros T
影响因子：
--
作者：
Yim S;Yu H;Jang D;Lee D
通讯作者：
Lee D
影响因子：
4.9
作者：
Di Benedetto P;Ruscitti P;Berardicurti O;Panzera N;Grazia N;Di Vito Nolfi M;Di Francesco B;Navarini L;Maurizi A;Rucci N;Teti AM;Zazzeroni F;Guggino G;Ciccia F;Dolo V;Alesse E;Cipriani P;Giacomelli R
通讯作者：
Giacomelli R