Szegvari Gabor, Dora David, Lohinai Zoltan
Translational Medicine Institute, Semmelweis University, 1094 Budapest, Hungary.
Department of Anatomy, Histology and Embryology, Semmelweis University, 1094 Budapest, Hungary.
Biology (Basel). 2023 Feb 27;12(3):376. doi: 10.3390/biology12030376.
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.
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.
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.
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.
巨噬细胞的功能和极化对许多疾病的结局有重大影响。由于异质性肿瘤微环境（TME）在体外的可重复性较低，靶向肿瘤相关巨噬细胞（TAM）是需要解决的最大挑战之一。为了创建一个更全面的模型，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模拟方法。
基于对科学文献的系统人工整理构建了一个布尔控制网络，通过将细胞外信号（输入）与基因转录（输出）相连接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。边的方向和极性经过人工验证，只有在文献明确支持这些参数时才纳入模型。模拟单一或联合抑制以模仿治疗干预，并分析输出模式以解释极化和细胞功能的变化。
我们表明，抑制单个靶点不足以改变已建立的极化，在联合治疗中，通常需要抑制多个具有个体微小效应的靶点。我们的研究结果表明，JAK1、JAK3和STAT6，以及在较小程度上的STK4、Sp1和Tyk2在建立M1样促炎极化中具有重要作用，而NFAT5在创建抗炎M2样表型中具有重要作用。
在此，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传导，提供了治疗性再极化的可能性，并为多靶点方法提供了证据。
