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Updated: Aug 5, 2025
Reprograming Model of Human Monocyte-derived Macrophages for In-vitro Assays
Published on: April 18, 2025
Effective Reversal of Macrophage Polarization by Inhibitory Combinations Predicted by a Boolean Protein-Protein
Gabor Szegvari1, David Dora2, Zoltan Lohinai1,3
1Translational Medicine Institute, Semmelweis University, 1094 Budapest, Hungary.
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.
Insights
Targeting macrophages requires multi-drug therapies. This study developed an in silico model to identify key targets for reprogramming macrophage polarization, crucial for disease treatment.
Area of Science:
- Immunology
- Computational Biology
- Systems Biology
Background:
- Macrophage polarization significantly impacts disease outcomes.
- Tumor-associated macrophages (TAMs) present therapeutic challenges due to the heterogeneous tumor microenvironment (TME).
- In vitro models struggle with TME reproducibility, necessitating novel approaches.
Purpose of the Study:
- To develop a computational model of macrophage polarization.
- To understand macrophage dependence on extracellular signals.
- To identify potential therapeutic targets for macrophage repolarization.
Main Methods:
- A Boolean control network was constructed from literature data.
- The network models early macrophage response events, linking extracellular signals to gene transcription.
- Simulations of single and combinatorial inhibitions were performed to analyze polarization changes.
Main Results:
- Single-target inhibition is insufficient to alter established macrophage polarization.
- Combination therapy targeting multiple nodes is frequently required for effective repolarization.
- Key regulators identified include JAK1, JAK3, STAT6 for M1 polarization and NFAT5 for M2 polarization.
Conclusions:
- A protein-protein interaction (PPI) network model for macrophage polarization was developed.
- The model supports the potential for therapeutic repolarization of macrophages.
- Evidence suggests multi-target strategies are necessary for effective macrophage-based therapies.
