PhD Scientific Days 2024

Budapest, 9-10 July 2024

Theoretical and Translational Medicine II.

Boolean Control Network Model of Macrophage Polarization for the Identification of Molecular Targets

Author(s)

Gábor Szegvári1, Dávid Dóra2, Zoltán Lohinai3
1: Institute of Translational Medicine, Semmelweis University
2: Department of Anatomy, Histology and Embryology, Semmelweis University
3: Pulmonology Center of the Reformed Church

Text of the abstract

Introduction: Macrophages can serve greatly different functions based on their environment, adapting by undergoing a process called polarization. This is of importance in multiple diseases, especially in cancer due to the unique local tumor microenvironment (TME). The numerous factors comprising the TME are hard to reproduce in vitro, limiting efforts to target these tumor-associated macrophages (TAMs) with therapeutic interventions.
Aims: To create a comprehensive in silico model of macrophage polarization, taking into account different possible states of the TME as extracellular signals. To use this model in search of targets with translational relevance.
Methods: I have built a Boolean control network, modeling early response events to polarizing signals on the molecular level. The change in cellular state is represented by gene transcription. Information was gathered from available scientific literature with a systematic search and manually curated, including verification of the direction and polarity of interactions. The model was used to run simulations of therapeutic interventions, inhibiting singular proteins or a combination of two.
Results: The model contains 106 nodes and 217 edges. It is verified with experimental data from the literature and integrates 9 input signals and examines their impact on 22 output nodes and a polarization index calculated from them. Simulations indicate that inhibition of a single target is not effective in modifying an established polarization state. However, combinatorial inhibition of pairs of proteins highlights several potential targets, some of which have negligible effect on their own. Pushing towards an M1-like pro-inflammatory polarization JAK1, JAK3 and STAT6 emerge as main targets with STK4, Sp1 and Tyk2 to a lesser extent. In the other direction of a regenerative M2-like state NFAT5 is indicated.
Conclusions: I have established a verified computational network model of macrophage polarization on the level of protein–protein interactions (PPI). Using this model I have identified potential therapeutic targets for modulating macrophage function and underlined the importance of a combinatorial approach.
Funding: Hungarian National Research, Development and Innovation Office (#146775)