(Bolyai Research Fellowship of the Hungarian Academy of Sciences)
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.
