# An integrative model of pro- and anti-inflammatory signaling pathways in macrophage differentiation: the role of NF-κB and CREB

## TL;DR

This paper presents a mathematical model of macrophage differentiation, showing how signaling pathways like NF-κB and CREB regulate pro- and anti-inflammatory responses.

## Contribution

A novel integrative model of macrophage signaling pathways using a 128-node network and ODEs to simulate immune response dynamics.

## Key findings

- The model identifies NF-κB activation in pro-inflammatory M1 macrophages and CREB1 in anti-inflammatory M2 profiles.
- Akt isoforms Akt1 and Akt3 are shown to regulate CREB1 inhibition via GSK3β in response to IFN-γ.
- Stochastic modeling confirms the robustness of macrophage differentiation into stable M1 and M2 profiles.

## Abstract

Monocytes are essential players of the innate immune response and adapt their functional states in response to different antigenic and cytokine environments. Integrating the complexity of monocyte intracellular signaling into a mathematical model can support the understanding of dynamic transitions that are crucial for immune regulation.

To formulate a comprehensive mathematical model of monocyte activation, differentiation, and metabolic adaptation dynamics in response to a variety of stimulus and cytokine microenvironment.

The model comprises a 128-node complex regulatory network based on known components of monocyte activation signal pathways. Node interactions are described by continuous fuzzy logic rules, and includes signaling events induced by LPS, activating IgG immune complexes, ssRNA, and the IFN-γ, IL-4 and IL-10 cytokines. Autocrine feedback loops for IL-10 and TNF-α, and a metabolism subnetwork were included. The network was analyzed by a set of ordinary differential equations (ODEs) system. The system outputs describe the dynamics of cell metabolic activity, activation of transcription factors, cytokine production and phagocytosis. An interactive program was developed as a tool to test the dynamical expression of the monocyte features under different initial conditions (see the https://grci.mx/modelos.html website).

The model captures the dynamics of the main events rendering stable states corresponding to the M1, M2, M2b and M2c macrophage profiles. Results are compatible with the predominance of glycolysis in the M1 and M2b, and oxidative phosphorylation in the M2a and M2c responses. The model shows the convergence to the activation of the NF-κB transcription factor in the pro-inflammatory response, while anti-inflammatory profiles are related to the induction of CREB1, a NF-κB inhibitor and promoter of IL-10 synthesis. Modelling supports a fundamental role of the Akt isoforms Akt1 and Akt3 in the induction of the activity the CREB1 inhibitor GSK3β upon IFN-γ signaling, so enabling the pro-inflammatory response. The anti-NF-κB activity of IL-4 signaling can turn the response into an M2 profile. The model predicts the relative levels of IFN-γ necessary to sustain the inflammatory response. Stochastic modelling proved the robustness of the macrophage differentiation process.

The complex network approach presented here integrates diverse cytokine and antigenic signaling leading to macrophage responses. It supports a mechanism for the IFN-γ mediated inhibition of CREB in the balance between pro-inflammatory and anti-inflammatory signals.