Boolean modeling reveals that cyclic attractors in macrophage polarization serve as reservoirs of states to balance external perturbations from the tumor microenvironment

Boolean model of the gene regulatory network of Pseudomonas aeruginosa CCBH4851

Introduction

Pseudomonas aeruginosa infections are one of the leading causes of death in immunocompromised patients with cystic fibrosis, diabetes, and lung diseases such as pneumonia and bronchiectasis. Furthermore, P. aeruginosa is one of the main multidrug-resistant bacteria responsible for nosocomial infections worldwide, including the multidrug-resistant CCBH4851 strain isolated in Brazil.

Methods

One way to analyze their dynamic cellular behavior is through computational modeling of the gene regulatory network, which represents interactions between regulatory genes and their targets. For this purpose, Boolean models are important predictive tools to analyze these interactions. They are one of the most commonly used methods for studying complex dynamic behavior in biological systems.

Results and discussion

Therefore, this research consists of building a Boolean model of the gene regulatory network of P. aeruginosa CCBH4851 using data from RNA-seq experiments. Next, the basins of attraction are estimated, as these regions and the transitions between them can help identify the attractors, representing long-term behavior in the Boolean model. The essential genes of the basins were associated with the phenotypes of the bacteria for two conditions: biofilm formation and polymyxin B treatment. Overall, the Boolean model and the analysis method proposed in this work can identify promising control actions and indicate potential therapeutic targets, which can help pinpoint new drugs and intervention strategies.

M1/M2 macrophages and their overlaps - myth or reality?

Macrophages represent heterogeneous cell population with important roles in defence mechanisms and in homoeostasis. Tissue macrophages from diverse anatomical locations adopt distinct activation states. M1 and M2 macrophages are two polarized forms of mononuclear phagocyte in vitro differentiation with distinct phenotypic patterns and functional properties, but in vivo, there is a wide range of different macrophage phenotypes in between depending on the microenvironment and natural signals they receive. In human infections, pathogens use different strategies to combat macrophages and these strategies include shaping the macrophage polarization towards one or another phenotype. Macrophages infiltrating the tumours can affect the patient's prognosis. M2 macrophages have been shown to promote tumour growth, while M1 macrophages provide both tumour-promoting and anti-tumour properties. In autoimmune diseases, both prolonged M1 activation, as well as altered M2 function can contribute to their onset and activity. In human atherosclerotic lesions, macrophages expressing both M1 and M2 profiles have been detected as one of the potential factors affecting occurrence of cardiovascular diseases. In allergic inflammation, T2 cytokines drive macrophage polarization towards M2 profiles, which promote airway inflammation and remodelling. M1 macrophages in transplantations seem to contribute to acute rejection, while M2 macrophages promote the fibrosis of the graft. The view of pro-inflammatory M1 macrophages and M2 macrophages suppressing inflammation seems to be an oversimplification because these cells exploit very high level of plasticity and represent a large scale of different immunophenotypes with overlapping properties. In this respect, it would be more precise to describe macrophages as M1-like and M2-like.