Boolean model of the gene regulatory network of Pseudomonas aeruginosa CCBH4851

Márcia da Silva Chagas; Marcelo Trindade Dos Santos; Marcio Argollo de Menezes; Fabricio Alves Barbosa da Silva

doi:10.3389/fmicb.2023.1274740

Boolean model of the gene regulatory network of Pseudomonas aeruginosa CCBH4851

Front Microbiol. 2023 Nov 30:14:1274740. doi: 10.3389/fmicb.2023.1274740. eCollection 2023.

Authors

Márcia da Silva Chagas¹, Marcelo Trindade Dos Santos², Marcio Argollo de Menezes³, Fabricio Alves Barbosa da Silva¹

Affiliations

¹ Scientific Computing Program (PROCC), FIOCRUZ, Rio de Janeiro, Brazil.
² National Laboratory of Scientific Computing, Rio de Janeiro, Brazil.
³ Institute of Physics, Fluminense Federal University, Niterói, Brazil.

Abstract

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.

Keywords: Boolean model; gene regulatory network (GRN); multidrug resistance (MDR); pseudomonas aeruginosa; system biology and systems modeling.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by INOVA-Fiocruz (grant #VPPCB-007-FIO-18-2-117) and FAPERJ (grant #269346).