Transcriptional Regulatory Systems in Pseudomonas: A Comparative Analysis of Helix-Turn-Helix Domains and Two-Component Signal Transduction Networks

Abstract

Bacterial communities in diverse environmental niches respond to various external stimuli for survival. A primary means of communication between bacterial cells involves one-component (OC) and two-component signal transduction systems (TCSs). These systems are key for sensing environmental changes and regulating bacterial physiology. TCSs, which are the more complex of the two, consist of a sensor histidine kinase for receiving an external input and a response regulator to convey changes in bacterial cell physiology. For numerous reasons, TCSs have emerged as significant targets for antibacterial drug design due to their role in regulating expression level, bacterial viability, growth, and virulence. Diverse studies have shown the molecular mechanisms by which TCSs regulate virulence and antibiotic resistance in pathogenic bacteria. In this study, we performed a thorough analysis of the data from multiple public databases to assemble a comprehensive catalog of the principal detection systems present in both the non-pathogenic Pseudomonas putida KT2440 and the pathogenic Pseudomonas aeruginosa PAO1 strains. Additionally, we conducted a sequence analysis of regulatory elements associated with transcriptional proteins. These were classified into regulatory families based on Helix-turn-Helix (HTH) protein domain information, a common structural motif for DNA-binding proteins. Moreover, we highlight the function of bacterial TCSs and their involvement in functions essential for bacterial survival and virulence. This comparison aims to identify novel targets that can be exploited for the development of advanced biotherapeutic strategies, potentially leading to new treatments for bacterial infections.

Keywords: Pseudomonas aeruginosa PAO1; Pseudomonas putida KT2440; gene expression; metabolism; one-component system (OCS); pathogenicity; two-component system (TCS).

Figure 1

Airway epithelial adaptation of pathogens in cystic fibrosis and chronic obstructive pulmonary disease. TLRs’ activation (TLR2, TLR4, and TLR5) triggers MyD88-dependent and -independent responses. The MyD88-dependent pathway involves TIRAP and leads to NF-κB activation, resulting in cytokine production (IL-8, TNF-α). In contrast, the MyD88-independent pathway, mediated by TRIF, activates IRF3 and also contributes to immune responses. Defective CFTR in cystic fibrosis disrupts ion transport, leading to constitutive NF-κB activation, chronic inflammation, and cytokine production, contributing to disease pathogenesis.

Figure 2

Schematic representation of one-component system (OCS) and two-component system (TCS) classes of P. putida KT2440 and P. aeruginosa PAO1 transcriptional regulators. R: Response regulatory protein; E: ligand-binding domain; S: signal molecules; K: histidine kinase, involved in phosphorylation/dephosphorylation events that activate the response regulator.

Figure 3

Circular genome analysis of (A) Pseudomonas putida KT2440 and (B) Pseudomonas aeruginosa PAO1 using the CGView tool (http://genocat.tools/tools/cgview_server.html) (Version not specified). The blue circus ring represents the distribution of genes identified as transcriptional regulatory.

Figure 4

Pseudomonas putida KT2440 (A) and Pseudomonas aeruginosa PAO1 (B) phylogenetic tree representation of transcriptional regulators of the Helix-turn-Helix family. The colored area corresponds to sequences with an HTH family domain.

Figure 5

Categorization of DNA-binding transcriptional regulators in P. putida KT2440 and P. aeruginosa PAO1 into distinct regulatory protein families. The names of the identified regulatory protein families are shown. These families are labeled based on designations from the Pfam database.

Figure 6

Functional comparison of genes from TCSs in P. putida KT2440 and P. aeruginosa PAO1. Functions are categorized into four groups: molecular function, biological process, pathway, and protein class. The primary functional Panther-GO categories are represented as a percentage of transcriptional regulators compared to the total number of HTH regulatory genes.