FleQ of Pseudomonas putida KT2440 is a multimeric cyclic diguanylate binding protein that differentially regulates expression of biofilm matrix components.

The intracellular signal molecule cyclic di-GMP (c-di-GMP) is an important element in regulation of biofilm formation by bacteria. In Pseudomonas aeruginosa, FleQ functions as a c-di-GMP-dependent transcriptional regulator of expression of flagellar genes and the exopolysaccharide (EPS) Pel, a component of the biofilm extracellular matrix. In the plant-beneficial bacterium Pseudomonas putida KT2440, a mutation in fleQ reduces biofilm formation and colonization of plant surfaces.

Molecular Binding Mechanism of TtgR Repressor to Antibiotics and Antimicrobials.

A disturbing phenomenon in contemporary medicine is the prevalence of multidrug-resistant pathogenic bacteria. Efflux pumps contribute strongly to this antimicrobial drug resistance, which leads to the subsequent failure of clinical treatments. The TtgR protein of Pseudomonas putida is a HTH-type transcriptional repressor that controls expression of the TtgABC efflux pump, which is the main contributor to resistance against several antimicrobials and toxic compounds in this microbe.

Engineering Biological Approaches for Detection of Toxic Compounds: A New Microbial Biosensor Based on the Pseudomonas putida TtgR Repressor.

Environmental contamination by toxic organic compounds and antimicrobials is one of the causes for the recent surge of multidrug-resistant pathogenic bacteria. Monitoring contamination is therefore the first step in containment of antimicrobial resistance and requires the development of simple, sensitive, and quantitative tools that detect a broad spectrum of toxic compounds. In this study, we have engineered a new microbial biosensor based on the ttgR-regulated promoter that controls expression of the TtgABC extrusion efflux pump of Pseudomonas putida, coupled to a gfp reporter.

Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins.

We have developed a statistical mechanics algorithm, TANGO, to predict protein aggregation. TANGO is based on the physico-chemical principles of beta-sheet formation, extended by the assumption that the core regions of an aggregate are fully buried. Our algorithm accurately predicts the aggregation of a data set of 179 peptides compiled from the literature as well as of a new set of 71 peptides derived from human disease-related proteins, including prion protein, lysozyme and beta2-microglobulin.