Two Group A Streptococcal Peptide Pheromones Act through Opposing Rgg Regulators to Control Biofilm Development

Streptococcus pyogenes (Group A Streptococcus, GAS) is an important human commensal that occasionally causes localized infections and less frequently causes severe invasive disease with high mortality rates. How GAS regulates expression of factors used to colonize the host and avoid immune responses remains poorly understood. Intercellular communication is an important means by which bacteria coordinate gene expression to defend against host assaults and competing bacteria, yet no conserved cell-to-cell signaling system has been elucidated in GAS. Encoded within the GAS genome are four rgg-like genes, two of which (rgg2 and rgg3) have no previously described function. We tested the hypothesis that rgg2 or rgg3 rely on extracellular peptides to control target-gene regulation. We found that Rgg2 and Rgg3 together tightly regulate two linked genes encoding new peptide pheromones. Rgg2 activates transcription of and is required for full induction of the pheromone genes, while Rgg3 plays an antagonistic role and represses pheromone expression. The active pheromone signals, termed SHP2 and SHP3, are short and hydrophobic (DI[I/L]IIVGG), and, though highly similar in sequence, their ability to disrupt Rgg3-DNA complexes were observed to be different, indicating that specificity and differential activation of promoters are characteristics of the Rgg2/3 regulatory circuit. SHP-pheromone signaling requires an intact oligopeptide permease (opp) and a metalloprotease (eep), supporting the model that pro-peptides are secreted, processed to the mature form, and subsequently imported to the cytoplasm to interact directly with the Rgg receptors. At least one consequence of pheromone stimulation of the Rgg2/3 pathway is increased biogenesis of biofilms, which counteracts negative regulation of biofilms by RopB (Rgg1). These data provide the first demonstration that Rgg-dependent quorum sensing functions in GAS and substantiate the role that Rggs play as peptide receptors across the Firmicute phylum

In silico approaches for unearthing bacterial quorum-sensing inhibitors against pathogenic bacteria

The bacterial phenotypic traits of biofilm formation, bioluminescence, swarming motility, and even virulence are being highly influenced by the phenomenon of cell density-dependent gene regulation a.k.a. quorum sensing (QS) through which the bacteria communicate within themselves. Essentially, QS is an intracellular signaling system which are different for the different gram characters of bacteria. While gram-negative bacteria use chemical autoinducer molecules like acyl-homoserine lactones (AHLs) for such signaling, the gram-positive bacteria use peptide-based signaling systems. These quorum-sensing peptides (QSPs) can initiate a signaling cascade of events via two-component system or even by direct binding to transcription factors. After the detection of QSPs by bacteria, response regulators or transcriptional factors are activated, which further stimulates change in the target gene expression. Owing to the therapeutic potential of the AHLs and QSPs as drug targets, different in silico approaches were utilized for the identification of inhibitors and their modeling which can help in combatingthe respective bacterial pathogenicity. Thus, certain group of researchers also developed machine learning tools based on support vector machine (SVM) and hidden Markov models (HMM) for the identification of novel and effective biofilm inhibitory peptides (BIPs), while others used in silico approaches for predicting and designing of antibiofilm peptides usingbidirectional recursive neural network (BRNN) and Random Forest (RF) algorithms. Moreover, biological network visualization techniques and analysis enabled the identification of QSPs in different bacteria using related information from the curated databases. To this end, identification of the binding pocket(s), motif search, and other physicochemical properties will help in predicting the three-dimensional structure of such target. Furthermore, ultra-high-throughput screening is another approach which unveils QS inhibitors (QSI) based on the characterization of natural products and screening for naturally occurring enzymes. This review specifically focuses on all such in silico approaches in predicting QSI in different bacterial species. Such in silico QSI predictions and their docking onto QS targets can help to shape up a promising future for making newer therapeutic options available against different pathogenic bacteria