Small RNA-mediated switch-like regulation in bacterial quorum sensing

Molecular Aspects of the Functioning of Pathogenic Bacteria Biofilm Based on Quorum Sensing (QS) Signal-Response System and Innovative Non-Antibiotic Strategies for Their Elimination

One of the key mechanisms enabling bacterial cells to create biofilms and regulate crucial life functions in a global and highly synchronized way is a bacterial communication system called quorum sensing (QS). QS is a bacterial cell-to-cell communication process that depends on the bacterial population density and is mediated by small signalling molecules called autoinducers (AIs). In bacteria, QS controls the biofilm formation through the global regulation of gene expression involved in the extracellular polymeric matrix (EPS) synthesis, virulence factor production, stress tolerance and metabolic adaptation. Forming biofilm is one of the crucial mechanisms of bacterial antimicrobial resistance (AMR). A common feature of human pathogens is the ability to form biofilm, which poses a serious medical issue due to their high susceptibility to traditional antibiotics. Because QS is associated with virulence and biofilm formation, there is a belief that inhibition of QS activity called quorum quenching (QQ) may provide alternative therapeutic methods for treating microbial infections. This review summarises recent progress in biofilm research, focusing on the mechanisms by which biofilms, especially those formed by pathogenic bacteria, become resistant to antibiotic treatment. Subsequently, a potential alternative approach to QS inhibition highlighting innovative non-antibiotic strategies to control AMR and biofilm formation of pathogenic bacteria has been discussed.

PresRAT: a server for identification of bacterial small-RNA sequences and their targets with probable binding region

Bacterial small-RNA (sRNA) sequences are functional RNAs, which play an important role in regulating the expression of a diverse class of genes. It is thus critical to identify such sRNA sequences and their probable mRNA targets. Here, we discuss new procedures to identify and characterize sRNA and their targets via the introduction of an integrated online platform 'PresRAT'. PresRAT uses the primary and secondary structural attributes of sRNA sequences to predict sRNA from a given sequence or bacterial genome. PresRAT also finds probable target mRNAs of sRNA sequences from a given bacterial chromosome and further concentrates on the identification of the probable sRNA-mRNA binding regions. Using PresRAT, we have identified a total of 66,209 potential sRNA sequences from 292 bacterial genomes and 2247 potential targets from 13 bacterial genomes. We have also implemented a protocol to build and refine 3D models of sRNA and sRNA-mRNA duplex regions and generated 3D models of 50 known sRNAs and 81 sRNA-mRNA duplexes using this platform. Along with the server part, PresRAT also contains a database section, which enlists the predicted sRNA sequences, sRNA targets, and their corresponding 3D models with structural dynamics information.

Mathematical Modelling of Bacterial Quorum Sensing: A Review

Bacterial quorum sensing (QS) refers to the process of cell-to-cell bacterial communication enabled through the production and sensing of the local concentration of small molecules called autoinducers to regulate the production of gene products (e.g. enzymes or virulence factors). Through autoinducers, bacteria interact with individuals of the same species, other bacterial species, and with their host. Among QS-regulated processes mediated through autoinducers are aggregation, biofilm formation, bioluminescence, and sporulation. Autoinducers are therefore "master" regulators of bacterial lifestyles. For over 10 years, mathematical modelling of QS has sought, in parallel to experimental discoveries, to elucidate the mechanisms regulating this process. In this review, we present the progress in mathematical modelling of QS, highlighting the various theoretical approaches that have been used and discussing some of the insights that have emerged. Modelling of QS has benefited almost from the onset of the involvement of experimentalists, with many of the papers which we review, published in non-mathematical journals. This review therefore attempts to give a broad overview of the topic to the mathematical biology community, as well as the current modelling efforts and future challenges.