Stochastic Gene Expression Influences the Selection of Antibiotic Resistance Mutations

Plasmid-mediated phenotypic noise leads to transient antibiotic resistance in bacteria

The rise of antibiotic resistance is a critical public health concern, requiring an understanding of mechanisms that enable bacteria to tolerate antimicrobial agents. Bacteria use diverse strategies, including the amplification of drug-resistance genes. In this paper, we showed that multicopy plasmids, often carrying antibiotic resistance genes in clinical bacteria, can rapidly amplify genes, leading to plasmid-mediated phenotypic noise and transient antibiotic resistance. By combining stochastic simulations of a computational model with high-throughput single-cell measurements of blaTEM-1 expression in Escherichia coli MG1655, we showed that plasmid copy number variability stably maintains populations composed of cells with both low and high plasmid copy numbers. This diversity in plasmid copy number enhances the probability of bacterial survival in the presence of antibiotics, while also rapidly reducing the burden of carrying multiple plasmids in drug-free environments. Our results further support the tenet that multicopy plasmids not only act as vehicles for the horizontal transfer of genetic information between cells but also as drivers of bacterial adaptation, enabling rapid modulation of gene copy numbers. Understanding the role of multicopy plasmids in antibiotic resistance is critical, and our study provides insights into how bacteria can transiently survive lethal concentrations of antibiotics.

The Impact of Pseudomonas aeruginosa Infection in Adult Cystic Fibrosis Patients-A Single Polish Centre Study

BACKGROUND

Pseudomonas aeruginosa (PA) is one of the most predominant pathogens of lung infections, often causing exacerbations in adult patients with cystic fibrosis (CF).

MATERIALS AND METHODS

Microbiological characterization of 74 PA isolates and to evaluate the correlations between the bacterial features and 44 adult Polish CF cohort clinical parameters.

RESULTS

The most common variant in the CF transmembrane conductance regulator (CFTR) gene was F508del (76.3%), followed by 3849+10kbC>T (26.3%). A total of 39.4% of the PA isolates showed multiple resistances. In patients with parameters pointing to a decline in lung function, there was a statistically significant moderate correlation with β-lactam resistance and a weak correlation between hospital frequency and colistin resistance. The mucoidity did not correlate with the biofilm formation ability, which showed 41.9% of the isolates. Proteolytic activity, observed in 60.8% of the clinical isolates, was weakly associated with motility detected in 78.4% of the strains. The genetic profiles of the PA were highly heterogeneous, and a weak positive correlation was established between cluster group and biofilm formation.

CONCLUSION

The findings suggest that there is a high variety in P. aeruginosa populations in adult CF patients. There is a need to monitor PA strains in groups of patients with cystic fibrosis, in particular, in terms of the occurrence of antibiotic resistance related to a decline in lung function.

The Extent of Antimicrobial Resistance Due to Efflux Pump Regulation

A key mechanism driving antimicrobial resistance (AMR) stems from the ability of bacteria to up-regulate efflux pumps upon exposure to drugs. The resistance gained by this up-regulation is pliable because of the tight regulation of efflux pump levels. This leads to temporary enhancement in survivability of bacteria due to higher efflux pump levels in the presence of antibiotics, which can be reversed when the cells are no longer exposed to the drug. Knowledge of the extent of resistance thus gained would inform intervention strategies aimed at mitigating AMR. Here, we combine mathematical modeling and experiments to quantify the maximum extent of resistance that efflux pump up-regulation can confer via phenotypic induction in the presence of drugs and genotypic abrogation of regulation. Our model describes the dynamics of drug transport in and out of cells coupled with the associated regulation of efflux pump levels and predicts the increase in the minimum inhibitory concentration (MIC) of drugs due to such regulation. To test the model, we measured the uptake and efflux as well as the MIC of the compound ethidium bromide (EtBr), a substrate of the efflux pump LfrA, in wild-type Mycobacterium smegmatis mc²155, as well as in two laboratory-generated strains. Our model captured the observed EtBr levels and MIC fold-changes quantitatively. Further, the model identified key parameters associated with the resulting resistance, variations in which could underlie the extent to which such resistance arises across different drug-bacteria combinations, potentially offering tunable handles to optimize interventions aimed at minimizing AMR.

Strategies to prevent, curb and eliminate biofilm formation based on the characteristics of various periods in one biofilm life cycle

Biofilms are colonies of bacteria embedded inside a complicated self-generating intercellular. The formation and scatter of a biofilm is an extremely complex and progressive process in constant cycles. Once formed, it can protect the inside bacteria to exist and reproduce under hostile conditions by establishing tolerance and resistance to antibiotics as well as immunological responses. In this article, we reviewed a series of innovative studies focused on inhibiting the development of biofilm and summarized a range of corresponding therapeutic methods for biological evolving stages of biofilm. Traditionally, there are four stages in the biofilm formation, while we systematize the therapeutic strategies into three main periods precisely:(i) period of preventing biofilm formation: interfering the colony effect, mass transport, chemical bonds and signaling pathway of plankton in the initial adhesion stage; (ii) period of curbing biofilm formation:targeting several pivotal molecules, for instance, polysaccharides, proteins, and extracellular DNA (eDNA) via polysaccharide hydrolases, proteases, and DNases respectively in the second stage before developing into irreversible biofilm; (iii) period of eliminating biofilm formation: applying novel multifunctional composite drugs or nanoparticle materials cooperated with ultrasonic (US), photodynamic, photothermal and even immune therapy, such as adaptive immune activated by stimulated dendritic cells (DCs), neutrophils and even immunological memory aroused by plasmocytes. The multitargeted or combinational therapies aim to prevent it from developing to the stage of maturation and dispersion and eliminate biofilms and planktonic bacteria simultaneously.

Cis-Regulatory Logic Produces Gene-Expression Noise Describing Phenotypic Heterogeneity in Bacteria

Gene transcriptional process is random. It occurs in bursts and follows single-molecular kinetics. Intermittent bursts are measured based on their frequency and size. They influence temporal fluctuations in the abundance of total mRNA and proteins by generating distinct transcriptional variations referred to as “noise”. Noisy expression induces uncertainty because the association between transcriptional variation and the extent of gene expression fluctuation is ambiguous. The promoter architecture and remote interference of different cis-regulatory elements are the crucial determinants of noise, which is reflected in phenotypic heterogeneity. An alternative perspective considers that cellular parameters dictating genome-wide transcriptional kinetics follow a universal pattern. Research on noise and systematic perturbations of promoter sequences reinforces that both gene-specific and genome-wide regulation occur across species ranging from bacteria and yeast to animal cells. Thus, deciphering gene-expression noise is essential across different genomics applications. Amidst the mounting conflict, it is imperative to reconsider the scope, progression, and rational construction of diversified viewpoints underlying the origin of the noise. Here, we have established an indication connecting noise, gene expression variations, and bacterial phenotypic variability. This review will enhance the understanding of gene-expression noise in various scientific contexts and applications.

Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging

Mesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative medicine, tissue repair, and immunotherapy. Unfortunately, the outcomes of MSC-based research and therapies can be highly inconsistent and difficult to reproduce, largely due to the inherently significant heterogeneity in MSCs, which has not been well investigated. To quantify cell heterogeneity, a standard approach is to measure marker expression on the protein level via immunochemistry assays. Performing such measurements non-invasively and at scale has remained challenging as conventional methods such as flow cytometry and immunofluorescence microscopy typically require cell fixation and laborious sample preparation. Here, we developed an artificial intelligence (AI)-based method that converts transmitted light microscopy images of MSCs into quantitative measurements of protein expression levels. By training a U-Net+ conditional generative adversarial network (cGAN) model that accurately (mean [Formula: see text] = 0.77) predicts expression of 8 MSC-specific markers, we showed that expression of surface markers provides a heterogeneity characterization that is complementary to conventional cell-level morphological analyses. Using this label-free imaging method, we also observed a multi-marker temporal-spatial fluctuation of protein distributions in live MSCs. These demonstrations suggest that our AI-based microscopy can be utilized to perform quantitative, non-invasive, single-cell, and multi-marker characterizations of heterogeneous live MSC culture. Our method provides a foundational step toward the instant integrative assessment of MSC properties, which is critical for high-throughput screening and quality control in cellular therapies.