Dataset Information

Sub-Inhibitory Concentrations of Chlorhexidine Induce Resistance to Chlorhexidine and Decrease Antibiotic Susceptibility in Neisseria gonorrhoeae.

ABSTRACT: Objectives: Chlorhexidine digluconate (chlorhexidine) and Listerine® mouthwashes are being promoted as alternative treatment options to prevent the emergence of antimicrobial resistance in Neisseria gonorrhoeae. We performed in vitro challenge experiments to assess induction and evolution of resistance to these two mouthwashes and potential cross-resistance to other antimicrobials. Methods: A customized morbidostat was used to subject N. gonorrhoeae reference strain WHO-F to dynamically sustained Listerine® or chlorhexidine pressure for 18 days and 40 days, respectively. Cultures were sampled twice a week and minimal inhibitory concentrations (MICs) of Listerine®, chlorhexidine, ceftriaxone, ciprofloxacin, cefixime and azithromycin were determined using the agar dilution method. Isolates with an increased MIC for Listerine® or chlorhexidine were subjected to whole genome sequencing to track the evolution of resistance. Results: We were unable to increase MICs for Listerine®. Three out of five cultures developed a 10-fold increase in chlorhexidine MIC within 40 days compared to baseline (from 2 to 20 mg/L). Increases in chlorhexidine MIC were positively associated with increases in the MICs of azithromycin and ciprofloxacin. Low-to-higher-level chlorhexidine resistance (2-20 mg/L) was associated with mutations in NorM. Higher-level resistance (20 mg/L) was temporally associated with mutations upstream of the MtrCDE efflux pump repressor (mtrR) and the mlaA gene, part of the maintenance of lipid asymmetry (Mla) system. Conclusion: Exposure to sub-lethal chlorhexidine concentrations may not only enhance resistance to chlorhexidine itself but also cross-resistance to other antibiotics in N. gonorrhoeae. This raises concern regarding the widespread use of chlorhexidine as an oral antiseptic, for example in the field of dentistry.

SUBMITTER: Laumen JGE

PROVIDER: S-EPMC8660576 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The lowest concentration of an antimicrobial agent that can inhibit the visible growth of a microorganism after overnight incubation is called as minimum inhibitory concentration (MIC) and the drug prescriptions are made on the basis of MIC data to ensure successful treatment outcomes. Therefore, reliable antimicrobial susceptibility data is crucial, and it will help clinicians about which drug to prescribe. Although few prediction studies based on strategies have been conducted, however, no single machine learning (ML) modelling has been carried out to predict MICs in N. gonorrhoeae. In this study, we propose a ML based approach that can predict MICs of a specific antibiotic using unitigs sequences data. We retrieved N. gonorrhoeae genomes from European Nucleotide Archive and NCBI and analysed them combined with their respective MIC data for cefixime, ciprofloxacin, and azithromycin and then we constructed unitigs by using de Brujin graphs. We built and compared 35 different ML regression models to predict MICs. Our results demonstrate that RandomForest and CATBoost models showed best performance in predicting MICs of the three antibiotics. The coefficient of determination, R2, (a statistical measure of how well the regression predictions approximate the real data points) for cefixime, ciprofloxacin, and azithromycin was 0.75787, 0.77241, and 0.79009 respectively using RandomForest. For CATBoost model, the R2 value was 0.74570, 0.77393, and 0.79317 for cefixime, ciprofloxacin, and azithromycin respectively. Lastly, using feature importance, we explore the important genomic regions identified by the models for predicting MICs. The major mutations which are responsible for resistance against these three antibiotics were chosen by ML models as a top feature in case of each antibiotics. CATBoost, DecisionTree, GradientBoosting, and RandomForest regression models chose the same unitigs which are responsible for resistance. This unitigs-based strategy for developing models for MIC prediction, clinical diagnostics, and surveillance can be applicable for other critical bacterial pathogens.

Project description:Pseudomonas aeruginosa chronically infects in the lungs of people with cystic fibrosis and other forms of lung disease. Infections are treated with antibiotics, but over time, the bacteria acquire mutations that reduce their antibiotic susceptibility. The effects of inhibitory amounts of antibiotics in selecting for antibiotic-resistant mutants have been well studied. However, the concentrations of antibiotics that reach infecting bacteria can be sub-inhibitory and but may nonetheless promote emergence of antibiotic-resistant bacteria. Therefore, the aim of this research was to investigate the effects of sub-inhibitory concentrations of antibiotics on the antibiotic susceptibility of P. aeruginosa. Two P. aeruginosa reference strains, PAO1 and PA14, and six isolates from individuals with cystic fibrosis were studied. The bacteria were passaged in the presence of antibiotics (ceftazidime, ciprofloxacin, meropenem or tobramycin) at sub-inhibitory amounts. Fifteen populations of bacteria (up to five per strain) were exposed to each of the four antibiotics. Antibiotic susceptibility was determined following 10 passages on agar supplemented with antibiotic and compared with susceptibility prior to antibiotic exposure. Antibiotic exposure resulted in susceptibility being significantly (>2-fold) reduced for 13 of the 60 populations. Seven samples had reduced susceptibility to ciprofloxacin, three to tobramycin, two to ceftazidime and one to meropenem. Whole-genome sequencing revealed the mutations arising following antibiotic exposure. Mutants with reduced antibiotic susceptibility had mutations in genes known to affect antibiotic resistance, including regulators of efflux pumps (mexR, mexS, mexZ and nalC) and the fusA1 gene that is associated with aminoglycoside resistance. Genes not previously associated with resistance, including gacS, sigX and crfX and two genes with no known function, were also mutated in some isolates with reduced antibiotic susceptibility. Our results show that exposure to sub-inhibitory amounts of antibiotics can select for mutations that reduce the susceptibility of P. aeruginosa to antibiotics and that the profile of mutations is different from that arising during selection with inhibitory antibiotic concentrations. It is likely that exposure to sub-inhibitory amounts of antibiotics during infection contributes to P. aeruginosa becoming antibiotic-resistant.

Dataset Information

Sub-Inhibitory Concentrations of Chlorhexidine Induce Resistance to Chlorhexidine and Decrease Antibiotic Susceptibility in Neisseria gonorrhoeae.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets