Bertrand, X. | bioRxiv Science

bioRxiv2020

The rise and the fall of a Pseudomonas aeruginosa epidemic lineage in a hospital

The biological features that allow a pathogen to survive in the hospital environment are mostly unknown. The extinction of bacterial epidemics in hospitals is mostly attributed to changes in medical practice, including infection control, but the role of bacterial adaptation has never been documented. We analyzed a collection of Pseudomonas aeruginosa isolates belonging to the Besancon Epidemic Strain (BES), responsible for a 12-year nosocomial outbreak, using a genotype-to-phenotype approach. Bayesian analysis estimated the emergence of the clone in the hospital five years before its opening, during the creation of its water distribution network made of copper. BES survived better than the reference strains PAO1 and PA14 in a copper solution due to a genomic island containing 13 metal-resistance genes and was specifically able to proliferate in the ubiquitous amoeba Vermamoeba vermiformis. Mutations affecting amino-acid metabolism, antibiotic resistance, lipopolysaccharide biosynthesis, and regulation were enriched during the spread of BES. Seven distinct regulatory mutations attenuated the overexpression of the genes encoding the efflux pump MexAB-OprM over time. The fitness of BES decreased over time in correlation with its genome size. Overall, the resistance to inhibitors and predators presumably aided the proliferation and propagation of BES in the plumbing system of the hospital. The pathogen further spread among patients via multiple routes of contamination. The decreased prevalence of patients infected by BES mirrored the parallel and convergent genomic evolution and reduction that affected bacterial fitness. Along with infection control measures, this may have participated in the extinction of BES in the hospital setting. ImportanceBacterial pathogens are responsible for nosocomial outbreaks, but the sources of contamination of the hospitals are mostly unclear and the role of bacterial evolution in the extinction of outbreaks has never been considered. Here, we found that an epidemic strain of the pathogen Pseudomonas aeruginosa contaminated the drinking water network of a hospital due to its tolerance to copper and predatory amoeba, both present in the water pipes. The extinction of the outbreak occurred concomitantly with parallel and convergent genome evolution and a reduction in the size of the bacterial genome that correlated with the fitness of the pathogen. Our data suggest that pathogen evolution participated in the extinction of an outbreak in a hospital setting.

microbiology↗

bioRxiv2018

Tracking Pseudomonas aeruginosa transmissions due to environmental contamination after discharge in ICUs using mathematical models

Pseudomonas aeruginosa (P. aeruginosa) is an important cause of healthcare-associated infections, particularly in immunocompromised patients. Understanding how this multi-drug resistant pathogen is transmitted within intensive care units (ICUs) is crucial for devising and evaluating successful control strategies. While it is known that moist environments serve as natural reservoirs for P. aeruginosa, there is little quantitative evidence regarding the contribution of environmental contamination to its transmission within ICUs. Previous studies on other nosocomial pathogens rely on deploying specific values for environmental parameters derived from costly and laborious genotyping. Using solely longitudinal surveillance data, we estimated the relative importance of P. aeruginosa transmission routes by exploiting the fact that different routes cause different pattern of fluctuations in the prevalence.\n\nWe developed a mathematical model including endogenous colonization, cross-transmission and environmental contamination. Patients contribute to a pool of pathogens by shedding bacteria to the environment. Natural decay and cleaning of the environment lead to a reduction of that pool. By assigning the bacterial load shed during an ICU stay to cross-transmission, we were able to disentangle environmental contamination during and after a patients stay. Based on a data-augmented Markov Chain Monte Carlo method the relative importance of the considered acquisition routes is determined for two ICUs of the University hospital in Besancon (France). We used information about the admission and discharge days, screening days and screening results of the ICU patients.\n\nBoth cross-transmission and endogenous transmission play a significant role in the transmission process in both ICUs. In contrast, only about 1% of the total transmissions were due to environmental contamination after discharge.\n\nImproved cleaning of the environment after discharge would have only a limited impact regarding the prevention of P.A. infections in the two considered ICUs. Our model was developed for P. aeruginosa but can be easily applied to other pathogens as well.\n\nAuthor summaryUnderstanding the transmission dynamics of multi-drug resistant pathogens in intensive-care units is essential for designing successful infection control strategies. We developed a method that estimates the relative importance of several transmission routes of Pseudomonas aeruginosa (P. aeruginosa), a bacterium intrinsically resistant to multiple antibiotics and known to be a major contributor to hospital-acquired infections. Our model includes three different routes: endogenous colonization, cross-transmission and environmental contamination. Since moist environments may serve as natural reservoirs for P. aeruginosa, we focused our study on environmental contamination. Patients contribute to a pool of pathogens by shedding bacteria to the environment. Natural decay and cleaning of the environment lead to a reduction of that pool. By assigning the bacterial load shed during an ICU stay to cross-transmission, we disentangled environmental contamination during and after a patients stay. Previous studies were able to assess the role of environmental contamination for specific hospitals using laborious and costly genotyping methods. In contrast, we only used surveillance screening data to estimate the relative contributions of the considered transmission routes. Our results can be used to tailor or assess the effect of interventions. Our model was developed for P. aeruginosa but can be easily applied to other pathogens as well.

epidemiology↗