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

ABSTRACT: 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. We developed a mathematical model including background transmission, 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 patient's 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 Besançon (France). We used information about the admission and discharge days, screening days and screening results of the ICU patients. Both background and cross-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. Based on longitudinal surveillance data, we conclude that cleaning improvement of the environment after discharge might have only a limited impact regarding the prevention of P.A. infections in the two considered ICUs of the University hospital in Besançon. Our model was developed for P. aeruginosa but can be easily applied to other pathogens as well.

SUBMITTER: Pham TM

PROVIDER: S-EPMC6736315 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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

Pham Thi Mui TM Kretzschmar Mirjam M Bertrand Xavier X Bootsma Martin M

PLoS computational biology 20190828 8

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 ...[more]

PMID: 31461450

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Project description:Pseudomonas aeruginosa is an opportunistic pathogen which is evolving resistance to many currently used antibiotics. While much research has been devoted to the roles of pathogenic P. aeruginosa in cystic fibrosis (CF) patients, less is known of its ecological properties. P. aeruginosa dominates the lungs during chronic infection in CF patients, yet its abundance in some environments is less than that of other diverse groups of pseudomonads. Here, we sought to determine if clinical isolates of P. aeruginosa are vulnerable to environmental pseudomonads that dominate soil and water habitats in one-to-one competitions which may provide a source of inhibitory factors. We isolated a total of 330 pseudomonads from diverse habitats of soil and freshwater ecosystems and competed these strains against one another to determine their capacity for antagonistic activity. Over 900 individual inhibitory events were observed. Extending the analysis to P. aeruginosa isolates revealed that clinical isolates, including ones with increased alginate production, were susceptible to competition by multiple environmental strains. We performed transposon mutagenesis on one isolate and identified an ∼14.8-kb locus involved in antagonistic activity. Only two other environmental isolates were observed to carry the locus, suggesting the presence of additional unique compounds or interactions among other isolates involved in outcompeting P. aeruginosa This collection of strains represents a source of compounds that are active against multiple pathogenic strains. With the evolution of resistance of P. aeruginosa to currently used antibiotics, these environmental strains provide opportunities for novel compound discovery against drug-resistant clinical strains.ImportanceWe demonstrate that clinical CF-derived isolates of P. aeruginosa are susceptible to competition in the presence of environmental pseudomonads. We observed that many diverse environmental strains exhibited varied antagonistic profiles against a panel of clinical P. aeruginosa isolates, suggesting the presence of distinct mechanisms of inhibition among these ecological strains. Understanding the properties of these antagonistic events offers the potential for discoveries of antimicrobial compounds or metabolic pathways important to the development of novel treatments for P. aeruginosa infections.

Project description:UnlabelledMicrobial population polymorphisms are commonly observed in natural environments, including long-term infected hosts. However, the underlying processes promoting and stabilizing diversity are difficult to unravel and are not well understood. Here, we use chronic infection of cystic fibrosis airways by the opportunistic pathogen Pseudomonas aeruginosa as a system for investigating bacterial diversification processes during the course of infection. We analyze clonal bacterial isolates sampled during a 32-year period and map temporal and spatial variations in population diversity to different infection sites within the infected host. We show that the ancestral infecting strain diverged into distinct sublineages, each with their own functional and genomic signatures and rates of adaptation, immediately after initial colonization. The sublineages coexisted in the host for decades, suggesting rapid evolution of stable population polymorphisms. Critically, the observed generation and maintenance of population diversity was the result of partitioning of the sublineages into physically separated niches in the CF airway. The results reveal a complex within-host population structure not previously realized and provide evidence that the heterogeneity of the highly structured and complex host environment promotes the evolution and long-term stability of pathogen population diversity during infection.ImportanceWithin-host pathogen evolution and diversification during the course of chronic infections is of importance in relation to therapeutic intervention strategies, yet our understanding of these processes is limited. Here, we investigate intraclonal population diversity in P. aeruginosa during chronic airway infections in cystic fibrosis patients. We show the evolution of a diverse population structure immediately after initial colonization, with divergence into multiple distinct sublineages that coexisted for decades and occupied distinct niches. Our results suggest that the spatial heterogeneity in CF airways plays a major role in relation to the generation and maintenance of population diversity and emphasize that a single isolate in sputum may not represent the entire pathogen population in the infected individual. A more complete understanding of the evolution of distinct clonal variants and their distribution in different niches could have positive implications for efficient therapy.

Dataset Information

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

Publications

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

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