Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most important multidrug-resistant nosocomial pathogens worldwide with infections leading to high rates of morbidity and mortality, a significant burden to human and veterinary clinical practices. The ability of S. aureus colonies to form biofilms on biotic and abiotic surfaces contributes further to its high antimicrobial resistance (AMR) rates and persistence in both host and non-host environments, adding a major ecological dimension to the problem. While there is a lot of information on MRSA prevalence in humans, data about MRSA in animal populations is scarce, incomplete and dispersed. This project is an attempt to evaluate the current epidemiological status of MRSA in Portugal by making a single case study from a One Health perspective. We aim to determine the prevalence of MRSA in anthropogenic sources liable to contaminate different animal habitats. The results obtained will be compiled with existing data on antibiotic resistant staphylococci from Portugal in a user-friendly database, to generate a geographically detailed epidemiological output for surveillance of AMR in MRSA. To achieve this, we will first characterize AMR and genetic lineages of MRSA circulating in northern Portugal in hospital wastewaters, farms near hospitals, farm animals that contact with humans, and wild animals. This will indicate the extent of the AMR problem in the context of local and regional human-animal-environment interactions. MRSA strains will then be tested for their ability to form biofilms. The proteomes of the strains will be compared to better elucidate their AMR mechanisms. Proteomics data will be integrated with the genomic and transcriptomic data obtained. The vast amount of information expected from this omics approach will improve our understanding of AMR in MRSA biofilms, and help us identify new vaccine candidates and biomarkers for early diagnosis and innovative therapeutic strategies to tackle MRSA biofilm-associated infections and potentially other AMR superbugs.
Highlights
Staphylococcus aureus is a Gram-positive facultative anaerobe frequently present in the natural human microbiota of the nose and skin that can cause a range of illnesses from minor skin infections and food poisoning to life-threatening diseases such as pneumonia, toxic shock syndrome and sepsis (Sousa et al, 2017)
methicillin-resistant S. aureus (MRSA) is resistant to almost all beta-lactams and frequently carries other major classes of antimicrobial resistance (AMR)
The One Health approach is the European Commission strategy to tackle AMR, as it recognizes that the health of people, animals and the environment are inextricably linked
Summary
Staphylococcus aureus is a Gram-positive facultative anaerobe frequently present in the natural human microbiota of the nose and skin that can cause a range of illnesses from minor skin infections and food poisoning to life-threatening diseases such as pneumonia, toxic shock syndrome and sepsis (Sousa et al, 2017). MRSA is resistant to almost all beta-lactams and frequently carries other major classes of antimicrobial resistance (AMR). Most AMR research has been focused on bacteria growing in planktonic cultures and antimicrobials were originally developed to target individual bacterial cells. Recent advances in proteomics techniques have enabled a more in-depth analysis of the possible mechanisms responsible for biofilm AMR and the identification of new anti-biofilm targets (Seneviratne et al, 2012; Azeredo et al, 2017). The use of prefractionation techniques to extract subproteomes significantly enhanced protein identification and coverage of the biofilm proteome (Seneviratne et al, 2012). New shotgun proteomics workflows based on high-resolution tandem mass spectrometry (MS/MS) directly coupled to high performance liquid chromatography (LC) require less protein than conventional two-dimensional gel electrophoresis (2-DE) approaches, allowing a more exhaustive analysis of proteomes or subproteomes and the performance of label-free semi-quantitative comparisons (Azeredo et al, 2017)
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