Molecular Epidemiology of Pseudomonas aeruginosa in Brazil: A Systematic Review and Meta-Analysis

Globally, Pseudomonas aeruginosa is a high-priority opportunistic pathogen which displays several intrinsic and acquired antimicrobial resistance (AMR) mechanisms, leading to challenging treatments and mortality of patients. Moreover, its wide virulence arsenal, particularly the type III secretion system (T3SS) exoU virulotype, plays a crucial role in pathogenicity and poor outcome of infections. In depth insights into the molecular epidemiology of P. aeruginosa, especially the prevalence of high-risk clones (HRCs), are crucial for the comprehension of virulence and AMR features and their dissemination among distinct strains. This study aims to evaluate the prevalence and distribution of HRCs and non-HRCs among Brazilian isolates of P. aeruginosa.

A systematic review and meta-analysis were conducted on studies published between 2011 and 2023, focusing on the prevalence of P. aeruginosa clones determined by multilocus sequence typing (MLST) in Brazil. Data were extracted from retrospective cross-sectional and case-control studies, encompassing clinical and non-clinical samples. The analysis included calculating the prevalence rates of various sequence types (STs) and assessing the regional variability in the distribution of HRCs and non-HRCs.

A total of 872 samples were analyzed within all studies, of which 298 (34.17%) were MLST typed, identifying 78 unique STs. HRCs accounted for 48.90% of the MLST-typed isolates, with ST277 being the most prevalent (100/298-33.55%), followed by ST244 (29/298-9.73%), ST235 (13/298-4.36%), ST111 (2/298-0.67%), and ST357 (2/298-0.67%). Significant regional variability was observed, with the Southeast region showing a high prevalence of ST277, while the North region shows a high prevalence of MLST-typed samples and HRCs.

Finally, this systematic review and meta-analysis highlight the role of P. aeruginosa clones in critical issue of AMR in P. aeruginosa in Brazil and the need of integration of comprehensive data from individual studies.