Pseudomonas aeruginosa: a clinical and genomics update.
Andreu Coello PelegrinMattia PalmieriCaroline MirandeAntonio OliverPieter MoonsHerman GoossensAlex van BelkumPublished in: FEMS microbiology reviews (2022)
Antimicrobial resistance (AMR) has become a global medical priority that needs urgent resolution. Pseudomonas aeruginosa is a versatile, adaptable bacterial species with widespread environmental occurrence, strong medical relevance, a diverse set of virulence genes and a multitude of intrinsic and possibly acquired antibiotic resistance traits. Pseudomonas aeruginosa causes a wide variety of infections and has an epidemic-clonal population structure. Several of its dominant global clones have collected a wide variety of resistance genes rendering them multi-drug resistant (MDR) and particularly threatening groups of vulnerable individuals including surgical patients, immunocompromised patients, Caucasians suffering from cystic fibrosis (CF) and more. AMR and MDR especially are particularly problematic in P. aeruginosa significantly complicating successful antibiotic treatment. In addition, antimicrobial susceptibility testing (AST) of P. aeruginosa can be cumbersome due to its slow growth or the massive production of exopolysaccharides and other extracellular compounds. For that reason, phenotypic AST is progressively challenged by genotypic methods using whole genome sequences (WGS) and large-scale phenotype databases as a framework of reference. We here summarize the state of affairs and the quality level of WGS-based AST for P. aeruginosa mostly from clinical origin.
Keyphrases
- pseudomonas aeruginosa
- cystic fibrosis
- antimicrobial resistance
- drug resistant
- acinetobacter baumannii
- multidrug resistant
- biofilm formation
- genome wide
- healthcare
- lung function
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- risk assessment
- prognostic factors
- staphylococcus aureus
- escherichia coli
- dna methylation
- single cell
- big data
- gene expression
- patient reported outcomes
- combination therapy
- human health
- chronic obstructive pulmonary disease
- machine learning
- intensive care unit
- smoking cessation
- replacement therapy