Worldwide Dissemination of bla KPC Gene by Novel Mobilization Platforms in Pseudomonas aeruginosa : A Systematic Review.
Daniela Forero-HurtadoZayda Lorena Corredor-RozoJulián Santiago Ruiz-CastellanosRicaurte Alejandro Marquez-OrtizDeisy AbrilNatasha VanegasGloria Inés LafaurieLeandro ChambroneJavier Escobar-PérezPublished in: Antibiotics (Basel, Switzerland) (2023)
The dissemination of bla KPC -harboring Pseudomonas aeruginosa (KPC- Pa ) is considered a serious public health problem. This study provides an overview of the epidemiology of these isolates to try to elucidate novel mobilization platforms that could contribute to their worldwide spread. A systematic review in PubMed and EMBASE was performed to find articles published up to June 2022. In addition, a search algorithm using NCBI databases was developed to identify sequences that contain possible mobilization platforms. After that, the sequences were filtered and pair-aligned to describe the bla KPC genetic environment. We found 691 KPC- Pa isolates belonging to 41 different sequence types and recovered from 14 countries. Although the bla KPC gene is still mobilized by the transposon Tn 4401 , the non-Tn 4401 elements (NTE KPC ) were the most frequent. Our analysis allowed us to identify 25 different NTE KPC , mainly belonging to the NTE KPC -I, and a new type (proposed as IVa) was also observed. This is the first systematic review that consolidates information about the behavior of the bla KPC acquisition in P. aeruginosa and the genetic platforms implied in its successful worldwide spread. Our results show high NTE KPC prevalence in P. aeruginosa and an accelerated dynamic of unrelated clones. All information collected in this review was used to build an interactive online map.
Keyphrases
- klebsiella pneumoniae
- multidrug resistant
- escherichia coli
- pseudomonas aeruginosa
- systematic review
- public health
- genome wide
- acinetobacter baumannii
- drug resistant
- machine learning
- social media
- risk factors
- healthcare
- transcription factor
- deep learning
- staphylococcus aureus
- health information
- artificial intelligence
- meta analyses