English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Antibiotic resistance has become a serious threat to human health (WHO Antibacterial Agents in Clinical Development: an Analysis of the Antibacterial Clinical Development Pipeline, Including Tuberculosis . Geneva: World Health Organization; 2017), and the ability to predict antibiotic resistance from genome sequencing has become a focal point for the medical community. With this genocentric prediction in mind, we were intrigued about two particular findings for a collection of clinical Pseudomonas aeruginosa isolates (Marvig et al . Nature Genetics 2015;47:57-64; Frimodt-Møller et al . Scientific Reports 2018;8:12512; Bartell et al . Nature Communications 2019;10:629): (i) 15 out of 52 genes found to be frequently targeted by adaptive mutations during the initial infection stage of cystic fibrosis airways ('candidate pathoadaptive genes') (Marvig et al . Nature Genetics 2015;47:57-64) were associated with antibiotic resistance (López-Causapé et al . Fronters in Microbiology 2018;9:685; López-Causapé et al . Antimicrobal Agents and Chemotherapy 2018;62:e02583-17); (ii) there was a parallel lack of resistance development and linkage to the genetic changes in these antibiotic-resistance-associated genes (Frimodt-Møller et al . Scientific Reports 2018;8:12512; Bartell et al . Nature Communications 2019;10:629). In this review, we highlight alternative selective forces that potentially enhance the infection success of P. aeruginosa and focus on the linkage to the 15 pathoadaptive antibiotic-resistance-associated genes, thereby showing the problems we may face when using only genomic information to predict and inform about relevant antibiotic treatment.

Antibiotic resistance in Pseudomonas aeruginosa and adaptation to complex dynamic environments