1887

Abstract

Antibiotic resistance has become a serious threat to human health (WHO . 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 isolates (Marvig . 2015;47:57–64; Frimodt-Møller . 2018;8:12512; Bartell . 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 . 2015;47:57–64) were associated with antibiotic resistance (López-Causapé . 2018;9:685; López-Causapé . 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 . 2018;8:12512; Bartell . 2019;10:629). In this review, we highlight alternative selective forces that potentially enhance the infection success of 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.

Funding
This study was supported by the:
  • Søren Molin , Novo Nordisk Fonden Center for Biosustainability (DK)
  • Helle K. Johansen , Sundhed og Sygdom, Det Frie Forskningsråd , (Award FTP-4183-00051)
  • Helle K. Johansen , RegionH Rammebevilling , (Award R144-A5287)
  • Helle K. Johansen , Novo Nordisk Fonden , (Award NNF15OC0017444)
  • Helle K. Johansen , Rigshospitalet (DK) , (Award R88-A3537)
  • Helle K. Johansen , Novo Nordisk Fonden , (Award NNF12OC1015920)
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2020-04-29
2020-06-02
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