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A biosensing strategy for the rapid detection and classification of antibiotic resistance.

Chen, Qun LU ; Andersson, Anneli LU ; Mecklenburg, Michael and Xie, Bin LU (2015) In Biosensors & Bioelectronics 73. p.251-255
Abstract
Antibiotic resistance (AR) poses an ever growing threat to global public health. Methods are urgently needed that simplify and accelerate the clinical detection and classification of AR. Here we describe a function-based antibiotic resistance assay (FARA) biosensing strategy. The scheme comprises three key components: i) FARA directly measures the thermal signal generated from the catalytic break-down of antibiotics by AR enzymes, ii) a sample specific AR profile is created by analyzing a panel of antibiotics which enhances informational content and iii) meta-analysis of the AR profile database to correlate profiles with diagnosis, treatments and outcomes. In order to test the ability of the scheme to identify and classify AR, two... (More)
Antibiotic resistance (AR) poses an ever growing threat to global public health. Methods are urgently needed that simplify and accelerate the clinical detection and classification of AR. Here we describe a function-based antibiotic resistance assay (FARA) biosensing strategy. The scheme comprises three key components: i) FARA directly measures the thermal signal generated from the catalytic break-down of antibiotics by AR enzymes, ii) a sample specific AR profile is created by analyzing a panel of antibiotics which enhances informational content and iii) meta-analysis of the AR profile database to correlate profiles with diagnosis, treatments and outcomes. In order to test the ability of the scheme to identify and classify AR, two well-studied antibiotic resistance enzymes, penicillinase and metallo-beta-lactamase (MBL), were profiled using a panel of 5 antibiotics: penicillin G, penicillin V, ampicillin, oxacillin and imipenem. The results show that the profiles of the two enzymes could easily detect AR and differentially classified these enzymes. More importantly, both enzymes showed a significant and distinct secondary catalytic profile, which dramatically increases informational content. FARA profiles can be generated and analyzed in 1h. FARA is a fast, simple, cost effective alternative for detecting and classifying AR. FARA will speed up AR detection and classification will allow more accurate individualized treatment. This will reduce the spread of resistance and personalized treatments will improve patient outcomes. Other potential applications of FARA technology are discussed, including the possibility of developing an in vitro blood model for studying AR. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Biosensors & Bioelectronics
volume
73
pages
251 - 255
publisher
Elsevier
external identifiers
  • pmid:26092129
  • wos:000358823900035
  • scopus:84936084896
ISSN
1873-4235
DOI
10.1016/j.bios.2015.06.011
language
English
LU publication?
yes
id
1712c765-968a-4bf7-8bcb-9465e2d7438a (old id 7484500)
date added to LUP
2015-07-09 13:03:22
date last changed
2017-01-01 04:18:48
@article{1712c765-968a-4bf7-8bcb-9465e2d7438a,
  abstract     = {Antibiotic resistance (AR) poses an ever growing threat to global public health. Methods are urgently needed that simplify and accelerate the clinical detection and classification of AR. Here we describe a function-based antibiotic resistance assay (FARA) biosensing strategy. The scheme comprises three key components: i) FARA directly measures the thermal signal generated from the catalytic break-down of antibiotics by AR enzymes, ii) a sample specific AR profile is created by analyzing a panel of antibiotics which enhances informational content and iii) meta-analysis of the AR profile database to correlate profiles with diagnosis, treatments and outcomes. In order to test the ability of the scheme to identify and classify AR, two well-studied antibiotic resistance enzymes, penicillinase and metallo-beta-lactamase (MBL), were profiled using a panel of 5 antibiotics: penicillin G, penicillin V, ampicillin, oxacillin and imipenem. The results show that the profiles of the two enzymes could easily detect AR and differentially classified these enzymes. More importantly, both enzymes showed a significant and distinct secondary catalytic profile, which dramatically increases informational content. FARA profiles can be generated and analyzed in 1h. FARA is a fast, simple, cost effective alternative for detecting and classifying AR. FARA will speed up AR detection and classification will allow more accurate individualized treatment. This will reduce the spread of resistance and personalized treatments will improve patient outcomes. Other potential applications of FARA technology are discussed, including the possibility of developing an in vitro blood model for studying AR.},
  author       = {Chen, Qun and Andersson, Anneli and Mecklenburg, Michael and Xie, Bin},
  issn         = {1873-4235},
  language     = {eng},
  pages        = {251--255},
  publisher    = {Elsevier},
  series       = {Biosensors & Bioelectronics},
  title        = {A biosensing strategy for the rapid detection and classification of antibiotic resistance.},
  url          = {http://dx.doi.org/10.1016/j.bios.2015.06.011},
  volume       = {73},
  year         = {2015},
}