title: A biosensing strategy for the rapid detection and classification of antibiotic resistance.
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.
authors.affiliation: Institute of Endemic Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, 710061 Xi'an, China; Department of Pure and Applied Biochemistry, Lund University, P.O. Box 124, SE-22100 Lund, Sweden.
authors.forename: Qun
authors.initials: Q
authors.lastname: Chen
authors.affiliation: Department of Pure and Applied Biochemistry, Lund University, P.O. Box 124, SE-22100 Lund, Sweden.
authors.forename: Anneli
authors.initials: A
authors.lastname: Andersson
authors.affiliation: BT Biomedical Technology AB, SE-22738 Lund, Sweden.
authors.forename: Michael
authors.initials: M
authors.lastname: Mecklenburg
authors.affiliation: Department of Pure and Applied Biochemistry, Lund University, P.O. Box 124, SE-22100 Lund, Sweden. Electronic address: bin.xie@tbiokem.lth.se.
authors.forename: Bin
authors.initials: B
authors.lastname: Xie
mesh_terms: D000900:Anti-Bacterial Agents / Q000378:metabolism / Q000494:pharmacology; D015374:Biosensing Techniques / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data; D016208:Databases, Factual; D004352:Drug Resistance, Microbial; D004867:Equipment Design; D006801:Humans; D010405:Penicillinase / Q000378:metabolism; D015203:Reproducibility of Results; D013379:Substrate Specificity; D001618:beta-Lactamases / Q000378:metabolism
chemical_list: D000900:Anti-Bacterial Agents; D010405:Penicillinase; D001618:beta-Lactamases
keywords: Antibiotic resistance; Antibiotics; Profiling; Thermal biosensor; β-lactamase