Rapid personalized AMR diagnostics using two-dimensional antibiotic resistance profiling strategy employing a thermometric NDM-1 biosensor

Qinglai Meng, Shichao Liu, Jinhua Meng, Jiao Feng, Michael Mecklenburg, Lei Zhu, Lifang Zhou, Leif Bülow, Jianyi Liu, Dewei Song, Changxin Wu, Bin Xie

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

Sammanfattning

Antimicrobial resistance (AMR) threatens global public health and modern surgical medicine. Expression of β-lactamase genes is the major mechanism by which pathogens become antibiotic resistant. Pathogens expressing extended spectrum β-lactamases (ESBL) and carbapenemases (CP) are especially difficult to treat and are associated with increased hospitalization and mortality rates. Despite considerable effort, identification of ESBLs and CPs in a clinically relevant timeframe remains challenging. In this study, a two-dimensional AMR profiling assay strategy was developed employing panels of antibiotics (penicillins, cephamycins, oximino-cephalosporins and carbapenems) and β-lactamases inhibitors (avibactam and EDTA). The assay required the development of a novel biosensor that employed New Delhi metallo-β-lactamase-1 (NDM-1) as the sensing element. Functionally probing β-lactamase activity using substrates and inhibitors combinatorically increased the informational content that enabled the development of assays capable of simultaneous, differential identification of multiple β-lactamases expressed in a single bacterial isolate. More specifically, the assay enabled the simultaneous identification of ESBL and CP in mock samples, as well as in an engineered construct which co-expressed these β-lactamases. The NDM-1 biosensor assay was 16 times and 8 times more sensitive than the ESBL Nordmann/Dortet/Poirel (NDP) and Carba Nordmann/Poirel (NP) assays, respectively. In a retrospective study, NDM-1 biosensor assays were able to differentially identify ESBLs, metallo-CPs and serine-CPs β-lactamases in 23 clinical isolates with 100% accuracy. An assay algorithm was developed which accelerated data analytics reducing turnaround to <1 h. The assay strategy integrated with AI-based data analytics has the potential to provide physicians with a comprehensive readout of patient AMR status.

Originalspråkengelska
Artikelnummer113526
TidskriftBiosensors and Bioelectronics
Volym193
DOI
StatusPublished - 2021

Ämnesklassifikation (UKÄ)

  • Mikrobiologi inom det medicinska området
  • Infektionsmedicin

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