Biosensors & bioelectronics · 2021
Rapid personalized AMR diagnostics using two-dimensional antibiotic resistance profiling strategy employing a thermometric NDM-1 biosensor.
СтатьяНазвание и аннотация: Перевод в очереди
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Meng Qinglai, Liu Shichao, Meng Jinhua, Feng Jiao, Mecklenburg Michael, Zhu Lei, Zhou Lifang, Bülow Leif, Liu Jianyi, Song Dewei, Wu Changxin, Xie Bin
Аннотация
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.