Journal
EMERGING MICROBES & INFECTIONS
Volume 11, Issue 1, Pages 2034-2044Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/22221751.2022.2113746
Keywords
Piperacillin; tazobactam; Escherichia coli; resistance; beta-lactamase; beta-lactamase inhibitor; machine learning; algorithm
Categories
Funding
- Proyectos de Investigacion en Salud, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovacion y Universidades of Spain - European Development Regional Fund A way to achieve Europe, operative program Intelligent Growth 2014-2020 [PI19/01009]
- subprograma Miguel Servet Tipo II, Instituto de Salud Carlos III, Ministerio de Ciencia e Innovacion of Spain [CP20/00018]
- subprograma Juan Rodes, Instituto de Salud Carlos III, Ministerio de Ciencia e Innovacion of Spain [JR20/00023]
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The study demonstrates that the MALDIpiptaz test can efficiently and rapidly discriminate between P/T-resistant, ESRI developers, and P/T-susceptible isolates in E. coli. Its combination of excellent performance and cost-effectiveness make it a useful tool for the rapid determination of P/T resistance in clinically relevant E. coli isolates.
Background The excessive use of piperacillin/tazobactam (P/T) has promoted the emergence of P/T-resistant Enterobacterales. We reported that in Escherichia coli, P/T contributes to the development of extended-spectrum resistance to beta-lactam/beta-lactamase inhibitor (BL/BLI) (ESRI) in isolates that are P/T susceptible but have low-level resistance to BL/BLI. Currently, the detection of P/T resistance relying on conventional methods is time-consuming. To overcome this issue, we developed a cost-effective test based on MALDI-MS technology, called MALDIpiptaz, which aims to detect P/T resistance and ESRI developers in E. coli. Methods We used automated Clover MS Data Analysis software to analyse the protein profile spectra obtained by MALDI-MS from a collection of 248 E. coli isolates (91 P/T-resistant, 81 ESRI developers and 76 P/T-susceptible). This software allowed to preprocess all the spectra to build different peak matrices that were analysed by machine learning algorithms. Results We demonstrated that MALDIpiptaz can efficiently and rapidly (15 min) discriminate between P/T-resistant, ESRI developer and P/T-susceptible isolates and allowed the correct classification between ESRI developers from their isogenic resistance to P/T. Conclusion The combination of excellent performance and cost-effectiveness are all desirable attributes, allowing the MALDIpiptaz test to be a useful tool for the rapid determination of P/T resistance in clinically relevant E. coli isolates.
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