Ultrasensitive and rapid identification of ESRI developer- and piperacillin/tazobactam-resistant Escherichia coli by the MALDIpiptaz test

ABSTRACT

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 β-lactam/β-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.

KEYWORDS:

• Piperacillin
• tazobactam
• Escherichia coli
• resistance
• beta-lactamase
• beta-lactamase inhibitor
• machine learning
• algorithm

ACKNOWLEDGMENTS

We thank María del Mar Tomás and Antonio Oliver for the kind gift of the P/T-resistant E. coli isolates.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflict of interest

Manuel J. Arroyo, Gema Méndez and Luis Mancera were employed by Clover Bioanalytical Software.

None of the remaining authors has a conflict of interest to declare.

Additional information

Funding

This study was supported by the [Proyectos de Investigación en Salud, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades of Spain] under grant [number PI19/01009] cofinanced by European Development Regional Fund “A way to achieve Europe”, operative program Intelligent Growth 2014-2020. Y.S. was supported by the [subprograma Miguel Servet Tipo II, Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación of Spain] under grant [number CP20/00018]. A.R.-V. was supported by the [subprograma Juan Rodés, Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación of Spain] under grant [number JR20/00023].