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.

A high-resolution genomic and phenotypic analysis of resistance evolution of anEscherichia colistrain from a critical care patient treated with piperacillin/tazobactam

“…Scores for BL/BLI resistance prediction from genomic data have been developed (72) in addition to β-lactamase copy number calculators (73) and tools like these will be vital to support decisions for use of new BL/BLI combinations and ensure their future efficacy is safeguarded. Alternative diagnostic methods, based on protein detection, such as matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) technology, may provide an alternative strategy to rapidly detect β-lactamase hyperproduction (74).…”

Direct Detection of Carbapenemase-Producing Klebsiella pneumoniae by MALDI-TOF Analysis of Full Spectra Applying Machine Learning

“…The importance of a feature is computed (the normalized values of the array sum to 100%) as the total reduction of the criterion brought by that feature. This is also known as the Gini importance ( 33 ). Shapley values measure the feature importance in a multivariate way.…”

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

“…Machine learning algorithms were implemented while developing antimicrobial resistance prediction models for Escherichia coli [84,85]. These algorithms were successfully utilised to enhance methods for discrimination between piperacillin/tazobactam-resistant and susceptible Escherichia coli isolates [86]. Furthermore, deep learning of single-cell subcellular phenotypes was utilised for determining antimicrobial susceptibility in Escherichia coli for ciprofloxacin, gentamicin, rifampicin, and co-amoxiclav These methods can be used to predict pyrazinamide resistance of Mycobacterium tuberculosis and to pinpoint genes and mutations responsible for the resistance [107,108].…”