Surface enhanced Raman spectroscopy and machine learning for identification of beta-lactam antibiotics resistance gene fragment in bacterial plasmid.

BACKGROUND

Antibiotic resistance stands as a critical medical concern, notably evident in commonly prescribed beta-lactam antibiotics. The imperative need for expeditious and precise early detection methods underscores their role in facilitating timely intervention, curbing the propagation of antibiotic resistance, and enhancing patient outcomes.

RESULTS

This study introduces the utilization of surface-enhanced Raman spectroscopy (SERS) in tandem with machine learning (ML) for the sensitive detection of characteristic gene fragments responsible for antibiotic resistance appearance and spreading. To make the detection procedure close to the real case, we used bacterial plasmids as starting biological objects, containing or not the characteristic gene fragment (up to 1:10 ratio), encoding beta-lactam antibiotics resistance. The plasmids were subjected to enzymatic digestion and without preliminary purification or isolation the created fragments were captured by functional SERS substrates. Based on subsequent SERS measurements, a database was created for the training and validation of ML. Method validation was performed using separately measured spectra, which did not overlap with the database used for ML training. To check the efficiency of recognising the target fragment, control experiments involved bacterial plasmids containing different resistance genes, the use of inappropriate enzymes, or the absence of plasmid.

SIGNIFICANCE

SERS-ML allowed express detection of bacterial plasmids containing a characteristic gene fragment up to the 10

Informations de copyright

Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest X The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.