Abstract

The rise of carbapenem-resistant E. coli, specifically carbapenemase-producing (CP) strains, in biological samples needs attention. Early detection is crucial in minimizing and controlling their detrimental impact. However, their rapid identification directly from contaminated samples has not been documented well. This study presented an affordable and rapid platform for extraction and detection of CP E. coli, particularly Klebsiella pneumoniae carbapenemase (KPC)-producing E. coli from spiked food and water samples. Glycan-coated magnetic nanoparticles (gMNPs) were utilized to extract the bacteria from contaminated buffer solution, tap water, romaine lettuce, ground beef, and chicken breast samples. The bacterial cell concentration by gMNPs was achieved in the presence of food microparticles and natural microflora. Transmission Electron Microscopy (TEM) illustrated the successful isolation of bacteria and their interaction with gMNPs. For detection, a parallel plasmonic biosensor using dextrin-coated gold nanoparticles (dGNPs) was developed. The biosensor successfully detected KPC-producing E. coli from pure culture by targeting uidA and blaKPC genes and did not require any PCR amplification. The stability of dGNPs, shown by their red appearance, indicated the presence of target DNA. In contrast, their agglomeration, given by their blue or purple color, indicated the absence of target DNA. This stability and agglomeration of the dGNPs were confirmed by TEM and quantified by absorbance spectrum measurements. Further, the designed biosensor platform successfully identified the DNA extracted from water and food samples contaminated with KPC-producing E. coli, with no false positives for the samples contaminated with non-target bacteria. The biosensor successfully detected the target organism, where the original concentration prior to magnetic extraction was 103 CFU/mL. The biosensor results were further confirmed with the standard PCR assay. As a proof-of-concept, these findings indicate promising applications of the integrated platform for cost-effective and rapid bacterial detection from complex matrices within <7 h. The gained insights may facilitate future application of this platform for its complete validation study to improve real-world applicability.

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