Validating phosphoethanolamine modification as a potential spectral marker of colistin resistance

The antibiotic colistin is regarded as the final line of defense for treating infections caused by Gram-negative bacteria. The combination of Raman spectroscopy (RS) with diverse machine learning methods has helped unravel the complexity of various microbiology problems. This approach offers a culture-free, rapid, and objective tool for identifying antimicrobial resistance (AMR). In this study, we employed the combinatorial approach of machine learning and RS to identify a novel spectral marker associated with phosphoethanolamine modification in the lipid A moiety of colistin-resistant Gram-negative Escherichia coli. The visible spectral fingerprints of this marker have been validated using partial least squares regression and discriminant analysis. The origin of the spectral feature was confirmed through hyperspectral imaging and K-means clustering of a single bacterial cell. The chemical structure of the modified lipid A moiety was verified by employing gold standard MALDI-TOF mass spectrometry. Our findings support the futuristic applicability of this spectroscopic marker in objectively identifying colistin-sensitive and -resistant strains.