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 . 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.