Validating phosphoethanolamine modification as a potential spectral marker of colistin resistance

Colistin antibiotic 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 to 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 lipid A moiety of colistin resistant gram-negative Escherichia coli. The visible spectral fingerprints of this marker have been validated by partial least square regression and discriminant analysis. The origin of the spectral feature has been confirmed by hyperspectral imaging and K-means clustering of a single bacterial cell. The chemical structure of the modified lipid A moiety has been verified by gold standard MALDI-TOF mass spectrometry. Our findings support futuristic applicability of this spectroscopic marker in objectively identifying colistin-sensitive and resistant.