Early cell-wall stress signatures enable 20-min ATR-FTIR discrimination of MRSA and MSSA

Rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) is crucial for early optimization of antibiotic treatment, but current routine susceptibility testing typically requires 48-72 h. Attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy has emerged as a promising approach for bacterial identification and has recently been used to distinguish MRSA from methicillin-sensitive S. aureus (MSSA) after 60-120 min of β-lactam exposure. Here, we test whether ATR-FTIR can resolve MRSA versus MSSA within the first hour of antibiotic challenge. We exposed three MSSA (ATCC 6538, WKZ1, RN4220) and four MRSA (ATCC 43300, USA300-JE2, WKZ2, CA629) strains to sub-MIC ampicillin (0.5 μg/mL) and acquired spectra from 800 to 1800 cm^-1 at 0, 20, 30, and 60 min. We compared classification pipelines based on the full spectrum, PCA-reduced features, and LASSO-selected bands, coupled with linear discriminant analysis, partial least-squares discriminant analysis, and support vector machines. Models based on LASSO-selected features achieved the strongest early performance, with strain-aware classification accuracies of 0.91 at 20 min and 0.92 at 30 min. Leave-one-strain-out cross-validation (LOSO-CV) further showed that focusing on mechanistically relevant difference spectra enabled robust across-strain discrimination, with balanced accuracies of 0.91 at 20 min and 0.90 at 30 min. The most informative early bands mapped primarily to peptidoglycan and carbohydrate precursor regions, while later discrimination increasingly involved lipid-associated bands. Transmission electron microscopy and atomic force microscopy at 20 min independently confirmed antibiotic-induced cell-wall thickening and structural disruption in susceptible strains but not in resistant strains. Together, these results establish a proof of concept that early cell-wall stress signatures captured by ATR-FTIR, combined with lightweight and interpretable machine-learning models, can deliver rapid and accurate phenotypic discrimination between MRSA and MSSA.