Profiling antibiotic resistance inEscherichia colistrains displaying differential antibiotic susceptibilities using Raman spectroscopy

The rapid identification of antibiotic resistant bacteria is important for public health. In the environment, bacteria are exposed to sub-inhibitory antibiotic concentrations which has implications in the generation of multi-drug resistant strains. To better understand these issues, Raman spectroscopy was employed coupled with partial least squares-discriminant analysis to profileEscherichia colistrains treated with sub-inhibitory concentrations of antibiotics. Clear differences were observed between cells treated with bacteriostatic (tetracycline and rifampicin) and bactericidal (ampicillin, ciprofloxacin, and ceftriaxone) antibiotics for 6 hr: First, atomic force microscopy revealed that bactericidal antibiotics cause extensive cell elongation whereas short filaments are observed with bacteriostatic antibiotics. Second, Raman spectral analysis revealed that bactericidal antibiotics lower nucleic acid to protein (I-812/I-830) and nucleic acid to lipid ratios (I-1483/I-1452) whereas the opposite is seen with bacteriostatic antibiotics. Third, the protein to lipid ratio (I-2936/I(2885)and I-2936/I-2850) is a Raman stress signature common to both the classes. These signatures were validated using two mutants, Delta lon and Delta acrB, that exhibit relatively high and low resistance towards antibiotics, respectively. In addition, these spectral markers correlated with the emergence of phenotypic antibiotic resistance. Overall, this study demonstrates the efficacy of Raman spectroscopy to identify resistance in bacteria to sub-lethal concentrations of antibiotics.

Automated summary

The study demonstrates the efficacy of Raman spectroscopy in identifying bacterial resistance to sub-lethal concentrations of antibiotics. Differences between bacteriostatic and bactericidal antibiotics were observed in terms of cell morphology and biochemical compositions, showing correlations with phenotypic antibiotic resistance. This study validates spectral markers that could potentially help in identifying antibiotic resistant bacteria in the future.