Time Evolution of Bacterial Resistance Observed with Principal Component Analysis

Area of Science:

• Biomolecular analysis
• Microbiology
• Spectroscopy

Background:

• Antibiotic resistance in bacteria is a growing global health concern.
• Principal Component Analysis (PCA) and Fourier Transformation Infrared (FTIR) spectroscopy offer powerful methods for analyzing microbial biomolecular changes.
• Previous work demonstrated the utility of PCA and FTIR for detecting antibiotic resistance in bacteria.

Purpose of the Study:

• To analyze biochemical structural changes in Staphylococcus aureus over time during antibiotic exposure.
• To identify trends in bacterial samples developing resistance to antibiotics.
• To understand the evolution of antibiotic resistance in Staphylococcus aureus.

Main Methods:

• FTIR spectra were collected from Staphylococcus aureus samples with induced resistance to Azithromycin, Oxacillin, or Trimethoprim/Sulfamethoxazole.
• Antibiotic resistance development was monitored over four increasing exposure periods.
• Machine learning algorithms and PCA were applied to the FTIR spectral data.

Main Results:

• Distinct patterns were identified in the FTIR spectral data correlating with minimum inhibitory concentration (MIC) values.
• Hierarchical classification and PCA revealed clusters associated with different exposure times and resistance levels.
• Machine learning algorithms successfully identified trends indicative of evolving antibiotic resistance.

Conclusions:

• FTIR spectroscopy combined with machine learning provides a method to observe bacterial resistance development pathways.
• This approach allows for the determination of the current stage of antibiotic resistance in a bacterial sample.
• The findings support faster and more effective infection treatment strategies in healthcare settings.