Time Evolution of Bacterial Resistance Observed with Principal Component Analysis

Author(s)

Barrera Patino, Claudia P. ; Bonner, Mitchell ; Borsatto, Andrew Ramos ; Soares, Jennifer M. ; Blanco, Kate C. ; Bagnato, Vanderlei S.

• Document type: Journal article
• Source: ANTIBIOTICS-BASEL; v. 14, n. 7, p. 20-pg., 2025-07-20.

Abstract

Background/Objectives: In recent work, we have demonstrated that principal component analysis (PCA) and Fourier Transformation Infrared (FTIR) spectra are powerful tools for analyzing the changes in microorganisms at the biomolecular level to detect changes in bacteria with resistance to antibiotics. Here biochemical structural changes in Staphylococcus aureus were analyzed over exposure time with the goal of identifying trends inside the samples that have been exposed to antibiotics for increasing amounts of time and developed resistance. Methods: All studied data was obtained from FTIR spectra of samples with induced antibiotic resistance to either Azithromycin, Oxacillin, or Trimethoprim/Sulfamethoxazole following the evolution of this development over four increasing antibiotic exposure periods. Results: The processing and data analysis with machine learning algorithms performed on this FTIR spectral database allowed for the identification of patterns across minimum inhibitory concentration (MIC) values associated with different exposure times and both clusters from hierarchical classification and PCA. Conclusions: The results enable the observation of resistance development pathways for the sake of knowing the present stage of resistance of a bacterial sample. This is carried out via machine learning methods for the purpose of faster and more effective infection treatment in healthcare settings. (AU)

• FAPESP's process: 14/50857-8 - National Institute in Basic Optics and Applied to Life Sciences
• Grantee: Vanderlei Salvador Bagnato
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 24/00100-0 - Combating antimicrobial resistance in multidrug-resistant bacteria through photodynamic action
• Grantee: Jennifer Machado Soares
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 23/17384-8 - Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups with the Implementation of Machine Learning Algorithms.
• Grantee: Claudia Patricia Barrera Patiño
• Support Opportunities: Scholarships in Brazil - Technical Training Program - Technical Training
• FAPESP's process: 13/07276-1 - CEPOF - Optics and Photonic Research Center
• Grantee: Vanderlei Salvador Bagnato
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC