Ramanomics providing molecular insight into antibacterial Mechanisms of Action

Thursday, October 9, 2025

10:30 AM - 10:50 AM EDT

Location: Meeting Room #6

Presenting Author(s)

TC

Tom Cambron

Director Principle Scientist Corporate Functions
Procter & Gamble
Mason, Ohio, United States

Non-Presenting Author(s)

TR

Tim Reichling

Procter & Gamble
Mason, Ohio, United States
JC

Justin Caserta

Procter & Gamble
Mason, Ohio, United States
ED

Emily Duderstadt

Procter & Gamble
Mason, Ohio, United States
XW

Xingtao Wei

Procter & Gamble
Mason, Ohio, United States

In fifteen words or less, explain the significance of this contribution (Novel Aspect).: Novel Ramanomics methodology, enabling precise identification of unknown antibacterial mechanisms and enhancing treatment strategies.

Abstract Text: Antibiotic and antibacterial resistance remain a significant global health challenge, necessitating a deeper understanding of the mechanisms of action (MoA) of antibacterial agents for the development of effective treatments. Traditional methods, such as transcriptomic RNA sequencing and mass spectrometry, provide insights into genetic and proteomic responses to antibacterial insults. In this presentation, we introduce a novel Ramanomic methodology that enhances our understanding of cellular phenotypes associated with various antibacterial agents. Utilizing Raman spectroscopy, we analyzed the effects of several agents on Escherichia coli, each known for different MoAs, including oxidative stress, metabolic disruption, and membrane lysis. Our innovative chemometric approaches enabled the extraction of spectral information associated with the Ramanome phenotype specific to each MoA, facilitating the screening of new antibacterial agents with unknown mechanisms. Through partial least-squares (PLS) analysis, we established a robust correlation between changes in Raman spectra and bacterial viability, achieving high predictive accuracy. This methodology allows for the comparison of the Ramanome phenotype of unknown antibacterial agents against a library of standards with known MoAs, thereby providing insights into their likely mechanisms. Additionally, we have developed a chemometric framework that directly identifies the most probable MoA of an unknown agent, offering a powerful tool for antibacterial research and development. This work represents a significant advancement in the field, paving the way for more targeted and effective antibacterial therapies.