Poster Presentation Australian Society for Microbiology Annual Scientific Meeting 2022

Light and fast – high-throughput UV-Visible method for assessing antimicrobial activity (#108)

Rebecca Orrell-Trigg 1 , Sheeana Gangadoo 1 , Miyah Awad 1 , Daniel Cozzolino 2 , Vi Khanh Truong 3 , James Chapman 1
  1. Applied Chemistry, RMIT University, Melbourne, Victoria, Australia
  2. Centre for Nutrition and Food Sciences, The University of Queensland, Brisbane, Queensland, Australia
  3. College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia

Antimicrobial resistance is a global health issue which is projected to cause 10 million deaths a year by 2050 [1]. The current suite of laboratory techniques used to understand antimicrobial activity and the development of antimicrobial resistance are often labour and time intensive, sometimes requiring many days of preparation and analysis and therefore diagnosis. Here, we present a novel high-throughput technique for the assessment of both activity and resistance development using an automated UV-Vis spectrophotometer and chemometric analysis [2]. The technique was developed using existing classical antimicrobial agents such as tetracycline and amoxicillin, and novel agents such as inorganic nanoparticles. An automated spectrophotometer allows testing of many samples over a specified time period, while chemometrics gives in-depth chemical information on the system and insight into the biochemical changes occurring in the cells and liquid media [3]. This work has focused on a range of Gram-positive and Gram-negative bacteria but may be expanded to the study of fungi and other organisms in the future. The outcomes of this work will allow for the rapid testing of novel antimicrobial agents with minimal input from the researcher and insight into the biochemical interactions in the systems.

  1. Review on Antimicrobial Resistance, “Tackling Drug-Resistant Infections Globally: Final report and recommendations - The review on antimicrobial resistance” (2016)
  2. Chapman, J., et al., “A high-throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV-visible spectrophotometry with principal component analysis” Biotechnology and Bioengineering, 118(4): p. 1511-1519 (2021)
  3. Alupoaei, C.E. and L.H. García-Rubio, “An Interpretation Model For The UV-VIS Spectra Of Microorganisms” Chem. Eng. Comm., 192(2): p. 198-218 (2005)