A systems biology approach to investigate the antimicrobial activity of oleuropein

Authors: LI, XIANHUA - Villanova University; Liu, Yanhong; JIA, QIAN - Rowan University; LAMACCHIA, VIRGINIA - Villanova University; O'DONOGHUE, KATHRYN - Villanova University; HUANG, ZUYI - Villanova University

Submitted to: Journal of Industrial Microbiology and Biotechnology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/23/2016
Publication Date: 12/1/2016
Citation: Li, X., Liu, Y., Jia, Q., Lamacchia, V., O'Donoghue, K., Huang, Z. 2016. A systems biology approach to investigate the antimicrobial activity of oleuropein. Journal of Industrial Microbiology and Biotechnology. 43(12):1705-1717.

Interpretive Summary: Staphylococcus aureus is a pathogenic bacterium that causes foodborne illness and outbreaks. Olive leaf extract is an herbal supplement that boosts immunity, and it also can destroy pathogenic bacteria. Oleuropein is the key compound in olive leaf extract that has these activities. In this study, a modeling approach based on genomic (relating to the complete set of DNA of an organism) information from S. aureus was developed to predict the systems-level metabolic changes in S. aureus triggered by oleuropein. The model identified S. aureus enzymes that were affected by the treatment with oleuropein. Thus, this genome scale modeling approach has the potential to be used for oleuropein dose optimization to control foodborne pathogens.

Technical Abstract: Oleuropein and its hydrolysis products are olive phenolic compounds that have antimicrobial effects on a variety of pathogens, with the potential to be utilized in food and pharmaceutical products. While the existing research is mainly focused on individual genes or enzymes that are regulated by oleuropein for antimicrobial activities, little work has been done to integrate genes, enzymes and metabolic reactions for a systematic investigation of the antimicrobial mechanism of oleuropein. In this study, the first genome-scale modeling method was developed to predict the systems-level changes of intracellular metabolism triggered by oleuropein in Staphylococcus aureus, a common food-borne pathogen. In order to simulate the antimicrobial effect, an existing S. aureus genome-scale metabolic model was extended by adding the missing nitric oxide reactions, and the exchange rates of potassium, phosphate and glutamate were adjusted in the model as suggested by previous research to mimic the stress imposed by oleuropein on S. aureus. The developed modeling approach was able to match S. aureus growth rates with experimental data for five oleuropein concentrations. The reactions with large flux changes were identified and the enzymes of fifteen of these reactions were validated by existing research for their important roles in oleuropein metabolism. When compared with experimental data, the changes in expression of the genes encoding 80% of these enzymes were correctly predicted by our modeling approach. This study indicates that the genome-scale modeling approach provides a promising avenue for revealing the intracellular metabolism of oleuropein antimicrobial properties.