Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes

Zachary P. Metz, Tong Ding, David J. Baumler

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Listeria monocytogenes is a microorganism of great concern for the food industry and the cause of human foodborne disease. Therefore, novel methods of control are needed, and systems biology is one such approach to identify them. Using a combination of computational techniques and laboratory methods, genome-scale metabolic models (GEMs) can be created, validated, and used to simulate growth environments and discern metabolic capabilities of microbes of interest, including L. monocytogenes. The objective of the work presented here was to generate GEMs for six different strains of L. monocytogenes, and to both qualitatively and quantitatively validate these GEMs with experimental data to examine the diversity of metabolic capabilities of numerous strains from the three different serovar groups most associated with foodborne outbreaks and human disease. Following qualitative validation, 57 of the 95 carbon sources tested experimentally were present in the GEMs, and; therefore, these were the compounds from which comparisons could be drawn. Of these 57 compounds, agreement between in silico predictions and in vitro results for carbon source utilization ranged from 80.7% to 91.2% between strains. Nutrient utilization agreement between in silico predictions and in vitro results were also conducted for numerous nitrogen, phosphorous, and sulfur sources. Additionally, quantitative validation showed that the L. monocytogenes GEMs were able to generate in silico predictions for growth rate and growth yield that were strongly and significantly (p < 0.0013 and p < 0.0015, respectively) correlated with experimental results. These findings are significant because they show that these GEMs for L. monocytogenes are comparable to published GEMs of other organisms for agreement between in silico predictions and in vitro results. Therefore, as with the other GEMs, namely those for Escherichia coli, Staphylococcus aureus, Vibrio vulnificus, and Salmonella spp., they can be used to determine new methods of growth control and disease treatment.

Original languageEnglish (US)
Article numbere0198584
JournalPloS one
Issue number6
StatePublished - Jun 2018

Bibliographical note

Funding Information:
We would like to thank the University of Minnesota and the Department of Food Science and Nutrition and the Grant-In-Aid award #147543 (DJB), for providing the funds needed to perform and complete this study. In addition, part of this work was supported by a MN Drive Graduate Student Fellowship (ZM).

Publisher Copyright:
© 2018 Metz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Dive into the research topics of 'Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes'. Together they form a unique fingerprint.

Cite this