T5-04 Using Genome-scale Metabolic Modeling to Compare Strains of the Foodborne Pathogen Listeria monocytogenes

Monday, August 1, 2016: 2:15 PM
241 (America's Center - St. Louis)
Zachary Metz, University of Minnesota, St. Paul, MN
David Baumler, University of Minnesota-Twin Cities, St. Paul, MN
Introduction: Listeria monocytogenes is a microorganism of great concern for the food industry.  Using a combination of computational techniques and laboratory methods, genome-scale metabolic models (GEMs) can be created, validated, and used to simulate metabolic capabilities of microbes of interest—including L. monocytogenes.

Purpose: The objective of this study was to generate GEMs for six different strains of L. monocytogenes, and to compare nutrient utilization predictions of these models to in vitro results.

Methods: Genomes for the six different strains of L. monocytogenes were taken from the NCBI database and uploaded to KBase—a semi-automated program used to generate the GEMs.  These models were then used to generate nutrient utilization predictions for sources of carbon, nitrogen, phosphorus, and sulfur using General Algebraic Modeling System (GAMS) software.  In silico predictions were then compared to in vitro experiments performed using Biolog phenotypic microarray plates.  Following this comparison, GEMs were manually curated to increase agreement between in silico predictions and in vitro results.

Results: A total of 58 of the 95 carbon sources tested in vitro were present in the models, and; therefore, these were the compounds from which comparisons could be drawn.  Of these 58 compounds, agreement between in silico predictions and in vitro results ranged from 79.3% to 89.7% between strains.  For nitrogen, 62 of the 95 compounds were present, and agreement ranged from 59.7% to 66.1%.  For phosphorus and sulfur, 33 of the 94 compounds were comparable, and agreement ranged from 36.4% to 45.5%.

Significance: These findings are significant because they show that these GEMs for L. monocytogenes are comparable in agreement between in silico predictions and in vitro results to published models of other organisms.  Therefore, as with the other models—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.