Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes.
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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC6012718?pdf=render |
| _version_ | 1819109264212361216 |
|---|---|
| author | Zachary P Metz Tong Ding David J Baumler |
| author_facet | Zachary P Metz Tong Ding David J Baumler |
| author_sort | Zachary P Metz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T03:23:03Z |
| format | Article |
| id | doaj.art-1d1f1ba0e974408dbfc8cc714cc6c748 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T03:23:03Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-1d1f1ba0e974408dbfc8cc714cc6c7482022-12-21T18:40:40ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01136e019858410.1371/journal.pone.0198584Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes.Zachary P MetzTong DingDavid J BaumlerListeria 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.http://europepmc.org/articles/PMC6012718?pdf=render |
| spellingShingle | Zachary P Metz Tong Ding David J Baumler Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes. PLoS ONE |
| title | Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes. |
| title_full | Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes. |
| title_fullStr | Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes. |
| title_full_unstemmed | Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes. |
| title_short | Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes. |
| title_sort | using genome scale metabolic models to compare serovars of the foodborne pathogen listeria monocytogenes |
| url | http://europepmc.org/articles/PMC6012718?pdf=render |
| work_keys_str_mv | AT zacharypmetz usinggenomescalemetabolicmodelstocompareserovarsofthefoodbornepathogenlisteriamonocytogenes AT tongding usinggenomescalemetabolicmodelstocompareserovarsofthefoodbornepathogenlisteriamonocytogenes AT davidjbaumler usinggenomescalemetabolicmodelstocompareserovarsofthefoodbornepathogenlisteriamonocytogenes |