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A Three-Tiered Study of Differences in Murine Intrahost Immune Response to Multiple Pneumococcal Strains.

Mochan-Keef E, Swigon D, Ermentrout GB, Clermont G - PLoS ONE (2015)

Bottom Line: We apply a previously developed 4-variable ordinary differential equation model of in-host immune response to pneumococcal pneumonia to study the variability of the immune response of MF1 mice and to explore bacteria-driven differences in disease progression and outcome.The model accurately reproduces infection kinetics in all cases and provides information about which mechanisms in the immune response have the greatest effect in each case.Results suggest that differences in the ability of bacteria to defeat immune response are primarily due to the ability of the bacteria to elude nonspecific clearance in the lung tissue as well as the ability to create damage to the lung epithelium.

View Article: PubMed Central - PubMed

Affiliation: Joint Carnegie Mellon University-University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, United States of America.

ABSTRACT
We apply a previously developed 4-variable ordinary differential equation model of in-host immune response to pneumococcal pneumonia to study the variability of the immune response of MF1 mice and to explore bacteria-driven differences in disease progression and outcome. In particular, we study the immune response to D39 strain of bacteria missing portions of the pneumolysin protein controlling either the hemolytic activity or complement-activating activity, the response to D39 bacteria deficient in either neuraminidase A or B, and the differences in the response to D39 (serotype 2), 0100993 (serotype 3), and TIGR4 (serotype 4) bacteria. The model accurately reproduces infection kinetics in all cases and provides information about which mechanisms in the immune response have the greatest effect in each case. Results suggest that differences in the ability of bacteria to defeat immune response are primarily due to the ability of the bacteria to elude nonspecific clearance in the lung tissue as well as the ability to create damage to the lung epithelium.

No MeSH data available.


Related in: MedlinePlus

Ensemble trajectories of neuraminidase study with intravenous infection data.Ensemble fits of each strain for lung pathogen (PL), blood pathogen (Pb), epithelial damage (D), and activated phagocytic cells (N). The black line represents the median trajectory, the inner dark gray area represents the 25th to 75th quantiles of trajectories, and the outer light gray envelope represents 90% of the trajectories (5th to 95th quantiles). Data points with standard deviations are represented by the black triangles with error bars. Data were taken at 0, 3, 6, 24, and 48 hours post-infection with five mice in each group. Trajectories are simulated over two days, with infection occurring on day 0 in the blood. The top row shows ensembles for NanA− bacteria, the middle row shows ensembles for NanB− bacteria, and the bottom row shows ensembles for the wild-type (WT) bacteria.
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pone.0134012.g004: Ensemble trajectories of neuraminidase study with intravenous infection data.Ensemble fits of each strain for lung pathogen (PL), blood pathogen (Pb), epithelial damage (D), and activated phagocytic cells (N). The black line represents the median trajectory, the inner dark gray area represents the 25th to 75th quantiles of trajectories, and the outer light gray envelope represents 90% of the trajectories (5th to 95th quantiles). Data points with standard deviations are represented by the black triangles with error bars. Data were taken at 0, 3, 6, 24, and 48 hours post-infection with five mice in each group. Trajectories are simulated over two days, with infection occurring on day 0 in the blood. The top row shows ensembles for NanA− bacteria, the middle row shows ensembles for NanB− bacteria, and the bottom row shows ensembles for the wild-type (WT) bacteria.

Mentions: Fig 4 demonstrates the ensemble solutions for the intravenous infection experiments. Bacteria are introduced into the blood at day 0 at an initial level of 105 CFU. NanA− bacteria are cleared from the blood within about 12 hours post-infection, and while they are able to reach the lungs relatively quickly, they are cleared from the tissue quickly as well. NanB− bacteria show an initial steep drop in blood levels as they move into the lungs. These bacteria are not fully cleared from the blood until about 2 days post-infection. While the lung bacteria levels have not been eliminated at this point, all trajectories will eventually lead to total clearance of bacteria from both compartments. Again, the wild-type bacteria are the most virulent in these experiments. These are the only bacteria able to cause significant damage, and as such the bacteria levels in both compartments rise over the first 12 hours until they hit a carrying capacity in the blood and cause morbidity of the host.


A Three-Tiered Study of Differences in Murine Intrahost Immune Response to Multiple Pneumococcal Strains.

Mochan-Keef E, Swigon D, Ermentrout GB, Clermont G - PLoS ONE (2015)

Ensemble trajectories of neuraminidase study with intravenous infection data.Ensemble fits of each strain for lung pathogen (PL), blood pathogen (Pb), epithelial damage (D), and activated phagocytic cells (N). The black line represents the median trajectory, the inner dark gray area represents the 25th to 75th quantiles of trajectories, and the outer light gray envelope represents 90% of the trajectories (5th to 95th quantiles). Data points with standard deviations are represented by the black triangles with error bars. Data were taken at 0, 3, 6, 24, and 48 hours post-infection with five mice in each group. Trajectories are simulated over two days, with infection occurring on day 0 in the blood. The top row shows ensembles for NanA− bacteria, the middle row shows ensembles for NanB− bacteria, and the bottom row shows ensembles for the wild-type (WT) bacteria.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4526468&req=5

pone.0134012.g004: Ensemble trajectories of neuraminidase study with intravenous infection data.Ensemble fits of each strain for lung pathogen (PL), blood pathogen (Pb), epithelial damage (D), and activated phagocytic cells (N). The black line represents the median trajectory, the inner dark gray area represents the 25th to 75th quantiles of trajectories, and the outer light gray envelope represents 90% of the trajectories (5th to 95th quantiles). Data points with standard deviations are represented by the black triangles with error bars. Data were taken at 0, 3, 6, 24, and 48 hours post-infection with five mice in each group. Trajectories are simulated over two days, with infection occurring on day 0 in the blood. The top row shows ensembles for NanA− bacteria, the middle row shows ensembles for NanB− bacteria, and the bottom row shows ensembles for the wild-type (WT) bacteria.
Mentions: Fig 4 demonstrates the ensemble solutions for the intravenous infection experiments. Bacteria are introduced into the blood at day 0 at an initial level of 105 CFU. NanA− bacteria are cleared from the blood within about 12 hours post-infection, and while they are able to reach the lungs relatively quickly, they are cleared from the tissue quickly as well. NanB− bacteria show an initial steep drop in blood levels as they move into the lungs. These bacteria are not fully cleared from the blood until about 2 days post-infection. While the lung bacteria levels have not been eliminated at this point, all trajectories will eventually lead to total clearance of bacteria from both compartments. Again, the wild-type bacteria are the most virulent in these experiments. These are the only bacteria able to cause significant damage, and as such the bacteria levels in both compartments rise over the first 12 hours until they hit a carrying capacity in the blood and cause morbidity of the host.

Bottom Line: We apply a previously developed 4-variable ordinary differential equation model of in-host immune response to pneumococcal pneumonia to study the variability of the immune response of MF1 mice and to explore bacteria-driven differences in disease progression and outcome.The model accurately reproduces infection kinetics in all cases and provides information about which mechanisms in the immune response have the greatest effect in each case.Results suggest that differences in the ability of bacteria to defeat immune response are primarily due to the ability of the bacteria to elude nonspecific clearance in the lung tissue as well as the ability to create damage to the lung epithelium.

View Article: PubMed Central - PubMed

Affiliation: Joint Carnegie Mellon University-University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, United States of America.

ABSTRACT
We apply a previously developed 4-variable ordinary differential equation model of in-host immune response to pneumococcal pneumonia to study the variability of the immune response of MF1 mice and to explore bacteria-driven differences in disease progression and outcome. In particular, we study the immune response to D39 strain of bacteria missing portions of the pneumolysin protein controlling either the hemolytic activity or complement-activating activity, the response to D39 bacteria deficient in either neuraminidase A or B, and the differences in the response to D39 (serotype 2), 0100993 (serotype 3), and TIGR4 (serotype 4) bacteria. The model accurately reproduces infection kinetics in all cases and provides information about which mechanisms in the immune response have the greatest effect in each case. Results suggest that differences in the ability of bacteria to defeat immune response are primarily due to the ability of the bacteria to elude nonspecific clearance in the lung tissue as well as the ability to create damage to the lung epithelium.

No MeSH data available.


Related in: MedlinePlus