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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 intranasal 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, 2, 4, 6, 12, 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 lungs. 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.g003: Ensemble trajectories of neuraminidase study with intranasal 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, 2, 4, 6, 12, 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 lungs. 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 3 shows the ensemble fits to intranasal infection data for wild-type, NanA−, and NanB− D39 bacteria. NanA− bacteria are unable to adequately bind to the epithelium, and they are cleared from the lungs within 12 hours. NanA− bacteria are unable to cause any appreciable damage or sustain a population in the blood for more than a few hours in our predicted trajectories. Experiments verified that these bacteria were not detected in the blood at any point in the experiments. NanB− bacteria can persist in the lungs longer than NanA−, but these bacteria will eventually clear as well. While our ensembles show some presence of bacteria in the blood, these bacteria are cleared within about one day, thus not causing severe bacteremia, again aligning with the findings of the authors [23]. The wild-type bacteria are highly virulent, causing sepsis and eventual death to the mice about 1 day post-infection. Our ensembles match the lung data well and show a quick rise in blood bacteria levels as well as epithelial damage. Though the activated phagocytic cell population is highest in the simulated wild-type bacteria ensemble, these cells are unable to contain the bacterial population in either compartment.


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 intranasal 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, 2, 4, 6, 12, 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 lungs. 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

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

pone.0134012.g003: Ensemble trajectories of neuraminidase study with intranasal 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, 2, 4, 6, 12, 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 lungs. 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 3 shows the ensemble fits to intranasal infection data for wild-type, NanA−, and NanB− D39 bacteria. NanA− bacteria are unable to adequately bind to the epithelium, and they are cleared from the lungs within 12 hours. NanA− bacteria are unable to cause any appreciable damage or sustain a population in the blood for more than a few hours in our predicted trajectories. Experiments verified that these bacteria were not detected in the blood at any point in the experiments. NanB− bacteria can persist in the lungs longer than NanA−, but these bacteria will eventually clear as well. While our ensembles show some presence of bacteria in the blood, these bacteria are cleared within about one day, thus not causing severe bacteremia, again aligning with the findings of the authors [23]. The wild-type bacteria are highly virulent, causing sepsis and eventual death to the mice about 1 day post-infection. Our ensembles match the lung data well and show a quick rise in blood bacteria levels as well as epithelial damage. Though the activated phagocytic cell population is highest in the simulated wild-type bacteria ensemble, these cells are unable to contain the bacterial population in either compartment.

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