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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 pneumolysin activity study.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, 12, 24, and 48 hours post-infection with ten mice in each group. Trajectories are simulated over two days, with infection occurring on day 0. The top row shows ensembles for H+/C- bacteria, the middle row shows ensembles for H2-/C+ bacteria, and the bottom row shows ensembles for the wild-type (WT) bacteria.
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pone.0134012.g001: Ensemble trajectories of pneumolysin activity study.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, 12, 24, and 48 hours post-infection with ten mice in each group. Trajectories are simulated over two days, with infection occurring on day 0. The top row shows ensembles for H+/C- bacteria, the middle row shows ensembles for H2-/C+ bacteria, and the bottom row shows ensembles for the wild-type (WT) bacteria.

Mentions: Fig 1 displays the ensemble fits to data for wild-type, H+/C-, and H2-/C+ D39 bacteria. On each ensemble trajectory plot, we represent the median trajectory as a solid black line, with the 25–75% quantiles in dark gray and 5–95% percentiles in light gray. Mean experimental data are represented by the black triangles, with standard deviations presented by the error bars.


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 pneumolysin activity study.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, 12, 24, and 48 hours post-infection with ten mice in each group. Trajectories are simulated over two days, with infection occurring on day 0. The top row shows ensembles for H+/C- bacteria, the middle row shows ensembles for H2-/C+ 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.g001: Ensemble trajectories of pneumolysin activity study.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, 12, 24, and 48 hours post-infection with ten mice in each group. Trajectories are simulated over two days, with infection occurring on day 0. The top row shows ensembles for H+/C- bacteria, the middle row shows ensembles for H2-/C+ bacteria, and the bottom row shows ensembles for the wild-type (WT) bacteria.
Mentions: Fig 1 displays the ensemble fits to data for wild-type, H+/C-, and H2-/C+ D39 bacteria. On each ensemble trajectory plot, we represent the median trajectory as a solid black line, with the 25–75% quantiles in dark gray and 5–95% percentiles in light gray. Mean experimental data are represented by the black triangles, with standard deviations presented by the error bars.

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