Limits...
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 serotype 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 12, 24, and 48 hours post-infection with three 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 0100993 bacteria, the middle row shows ensembles for TIGR4 bacteria, and the bottom row shows ensembles for the D39 bacteria.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4526468&req=5

pone.0134012.g006: Ensemble trajectories of serotype 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 12, 24, and 48 hours post-infection with three 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 0100993 bacteria, the middle row shows ensembles for TIGR4 bacteria, and the bottom row shows ensembles for the D39 bacteria.

Mentions: Fig 6 shows the ensemble trajectories fit to data for D39, 0100993, and TIGR4 bacteria up to 48 hours post-infection. Trajectories generally fit data tightly, with most variation in predicted trajectories occurring in D and N. TIGR4 tend to create the most damage, while very little damage is seen for the D39 ensemble. Each strain varies significantly in the first 12 hours, represented by the first data point. 0100993 bacteria remain relatively high, exhibiting little nonspecific clearance. TIGR4 and D39 bacteria levels in the lung decrease by several orders of magnitude during the first 12 hours, showing both a greater susceptibility to this initial clearance and faster movement into the bloodstream.


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 serotype 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 12, 24, and 48 hours post-infection with three 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 0100993 bacteria, the middle row shows ensembles for TIGR4 bacteria, and the bottom row shows ensembles for the D39 bacteria.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0134012.g006: Ensemble trajectories of serotype 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 12, 24, and 48 hours post-infection with three 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 0100993 bacteria, the middle row shows ensembles for TIGR4 bacteria, and the bottom row shows ensembles for the D39 bacteria.
Mentions: Fig 6 shows the ensemble trajectories fit to data for D39, 0100993, and TIGR4 bacteria up to 48 hours post-infection. Trajectories generally fit data tightly, with most variation in predicted trajectories occurring in D and N. TIGR4 tend to create the most damage, while very little damage is seen for the D39 ensemble. Each strain varies significantly in the first 12 hours, represented by the first data point. 0100993 bacteria remain relatively high, exhibiting little nonspecific clearance. TIGR4 and D39 bacteria levels in the lung decrease by several orders of magnitude during the first 12 hours, showing both a greater susceptibility to this initial clearance and faster movement into the bloodstream.

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