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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

Analysis of bacteria-dependent parameters in the sesrotype study.(A) One-dimensional parameter distributions of bacteria-dependent parameters: h, q, a, ν, ξnl, ξnb. The top row (green) shows ensembles for 0100993 bacteria. The middle row (red) shows ensembles for TIGR4 bacteria. The bottom row (blue) shows ensembles for D39) bacteria. (B) Principal values computed from singular-value decomposition of ensembles for each bacterial strain. (C-E) Composition of the last principal component for 0100993 (C) TIGR4 (D) and wild-type (E) bacteria. Warm colors represent a negative contribution to sensitivity, and cool colors represent a positive contribution.
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pone.0134012.g007: Analysis of bacteria-dependent parameters in the sesrotype study.(A) One-dimensional parameter distributions of bacteria-dependent parameters: h, q, a, ν, ξnl, ξnb. The top row (green) shows ensembles for 0100993 bacteria. The middle row (red) shows ensembles for TIGR4 bacteria. The bottom row (blue) shows ensembles for D39) bacteria. (B) Principal values computed from singular-value decomposition of ensembles for each bacterial strain. (C-E) Composition of the last principal component for 0100993 (C) TIGR4 (D) and wild-type (E) bacteria. Warm colors represent a negative contribution to sensitivity, and cool colors represent a positive contribution.

Mentions: Fig 7A shows the distributions of bacteria-strain-dependent parameters for each of the three serotypes. The largest disparities between strains exist in distributions for a, ξnl, ξnb and ν populations. 0100993 bacteria tend to have a low value for a, the damage-independent movement of bacteria from the bloodstream to the tissue. Since these serotype 3 bacteria typically remain higher in the lung tissue than in the blood, we would expect the effect of this motion to be minimal. In contrast, TIGR4 bacteria tend to have a high a value, as these bacteria readily cause sepsis. D39, known to cause both severe pneumonia and sepsis in MF1 mice, have a values concentrated between these two extremes.


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)

Analysis of bacteria-dependent parameters in the sesrotype study.(A) One-dimensional parameter distributions of bacteria-dependent parameters: h, q, a, ν, ξnl, ξnb. The top row (green) shows ensembles for 0100993 bacteria. The middle row (red) shows ensembles for TIGR4 bacteria. The bottom row (blue) shows ensembles for D39) bacteria. (B) Principal values computed from singular-value decomposition of ensembles for each bacterial strain. (C-E) Composition of the last principal component for 0100993 (C) TIGR4 (D) and wild-type (E) bacteria. Warm colors represent a negative contribution to sensitivity, and cool colors represent a positive contribution.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0134012.g007: Analysis of bacteria-dependent parameters in the sesrotype study.(A) One-dimensional parameter distributions of bacteria-dependent parameters: h, q, a, ν, ξnl, ξnb. The top row (green) shows ensembles for 0100993 bacteria. The middle row (red) shows ensembles for TIGR4 bacteria. The bottom row (blue) shows ensembles for D39) bacteria. (B) Principal values computed from singular-value decomposition of ensembles for each bacterial strain. (C-E) Composition of the last principal component for 0100993 (C) TIGR4 (D) and wild-type (E) bacteria. Warm colors represent a negative contribution to sensitivity, and cool colors represent a positive contribution.
Mentions: Fig 7A shows the distributions of bacteria-strain-dependent parameters for each of the three serotypes. The largest disparities between strains exist in distributions for a, ξnl, ξnb and ν populations. 0100993 bacteria tend to have a low value for a, the damage-independent movement of bacteria from the bloodstream to the tissue. Since these serotype 3 bacteria typically remain higher in the lung tissue than in the blood, we would expect the effect of this motion to be minimal. In contrast, TIGR4 bacteria tend to have a high a value, as these bacteria readily cause sepsis. D39, known to cause both severe pneumonia and sepsis in MF1 mice, have a values concentrated between these two extremes.

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