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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 neuraminidase study.(A) One-dimensional parameter distributions of bacteria-dependent parameters: q, a, ν, ξnl, ξnb. The top row (green) shows ensembles for NanA− bacteria. The middle row (red) shows ensembles for NanB− bacteria. The bottom row (blue) shows ensembles for wild-type (WT) bacteria. (B) Principal values computed from singular-value decomposition of ensembles for each bacterial strain. (C-E) Two-dimensional parameter correlations for NanA− (C) NanB− (D) and wild-type (E) bacteria.
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pone.0134012.g005: Analysis of bacteria-dependent parameters in the neuraminidase study.(A) One-dimensional parameter distributions of bacteria-dependent parameters: q, a, ν, ξnl, ξnb. The top row (green) shows ensembles for NanA− bacteria. The middle row (red) shows ensembles for NanB− bacteria. The bottom row (blue) shows ensembles for wild-type (WT) bacteria. (B) Principal values computed from singular-value decomposition of ensembles for each bacterial strain. (C-E) Two-dimensional parameter correlations for NanA− (C) NanB− (D) and wild-type (E) bacteria.

Mentions: Distributions of our bacteria-dependent parameters show marked differences across these three strains (Fig 5A). NanA− bacteria are essentially insensitive to q and a, as the bacteria are unable to maintain their population for more than a few hours in either the intranasal or the intravenous experiments. Clearance rates of the NanA− bacteria both by nonspecific means (ν) and by phagocytic cells (ξnl, ξnb) tend towards the upper end of the spectrum, meaning these bacteria are easily cleared by the immune system. This result is further verified by the evidence that NanA prevents opsonization by neutrophils [28].


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 neuraminidase study.(A) One-dimensional parameter distributions of bacteria-dependent parameters: q, a, ν, ξnl, ξnb. The top row (green) shows ensembles for NanA− bacteria. The middle row (red) shows ensembles for NanB− bacteria. The bottom row (blue) shows ensembles for wild-type (WT) bacteria. (B) Principal values computed from singular-value decomposition of ensembles for each bacterial strain. (C-E) Two-dimensional parameter correlations for NanA− (C) NanB− (D) and wild-type (E) bacteria.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0134012.g005: Analysis of bacteria-dependent parameters in the neuraminidase study.(A) One-dimensional parameter distributions of bacteria-dependent parameters: q, a, ν, ξnl, ξnb. The top row (green) shows ensembles for NanA− bacteria. The middle row (red) shows ensembles for NanB− bacteria. The bottom row (blue) shows ensembles for wild-type (WT) bacteria. (B) Principal values computed from singular-value decomposition of ensembles for each bacterial strain. (C-E) Two-dimensional parameter correlations for NanA− (C) NanB− (D) and wild-type (E) bacteria.
Mentions: Distributions of our bacteria-dependent parameters show marked differences across these three strains (Fig 5A). NanA− bacteria are essentially insensitive to q and a, as the bacteria are unable to maintain their population for more than a few hours in either the intranasal or the intravenous experiments. Clearance rates of the NanA− bacteria both by nonspecific means (ν) and by phagocytic cells (ξnl, ξnb) tend towards the upper end of the spectrum, meaning these bacteria are easily cleared by the immune system. This result is further verified by the evidence that NanA prevents opsonization by neutrophils [28].

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