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Updated: Jul 11, 2026
Evaluation of Host-Pathogen Responses and Vaccine Efficacy in Mice
Published on: February 22, 2019
Quantifying the relationship between multiple immunological parameters and host resistance: probing the limits of
D Keil1, R W Luebke, S B Pruett
1Department of Biological Sciences, Mississippi State University, Mississippi State, MS 39762, USA.
Abstract:
Although reductionist experimental designs are excellent for identifying cells, molecules, or functions involved in resistance to particular microbes or cancer cells, they do not provide an integrated, quantitative view of immune function. In the present study, mice were treated with either dexamethasone (DEX) or cyclosporin A (CyA), and immune function and host resistance were evaluated. Multivariate statistical methods were used to describe the relative importance of a broad range of immunological parameters for host resistance in mice treated with various dosages of DEX. Multiple regression and logistic regression analysis indicated that changes in 24 immunological parameters explained a substantial portion of the changes in resistance to B16F10 tumor cells or streptococcus group B. However, at least 40% of the change in host resistance remained unexplained. DEX at all dosages substantially suppressed numerous relevant immunological parameters, but significantly decreased resistance to Listeria monocytogenes only at the highest dosage. In contrast, CyA substantially decreased resistance to L. monocytogenes at dosages that caused relatively minor suppression of just a few immunological parameters (unfortunately, CyA data and host resistance data for L. monocytogenes were not suitable for multivariate analysis). These results illustrate that mathematical models can be used to explain changes in host resistance on the basis of changes in immune parameters, and that moderate changes in relevant immunological parameters may not produce the types of changes in host resistance expected on the basis of results from reductionist experimental designs.
Insights
Quantitative analysis reveals that while immune parameters predict host resistance, a significant portion remains unexplained. Mathematical models show moderate immune changes may not alter resistance as expected from reductionist studies.
Area of Science:
- Immunology
- Mathematical Biology
- Pharmacology
Background:
- Reductionist experimental designs identify specific immune components but lack a holistic view of host resistance.
- Understanding the quantitative relationship between immune function and host resistance is crucial for effective therapeutic strategies.
Purpose of the Study:
- To evaluate immune function and host resistance in mice treated with dexamethasone (DEX) or cyclosporin A (CyA).
- To utilize multivariate statistical methods to determine the importance of immunological parameters in host resistance.
- To compare the effects of DEX and CyA on host resistance and immune parameters.
Main Methods:
- Mice were treated with varying dosages of DEX or CyA.
- Immune function and host resistance were assessed.
- Multivariate statistical analyses, including multiple and logistic regression, were applied to 24 immunological parameters.
Main Results:
- Changes in 24 immunological parameters explained a significant portion of host resistance variation against B16F10 tumor cells and Streptococcus group B.
- However, over 40% of the change in host resistance remained unexplained by these parameters.
- Dexamethasone (DEX) suppressed multiple immune parameters but only decreased resistance to Listeria monocytogenes at high doses.
- Cyclosporin A (CyA) significantly reduced resistance to L. monocytogenes with minimal suppression of immune parameters.
Conclusions:
- Mathematical models can quantitatively link changes in immune parameters to host resistance.
- Moderate alterations in immunological parameters may not predictably alter host resistance as suggested by reductionist approaches.
- Integrated, quantitative assessments are necessary to fully understand immune function and host resistance dynamics.
