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Predicting the Role of IL-10 in the Regulation of the Adaptive Immune Responses in Mycobacterium avium Subsp. paratuberculosis Infections Using Mathematical Models.

Magombedze G, Eda S, Stabel J - PLoS ONE (2015)

Bottom Line: The Th1 response wanes with disease progression and is supplanted by a non-protective humoral immune response (Th2-type).We tested our models with IL-4, IL-10, IFN-γ, and MAP fecal shedding data collected from calves that were experimentally infected and followed over a period of 360 days in the study of Stabel and Robbe-Austerman (2011).In these predicted roles, suppression of Th1 responses was correlated with increased number of MAP.

View Article: PubMed Central - PubMed

Affiliation: National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Tennessee, 37996-1527, United States of America.

ABSTRACT
Mycobacterium avium subsp. paratuberculosis (MAP) is an intracellular bacterial pathogen that causes Johne's disease (JD) in cattle and other animals. The hallmark of MAP infection in the early stages is a strong protective cell-mediated immune response (Th1-type), characterized by antigen-specific γ-interferon (IFN-γ). The Th1 response wanes with disease progression and is supplanted by a non-protective humoral immune response (Th2-type). Interleukin-10 (IL-10) is believed to play a critical role in the regulation of host immune responses to MAP infection and potentially orchestrate the reversal of Th1/Th2 immune dominance during disease progression. However, how its role correlates with MAP infection remains to be completely deciphered. We developed mathematical models to explain probable mechanisms for IL-10 involvement in MAP infection. We tested our models with IL-4, IL-10, IFN-γ, and MAP fecal shedding data collected from calves that were experimentally infected and followed over a period of 360 days in the study of Stabel and Robbe-Austerman (2011). Our models predicted that IL-10 can have different roles during MAP infection, (i) it can suppress the Th1 expression, (ii) can enhance Th2 (IL-4) expression, and (iii) can suppress the Th1 expression in synergy with IL-4. In these predicted roles, suppression of Th1 responses was correlated with increased number of MAP. We also predicted that Th1-mediated responses (IFN-γ) can lead to high expression of IL-10 and that infection burden regulates Th2 suppression by the Th1 response. Our models highlight areas where more experimental data is required to refine our model assumptions, and further test and investigate the role of IL-10 in MAP infection.

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Cell compartmental model data fitting.The best fit for Calf A was obtained with a model that assumed IL-10 inhibits Th1 response expansion. Calf B data was best explained by a model that assumed IL-10 enhances Th2 proliferation with Th1 and Th2 differentiation cross inhibition. To obtain the best fit for Calf C, a model that assumed Th1 inhibition by IL-10 and Th2 inhibition by Th1 was used. The estimated parameters are given in Table 3 and the parameters that were fixed during model fitting are given in Table 2.
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pone.0141539.g003: Cell compartmental model data fitting.The best fit for Calf A was obtained with a model that assumed IL-10 inhibits Th1 response expansion. Calf B data was best explained by a model that assumed IL-10 enhances Th2 proliferation with Th1 and Th2 differentiation cross inhibition. To obtain the best fit for Calf C, a model that assumed Th1 inhibition by IL-10 and Th2 inhibition by Th1 was used. The estimated parameters are given in Table 3 and the parameters that were fixed during model fitting are given in Table 2.

Mentions: The data can also be explained using the cell compartmental model as illustrated in Fig 3. The patterns observed in the data can be explained using a combination of immune response, infection and shedding parameters (see Table 3). The parameters ki, kb, λ1, and δm were predicted to be essential to explain data for all calves. Also the parameters θ2, ucf, p2, δTreg, δB, were predicted to be either relevant or not relevant between the calves. In Table 3, a dash (-) implies that a given parameter was not essential to explain the data kinetics of a given calf. The magnitude of the parameter quantifies the importance of the parameter or the influence of the associated biological mechanism in the dynamics, hence predicting different and similar biological processes between the animals. These predicted parameters can be grouped into 3 categories (i) the infection parameters (ki, kb), (ii) immune response parameters (δm, δB, δTreg, θ2, u1, u2, p2) and (iii) the MAP bacteria shedding parameters (λ1, ucf). In our model fitting, we assumed that IL-10 is produced by Treg cells indirectly through the density of infected macrophages (see S2 Table for more details on how this term was selected) [24, 29], that is IL-10 is assumed to be generated in proportion to the population of infected macrophages using either a mass action term (or density dependent term) between Th0 cells and infected macrophages. Through this analysis we identified that different roles of IL-10 can generate different immune responses. Calf A, was best explained with a model with IL-10 inhibiting Th1 cell differentiation, Calf B data was best matched when IL-10 promoted Th2 cell proliferation and in Calf C, when IL10 enhanced Th2 cell differentiation. We also tested these models against all the calves (see Table 4) and in some cases we could not find statistical evidence to select one model over the others, however, through visiual exmanination of the fits we could clearly discriminate the best models (see S1 Fig for an illustration of the visiual examination). Also, we observe (S3 Fig) similar predictions when we fit for the initial populations of uninfected and infected macrophages, free bacteria and naïve T cells. However, fixing the initial populations of these cells at the same values during fitting gives a better platform for comparing mechanisms between the animals, since altering initial conditions affects the transient kinetics of the disease but not its long term dynamics (see S4 Fig).


Predicting the Role of IL-10 in the Regulation of the Adaptive Immune Responses in Mycobacterium avium Subsp. paratuberculosis Infections Using Mathematical Models.

Magombedze G, Eda S, Stabel J - PLoS ONE (2015)

Cell compartmental model data fitting.The best fit for Calf A was obtained with a model that assumed IL-10 inhibits Th1 response expansion. Calf B data was best explained by a model that assumed IL-10 enhances Th2 proliferation with Th1 and Th2 differentiation cross inhibition. To obtain the best fit for Calf C, a model that assumed Th1 inhibition by IL-10 and Th2 inhibition by Th1 was used. The estimated parameters are given in Table 3 and the parameters that were fixed during model fitting are given in Table 2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664406&req=5

pone.0141539.g003: Cell compartmental model data fitting.The best fit for Calf A was obtained with a model that assumed IL-10 inhibits Th1 response expansion. Calf B data was best explained by a model that assumed IL-10 enhances Th2 proliferation with Th1 and Th2 differentiation cross inhibition. To obtain the best fit for Calf C, a model that assumed Th1 inhibition by IL-10 and Th2 inhibition by Th1 was used. The estimated parameters are given in Table 3 and the parameters that were fixed during model fitting are given in Table 2.
Mentions: The data can also be explained using the cell compartmental model as illustrated in Fig 3. The patterns observed in the data can be explained using a combination of immune response, infection and shedding parameters (see Table 3). The parameters ki, kb, λ1, and δm were predicted to be essential to explain data for all calves. Also the parameters θ2, ucf, p2, δTreg, δB, were predicted to be either relevant or not relevant between the calves. In Table 3, a dash (-) implies that a given parameter was not essential to explain the data kinetics of a given calf. The magnitude of the parameter quantifies the importance of the parameter or the influence of the associated biological mechanism in the dynamics, hence predicting different and similar biological processes between the animals. These predicted parameters can be grouped into 3 categories (i) the infection parameters (ki, kb), (ii) immune response parameters (δm, δB, δTreg, θ2, u1, u2, p2) and (iii) the MAP bacteria shedding parameters (λ1, ucf). In our model fitting, we assumed that IL-10 is produced by Treg cells indirectly through the density of infected macrophages (see S2 Table for more details on how this term was selected) [24, 29], that is IL-10 is assumed to be generated in proportion to the population of infected macrophages using either a mass action term (or density dependent term) between Th0 cells and infected macrophages. Through this analysis we identified that different roles of IL-10 can generate different immune responses. Calf A, was best explained with a model with IL-10 inhibiting Th1 cell differentiation, Calf B data was best matched when IL-10 promoted Th2 cell proliferation and in Calf C, when IL10 enhanced Th2 cell differentiation. We also tested these models against all the calves (see Table 4) and in some cases we could not find statistical evidence to select one model over the others, however, through visiual exmanination of the fits we could clearly discriminate the best models (see S1 Fig for an illustration of the visiual examination). Also, we observe (S3 Fig) similar predictions when we fit for the initial populations of uninfected and infected macrophages, free bacteria and naïve T cells. However, fixing the initial populations of these cells at the same values during fitting gives a better platform for comparing mechanisms between the animals, since altering initial conditions affects the transient kinetics of the disease but not its long term dynamics (see S4 Fig).

Bottom Line: The Th1 response wanes with disease progression and is supplanted by a non-protective humoral immune response (Th2-type).We tested our models with IL-4, IL-10, IFN-γ, and MAP fecal shedding data collected from calves that were experimentally infected and followed over a period of 360 days in the study of Stabel and Robbe-Austerman (2011).In these predicted roles, suppression of Th1 responses was correlated with increased number of MAP.

View Article: PubMed Central - PubMed

Affiliation: National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Tennessee, 37996-1527, United States of America.

ABSTRACT
Mycobacterium avium subsp. paratuberculosis (MAP) is an intracellular bacterial pathogen that causes Johne's disease (JD) in cattle and other animals. The hallmark of MAP infection in the early stages is a strong protective cell-mediated immune response (Th1-type), characterized by antigen-specific γ-interferon (IFN-γ). The Th1 response wanes with disease progression and is supplanted by a non-protective humoral immune response (Th2-type). Interleukin-10 (IL-10) is believed to play a critical role in the regulation of host immune responses to MAP infection and potentially orchestrate the reversal of Th1/Th2 immune dominance during disease progression. However, how its role correlates with MAP infection remains to be completely deciphered. We developed mathematical models to explain probable mechanisms for IL-10 involvement in MAP infection. We tested our models with IL-4, IL-10, IFN-γ, and MAP fecal shedding data collected from calves that were experimentally infected and followed over a period of 360 days in the study of Stabel and Robbe-Austerman (2011). Our models predicted that IL-10 can have different roles during MAP infection, (i) it can suppress the Th1 expression, (ii) can enhance Th2 (IL-4) expression, and (iii) can suppress the Th1 expression in synergy with IL-4. In these predicted roles, suppression of Th1 responses was correlated with increased number of MAP. We also predicted that Th1-mediated responses (IFN-γ) can lead to high expression of IL-10 and that infection burden regulates Th2 suppression by the Th1 response. Our models highlight areas where more experimental data is required to refine our model assumptions, and further test and investigate the role of IL-10 in MAP infection.

Show MeSH
Related in: MedlinePlus