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Immune Tolerance Maintained by Cooperative Interactions between T Cells and Antigen Presenting Cells Shapes a Diverse TCR Repertoire.

Best K, Chain B, Watkins C - Front Immunol (2015)

Bottom Line: We show further that the size of individual clones in the model repertoire becomes heterogeneous, and that new clones can establish themselves even when the repertoire has stabilized.Our study combines the salient features of the "danger" model of self/non-self discrimination with the concepts of quorum sensing, and extends repertoire generation models to encompass the establishment of tolerance.Furthermore, the dynamic and continuous repertoire reshaping, which underlies tolerance in this model, suggests opportunities for therapeutic intervention to achieve long-term tolerance following transplantation.

View Article: PubMed Central - PubMed

Affiliation: Division of Infection and Immunity, University College London , London , UK ; Centre for Mathematics, Physics and Engineering in the Life Sciences and Experimental Biology (CoMPLEX), University College London , London , UK.

ABSTRACT
The T cell population in an individual needs to avoid harmful activation by self peptides while maintaining the ability to respond to an unknown set of foreign peptides. This property is acquired by a combination of thymic and extra-thymic mechanisms. We extend current models for the development of self/non-self discrimination to consider the acquisition of self-tolerance as an emergent system level property of the overall T cell receptor repertoire. We propose that tolerance is established at the level of the antigen presenting cell/T cell cluster, which facilitates and integrates cooperative interactions between T cells of different specificities. The threshold for self-reactivity is therefore imposed at a population level, and not at the level of the individual T cell/antigen encounter. Mathematically, the model can be formulated as a linear programing optimization problem that can be implemented as a multiplicative update algorithm, which shows a rapid convergence to a stable state. The model constrains self-reactivity within a predefined threshold, but maintains repertoire diversity and cross reactivity which are key characteristics of human T cell immunity. We show further that the size of individual clones in the model repertoire becomes heterogeneous, and that new clones can establish themselves even when the repertoire has stabilized. Our study combines the salient features of the "danger" model of self/non-self discrimination with the concepts of quorum sensing, and extends repertoire generation models to encompass the establishment of tolerance. Furthermore, the dynamic and continuous repertoire reshaping, which underlies tolerance in this model, suggests opportunities for therapeutic intervention to achieve long-term tolerance following transplantation.

No MeSH data available.


Related in: MedlinePlus

Broad coverage to non-self is maintained during the development of a self-tolerance repertoire. (A) The mean (±standard deviation) total T cell response to self (blue) or non-self (green) pMHC profiles over time with N = 2,000 and M = 200. (B) After 30,000 iterations of the update algorithm with parameters as in (A), the distribution of total T cell response to self (blue) and non-self (green) pMHC profiles. (C) The ability of the repertoire to successfully mount an immune response to non-self pMHC profiles, measured as the average total response to a non-self profile divided by the average total response to a self profile, over time. The number of T cell clonotypes in a simulation is indicated by color, with the number of self profiles simulated ranging between 100 and 800. (D) The relationship between number of T cell clonotypes and the average total response to a non-self profile divided by the average total response to a self profile after 30,000 iterations of the update algorithm. (E) The proportion of non-self profiles that have a total T cell response greater than the mean response toward self profiles over time. The number of T cell clonotypes is indicated by color. (F) The relationship between the number of T cell clonotypes and the proportion of non-self profiles having a stronger total T cell response than the mean response to self profiles after 30,000 cycles of the update algorithm. Other model parameters for all panels are: self-response threshold τ = 1, growth rate ν = ln 2 δt–1, learning rate η = 0.002001 δt–1 and proportion of non-zero affinities γ = 0.01.
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Figure 3: Broad coverage to non-self is maintained during the development of a self-tolerance repertoire. (A) The mean (±standard deviation) total T cell response to self (blue) or non-self (green) pMHC profiles over time with N = 2,000 and M = 200. (B) After 30,000 iterations of the update algorithm with parameters as in (A), the distribution of total T cell response to self (blue) and non-self (green) pMHC profiles. (C) The ability of the repertoire to successfully mount an immune response to non-self pMHC profiles, measured as the average total response to a non-self profile divided by the average total response to a self profile, over time. The number of T cell clonotypes in a simulation is indicated by color, with the number of self profiles simulated ranging between 100 and 800. (D) The relationship between number of T cell clonotypes and the average total response to a non-self profile divided by the average total response to a self profile after 30,000 iterations of the update algorithm. (E) The proportion of non-self profiles that have a total T cell response greater than the mean response toward self profiles over time. The number of T cell clonotypes is indicated by color. (F) The relationship between the number of T cell clonotypes and the proportion of non-self profiles having a stronger total T cell response than the mean response to self profiles after 30,000 cycles of the update algorithm. Other model parameters for all panels are: self-response threshold τ = 1, growth rate ν = ln 2 δt–1, learning rate η = 0.002001 δt–1 and proportion of non-zero affinities γ = 0.01.

Mentions: A successful T cell population needs to be able to control immune response to self but at the same time must provide broad coverage against a range of unknown non-self antigens that the individual might encounter. The mean total potential T cell response to self and non-self profiles (±standard deviation) across iterations is shown for one set of simulation parameters in Figure 3A. This shows that the response to self is well controlled at the allowed threshold τ. By contrast, the average response to non-self pMHC profiles becomes higher as the model shapes the repertoire. However, the non-self responses become very heterogeneous. After 30,000 iterations, the response to all self profiles is at or near the allowed threshold while the majority of non-self profiles result in more T cell binding, and therefore a larger potential T cell response (Figure 3B). However, there are also a number of non-self profiles that create a lower response than that of self profiles. These presumably represent “holes” in the repertoire coverage.


Immune Tolerance Maintained by Cooperative Interactions between T Cells and Antigen Presenting Cells Shapes a Diverse TCR Repertoire.

Best K, Chain B, Watkins C - Front Immunol (2015)

Broad coverage to non-self is maintained during the development of a self-tolerance repertoire. (A) The mean (±standard deviation) total T cell response to self (blue) or non-self (green) pMHC profiles over time with N = 2,000 and M = 200. (B) After 30,000 iterations of the update algorithm with parameters as in (A), the distribution of total T cell response to self (blue) and non-self (green) pMHC profiles. (C) The ability of the repertoire to successfully mount an immune response to non-self pMHC profiles, measured as the average total response to a non-self profile divided by the average total response to a self profile, over time. The number of T cell clonotypes in a simulation is indicated by color, with the number of self profiles simulated ranging between 100 and 800. (D) The relationship between number of T cell clonotypes and the average total response to a non-self profile divided by the average total response to a self profile after 30,000 iterations of the update algorithm. (E) The proportion of non-self profiles that have a total T cell response greater than the mean response toward self profiles over time. The number of T cell clonotypes is indicated by color. (F) The relationship between the number of T cell clonotypes and the proportion of non-self profiles having a stronger total T cell response than the mean response to self profiles after 30,000 cycles of the update algorithm. Other model parameters for all panels are: self-response threshold τ = 1, growth rate ν = ln 2 δt–1, learning rate η = 0.002001 δt–1 and proportion of non-zero affinities γ = 0.01.
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Related In: Results  -  Collection

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Figure 3: Broad coverage to non-self is maintained during the development of a self-tolerance repertoire. (A) The mean (±standard deviation) total T cell response to self (blue) or non-self (green) pMHC profiles over time with N = 2,000 and M = 200. (B) After 30,000 iterations of the update algorithm with parameters as in (A), the distribution of total T cell response to self (blue) and non-self (green) pMHC profiles. (C) The ability of the repertoire to successfully mount an immune response to non-self pMHC profiles, measured as the average total response to a non-self profile divided by the average total response to a self profile, over time. The number of T cell clonotypes in a simulation is indicated by color, with the number of self profiles simulated ranging between 100 and 800. (D) The relationship between number of T cell clonotypes and the average total response to a non-self profile divided by the average total response to a self profile after 30,000 iterations of the update algorithm. (E) The proportion of non-self profiles that have a total T cell response greater than the mean response toward self profiles over time. The number of T cell clonotypes is indicated by color. (F) The relationship between the number of T cell clonotypes and the proportion of non-self profiles having a stronger total T cell response than the mean response to self profiles after 30,000 cycles of the update algorithm. Other model parameters for all panels are: self-response threshold τ = 1, growth rate ν = ln 2 δt–1, learning rate η = 0.002001 δt–1 and proportion of non-zero affinities γ = 0.01.
Mentions: A successful T cell population needs to be able to control immune response to self but at the same time must provide broad coverage against a range of unknown non-self antigens that the individual might encounter. The mean total potential T cell response to self and non-self profiles (±standard deviation) across iterations is shown for one set of simulation parameters in Figure 3A. This shows that the response to self is well controlled at the allowed threshold τ. By contrast, the average response to non-self pMHC profiles becomes higher as the model shapes the repertoire. However, the non-self responses become very heterogeneous. After 30,000 iterations, the response to all self profiles is at or near the allowed threshold while the majority of non-self profiles result in more T cell binding, and therefore a larger potential T cell response (Figure 3B). However, there are also a number of non-self profiles that create a lower response than that of self profiles. These presumably represent “holes” in the repertoire coverage.

Bottom Line: We show further that the size of individual clones in the model repertoire becomes heterogeneous, and that new clones can establish themselves even when the repertoire has stabilized.Our study combines the salient features of the "danger" model of self/non-self discrimination with the concepts of quorum sensing, and extends repertoire generation models to encompass the establishment of tolerance.Furthermore, the dynamic and continuous repertoire reshaping, which underlies tolerance in this model, suggests opportunities for therapeutic intervention to achieve long-term tolerance following transplantation.

View Article: PubMed Central - PubMed

Affiliation: Division of Infection and Immunity, University College London , London , UK ; Centre for Mathematics, Physics and Engineering in the Life Sciences and Experimental Biology (CoMPLEX), University College London , London , UK.

ABSTRACT
The T cell population in an individual needs to avoid harmful activation by self peptides while maintaining the ability to respond to an unknown set of foreign peptides. This property is acquired by a combination of thymic and extra-thymic mechanisms. We extend current models for the development of self/non-self discrimination to consider the acquisition of self-tolerance as an emergent system level property of the overall T cell receptor repertoire. We propose that tolerance is established at the level of the antigen presenting cell/T cell cluster, which facilitates and integrates cooperative interactions between T cells of different specificities. The threshold for self-reactivity is therefore imposed at a population level, and not at the level of the individual T cell/antigen encounter. Mathematically, the model can be formulated as a linear programing optimization problem that can be implemented as a multiplicative update algorithm, which shows a rapid convergence to a stable state. The model constrains self-reactivity within a predefined threshold, but maintains repertoire diversity and cross reactivity which are key characteristics of human T cell immunity. We show further that the size of individual clones in the model repertoire becomes heterogeneous, and that new clones can establish themselves even when the repertoire has stabilized. Our study combines the salient features of the "danger" model of self/non-self discrimination with the concepts of quorum sensing, and extends repertoire generation models to encompass the establishment of tolerance. Furthermore, the dynamic and continuous repertoire reshaping, which underlies tolerance in this model, suggests opportunities for therapeutic intervention to achieve long-term tolerance following transplantation.

No MeSH data available.


Related in: MedlinePlus