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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

Clonotype diversity and pMHC profile cross-reactivity are preserved by the update algorithm. (A) Blue: The proportion of clonotypes (after positive selection) that are present over time during simulation of the update algorithm. Red: The Shannon entropy of the repertoire over time. Simulation implemented with N = 1,600 and M = 400. (B) Relationship between number of clonotypes in the simulation and proportion of clonotypes remaining (blue) or Shannon entropy of the repertoire (red) after 30,000 iterations of the update algorithm. Simulation implemented with values of N between 400 and 25,600 and M between 100 and 800, with M < N. (C) Cross-reactivity of T cell clonotypes against self (blue) and non-self (green) pMHC profiles over time, run with N = 3,200 and M = 400. Cross-reactivity is measured as the proportion of present clonotypes that have non-zero binding strength for a given profile. Data shown is mean cross-reactivity across all profiles ± standard deviation. (D) Distribution of cross-reactivity across all self (blue) and non-self (green) pMHC profiles after 30,000 iterations of the update algorithm with N = 3,200 and M = 400. Cross-reactivity is measured as the absolute number of present clonotypes that have non-zero binding strength to a profile. (E) Relationship between the number of clonotypes present at the start of the update algorithm and the ratio of the mean cross-reactivity against non-self profiles to the mean cross-reactivity against self profiles after 30,000 cycles of the update algorithm. (F) Relationship between the number of self profiles in the update algorithm and the ratio of the mean cross-reactivity against non-self profiles to the mean cross-reactivity against 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 4: Clonotype diversity and pMHC profile cross-reactivity are preserved by the update algorithm. (A) Blue: The proportion of clonotypes (after positive selection) that are present over time during simulation of the update algorithm. Red: The Shannon entropy of the repertoire over time. Simulation implemented with N = 1,600 and M = 400. (B) Relationship between number of clonotypes in the simulation and proportion of clonotypes remaining (blue) or Shannon entropy of the repertoire (red) after 30,000 iterations of the update algorithm. Simulation implemented with values of N between 400 and 25,600 and M between 100 and 800, with M < N. (C) Cross-reactivity of T cell clonotypes against self (blue) and non-self (green) pMHC profiles over time, run with N = 3,200 and M = 400. Cross-reactivity is measured as the proportion of present clonotypes that have non-zero binding strength for a given profile. Data shown is mean cross-reactivity across all profiles ± standard deviation. (D) Distribution of cross-reactivity across all self (blue) and non-self (green) pMHC profiles after 30,000 iterations of the update algorithm with N = 3,200 and M = 400. Cross-reactivity is measured as the absolute number of present clonotypes that have non-zero binding strength to a profile. (E) Relationship between the number of clonotypes present at the start of the update algorithm and the ratio of the mean cross-reactivity against non-self profiles to the mean cross-reactivity against self profiles after 30,000 cycles of the update algorithm. (F) Relationship between the number of self profiles in the update algorithm and the ratio of the mean cross-reactivity against non-self profiles to the mean cross-reactivity against 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: We first consider the proportion of starting clonotypes surviving (i.e., with an abundance greater than the lower limit defined above) as a function of time. The proportion of clonotypes present in the repertoire falls rapidly in the initial stages of repertoire reshaping and then stabilizes (Figure 4A, blue). The proportion of the initial clonotypes that remain after 30,000 cycles of the update algorithm is inversely correlated to the number of clonotypes in the simulation (Figure 4B, blue).


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)

Clonotype diversity and pMHC profile cross-reactivity are preserved by the update algorithm. (A) Blue: The proportion of clonotypes (after positive selection) that are present over time during simulation of the update algorithm. Red: The Shannon entropy of the repertoire over time. Simulation implemented with N = 1,600 and M = 400. (B) Relationship between number of clonotypes in the simulation and proportion of clonotypes remaining (blue) or Shannon entropy of the repertoire (red) after 30,000 iterations of the update algorithm. Simulation implemented with values of N between 400 and 25,600 and M between 100 and 800, with M < N. (C) Cross-reactivity of T cell clonotypes against self (blue) and non-self (green) pMHC profiles over time, run with N = 3,200 and M = 400. Cross-reactivity is measured as the proportion of present clonotypes that have non-zero binding strength for a given profile. Data shown is mean cross-reactivity across all profiles ± standard deviation. (D) Distribution of cross-reactivity across all self (blue) and non-self (green) pMHC profiles after 30,000 iterations of the update algorithm with N = 3,200 and M = 400. Cross-reactivity is measured as the absolute number of present clonotypes that have non-zero binding strength to a profile. (E) Relationship between the number of clonotypes present at the start of the update algorithm and the ratio of the mean cross-reactivity against non-self profiles to the mean cross-reactivity against self profiles after 30,000 cycles of the update algorithm. (F) Relationship between the number of self profiles in the update algorithm and the ratio of the mean cross-reactivity against non-self profiles to the mean cross-reactivity against 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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Show All Figures
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Figure 4: Clonotype diversity and pMHC profile cross-reactivity are preserved by the update algorithm. (A) Blue: The proportion of clonotypes (after positive selection) that are present over time during simulation of the update algorithm. Red: The Shannon entropy of the repertoire over time. Simulation implemented with N = 1,600 and M = 400. (B) Relationship between number of clonotypes in the simulation and proportion of clonotypes remaining (blue) or Shannon entropy of the repertoire (red) after 30,000 iterations of the update algorithm. Simulation implemented with values of N between 400 and 25,600 and M between 100 and 800, with M < N. (C) Cross-reactivity of T cell clonotypes against self (blue) and non-self (green) pMHC profiles over time, run with N = 3,200 and M = 400. Cross-reactivity is measured as the proportion of present clonotypes that have non-zero binding strength for a given profile. Data shown is mean cross-reactivity across all profiles ± standard deviation. (D) Distribution of cross-reactivity across all self (blue) and non-self (green) pMHC profiles after 30,000 iterations of the update algorithm with N = 3,200 and M = 400. Cross-reactivity is measured as the absolute number of present clonotypes that have non-zero binding strength to a profile. (E) Relationship between the number of clonotypes present at the start of the update algorithm and the ratio of the mean cross-reactivity against non-self profiles to the mean cross-reactivity against self profiles after 30,000 cycles of the update algorithm. (F) Relationship between the number of self profiles in the update algorithm and the ratio of the mean cross-reactivity against non-self profiles to the mean cross-reactivity against 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: We first consider the proportion of starting clonotypes surviving (i.e., with an abundance greater than the lower limit defined above) as a function of time. The proportion of clonotypes present in the repertoire falls rapidly in the initial stages of repertoire reshaping and then stabilizes (Figure 4A, blue). The proportion of the initial clonotypes that remain after 30,000 cycles of the update algorithm is inversely correlated to the number of clonotypes in the simulation (Figure 4B, blue).

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