Limits...
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

Optimization of the T cell population to avoid autoimmunity while maximizing T cell numbers in a simplified system with two clonotypes and three spMHC profiles. The update algorithm is initiated with different initial clonotype abundances each represented by a different color. The colored lines track the changes in clonotype abundance over iterations of the update algorithm (A–E). The clone abundances after (A) 5, (B) 10, (C) 100, (D) 1,000, or (E) 10,000 iterations of the update algorithm. The gray lines indicate the constraint that total T cell response should be less than the threshold for each of the spMHC profiles (F). For each of the starting repertoire configurations, the relationship between the Euclidean distance moved by the repertoire configuration in an iteration of the update algorithm and the distance from the furthest violated threshold, or if there are no violations the distance to the nearest threshold (G). The affinities between clonotypes and spMHC profiles. Other model parameters for all panels are: τ = 1, the self-response threshold for each spMHC profile, ν = ln 2 δt–1, the growth rate and η = 0.01001 δt–1, the learning rate.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4528093&req=5

Figure 1: Optimization of the T cell population to avoid autoimmunity while maximizing T cell numbers in a simplified system with two clonotypes and three spMHC profiles. The update algorithm is initiated with different initial clonotype abundances each represented by a different color. The colored lines track the changes in clonotype abundance over iterations of the update algorithm (A–E). The clone abundances after (A) 5, (B) 10, (C) 100, (D) 1,000, or (E) 10,000 iterations of the update algorithm. The gray lines indicate the constraint that total T cell response should be less than the threshold for each of the spMHC profiles (F). For each of the starting repertoire configurations, the relationship between the Euclidean distance moved by the repertoire configuration in an iteration of the update algorithm and the distance from the furthest violated threshold, or if there are no violations the distance to the nearest threshold (G). The affinities between clonotypes and spMHC profiles. Other model parameters for all panels are: τ = 1, the self-response threshold for each spMHC profile, ν = ln 2 δt–1, the growth rate and η = 0.01001 δt–1, the learning rate.

Mentions: We first simulate a very simplified repertoire to allow us to visualize the action of the update algorithm. We start with two T cell clonotypes and three spMHC profiles. The clonotypes have binding strengths for each of the profiles as detailed in Figure 1G. In this simulation, each profile is given the same total response threshold (=1), above which there will be harmful autoimmunity. The other parameters of the update algorithm are set out in the legend of Figure 1.


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)

Optimization of the T cell population to avoid autoimmunity while maximizing T cell numbers in a simplified system with two clonotypes and three spMHC profiles. The update algorithm is initiated with different initial clonotype abundances each represented by a different color. The colored lines track the changes in clonotype abundance over iterations of the update algorithm (A–E). The clone abundances after (A) 5, (B) 10, (C) 100, (D) 1,000, or (E) 10,000 iterations of the update algorithm. The gray lines indicate the constraint that total T cell response should be less than the threshold for each of the spMHC profiles (F). For each of the starting repertoire configurations, the relationship between the Euclidean distance moved by the repertoire configuration in an iteration of the update algorithm and the distance from the furthest violated threshold, or if there are no violations the distance to the nearest threshold (G). The affinities between clonotypes and spMHC profiles. Other model parameters for all panels are: τ = 1, the self-response threshold for each spMHC profile, ν = ln 2 δt–1, the growth rate and η = 0.01001 δt–1, the learning rate.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528093&req=5

Figure 1: Optimization of the T cell population to avoid autoimmunity while maximizing T cell numbers in a simplified system with two clonotypes and three spMHC profiles. The update algorithm is initiated with different initial clonotype abundances each represented by a different color. The colored lines track the changes in clonotype abundance over iterations of the update algorithm (A–E). The clone abundances after (A) 5, (B) 10, (C) 100, (D) 1,000, or (E) 10,000 iterations of the update algorithm. The gray lines indicate the constraint that total T cell response should be less than the threshold for each of the spMHC profiles (F). For each of the starting repertoire configurations, the relationship between the Euclidean distance moved by the repertoire configuration in an iteration of the update algorithm and the distance from the furthest violated threshold, or if there are no violations the distance to the nearest threshold (G). The affinities between clonotypes and spMHC profiles. Other model parameters for all panels are: τ = 1, the self-response threshold for each spMHC profile, ν = ln 2 δt–1, the growth rate and η = 0.01001 δt–1, the learning rate.
Mentions: We first simulate a very simplified repertoire to allow us to visualize the action of the update algorithm. We start with two T cell clonotypes and three spMHC profiles. The clonotypes have binding strengths for each of the profiles as detailed in Figure 1G. In this simulation, each profile is given the same total response threshold (=1), above which there will be harmful autoimmunity. The other parameters of the update algorithm are set out in the legend of Figure 1.

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