On how monospecific memory-like autoregulatory CD8+ T cells can blunt diabetogenic autoimmunity: a computational approach

Theoretical quantification of the polyvalent binding of nanoparticles coated with peptide-major histocompatibility complex to T cell receptor-nanoclusters

Nanoparticles (NPs) coated with peptide-major histocompatibility complexes (pMHCs) can be used as a therapy to treat autoimmune diseases. They do so by inducing the differentiation and expansion of disease-suppressing T regulatory type 1 (Tr1) cells by binding to their T cell receptors (TCRs) expressed as TCR-nanoclusters (TCR_nc). Their efficacy can be controlled by adjusting NP size and number of pMHCs coated on them (referred to as valence). The binding of these NPs to TCR_nc on T cells is thus polyvalent and occurs at three levels: the TCR-pMHC, NP-TCR_nc and T cell levels. In this study, we explore how this polyvalent interaction is manifested and examine if it can facilitate T cell activation downstream. This is done by developing a multiscale biophysical model that takes into account the three levels of interactions and the geometrical complexity of the binding. Using the model, we quantify several key parameters associated with this interaction analytically and numerically, including the insertion probability that specifies the number of remaining pMHC binding sites in the contact area between T cells and NPs, the dwell time of interaction between NPs and TCR_nc, carrying capacity of TCR_nc, the distribution of covered and bound TCRs, and cooperativity in the binding of pMHCs within the contact area. The model was fit to previously published dose-response curves of interferon-γ obtained experimentally by stimulating a population of T cells with increasing concentrations of NPs at various valences and NP sizes. Exploring the parameter space of the model revealed that for an appropriate choice of the contact area angle, the model can produce moderate jumps between dose-response curves at low valences. This suggests that the geometry and kinetics of NP binding to TCR_nc can act in synergy to facilitate T cell activation.

NKG2D Signaling Within the Pancreatic Islets Reduces NOD Diabetes and Increases Protective Central Memory CD8+ T-Cell Numbers

NKG2D is implicated in autoimmune diabetes. However, the role of this receptor in diabetes pathogenesis is unclear owing to conflicting results with studies involving global inhibition of NKG2D signaling. We found that NKG2D and its ligands are present in human pancreata, with expression of NKG2D and its ligands increased in the islets of patients with type 1 diabetes. To directly assess the role of NKG2D in the pancreas, we generated NOD mice that express an NKG2D ligand in β-islet cells. Diabetes was reduced in these mice. The reduction corresponded with a decrease in the effector to central memory CD8⁺ T-cell ratio. Further, NKG2D signaling during in vitro activation of both mouse and human CD8⁺ T cells resulted in an increased number of central memory CD8⁺ T cells and diabetes protection by central memory CD8⁺ T cells in vivo. Taken together, these studies demonstrate that there is a protective role for central memory CD8⁺ T cells in autoimmune diabetes and that this protection is enhanced with NKG2D signaling. These findings stress the importance of anatomical location when determining the role NKG2D signaling plays, as well as when developing therapeutic strategies targeting this pathway, in type 1 diabetes development.

Continuum model of T-cell avidity: Understanding autoreactive and regulatory T-cell responses in type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease that results from the destruction of insulin-secreting pancreatic β cells, leading to abolition of insulin secretion and onset of diabetes. Cytotoxic CD4(+) and CD8(+) T cells, activated by antigen presenting cells (APCs), are both implicated in disease onset and progression. Regulatory T cells (Tregs), on the other hand, play a leading role in regulating immunological tolerance and resistant homoeostasis in T1D by suppressing effector T cells (Teffs). Recent data indicates that after activation, conventional Teffs transiently produce interleukin IL-2, a cytokine that acts as a growth factor for both Teffs and Tregs. Tregs suppress Teffs through IL-2 deprivation, competition and Teff conversion into inducible Tregs (iTregs). To investigate the interactions of these components during T1D progression, a mathematical model of T-cell dynamics is developed as a predictor of β-cell loss, with the underlying hypothesis that avidity of Teffs and Tregs, i.e., the binding affinity of T-cell receptors to peptide-major histocompatibility complexes on host cells, is continuum. The model is used to infer a set of criteria that determines susceptibility to T1D in high risk subjects. Our findings show that diabetes onset is guided by the absence of Treg-to-Teff dominance at specific high avidities, rather than over the whole range of avidity, and that the lack of overall dominance of Teffs-to-Tregs over time is the underlying cause of the "honeymoon period", the remission phase observed in some T1D patients. The model also suggests that competition between Teffs and Tregs is more effective than Teff-induction into iTregs in suppressing Teffs, and that a prolonged full width at half maximum of IL-2 release is a necessary condition for curbing disease onset. Finally, the model provides a rationale for observing rapid and slow progressors of T1D based on modest heterogeneity in the kinetic parameters.

Unraveling the contribution of pancreatic beta-cell suicide in autoimmune type 1 diabetes

In type 1 diabetes, an autoimmune disease mediated by autoreactive T-cells that attack insulin-secreting pancreatic beta-cells, it has been suggested that disease progression may additionally require protective mechanisms in the target tissue to impede such auto-destructive mechanisms. We hypothesize that the autoimmune attack against beta-cells causes endoplasmic reticulum stress by forcing the remaining beta-cells to synthesize and secrete defective insulin. To rescue beta-cell from the endoplasmic reticulum stress, beta-cells activate the unfolded protein response to restore protein homeostasis and normal insulin synthesis. Here we investigate the compensatory role of unfolded protein response by developing a multi-state model of type 1 diabetes that takes into account beta-cell destruction caused by pathogenic autoreactive T-cells and apoptosis triggered by endoplasmic reticulum stress. We discuss the mechanism of unfolded protein response activation and how it counters beta-cell extinction caused by an autoimmune attack and/or irreversible damage by endoplasmic reticulum stress. Our results reveal important insights about the balance between beta-cell destruction by autoimmune attack (beta-cell homicide) and beta-cell apoptosis by endoplasmic reticulum stress (beta-cell suicide). It also provides an explanation as to why the unfolded protein response may not be a successful therapeutic target to treat type 1 diabetes.

Autoimmune responses in T1DM: quantitative methods to understand onset, progression, and prevention of disease

Understanding the physiological processes that underlie autoimmune disorders and identifying biomarkers to predict their onset are two pressing issues that need to be thoroughly sorted out by careful thought when analyzing these diseases. Type 1 diabetes (T1D) is a typical example of such diseases. It is mediated by autoreactive cytotoxic CD4⁺ and CD8⁺ T-cells that infiltrate the pancreatic islets of Langerhans and destroy insulin-secreting β-cells, leading to abnormal levels of glucose in affected individuals. The disease is also associated with a series of islet-specific autoantibodies that appear in high-risk subjects (HRS) several years prior to the onset of diabetes-related symptoms. It has been suggested that T1D is relapsing-remitting in nature and that islet-specific autoantibodies released by lymphocytic B-cells are detectable at different stages of the disease, depending on their binding affinity (the higher, the earlier they appear). The multifaceted nature of this disease and its intrinsic complexity make this disease very difficult to analyze experimentally as a whole. The use of quantitative methods, in the form of mathematical models and computational tools, to examine the disease has been a very powerful tool in providing predictions and insights about the underlying mechanism(s) regulating its onset and development. Furthermore, the models developed may have prognostic implications by aiding in the enrollment of HRS into trials for T1D prevention. In this review, we summarize recent advances made in determining T- and B-cell involvement in T1D using these quantitative approaches and delineate areas where mathematical modeling can make further contributions in unraveling certain aspect of this disease.

Human systems immunology: hypothesis-based modeling and unbiased data-driven approaches

Systems immunology is an emerging paradigm that aims at a more systematic and quantitative understanding of the immune system. Two major approaches have been utilized to date in this field: unbiased data-driven modeling to comprehensively identify molecular and cellular components of a system and their interactions; and hypothesis-based quantitative modeling to understand the operating principles of a system by extracting a minimal set of variables and rules underlying them. In this review, we describe applications of the two approaches to the study of viral infections and autoimmune diseases in humans, and discuss possible ways by which these two approaches can synergize when applied to human immunology.

Quantifying the importance of pMHC valency, total pMHC dose and frequency on nanoparticle therapeutic efficacy

Nanoparticles (NPs) coated with β-cell-specific peptide major histocompatibility complex (pMHC) class I molecules can effectively restore normoglycemia in spontaneously diabetic nonobese diabetic mice. They do so by expanding pools of cognate memory autoreactive regulatory CD8+ T cells that arise from naive low-avidity T-cell precursors to therapeutic levels. Here we develop our previously constructed mathematical model to explore the effects of compound design parameters (NP dose and pMHC valency) on therapeutic efficacy with the underlying hypothesis that the functional correlates of the therapeutic response (expansion of autoregulatory T cells and deletion of autoantigen-loaded antigen-presenting cells by these T cells) are biphasic. We show, using bifurcation analysis, that the model exhibits a 'resonance'-like behavior for a given range of NP dose in which bistability between the healthy state (possessing zero level of effector T-cell population) and autoimmune state (possessing elevated level of the same population) disappears. A heterogeneous population of model mice subjected to several treatment protocols under these new conditions is conducted to quantify both the average percentage of autoregulatory T cells in responsive and nonresponsive model mice, and the average valency-dependent minimal optimal dose needed for effective therapy. Our results reveal that a moderate increase (≥1.6-fold) in the NP-dependent expansion rate of autoregulatory T-cell population leads to a significant increase in the efficacy and the area corresponding to the effective treatment regimen, provided that NP dose ≥8 μg. We expect the model developed here to generalize to other autoimmune diseases and serve as a computational tool to understand and optimize pMHC-NP-based therapies.

Regulation of self-tolerance by Qa-1-restricted CD8(+) regulatory T cells

Mounting an efficient immune response to pathogens while avoiding damage to host tissues is the central task of the immune system. Emerging evidence has highlighted the contribution of the CD8(+) lineage of regulatory T cells to the maintenance of self-tolerance. Specific recognition of the MHC class Ib molecule Qa-1 complexed to peptides expressed by activated CD4(+) T cells by regulatory CD8(+) T cells triggers an inhibitory interaction that prevents autoimmune responses. Conversely, defective Qa-1-restricted CD8(+) regulatory activity can result in development of systemic autoimmune disease. Here, we review recent research into the cellular and molecular basis of these regulatory T cells, their mechanism of suppressive activity and the potential application of these insights into new treatments for autoimmune disease and cancer.

Investigating the role of T-cell avidity and killing efficacy in relation to type 1 diabetes prediction

During the progression of the clinical onset of Type 1 Diabetes (T1D), high-risk individuals exhibit multiple islet autoantibodies and high-avidity T cells which progressively destroy beta cells causing overt T1D. In particular, novel autoantibodies, such as those against IA-2 epitopes (aa1-577), had a predictive rate of 100% in a 10-year follow up (rapid progressors), unlike conventional autoantibodies that required 15 years of follow up for a 74% predictive rate (slow progressors). The discrepancy between these two groups is thought to be associated with T-cell avidity, including CD8 and/or CD4 T cells. For this purpose, we build a series of mathematical models incorporating first one clone then multiple clones of islet-specific and pathogenic CD8 and/or CD4 T cells, together with B lymphocytes, to investigate the interaction of T-cell avidity with autoantibodies in predicting disease onset. These models are instrumental in examining several experimental observations associated with T-cell avidity, including the phenomenon of avidity maturation (increased average T-cell avidity over time), based on intra- and cross-clonal competition between T cells in high-risk human subjects. The model shows that the level and persistence of autoantibodies depends not only on the avidity of T cells, but also on the killing efficacy of these cells. Quantification and modeling of autoreactive T-cell avidities can thus determine the level of risk associated with each type of autoantibodies and the timing of T1D disease onset in individuals that have been tested positive for these autoantibodies. Such studies may lead to early diagnosis of the disease in high-risk individuals and thus potentially serve as a means of staging patients for clinical trials of preventive or interventional therapies far before disease onset.