32 research outputs found
T Cell/B Cell Collaboration and Autoimmunity: An Intimate Relationship
Co-ordinated interaction between distinct cell types is a hallmark of successful immune function. A striking example of this is the carefully orchestrated cooperation between helper T cells and B cells that occurs during the initiation and fine-tuning of T-cell dependent antibody responses. While these processes have evolved to permit rapid immune defense against infection, it is becoming increasingly clear that such interactions can also underpin the development of autoimmunity. Here we discuss a selection of cellular and molecular pathways that mediate T cell/B cell collaboration and highlight how in vivo models and genome wide association studies link them with autoimmune disease. In particular, we emphasize how CTLA-4-mediated regulation of CD28 signaling controls the engagement of secondary costimulatory pathways such as ICOS and OX40, and profoundly influences the capacity of T cells to provide B cell help. While our molecular understanding of the co-operation between T cells and B cells derives from analysis of secondary lymphoid tissues, emerging evidence suggests that subtly different rules may govern the interaction of T and B cells at ectopic sites during autoimmune inflammation. Accordingly, the phenotype of the T cells providing help at these sites includes notable distinctions, despite sharing core features with T cells imparting help in secondary lymphoid tissues. Finally, we highlight the interdependence of T cell and B cell responses and suggest that a significant beneficial impact of B cell depletion in autoimmune settings may be its detrimental effect on T cells engaged in molecular conversation with B cells
Follicular helper T cell profiles predict response to costimulation blockade in type 1 diabetes
Follicular helper T (TFH) cells are implicated in type 1 diabetes (T1D), and their development has been linked to CD28 costimulation. We tested whether TFH cells were decreased by costimulation blockade using the CTLA-4–immunoglobulin (Ig) fusion protein (abatacept) in a mouse model of diabetes and in individuals with new-onset T1D. Unbiased bioinformatics analysis identified that inducible costimulatory molecule (ICOS)+ TFH cells and other ICOS+ populations, including peripheral helper T cells, were highly sensitive to costimulation blockade. We used pretreatment TFH profiles to derive a model that could predict clinical response to abatacept in individuals with T1D. Using two independent approaches, we demonstrated that higher frequencies of ICOS+ TFH cells at baseline were associated with a poor clinical response following abatacept administration. Therefore, TFH analysis may represent a new stratification tool, permitting the identification of individuals most likely to benefit from costimulation blockade
Stratification of PD-1 blockade response in melanoma using pre- and post-treatment immunophenotyping of peripheral blood
Efficacy of checkpoint inhibitor therapies in cancer varies greatly, with some patients showing complete responses while others do not respond and experience progressive disease. We aimed to identify correlates of response and progression following PD-1-directed therapy by immunophenotyping peripheral blood samples from 20 patients with advanced malignant melanoma before and after treatment with the PD-1 blocking antibody pembrolizumab. Our data reveal that individuals responding to PD-1 blockade were characterised by increased CD8 T cell proliferation following treatment, while progression was associated with an increase in CTLA-4-expressing Treg. Remarkably, unsupervised clustering analysis of pre-treatment T cell subsets revealed differences in individuals that went on to respond to PD-1 blockade compared to individuals that did not. These differences mapped to expression of the proliferation marker Ki67 and the costimulatory receptor CD28 as well as the inhibitory molecules 2B4 and KLRG1. While these results require validation in larger patient cohorts, they suggest that flow cytometric analysis of a relatively small number of T cell markers in peripheral blood could potentially allow stratification of PD-1 blockade treatment response prior to therapy initiation
IL-21 shapes germinal center polarization via light zone B cell selection and cyclin D3 upregulation
Germinal center (GC) dysregulation has been widely reported in the context of autoimmunity. Here, we show that interleukin 21 (IL-21), the archetypal follicular helper T cell (Tfh) cytokine, shapes the scale and polarization of spontaneous chronic autoimmune as well as transient immunization-induced GC. We find that IL-21 receptor deficiency results in smaller GC that are profoundly skewed toward a light zone GC B cell phenotype and that IL-21 plays a key role in selection of light zone GC B cells for entry to the dark zone. Light zone skewing has been previously reported in mice lacking the cell cycle regulator cyclin D3. We demonstrate that IL-21 triggers cyclin D3 upregulation in GC B cells, thereby tuning dark zone inertial cell cycling. Lastly, we identify Foxo1 regulation as a link between IL-21 signaling and GC dark zone formation. These findings reveal new biological roles for IL-21 within GC and have implications for autoimmune settings where IL-21 is overproduced
IL-21 shapes germinal center polarization via light zone B cell selection and cyclin D3 upregulation
CTLA-4-mediated Trans-endocytosis in the Control of T Cell Co-stimulation
The signal threshold for the activation of a naïve T cell upon recognising its antigen is regulated tightly by the CD28/CTLA-4 – CD80/86 pathway. Within this framework, CTLA-4 has been shown to limit the availability of co-stimulation by physical removal of CD80 and CD86 from the APC, via the mechanism of trans-endocytosis (TE). The study presented in this thesis explores CTLA-4-mediated modulation of CD80 and CD86 as it occurs in vitro and in vivo. It is revealed that regulatory T cells, highly enriched in CTLA-4, are able to rapidly recruit it to the cell surface and trans-endocytose CD80 and CD86 upon stimulation. As a result, Treg, but not Tconv, within hours can significantly reduce the level of co-stimulation on DCs. Although both CD80 and CD86 can be readily taken up by CTLA-4, CD80 is the dominant ligand that it preferentially downregulated from APCs in a competitive scenario. Furthermore, CD28 signalling, in addition to TCR, is identified as a requirement for effective CTLA-4 function. In vivo, co-stimulation is shown to dictate the magnitude of a T cell response in a digital manner. Accordingly, Treg suppress Tconv expansion in a way that is consistent with reduced co-stimulation as a consequence of TE. By using antigen presenting cells that express fluorescently-tagged CD80 and CD86, it is revealed that Treg use CTLA-4 to capture its ligands across tissues in the steady state. Thereby, the phenotype of the APC is constantly modulated. Perturbing CTLA-4 identifies that the lymph node migratory DCs are the main target for CTLA-4 control, while CD80 is confirmed to be the preferred ligand. Finally, Treg-expressed CD80 and CD86 is shown to act as a tuning mechanism that restricts homeostatic activation, suppressive capability and TE. Together, these data provide new insight into how CTLA-4-mediated trans-endocytosis may routinely prevent self-reactive immune activation
Regulatory T Cells in Autoimmune Diabetes: Mechanisms of Action and Translational Potential
Since the discovery of specialized T cells with regulatory function, harnessing the power of
these cells to ameliorate autoimmunity has been a major goal. Here we collate the evidence
that regulatory T cells (Treg) can inhibit Type 1 Diabetes in animal models and humans. We
discuss the anatomical sites and molecular mechanisms of Treg suppressive function in the
Type 1 Diabetes setting, citing evidence that Treg can function in both the pancreatic lymph
nodes and within the pancreatic lesion. Involvement of the CTLA-4 pathway, as well as TGFβ and IL-2 deprivation will be considered. Finally we summarize current efforts to manipulate
Treg therapeutically in individuals with Type 1 Diabetes. The translation of this research area
from bench to bedside is still in its infancy, but the remarkable therapeutic potential of
successfully manipulating Treg populations is clear to see
The kinetics of cytokine expression during Th1/Tc1 and Th17/Tc17 polarisation.
<p>Naïve CD4<sup>+</sup> or CD8<sup>+</sup> cells from AhR<sup>+/−</sup> mice were cultured for 96 h under Tc1/Th1 (open symbols) or Th17/Tc17 (closed symbols) polarising conditions in the presence of 3 µM CH-223191 (•,○) or 300 nM FICZ (▴,Δ) formulated in DMSO or with an equivalent amount of DMSO alone (▪,□). At selected time points (12–96 h) RNA was isolated and levels of IFN-γ (A, B), IL-17 (C, D) and IL-22 (E, F) mRNA transcripts were analysed by RT-PCR using the ΔΔ Ct method. Results were normalised against naive CD4<sup>+</sup> or CD8<sup>+</sup> cells and the housekeeping gene HPRT. A single experiment is shown as a function of relative mRNA expression on a logarithmic scale.</p
Cytokine mRNA and protein expression profiles of Th17 and Tc17 cells: effect of AhR modulation.
<p>Naïve CD4<sup>+</sup> cells from AhR<sup>+/−</sup> (black bar) or AhR<sup>−/−</sup> mice (white bar) and naïve CD8<sup>+</sup> cells from AhR<sup>+/−</sup> (grey bar) or AhR<sup>−/−</sup> mice (striped bar) were polarised under Th17/Tc17 conditions for 5 days in the presence of AhR antagonist (CH-223191) or AhR agonist (FICZ) both formulated in DMSO or with an equivalent amount of DMSO alone. Cells were harvested and total RNA prepared for the analysis of mRNA levels for IL-17A, IL-22, and IFN-γ using RT-PCR and the ΔΔ Ct method (A, C and E). Results were normalised against naive CD4<sup>+</sup> or CD8<sup>+</sup> cells and the housekeeping gene HPRT. Supernatants were also analysed for secreted cytokine by ELISA (B, D and F). Results are shown as mean ± SE for n = 3 independent experiments. Statistical significance of differences between DMSO control and AhR antagonist/agonist treated cells was analysed by one-way ANOVA. *, <i>p</i> < 0.05; **, <i>p</i> < 0.01; ***, <i>p</i> < 0.001.</p