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The generation of regulatory T (T(Reg)) cells in the thymus is crucial for immune homeostasis and self-tolerance. Recent discoveries have revealed the cellular and molecular mechanisms that govern the differentiation of a subset of developing thymocytes into natural T(Reg) cells. Several models, centred on the self-reactivity of the T cell receptor (TCR), have been proposed to explain the generation of a T(Reg) cell population that is cognizant of self. Several molecular pathways link TCR and cytokine signalling with the expression of the T(Reg) cell-associated transcription factor forkhead box P3 (FOXP3). Moreover, interplay between thymocytes and thymic antigen-presenting cells is also involved in T(Reg) cell generation.

Summary The degree of T cell self-reactivity considered dangerous by the immune system, thereby requiring thymic selection processes to prevent autoimmunity, is unknown. Here, we analyzed a panel of T cell receptors (TCRs) with a broad range of reactivity to ovalbumin (OVA323-339) in the rat insulin promoter (RIP)-mOVA self-antigen model for their ability to trigger thymic self-tolerance mechanisms. Thymic regulatory T (Treg) cell generation in vivo was directly correlated with in vitro TCR reactivity to OVA-peptide in a broad ~1,000-fold range. Interestingly, higher TCR affinity was associated with a larger Treg cell developmental “niche” size, even though the amount of antigen should remain constant. The TCR-reactivity threshold to elicit thymic negative selection and peripheral T cell responses was ~100 fold higher than that of Treg cell differentiation. Thus, these data suggest that the broad range of self-reactivity that elicits thymic Treg cell generation is tuned to secure peripheral tolerance to self.

Reciprocal interactions between B and follicular T helper (Tfh) cells orchestrate the germinal center (GC) reaction, a hallmark of humoral immunity. Abnormal GC responses could lead to the production of pathogenic autoantibodies and the development of autoimmunity. Here we show that miR-146a controls GC responses by targeting multiple CD40 signaling pathway components in B cells; by contrast, loss of miR-146a in T cells does not alter humoral responses. However, specific deletion of both miR-146a and its paralog, miR-146b, in T cells increases Tfh cell numbers and enhanced GC reactions. Thus, our data reveal differential cell-intrinsic regulations of GC B and Tfh cells by miR-146a and miR-146b. Together, members of the miR-146 family serve as crucial molecular brakes to coordinately control GC reactions to generate protective humoral responses without eliciting unwanted autoimmunity.

Background: Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-ago go related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore, a virtual screening method to predict drug-induced hERG-related cardiotoxicity could facilitate drug discovery by filtering out toxic drug candidates. Result: In this study, we generated a reliable hERG-related cardiotoxicity dataset composed of 2130 compounds, which were carried out under constant conditions. Based on our dataset, we developed a computational hERG-related cardiotoxicity prediction model. The neural network model achieved an area under the receiver operating characteristic curve (AUC) of 0.764, with an accuracy of 90.1%, a Matthews correlation coefficient (MCC) of 0.368, a sensitivity of 0.321, and a specificity of 0.967, when tenfold cross-validation was performed. The model was further evaluated using ten drug compounds tested on guinea pigs and showed an accuracy of 80.0%, an MCC of 0.655, a sensitivity of 0.600, and a specificity of 1.000, which were better than the performances of existing hERG-toxicity prediction models. Conclusion: The neural network model can predict hERG-related cardiotoxicity of chemical compounds with a high accuracy. Therefore, the model can be applied to virtual high-throughput screening for drug candidates that do not cause cardiotoxicity. The prediction tool is available as a web-tool at http://ssbio.cau.ac.kr/CardPred.