A NOVEL T CELL ACTIVATION CONTROLLED BY A DISEASE GENE

描述：(改编自《调查员摘要》)： BB大鼠的自发性糖尿病需要一种名为LYP的糖尿病基因。 LYP是根据它产生的表型--外周T细胞淋巴细胞减少症而命名的。 大多数来自淋巴细胞减少动物的T细胞寿命显著缩短 当它们不能成为长寿外周T细胞池的成员时 创造了淋巴细胞减少的环境。我们工作的一个主要重点是 旨在了解LYP基因和由此导致的淋巴细胞减少症 会导致疾病。我们最近的观察表明，大多数来自 易患糖尿病的BB动物表达一种新的激活状态，由一种 细胞表面静止，但激活了细胞周期机制的内部测量。 我们认为，这种新的激活状态是通过 自体反应性T细胞被激活。在此应用程序中，我们将测试几个 根据我们的数据提出的假设。首先，淋巴细胞减少的原因是 LYP基因介导的T细胞细胞周期停滞 G1期至S期。其次，淋巴细胞减少症在外围创造了空间。空间 为携带T细胞的近期胸腺移居者提供克隆性扩增信号 自身抗原的受体。第三，自身反应性T细胞可以克服LYP 基因介导的细胞周期停滞因为它们表达高亲和力受体 用于自身抗原。如果我们的假设是正确的，就像在实验中测试的那样 在本申请中提出的，生成的数据将使我们能够理解 至少一个遗传易感区域是如何导致这一地区疾病的 大鼠模型。

DESCRIPTION: (Adapted from the Investigator's abstract): The development of diabetes spontaneously in the BB rat requires a diabetogenic gene termed lyp. Lyp is named after the phenotype it creates, peripheral T cell lymphopenia. Most T cells from lymphopenic animals have a dramatically shortened life span and when they fail to become members of the long lived peripheral T cell pool the lymphopenic environment is created. A major focus of our work has been directed to understanding how the lyp gene and the resultant lymphopenia contribute to disease. Our recent observations indicate that most T cells from diabetes-prone BB animals express a novel activation state, defined by a quiescent cell surface but activated internal measures of cell cycle machinery. We believe this novel activation state is the impetus through which autoreactive T cells become activated. In this application we will test several hypotheses formulated from our data. First, the cause of the lymphopenia is the lyp gene mediated cell cycle arrest of T cells as they attempt to progress from G1 to S phase. Second, lymphopenia creates space in the periphery. Space provides clonal expansion signals for recent thymic emigrants that bear T cell receptors for self antigens. Third, autoreactive T cells can overcome the lyp gene mediated cell cycle arrest because they express high affinity receptors for self antigens. If our hypotheses are correct, as tested in the experiments proposed in this application, the data generated will permit us to understand how at least one genetic susceptibility region contributes to disease in this rat model.