Gene Regulation as a Foundation for Autoimmune Disease Prevention

抽象的。 我们建议成立一个针对自身免疫性疾病预防倡议的合作研究小组，该小组将为 对于实现设计和管理实际干预措施以防止一种或多种疾病的目标至关重要的知识 更多的自身免疫性疾病“(引用自RFA-AI-16-003)。我们的建议是将基因的作用定义为 自身免疫性疾病的易感性和环境影响“，以”确定 定义的遗传风险等位基因会影响自身免疫性疾病的易感性“，并有助于”识别 并阐明可能为预防性干预提供目标的细胞和免疫途径“。 初步数据显示，一组~25个转录因子和辅助因子(TF)免疫沉淀DNA 疾病风险基因座的序列(CHIP-SEQ)产生强大的关联(2.7Risk≤≤40，1.5E- 06≥PC≥4.6E-53)，适用于7种自身免疫性疾病：多发性硬化症(MS)、1型糖尿病(T1D)、 类风湿关节炎(RA)、炎症性肠病(IBD)、幼年特发性关节炎(JIA)、乳糜泻 疾病(CelD)和系统性红斑狼疮(SLE)。这些TF突出地包括一个 由一种病毒编码，EB病毒(EBV)核抗原2(EBNA2)，与近一半的相关 已知转录因子是EBV相关超级增强子的组成部分。虽然新的结果令人信服； 基础数据目前不完整，一般不足以解决技术和研究设计问题； 然而，我们已经确定了几个令人信服的例子，表明Tf与Tf结合的差异依赖于等位基因。 来自疾病风险基因座的看似合理的因果变异(例如，IL-10、CD37和TMBIM1)。这些说明了 提名可能对随后的自身免疫性疾病有贡献的特定分子相互作用； 此外，它们还为应开发的数据提供了强有力的指导，以便理解 环境(这里是EBNA2)与危险基因的相互作用产生自身免疫性疾病的机制。 我们提出了四个目标：我们将测试调节分子(TF， MicroRNAs、长非编码RNAs和RNA结合蛋白)与自身免疫性疾病基因座(目标1)；我们将 生成实验性的调节分子结合数据，以揭示可能的致病机制(目的 2)；我们将在体外测试特定的基因调控假说，并将基因调控效应定位于个体 用染色质编辑的细胞系的变体(目标3)；我们将把我们的新方法扩展到我们的自身免疫 预防研究小组的同事和他们的项目，作为我们信息学和实验的“阿尔法测试者” 方法，以便尽快推进我们对自身免疫的基本理解(目标4)。总的来说， 该项目将为自身免疫性疾病提供病理生理学机制，耦合致病基因。 对于环境，至少以EBV为例，将展示更大的数据资源如何提供 将洞察力转化为许多疾病，并将产生提供试剂和数据的体外模型 以便利随后对治疗和预防策略的评估。

ABSTRACT. We propose a Cooperative Study Group for the Autoimmune Disease Prevention initiative that will “contribute knowledge critical to achieving the goal of designing and administering practical interventions to prevent one or more autoimmune diseases” (quotes are from RFA-AI-16-003). Our proposal is to define “the role of genetic susceptibility and environmental influences in autoimmune disease”, to “determine the mechanisms by which defined genetic risk alleles influence susceptibility to autoimmune disease”, and to facilitate the “identification and elucidation of cellular and immune pathways that may provide targets for preventive interventions”. Preliminary data show sets of ~25 transcription factors and co-factors (TFs) that immunoprecipitate DNA sequences (ChIP-seq) at disease risk loci producing powerful associations (2.7≤Relative Risk≤40, 1.5E- 06≥pc≥4.6E-53) for each of 7 autoimmune diseases: multiple sclerosis (MS), type 1 diabetes (T1D), rheumatoid arthritis (RA), Inflammatory bowel disease (IBD), juvenile idiopathic arthritis (JIA), celiac disease (CelD), and systemic lupus erythematosus (SLE). These TFs prominently include one that is encoded by a virus, Epstein-Barr virus (EBV) nuclear antigen 2 (EBNA2), and nearly half of the associated TFs are known to be components of EBV associated super-enhancers. While the new results are compelling; the underlying data are currently incomplete and generally inadequate for technical and study design issues; nevertheless, we have identified several convincing examples of allele-dependent differences in TF binding to plausible causal variants from disease risk loci (e.g., at IL-10, CD37, and TMBIM1). These illustrate the nomination of specific molecular interactions for possible contributions to the consequent autoimmune disease; in addition, they provide powerful guidance for the data that should be developed in order to understand the interaction of environment (here EBNA2) with risk loci to produce autoimmune disease mechanisms. We propose 4 aims: We will test the association of nucleic acid binding by regulatory molecules (TFs, microRNAs, long non-coding RNAs, and RNA binding proteins) with autoimmune disease loci (Aim 1); we will generate experimental regulatory molecule binding data to reveal possible disease generating mechanisms (Aim 2); we will test specific hypotheses of gene regulation ex vivo and localize gene regulatory effects to individual variants with chromatin edited cell lines (Aim 3); and we will extend our new approach to our Autoimmune Prevention Study Group colleagues and their projects, as “alpha testers” of our informatics and experimental approaches, in order to advance our basic understanding of autoimmunity as rapidly as possible (Aim 4). Overall, this project will provide pathophysiological mechanisms for autoimmune diseases, will couple the causal genes to environment for at least the example of EBV, will demonstrate how a much larger data resource could provide transforming insights into many diseases, and will generate ex vivo models that will provide reagents and data to facilitate the subsequent evaluation of therapies and preventive strategies.