Gene Regulation as a Foundation for Autoimmune Disease Prevention

摘要。 我们建议成立一个自身免疫性疾病预防合作研究小组， 对实现设计和管理实际干预措施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）。我们的建议是定义“基因的作用”， 自身免疫性疾病的易感性和环境影响”，以“确定机制， 确定的遗传风险等位基因影响自身免疫性疾病的易感性”，并促进“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，免疫沉淀DNA的转录因子和辅因子（TF）约有25种 在疾病风险基因座的ChIP-seq序列产生强大的关联（2.7≤相对风险≤40，1.5E- 06≥pc≥4.6E-53），用于7种自身免疫性疾病：多发性硬化症（MS）、1型糖尿病（T1 D）、 类风湿性关节炎（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 EB病毒（EBV）核抗原2（EBNA 2）和近一半的相关基因编码。 已知TF是EBV相关超级增强子的组分。虽然新的结果令人信服; 基本数据目前不完整，一般不足以解决技术和研究设计问题; 尽管如此，我们已经确定了几个令人信服的例子，等位基因依赖的差异，TF结合， 来自疾病风险基因座的可能的因果变异（例如，在IL-10、CD 37和TMBIM 1处）。这些说明了 指定特定的分子相互作用可能有助于随之而来的自身免疫性疾病; 此外，它们还为应该开发的数据提供了强有力的指导，以便了解 环境（此处为EBNA 2）与风险基因座的相互作用产生自身免疫性疾病机制。 我们提出了4个目标：我们将测试调节分子（TF， microRNA、长链非编码RNA和RNA结合蛋白）与自身免疫性疾病基因座（Aim 1）;我们将 生成实验性调节分子结合数据，以揭示可能的疾病发生机制（Aim 2）;我们将测试离体基因调控的特定假设，并将基因调控效应定位到个体 与染色质编辑的细胞系的变体（目标3）;我们将扩展我们的新方法，我们的自身免疫 预防研究小组的同事和他们的项目，作为我们的信息学和实验的“阿尔法测试员”， 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， 该项目将提供自身免疫性疾病的病理生理机制， 环境，至少以EBV为例，将演示如何通过更大的数据资源提供 将洞察力转化为许多疾病，并将产生离体模型， 以便于随后对治疗和预防策略进行评估。

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.