Estrogen receptor control of inflammatory gene expression

更年期会引发全身炎症，导致内分泌和骨功能受损， 代谢性疾病、肥胖，以及自身免疫力和认知能力下降。这些观察结果突出了一个 迫切需要了解雌激素信号和炎症之间的关系。最重要的是 在我们对核受体的基本理解和治疗靶向方面的问题仍然几乎 完全不了解配体之间的微小差异如何会导致转录水平的巨大差异 结果，包括途径和组织选择性信号。目标是定义化学、结构和 雌激素受体-α(ER-α)配体调控炎症基因表达的分子规律 直接或间接地与DNA结合，改变辅助调节因子的招募，进而控制基因 表情。我们将用系统生物学的方法来研究化学生物学和结构生物学，以揭示规律。 了解配体如何实现信号特异性。具体目标1.发现和表征表观遗传学 调节ERα配体对炎症反应的不同效应的调节组。功能性 基因组学方法将被用来识别我们假设将会发生的三组炎症基因集 显示出不同的基因控制规则：1)肿瘤坏死因子诱导的、带有复合ERE/κB位点的E2调控基因2) 3)肿瘤坏死因子诱导的基因；3)肿瘤坏死因子诱导的基因 ERα绑定但不进行调制的位置。我们将从每个类别中挑选3-4个有代表性的基因作为靶点 通过针对所有协调控子的shRNA筛选(~1200个shRNAmir针对~300个基因)来识别这些基因 ERα调制所需。将构建协调节器-ERα交互屏幕，并使用化合物进行分析 500个ERα配体的文库。统计模型将使识别特定的协同调节相互作用成为可能 预测不同类型配体的炎症反应，并将它们与特定的结构联系起来 微扰。具体目标2.确定ERα转导的化学和结构规则 控制炎症基因的特定信号复合体的化学结构 表情。分析&gt；100晶体结构将用于计算所有原子距离矩阵并使用 主成分分析和多元线性回归用于识别预测配体诱导的扰动 (1)对特定基因的调控，以及(2)与特定目标中定义的特定辅助调节因子的相互作用1.影响： 这项工作将确定ERα配体如何差异调控炎症基因表达的结构规则 通过配体-受体界面变构控制特定的 共同调节复合体。识别配体如何实现信号特异性影响药物的因果模型 多种激素敏感生理学的发现和我们的理解 系统和疾病状态，并应易于翻译到其他变构信号系统，包括 其他核受体和GPCRs。

Menopause induces systemic inflammation that contributes to impaired endocrine and bone function, metabolic disease, obesity, as well as autoimmunity and cognitive decline. These observations highlight a critical need to understand the relationship between estrogen signaling and inflammation. The overarching problem in both our basic understanding and therapeutic targeting of nuclear receptors remains an almost complete lack of understanding of how small differences in ligands can drive widely different transcriptional outcomes, including pathway and tissue-selective signaling. The goal is to define the chemical, structural, and molecular rules for how the estrogen receptor-α (ERα) ligands control inflammatory gene expression through binding directly or indirectly to DNA and altering coregulator recruitment, which in turn controls gene expression. A systems biology approach to chemical biology and structural biology will be used to reveal rules for how ligands achieve signaling specificity. Specific Aim 1. Discover and characterize the epigenetic regulome that mediates differential effects of ERα ligands on the inflammatory response. Functional genomics approaches will be used to identify three sets of inflammatory gene sets that we hypothesize will show distinct rules for gene control: 1) TNF-induced, E2 modulated genes with composite ERE/κB sites 2) TNF-induced, E2 modulated genes with ERα tethering to κB response elements; and 3) TNF-induce genes where ERα bounds but does not modulate. We will pick 3-4 representative genes from each category to target with an shRNA screen targeting all coregulators (~1200 shRNAmir targeting ~300 genes) to identify those required for ERα modulation. Coregulator-ERα interaction screens will be built and profiled with a compound library of 500 ERα ligands. Statistical models will enable identification of specific coregulator interactions that predict the inflammatory response to different classes of ligands and ties them to specific structural perturbations. Specific Aim 2. Identify the chemical and structural rules by which ERα transduces chemical structure into recruitment of specific signaling complexes that control inflammatory gene expression. Analysis of >100 crystal structures will be used to compute all atom distance matrices and use principal component analysis and multiple linear regression to identify ligand-induced perturbations that predict (1) regulation of specific genes, and (2) interaction with specific coregulators defined in Specific Aim 1. Impact: This work will identify structural rules for how ERα ligands differentially regulate inflammatory gene expression through the ligand-receptor interface allosterically controlling recruitment of an ensemble of specific coregulatory complexes. Identifying causal models for how ligands achieve signaling specificity impacts drug discovery and our understanding of the physiology underlying a variety of hormone-sensitive physiological systems and disease states, and should be readily translatable to other allosteric signaling systems including other nuclear receptors and GPCRs.