Modulation of Lung Disease by Genetic/Epigenetic Profiling

通过遗传/表观遗传分析调节肺部疾病

基本信息

批准号：
10736913
负责人：
William Edward Balch
金额：
$ 45.25万
依托单位：
SCRIPPS RESEARCH INSTITUTE, THE
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-04-01 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10736913
关键词：
Accounting Acetylation Affect Aging Automobile Driving Bacterial Infections Biology Cell Death Cell physiology Cells Cellular Stress Cessation of life Chlorides Chronic Clinic Clinical Complex Cystic Fibrosis Cystic Fibrosis Transmembrane Conductance Regulator Cytoprotection Deacetylation Delta F508 mutation Development Disease Environment Epigenetic Process Equilibrium Event Fibrosis Functional disorder Funding Gene Expression Gene Proteins Genetic Genetic Diseases Genetic Variation Genomics Genotype HDAC7 histone deacetylase Histone Acetylation Histone Deacetylase Histone Deacetylase Inhibitor Hospitalization Immune response Individual Infection Inflammation Inflammatory Response Intestines Knowledge Lead Link Logistics Lung Lung diseases Machine Learning Maps Membrane Mendelian disorder Mucociliary Clearance Organ Outcome Pancreas Paper Pathology Pathway interactions Patients Performance Personal Satisfaction Phenotype Play Population Population Heterogeneity Post-Translational Protein Processing Process Protein Isoforms Proteins Proteomics Publications Pulmonary Function Test/Forced Expiratory Volume 1 Resolution Role Small Interfering RNA Stress Structure Symptoms Technology Therapeutic Time Tissues Treatment Efficacy Variant clinical phenotype epigenetic profiling epigenome epigenomics genomic platform improved inhibitor loss of function misfolded protein novel novel strategies patient population prevent programs protein folding protein function pulmonary function response synergism tool trafficking transcriptome

项目摘要

Project Summary/Abstract: Therapeutic management of lung disorders triggered by the loss-of-function of the cystic fibrosis (CF) transmembrane conductance regulator (CFTR) function in response to leading to CF are challenged by genetic and epigenetic diversity found in the CF population. The highly effective modulator therapy (HEMT) Trikafta has a pronounced but incomplete and variable impact on the pathology of disease in the clinic. We now need to discover new approaches to further improve clinical outcome. CF is not a simple monogenic disease but rather a complex disease impacted by membrane trafficking and channel function of CFTR- as well as diverse clinical features including inflammation, mucociliary clearance, and bacterial infection. These environmental features of disease lead to lung dysfunction as well as multi-organ symptoms including pancreatic and intestinal dysfunction. New approaches that capture the link between the genotype and cellular dysfunctional phenotypes as a collective of covariant events in the individual will require a deeper understanding of the fundamental principles dictating disease influenced by genetic and epigenetic diversity of the population. This proposal is about understanding the role of the epigenetic environment in management of CF in response to the histone deacetylase (HDAC) program controlling gene expression during development, aging and in response to cellular stress in disease. During the previous funding period, we have shown that the collective of variation found in the CF population can be used to define sequence-to-function-to-structure relationships responsive to HDAC inhibitors (HDACi). We will study the interlinked roles of genetic and epigenetic diversity using novel machine learning computational approaches we developed during the previous funding period that can be integrated with experimental/clinical features to discover therapeutics that could considerably improve patient well-being in response to the HEMT Trikafta. To understand the impact of complex epigenetic pathways in CF to improve Trikafta performance, we will apply our new Gaussian process (GP) based platform, referred to as variation spatial profiling (VSP) based variation capture (VarC) mapping, to profile at a residue-residue basis at atomic resolution a map of hidden spatial covariant (SCV) interactions that can resolve complex phenotypic relationships in response to genetic/epigenetic diversity. During the previous funding period, VSP/VarC mapping revealed a hidden ‘YKDAD’ energetic core in the CFTR fold that is the foundational basis for disease in the majority of the CF population that is not corrected by the HEMT Trikafta- limiting its impact in the clinic. In Aim 1 we will use VSP/VarC mapping to inform us of the complex disease states disrupted by CFTR misfolding, trafficking and function affecting inflammation, mucociliary clearance and infection to predict how to more effectively treat the patient through use of HDAC inhibitors (HDACi). In Aim 2, we will specifically explore the role of HDAC7, which we have previously shown to correct CFTR function. We hypothesize that knowledge of the role of the HDAC epigenetic program can be used to enhance HEMT efficacy in the path to a cure.

项目摘要/摘要：囊性纤维化丧失（CF）触发的肺部疾病治疗治疗跨膜电导调节剂（CFTR）响应导致CF的功能受到遗传的挑战以及在CF人群中发现的表观遗传多样性。高效调节剂治疗（HEMT）Trikafta具有对诊所疾病病理学的明显但不完整和可变影响。我们现在需要发现新的方法以进一步改善临床结果。 CF不是一种简单的单基因疾病，而是由膜运输和CFTR和潜水员临床的膜运输和通道功能影响的复杂疾病包括感染，粘膜缩减清除率和细菌感染的特征。这些环境特征的疾病导致肺功能障碍以及包括胰腺和肠功能障碍在内的多器官症状。捕获基因型和细胞功能障碍表型之间联系的新方法个人中的协方差事件将需要更深入地了解基本原则由人群的遗传和表观遗传学多样性影响疾病。该建议是关于了解表观遗传环境在响应组蛋白的CF管理中的作用脱乙酰基酶（HDAC）程序控制在发育，衰老和对细胞的反应过程中控制基因表达疾病的压力。在上一个资金期间，我们已经证明了在 CF人群可用于定义对HDAC响应的序列到功能的结构关系抑制剂（HDACI）。我们将使用新机器研究遗传和表观遗传多样性的相互联系的作用我们在上一个资金期间开发的计算方法可以与实验/临床特征发现可以考虑改善患者福祉的疗法对Hemt Trikafta的反应。了解复杂表观遗传途径在CF中的影响 Trikafta性能，我们将应用基于新的高斯流程（GP）平台，称为变体基于空间分析（VSP）的变化捕获（VARC）映射，在原子上以退休救助为基础分辨率一张可以解决复杂表型关系的隐藏空间协变（SCV）相互作用的图响应遗传/表观遗传学多样性。在上一个资金期间，VSP/VARC映射显示 CFTR折叠中隐藏的“ ykdad”能量核心，这是大多数疾病的基础基础 CF人群未通过HEMT Trikafta限制其在诊所的影响而纠正。在AIM 1中，我们将使用 VSP/VARC映射，以告知我们CFTR不折叠，贩运和影响感染，粘膜缩减清除和感染的功能，以预测如何更有效地治疗通过使用HDAC抑制剂（HDACI）患者。在AIM 2中，我们将专门探讨HDAC7的作用，我们先前已显示纠正CFTR函数。我们假设对HDAC角色的知识表观遗传程序可用于提高治疗路径中的HEMT效率。