Developing novel strategies for personalized treatment and prevention of ALS: Leveraging the global exposome, genome, epigenome, metabolome, and inflammasome with data science in a case/control cohort

制定个性化治疗和预防 ALS 的新策略：在病例/对照队列中利用数据科学的全局暴露组、基因组、表观基因组、代谢组和炎症小体

• 批准号: 10271663
• 负责人: STUART A BATTERMAN
• 金额: $ 99万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10271663
• 关键词: Address; Air Pollution; Alzheimer' s Disease; Amyotrophic Lateral Sclerosis; Area; Award; Biological Markers; C9ORF72; Cell Survival; Complex; DNA; Data; Data Science; Data Set; Development; Diagnostic; Disease; Drug Targeting; Environment; Environmental Exposure; Environmental Risk Factor; Epigenetic Process; Exposure to; FDA approved; Gene Expression; Gene Targeting; Genes; Genetic; Genetic Polymorphism; Genetic Risk; Genetic study; Genome; Genomics; Goals; Grouping; Heritability; Immune; Immunophenotyping; Inflammasome; Inflammation; Intervention; Link; Measures; Messenger RNA; Metabolic; Metals; MicroRNAs; Modeling; Modification; Nature; Neurodegenerative Disorders; Occupational; Parkinson Disease; Pathogenesis; Pathway interactions; Patient Self-Report; Patients; Persons; Pesticides; Pharmaceutical Preparations; Phenotype; Plasma; Play; Population; Precision Health; Prevalence; Prevention; Prevention strategy; Public Health; Quantitative Trait Loci; Recording of previous events; Research; Resolution; Risk; Risk Factors; Role; Science; Series; Techniques; Therapeutic; Time; amyotrophic lateral sclerosis therapy; base; case control; cohort; differential expression; disorder prevention; disorder risk; epigenome; epigenomics; improved; individualized prevention; innovation; insight; metabolome; metabolomics; methylome; middle age; multiple omics; new therapeutic target; novel strategies; personalized medicine; pollutant; polygenic risk score; predictive modeling; prevent; prognostic; risk prediction model; therapeutic target; toxicant; transcriptome; transcriptomics; treatment strategy

ABSTRACT Genetic heritability incompletely explains amyotrophic lateral sclerosis (ALS), and the pace of ALS genetic dis- coveries has slowed, meaning entirely new research directions are needed to unravel disease mechanisms and identify therapies. Our goal is to understand, cure, and prevent ALS. Our overall approach is to identify the intersection of exposures, genomics, epigenomics, transcriptomics, metabolomics, and inflammation on ALS. Our rationale is that prior environmental risk scores (ERS) based on even crude plasma measures of limited classes of pollutants associate with a 7-fold increase in ALS risk and 2-fold decrease in survival, therefore a detailed understanding of the exposome with other omics can immediately provide new, much needed strate- gies for both ALS treatment and prevention. We propose 3 aims: 1) comprehensively assess environmental exposures and polygenic factors in ALS versus control subjects to identify synergistic environment-polygenic associations that increase ALS risk; 2) define exposome signatures in the ALS epigenome, transcriptome, and metabolome; and 3) determine how environmental exposures alter ALS immune profiles and identify drug tar- gets. First, we account for the complex exposure data from self-reports, geospatial analysis, and biospecimens using component-ERS (cERS) for specific exposure types (e.g. pesticides, metals, air pollution) and a poly- ERS for combined exposures. We account for genetic risk using polygenic risk scores (PRS) and C9ORF72 status. We will build ALS risk and prediction models based on ERS and PRS. Next, using cERS, poly-ERS, and PRS, we determine the environmental signature on the DNA methylome, mRNA and microRNA, to identify exposures that associate with differentially expressed genes and target pathways. Expression quantitative trait loci (eQTL) analyses will define the relative contribution of polymorphisms vs exposures on gene expression. High resolution untargeted metabolomics will reveal the environmental signature of the ALS metabolome and identify new toxicants. All datasets will be integrated using pan-omics techniques to identify gene-metabolite networks that are disease targets. Finally, we will classify immune profiles that associate with cERS and poly- ERS to identify therapeutic targets using existing FDA approved drugs. Our proposal is highly innovative; it de- fines for each patient, (i) their exposome, summarized with cERS/poly-ERS; (ii) their genome summarized by PRS and its association with ERS to understand the combined gene/environment risk; (iii) their multi-omic en- vironmental signatures from the epigenome, transcriptome, metabolome, and inflammasome; (iv) their dysreg- ulated pathways, ranked by their association to ALS risk and progression to identify personalized mechanism- based drug-targets; and (v) ALS prediction models and preventative strategies via risk factor modification. Our parallel, multi-omics approach is significantly faster than serial, single-toxicant/omic approaches, and its inte- grated nature captures the full spectrum of omics intersections, accelerating scientific discovery. We will make significant strides in finding completely new therapeutic targets and public health preventative strategies.

抽象的 遗传性不能完全解释肌萎缩侧索硬化症 (ALS)，而且 ALS 遗传疾病的进展速度 覆盖率已经放缓，这意味着需要全新的研究方向来揭示疾病机制 并确定治疗方法。我们的目标是了解、治愈和预防 ALS。我们的总体方法是确定 ALS 的暴露、基因组学、表观基因组学、转录组学、代谢组学和炎症的交叉。 我们的理由是，先前的环境风险评分（ERS）基于有限的原始血浆测量值 污染物类别与 ALS 风险增加 7 倍和生存率下降 2 倍相关，因此 对暴露组与其他组学的详细了解可以立即提供新的、急需的策略 为 ALS 治疗和预防提供帮助。我们提出3个目标：1）全面评估环境 ALS 与对照受试者的暴露和多基因因素，以确定协同环境多基因 增加 ALS 风险的协会； 2) 定义 ALS 表观基因组、转录组中的暴露组特征，以及 代谢组； 3) 确定环境暴露如何改变 ALS 免疫特征并识别药物焦油 得到。首先，我们考虑来自自我报告、地理空间分析和生物样本的复杂暴露数据 使用针对特定暴露类型（例如农药、金属、空气污染）的组分-ERS（cERS）和多 综合暴露的 ERS。我们使用多基因风险评分 (PRS) 和 C9ORF72 来解释遗传风险 地位。我们将基于ERS和PRS建立ALS风险和预测模型。接下来，使用 cERS、poly-ERS， 和 PRS，我们确定 DNA 甲基化组、mRNA 和 microRNA 上的环境特征，以识别 与差异表达基因和目标途径相关的暴露。表达数量性状 位点 (eQTL) 分析将定义多态性与暴露对基因表达的相对贡献。 高分辨率非靶向代谢组学将揭示 ALS 代谢组的环境特征 识别新的毒物。所有数据集将使用泛组学技术进行整合，以识别基因代谢物 作为疾病目标的网络。最后，我们将对与 cERS 和多聚相关的免疫特征进行分类 ERS 使用 FDA 批准的现有药物来确定治疗靶点。我们的建议极具创新性；它去- 每个患者的罚款，(i) 他们的暴露组，用 cERS/poly-ERS 汇总； (ii) 他们的基因组总结为 PRS 及其与 ERS ​​的关联，以了解基因/环境组合风险； (iii) 他们的多组学en- 来自表观基因组、转录组、代谢组和炎症体的环境特征； (iv) 他们的失调 制定的途径，根据其与 ALS 风险和进展的关联进行排序，以确定个性化机制 - 基于药物靶点； (v) ALS 预测模型和通过风险因素修正的预防策略。我们的 并行的多组学方法比串行的单毒物/组学方法要快得多，并且其内部 精细的自然捕捉了组学交叉点的全部光谱，加速了科学发现。我们将使 在寻找全新的治疗靶点和公共卫生预防策略方面取得了重大进展。