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: 10493151
• 负责人: STUART A BATTERMAN
• 金额: $ 98.43万
• 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/10493151
• 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.

摘要 遗传性不能完全解释肌萎缩侧索硬化症（ALS），ALS遗传性疾病的进展速度， 覆盖速度放缓，这意味着需要全新的研究方向来解开疾病机制 并确定治疗方法。我们的目标是了解，治疗和预防ALS。我们的总体方法是确定 暴露、基因组学、表观基因组学、转录组学、代谢组学和炎症对ALS的交叉作用。 我们的理由是，即使是基于有限的粗血浆测量的先前环境风险评分（ERS）， 各类污染物与ALS风险增加7倍和生存率降低2倍相关，因此， 详细了解与其他组学的麻烦，可以立即提供新的，急需的战略， ALS的治疗和预防。我们提出了3个目标：1）全面评估环境 暴露和多基因因素在ALS与对照受试者，以确定协同环境多基因 增加ALS风险的关联; 2）定义ALS表观基因组，转录组和 代谢组;和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，我们解释了来自自我报告、地理空间分析和生物标本的复杂暴露数据 对特定的暴露类型（如农药、金属、空气污染）使用成分-ERS（cERS）， ERS用于组合暴露。我们使用多基因风险评分（PRS）和C9 ORF 72来解释遗传风险 status.我们将建立基于ERS和PRS的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 与差异表达基因和靶向通路相关的暴露。表达量性状 基因座（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， 发现新的毒物将使用泛组学技术整合所有数据集，以识别基因代谢产物 这些网络是疾病的目标。最后，我们将对与cERS和多聚- ERS使用现有FDA批准的药物确定治疗靶点。我们的建议极具创新性，它... 每个患者的罚款，（i）他们的麻烦，总结与cERS/聚-ERS;（ii）他们的基因组总结， PRS及其与ERS的关联，以了解基因/环境组合风险;（iii）它们的多基因组基因， 来自表观基因组、转录组、代谢组和炎性体的标记;（iv）它们的dysreg-regenerative- 根据其与ALS风险和进展的相关性进行排名，以确定个性化机制- 基于药物的靶标;和（v）ALS预测模型和通过风险因素修改的预防策略。我们 平行的，多组学方法比串行的，单一毒物/组学方法快得多， grated nature捕捉了组学交叉点的全部频谱，加速了科学发现。的中国梦作出 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。