Building Novel Predictive Networks for high-throughput, in-silico Key Driver Prioritization to Enhance Drug Target Discovery in AMP-AD and M2OVE-AD

构建新型预测网络以实现高通量、计算机内关键驱动程序优先级排序，以增强 AMP-AD 和 M2OVE-AD 中的药物靶标发现

• 批准号: 9423217
• 负责人: Rui Chang
• 金额: $ 21.85万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2018-03-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9423217
• 关键词: Affect; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease model; Animal Model; Astrocytes; Brain; Cell model; Cells; Collaborations; Computer Simulation; Data; Data Set; Dementia; Development; Disease; Drug Targeting; Gene Expression; Gene Proteins; Genes; Goals; Heterogeneity; Human; Learning; Metabolic; Microglia; Molecular; Molecular Profiling; Network-based; Neurons; Oligodendroglia; Pathway interactions; Phenotype; Proteomics; Recipe; Reproducibility; Therapeutic; Tissues; Treatment Efficacy; Update; Validation; cell type; combinatorial; drug development; experimental study; improved; induced pluripotent stem cell; molecular phenotype; monocyte; multi-scale modeling; multidisciplinary; network models; new therapeutic target; novel; novel therapeutics; open data; pre-clinical; programs; protein metabolite; screening; small hairpin RNA; success; therapeutic target; therapy development; transcriptome sequencing

Project Summary We respond to the RFA (AG-17-054) with the goals of 1) applying new computational approaches, i.e. top- down and bottom-up predictive network (predictive network for short) to the existing rich datasets generated by the AMP-AD and M2OVE-AD consortia to discover novel targets that can guide therapy development; 2) performing experimental validation of the novel targets using human cellular models and generating RNA-seq data to systematically validate in-silico prediction; 3) integrating the new RNA-seq data to further improve the predictive network built on existing AMP-AD data and to enhance the quality of the targets. Alzheimer's disease is the most common form of Dementia estimated to affect 36 million people worldwide. This number is expected to rise to 115 million by 2050 unless an effective therapeutic is developed. The AMP-AD Target Discovery and Preclinical Validation and M2OVE-AD programs are large-scale, open science consortia aimed at building a predictive, multi-scale model of AD that better reflects its heterogeneity and at discovering the next-generation therapeutic targets through integrative, data-driven approaches. While the four multi- institutional, multidisciplinary teams in AMP-AD engaged cutting-edge and agnostic analysis efforts to reconstruct the molecular network of the gene, protein, and metabolite in AD and to discover novel therapeutic targets evaluated by multiple model organisms, the reproducibility of the drug targets proposed across all teams is low (1 replicate out of total 80 targets proposed in Sept. 2016) given the considerable complexity of the disease and differences that exist in the data types and the approaches used to generate and analyze the data. This variability undermined the confidence of each candidate target and defocused the efforts of experiment validation. Current key driver screening lacks a high-throughput, in-silico screening component to directly evaluate the efficacy of a target by predicting the downstream molecular phenotype given its perturbation. This gap between target discovery and validation further reduces the overall rate of success, efficacy and efficiency of drug development. In this proposal, we will apply the predictive network pipeline with the in-silico screening component to existing data in AMP-AD in collaboration with all teams in AMP-AD and M2OVE-AD to build causal and predictive networks and to identify and prioritize key drivers, which will be evaluated by experiment validation. The RNA-seq data generated by experiment validation will be used to systematically evaluate the in-silico phenotypic prediction to determine the confidence of proposed therapeutic recipes and to be integrated with the existing predictive networks to enhance drug target discovery.

项目摘要 我们对RFA（AG-17-054）做出了回应，其目标是1）采用新的计算方法，即顶级 向下和自下而上的预测网络（简称预测网络）到由现有的丰富数据集生成 AMP-AD和M2OVE-AD联盟发现可以指导治疗发展的新目标； 2） 使用人类细胞模型对新目标进行实验验证并产生RNA-Seq 数据以系统地验证核内预测； 3）整合新的RNA-seq数据以进一步改善 预测网络基于现有的AMP-AD数据并提高目标质量。阿尔茨海默氏症 疾病是估计影响全球3600万人的最常见痴呆症形式。这个数字是 除非开发有效的治疗性，否则预计到2050年将上升到1.15亿。 AMP-AD目标 发现和临床前验证以及M2OVE-AD计划是针对大规模的开放科学联盟 在建立一个预测性的多尺度AD模型时，可以更好地反映其异质性并发现 下一代治疗靶标通过集成，数据驱动的方法。而四个多 在AMP-AD参与尖端和不可知论分析的机构多学科团队中 在AD中重建基因，蛋白质和代谢物的分子网络，并发现新型治疗 通过多种模型生物评估的靶标，所有在所有模型生物中提出的药物靶标的可重复性 鉴于相当复杂的复杂性 数据类型中存在的疾病和差异以及用于生成和分析的方法 数据。这种可变性破坏了每个候选目标的信心，并宣传了 实验验证。当前的关键驱动程序筛查缺乏高通量的，内部的筛选组件 直接通过预测下游分子表型来直接评估目标的疗效 扰动。目标发现与验证之间的差距进一步降低了成功的总体成功率， 药物开发的功效和效率。在此提案中，我们将使用预测网络管道 与AMP-AD和AMP-AD和 m2ove-ad建立因果和预测网络并确定和确定关键驱动因素的优先级，这将是 通过实验验证评估。实验验证生成的RNA-seq数据将用于 系统地评估Silico表型预测，以确定所提出的治疗的置信度 食谱并与现有的预测网络集成，以增强药物靶标的发现。