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

项目摘要 我们响应RFA(AG-17-054)的目标是：1)应用新的计算方法，即TOP- 向下和自下而上的预测网络(简称预测网络)向现有的丰富数据集生成 AMP-AD和M2OVE-AD联合体发现可指导治疗发展的新靶点；2) 使用人类细胞模型对新靶点进行实验验证并生成RNA-SEQ 3)整合新的rna-seq数据，以进一步改进 建立基于现有AMP-AD数据的预测网络，以提高目标的质量。阿尔茨海默氏症 疾病是痴呆症最常见的形式，据估计，全世界有3600万人受到影响。这个号码是 预计到2050年将上升到1.15亿，除非开发出有效的治疗方法。AMP-AD目标 发现和临床前验证以及M2OVE-AD计划是大规模开放科学联盟的目标 在构建更好地反映其异质性的AD的预测性、多尺度模型以及在发现 通过综合的、数据驱动的方法实现下一代治疗目标。而四个多- AMP-AD的机构、多学科团队参与了尖端和不可知的分析工作，以 重建阿尔茨海默病基因、蛋白质和代谢物的分子网络并发现新的治疗方法 由多个模式生物评估的靶标，提出的所有药物靶标的重复性 团队数量较低(9月1日提出的80个目标中有1个重复。2016)考虑到 数据类型中存在的疾病和差异，以及用于生成和分析 数据。这种可变性破坏了每个候选目标的信心，并分散了 实验验证。当前的关键驱动因素筛查缺乏高通量的硅内筛查组件 通过预测下游分子表型来直接评估靶标的有效性 微扰。目标发现和验证之间的这种差距进一步降低了总体成功率， 药物开发的功效和效率。在本提案中，我们将使用预测网络管道 与AMP-AD和AMP-AD中的所有团队协作，对AMP-AD中的现有数据进行电子筛选组件 M2OVE-AD，用于构建因果和预测性网络，并确定关键驱动因素的优先顺序，这将是 通过实验验证进行评估。实验验证产生的rna-seq数据将用于 系统地评估计算机内表型预测，以确定拟议治疗的可信度 并与现有的预测网络相结合，以加强药物靶标发现。

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.