Repurpose open data to discover therapeutics for understudied diseases

父R 01的目标是重用开放数据，以发现未充分研究的疾病的治疗方法。To respond 为了这个补充奖的具体利益，我们建议扩大我们在 亲本R 01来鉴定阿尔茨海默病及其亚型的再利用候选者。整合这些 表达谱与其他开放数据的结合为深入了解疾病提供了巨大的机会 机制和识别新的治疗方法。我们利用了一种基于系统的方法， 疾病样品的表达谱和来自癌细胞系的药物诱导的基因表达谱， 预测肝细胞癌、尤文肉瘤和基底细胞癌的新治疗候选者。所有 这些候选物在临床前模型中得到成功验证。这种方法的成功依赖于 多尺度程序，如疾病样本的质量控制，选择适当的参考组织， 疾病标记的评估和细胞系的加权。有大量的相关数据集和分析 公开可用的模块，但隔离在不同的筒仓中，使得实现这种方法变得繁琐 in translational翻译research研究.一个集中的信息系统，允许预测治疗，以进一步 因此，实验验证对于研究未充分研究的疾病的研究人员具有极大的兴趣。因此，委员会认为， 我们提出了四个具体目标：1）开发新的深度学习方法来选择精确的参考法线 2）开发计算方法以重新使用来自其他组织的药物谱， 用于药物预测的疾病模型，3）整合开放的功效数据以从系统中识别新靶点- 4）建立一个集中的平台，并在科学界推广该平台。 成功实施基于系统的方法可以作为使用其他大型开放 组学（蛋白质，代谢物），以发现未满足需求的疾病的治疗方法。阿尔茨海默病（AD） 影响着全世界数百万的患者，但没有有效的治疗方法。使用类似的方法，我们 合作者发现布美他尼作为与AD相关的APOE 4的候选物，并观察到AD的逆转 在小鼠模型中药物治疗后的基因表达，表明了扩展这种方法的潜力。 最近的努力已经为AD研究产生了大量的数据，包括单细胞RNA-seq和 来自患者和临床前模型的样品的空间转录组学，以及药物功效数据。在我们 最近在COVID-19药物再利用方面的努力，我们发现只有不到10%的疾病特征是 在治疗发现中提供信息。因此，本附录将系统评价AD特征 来源于患者和小鼠模型的批量RNA-seq、单细胞RNA-seq和空间转录组学。的 信息丰富的AD签名将部署到我们的药物发现管道中，以识别新的候选药物。

The goal of the parent R01 is to reuse open data to discover therapeutics for understudied diseases. To respond to the specific interest of this supplement award, we propose to expand the tools we have developed in the parent R01 to identify repurposing candidates for Alzheimer’s disease and its subtypes. Integrating these expression profiles with other open data provides tremendous opportunities to gain insights into disease mechanisms and identify new therapeutics. We have utilized a systems-based approach that employs gene expression profiles of disease samples and drug-induced gene expression profiles from cancer cell lines to predict new therapeutic candidates for hepatocellular carcinoma, Ewing sarcoma, and basal cell carcinoma. All these candidates were successfully validated in preclinical models. The success of this approach relies on multiscale procedures, such as quality control of disease samples, selection of appropriate reference tissues, evaluation of disease signatures, and weighting cell lines. There is a plethora of relevant datasets and analysis modules that are publicly available, yet are isolated in distinct silos, making it tedious to implement this approach in translational research. A centralized informatics system that allows prediction of therapeutics for further experimental validation is thus of great interest to researchers working on understudied diseases. Accordingly, we propose four specific aims: 1) developing novel deep learning methods to select precise reference normal tissues for disease signature creation, 2) developing computational methods to reuse drug profiles from other disease models for drug prediction, 3) integrating open efficacy data to identify new targets from the systems- based approach, and 4) developing a centralized platform and promoting the platform in the scientific community. Successful implementation of the systems-based approach can be used as a model for using other large open omics (proteins, metabolites) to discover therapeutics for diseases with unmet needs. Alzheimer’s disease (AD) affects millions of patients worldwide, yet there is no effective treatment. Using a similar approach, our collaborator discovered bumetanide as a candidate in APOE4 related to AD and observed the reversal of AD gene expression after drug treatment in a mouse model, suggesting the potential of expanding this approach. The recent endeavors have generated a huge amount of data for AD research including single cell RNA-seq and spatial transcriptomics of samples from patients and preclinical models, as well as drug efficacy data. In our recent effort in COVID-19 drug repurposing, we discovered only less than 10% of disease signatures were informative in therapeutic discovery. Therefore, this supplement will systematically evaluate AD signatures derived from bulk RNA-seq, single-cell RNA-seq and spatial transcriptomics of patients and mouse models. The informative AD signatures will be deployed to our drug discovery pipeline to identify new candidates.