Repurpose open data to discover therapeutics for understudied diseases

Michigan State University, East Lansing MI

Investigators

Linked publications, trials & patents

Abstract

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.