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A Multimodal Infrastructure for Alzheimer’s MultiOme Data Repurposing: Artificial Intelligence, Network Medicine, and Therapeutics Discovery

PI
Cheng, Feixiong
DAR ID
10373
Initial Approval Date
June 9, 2021
Current DAR Status
Active
PI Institution
Cleveland Clinic Lerner College of Medicine of CWRU
PI Institution Country
United States
PI Institution Type
Non-Profit

Requested Datasets (12)

NG00067Alzheimer's Disease Sequencing Project Umbrella StudyNG00108Profiling microglia expression profiles in AD using single-nuclei RNA-seqNG00105MiGA – Microglia Genomic AtlasNG00113Metabolomic and lipidomic signatures in Alzheimer disease brainsNG00130Large scale CSF proteogenomic atlasNG00160Sex-specific genetic architecture of late-life memory performanceNG00158Sex differences in the genetic predictors of Alzheimer’s pathology – Dumitrescu, et al. 2019NG00161Sex differences in the genetic architecture of cognitive resilience to Alzheimer's disease – Eissman, et al. 2022NG00162Oligodendrocytes single-cell whole genome and RNA sequencingNG00172GWAS Summary Statistics of SNVs/INDELs/SVs for Progressive Supranuclear Palsy (PSP) – Wang et al., 2024NG00174Whole Genome Sequencing (WGS) and SNP Array data for the SEA-AD Consortium CohortNG00178Multi-brain region mRNA-seq of sporadic ALS patients with pTDP-43 pathological staging

Research Use Statement
We propose to develop capable and intelligent computer-based toolboxes that enable searching, sharing, visualizing, querying, and analyzing genetics, genomics, multi-omics, and clinical data for AD. The central unifying hypothesis of this project (1U01AG073323-01 [pending for Council meeting at May/2021) is that a genome-wide, multimodal artificial intelligence (AI) framework to identify novel risk genes and networks from human WGS/WES and multi-omics findings will offer drug targets for targeted therapeutic development in AD. Aim 1 will identify rare coding variant-based risk genes using a sequence and structure-based deep learning model. Aim 2 will identify rare non-coding variant-based risk genes using a multiple kernel learning approach. Aim 3 will test whether GWAS common variants linked to AD pathobiology and endophenotypes are enriched in gene regulatory networks in a cell-type specific manner using a Bayesian framework. These analyses will leverage variants from ethnically diverse WGS/WES and clinical data (i.e., imaging, biomarkers, and cognitive measures) from Alzheimer's Disease Sequencing Project (ADSP), and publicly available chromatin interactomic data from NIH RoadMap, FANTOM5, and NIH 4D Nucleome. We will validate our findings using WGS/WES data and protein expression data from our existing cohorts: The Cleveland Clinic Lou Ruvo Center for Brain Health Aging and Neurodegenerative Disease Biobank (CBH-Biobank) and the Cleveland Alzheimer's Disease Research Center (CADRC). We will compile information for clinical data harmonization, including functional imaging, AD biomarkers, and cognitive measures for all integrative analyses. There are no any PHI information will collected or used in the data analysis. We don’t have the planned collaboration with researchers outside Cleveland Clinic in the current analytic plans.

Non-Technical Summary
It is estimated that more than 16 million people with AD live in the United States by 2050 and the predisposition to AD involves a complex, polygenic, and pleiotropic genetic architecture. This project will develop intelligent computer-based network medicine and systems biology tools, capable of identifying and validating human genome sequencing findings for novel risk gene discoveries and targeted therapeutic development in AD. The innovative network-based, artificial intelligence toolboxes and novel risk genes and biologically relevant targeted therapeutic approaches developed in this proposal will prove to be novel and effective ways to improve outcomes in long-term brain care for the rapidly growing AD population, an essential goal of AD precision medicine.

Reported Publications (13)

Publications shown are those reported by investigators through NIAGADS DAR reporting and renewal processes and may not represent a complete list of publications associated with the project.

  1. Single-microglia transcriptomic transition network-based prediction and real-world patient data validation identifies ketorolac as a repurposable drug for Alzheimer's disease. Xu J, et al. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2025. PMID: 39641322
  2. Alzheimer's disease drug development pipeline: 2024. Cummings J, et al. Alzheimer's & dementia (New York, N. Y.). 2024. PMID: 38659717
  3. Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease. Cheng F, et al. Cell reports. Medicine. 2024. PMID: 38382465
  4. Artificial intelligence for drug discovery and development in Alzheimer's disease. Qiu Y, et al. Current opinion in structural biology. 2024. PMID: 38335558
  5. Microglial immunometabolism endophenotypes contribute to sex difference in Alzheimer's disease. Hou Y, et al. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2024. PMID: 37985399
  6. Sildenafil as a Candidate Drug for Alzheimer's Disease: Real-World Patient Data Observation and Mechanistic Observations from Patient-Induced Pluripotent Stem Cell-Derived Neurons. Gohel D, et al. Journal of Alzheimer's disease : JAD. 2024. PMID: 38427489
  7. Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease. Qiu Y, et al. Cell reports. 2024. PMID: 38652661
  8. Population-based discovery and Mendelian randomization analysis identify telmisartan as a candidate medicine for Alzheimer's disease in African Americans. Zhang P, et al. Alzheimer's & dementia : the journal of the Alzheimer's Association. 2023. PMID: 36331056
  9. Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease. Fang J, et al. Alzheimer's research & therapy. 2022. PMID: 35012639
  10. Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease. Martin W, et al. Current opinion in structural biology. 2022. PMID: 34628220
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  1. Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease. Xu J, et al. Cell reports. 2022. PMID: 36450252
  2. The Alzheimer's Cell Atlas (TACA): A single-cell molecular map for translational therapeutics accelerator in Alzheimer's disease. Zhou Y, et al. Alzheimer's & dementia (New York, N. Y.). 2022. PMID: 36254161
  3. Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer's disease. Fang J, et al. Nature aging. 2021. PMID: 35572351