DECODER: A probabilistic approach to integrate big data reveals mitochondrial Complex I as a potential therapeutic target for Alzheimer’s disease

Year: 2018

Type: preprint

Abstract: Identifying gene expression markers for Alzheimer’s disease (AD) neuropathology through meta-analysis is a complex undertaking because available data are often from different studies and/or brain regions involving study-specific confounders and/or region-specific biological processes. Here we introduce a novel probabilistic model-based framework, DECODER, leveraging these discrepancies to identify robust biomarkers for complex phenotypes. Our experiments present: (1) DECODER’s potential as a general meta-analysis framework widely applicable to various diseases (e.g., AD and cancer) and phenotypes (e.g., Amyloid-β (Aβ) pathology, tau pathology, and survival), (2) our results from a meta-analysis using 1,746 human brain tissue samples from nine brain regions in three studies — the largest expression meta-analysis for AD, to our knowledge —, and (3) in vivo validation of identified modifiers of Aβ toxicity in a transgenic Caenorhabditis elegans model expressing AD-associated Aβ, which pinpoints mitochondrial Complex I as a critical mediator of proteostasis and a promising pharmacological avenue toward treating AD.

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.1101/302737

Authors: Safiye Celik, Josh C. Russell, Cezar R. Pestana, Ting-I Lee, Shubhabrata Mukherjee, Paul K. Crane, C. Dirk Keene, Jennifer F. Bobb, Matt Kaeberlein, Su-In Lee

Institutions: University of Washington, Kaiser Permanente Washington Health Research Institute

Language: English

Topic: Alzheimer's disease research and treatments

Subfield: Physiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Science Foundation, Paul G. Allen Family Foundation, Illinois Department of Public Health, Nancy and Buster Alvord Endowment, Translational Genomics Research Institute, National Institutes of Health, National Institute on Aging

OpenAlex ID: W2805562016