Comparative Gene Expression Studies in AD and Other Conditions
More than 98 percent of the human genome is composed of noncoding DNA. It is postulated that much of the genetic variation that influences risk of common and complex human diseases such as Alzheimer's disease (AD) and other neurodegenerative diseases will reside in noncoding through functional, regulatory regions of the genome and will alter disease risk by affecting gene expression.
If this hypothesis is correct, then many functional, regulatory genetic variants or their proxies will associate with both disease risk and gene expression levels. Further, gene transcripts, which are regulated by functional disease risk variants, may show differential levels in subjects that have the disease either clinically or prodromally in comparison with control subjects.
In support of this hypothesis, the lab has determined that genetic variants near many genetic risk loci for late-onset Alzheimer's disease (LOAD) associate with brain levels of nearby genes. Additionally, genetic variants that have strong association with brain gene expression levels are enriched for human disease-associated variants, including those for LOAD.
The lab has several ongoing projects that are aimed at harnessing this dual information on gene expression levels and disease risk. The goal is to identify and characterize novel genes, transcripts and genetic risk variants for LOAD.
These projects include:
1. Target pathway discovery in AD using transcriptomics
The team has already determined that the strongest genetic risk variants for AD — detected in recent genomic screens — also associate with the brain expression levels of genes located close to the strong risk variants. These findings suggest that genetic variants that confer risk of AD can be found by modifying the expression levels of genes in the brain.
Many genes exist in different forms, called isoforms, which result in different protein products with diverse functions. Identifying the precise changes in brain levels of these gene isoforms enhances the understanding of risk mechanisms in AD and enables future novel therapeutic approaches.
In this project, the lab aims to uncover specific isoform level changes for the strongest Alzheimer's risk genes using deceased-donor brains of people who had AD. Additionally, researchers will identify the biological consequences of altering specific isoform levels for some of the risk genes by decreasing or increasing their levels in cellular models, as well as investigating functional outcomes such as cell survival.
The goal is to uncover changes to the gene isoform level that implicates novel pathways in AD that may guide future drug discovery efforts and revolutionize therapeutic approaches.
2. A systems approach to targeting innate immunity in AD
As part of a multi-institutional project co-led by Nilufer Ertekin-Taner, M.D., Ph.D.; Todd Golde, M.D., Ph.D., at the University of Florida; Nathan Price, Ph.D., at the Institute for Systems Biology; and Steven G. Younkin, M.D., Ph.D., at Mayo Clinic, the laboratory is leading the aim to identify gene expression changes in innate immunity pathways.
Using next-generation RNA with more targeted gene expression arrays and through mining existing data, the team will define how innate immunity is altered in AD, primary tauopathies and pathological aging (700 brain samples from 350 subjects). Additionally, transgenic mouse models that develop AD relevant pathologies (amyloid-beta and tau with two models each) will be used.
The focus is to validate key alterations in innate immune gene expression at the protein level in biological tissues and fluids. Though preliminary data has already revealed new targets for intervention, this study enables more broad and rational identification of key nodes within the innate immune signaling pathways.
3. An innovative omics approach for biomarker discovery in AD
The lab postulates that transcript level changes that occur due to regulatory genetic variants, which also influence disease risk, can be detected prior to the development of clinical AD. The underlying premise is that since disease risk variants are expected to be more frequent in the high-risk preclinical AD population compared with controls, the downstream regulatory effects of transcript level changes also can be detected preclinically.
Indeed, the strongest known genetic risk factor for LOAD, apolipoprotein ε4 (APOE ε4), has higher frequency in subjects with mild cognitive impairment (MCI). This is a high-risk state considered to be a prodrome for AD, especially for amnestic MCI (aMCI). Although APOE ε4 reflects coding and not regulatory polymorphisms, if these observations apply to other disease risk variants, then regulatory variants that influence AD risk and also associate with levels of gene transcripts will result in transcript level differences. These include subjects with AD versus controls and high-risk, preclinical subjects (such as aMCI) versus controls.
If this hypothetical model is correct, it provides a strong rationale for the use of transcript levels as genetically-driven biomarkers in preclinical AD.
In this project, the team is measuring whole transcriptome gene levels collected from blood RNA of a subset of subjects from the Mayo Clinic Study of Aging. Transcript levels will be measured in subjects who are cognitively normal, incident MCI and incident AD.
The goal is to identify genetically-driven premorbid blood transcript biomarkers for AD.