Gene expression profile biomarkers and therapeutic targets for brain aging and age-related cognitive impairment

Abstract

A statistical and functional correlation strategy to identify changes in cellular pathways specifically linked to impaired cognitive function with aging. Analyses using the strategy identified multiple groups of genes expressed in the hippocampi of mammals, where the genes were expressed at different levels for several ages. The aging changes in expression began before mid-life. Many of the genes were involved in specific neuronal and glial pathways with previously unrecognized relationships to aging and / or cognitive decline. The processes identified by the strategy suggest a new hypothesis of brain aging in which initially decreased neuronal activity and / or oxidative metabolism trigger separate but parallel genomic cascades in neurons and glia. In neurons, the cascade results in elevations in calcium signaling and reductions of immediate early gene signaling, biosynthesis, synaptogenesis and neurite remodeling. In contrast, glia undergo increased lipid metabolism and mediate a cycle of demyelination and remyelination that induces antigen presentation, inflammation, oxidative stress and extracellular restructuring. These identified genes and the proteins they encode can be used as novel biomarkers of brain aging and as targets for developing treatment methods against age-related cognitive decline, Alzheimer's Disease and Parkinson's Disease.

Description

FIELD OF THE INVENTION The invention relates generally to genetic algorithms, and more particularly to the identification of gene expression profile biomarkers and therapeutic targets for brain aging. BACKGROUND OF THE INVENTION Brain aging processes are enormously complex phenomena that affect multiple systems, cell types and pathways, and result in cognitive decline and increased risk of Alzheimer's disease (AD). Landfield P W et al., J Neurobiol 23: 1247-1260 (1992). Although several biological mechanisms have been putatively linked to brain aging or Alzheimer's disease, including inflammation, oxidative stress, Ca2+ dyshomeostasis (Landfield, P W & Pitler T A, Science 226: 1089-1092 (1984); Landfield P W et al., J Neurobiol 23: 1247-1260 (1992)), mitochondrial dysfunction and chronic exposure to adrenal stress hormones (Landfield P W et al., Science 214: 581-584 (1981); Porter N M & Landfield P W, Nature Neurosci 1: 3-4 (1998)), the specific mechanisms and pathways, if any, th...

AI Technical Summary

Benefits of technology

The invention provides a statistical and functional correlation strategy to identify changes in cellular pathways specifically linked to impaired cognitive function with aging. The bioinformatics and functional correlation strategy improves the power of microarray analyses and provides the ability to test whether alterations in specific hippocampal pathways are correlated with aging-related cognitive impairment. The invention is useful for application in large, well-powered groups and for controlling type I error (false positives), enhancing detection sensitivity (reducing type II false negatives) and determining which aging changes in expression are most closely correlated with declining brain function.

Problems solved by technology

Brain aging processes are enormously complex phenomena that affect multiple systems, cell types and pathways, and result in cognitive decline and increased risk of Alzheimer's disease (AD).

Thus, there is a clear need for identifying ACGs but, to date, such genes have not been discovered for any mammal.

To date, however, its impact has been limited by statistical problems, small sample sizes, and difficulty in assessing functional relevance.

Moreover, studies that have examined gene expression during brain aging using microarrays have not used sample sizes large enough to provide adequate statistical power for formal statistical testing.

The extremely large data sets generated by microarrays pose formidable bioinformatics and resource problems that have to date limited the impact of this powerful technology.

However, neither fold change analyses nor the small sample protocols widely used allow the direct estimates of variance necessary for defining type I error (false positives).

Therefore, they exhibit low detection sensitivity (high false negatives, or type II error), and are unable to identify the modest changes that often characterize functionally important (and, therefore, tightly regulated) genes.

The inability to assign type I error is a particularly critical problem for microarray studies because the thousands of comparisons of gene expression in such analyses greatly increase the expected false positives.

Several strategies to improve statistical confidence have been developed for small-sample microarray studies, but these generally rely on indirect estimates of variance and / or greatly sacrifice sensitivity (i.e., stringent p-values).

Another highly important problem of microarray studies is that of determining which of the hundreds of expression changes that may be observed are likely to be functionally relevant.

Nonetheless, there have been few correlation studies attempting to link cognitive dysfunction with univariate gene expression patterns across individual subjects, much less using the massive amounts of data generated in microarray analyses.

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