Gene Expression Biomarkers in the Brain of a Mouse Model for Alzheimer's Disease: Mining of Microarray Data by Logic Classification and Feature Selection

Abstract

The identification of early and stage-specific biomarkers for Alzheimer's disease (AD) is critical, as the development of disease-modification therapies may depend on the discovery and validation of such markers. The identification of early reliable biomarkers depends on the development of new diagnostic algorithms to computationally exploit the information in large biological datasets. To identify potential biomarkers from mRNA expression profile data, we used the Logic Mining method for the unbiased analysis of a large microarray expression dataset from the anti-NGF AD11 transgenic mouse model. The gene expression profile of AD11 brain regions was investigated at different neurodegeneration stages by whole genome microarrays. A new implementation of the Logic Mining method was applied both to early (1-3 months) and late stage (6-15 months) expression data, coupled to standard statistical methods. A small number of "fingerprinting" formulas was isolated, encompassing mRNAs whose expression levels were able to discriminate between diseased and control mice. We selected three differential "signature" genes specific for the early stage (Nudt19, Arl16, Aph1b), five common to both groups (Slc15a2, Agpat5, Sox2ot, 2210015, D19Rik, Wdfy1), and seven specific for late stage (D14Ertd449, Tia1, Txnl4, 1810014B01Rik, Snhg3, Actl6a, Rnf25). We suggest these genes as potential biomarkers for the early and late stage of AD-like neurodegeneration in this model and conclude that Logic Mining is a powerful and reliable approach for large scale expression data analysis. Its application to large expression datasets from brain or peripheral human samples may facilitate the discovery of early and stage-specific AD biomarkers.

A Gene Expression Profile of Alzheimer’s Disease Using Microarray Technology

“…In a research conducted in 2001, researchers identified Alzheimer's biomarkers in a few mice [17], and again we had common results to an extent. However, it was necessary to find more accurate and more reliable results, so that this study would be conducted on human samples and with sampling of different brain segments.…”

Identification of Genes Involved in the Early Stages of Alzheimer Disease Using a Neural Network Algorithm

“…In a research conducted in 2001 in Saint Mateu, California, on Alzheimer disease, 31 genes had an over expression and 87 genes had a down expression which was completely consistent with the current study samples and some of the unknown genes were also added to the list of genes that previously had a role in the incidence of Alzheimer disease (21).…”

The solution of large-scale Minimum Cost SAT Problem as a tool for data analysis in bioinformatics

“…A long array of methods are used to perform such task, ranging from the more established parametric statistical analysis to non parametric techniques, to classification methods that have been developed in knowledge engineering and artificial intelligence. In this paper, we consider a method for extracting logic formulas from data that relies on a large body of literature in integer and logic optimization, originally presented in [1], that has been largely and successfully applied to different problems in bioinformatics ( [2], [3], [4], [5], [6]). Such method is based on the iterative solution of Minimum Cost SAT Problems and is able to extract logic formulas in DNF form that possess interesting features for their interpretation.…”