Kuey Chu Chen | PubMed Health

PubMed2004

Incipient Alzheimer's disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses.

The pathogenesis of incipient Alzheimer's disease (AD) has been resistant to analysis because of the complexity of AD and the overlap of its early-stage markers with normal aging. Gene microarrays provide new tools for addressing complexity because they allow overviews of the simultaneous activity of multiple cellular pathways. However, microarray data interpretation is often hindered by low statistical power, high false positives or false negatives, and by uncertain relevance to functional endpoints. Here, we analyzed hippocampal gene expression of nine control and 22 AD subjects of varying severity on 31 separate microarrays. We then tested the correlation of each gene's expression with MiniMental Status Examination (MMSE) and neurofibrillary tangle (NFT) scores across all 31 subjects regardless of diagnosis. These well powered tests revealed a major transcriptional response comprising thousands of genes significantly correlated with AD markers. Several hundred of these genes were also correlated with AD markers across only control and incipient AD subjects (MMSE > 20). Biological process categories associated with incipient AD-correlated genes were identified statistically (ease program) and revealed up-regulation of many transcription factor/signaling genes regulating proliferation and differentiation, including tumor suppressors, oligodendrocyte growth factors, and protein kinase A modulators. In addition, up-regulation of adhesion, apoptosis, lipid metabolism, and initial inflammation processes occurred, and down-regulation of protein folding/metabolism/transport and some energy metabolism and signaling pathways took place. These findings suggest a new model of AD pathogenesis in which a genomically orchestrated up-regulation of tumor suppressor-mediated differentiation and involution processes induces the spread of pathology along myelinated axons.

Algorithms↗

PubMed2003

Statistical implications of pooling RNA samples for microarray experiments.

BACKGROUND: Microarray technology has become a very important tool for studying gene expression profiles under various conditions. Biologists often pool RNA samples extracted from different subjects onto a single microarray chip to help defray the cost of microarray experiments as well as to correct for the technical difficulty in getting sufficient RNA from a single subject. However, the statistical, technical and financial implications of pooling have not been explicitly investigated. RESULTS: Modeling the resulting gene expression from sample pooling as a mixture of individual responses, we derived expressions for the experimental error and provided both upper and lower bounds for its value in terms of the variability among individuals and the number of RNA samples pooled. Using "virtual" pooling of data from real experiments and computer simulations, we investigated the statistical properties of RNA sample pooling. Our study reveals that pooling biological samples appropriately is statistically valid and efficient for microarray experiments. Furthermore, optimal pooling design(s) can be found to meet statistical requirements while minimizing total cost. CONCLUSIONS: Appropriate RNA pooling can provide equivalent power and improve efficiency and cost-effectiveness for microarray experiments with a modest increase in total number of subjects. Pooling schemes in terms of replicates of subjects and arrays can be compared before experiments are conducted.

Computational Biology↗