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

Eric Blalock

James Geddes

Kuey Chen

Nada Porter

William Markesbery

Philip Landfield

^{Department of Molecular and Biomedical Pharmacology,}^{Spinal Cord and Brain Injury Research Center, and}^{Sanders-Brown Research Center on Aging, University of Kentucky College of Medicine, Lexington, KY 40536}

^{To whom correspondence should be addressed at: Department of Molecular and Biomedical Pharmacology, University of Kentucky Medical Center, 800 Rose Street, MS309, Lexington, KY 40536. E-mail:}ude.yku@dnalwp.

Communicated by James L. McGaugh, University of California, Irvine, CA, December 19, 2003

Received 2003 Sep 5

Abstract

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.

Abstract

Alzheimer's disease (AD) has received intense study during past decades. Multiple processes have been implicated in AD, notably including abnormal β-amyloid (Aβ) production (1-7), tau hyperphosphorylation and neurofibrillary tangles (NFTs) (8, 9), synaptic pathology (10-12), oxidative stress (13-15), inflammation (5, 16-19), protein processing or misfolding (20, 21), calcium dyshomeostasis (15, 20-26), aberrant reentry of neurons into the cell cycle (27, 28), cholesterol synthesis (29, 30), and effects of hormones (23, 31) or growth factors (17, 32). Nevertheless, the pathogenic factors that initiate these processes remain elusive.

Several reasons account for the substantial resistance of AD pathogenesis to analysis. One is the vast extent and complexity of the disease, which affects numerous molecules, cells, and systems and impedes attempts to determine which alterations are specifically associated with early pathology. Another is that clinically normal subjects may exhibit considerable AD pathology, blurring criteria for distinguishing subjects with normal aging, mild cognitive impairment, or incipient AD (33-35).

We addressed the problems of high complexity and overlapping criteria by using a strategy combining powerful new gene microarray technology, which permits measurement of the expression of many thousands of genes simultaneously (36, 37), with statistical correlation analyses. This strategy allowed the linking of gene expression to cognitive and pathological markers of AD independently of AD diagnosis. We also focused on subjects with the earliest signs of AD. Several microarray studies of AD brain (38-42) and/or mouse models of AD (43) have been published. These studies have yielded important new insights, in particular, regarding changes in plasticity-related genes (e.g., ref. 43). However, few microarray studies use independent sample sizes sufficient to provide the statistical power needed to avoid high false positive (type I) and/or high false negative (type II) error (44, 45). In the present study, we ensured adequate power by using a separate array for each hippocampal sample of a large group of subjects (n = 31) and correlated the expression values of each of thousands of genes with pathological and cognitive indexes of incipient AD. Together, these approaches revealed a major and previously unrecognized transcriptional response with potentially important implications for the early pathogenesis of AD.

Subjects were assigned to four groups reflecting different levels of AD severity or Control (see Methods). n, number of subjects in each group; Age, age at death; NFT, neurofibrillary tangle count; Braak, Braak stage; MMSE, adjusted Minimental Status Exam (see Methods); PMI, postmortem interval. Values are mean ± SEM.

Biological process categories significantly overrepresented by ADGs (P ≤ 0.05; ease score) and a few other selected categories are shown. Numerous other similar significant categories are not included to reduce redundancy. Significant functional categories are those with a higher ratio of identified genes to all genes tested on the array for associations with that category, relative to the ratio of total identified genes in the study to all genes tested on the array for associations with all categories. Association numbers approximate but are not exactly equal to gene numbers in a category. After each category description (in parentheses) is the ratio of associations for that category and the percentage represented by that ratio. The analogous ratios for total identified up-regulated and down-regulated genes are shown in the headings (Total). ease, modified Fisher's exact test P value; N/M/B, percentage of genes included in category because they were significant by NFT correlation (N), MMSE correlation (M), or both (B). (The complete list of ADGs is given alphabetically in Table 5).

Biological process categories significantly overrepresented by IADGs (P ≤ 0.15; ease score) and a few other selected categories are shown. Numerous other similar significant categories are not included to reduce redundancy. Significant functional categories are those with a higher ratio of identified genes to all genes tested on the array for associations with that category, relative to the ratio of total identified genes in the study to all genes tested on the array for associations with all categories. The association numbers approximate but are not exactly equal to gene numbers in a category. After each category description (in parentheses) is the ratio of associations for that category and the percentage represented by that ratio. The analogous ratios for total identified up-regulated and down-regulated genes are shown in the headings (Total). ease, modified Fisher's exact test P value; N/M/B, percentage of genes included in category because they were significant by NFT correlation (N), MMSE correlation (M), or both (B). (The complete list of IADGs is given alphabetically in Table 6).

Gene symbols for TF IADGs positively correlated with NFT, negatively correlated with MMSE, or both (^{) are shown separately (only those with}P ≤ 0.025). IADGs for TS (boldface) or lipogenic (underlined) functions are highlighted. (Full descriptions of all IADGs, alphabetically listed, are available in Table 6).

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Acknowledgments

We thank Dr. Stephen Snyder for drawing our attention to possible changes in semaphorins and their potential relevance to axonal pathology and Drs. David Wekstein and Daron Davis for important contributions to subject recruitment and assessment. This research was supported by National Institute on Aging Grants AG10836 and AG05144.

Acknowledgments

Notes

Abbreviations: MMSE, MiniMental Status Examination; NFT, neurofibrillary tangle; AD, Alzheimer's disease; ADG, AD-related gene; IADG, incipient ADG; TS, tumor suppressor; TF, transcription factor; RB, retinoblastoma; ECM, extracellular matrix; GF, growth factor; OG, oligodendrocyte.

Notes

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