Multivariate Protein Signatures of Pre-Clinical Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Plasma Proteome Dataset

Background: Recent Alzheimer's disease (AD) research has focused on finding biomarkers to identify disease at the pre-clinical stage of mild cognitive impairment (MCI), allowing treatment to be initiated before irreversible damage occurs. Many studies have examined brain imaging or cerebrospinal fluid but there is also growing interest in blood biomarkers. The Alzheimer's Disease Neuroimaging Initiative (ADNI) has generated data on 190 plasma analytes in 566 individuals with MCI, AD or normal cognition. We conducted independent analyses of this dataset to identify plasma protein signatures predicting pre-clinical AD. Methods and Findings: We focused on identifying signatures that discriminate cognitively normal controls (n = 54) from individuals with MCI who subsequently progress to AD (n = 163). Based on p value, apolipoprotein E (APOE) showed the strongest difference between these groups (p = 2.3×10−13). We applied a multivariate approach based on combinatorial optimization ((α,β)-k Feature Set Selection), which retains information about individual participants and maintains the context of interrelationships between different analytes, to identify the optimal set of analytes (signature) to discriminate these two groups. We identified 11-analyte signatures achieving values of sensitivity and specificity between 65% and 86% for both MCI and AD groups, depending on whether APOE was included and other factors. Classification accuracy was improved by considering “meta-features,” representing the difference in relative abundance of two analytes, with an 8-meta-feature signature consistently achieving sensitivity and specificity both over 85%. Generating signatures based on longitudinal rather than cross-sectional data further improved classification accuracy, returning sensitivities and specificities of approximately 90%. Conclusions: Applying these novel analysis approaches to the powerful and well-characterized ADNI dataset has identified sets of plasma biomarkers for pre-clinical AD. While studies of independent test sets are required to validate the signatures, these analyses provide a starting point for developing a cost-effective and minimally invasive test capable of diagnosing AD in its pre-clinical stages

Molecular Insights into the Pathogenesis of Alzheimer's Disease and Its Relationship to Normal Aging

Alzheimer's disease (AD) is a complex neurodegenerative disorder that diverges from the process of normal brain aging by unknown mechanisms. We analyzed the global structure of age- and disease-dependent gene expression patterns in three regions from more than 600 brains. Gene expression variation could be almost completely explained by four transcriptional biomarkers that we named BioAge (biological age), Alz (Alzheimer), Inflame (inflammation), and NdStress (neurodegenerative stress). BioAge captures the first principal component of variation and includes genes statistically associated with neuronal loss, glial activation, and lipid metabolism. Normally BioAge increases with chronological age, but in AD it is prematurely expressed as if some of the subjects were 140 years old. A component of BioAge, Lipa, contains the AD risk factor APOE and reflects an apparent early disturbance in lipid metabolism. The rate of biological aging in AD patients, which cannot be explained by BioAge, is associated instead with NdStress, which includes genes related to protein folding and metabolism. Inflame, comprised of inflammatory cytokines and microglial genes, is broadly activated and appears early in the disease process. In contrast, the disease-specific biomarker Alz was selectively present only in the affected areas of the AD brain, appears later in pathogenesis, and is enriched in genes associated with the signaling and cell adhesion changes during the epithelial to mesenchymal (EMT) transition. Together these biomarkers provide detailed description of the aging process and its contribution to Alzheimer's disease progression

Clusterin regulates β-amyloid toxicity via Dickkopf-1-driven induction of the wnt-PCP-JNK pathway.

Although the mechanism of Aβ action in the pathogenesis of Alzheimer's disease (AD) has remained elusive, it is known to increase the expression of the antagonist of canonical wnt signalling, Dickkopf-1 (Dkk1), whereas the silencing of Dkk1 blocks Aβ neurotoxicity. We asked if clusterin, known to be regulated by wnt, is part of an Aβ/Dkk1 neurotoxic pathway. Knockdown of clusterin in primary neurons reduced Aβ toxicity and DKK1 upregulation and, conversely, Aβ increased intracellular clusterin and decreased clusterin protein secretion, resulting in the p53-dependent induction of DKK1. To further elucidate how the clusterin-dependent induction of Dkk1 by Aβ mediates neurotoxicity, we measured the effects of Aβ and Dkk1 protein on whole-genome expression in primary neurons, finding a common pathway suggestive of activation of wnt-planar cell polarity (PCP)-c-Jun N-terminal kinase (JNK) signalling leading to the induction of genes including EGR1 (early growth response-1), NAB2 (Ngfi-A-binding protein-2) and KLF10 (Krüppel-like factor-10) that, when individually silenced, protected against Aβ neurotoxicity and/or tau phosphorylation. Neuronal overexpression of Dkk1 in transgenic mice mimicked this Aβ-induced pathway and resulted in age-dependent increases in tau phosphorylation in hippocampus and cognitive impairment. Furthermore, we show that this Dkk1/wnt-PCP-JNK pathway is active in an Aβ-based mouse model of AD and in AD brain, but not in a tau-based mouse model or in frontotemporal dementia brain. Thus, we have identified a pathway whereby Aβ induces a clusterin/p53/Dkk1/wnt-PCP-JNK pathway, which drives the upregulation of several genes that mediate the development of AD-like neuropathologies, thereby providing new mechanistic insights into the action of Aβ in neurodegenerative diseases.Molecular Psychiatry advance online publication, 20 November 2012; doi:10.1038/mp.2012.163.JOURNAL ARTICLESCOPUS: ar.jinfo:eu-repo/semantics/publishe

Novel biomarkers for prostate cancer revealed by (α,β)-k-feature sets

In this chapter we present a method based on the (α,β)-k-feature set problem for identifying relevant attributes in high-dimensional datasets for classification purposes. We present a case-study of biomedical interest. Using the gene expression of thousands of genes, we show that the method can give a reduced set that can identify samples as belonging to prostate cancer tumors or not. We thus address the need of finding novel methods that can deal with classification problems that involve feature selection from several thousand features, while we only have on the order of one hundred samples. The methodology appears to be very robust in this prostate cancer case study. It has lead to the identification of a set of differentially expressed genes that are highly predictive of the cells transition to a more malignant type, thus departing from the profile which is characteristic of its originating tissue. Although the method is presented with a particular bioinformatics application in mind, it can clearly be used in other domains. A biological analysis illustrates on the relevance of the genes found, and links to the most current developments in prostate cancer biomarker studies