Variation in RTN3 and PPIL2 Genes Does not Influence Platelet Membrane β-Secretase Activity or Susceptibility to Alzheimer’s Disease in the Northern Irish Population

beta-site amyloid precursor protein cleaving enzyme (BACE1) is the rate-limiting enzyme for production of beta-amyloid peptides (A beta), which are proposed to drive the pathological changes found in Alzheimer's disease (AD). Reticulon 3 (RTN3) is a negative modulator of BACE1 (beta-secretase) proteolytic activity, while peptidylprolyl isomerase (cyclophilin)-like 2 (PPIL2) positively regulates BACE1 expression. The present study investigated whether there was any association between genetic variation in RTN3 and PPIL2, and either risk for AD, or levels of platelet beta-secretase activity, in a large Northern Irish case-control sample. Four hundred and sixty-nine patients with a diagnosis of probable AD (NINCDS-ADRDA criteria) and 347 control individuals (MMSE > 28/30) were genotyped. SNPs in both genes were selected by downloading genotype data from the International HapMap Project (Phase II) and tags selected using multimarker approach in Haploview, where r (2) > 0.8 and LOD > 3.0. Non-synonymous SNPs of interest were also included. Genotyping was performed by Sequenom iPLEX and TaqMan technologies. Alleles, genotypes and multi-marker haplotypes were tested for association with AD, and platelet beta-secretase activities were measured for a subset of individuals (n = 231). Eight SNPs in RTN3 and 7 in PPIL2 were genotyped. We found no significant associations between allele, genotype or haplotype frequencies and risk of AD. Further, there was no effect of genotype on platelet membrane beta-secretase activity. We conclude that common or potentially functional genetic variation in these BACE1 interacting proteins does not affect platelet membrane beta-secretase activity or contribute to risk of AD in this population.

Replication of EPHA1 and CD33 associations with late-onset Alzheimer’s disease: a multi-centre case-control study

A recently published genome-wide association study (GWAS) of late-onset Alzheimer's disease (LOAD) revealed genome-wide significant association of variants in or near MS4A4A, CD2AP, EPHA1 and CD33. Meta-analyses of this and a previously published GWAS revealed significant association at ABCA7 and MS4A, independent evidence for association of CD2AP, CD33 and EPHA1 and an opposing yet significant association of a variant near ARID5B. In this study, we genotyped five variants (in or near CD2AP, EPHA1, ARID5B, and CD33) in a large (2,634 LOAD, 4,201 controls), independent dataset comprising six case-control series from the USA and Europe. We performed meta-analyses of the association of these variants with LOAD and tested for association using logistic regression adjusted by age-at-diagnosis, gender, and APOE e4 dosage.

No Evidence that Extended Tracts of Homozygosity are Associated with Alzheimer’s Disease.

We sought to investigate the contribution of extended runs of homozygosity in a genome-wide association dataset of 1,955 Alzheimer's disease cases and 955 elderly screened controls genotyped for 529,205 autosomal single nucleotide polymorphisms. Tracts of homozygosity may mark regions inherited from a common ancestor and could reflect disease loci if observed more frequently in cases than controls. We found no excess of homozygous tracts in Alzheimer's disease cases compared to controls and no individual run of homozygosity showed association to Alzheimer's disease.

Investigation of the Human Brain Metabolome to Identify Potential Markers for Early Diagnosis and Therapeutic Targets of Alzheimer’s Disease

A study combining high resolution mass spectrometry (liquid chromatography-quadrupole time-of-flight-mass spectrometry, UPLC-QTof-MS) and chemometrics for the analysis of post-mortem brain tissue from subjects with Alzheimer’s disease (AD) (n = 15) and healthy age-matched controls (n = 15) was undertaken. The huge potential of this metabolomics approach for distinguishing AD cases is underlined by the correct prediction of disease status in 94–97% of cases. Predictive power was confirmed in a blind test set of 60 samples, reaching 100% diagnostic accuracy. The approach also indicated compounds significantly altered in concentration following the onset of human AD. Using orthogonal partial least-squares discriminant analysis (OPLS-DA), a multivariate model was created for both modes of acquisition explaining the maximum amount of variation between sample groups (Positive Mode-R2 = 97%; Q2 = 93%; root mean squared error of validation (RMSEV) = 13%; Negative Mode-R2 = 99%; Q2 = 92%; RMSEV = 15%). In brain extracts, 1264 and 1457 ions of interest were detected for the different modes of acquisition (positive and negative, respectively). Incorporation of gender into the model increased predictive accuracy and decreased RMSEV values. High resolution UPLC-QTof-MS has not previously been employed to biochemically profile post-mortem brain tissue, and the novel methods described and validated herein prove its potential for making new discoveries related to the etiology, pathophysiology, and treatment of degenerative brain disorders.

Untargeted Metabolomic Analysis of Human Plasma Indicates Differentially Affected Polyamine and L-Arginine Metabolism in Mild Cognitive Impairment Subjects Converting to Alzheimer’s Disease

This study combined high resolution mass spectrometry (HRMS), advanced chemometrics and pathway enrichment analysis to analyse the blood metabolome of patients attending the memory clinic: cases of mild cognitive impairment (MCI; n = 16), cases of MCI who upon subsequent follow-up developed Alzheimer's disease (MCI_AD; n = 19), and healthy age-matched controls (Ctrl; n = 37). Plasma was extracted in acetonitrile and applied to an Acquity UPLC HILIC (1.7μm x 2.1 x 100 mm) column coupled to a Xevo G2 QTof mass spectrometer using a previously optimised method. Data comprising 6751 spectral features were used to build an OPLS-DA statistical model capable of accurately distinguishing Ctrl, MCI and MCI_AD. The model accurately distinguished (R2 = 99.1%; Q2 = 97%) those MCI patients who later went on to develop AD. S-plots were used to shortlist ions of interest which were responsible for explaining the maximum amount of variation between patient groups. Metabolite database searching and pathway enrichment analysis indicated disturbances in 22 biochemical pathways, and excitingly it discovered two interlinked areas of metabolism (polyamine metabolism and L-Arginine metabolism) were differentially disrupted in this well-defined clinical cohort. The optimised untargeted HRMS methods described herein not only demonstrate that it is possible to distinguish these pathologies in human blood but also that MCI patients 'at risk' from AD could be predicted up to 2 years earlier than conventional clinical diagnosis. Blood-based metabolite profiling of plasma from memory clinic patients is a novel and feasible approach in improving MCI and AD diagnosis and, refining clinical trials through better patient stratification.