Aoife Keohane

Plasma proteins predict conversion to dementia from prodromal disease

The study aimed to validate previously discovered plasma biomarkers associated with AD, using a design based on imaging measures as surrogate for disease severity and assess their prognostic value in predicting conversion to dementia.

A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status

BackgroundDiagnostics of the human ageing process may help predict future healthcare needs or guide preventative measures for tackling diseases of older age. We take a transcriptomics approach to build the first reproducible multi-tissue RNA expression signature by gene-chip profiling tissue from sedentary normal subjects who reached 65 years of age in good health.ResultsOne hundred and fifty probe-sets form an accurate classifier of young versus older muscle tissue and this healthy ageing RNA classifier performed consistently in independent cohorts of human muscle, skin and brain tissue (n = 594, AUC = 0.83–0.96) and thus represents a biomarker for biological age. Using the Uppsala Longitudinal Study of Adult Men birth-cohort (n = 108) we demonstrate that the RNA classifier is insensitive to confounding lifestyle biomarkers, while greater gene score at age 70 years is independently associated with better renal function at age 82 years and longevity. The gene score is ‘up-regulated’ in healthy human hippocampus with age, and when applied to blood RNA profiles from two large independent age-matched dementia case–control data sets (n = 717) the healthy controls have significantly greater gene scores than those with cognitive impairment. Alone, or when combined with our previously described prototype Alzheimer disease (AD) RNA ‘disease signature’, the healthy ageing RNA classifier is diagnostic for AD.ConclusionsWe identify a novel and statistically robust multi-tissue RNA signature of human healthy ageing that can act as a diagnostic of future health, using only a peripheral blood sample. This RNA signature has great potential to assist research aimed at finding treatments for and/or management of AD and other ageing-related conditions.

Alzheimer's disease susceptibility variants in the MS4A6A gene are associated with altered levels of MS4A6A expression in blood

An increased risk of developing Alzheimers disease (AD) has previously been found to be associated with variants at the MS4A6A locus. We sought to identify which genes and transcripts in this region have altered expression in AD and mild cognitive impairment (MCI) and are influenced by the AD risk variant(s), as a first step to understanding the molecular basis of AD susceptibility at this locus. Common variants located within highly expressed MS4A6A transcripts were significantly associated with AD and MS4A6A expression levels in blood from MCI and AD subjects (p < 0.05, rs610932, rs7232, rs583791). More copies of the protective (minor) allele were associated with lower MS4A6A expression of each transcript (e.g., p = 0.019; rs610932-total MS4A6A). Furthermore, in heterozygous AD subjects, relative expression of the protective allele of V4-MS4A6A transcripts was lower (p < 0.008). Irrespective of genotype, MS4A6A transcripts were increased in blood from people with AD (p < 0.003), whereas lower expression of full length V1-MS4A6A (p = 0.002) and higher expression of V4-MS4A6A (p = 1.8 × 10(-4)) were observed in MCI, relative to elderly controls. The association between genotype and expression was less consistent in brain, although BA9 did have a similar genotype association with V4-MS4A6A transcripts as in blood. MS4A6A transcripts were widely expressed in tissues and cells, with the exception of V4-MS4A6A, which was not expressed in neuronal cells. Together these results suggest that high levels of MS4A6A in emerging AD pathology are detrimental. Persons with MCI may lower MS4A6A expression to minimize detrimental disease associated MS4A6A activity. However, those with the susceptibility allele appear unable to decrease expression sufficiently, which may explain their increased risk for developing AD. Inhibiting MS4A6A may therefore promote a more neuroprotective phenotype, although further work is needed to establish whether this is the case.

Mitochondrial genes are altered in blood early in Alzheimer's disease

Although mitochondrial dysfunction is a consistent feature of Alzheimers disease in the brain and blood, the molecular mechanisms behind these phenomena are unknown. Here we have replicated our previous findings demonstrating reduced expression of nuclear-encoded oxidative phosphorylation (OXPHOS) subunits and subunits required for the translation of mitochondrial-encoded OXPHOS genes in blood from people with Alzheimers disease and mild cognitive impairment. Interestingly this was accompanied by increased expression of some mitochondrial-encoded OXPHOS genes, namely those residing closest to the transcription start site of the polycistronic heavy chain mitochondrial transcript (MT-ND1, MT-ND2, MT-ATP6, MT-CO1, MT-CO2, MT-C03) and MT-ND6 transcribed from the light chain. Further we show that mitochondrial DNA copy number was unchanged suggesting no change in steady-state numbers of mitochondria. We suggest that an imbalance in nuclear and mitochondrial genome-encoded OXPHOS transcripts may drive a negative feedback loop reducing mitochondrial translation and compromising OXPHOS efficiency, which is likely to generate damaging reactive oxygen species.

A pathway based classification method for analyzing gene expression for Alzheimer's disease diagnosis

Background: Recent studies indicate that gene expression levels in blood may be able to differentiate subjects with Alzheimer’s disease (AD) from normal elderly controls and mild cognitively impaired (MCI) subjects. However, there is limited replicability at the single marker level. A pathway-based interpretation of gene expression may prove more robust. Objectives: This study aimed to investigate whether a case/control classification model built on pathway level data was more robust than a gene level model and may consequently perform better in test data. The study used two batches of gene expression data from the AddNeuroMed (ANM) and Dementia Case Registry (DCR) cohorts. Methods: Our study used Illumina Human HT-12 Expression BeadChips to collect gene expression from blood samples. Random forest modeling with recursive feature elimination was used to predict case/control status. Age and APOE ɛ4 status were used as covariates for all analysis. Results: Gene and pathway level models performed similarly to each other and to a model based on demographic information only. Conclusions: Any potential increase in concordance from the novel pathway level approach used here has not lead to a greater predictive ability in these datasets. However, we have only tested one method for creating pathway level scores. Further, we have been able to benchmark pathways against genes in datasets that had been extensively harmonized. Further work should focus on the use of alternative methods for creating pathway level scores, in particular those that incorporate pathway topology, and the use of an endophenotype based approach.

Investigation of common, low-frequency and rare genome-wide variation in anorexia nervosa

Anorexia nervosa (AN) is a complex neuropsychiatric disorder presenting with dangerously low body weight, and a deep and persistent fear of gaining weight. To date, only one genome-wide significant locus associated with AN has been identified. We performed an exome-chip based genome-wide association studies (GWAS) in 2158 cases from nine populations of European origin and 15 485 ancestrally matched controls. Unlike previous studies, this GWAS also probed association in low-frequency and rare variants. Sixteen independent variants were taken forward for in silico and de novo replication (11 common and 5 rare). No findings reached genome-wide significance. Two notable common variants were identified: rs10791286, an intronic variant in OPCML (P=9.89 × 10−6), and rs7700147, an intergenic variant (P=2.93 × 10−5). No low-frequency variant associations were identified at genome-wide significance, although the study was well-powered to detect low-frequency variants with large effect sizes, suggesting that there may be no AN loci in this genomic search space with large effect sizes.

Does genetic risk help to predict amyloid burden in a non-demented population? A Bayesian approach.

INTRODUCTION In this study we investigate the association between Aβ levels in cerebrospinal fluid (CSF) and genetic risk in a non-demented population. This paper presents the first analysis to use a Bayesian methodology in this area. METHODS Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the EDAR* and DESCRIPA** studies was used in a Bayesian logistic regression analysis. We modeled CSF Aβ burden using age, diagnosis (healthy control or mild cognitive impairment), APOE and a polygenic risk score (PGRS) associated with Alzheimer’s Disease (AD). We compared models built using informative priors on age, diagnosis and APOE with non-informative priors on all variables. RESULTS The use of informative priors did not improve model performance in the majority of cases. Models using only age, diagnosis and APOE genotype showed the best predictive ability. DISCUSSION A previous study indicated that a PGRS of AD case/control status was associated with CSF Aβ burden in healthy controls. The current study suggests that this association does not lead to models that are more predictive of amyloid positivity than already known factors such as age and APOE. *‘Beta amyloid oligomers in the early diagnosis of AD and as marker for treatment response’ **‘Development of screening guidelines and criteria for pre-dementia Alzheimers disease’

USING GENE EXPRESSION AND GENETICS TO PREDICT AMYLOID BURDEN BEFORE DEMENTIA

PREDICTAMYLOID BURDEN BEFORE DEMENTIA Nicola Voyle, Willemijn J. Jansen, Aoife Keohane, Hamel Patel, Kuang Lin, Pieter Jelle Visser, Angela Hodges, Richard JB. Dobson, Steven John Kiddle, King’s College London, London, United Kingdom; 2 Maastricht University, Alzheimer Center Limburg, School for Mental Health and Neuroscience, Maastricht, Netherlands; King’s College London, London, United Kingdom; King’s College London, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom; 5 Department of Neurology, Alzheimer Centre, VU Medical Centre, Amsterdam, Netherlands; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, United Kingdom; 7 Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom; MRC Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, United Kingdom; 9 Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom. Contact e-mail: steven.kiddle@kcl.ac.uk

GENOME-WIDE META-ANALYSIS OF TRANSCRIPTOME PROFILING IDENTIFIES NOVEL DYSREGULATED GENES IMPLICATED IN ALZHEIMER’S DISEASE

(Fig. 1). Individuals with lower expression showed greater cortical thickness (corrected p<0.05). Higher ABCA7 expression was also associated with decreased metabolic activity ([F] FDG PET) in Hippocampus (p1⁄40.033; Fig. 2). cis-eQTL mapping analyses of ABCA7 detected 63 significant associations with p < 2.5x10 (Fig. 3). The most significant cis-eQTL SNP (rs5638817) was replicated (Fig. 2) and was significantly associated with increased metabolic activity ([F] FDG PET) in Hippocampus (p<0.05) (Fig. 4). Conclusions:This is the first study to show that LOAD susceptibility gene ABCA7 expression in blood is associated with decreased cortical thickness and reduced metabolic activity in AD-related brain regions and CD33 expression is associated with decreased cortical thickness.