Laura Ibanez, United States of America
Washington University School of Medicine PsychiatryAuthor Of 4 Presentations
CIRCULAR RNAS ARE SIGNIFICANTLY ASSOCIATED WITH BOTH SYMPTOMATIC AND PRE-SYMPTOMATIC ALZHEIMER’S DISEASE
Abstract
Aims
Circular RNAs (circRNAs) are a class of RNAs highly expressed in the nervous system and enriched in synaptoneurosomes. A recent study suggest that circCDR1-as is downregulated in the frontal cortex of Alzheimer’s Disease (AD) patients. A study investigating whether other circRNAs are differentially expressed in the context of AD remains outstanding. Here, we conduct an analysis of circRNA expression to explore the relevance of circRNA expression in AD.
Methods
We generated RNA-seq data from 83 individuals with AD and 16 control individuals. We performed circRNA differential expression (DE) analysis on the basis of clinical dementia rating (CDR). We replicated the DE analyses using publically available superior temporal cortex RNA sequencing data from the Mount Sinai Brain Bank (173 AD cases and 63 controls) and performed a meta-analysis.
Results
On meta-analysis, we observed six circRNAs to be significantly DE on the basis of CDR. This included the previously reported circCDR1-as (p-value: 1.66×10-6), as well as five novel ones: circHOMER1 (p-value: 3.30×10-9), circDOCK1 (p-value: 4.17×10-9), circPEX5L (p-value: 6.12×10-6), circKCNN2 (p-value: 6.12 × 10-6), and circMLIP (p-value: 8.45 × 10-6). When included in a model with AD risk factors (number of APOE-4 alleles, gender, age at death, and ethnicity), the circRNAs collectively contributed more than 34% of the variation in CDR in our parietal dataset. In contrast, number of APOE-4 alleles, the most common genetic risk factor, only explained 9% of the variation in CDR. Multiple circRNAs are differentially expressed in AD brain tissues.
Conclusions
Together these circRNAs contribute substantially to the variation in CDR.
IDENTIFICATION OF GENETIC MODIFIERS FOR ALZHEIMER DISEASE – THE FAMILIAL ALZHEIMER SEQUENCING (FASE) PROJECT
Abstract
Aims
The Familial Alzheimer Sequencing (FASe) project aims to identify rare and high penetrant variants that have strong effect in the etiology of Alzheimer Disease (AD) by using sequencing data from families densely affected by late onset AD (fLOAD).
Methods
We have generated whole genome sequence (WGS) data for 952 samples (758 cases, 194 controls) from the Knight-ADRC at Washington University (WASHU), the NIALOAD and NCRAD repositories. These samples are being added to our current dataset of whole exome (WES) and WGS from 1,235 non-hispanic white participants (824 cases, 411 controls) across 285 fLOAD families. These samples have no or minimum overlap with the families sequenced by the ADSP consortia which will also be incorporated to our dataset; a total of 440 families and 3,187 samples (average of 5 cases and 2 controls per family) will be analyzed. We are processing all the data using the same bioinformatics pipeline. Briefly, sequence reads are aligned against reference build GRCh38 using BWA; variant calling is restricted to exonic regions following GATK v4.1.2 best practices. Data analysis includes single variant association, segregation, gene-based and pathway analysis.
Results
We have detected a genetic cross-over between AD, Frontotemporal Dementia and Parkinson disease, and we also identified rare variants in novel candidate genes for AD (PLD3, UNC5C, CPAMD8) highlighting the power of our dataset and the feasibility of our approach.
Conclusions
We hope to identify novel variants and pathways implicated on AD, which will be followed-up in the case-control ADSP.
LIVE DISCUSSION
PREDICTION OF ALZHEIMER DISEASE USING PLASMA RNA SEQUENCES
Abstract
Aims
The aim of this study was to generate predictive models for AD using plasma cell-free RNA species at different stages of the disease.
Methods
We generated cfRNA-Sequence data from AD cases at CDR=1 (N=44) and controls (N=45) and applied standard quality control. Gene expression was quantified with Salmon and corrected by library complexity and log transformed prior to analysis. Genes known to be involved in AD and other neurodegenerative diseases (N=25) were used to create a predictive model using step-wise discriminant analysis in the CDR=1. APOE genotype was included in the model afterwards. The predictive power was tested in early (N=27) and pre-symptomatic (N=21) stages of the disease.
Results
Out of the 25 genes, eight were included in the predictive model after step-wise discriminant analysis. After inclusion of APOE genotype, the area under the ROC curve was 0.96, 0.99 and 0.82 for CDR=1, CDR=0.5 and pre-symptomatic stages respectively (Figure1).
Conclusions
Cell-free RNA is a promising minimally invasive biomarker for AD with an accuracy comparable to the one obtained using CSF biomarkers. This approach can provide a new screening tool for AD that can be used at population level and to evaluate disease-modifying therapies that target amyloid beta and tau.
Presenter of 2 Presentations
LIVE DISCUSSION
PREDICTION OF ALZHEIMER DISEASE USING PLASMA RNA SEQUENCES
Abstract
Aims
The aim of this study was to generate predictive models for AD using plasma cell-free RNA species at different stages of the disease.
Methods
We generated cfRNA-Sequence data from AD cases at CDR=1 (N=44) and controls (N=45) and applied standard quality control. Gene expression was quantified with Salmon and corrected by library complexity and log transformed prior to analysis. Genes known to be involved in AD and other neurodegenerative diseases (N=25) were used to create a predictive model using step-wise discriminant analysis in the CDR=1. APOE genotype was included in the model afterwards. The predictive power was tested in early (N=27) and pre-symptomatic (N=21) stages of the disease.
Results
Out of the 25 genes, eight were included in the predictive model after step-wise discriminant analysis. After inclusion of APOE genotype, the area under the ROC curve was 0.96, 0.99 and 0.82 for CDR=1, CDR=0.5 and pre-symptomatic stages respectively (Figure1).
Conclusions
Cell-free RNA is a promising minimally invasive biomarker for AD with an accuracy comparable to the one obtained using CSF biomarkers. This approach can provide a new screening tool for AD that can be used at population level and to evaluate disease-modifying therapies that target amyloid beta and tau.