Systematic Analysis and Biomarker Study for Alzheimer’s Disease

• Published in: Scientific reports Vol. 8; no. 1; pp. 17394 - 14
• Main Authors: Li, Xinzhong, Wang, Haiyan, Long, Jintao, Pan, Genhua, He, Taigang, Anichtchik, Oleg, Belshaw, Robert, Albani, Diego, Edison, Paul, Green, Elaine K, Scott, James
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26.11.2018; Nature Publishing Group
• Subjects: 45/43; 45/61; 631/553/1833; 692/53/2421; Age; Alzheimer Disease - blood; Alzheimer Disease - genetics; Alzheimer Disease - metabolism; Alzheimer's disease; Biomarkers; Biomarkers - blood; Biomarkers - metabolism; Blood; Blood - metabolism; Brain; Brain - metabolism; Case-Control Studies; Cognitive ability; Cognitive Dysfunction - blood; Cognitive Dysfunction - genetics; Cognitive Dysfunction - metabolism; Datasets; Dementia; Gender; Gene expression; Genome-wide association studies; Genome-Wide Association Study; Genomes; Genomics; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; Mitochondria; Mitochondria - genetics; multidisciplinary; NF-kappa B - genetics; NF-κB protein; Nitric Oxide Synthase Type II - genetics; Nitric-oxide synthase; Parkinson's disease; Ribosomes - genetics; Science; Science (multidisciplinary); Sensitivity and Specificity; Signal transduction; Signal Transduction - genetics; Tyk2 protein; Genome-wide Association Studies; Area Under Precision-recall Curve (AUPR); International Genomics Of Alzheimer’s Project (IGAP); Differentially Expressed Genes (DEGs); Least Absolute Shrinkage And Selection Operator (LASSO)
• DOI: 10.1038/s41598-018-35789-3
• ISSN: 2045-2322

Revealing the relationship between dysfunctional genes in blood and brain tissues from patients with Alzheimer’s Disease (AD) will help us to understand the pathology of this disease. In this study, we conducted the first such large systematic analysis to identify differentially expressed genes (DEGs) in blood samples from 245 AD cases, 143 mild cognitive impairment (MCI) cases, and 182 healthy control subjects, and then compare these with DEGs in brain samples. We evaluated our findings using two independent AD blood datasets and performed a gene-based genome-wide association study to identify potential novel risk genes. We identified 789 and 998 DEGs common to both blood and brain of AD and MCI subjects respectively, over 77% of which had the same regulation directions across tissues and disease status, including the known ABCA7 , and the novel TYK2 and TCIRG1 . A machine learning classification model containing NDUFA1 , MRPL51, and RPL36AL , implicating mitochondrial and ribosomal function, was discovered which discriminated between AD patients and controls with 85.9% of area under the curve and 78.1% accuracy (sensitivity = 77.6%, specificity = 78.9%). Moreover, our findings strongly suggest that mitochondrial dysfunction, NF-κB signalling and iNOS signalling are important dysregulated pathways in AD pathogenesis.