There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Wei Dong |
| Institution: | China National Clinical Research Center for Neurological Diseases |
| Department: | Human brain banking |
| Country: | |
| Proposed Analysis: | Research Proposal Title: The Impacts of Autophagy-Related Genetic Variants on the Related Biomarkers in Alzheimer's Disease Background Alzheimer's disease (AD) is the most common neurodegenerative disorder in the elderly. The molecular mechanisms of AD have been related to deficiency in protein degradation. Autophagy is responsible for the clearance of long-lived, aggregated or misfolded proteins and damaged organelles to preserve intracellular homeostasis[1]. Autophagy is a highly conserved pathway for the degeneration of misfolded or aggregated proteins, Aβ, tau and damaged organelles in the cell[2]. Autophagy is a self-degradative process which involves cellular growth, metabolism, and antioxidant defence. It functions as a protective factor against AD progression associated with intracellular toxic Aβ and tau aggregates[3, 4]. The upregulation of autophagy can also be beneficial in AD treatment[5]. Genetic factors have important contribution to the occurrence of AD. Previous study has demonstrated how the AD-associated genes are related to autophagy[6]. However, whether the genetic variants of autophagy-related genes affect the onset and severities of clinical phenotypes and plasma levels of biomarkers in AD have not been elucidated. Hypothesis 1.The genetic variants of autophagy-related genes is associated with the risk of AD. 2.The genetic variants of autophagy-related genes is associated with the biomarkers in AD. 3.The genetic variants of autophagy-related genes is associated with the clinical characteristics of AD. Aims of the Project 1.To identify genetic variants of autophagy-related genes in developing AD (AD and controls association study) 2.To determine if autophagy-related genetic variants associate with the clinical characteristics of AD. 3.To determine if autophagy-related genetic variants associate with the levels of biomarkers in CSF and plasma in AD and controls. 4.To elucidate the comprehensive impacts of autophagy-related genes on the pathogenesis of AD, assisting the diagnosis and predicting the progression of AD. Research Plan 1.Dec. 20. 2020 to Jan. 20. 2021: Apply for accessing to ADNI database and Apply for the ethic approval from local institution to access and analyse the ADNI database. 2.Jan. 20. 2020 to Feb. 20. 2021: Using the “gene” and “SNP” search engines in NCBI database (https://www.ncbi.nlm.nih.gov/gene, https://www.ncbi.nlm.nih.gov/snp) to retrieve the autophagy-related genes. 3.Feb. 20. 2020 to April. 20. 2021: Access the database resources, and the clinical, genetic and biomarkers information will be extracted. 4.April. 20. 2020 to June. 20. 2021: Finish the clinical, genetic and biomarkers information analysis by multiple statistical methods. 5.June. 20. 2020 to August. 20. 2021: Based on the results, finish the paper and submit for publication. Data Request To achieve the above aims, we need to access the following data: 1.Both AD patients and healthy controls are required. 2.Clinical demographic information: Gender, age at assessment, origin, age at onset, neurological examination, medications, etc. 3.Clinical quantitative measurements: different cognitive assessments, such as MMSE, MoCA, etc. 4.Genetic data: sequencing data, genotyping data and whole genome sequencing data. 5.Biomarker data: homocysteine, species of isoprostanes, tau, sAPPβ, BACE, enzyme activity, plasma Aβ 40 and Aβ 42, other promising biomarkers in CSF and plasma, based on ongoing multiplex immunoassay studies and mass spectrometry MRM studies. Data Management and Statistical Analysis Methods 1. Data will be transformed into Z scores using SPSS descriptive analysis. Missing data will be replaced using SPSS missing data calculation. 2. The SPSS-26 and R software will be used to analyse and a p<0.05 will be considered statistically significant. T-test will be used to test differences in continuous variables (age, education years, cognitive scores, age at onset, etc.) and chi-square test is used to examine categorical data (gender, etc). The correlation between autophagy-related SNPs and the levels of multiple AD biomarkers or clinical quantitative measures in all cohorts will be evaluated with multiple linear regression models. Bonferroni correction will be used to control the false discovery rate of multiple tests. Furthermore, polygenetic risk scores (PRS)will be calculated to evaluate the synergistic genetic effects on the clinical and biomarkers features. 3. The hierarchical all-against-all association (HALLA) will be used to demonstrate the associations among the three datasets: genetics, clinical features and CSF biomarkers. Research Team 1. Wei Dong, MD, Ph.D candidate, Capital Medical University (CMU), Beijing, China 2. Huan Liu, MA, Data Manager, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, CMU, China 3. Yue Huang, MD, Ph.D, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, CMU, Beijing, China References [1] Liu J, Kuang F, Kroemer G, et al. Autophagy-dependent ferroptosis: machinery and regulation.[J]. Cell Chem Biol,2020,27(4):420-435. [2] Chen S, Zhou Q, Ni Y, et al. Autophagy and Alzheimer's disease[J]. Adv Exp Med Biol,2020,1207:3-19. [3] Zhang Z, Jing Y, Ma Y, et al. Driving GABAergic neurons optogenetically improves learning, reduces amyloid load and enhances autophagy in a mouse model of Alzheimer's disease[J]. Biochem Biophys Res Commun,2020,525(4):928-935. [4] Silva M C, Nandi G A, Tentarelli S, et al. Prolonged tau clearance and stress vulnerability rescue by pharmacological activation of autophagy in tauopathy neurons[M]. 11.2020.3258. [5] Di Meco A, Curtis M E, Lauretti E, et al. Autophagy dysfunction in Alzheimer's disease: Mechanistic insights and new therapeutic opportunities[J]. Biol Psychiatry,2020,87(9):797-807. [6] Yoon S Y, Kim D H. Alzheimer's disease genes and autophagy[J]. Brain Res,2016,1649(Pt B):201-209. ATTACHMENT – Resumes of The Research Team Name Position Wei Dong Ph.D candidate, Capital Medical University (CMU), Beijing, China. Education Capital Medical University, China 2019-Now Ph.D Neurology Capital Medical University, China 2016-2019 MD Neurology Sun Yat-Sen University, China 2011-2016 MBBS Medicine Name Position Huan Liu Data Manager, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, CMU. Education Shenyang University of Technology, China 2014-2017 MA Biomedical Engineering Shenyang University of Technology, China 2010-2014 BA Biomedical Engineering Name Position Yue Huang Director, Human Brain Bank, China National Clinical Research Centre for Neurological Diseases, Beijing Tiantan Hospital, CMU. Education University of New South Wales, Sydney, Australia 2003-2008 Post-doc Neuropathology Harbin Medical University, China 2000-2003 Ph.D Neuroscience Harbin Medical University, China 1997-1999 MD Neurology Harbin Medical University, China 1986-1991 MBBS Medicine Positions and Honors 2017.12 - Professor of Neurology, Capital Medical University, Beijing, China. 2017.12- Director, Brain Bank, China National Clinical Research Centre for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China 2012.08-2017.12. conjoint associate professor, Faculty of Medicine, UNSW, Sydney, Australia. 2008-2012, Conjoint Senior Lecturer, Faculty of Medicine, UNSW, Sydney, Australia 2003-2008, Conjoint Lecturer, Faculty of Medicine, UNSW, Sydney, Australia 1991.09-2000.09 Neurologist, Harbin First Hospital, Harbin, China. Publication records Number of articles: 70 H-index: 23 Selected peer-reviewed publications on dementia 1.Lourenco GF, Janitz M, Huang Y, Halliday GM (2015) Long noncoding RNAs in TDP-43 and FUS/TLS-related frontotemporal lobar degeneration (FTLD). Neurobiol Dis. 82:445-454. (IF=5.332) 2.Xu B, Gao Y, Zhan S, Xiong F, Qiu W, Qian X, Wang T, Wang N, Zhang D, Yang Q, Wang R, Bao X, Dou W, Tian R, Meng S, Gai WP, Huang Y, Yan XX, Ge W, Ma C (2016) Quantitative Protein Profiling of Hippocampus During Human Aging. Neurobiology of Aging. 39(41): 46-56. 3.Huang Y, Lahiri DK (2016) Editorial (Thematic Issue: Translational Alzheimer’s Disease Research) 13:214. 4.Wang G*, Huang Y*, Wang LL*, Zhang YF*, Xu J, Zhou Y, Lourenco GF, Zhang B, Wang Y, Ren RJ, Halliday GM, Chen SD (2016) MicroRNA-146a suppresses ROCK1 allowing hyperphosphorylation of tau in Alzheimer’s disease. Scientific Reports. May 25;6:26697. doi: 10.1038/srep26697. 5.Zhang B, Wang LL, Ren RJ, Dammer EB, Zhang YF, Huang Y, Chen SD, Wang G. MicroRNA-146a represses LRP2 translation and leads to cell apoptosis in Alzheimer’s disease. FEBS letters. 2016 Jul;590(14):2190-200. 6.Yu Y, Liang X, Yu H, Zhao W, Lu Y, Huang Y, Yin C, Gong G, Han Y (2016) How does white matter microstructure differ between the vascular and amnestic mild cognitive impairment? Oncotarget. 2016 Dec 15. doi: 10.18632/oncotarget.13960. 7.Hao ZH, Huang Y, Wang MR, Huo TT, Jia Q, Feng RF, Fan P, Wang JH. SS31 Ameliorates age-related activation of NF-κB signaling in senile mice model, SAMP8. Oncotarget. 2016 Dec 21. doi: 10.18632/oncotarget.14077. 8.Qiu WY, Ma C, Zhu KQ, Bao AM, Huang Y, Yan XX, Zhang J, Zhong CJ, Zhou JN, Shen Y, Zheng XY, Zhang LW, Shu YS, Tang BS, Zhang ZX, Duan SM (2016) Standardised Operation Procedure for Human Brain Tissue Banking in China. Acta Anatomica Sinica. Accepted on 2016. Dec. 29. 9.Che XQ, Zhao QH, Huang Y, Li X, Ren RJ, Chen SD, Wang G, Guo QH. Clinical and Genetic Features of MAPT, GRN, C9orf72 and CHCHD10 Genes Mutations in Chinese Patients with Frontotemporal Dementia. Current Alzheimer Research. 2017;14(10):1102-1108. 10.Langenhove TV, Piguet O, Burrell JR, Leyton C, Foxe D, Abela M, Bartley L, Kim WS, Jary E, Huang Y, Dobson-Stone C, Kwok JB, Halliday GM, Hodges JR. Predicting development of amyotrophic lateral sclerosis in frontotemporal dementia. Journal of Alzheimer’s Disease. 2017;58(1):163-170. doi: 10.3233/JAD-161272. 11.Sheng C, Xia MR, Yu HK, Huang Y, Lu Y, Liu F, He Y, Han Y. Medial Temporal Atrophy and Abnormal Global Functional Network Connectivity in Patients with Amnestic Mild Cognitive Impairment. Plos One. 2017;12(6):e0179823. 12.Huang Y. Book Review: Landmark Papers in Neurology. Current Alzheimer Research. Curr Alzheimer Res. 2017;14(12):1348-1349 13.Qiu W-Y, Yang Q, Zhang W, Wang N, Zhang D, Huang Y, Ma C. The Correlations Between Postmortem Brain Pathologies and Cognitive Dysfunction in Aging and Alzheimer's Disease Curr Alzheimer Res. 2018;15(5):462-473. (IF=3.211) 14.Forrest SL, Kril JJ, Stevens CH, Kwok JB, Hallupp M, Kim WS, Huang Y, McGinley CV, Werka H, Kiernan MC. et al. Retiring the term FTDP-17 as MAPT mutations are genetic forms of sporadic frontotemporal tauopathies. Brain. 2018; 141(2):521-534. (IF=11.337) 15.Sheng C, Huang Y, Han Y. Dissection of prodromal Alzheimer’s disease. Front Biosci (Landmark Ed) 23: 1272-1291. 2018.01.01. (IF=2.747) 16.Che XQ*, Zhao QH*, Huang Y*, Li X, Ren RJ, Chen SD, Guo QH, Wang G. Mutation Screening of the CHCHD2 Gene for Alzheimer’s Disease and Frontotemporal Dementia in Chinese Mainland Population. Journal of Alzheimer’s Disease.2018; 61(4): 1283-1288. (IF=4.151) 17.Wang G, Cui HL, Huang Y. Reader response: Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology, 2018;90(6):38. (IF=8.320) 18.Qiu W, Zhang HL, Bao AM, Zhu KQ, Huang Y, Yan XX, Zhang J, Zhong C, Zhou JN, Shen Y, Zheng XY, Zhang LW, Shu YS, Tang BS, Zhang ZX, Wang G, Zhou R, Sun B, Gong C, Duan S, Ma C. Standardised operational protocol for human brain banking in China. Neuroscience Bulletin. 2019, 35(2):270-276. (IF=3.80) 19.Hu YB, Ren RJ, Zhang YF, Huang Y, Cui HL, Ma C, Qiu WY, Wang H, Cui PJ, Chen HZ, Wang G (2019) Rho‐associated coiled‐coil kinase 1 activation mediates amyloid precursor protein site‐specific Ser655 phosphorylation and triggers amyloid pathology. Aging cell. 2019.July 9, https://doi.org/10.1111/acel.13001 (IF=7.080) 20.Kong WL, Huang Y*, Qian E, Morris M*. Constipation and Probable Rapid Eye Movement Sleep Behavior Disorder Associate with Cognitive Processing Speed and Attention over Five Years Following Clinical Diagnosis in Males with Parkinson’s Disease. Scientific Report. Accepted on 2020. Spet.8 (IF=4.120) 21.Forrest SL, Halliday GM, Sizemova A, Roijen M, McGinley CV, Bright F, Kapur M, McGeachie AB, McCann H, Shepherd CE, Tan RH, Affleck AJ, Huang Y, Kril J. A Practical Approach to Differentiate the Frontotemporal Tauopathy Subtypes. Journal of Neuropathology and Experimental Neurology. 2020;79(10): 1122–1126 (IF=3.510) |
| Additional Investigators | |
| Investigator's Name: | Huan Liu |
| Proposed Analysis: | HALLA analysis for the data extracted from ADNI. |
| Investigator's Name: | Yue Huang |
| Proposed Analysis: | Oversee the project and ensure the integrity and completion of the project at appropriate time frame. |
