1
|
Zhang Z, Liu X, Zhang S, Song Z, Lu K, Yang W. A review and analysis of key biomarkers in Alzheimer's disease. Front Neurosci 2024; 18:1358998. [PMID: 38445255 PMCID: PMC10912539 DOI: 10.3389/fnins.2024.1358998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/02/2024] [Indexed: 03/07/2024] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.
Collapse
Affiliation(s)
- Zhihao Zhang
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Xiangtao Liu
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Suixia Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- State Key Laboratory of Pathogenesis, Prevention, Treatment of Central Asian High Incidence Diseases, First Affiliated Hospital of Xinjiang Medical University, Ürümqi, China
| | - Zhixin Song
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Ke Lu
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
| | - Wenzhong Yang
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
| |
Collapse
|
2
|
Zhang Q, Liu J, Liu H, Ao L, Xi Y, Chen D. Genome-wide epistasis analysis reveals gene-gene interaction network on an intermediate endophenotype P-tau/Aβ 42 ratio in ADNI cohort. Sci Rep 2024; 14:3984. [PMID: 38368488 PMCID: PMC10874417 DOI: 10.1038/s41598-024-54541-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 02/14/2024] [Indexed: 02/19/2024] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the elderly worldwide. The exact etiology of AD, particularly its genetic mechanisms, remains incompletely understood. Traditional genome-wide association studies (GWAS), which primarily focus on single-nucleotide polymorphisms (SNPs) with main effects, provide limited explanations for the "missing heritability" of AD, while there is growing evidence supporting the important role of epistasis. In this study, we performed a genome-wide SNP-SNP interaction detection using a linear regression model and employed multiple GPUs for parallel computing, significantly enhancing the speed of whole-genome analysis. The cerebrospinal fluid (CSF) phosphorylated tau (P-tau)/amyloid-[Formula: see text] (A[Formula: see text]) ratio was used as a quantitative trait (QT) to enhance statistical power. Age, gender, and clinical diagnosis were included as covariates to control for potential non-genetic factors influencing AD. We identified 961 pairs of statistically significant SNP-SNP interactions, explaining a high-level variance of P-tau/A[Formula: see text] level, all of which exhibited marginal main effects. Additionally, we replicated 432 previously reported AD-related genes and found 11 gene-gene interaction pairs overlapping with the protein-protein interaction (PPI) network. Our findings may contribute to partially explain the "missing heritability" of AD. The identified subnetwork may be associated with synaptic dysfunction, Wnt signaling pathway, oligodendrocytes, inflammation, hippocampus, and neuronal cells.
Collapse
Affiliation(s)
- Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Junfeng Liu
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Hongwei Liu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, 145 Nantong Street, Harbin, China
| | - Lang Ao
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Yang Xi
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Dandan Chen
- School of Automation Engineering, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China.
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, 145 Nantong Street, Harbin, China.
| |
Collapse
|
3
|
Li J, Chen D, Liu H, Xi Y, Luo H, Wei Y, Liu J, Liang H, Zhang Q. Identifying potential genetic epistasis implicated in Alzheimer's disease via detection of SNP-SNP interaction on quantitative trait CSF Aβ 42. Neurobiol Aging 2024; 134:84-93. [PMID: 38039940 DOI: 10.1016/j.neurobiolaging.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 12/03/2023]
Abstract
Although genome-wide association studies have identified multiple Alzheimer's disease (AD)-associated loci by selecting the main effects of individual single-nucleotide polymorphisms (SNPs), the interpretation of genetic variance in AD is limited. Based on the linear regression method, we performed genome-wide SNP-SNP interaction on cerebrospinal fluid Aβ42 to identify potential genetic epistasis implicated in AD, with age, gender, and diagnosis as covariates. A GPU-based method was used to address the computational challenges posed by the analysis of epistasis. We found 368 SNP pairs to be statistically significant, and highly significant SNP-SNP interactions were identified between the marginal main effects of SNP pairs, which explained a relatively high variance at the Aβ42 level. Our results replicated 100 previously reported AD-related genes and 5 gene-gene interaction pairs of the protein-protein interaction network. Our bioinformatics analyses provided preliminary evidence that the 5-overlapping gene-gene interaction pairs play critical roles in inducing synaptic loss and dysfunction, thereby leading to memory decline and cognitive impairment in AD-affected brains.
Collapse
Affiliation(s)
- Jin Li
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Dandan Chen
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China; School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Hongwei Liu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Yang Xi
- School of Computer Science, Northeast Electric Power University, Jilin, China
| | - Haoran Luo
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Yiming Wei
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Junfeng Liu
- School of Computer Science, Northeast Electric Power University, Jilin, China
| | - Hong Liang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
| | - Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, Jilin, China.
| |
Collapse
|
4
|
Wang KW, Yuan YX, Zhu B, Zhang Y, Wei YF, Meng FS, Zhang S, Wang JX, Zhou JY. X chromosome-wide association study of quantitative biomarkers from the Alzheimer's Disease Neuroimaging Initiative study. Front Aging Neurosci 2023; 15:1277731. [PMID: 38035272 PMCID: PMC10682795 DOI: 10.3389/fnagi.2023.1277731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 10/20/2023] [Indexed: 12/02/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a complex neurodegenerative disease with high heritability. Compared to autosomes, a higher proportion of disorder-associated genes on X chromosome are expressed in the brain. However, only a few studies focused on the identification of the susceptibility loci for AD on X chromosome. Methods Using the data from the Alzheimer's Disease Neuroimaging Initiative Study, we conducted an X chromosome-wide association study between 16 AD quantitative biomarkers and 19,692 single nucleotide polymorphisms (SNPs) based on both the cross-sectional and longitudinal studies. Results We identified 15 SNPs statistically significantly associated with different quantitative biomarkers of the AD. For the cross-sectional study, six SNPs (rs5927116, rs4596772, rs5929538, rs2213488, rs5920524, and rs5945306) are located in or near to six genes DMD, TBX22, LOC101928437, TENM1, SPANXN1, and ZFP92, which have been reported to be associated with schizophrenia or neuropsychiatric diseases in literature. For the longitudinal study, four SNPs (rs4829868, rs5931111, rs6540385, and rs763320) are included in or near to two genes RAC1P4 and AFF2, which have been demonstrated to be associated with brain development or intellectual disability in literature, while the functional annotations of other five novel SNPs (rs12157031, rs428303, rs5953487, rs10284107, and rs5955016) have not been found. Discussion 15 SNPs were found statistically significantly associated with the quantitative biomarkers of the AD. Follow-up study in molecular genetics is needed to verify whether they are indeed related to AD. The findings in this article expand our understanding of the role of the X chromosome in exploring disease susceptibility, introduce new insights into the molecular genetics behind the AD, and may provide a mechanistic clue to further AD-related studies.
Collapse
Affiliation(s)
- Kai-Wen Wang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yu-Xin Yuan
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Bin Zhu
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yi Zhang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yi-Fang Wei
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Fan-Shuo Meng
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Shun Zhang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jing-Xuan Wang
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
| | - Ji-Yuan Zhou
- State Key Laboratory of Organ Failure Research, Ministry of Education, Guangdong Provincial Key Laboratory of Tropical Disease Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | | |
Collapse
|
5
|
Stevenson-Hoare J, Heslegrave A, Leonenko G, Fathalla D, Bellou E, Luckcuck L, Marshall R, Sims R, Morgan BP, Hardy J, de Strooper B, Williams J, Zetterberg H, Escott-Price V. Plasma biomarkers and genetics in the diagnosis and prediction of Alzheimer's disease. Brain 2023; 146:690-699. [PMID: 35383826 PMCID: PMC9924904 DOI: 10.1093/brain/awac128] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/14/2022] [Accepted: 03/13/2022] [Indexed: 11/12/2022] Open
Abstract
Plasma biomarkers for Alzheimer's disease-related pathologies have undergone rapid developments during the past few years, and there are now well-validated blood tests for amyloid and tau pathology, as well as neurodegeneration and astrocytic activation. To define Alzheimer's disease with biomarkers rather than clinical assessment, we assessed prediction of research-diagnosed disease status using these biomarkers and tested genetic variants associated with the biomarkers that may reflect more accurately the risk of biochemically defined Alzheimer's disease instead of the risk of dementia. In a cohort of Alzheimer's disease cases [n = 1439, mean age 68 years (standard deviation = 8.2)] and screened controls [n = 508, mean age 82 years (standard deviation = 6.8)], we measured plasma concentrations of the 40 and 42 amino acid-long amyloid-β (Aβ) fragments (Aβ40 and Aβ42, respectively), tau phosphorylated at amino acid 181 (P-tau181), neurofilament light (NfL) and glial fibrillary acidic protein (GFAP) using state-of-the-art Single molecule array (Simoa) technology. We tested the relationships between the biomarkers and Alzheimer's disease genetic risk, age at onset and disease duration. We also conducted a genome-wide association study for association of disease risk genes with these biomarkers. The prediction accuracy of Alzheimer's disease clinical diagnosis by the combination of all biomarkers, APOE and polygenic risk score reached area under receiver operating characteristic curve (AUC) = 0.81, with the most significant contributors being ε4, Aβ40 or Aβ42, GFAP and NfL. All biomarkers were significantly associated with age in cases and controls (P < 4.3 × 10-5). Concentrations of the Aβ-related biomarkers in plasma were significantly lower in cases compared with controls, whereas other biomarker levels were significantly higher in cases. In the case-control genome-wide analyses, APOE-ε4 was associated with all biomarkers (P = 0.011-4.78 × 10-8), except NfL. No novel genome-wide significant single nucleotide polymorphisms were found in the case-control design; however, in a case-only analysis, we found two independent genome-wide significant associations between the Aβ42/Aβ40 ratio and WWOX and COPG2 genes. Disease prediction modelling by the combination of all biomarkers indicates that the variance attributed to P-tau181 is mostly captured by APOE-ε4, whereas Aβ40, Aβ42, GFAP and NfL biomarkers explain additional variation over and above APOE. We identified novel plausible genome wide-significant genes associated with Aβ42/Aβ40 ratio in a sample which is 50 times smaller than current genome-wide association studies in Alzheimer's disease.
Collapse
Affiliation(s)
| | - Amanda Heslegrave
- Dementia Research Institute, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
| | - Ganna Leonenko
- Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Dina Fathalla
- Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Eftychia Bellou
- Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Lauren Luckcuck
- Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Rachel Marshall
- Division of Neuroscience and Mental Health, Cardiff University, Cardiff, UK
| | - Rebecca Sims
- Division of Neuroscience and Mental Health, Cardiff University, Cardiff, UK
| | | | - John Hardy
- Dementia Research Institute, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
| | - Bart de Strooper
- Dementia Research Institute, University College London, London, UK
- VIB Center for Brain and Disease Research, 3000 Leuven, Belgium
- KU Leuven, Leuven Brain Institute, 3000 Leuven, Belgium
| | - Julie Williams
- Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Henrik Zetterberg
- Dementia Research Institute, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Valentina Escott-Price
- Dementia Research Institute, Cardiff University, Cardiff, UK
- Division of Neuroscience and Mental Health, Cardiff University, Cardiff, UK
| |
Collapse
|
6
|
Oatman SR, Reddy JS, Quicksall Z, Carrasquillo MM, Wang X, Liu CC, Yamazaki Y, Nguyen TT, Malphrus K, Heckman M, Biswas K, Nho K, Baker M, Martens YA, Zhao N, Kim JP, Risacher SL, Rademakers R, Saykin AJ, DeTure M, Murray ME, Kanekiyo T, Dickson DW, Bu G, Allen M, Ertekin-Taner N. Genome-wide association study of brain biochemical phenotypes reveals distinct genetic architecture of Alzheimer's disease related proteins. Mol Neurodegener 2023; 18:2. [PMID: 36609403 PMCID: PMC9825010 DOI: 10.1186/s13024-022-00592-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 12/19/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is neuropathologically characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. The main protein components of these hallmarks include Aβ40, Aβ42, tau, phosphor-tau, and APOE. We hypothesize that genetic variants influence the levels and solubility of these AD-related proteins in the brain; identifying these may provide key insights into disease pathogenesis. METHODS Genome-wide genotypes were collected from 441 AD cases, imputed to the haplotype reference consortium (HRC) panel, and filtered for quality and frequency. Temporal cortex levels of five AD-related proteins from three fractions, buffer-soluble (TBS), detergent-soluble (Triton-X = TX), and insoluble (Formic acid = FA), were available for these same individuals. Variants were tested for association with each quantitative biochemical measure using linear regression, and GSA-SNP2 was used to identify enriched Gene Ontology (GO) terms. Implicated variants and genes were further assessed for association with other relevant variables. RESULTS We identified genome-wide significant associations at seven novel loci and the APOE locus. Genes and variants at these loci also associate with multiple AD-related measures, regulate gene expression, have cell-type specific enrichment, and roles in brain health and other neuropsychiatric diseases. Pathway analysis identified significant enrichment of shared and distinct biological pathways. CONCLUSIONS Although all biochemical measures tested reflect proteins core to AD pathology, our results strongly suggest that each have unique genetic architecture and biological pathways that influence their specific biochemical states in the brain. Our novel approach of deep brain biochemical endophenotype GWAS has implications for pathophysiology of proteostasis in AD that can guide therapeutic discovery efforts focused on these proteins.
Collapse
Affiliation(s)
- Stephanie R. Oatman
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Joseph S. Reddy
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Zachary Quicksall
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | | | - Xue Wang
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Chia-Chen Liu
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Yu Yamazaki
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Thuy T. Nguyen
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Kimberly Malphrus
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Michael Heckman
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Kristi Biswas
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Kwangsik Nho
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN USA
| | - Matthew Baker
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Yuka A. Martens
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Na Zhao
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Jun Pyo Kim
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
| | - Shannon L. Risacher
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
| | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- VIB-UA Center for Molecular Neurology, VIB, University of Antwerp, Antwerp, Belgium
| | - Andrew J. Saykin
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN USA
| | - Michael DeTure
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Takahisa Kanekiyo
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN USA
- VIB-UA Center for Molecular Neurology, VIB, University of Antwerp, Antwerp, Belgium
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Birdsall 3, Jacksonville, FL 32224 USA
| | - Dennis W. Dickson
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Guojun Bu
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Birdsall 3, Jacksonville, FL 32224 USA
| |
Collapse
|
7
|
Ouhourane M, Yang Y, Benedet AL, Oualkacha K. Group penalized quantile regression. STAT METHOD APPL-GER 2022. [DOI: 10.1007/s10260-021-00580-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
8
|
Prediction of Medical Conditions Using Machine Learning Approaches: Alzheimer’s Case Study. MATHEMATICS 2022. [DOI: 10.3390/math10101767] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Alzheimer’s Disease (AD) is a highly prevalent condition and most of the people suffering from it receive the diagnosis late in the process. The diagnosis is currently established following an evaluation of the protein biomarkers in cerebrospinal fluid (CSF), brain imaging, cognitive tests, and the medical history of the individuals. While diagnostic tools based on CSF collections are invasive, the tools used for acquiring brain scans are expensive. Taking these into account, an early predictive system, based on Artificial Intelligence (AI) approaches, targeting the diagnosis of this condition, as well as the identification of lead biomarkers becomes an important research direction. In this survey, we review the state-of-the-art research on machine learning (ML) techniques used for the detection of AD and Mild Cognitive Impairment (MCI). We attempt to identify the most accurate and efficient diagnostic approaches, which employ ML techniques and therefore, the ones most suitable to be used in practice. Research is still ongoing to determine the best biomarkers for the task of AD classification. At the beginning of this survey, after an introductory part, we enumerate several available resources, which can be used to build ML models targeting the diagnosis and classification of AD, as well as their main characteristics. After that, we discuss the candidate markers which were used to build AI models with the best results in terms of diagnostic accuracy, as well as their limitations.
Collapse
|
9
|
Predictive classification of Alzheimer’s disease using brain imaging and genetic data. Sci Rep 2022; 12:2405. [PMID: 35165327 PMCID: PMC8844076 DOI: 10.1038/s41598-022-06444-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 01/24/2022] [Indexed: 02/06/2023] Open
Abstract
For now, Alzheimer’s disease (AD) is incurable. But if it can be diagnosed early, the correct treatment can be used to delay the disease. Most of the existing research methods use single or multi-modal imaging features for prediction, relatively few studies combine brain imaging with genetic features for disease diagnosis. In order to accurately identify AD, healthy control (HC) and the two stages of mild cognitive impairment (MCI: early MCI, late MCI) combined with brain imaging and genetic characteristics, we proposed an integrated Fisher score and multi-modal multi-task feature selection research method. We learned first genetic features with Fisher score to perform dimensionality reduction in order to solve the problem of the large difference between the feature scales of genetic and brain imaging. Then we learned the potential related features of brain imaging and genetic data, and multiplied the selected features with the learned weight coefficients. Through the feature selection program, five imaging and five genetic features were selected to achieve an average classification accuracy of 98% for HC and AD, 82% for HC and EMCI, 86% for HC and LMCI, 80% for EMCI and LMCI, 88% for EMCI and AD, and 72% for LMCI and AD. Compared with only using imaging features, the classification accuracy has been improved to a certain extent, and a set of interrelated features of brain imaging phenotypes and genetic factors were selected.
Collapse
|
10
|
Vogrinc D, Goričar K, Dolžan V. Genetic Variability in Molecular Pathways Implicated in Alzheimer's Disease: A Comprehensive Review. Front Aging Neurosci 2021; 13:646901. [PMID: 33815092 PMCID: PMC8012500 DOI: 10.3389/fnagi.2021.646901] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 02/16/2021] [Indexed: 12/14/2022] Open
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disease, affecting a significant part of the population. The majority of AD cases occur in the elderly with a typical age of onset of the disease above 65 years. AD presents a major burden for the healthcare system and since population is rapidly aging, the burden of the disease will increase in the future. However, no effective drug treatment for a full-blown disease has been developed to date. The genetic background of AD is extensively studied; numerous genome-wide association studies (GWAS) identified significant genes associated with increased risk of AD development. This review summarizes more than 100 risk loci. Many of them may serve as biomarkers of AD progression, even in the preclinical stage of the disease. Furthermore, we used GWAS data to identify key pathways of AD pathogenesis: cellular processes, metabolic processes, biological regulation, localization, transport, regulation of cellular processes, and neurological system processes. Gene clustering into molecular pathways can provide background for identification of novel molecular targets and may support the development of tailored and personalized treatment of AD.
Collapse
Affiliation(s)
| | | | - Vita Dolžan
- Pharmacogenetics Laboratory, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| |
Collapse
|
11
|
Li J, Bian C, Luo H, Chen D, Cao L, Liang H. Multi-dimensional persistent feature analysis identifies connectivity patterns of resting-state brain networks in Alzheimer's disease. J Neural Eng 2020; 18. [PMID: 33152713 DOI: 10.1088/1741-2552/abc7ef] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 11/05/2020] [Indexed: 01/21/2023]
Abstract
OBJECTIVE The characterization of functional brain network is crucial to understanding the neural mechanisms associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Some studies have shown that graph theoretical analysis could reveal changes of the disease-related brain networks by thresholding edge weights. But the choice of threshold depends on ambiguous cognitive conditions, which leads to the lack of interpretability. Recently, persistent homology (PH) was proposed to record the persistence of topological features of networks across every possible thresholds, reporting a higher sensitivity than graph theoretical features in detecting network-level biomarkers of AD. However, most research on PH focused on 0-dimensional features (persistence of connected components) reflecting the intrinsic topology of the brain network, rather than 1-dimensional features (persistence of cycles) with an interesting neurobiological communication pattern. Our aim is to explore the multi-dimensional persistent features of brain networks in the AD and MCI patients, and further to capture valuable brain connectivity patterns. APPROACH We characterized the change rate of the connected component numbers across graph filtration using the functional derivative curves, and examined the persistence landscapes that vectorize the persistence of cycle structures. After that, the multi-dimensional persistent features were validated in disease identification using a K-nearest neighbor algorithm. Furthermore, a connectivity pattern mining framework was designed to capture the disease-specific brain structures. MAIN RESULTS We found that the multi-dimensional persistent features can identify statistical group differences, quantify subject-level distances, and yield disease-specific connectivity patterns. Relatively high classification accuracies were received when compared with graph theoretical features. SIGNIFICANCE This work represents a conceptual bridge linking complex brain network analysis and computational topology. Our results can be beneficial for providing a complementary objective opinion to the clinical diagnosis of neurodegenerative diseases.
Collapse
Affiliation(s)
- Jin Li
- Harbin Engineering University, Harbin, Heilongjiang, CHINA
| | - Chenyuan Bian
- Harbin Engineering University, Harbin, Heilongjiang, CHINA
| | - Haoran Luo
- Harbin Engineering University, Harbin, Heilongjiang, CHINA
| | - Dandan Chen
- Harbin Engineering University, Harbin, Heilongjiang, CHINA
| | - Luolong Cao
- Harbin Engineering University, Harbin, Heilongjiang, CHINA
| | - Hong Liang
- Harbin Engineering University, Nantong street 145, Harbin, 150001, CHINA
| |
Collapse
|
12
|
Deruelle T, Kober F, Perles-Barbacaru A, Delzescaux T, Noblet V, Barbier EL, Dojat M. A Multicenter Preclinical MRI Study: Definition of Rat Brain Relaxometry Reference Maps. Front Neuroinform 2020; 14:22. [PMID: 32508614 PMCID: PMC7248563 DOI: 10.3389/fninf.2020.00022] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 04/21/2020] [Indexed: 11/13/2022] Open
Abstract
Similarly to human population imaging, there are several well-founded motivations for animal population imaging, the most notable being the improvement of the validity of statistical results by pooling a sufficient number of animal data provided by different imaging centers. In this paper, we demonstrate the feasibility of such a multicenter animal study, sharing raw data from forty rats and processing pipelines between four imaging centers. As specific use case, we focused on T1 and T2 mapping of the healthy rat brain at 7T. We quantitatively report about the variability observed across two MR data providers and evaluate the influence of image processing steps on the final maps, using three fitting algorithms from three centers. Finally, to derive relaxation times from different brain areas, two multi-atlas segmentation pipelines from different centers were performed on two different platforms. Differences between the two data providers were 2.21% for T1 and 9.52% for T2. Differences between processing pipelines were 1.04% for T1 and 3.33% for T2. These maps, obtained in healthy conditions, may be used in the future as reference when exploring alterations in animal models of pathology.
Collapse
Affiliation(s)
- Tristan Deruelle
- INSERM, U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, Grenoble, France
| | - Frank Kober
- CNRS, CRMBM, Aix-Marseille Université, Marseille, France
| | | | | | - Vincent Noblet
- CNRS, ICube - IMAGeS, Strasbourg University, Strasbourg, France
| | - Emmanuel L Barbier
- INSERM, U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, Grenoble, France
| | - Michel Dojat
- INSERM, U1216, Grenoble Institut des Neurosciences, Université Grenoble Alpes, Grenoble, France
| |
Collapse
|
13
|
Bi XA, Liu Y, Xie Y, Hu X, Jiang Q. Morbigenous brain region and gene detection with a genetically evolved random neural network cluster approach in late mild cognitive impairment. Bioinformatics 2020; 36:2561-2568. [PMID: 31971559 PMCID: PMC7178433 DOI: 10.1093/bioinformatics/btz967] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 12/12/2019] [Accepted: 01/18/2020] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION The multimodal data fusion analysis becomes another important field for brain disease detection and increasing researches concentrate on using neural network algorithms to solve a range of problems. However, most current neural network optimizing strategies focus on internal nodes or hidden layer numbers, while ignoring the advantages of external optimization. Additionally, in the multimodal data fusion analysis of brain science, the problems of small sample size and high-dimensional data are often encountered due to the difficulty of data collection and the specialization of brain science data, which may result in the lower generalization performance of neural network. RESULTS We propose a genetically evolved random neural network cluster (GERNNC) model. Specifically, the fusion characteristics are first constructed to be taken as the input and the best type of neural network is selected as the base classifier to form the initial random neural network cluster. Second, the cluster is adaptively genetically evolved. Based on the GERNNC model, we further construct a multi-tasking framework for the classification of patients with brain disease and the extraction of significant characteristics. In a study of genetic data and functional magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative, the framework exhibits great classification performance and strong morbigenous factor detection ability. This work demonstrates that how to effectively detect pathogenic components of the brain disease on the high-dimensional medical data and small samples. AVAILABILITY AND IMPLEMENTATION The Matlab code is available at https://github.com/lizi1234560/GERNNC.git.
Collapse
Affiliation(s)
- Xia-an Bi
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Yingchao Liu
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Yiming Xie
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Xi Hu
- Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qinghua Jiang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| |
Collapse
|
14
|
De Velasco Oriol J, Vallejo EE, Estrada K, Taméz Peña JG, Disease Neuroimaging Initiative TA. Benchmarking machine learning models for late-onset alzheimer's disease prediction from genomic data. BMC Bioinformatics 2019; 20:709. [PMID: 31842725 PMCID: PMC6915925 DOI: 10.1186/s12859-019-3158-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 10/14/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Late-Onset Alzheimer's Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available. RESULTS We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve. CONCLUSIONS Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease.
Collapse
Affiliation(s)
- Javier De Velasco Oriol
- Department of Bioinformatics, Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, 64710 Mexico
| | - Edgar E. Vallejo
- Department of Bioinformatics, Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, 64710 Mexico
| | - Karol Estrada
- Graduate Professional Studies, Brandeis University, Waltham, 02453 MA USA
| | - José Gerardo Taméz Peña
- Department of Bioinformatics, Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, 64710 Mexico
| | | |
Collapse
|
15
|
Yuan S, Li H, Xie J, Sun X. Quantitative Trait Module-Based Genetic Analysis of Alzheimer's Disease. Int J Mol Sci 2019; 20:E5912. [PMID: 31775305 PMCID: PMC6928939 DOI: 10.3390/ijms20235912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 11/21/2019] [Accepted: 11/22/2019] [Indexed: 01/02/2023] Open
Abstract
The pathological features of Alzheimer's Disease (AD) first appear in the medial temporal lobe and then in other brain structures with the development of the disease. In this work, we investigated the association between genetic loci and subcortical structure volumes of AD on 393 samples in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Brain subcortical structures were clustered into modules using Pearson's correlation coefficient of volumes across all samples. Module volumes were used as quantitative traits to identify not only the main effect loci but also the interactive effect loci for each module. Thirty-five subcortical structures were clustered into five modules, each corresponding to a particular brain structure/area, including the limbic system (module I), the corpus callosum (module II), thalamus-cerebellum-brainstem-pallidum (module III), the basal ganglia neostriatum (module IV), and the ventricular system (module V). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results indicate that the gene annotations of the five modules were distinct, with few overlaps between different modules. We identified several main effect loci and interactive effect loci for each module. All these loci are related to the function of module structures and basic biological processes such as material transport and signal transduction.
Collapse
Affiliation(s)
| | | | | | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China; (S.Y.)
| |
Collapse
|
16
|
Talwar P, Gupta R, Kushwaha S, Agarwal R, Saso L, Kukreti S, Kukreti R. Viral Induced Oxidative and Inflammatory Response in Alzheimer's Disease Pathogenesis with Identification of Potential Drug Candidates: A Systematic Review using Systems Biology Approach. Curr Neuropharmacol 2019; 17:352-365. [PMID: 29676229 PMCID: PMC6482477 DOI: 10.2174/1570159x16666180419124508] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 03/19/2018] [Accepted: 04/10/2018] [Indexed: 02/07/2023] Open
Abstract
Alzheimer's disease (AD) is genetically complex with multifactorial etiology. Here, we aim to identify the potential viral pathogens leading to aberrant inflammatory and oxidative stress response in AD along with potential drug candidates using systems biology approach. We retrieved protein interactions of amyloid precursor protein (APP) and tau protein (MAPT) from NCBI and genes for oxidative stress from NetAge, for inflammation from NetAge and InnateDB databases. Genes implicated in aging were retrieved from GenAge database and two GEO expression datasets. These genes were individually used to create protein-protein interaction network using STRING database (score≥0.7). The interactions of candidate genes with known viruses were mapped using virhostnet v2.0 database. Drug molecules targeting candidate genes were retrieved using the Drug- Gene Interaction Database (DGIdb). Data mining resulted in 2095 APP, 116 MAPT, 214 oxidative stress, 1269 inflammatory genes. After STRING PPIN analysis, 404 APP, 109 MAPT, 204 oxidative stress and 1014 inflammation related high confidence proteins were identified. The overlap among all datasets yielded eight common markers (AKT1, GSK3B, APP, APOE, EGFR, PIN1, CASP8 and SNCA). These genes showed association with hepatitis C virus (HCV), Epstein- Barr virus (EBV), human herpes virus 8 and Human papillomavirus (HPV). Further, screening of drugs targeting candidate genes, and possessing anti-inflammatory property, antiviral activity along with a suggested role in AD pathophysiology yielded 12 potential drug candidates. Our study demonstrated the role of viral etiology in AD pathogenesis by elucidating interaction of oxidative stress and inflammation causing candidate genes with common viruses along with the identification of potential AD drug candidates.
Collapse
Affiliation(s)
- Puneet Talwar
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi, India
| | - Renu Gupta
- Institute of Human Behaviour & Allied Sciences (IHBAS), Dilshad Garden, Delhi 110 095, India
| | - Suman Kushwaha
- Institute of Human Behaviour & Allied Sciences (IHBAS), Dilshad Garden, Delhi 110 095, India
| | - Rachna Agarwal
- Institute of Human Behaviour & Allied Sciences (IHBAS), Dilshad Garden, Delhi 110 095, India
| | - Luciano Saso
- Department of Physiology and Pharmacology, Sapienza University of Rome, Italy
| | | | - Ritushree Kukreti
- Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR), Delhi, India
| |
Collapse
|
17
|
Fang X, Tang W, Yang F, Lu W, Cai J, Ni J, Zhang J, Tang W, Li T, Zhang DF, Zhang C. A Comprehensive Analysis of the CaMK2A Gene and Susceptibility to Alzheimer's Disease in the Han Chinese Population. Front Aging Neurosci 2019; 11:84. [PMID: 31031618 PMCID: PMC6470288 DOI: 10.3389/fnagi.2019.00084] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Accepted: 03/26/2019] [Indexed: 02/05/2023] Open
Abstract
There is ample evidence suggesting that calcium/calmodulin-dependent protein kinase II alpha (CaMK2A) may play an important role in the pathophysiology of Alzheimer’s disease (AD). This genetic study aimed to investigate whether CaMK2A confers susceptibility to the development of AD in the Han Chinese population. A total of seven single nucleotide polymorphisms (SNPs) within CaMK2A were screened in two independent cohorts from southwestern China (333 AD patients and 334 controls) and eastern China (382 AD patients and 426 controls) to discern the potential association between this gene and AD. In addition, a cross-platform normalized expression resource was used to investigate whether CaMK2A is differentially expressed in the brain between individuals with AD and the controls. In addition, expression quantitative trait loci (eQTL) analysis was used to explore the differences in CaMK2A expression in the brain among different genotypes. The cross-platform normalized data showed significant differences in CaMK2A expression in the hippocampus, entorhinal cortex and temporal cortex between the AD patients and the control subjects (|log FC| > 0.1, P < 0.05); however, only the differences in the hippocampus and temporal cortex remained after the multiple comparisons correction [false discovery rate (FDR)-corrected, P < 0.05]. The frequency of the rs4958445 genotype was significantly different between the AD subjects and the controls from southwestern China (P = 0.013, P = 0.034 after FDR correction). When the two samples were combined, rs4958445 still showed a significant association with AD (P = 0.044). Haplotype analysis indicated that the T-A-C-A-T-C-C and T-G-C-A-T-C-C haplotypes in the southwestern cohort and the T-G-C-G-C-T-C haplotype in the eastern cohort, consisting of rs10051644, rs6869634, rs3797617, rs3756577, rs4958445, rs10515639 and rs6881743, showed a significant association with AD (P = 0.037, P = 0.026 and P = 0.045, respectively). Furthermore, the brain eQTL analysis revealed a significant association between the rs4958445 polymorphism and CaMK2A expression in the inferior olivary nucleus (P = 0.029). Our results suggest an important role for CaMK2A in the pathophysiology of AD in the Han Chinese population, especially the southwestern population.
Collapse
Affiliation(s)
- Xinyu Fang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Tang
- Wenzhou Kangning Hospital, Wenzhou Medical University, Wenzhou, China
| | - Fuyin Yang
- Wenzhou Kangning Hospital, Wenzhou Medical University, Wenzhou, China
| | - Weihong Lu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Cai
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jianliang Ni
- Tongde Hospital of Zhejiang Province, Hangzhou, China
| | | | - Wenxin Tang
- Hangzhou Seventh People's Hospital, Hangzhou, China
| | - Tao Li
- Huaxi Brain Research Centre, West China Hospital, Sichuan University, Sichuan, China
| | - Deng-Feng Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms, Kunming Institute of Zoology, Chinese Academy of Sciences, Yunnan, China
| | - Chen Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
18
|
Veitch DP, Weiner MW, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Understanding disease progression and improving Alzheimer's disease clinical trials: Recent highlights from the Alzheimer's Disease Neuroimaging Initiative. Alzheimers Dement 2018; 15:106-152. [PMID: 30321505 DOI: 10.1016/j.jalz.2018.08.005] [Citation(s) in RCA: 249] [Impact Index Per Article: 41.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Accepted: 08/21/2018] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI is a multisite, longitudinal, observational study that has collected many biomarkers since 2004. Recent publications highlight the multifactorial nature of late-onset AD. We discuss selected topics that provide insights into AD progression and outline how this knowledge may improve clinical trials. METHODS We used standard methods to identify nearly 600 publications using ADNI data from 2016 and 2017 (listed in Supplementary Material and searchable at http://adni.loni.usc.edu/news-publications/publications/). RESULTS (1) Data-driven AD progression models supported multifactorial interactions rather than a linear cascade of events. (2) β-Amyloid (Aβ) deposition occurred concurrently with functional connectivity changes within the default mode network in preclinical subjects and was followed by specific and progressive disconnection of functional and anatomical networks. (3) Changes in functional connectivity, volumetric measures, regional hypometabolism, and cognition were detectable at subthreshold levels of Aβ deposition. 4. Tau positron emission tomography imaging studies detailed a specific temporal and spatial pattern of tau pathology dependent on prior Aβ deposition, and related to subsequent cognitive decline. 5. Clustering studies using a wide range of modalities consistently identified a "typical AD" subgroup and a second subgroup characterized by executive impairment and widespread cortical atrophy in preclinical and prodromal subjects. 6. Vascular pathology burden may act through both Aβ dependent and independent mechanisms to exacerbate AD progression. 7. The APOE ε4 allele interacted with cerebrovascular disease to impede Aβ clearance mechanisms. 8. Genetic approaches identified novel genetic risk factors involving a wide range of processes, and demonstrated shared genetic risk for AD and vascular disorders, as well as the temporal and regional pathological associations of established AD risk alleles. 9. Knowledge of early pathological changes guided the development of novel prognostic biomarkers for preclinical subjects. 10. Placebo populations of randomized controlled clinical trials had highly variable trajectories of cognitive change, underscoring the importance of subject selection and monitoring. 11. Selection criteria based on Aβ positivity, hippocampal volume, baseline cognitive/functional measures, and APOE ε4 status in combination with improved cognitive outcome measures were projected to decrease clinical trial duration and cost. 12. Multiple concurrent therapies targeting vascular health and other AD pathology in addition to Aβ may be more effective than single therapies. DISCUSSION ADNI publications from 2016 and 2017 supported the idea of AD as a multifactorial disease and provided insights into the complexities of AD disease progression. These findings guided the development of novel biomarkers and suggested that subject selection on the basis of multiple factors may lower AD clinical trial costs and duration. The use of multiple concurrent therapies in these trials may prove more effective in reversing AD disease progression.
Collapse
Affiliation(s)
- Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Northern California Institute for Research and Education (NCIRE), Department of Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | |
Collapse
|
19
|
Liu C, Chyr J, Zhao W, Xu Y, Ji Z, Tan H, Soto C, Zhou X. Genome-Wide Association and Mechanistic Studies Indicate That Immune Response Contributes to Alzheimer's Disease Development. Front Genet 2018; 9:410. [PMID: 30319691 PMCID: PMC6166008 DOI: 10.3389/fgene.2018.00410] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Accepted: 09/05/2018] [Indexed: 12/19/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common cause of dementia. Although genome-wide association study (GWAS) have reported hundreds of single-nucleotide polymorphisms (SNPs) and genes linked to AD, the mechanisms about how these SNPs modulate the development of AD remain largely unknown. In this study, we performed GWAS for three traits in cerebrospinal fluid (CSF) and one clinical trait in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Our analysis identified five most significant AD related SNPs (FDR < 0.05) within or proximal to APOE, APOC1, and TOMM40. One of the SNPs was co-inherited with APOE allele 4, which is the most important genetic risk factor for AD. Three of the five SNPs were located in promoter or enhancer regions, and transcription factor (TF) binding affinity calculations showed dramatic changes (| Log2FC| > 2) of three TFs (PLAG1, RREB1, and ZBTB33) for two motifs containing SNPs rs2075650 and rs157580. In addition, our GWAS showed that both rs2075650 and rs157580 were significantly associated with the poliovirus receptor-related 2 (PVRL2) gene (FDR < 0.25), which is involved in spreading of herpes simplex virus (HSV). The altered regulation of PVRL2 may increase the susceptibility AD patients to HSV and other virus infections of the brain. Our work suggests that AD is a type of immune disorder driven by viral or microbial infections of the brain during aging.
Collapse
Affiliation(s)
- Changan Liu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Jacqueline Chyr
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Weiling Zhao
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Yungang Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Zhiwei Ji
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Hua Tan
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Claudio Soto
- Department of Neurology, The George and Cynthia W Mitchell Center for Alzheimer's Disease and Other Brain Related Illnesses, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Xiaobo Zhou
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | | |
Collapse
|
20
|
Gozes I. Specific protein biomarker patterns for Alzheimer's disease: improved diagnostics in progress. EPMA J 2017; 8:255-259. [PMID: 29021836 DOI: 10.1007/s13167-017-0110-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 08/10/2017] [Indexed: 12/12/2022]
Abstract
This short review looks at Alzheimer's disease (AD) diagnosis through my own point of view, going from imaging through cerebrospinal fluid to blood proteins. Over the last couple of years, we have published two papers targeted at Alzheimer's diagnosis. In one paper, we took an approach of selecting a specific target, namely, activity-dependent neuroprotective protein (ADNP), and our results tightened the association of ADNP blood expression with intelligence. In another paper, we took an unbiased approach of analysis of all genes expressed in lymphoblastoid cells lines and discovered changes in expression of the regulator of G-protein signaling 2 (RGS2) as a potential AD predictor. This review will assess our data in comparison to selected independent studies focusing on blood protein biomarkers as well as assessing saliva and urine samples with potential predictive value for AD. Furthermore, the review will provide directions for a combination of innovative markers, stratifying the population toward disease prevention and personalized medicine.
Collapse
Affiliation(s)
- Illana Gozes
- The Lily and Avraham Gildor Chair for the Investigation of Growth Factors, Elton Laboratory for Neuroendocrinology, Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Adams Super Center for Brain Studies and Sagol School of Neuroscience, Tel Aviv University, 69978 Tel Aviv, Israel
| |
Collapse
|