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Wang B, Martini-Stoica H, Qi C, Lu TC, Wang S, Xiong W, Qi Y, Xu Y, Sardiello M, Li H, Zheng H. TFEB-vacuolar ATPase signaling regulates lysosomal function and microglial activation in tauopathy. Nat Neurosci 2024; 27:48-62. [PMID: 37985800 DOI: 10.1038/s41593-023-01494-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 10/13/2023] [Indexed: 11/22/2023]
Abstract
Transcription factor EB (TFEB) mediates gene expression through binding to the coordinated lysosome expression and regulation (CLEAR) sequence. TFEB targets include subunits of the vacuolar ATPase (v-ATPase), which are essential for lysosome acidification. Single-nucleus RNA sequencing of wild-type and PS19 (Tau) transgenic mice expressing the P301S mutant tau identified three unique microglia subclusters in Tau mice that were associated with heightened lysosome and immune pathway genes. To explore the lysosome-immune relationship, we specifically disrupted the TFEB-v-ATPase signaling by creating a knock-in mouse line in which the CLEAR sequence of one of the v-ATPase subunits, Atp6v1h, was mutated. CLEAR mutant exhibited a muted response to TFEB, resulting in impaired lysosomal acidification and activity. Crossing the CLEAR mutant with Tau mice led to higher tau pathology but diminished microglia response. These microglia were enriched in a subcluster low in mTOR and HIF-1 pathways and were locked in a homeostatic state. Our studies demonstrate a physiological function of TFEB-v-ATPase signaling in maintaining lysosomal homeostasis and a critical role of the lysosome in mounting a microglia and immune response in tauopathy and Alzheimer's disease.
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Affiliation(s)
- Baiping Wang
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Heidi Martini-Stoica
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, USA
- Department of Otolaryngology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Chuangye Qi
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
| | - Tzu-Chiao Lu
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
| | - Shuo Wang
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
| | - Wen Xiong
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
| | - Yanyan Qi
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
| | - Yin Xu
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
- School of Mental Health and Psychological Sciences, Anhui Medical University, Anhui, China
| | - Marco Sardiello
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Dan and Jan Duncan Neurological Research Institute, Baylor College of Medicine, Houston, TX, USA
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA
| | - Hongjie Li
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Hui Zheng
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA.
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
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Rajabli F, Benchek P, Tosto G, Kushch N, Sha J, Bazemore K, Zhu C, Lee WP, Haut J, Hamilton-Nelson KL, Wheeler NR, Zhao Y, Farrell JJ, Grunin MA, Leung YY, Kuksa PP, Li D, Lucio da Fonseca E, Mez JB, Palmer EL, Pillai J, Sherva RM, Song YE, Zhang X, Iqbal T, Pathak O, Valladares O, Kuzma AB, Abner E, Adams PM, Aguirre A, Albert MS, Albin RL, Allen M, Alvarez L, Apostolova LG, Arnold SE, Asthana S, Atwood CS, Ayres G, Baldwin CT, Barber RC, Barnes LL, Barral S, Beach TG, Becker JT, Beecham GW, Beekly D, Benitez BA, Bennett D, Bertelson J, Bird TD, Blacker D, Boeve BF, Bowen JD, Boxer A, Brewer J, Burke JR, Burns JM, Buxbaum JD, Cairns NJ, Cantwell LB, Cao C, Carlson CS, Carlsson CM, Carney RM, Carrasquillo MM, Chasse S, Chesselet MF, Chin NA, Chui HC, Chung J, Craft S, Crane PK, Cribbs DH, Crocco EA, Cruchaga C, Cuccaro ML, Cullum M, Darby E, Davis B, De Jager PL, DeCarli C, DeToledo J, Dick M, Dickson DW, Dombroski BA, Doody RS, Duara R, Ertekin-Taner NI, Evans DA, Faber KM, Fairchild TJ, Fallon KB, Fardo DW, Farlow MR, Fernandez-Hernandez V, Ferris S, Foroud TM, Frosch MP, Fulton-Howard B, Galasko DR, Gamboa A, Gearing M, Geschwind DH, Ghetti B, Gilbert JR, Goate AM, Grabowski TJ, Graff-Radford NR, Green RC, Growdon JH, Hakonarson H, Hall J, Hamilton RL, Harari O, Hardy J, Harrell LE, Head E, Henderson VW, Hernandez M, Hohman T, Honig LS, Huebinger RM, Huentelman MJ, Hulette CM, Hyman BT, Hynan LS, Ibanez L, Jarvik GP, Jayadev S, Jin LW, Johnson K, Johnson L, Kamboh MI, Karydas AM, Katz MJ, Kauwe JS, Kaye JA, Keene CD, Khaleeq A, Kim R, Knebl J, Kowall NW, Kramer JH, Kukull WA, LaFerla FM, Lah JJ, Larson EB, Lerner A, Leverenz JB, Levey AI, Lieberman AP, Lipton RB, Logue M, Lopez OL, Lunetta KL, Lyketsos CG, Mains D, Margaret FE, Marson DC, Martin ERR, Martiniuk F, Mash DC, Masliah E, Massman P, Masurkar A, McCormick WC, McCurry SM, McDavid AN, McDonough S, McKee AC, Mesulam M, Miller BL, Miller CA, Miller JW, Montine TJ, Monuki ES, Morris JC, Mukherjee S, Myers AJ, Nguyen T, O'Bryant S, Olichney JM, Ory M, Palmer R, Parisi JE, Paulson HL, Pavlik V, Paydarfar D, Perez V, Peskind E, Petersen RC, Pierce A, Polk M, Poon WW, Potter H, Qu L, Quiceno M, Quinn JF, Raj A, Raskind M, Reiman EM, Reisberg B, Reisch JS, Ringman JM, Roberson ED, Rodriguear M, Rogaeva E, Rosen HJ, Rosenberg RN, Royall DR, Sager MA, Sano M, Saykin AJ, Schneider JA, Schneider LS, Seeley WW, Slifer SH, Small S, Smith AG, Smith JP, Sonnen JA, Spina S, St George-Hyslop P, Stern RA, Stevens AB, Strittmatter SM, Sultzer D, Swerdlow RH, Tanzi RE, Tilson JL, Trojanowski JQ, Troncoso JC, Tsuang DW, Van Deerlin VM, van Eldik LJ, Vance JM, Vardarajan BN, Vassar R, Vinters HV, Vonsattel JP, Weintraub S, Welsh-Bohmer KA, Whitehead PL, Wijsman EM, Wilhelmsen KC, Williams B, Williamson J, Wilms H, Wingo TS, Wisniewski T, Woltjer RL, Woon M, Wright CB, Wu CK, Younkin SG, Yu CE, Yu L, Zhu X, Kunkle BW, Bush WS, Wang LS, Farrer LA, Haines JL, Mayeux R, Pericak-Vance MA, Schellenberg GD, Jun GR, Reitz C, Naj AC. Multi-ancestry genome-wide meta-analysis of 56,241 individuals identifies LRRC4C, LHX5-AS1 and nominates ancestry-specific loci PTPRK , GRB14 , and KIAA0825 as novel risk loci for Alzheimer's disease: the Alzheimer's Disease Genetics Consortium. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.06.23292311. [PMID: 37461624 PMCID: PMC10350126 DOI: 10.1101/2023.07.06.23292311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Limited ancestral diversity has impaired our ability to detect risk variants more prevalent in non-European ancestry groups in genome-wide association studies (GWAS). We constructed and analyzed a multi-ancestry GWAS dataset in the Alzheimer's Disease (AD) Genetics Consortium (ADGC) to test for novel shared and ancestry-specific AD susceptibility loci and evaluate underlying genetic architecture in 37,382 non-Hispanic White (NHW), 6,728 African American, 8,899 Hispanic (HIS), and 3,232 East Asian individuals, performing within-ancestry fixed-effects meta-analysis followed by a cross-ancestry random-effects meta-analysis. We identified 13 loci with cross-ancestry associations including known loci at/near CR1 , BIN1 , TREM2 , CD2AP , PTK2B , CLU , SHARPIN , MS4A6A , PICALM , ABCA7 , APOE and two novel loci not previously reported at 11p12 ( LRRC4C ) and 12q24.13 ( LHX5-AS1 ). Reflecting the power of diverse ancestry in GWAS, we observed the SHARPIN locus using 7.1% the sample size of the original discovering single-ancestry GWAS (n=788,989). We additionally identified three GWS ancestry-specific loci at/near ( PTPRK ( P =2.4×10 -8 ) and GRB14 ( P =1.7×10 -8 ) in HIS), and KIAA0825 ( P =2.9×10 -8 in NHW). Pathway analysis implicated multiple amyloid regulation pathways (strongest with P adjusted =1.6×10 -4 ) and the classical complement pathway ( P adjusted =1.3×10 -3 ). Genes at/near our novel loci have known roles in neuronal development ( LRRC4C, LHX5-AS1 , and PTPRK ) and insulin receptor activity regulation ( GRB14 ). These findings provide compelling support for using traditionally-underrepresented populations for gene discovery, even with smaller sample sizes.
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Tian T, Sun J. Variable selection for nonparametric additive Cox model with interval-censored data. Biom J 2023; 65:e2100310. [PMID: 35923136 DOI: 10.1002/bimj.202100310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 05/05/2022] [Accepted: 05/28/2022] [Indexed: 01/17/2023]
Abstract
The standard Cox model is perhaps the most commonly used model for regression analysis of failure time data but it has some limitations such as the assumption on linear covariate effects. To relax this, the nonparametric additive Cox model, which allows for nonlinear covariate effects, is often employed, and this paper will discuss variable selection and structure estimation for this general model. For the problem, we propose a penalized sieve maximum likelihood approach with the use of Bernstein polynomials approximation and group penalization. To implement the proposed method, an efficient group coordinate descent algorithm is developed and can be easily carried out for both low- and high-dimensional scenarios. Furthermore, a simulation study is performed to assess the performance of the presented approach and suggests that it works well in practice. The proposed method is applied to an Alzheimer's disease study for identifying important and relevant genetic factors.
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Affiliation(s)
- Tian Tian
- Department of Statistics, University of Missouri, Columbia, USA, MO
| | - Jianguo Sun
- Department of Statistics, University of Missouri, Columbia, USA, MO
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4
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Zhang T, Liu N, Wei W, Zhang Z, Li H. Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer's Disease. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2021; 2021:9918498. [PMID: 34367470 PMCID: PMC8339876 DOI: 10.1155/2021/9918498] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 07/14/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease; however, there are no comprehensive therapeutic interventions. Therefore, this study is aimed at identifying novel molecular targets that may improve the diagnosis and treatment of patients with AD. METHODS In our study, GSE5281 microarray dataset from the GEO database was collected and screened for differential expression analysis. Genes with a P value of <0.05 and ∣log2FoldChange | >0.5 were considered differentially expressed genes (DEGs). We further profiled and identified AD-related coexpression genes using weighted gene coexpression network analysis (WGCNA). Functional enrichment analysis was performed to determine the characteristics and pathways of the key modules. We constructed an AD-related model based on hub genes by logistic regression and least absolute shrinkage and selection operator (LASSO) analyses, which was also verified by the receiver operating characteristic (ROC) curve. RESULTS In total, 4674 DEGs were identified. Nine distinct coexpression modules were identified via WGCNA; among these modules, the blue module showed the highest positive correlation with AD (r = 0.64, P = 3e - 20), and it was visualized by establishing a protein-protein interaction network. Moreover, this module was particularly enriched in "pathways of neurodegeneration-multiple diseases," "Alzheimer disease," "oxidative phosphorylation," and "proteasome." Sixteen genes were identified as hub genes and further submitted to a LASSO regression model, and six genes (EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF) were identified based on the model index. Additionally, we assessed the accuracy of the LASSO model by plotting an ROC curve (AUC = 0.940). CONCLUSIONS Using the WGCNA and LASSO models, our findings provide a better understanding of the role of biomarkers EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF and provide a basis for further studies on AD progression.
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Affiliation(s)
- Tingting Zhang
- College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China
| | - Nanyang Liu
- Department of Geratology, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China
| | - Wei Wei
- College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China
| | - Zhen Zhang
- Department of Geratology, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China
| | - Hao Li
- Department of Geratology, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China
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5
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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.
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Affiliation(s)
| | | | - Vita Dolžan
- Pharmacogenetics Laboratory, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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6
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Pellegrini C, Pirazzini C, Sala C, Sambati L, Yusipov I, Kalyakulina A, Ravaioli F, Kwiatkowska KM, Durso DF, Ivanchenko M, Monti D, Lodi R, Franceschi C, Cortelli P, Garagnani P, Bacalini MG. A Meta-Analysis of Brain DNA Methylation Across Sex, Age, and Alzheimer's Disease Points for Accelerated Epigenetic Aging in Neurodegeneration. Front Aging Neurosci 2021; 13:639428. [PMID: 33790779 PMCID: PMC8006465 DOI: 10.3389/fnagi.2021.639428] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 02/05/2021] [Indexed: 12/14/2022] Open
Abstract
Alzheimer's disease (AD) is characterized by specific alterations of brain DNA methylation (DNAm) patterns. Age and sex, two major risk factors for AD, are also known to largely affect the epigenetic profiles in brain, but their contribution to AD-associated DNAm changes has been poorly investigated. In this study we considered publicly available DNAm datasets of four brain regions (temporal, frontal, entorhinal cortex, and cerebellum) from healthy adult subjects and AD patients, and performed a meta-analysis to identify sex-, age-, and AD-associated epigenetic profiles. In one of these datasets it was also possible to distinguish 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) profiles. We showed that DNAm differences between males and females tend to be shared between the four brain regions, while aging differently affects cortical regions compared to cerebellum. We found that the proportion of sex-dependent probes whose methylation is modified also during aging is higher than expected, but that differences between males and females tend to be maintained, with only a few probes showing age-by-sex interaction. We did not find significant overlaps between AD- and sex-associated probes, nor disease-by-sex interaction effects. On the contrary, we found that AD-related epigenetic modifications are significantly enriched in probes whose DNAm varies with age and that there is a high concordance between the direction of changes (hyper or hypo-methylation) in aging and AD, supporting accelerated epigenetic aging in the disease. In summary, our results suggest that age-associated DNAm patterns concur to the epigenetic deregulation observed in AD, providing new insights on how advanced age enables neurodegeneration.
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Affiliation(s)
- Camilla Pellegrini
- Istituto di Ricovero e Cura a Carattere Scientifico Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Chiara Pirazzini
- Istituto di Ricovero e Cura a Carattere Scientifico Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Claudia Sala
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
| | - Luisa Sambati
- Istituto di Ricovero e Cura a Carattere Scientifico Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Igor Yusipov
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky University, Nizhny Novgorod, Russia
| | - Alena Kalyakulina
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky University, Nizhny Novgorod, Russia
| | - Francesco Ravaioli
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Katarzyna M. Kwiatkowska
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | - Danielle F. Durso
- Department of Neurology, University of Massachusetts Medical School, Worcester, MA, United States
| | - Mikhail Ivanchenko
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky University, Nizhny Novgorod, Russia
| | - Daniela Monti
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio,” University of Florence, Florence, Italy
| | - Raffaele Lodi
- Istituto di Ricovero e Cura a Carattere Scientifico Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Claudio Franceschi
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky University, Nizhny Novgorod, Russia
| | - Pietro Cortelli
- Istituto di Ricovero e Cura a Carattere Scientifico Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Paolo Garagnani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
- Department of Laboratory Medicine, Clinical Chemistry, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
- Applied Biomedical Research Center, Policlinico S.Orsola-Malpighi Polyclinic, Bologna, Italy
- National Research Council of Italy Institute of Molecular Genetics “Luigi Luca Cavalli-Sforza,” Unit of Bologna, Bologna, Italy
| | - Maria Giulia Bacalini
- Istituto di Ricovero e Cura a Carattere Scientifico Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
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7
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Zhou J, Hu L, Jiang Y, Liu L. A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning. BIOMED RESEARCH INTERNATIONAL 2021; 2021:8890513. [PMID: 33628827 PMCID: PMC7886593 DOI: 10.1155/2021/8890513] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 12/23/2020] [Accepted: 01/27/2021] [Indexed: 12/31/2022]
Abstract
Motivation. At present, the research methods for image genetics of Alzheimer's disease based on machine learning are mainly divided into three steps: the first step is to preprocess the original image and gene information into digital signals that are easy to calculate; the second step is feature selection aiming at eliminating redundant signals and obtain representative features; and the third step is to build a learning model and predict the unknown data with regression or bivariate correlation analysis. This type of method requires manual extraction of feature single-nucleotide polymorphisms (SNPs), and the extraction process relies on empirical knowledge to a certain extent, such as linkage imbalance and gene function information in a group sparse model, which puts forward certain requirements for applicable scenarios and application personnel. To solve the problems of insufficient biological significance and large errors in the previous methods of association analysis and disease diagnosis, this paper presents a method of correlation analysis and disease diagnosis between SNP and region of interest (ROI) based on a deep learning model. It is a data-driven method, which has no obvious feature selection process. Results. The deep learning method adopted in this paper has no obvious feature extraction process relying on prior knowledge and model assumptions. From the results of correlation analysis between SNP and ROI, this method is complementary to other regression model methods in application scenarios. In order to improve the disease diagnosis performance of deep learning, we use the deep learning model to integrate SNP characteristics and ROI characteristics. The SNP feature, ROI feature, and SNP-ROI joint feature were input into the deep learning model and trained by cross-validation technique. The experimental results show that the SNP-ROI joint feature describes the information of the samples from different angles, which makes the diagnosis accuracy higher.
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Affiliation(s)
- Juan Zhou
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Linfeng Hu
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Yu Jiang
- School of Software, East China Jiaotong University, Nanchang 330013, China
| | - Liyue Liu
- School of Software, East China Jiaotong University, Nanchang 330013, China
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8
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Zhou J, Qiu Y, Chen S, Liu L, Liao H, Chen H, Lv S, Li X. A Novel Three-Stage Framework for Association Analysis Between SNPs and Brain Regions. Front Genet 2020; 11:572350. [PMID: 33193677 PMCID: PMC7542238 DOI: 10.3389/fgene.2020.572350] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Accepted: 08/17/2020] [Indexed: 12/17/2022] Open
Abstract
Motivation: At present, a number of correlation analysis methods between SNPs and ROIs have been devised to explore the pathogenic mechanism of Alzheimer's disease. However, some of the deficiencies inherent in these methods, including lack of statistical efficacy and biological meaning. This study aims at addressing issues: insufficient correlation by previous methods (relative high regression error) and the lack of biological meaning in association analysis. Results: In this paper, a novel three-stage SNPs and ROIs correlation analysis framework is proposed. Firstly, clustering algorithm is applied to remove the potential linkage unbalanced structure of two SNPs. Then, the group sparse model is used to introduce prior information such as gene structure and linkage unbalanced structure to select feature SNPs. After the above steps, each SNP has a weight vector corresponding to each ROI, and the importance of SNPs can be judged according to the weights in the feature vector, and then the feature SNPs can be selected. Finally, for the selected feature SNPS, a support vector machine regression model is used to implement the prediction of the ROIs phenotype values. The experimental results under multiple performance measures show that the proposed method has better accuracy than other methods.
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Affiliation(s)
- Juan Zhou
- School of Software, East China Jiaotong University, Nanchang, China
| | - Yangping Qiu
- School of Software, East China Jiaotong University, Nanchang, China
| | - Shuo Chen
- School of Software, East China Jiaotong University, Nanchang, China
| | - Liyue Liu
- School of Software, East China Jiaotong University, Nanchang, China
| | - Huifa Liao
- School of Software, East China Jiaotong University, Nanchang, China
| | - Hongli Chen
- School of Software, East China Jiaotong University, Nanchang, China
| | - Shanguo Lv
- School of Software, East China Jiaotong University, Nanchang, China
| | - Xiong Li
- School of Software, East China Jiaotong University, Nanchang, China
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9
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Jian X, Sofer T, Tarraf W, Bressler J, Faul JD, Zhao W, Ratliff SM, Lamar M, Launer LJ, Laurie CC, Schneiderman N, Weir DR, Wright CB, Yaffe K, Zeng D, DeCarli C, Mosley TH, Smith JA, González HM, Fornage M. Genome-wide association study of cognitive function in diverse Hispanics/Latinos: results from the Hispanic Community Health Study/Study of Latinos. Transl Psychiatry 2020; 10:245. [PMID: 32699239 PMCID: PMC7376098 DOI: 10.1038/s41398-020-00930-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 06/19/2020] [Accepted: 07/03/2020] [Indexed: 12/13/2022] Open
Abstract
Cognitive function such as reasoning, attention, memory, and language is strongly correlated with brain aging. Compared to non-Hispanic whites, Hispanics/Latinos have a higher risk of cognitive impairment and dementia. The genetic determinants of cognitive function have not been widely explored in this diverse and admixed population. We conducted a genome-wide association analysis of cognitive function in up to 7600 middle aged and older Hispanics/Latinos (mean = 55 years) from the Hispanic Community Health Study / Study of Latinos (HCHS/SOL). Four cognitive measures were examined: the Brief Spanish English Verbal Learning Test (B-SEVLT), the Word Fluency Test (WFT), the Digit Symbol Substitution Test (DSST), the Six-Item Screener (SIS). Four novel loci were identified: one for B-SEVLT at 4p14, two for WFT at 3p14.1 and 6p21.32, and one for DSST at 10p13. These loci implicate genes highly expressed in brain and previously connected to neurological diseases (UBE2K, FRMD4B, the HLA gene complex). By applying tissue-specific gene expression prediction models to our genotype data, additional genes highly expressed in brain showed suggestive associations with cognitive measures possibly indicating novel biological mechanisms, including IFT122 in the hippocampus for SIS, SNX31 in the basal ganglia for B-SEVLT, RPS6KB2 in the frontal cortex for WFT, and CSPG5 in the hypothalamus for DSST. These findings provide new information about the genetic determinants of cognitive function in this unique population. In addition, we derived a measure of general cognitive function based on these cognitive tests and generated genome-wide association summary results, providing a resource to the research community for comparison, replication, and meta-analysis in future genetic studies in Hispanics/Latinos.
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Affiliation(s)
- Xueqiu Jian
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Tamar Sofer
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Wassim Tarraf
- Institute of Gerontology and Department of Health Care Sciences, Wayne State University, Detroit, MI, USA
| | - Jan Bressler
- Department of Epidemiology, Human Genetics and Environmental Sciences and Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wei Zhao
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Scott M Ratliff
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Melissa Lamar
- Department of Behavioral Sciences, Rush Medical College, Chicago, IL, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Science, National Institute on Aging, Bethesda, MD, USA
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington School of Public Health, Seattle, WA, USA
| | - Neil Schneiderman
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Clinton B Wright
- Division of Clinical Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Kristine Yaffe
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Charles DeCarli
- Department of Neurology, School of Medicine and Imaging of Dementia and Aging Laboratory, Center for Neuroscience, University of California, Davis, Sacramento, CA, USA
| | - Thomas H Mosley
- Memory Impairment and Neurodegenerative Dementia (MIND) Center and Department of Medicine, The University of Mississippi Medical Center, Jackson, MS, USA
| | - Jennifer A Smith
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Hector M González
- Department of Neurosciences and Shiley-Marcos Alzheimer's Disease Research Center, University of California, San Diego, La Jolla, CA, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA.
- Department of Epidemiology, Human Genetics and Environmental Sciences and Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA.
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10
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Wu Q, Zhao H, Zhu L, Sun J. Variable selection for high-dimensional partly linear additive Cox model with application to Alzheimer's disease. Stat Med 2020; 39:3120-3134. [PMID: 32652699 DOI: 10.1002/sim.8594] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 03/20/2020] [Accepted: 05/13/2020] [Indexed: 11/10/2022]
Abstract
Variable selection has been discussed under many contexts and especially, a large literature has been established for the analysis of right-censored failure time data. In this article, we discuss an interval-censored failure time situation where there exist two sets of covariates with one being low-dimensional and having possible nonlinear effects and the other being high-dimensional. For the problem, we present a penalized estimation procedure for simultaneous variable selection and estimation, and in the method, Bernstein polynomials are used to approximate the involved nonlinear functions. Furthermore, for implementation, a coordinate-wise optimization algorithm, which can accommodate most commonly used penalty functions, is developed. A numerical study is performed for the evaluation of the proposed approach and suggests that it works well in practical situations. Finally the method is applied to an Alzheimer's disease study that motivated this investigation.
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Affiliation(s)
- Qiwei Wu
- Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Hui Zhao
- School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China
| | - Liang Zhu
- Division of Clinical and Translational Sciences, Department of Internal Medicine, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Jianguo Sun
- Department of Statistics, University of Missouri, Columbia, Missouri, USA
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11
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Chen H, Lei X, Yuan D, Huang S. The relationship between the minor allele content and Alzheimer's disease. Genomics 2020; 112:2426-2432. [PMID: 31982476 DOI: 10.1016/j.ygeno.2020.01.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 11/24/2019] [Accepted: 01/22/2020] [Indexed: 01/21/2023]
Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disease. The genetic risk factors of AD remain better understood. Using previously published dataset of common single nucleotide polymorphisms (SNPs), we studied the association between the minor allele content (MAC) in an individual and AD. We found that AD patients have higher average MAC values than matched controls. We identified a risk prediction model that could predict 2.19% of AD cases. We also identified 49 genes whose expression levels correlated with both MAC and AD. By pathway and process enrichment analyses, these genes were found in pathways or processes closely related to AD. Our study suggests that AD may be linked with too many genetic variations over a threshold. The method of correlations with both MAC and traits appears to be effective in high efficiency identification of target genes for complex traits.
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Affiliation(s)
- Hongyao Chen
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, Hunan Key Laboratory of Animal Models for Human Diseases, School of Life Sciences, Central South University, 110 Xiangya Road, Changsha, Hunan 410078, China
| | - Xiaoyun Lei
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, Hunan Key Laboratory of Animal Models for Human Diseases, School of Life Sciences, Central South University, 110 Xiangya Road, Changsha, Hunan 410078, China
| | - Dejian Yuan
- Department of Birth Health and Heredity, Liuzhou Municipal Maternity and Child Healthcare Hospital, Liuzhou 545000, China
| | - Shi Huang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, Hunan Key Laboratory of Animal Models for Human Diseases, School of Life Sciences, Central South University, 110 Xiangya Road, Changsha, Hunan 410078, China.
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12
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Fernandes J, Chandler JD, Lili LN, Uppal K, Hu X, Hao L, Go YM, Jones DP. Transcriptome Analysis Reveals Distinct Responses to Physiologic versus Toxic Manganese Exposure in Human Neuroblastoma Cells. Front Genet 2019; 10:676. [PMID: 31396262 PMCID: PMC6668488 DOI: 10.3389/fgene.2019.00676] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 06/27/2019] [Indexed: 12/16/2022] Open
Abstract
Manganese (Mn) is an essential trace element, which also causes neurotoxicity in exposed occupational workers. Mn causes mitochondrial toxicity; however, little is known about transcriptional responses discriminated by physiological and toxicological levels of Mn. Identification of such mechanisms could provide means to evaluate risk of Mn toxicity and also potential avenues to protect against adverse effects. To study the Mn dose-response effects on transcription, analyzed by RNA-Seq, we used human SH-SY5Y neuroblastoma cells exposed for 5 h to Mn (0 to 100 μM), a time point where no immediate cell death occurred at any of the doses. Results showed widespread effects on abundance of protein-coding genes for metabolism of reactive oxygen species, energy sensing, glycolysis, and protein homeostasis including the unfolded protein response and transcriptional regulation. Exposure to a concentration (10 μM Mn for 5 h) that did not result in cell death after 24-h increased abundance of differentially expressed genes (DEGs) in the protein secretion pathway that function in protein trafficking and cellular homeostasis. These include BET1 (Golgi vesicular membrane-trafficking protein), ADAM10 (ADAM metallopeptidase domain 10), and ARFGAP3 (ADP-ribosylation factor GTPase-activating protein 3). In contrast, 5-h exposure to 100 μM Mn, a concentration that caused cell death after 24 h, increased abundance of DEGs for components of the mitochondrial oxidative phosphorylation pathway. Integrated pathway analysis results showed that protein secretion gene set was associated with amino acid metabolites in response to 10 μM Mn, while oxidative phosphorylation gene set was associated with energy, lipid, and neurotransmitter metabolites at 100 μM Mn. These results show that differential effects of Mn occur at a concentration which does not cause subsequent cell death compared to a concentration that causes subsequent cell death. If these responses translate to effects on the secretory pathway and mitochondrial functions in vivo, differential activities of these systems could provide a sensitive basis to discriminate sub-toxic and toxic environmental and occupational Mn exposures.
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Affiliation(s)
| | | | | | | | | | | | - Young-Mi Go
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University, Atlanta, GA, United States
| | - Dean P. Jones
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, Emory University, Atlanta, GA, United States
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