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Xu M, Liu Q, Bi R, Li Y, Li H, Kang WB, Yan Z, Zheng Q, Sun C, Ye M, Xiang BL, Luo XJ, Li M, Zhang DF, Yao YG. Coexistence of Multiple Functional Variants and Genes Underlies Genetic Risk Locus 11p11.2 of Alzheimer's Disease. Biol Psychiatry 2023; 94:743-759. [PMID: 37290560 DOI: 10.1016/j.biopsych.2023.05.020] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 06/10/2023]
Abstract
BACKGROUND Genome-wide association studies have identified dozens of genetic risk loci for Alzheimer's disease (AD), yet the underlying causal variants and biological mechanisms remain elusive, especially for loci with complex linkage disequilibrium and regulation. METHODS To fully untangle the causal signal at a single locus, we performed a functional genomic study of 11p11.2 (the CELF1/SPI1 locus). Genome-wide association study signals at 11p11.2 were integrated with datasets of histone modification, open chromatin, and transcription factor binding to distill potentially functional variants (fVars). Their allelic regulatory activities were confirmed by allele imbalance, reporter assays, and base editing. Expressional quantitative trait loci and chromatin interaction data were incorporated to assign target genes to fVars. The relevance of these genes to AD was assessed by convergent functional genomics using bulk brain and single-cell transcriptomic, epigenomic, and proteomic datasets of patients with AD and control individuals, followed by cellular assays. RESULTS We found that 24 potential fVars, rather than a single variant, were responsible for the risk of 11p11.2. These fVars modulated transcription factor binding and regulated multiple genes by long-range chromatin interactions. Besides SPI1, convergent evidence indicated that 6 target genes (MTCH2, ACP2, NDUFS3, PSMC3, C1QTNF4, and MADD) of fVars were likely to be involved in AD development. Disruption of each gene led to cellular amyloid-β and phosphorylated tau changes, supporting the existence of multiple likely causal genes at 11p11.2. CONCLUSIONS Multiple variants and genes at 11p11.2 may contribute to AD risk. This finding provides new insights into the mechanistic and therapeutic challenges of AD.
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Affiliation(s)
- Min Xu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Qianjin Liu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Rui Bi
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China; National Resource Center for Non-Human Primates, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Yu Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Hongli Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Wei-Bo Kang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Zhongjiang Yan
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Quanzhen Zheng
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Chunli Sun
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Maosen Ye
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Bo-Lin Xiang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Xiong-Jian Luo
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China
| | - Deng-Feng Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China; National Resource Center for Non-Human Primates, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
| | - Yong-Gang Yao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, China; National Resource Center for Non-Human Primates, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
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Gamache J, Gingerich D, Shwab EK, Barrera J, Garrett ME, Hume C, Crawford GE, Ashley-Koch AE, Chiba-Falek O. Integrative single-nucleus multi-omics analysis prioritizes candidate cis and trans regulatory networks and their target genes in Alzheimer's disease brains. Cell Biosci 2023; 13:185. [PMID: 37789374 PMCID: PMC10546724 DOI: 10.1186/s13578-023-01120-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 08/30/2023] [Indexed: 10/05/2023] Open
Abstract
BACKGROUND The genetic underpinnings of late-onset Alzheimer's disease (LOAD) are yet to be fully elucidated. Although numerous LOAD-associated loci have been discovered, the causal variants and their target genes remain largely unknown. Since the brain is composed of heterogenous cell subtypes, it is imperative to study the brain on a cell subtype specific level to explore the biological processes underlying LOAD. METHODS Here, we present the largest parallel single-nucleus (sn) multi-omics study to simultaneously profile gene expression (snRNA-seq) and chromatin accessibility (snATAC-seq) to date, using nuclei from 12 normal and 12 LOAD brains. We identified cell subtype clusters based on gene expression and chromatin accessibility profiles and characterized cell subtype-specific LOAD-associated differentially expressed genes (DEGs), differentially accessible peaks (DAPs) and cis co-accessibility networks (CCANs). RESULTS Integrative analysis defined disease-relevant CCANs in multiple cell subtypes and discovered LOAD-associated cell subtype-specific candidate cis regulatory elements (cCREs), their candidate target genes, and trans-interacting transcription factors (TFs), some of which, including ELK1, JUN, and SMAD4 in excitatory neurons, were also LOAD-DEGs. Finally, we focused on a subset of cell subtype-specific CCANs that overlap known LOAD-GWAS regions and catalogued putative functional SNPs changing the affinities of TF motifs within LOAD-cCREs linked to LOAD-DEGs, including APOE and MYO1E in a specific subtype of microglia and BIN1 in a subpopulation of oligodendrocytes. CONCLUSIONS To our knowledge, this study represents the most comprehensive systematic interrogation to date of regulatory networks and the impact of genetic variants on gene dysregulation in LOAD at a cell subtype resolution. Our findings reveal crosstalk between epigenetic, genomic, and transcriptomic determinants of LOAD pathogenesis and define catalogues of candidate genes, cCREs, and variants involved in LOAD genetic etiology and the cell subtypes in which they act to exert their pathogenic effects. Overall, these results suggest that cell subtype-specific cis-trans interactions between regulatory elements and TFs, and the genes dysregulated by these networks contribute to the development of LOAD.
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Affiliation(s)
- Julia Gamache
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, DUMC Box 2900, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Daniel Gingerich
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, DUMC Box 2900, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - E Keats Shwab
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, DUMC Box 2900, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Julio Barrera
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, DUMC Box 2900, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Melanie E Garrett
- Duke Molecular Physiology Institute, Duke University Medical Center, DUMC Box 104775, Durham, NC, 27701, USA
| | - Cordelia Hume
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, DUMC Box 2900, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Gregory E Crawford
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA.
- Department of Pediatrics, Division of Medical Genetics, Duke University Medical Center, DUMC Box 3382, Durham, NC, 27708, USA.
- Center for Advanced Genomic Technologies, Duke University Medical Center, Durham, NC, 27708, USA.
| | - Allison E Ashley-Koch
- Duke Molecular Physiology Institute, Duke University Medical Center, DUMC Box 104775, Durham, NC, 27701, USA.
- Department of Medicine, Duke University Medical Center, Durham, NC, 27708, USA.
| | - Ornit Chiba-Falek
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, DUMC Box 2900, Durham, NC, 27710, USA.
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA.
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Lutz MW, Chiba-Falek O. Bioinformatics pipeline to guide post-GWAS studies in Alzheimer's: A new catalogue of disease candidate short structural variants. Alzheimers Dement 2023; 19:4094-4109. [PMID: 37253165 PMCID: PMC10524333 DOI: 10.1002/alz.13168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/27/2023] [Accepted: 05/08/2023] [Indexed: 06/01/2023]
Abstract
BACKGROUND Short structural variants (SSVs), including insertions/deletions (indels), are common in the human genome and impact disease risk. The role of SSVs in late-onset Alzheimer's disease (LOAD) has been understudied. In this study, we developed a bioinformatics pipeline of SSVs within LOAD-genome-wide association study (GWAS) regions to prioritize regulatory SSVs based on the strength of their predicted effect on transcription factor (TF) binding sites. METHODS The pipeline utilized publicly available functional genomics data sources including candidate cis-regulatory elements (cCREs) from ENCODE and single-nucleus (sn)RNA-seq data from LOAD patient samples. RESULTS We catalogued 1581 SSVs in candidate cCREs in LOAD GWAS regions that disrupted 737 TF sites. That included SSVs that disrupted the binding of RUNX3, SPI1, and SMAD3, within the APOE-TOMM40, SPI1, and MS4A6A LOAD regions. CONCLUSIONS The pipeline developed here prioritized non-coding SSVs in cCREs and characterized their putative effects on TF binding. The approach integrates multiomics datasets for validation experiments using disease models.
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Affiliation(s)
- Michael W. Lutz
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA
| | - Ornit Chiba-Falek
- Division of Translational Brain Sciences, Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC 27710, USA
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Chen D, Li J, Liu H, Liu X, Zhang C, Luo H, Wei Y, Xi Y, Liang H, Zhang Q. Genome-Wide Epistasis Study of Cerebrospinal Fluid Hyperphosphorylated Tau in ADNI Cohort. Genes (Basel) 2023; 14:1322. [PMID: 37510227 PMCID: PMC10379656 DOI: 10.3390/genes14071322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
Alzheimer's disease (AD) is the main cause of dementia worldwide, and the genetic mechanism of which is not yet fully understood. Much evidence has accumulated over the past decade to suggest that after the first large-scale genome-wide association studies (GWAS) were conducted, the problem of "missing heritability" in AD is still a great challenge. Epistasis has been considered as one of the main causes of "missing heritability" in AD, which has been largely ignored in human genetics. The focus of current genome-wide epistasis studies is usually on single nucleotide polymorphisms (SNPs) that have significant individual effects, and the amount of heritability explained by which was very low. Moreover, AD is characterized by progressive cognitive decline and neuronal damage, and some studies have suggested that hyperphosphorylated tau (P-tau) mediates neuronal death by inducing necroptosis and inflammation in AD. Therefore, this study focused on identifying epistasis between two-marker interactions at marginal main effects across the whole genome using cerebrospinal fluid (CSF) P-tau as quantitative trait (QT). We sought to detect interactions between SNPs in a multi-GPU based linear regression method by using age, gender, and clinical diagnostic status (cds) as covariates. We then used the STRING online tool to perform the PPI network and identify two-marker epistasis at the level of gene-gene interaction. A total of 758 SNP pairs were found to be statistically significant. Particularly, between the marginal main effect SNP pairs, highly significant SNP-SNP interactions were identified, which explained a relatively high variance at the P-tau level. In addition, 331 AD-related genes were identified, 10 gene-gene interaction pairs were replicated in the PPI network. The identified gene-gene interactions and genes showed associations with AD in terms of neuroinflammation and neurodegeneration, neuronal cells activation and brain development, thereby leading to cognitive decline in AD, which is indirectly associated with the P-tau pathological feature of AD and in turn supports the results of this study. Thus, the results of our study might be beneficial for explaining part of the "missing heritability" of AD.
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Affiliation(s)
- Dandan Chen
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China
| | - Jin Li
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Hongwei Liu
- School of Computer Science, Northeast Electric Power University, Jilin 132012, China
| | - Xiaolong Liu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Chenghao Zhang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Haoran Luo
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Yiming Wei
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Yang Xi
- School of Computer Science, Northeast Electric Power University, Jilin 132012, China
| | - Hong Liang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, Jilin 132012, China
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Vitek MP, Araujo JA, Fossel M, Greenberg BD, Howell GR, Rizzo SJS, Seyfried NT, Tenner AJ, Territo PR, Windisch M, Bain LJ, Ross A, Carrillo MC, Lamb BT, Edelmayer RM. Translational animal models for Alzheimer's disease: An Alzheimer's Association Business Consortium Think Tank. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2021; 6:e12114. [PMID: 33457489 PMCID: PMC7798310 DOI: 10.1002/trc2.12114] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 07/04/2020] [Accepted: 07/09/2020] [Indexed: 12/12/2022]
Abstract
Over 5 million Americans and 50 million individuals worldwide are living with Alzheimer's disease (AD). The progressive dementia associated with AD currently has no cure. Although clinical trials in patients are ultimately required to find safe and effective drugs, animal models of AD permit the integration of brain pathologies with learning and memory deficits that are the first step in developing these new drugs. The purpose of the Alzheimer's Association Business Consortium Think Tank meeting was to address the unmet need to improve the discovery and successful development of Alzheimer's therapies. We hypothesize that positive responses to new therapies observed in validated models of AD will provide predictive evidence for positive responses to these same therapies in AD patients. To achieve this goal, we convened a meeting of experts to explore the current state of AD animal models, identify knowledge gaps, and recommend actions for development of next-generation models with better predictability. Among our findings, we all recognize that models reflecting only single aspects of AD pathogenesis do not mimic AD. Models or combinations of new models are needed that incorporate genetics with environmental interactions, timing of disease development, heterogeneous mechanisms and pathways, comorbidities, and other pathologies that lead to AD and related dementias. Selection of the best models requires us to address the following: (1) which animal species, strains, and genetic backgrounds are most appropriate; (2) which models permit efficient use throughout the drug development pipeline; (3) the translatability of behavioral-cognitive assays from animals to patients; and (4) how to match potential AD therapeutics with particular models. Best practice guidelines to improve reproducibility also need to be developed for consistent use of these models in different research settings. To enhance translational predictability, we discuss a multi-model evaluation strategy to de-risk the successful transition of pre-clinical drug assets to the clinic.
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Affiliation(s)
| | | | | | | | | | | | - Nicholas T. Seyfried
- Departments of Biochemistry and NeurologyEmory School of MedicineAtlantaGeorgiaUSA
| | - Andrea J. Tenner
- Department of Molecular Biology and BiochemistryUniversity of CaliforniaIrvineCaliforniaUSA
| | | | | | - Lisa J. Bain
- Independent Science and Medical WriterElversonPennsylvaniaUSA
| | - April Ross
- Former Alzheimer's Association EmployeeChicagoIllinoisUSA
| | | | - Bruce T. Lamb
- Indiana University School of MedicineStark Neurosciences Research InstituteIndianapolisIndianaUSA
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