1
|
Fröhlich A, Pfaff AL, Middlehurst B, Hughes LS, Bubb VJ, Quinn JP, Koks S. Deciphering the role of a SINE-VNTR-Alu retrotransposon polymorphism as a biomarker of Parkinson's disease progression. Sci Rep 2024; 14:10932. [PMID: 38740892 DOI: 10.1038/s41598-024-61753-5] [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: 04/27/2023] [Accepted: 05/09/2024] [Indexed: 05/16/2024] Open
Abstract
SINE-VNTR-Alu (SVA) retrotransposons are transposable elements which represent a source of genetic variation. We previously demonstrated that the presence/absence of a human-specific SVA, termed SVA_67, correlated with the progression of Parkinson's disease (PD). In the present study, we demonstrate that SVA_67 acts as expression quantitative trait loci, thereby exhibiting a strong regulatory effect across the genome using whole genome and transcriptomic data from the Parkinson's progression markers initiative cohort. We further show that SVA_67 is polymorphic for its variable number tandem repeat domain which correlates with both regulatory properties in a luciferase reporter gene assay in vitro and differential expression of multiple genes in vivo. Additionally, this variation's utility as a biomarker is reflected in a correlation with a number of PD progression markers. These experiments highlight the plethora of transcriptomic and phenotypic changes associated with SVA_67 polymorphism which should be considered when investigating the missing heritability of neurodegenerative diseases.
Collapse
Affiliation(s)
- Alexander Fröhlich
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- Perron Institute for Neurological and Translational Science, Perth, WA, Australia
| | - Abigail L Pfaff
- Perron Institute for Neurological and Translational Science, Perth, WA, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, WA, Australia
| | - Ben Middlehurst
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Lauren S Hughes
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Vivien J Bubb
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - John P Quinn
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.
| | - Sulev Koks
- Perron Institute for Neurological and Translational Science, Perth, WA, Australia.
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, WA, Australia.
| |
Collapse
|
2
|
Dobson-Stone C, Guennewig B, Mundell H, Kwok JB. Detecting and Validating MAPT Mutations in Neurodegeneration Patients and Analysis of Exon Splicing Consequences. Methods Mol Biol 2024; 2754:411-433. [PMID: 38512679 DOI: 10.1007/978-1-0716-3629-9_22] [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] [Indexed: 03/23/2024]
Abstract
Mutation of MAPT has been observed in patients with parkinsonism, progressive supranuclear palsy, and corticobasal degeneration and is a significant cause of frontotemporal dementia. In this chapter, we discuss considerations for next-generation sequencing analysis to identify MAPT mutations in patient genomic DNA and describe the validation of these mutations by Sanger sequencing. One of the most common effects of MAPT mutations is differential splicing of exon 10, which leads to an imbalance in the proportion of 3-repeat and 4-repeat tau isoforms. We describe how to investigate the effect of novel DNA variants on the splicing efficiency of this exon in vitro using the exon-trapping technique, also known as the splicing reporter minigene assay.
Collapse
Affiliation(s)
- Carol Dobson-Stone
- Brain and Mind Centre and School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia.
| | - Boris Guennewig
- Brain and Mind Centre and School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
| | - Hamish Mundell
- New South Wales Brain Tissue Resource Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
| | - John B Kwok
- Brain and Mind Centre and School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
| |
Collapse
|
3
|
Irving-Pease EK, Refoyo-Martínez A, Barrie W, Ingason A, Pearson A, Fischer A, Sjögren KG, Halgren AS, Macleod R, Demeter F, Henriksen RA, Vimala T, McColl H, Vaughn AH, Speidel L, Stern AJ, Scorrano G, Ramsøe A, Schork AJ, Rosengren A, Zhao L, Kristiansen K, Iversen AKN, Fugger L, Sudmant PH, Lawson DJ, Durbin R, Korneliussen T, Werge T, Allentoft ME, Sikora M, Nielsen R, Racimo F, Willerslev E. The selection landscape and genetic legacy of ancient Eurasians. Nature 2024; 625:312-320. [PMID: 38200293 PMCID: PMC10781624 DOI: 10.1038/s41586-023-06705-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 10/03/2023] [Indexed: 01/12/2024]
Abstract
The Holocene (beginning around 12,000 years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations. Using a dataset of more than 1,600 imputed ancient genomes1, we modelled the selection landscape during the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify key selection signals related to metabolism, including that selection at the FADS cluster began earlier than previously reported and that selection near the LCT locus predates the emergence of the lactase persistence allele by thousands of years. We also find strong selection in the HLA region, possibly due to increased exposure to pathogens during the Bronze Age. Using ancient individuals to infer local ancestry tracts in over 400,000 samples from the UK Biobank, we identify widespread differences in the distribution of Mesolithic, Neolithic and Bronze Age ancestries across Eurasia. By calculating ancestry-specific polygenic risk scores, we show that height differences between Northern and Southern Europe are associated with differential Steppe ancestry, rather than selection, and that risk alleles for mood-related phenotypes are enriched for Neolithic farmer ancestry, whereas risk alleles for diabetes and Alzheimer's disease are enriched for Western hunter-gatherer ancestry. Our results indicate that ancient selection and migration were large contributors to the distribution of phenotypic diversity in present-day Europeans.
Collapse
Affiliation(s)
- Evan K Irving-Pease
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
| | - Alba Refoyo-Martínez
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - William Barrie
- GeoGenetics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Andrés Ingason
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Roskilde, Denmark
| | - Alice Pearson
- Department of Genetics, University of Cambridge, Cambridge, UK
- Department of Zoology, University of Cambridge, Cambridge, UK
| | - Anders Fischer
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Department of Historical Studies, University of Gothenburg, Gothenburg, Sweden
- Sealand Archaeology, Kalundborg, Denmark
| | - Karl-Göran Sjögren
- Department of Historical Studies, University of Gothenburg, Gothenburg, Sweden
| | - Alma S Halgren
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
| | - Ruairidh Macleod
- GeoGenetics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- UCL Genetics Institute, University College London, London, UK
| | - Fabrice Demeter
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Eco-anthropologie, Muséum national d'Histoire naturelle, CNRS, Université Paris Cité, Musée de l'Homme, Paris, France
| | - Rasmus A Henriksen
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Tharsika Vimala
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Hugh McColl
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Andrew H Vaughn
- Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Leo Speidel
- UCL Genetics Institute, University College London, London, UK
- Ancient Genomics Laboratory, The Francis Crick Institute, London, UK
| | - Aaron J Stern
- Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Gabriele Scorrano
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Abigail Ramsøe
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Andrew J Schork
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Roskilde, Denmark
- Neurogenomics Division, The Translational Genomics Research Institute (TGEN), Phoenix, AZ, USA
| | - Anders Rosengren
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, Roskilde, Denmark
| | - Lei Zhao
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Kristiansen
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Department of Historical Studies, University of Gothenburg, Gothenburg, Sweden
| | - Astrid K N Iversen
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Lars Fugger
- Oxford Centre for Neuroinflammation, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Department of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark
- MRC Human Immunology Unit, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Peter H Sudmant
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Daniel J Lawson
- Institute of Statistical Sciences, School of Mathematics, University of Bristol, Bristol, UK
| | - Richard Durbin
- Department of Genetics, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Cambridge, UK
| | - Thorfinn Korneliussen
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Werge
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Institute of Biological Psychiatry, Mental Health Center Sct Hans, Copenhagen University Hospital, Copenhagen, Denmark
| | - Morten E Allentoft
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
- Trace and Environmental DNA (TrEnD) Laboratory, School of Molecular and Life Science, Curtin University, Perth, Western Australia, Australia
| | - Martin Sikora
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark
| | - Rasmus Nielsen
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
- Departments of Integrative Biology and Statistics, UC Berkeley, Berkeley, CA, USA.
| | - Fernando Racimo
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
| | - Eske Willerslev
- Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
- GeoGenetics Group, Department of Zoology, University of Cambridge, Cambridge, UK.
- MARUM Center for Marine Environmental Sciences and Faculty of Geosciences, University of Bremen, Bremen, Germany.
| |
Collapse
|
4
|
Ma DR, Li SJ, Shi JJ, Liang YY, Hu ZW, Hao XY, Li MJ, Guo MN, Zuo CY, Yu WK, Mao CY, Tang MB, Zhang C, Xu YM, Wu J, Sun SL, Shi CH. Shared Genetic Architecture between Parkinson's Disease and Brain Structural Phenotypes. Mov Disord 2023; 38:2258-2268. [PMID: 37990409 DOI: 10.1002/mds.29598] [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: 05/10/2023] [Revised: 08/02/2023] [Accepted: 08/21/2023] [Indexed: 11/23/2023] Open
Abstract
BACKGROUND Patients with Parkinson's disease (PD) have consistently demonstrated brain structure abnormalities, indicating the presence of shared etiological and pathological processes between PD and brain structures; however, the genetic relationship remains poorly understood. OBJECTIVE The aim of this study was to investigate the extent of shared genetic architecture between PD and brain structural phenotypes (BSPs) and to identify shared genomic loci. METHODS We used the summary statistics from genome-wide association studies to conduct MiXeR and conditional/conjunctional false discovery rate analyses to investigate the shared genetic signatures between PD and BSPs. Subsequent expression quantitative trait loci mapping in the human brain and enrichment analyses were also performed. RESULTS MiXeR analysis identified genetic overlap between PD and various BSPs, including total cortical surface area, average cortical thickness, and specific brain volumetric structures. Further analysis using conditional false discovery rate (FDR) identified 21 novel PD risk loci on associations with BSPs at conditional FDR < 0.01, and the conjunctional FDR analysis demonstrated that PD shared several genomic loci with certain BSPs at conjunctional FDR < 0.05. Among the shared loci, 16 credible mapped genes showed high expression in the brain tissues and were primarily associated with immune function-related biological processes. CONCLUSIONS We confirmed the polygenic overlap with mixed directions of allelic effects between PD and BSPs and identified multiple shared genomic loci and risk genes, which are likely related to immune-related biological processes. These findings provide insight into the complex genetic architecture associated with PD. © 2023 International Parkinson and Movement Disorder Society.
Collapse
Affiliation(s)
- Dong-Rui Ma
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Shuang-Jie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Jing-Jing Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Yuan-Yuan Liang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Zheng-Wei Hu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Xiao-Yan Hao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Meng-Jie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Meng-Nan Guo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Chun-Yan Zuo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Wen-Kai Yu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Cheng-Yuan Mao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Mi-Bo Tang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Chan Zhang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Yu-Ming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| | - Jun Wu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| | - Shi-Lei Sun
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| | - Chang-He Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| |
Collapse
|
5
|
Bhatnagar D, Ladhe S, Kumar D. Discerning the Prospects of miRNAs as a Multi-Target Therapeutic and Diagnostic for Alzheimer's Disease. Mol Neurobiol 2023; 60:5954-5974. [PMID: 37386272 DOI: 10.1007/s12035-023-03446-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 06/14/2023] [Indexed: 07/01/2023]
Abstract
Although over the last few decades, numerous attempts have been made to halt Alzheimer's disease (AD) progression and mitigate its symptoms, only a few have been proven beneficial. Most medications available, still only cater to the symptoms of the disease rather than fixing the cause at the root level. A novel approach involving the use of miRNAs, which work on the principle of gene silencing, is being explored by scientists. Naturally present miRNAs in the biological system help to regulate various genes than may be implicated in AD-like BACE-1 and APP. One miRNA thus, holds the power to keep a check on several genes, conferring it the ability to be used as a multi-target therapeutic. With aging and the onset of diseased pathology, dysregulation of these miRNAs is observed. This flawed miRNA expression is responsible for the unusual buildup of amyloid proteins, fibrillation of tau proteins in the brain, neuronal death and other hallmarks leading to AD. The use of miRNA mimics and miRNA inhibitors provides an attractive perspective for fixing the upregulation and downregulation of miRNAs that led to abnormal cellular activities. Furthermore, the detection of miRNAs in the CSF and serum of diseased patients might be considered an earlier biomarker for the disease. While most of the therapies designed around AD have not succeeded completely, the targeting of dysregulated miRNAs in AD patients might give a new direction to scholars to develop an effective treatment for Alzheimer's disease.
Collapse
Affiliation(s)
- Devyani Bhatnagar
- Department of Pharmaceutical Chemistry, Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to Be University), Erandwane, Pune, 411038, Maharashtra, India
| | - Shreya Ladhe
- Department of Pharmaceutical Chemistry, Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to Be University), Erandwane, Pune, 411038, Maharashtra, India
| | - Dileep Kumar
- Department of Pharmaceutical Chemistry, Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to Be University), Erandwane, Pune, 411038, Maharashtra, India.
- Department of Entomology, University of California, Davis, One Shields Ave, Davis, CA, 95616, USA.
- UC Davis Comprehensive Cancer Center, University of California, Davis, One Shields Ave, Davis, CA, 95616, USA.
| |
Collapse
|
6
|
Petrović N, Essack M, Šami A, Perry G, Gojobori T, Isenović ER, Bajić VP. MicroRNA networks linked with BRCA1/2, PTEN, and common genes for Alzheimer's disease and breast cancer share highly enriched pathways that may unravel targets for the AD/BC comorbidity treatment. Comput Biol Chem 2023; 106:107925. [PMID: 37487248 DOI: 10.1016/j.compbiolchem.2023.107925] [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: 08/15/2022] [Revised: 06/29/2023] [Accepted: 07/13/2023] [Indexed: 07/26/2023]
Abstract
MicroRNAs (miRNAs) are involved in the regulation of various cellular processes including pathological conditions. MiRNA networks have been extensively researched in age-related degenerative diseases, such as cancer, Alzheimer's disease (AD), and heart failure. Thus, miRNA has been studied from different approaches, in vivo, in vitro, and in silico including miRNA networks. Networks linking diverse biomedical entities unveil information not readily observable by other means. This work focuses on biological networks related to Breast cancer susceptibility 1 (BRCA1) in AD and breast cancer (BC). Using various bioinformatics approaches, we identified subnetworks common to AD and BC that suggest they are linked. According to our results, miR-107 was identified as a potentially good candidate for both AD and BC treatment (targeting BRCA1/2 and PTEN in both diseases), accompanied by miR-146a and miR-17. The analysis also confirmed the involvement of the miR-17-92 cluster, and miR-124-3p, and highlighted the importance of poorly researched miRNAs such as mir-6785 mir-6127, mir-6870, or miR-8485. After filtering the in silico analysis results, we found 49 miRNA molecules that modulate the expression of at least five genes common to both BC and AD. Those 49 miRNAs regulate the expression of 122 genes in AD and 93 genes in BC, from which 26 genes are common genes for AD and BC involved in neuron differentiation and genesis, cell differentiation and migration, regulation of cell cycle, and cancer development. Additionally, the highly enriched pathway was associated with diabetic complications, pointing out possible interplay among molecules underlying BC, AD, and diabetes pathology.
Collapse
Affiliation(s)
- Nina Petrović
- Laboratory for Radiobiology and Molecular Genetics, Department of Health and Environment, "VINČA "Institute of Nuclear Sciences-National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovića Alasa 12-14, 11001 Belgrade, Serbia; Department for Experimental Oncology, Institute for Oncology and Radiology of Serbia, Pasterova 14, 11000 Belgrade, Serbia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Ahmad Šami
- Cellular and Molecular Radiation Oncology Laboratory, Department of Radiation Oncology, Universitatsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - George Perry
- Department of Biology, The University of Texas at San Antonio, San Antonio, TX, USA
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Esma R Isenović
- Laboratory for Radiobiology and Molecular Genetics, Department of Health and Environment, "VINČA "Institute of Nuclear Sciences-National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovića Alasa 12-14, 11001 Belgrade, Serbia
| | - Vladan P Bajić
- Laboratory for Radiobiology and Molecular Genetics, Department of Health and Environment, "VINČA "Institute of Nuclear Sciences-National Institute of the Republic of Serbia, University of Belgrade, Mike Petrovića Alasa 12-14, 11001 Belgrade, Serbia.
| |
Collapse
|
7
|
Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, Davatzikos C. The Genetic Architecture of Multimodal Human Brain Age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.13.536818. [PMID: 37333190 PMCID: PMC10274645 DOI: 10.1101/2023.04.13.536818] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.
Collapse
Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | | | - Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yogasudha Veturi
- Department of Biobehavioral Health and Statistics, Penn State University, University Park, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| |
Collapse
|
8
|
Wainberg M, Andrews SJ, Tripathy SJ. Shared genetic risk loci between Alzheimer's disease and related dementias, Parkinson's disease, and amyotrophic lateral sclerosis. Alzheimers Res Ther 2023; 15:113. [PMID: 37328865 PMCID: PMC10273745 DOI: 10.1186/s13195-023-01244-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 05/16/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Genome-wide association studies (GWAS) have indicated moderate genetic overlap between Alzheimer's disease (AD) and related dementias (ADRD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS), neurodegenerative disorders traditionally considered etiologically distinct. However, the specific genetic variants and loci underlying this overlap remain almost entirely unknown. METHODS We leveraged state-of-the-art GWAS for ADRD, PD, and ALS. For each pair of disorders, we examined each of the GWAS hits for one disorder and tested whether they were also significant for the other disorder, applying Bonferroni correction for the number of variants tested. This approach rigorously controls the family-wise error rate for both disorders, analogously to genome-wide significance. RESULTS Eleven loci with GWAS hits for one disorder were also associated with one or both of the other disorders: one with all three disorders (the MAPT/KANSL1 locus), five with ADRD and PD (near LCORL, CLU, SETD1A/KAT8, WWOX, and GRN), three with ADRD and ALS (near GPX3, HS3ST5/HDAC2/MARCKS, and TSPOAP1), and two with PD and ALS (near GAK/TMEM175 and NEK1). Two of these loci (LCORL and NEK1) were associated with an increased risk of one disorder but decreased risk of another. Colocalization analysis supported a shared causal variant between ADRD and PD at the CLU, WWOX, and LCORL loci, between ADRD and ALS at the TSPOAP1 locus, and between PD and ALS at the NEK1 and GAK/TMEM175 loci. To address the concern that ADRD is an imperfect proxy for AD and that the ADRD and PD GWAS have overlapping participants (nearly all of which are from the UK Biobank), we confirmed that all our ADRD associations had nearly identical odds ratios in an AD GWAS that excluded the UK Biobank, and all but one remained nominally significant (p < 0.05) for AD. CONCLUSIONS In one of the most comprehensive investigations to date of pleiotropy between neurodegenerative disorders, we identify eleven genetic risk loci shared among ADRD, PD, and ALS. These loci support lysosomal/autophagic dysfunction (GAK/TMEM175, GRN, KANSL1), neuroinflammation/immunity (TSPOAP1), oxidative stress (GPX3, KANSL1), and the DNA damage response (NEK1) as transdiagnostic processes underlying multiple neurodegenerative disorders.
Collapse
Affiliation(s)
- Michael Wainberg
- Centre for Addiction and Mental Health, 250 College Street, Toronto, M5T 1R8, Canada
| | - Shea J Andrews
- Department of Psychiatry & Behavioral Sciences, University of California San Francisco, San Francisco, 94143, USA
| | - Shreejoy J Tripathy
- Centre for Addiction and Mental Health, 250 College Street, Toronto, M5T 1R8, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, M5S 1A8, Canada.
- Department of Psychiatry, University of Toronto, Toronto, M5T 1R8, Canada.
- Department of Physiology, University of Toronto, Toronto, M5S 1A8, Canada.
| |
Collapse
|
9
|
Chen Z, Wu B, Li G, Zhou L, Zhang L, Liu J. MAPT rs17649553 T allele is associated with better verbal memory and higher small-world properties in Parkinson's disease. Neurobiol Aging 2023; 129:219-231. [PMID: 37413784 DOI: 10.1016/j.neurobiolaging.2023.06.006] [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: 03/08/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 07/08/2023]
Abstract
Currently, over 90 genetic loci have been found to be associated with Parkinson's disease (PD) in genome-wide association studies, nevertheless, the effects of these genetic variants on the clinical features and brain structure of PD patients are largely unknown. This study investigated the effects of microtubule-associated protein tau (MAPT) rs17649553 (C>T), a genetic variant associated with reduced PD risk, on the clinical manifestations and brain networks of PD patients. We found MAPT rs17649553 T allele was associated with better verbal memory in PD patients. In addition, MAPT rs17649553 significantly shaped the topology of gray matter covariance network and white matter network. Both the network metrics in gray matter covariance network and white matter network were correlated with verbal memory, however, the mediation analysis showed that it was the small-world properties in white matter network that mediated the effects of MAPT rs17649553 on verbal memory. These results suggest that MAPT rs17649553 T allele is associated with higher small-world properties in structural network and better verbal memory in PD.
Collapse
Affiliation(s)
- Zhichun Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Department of Neurology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Bin Wu
- Department of Neurology, Xuchang Central Hospital Affiliated with Henan University of Science and Technology, Henan, China
| | - Guanglu Li
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Liche Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Lina Zhang
- Department of Biostatistics, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Liu
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| |
Collapse
|
10
|
Tigro H, Shimozawa M, Nilsson P, Lyashkov A, Khadeer M, Järving I, Ferrucci L, Shimmo R, Johansson J, Moaddel R. Identification of glycolytic proteins as binding partners of Bri2 BRICHOS domain. J Pharm Biomed Anal 2023; 232:115465. [PMID: 37220701 DOI: 10.1016/j.jpba.2023.115465] [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: 04/05/2023] [Revised: 04/30/2023] [Accepted: 05/16/2023] [Indexed: 05/25/2023]
Abstract
Human integral membrane protein 2B (ITM2B or Bri2) is a member of the BRICHOS family, that can attenuate Aβ pathology in the brain. As a result, the identification of novel Bri2 BRICHOS client proteins has been sought to help elucidate signaling pathways and the potential identification of novel therapeutic targets. To identify Bri2 BRICHOS interacting partners, we carried out a 'protein fishing' experiment using recombinant human (rh) Bri2 BRICHOS-coated magnetic particles, in combination with proteomic analysis on cytosolic and membrane fractions of cortical homogenates from C57BL/6 J WT mouse. We identified 4 proteins from the cytosolic fractions and 44 proteins from the membrane fractions that had significant interactions (p < 0.05) with Bri2 BRICHOS domain, of which 11 proteins were previously identified as proteins that interacted with Bri2 BRICHOS domain. Enrichment analysis of the retained proteins identified glycolysis/gluconeogenesis as the most enriched pathway, with several proteins identified playing roles in carbon metabolism, amino acid synthesis. The data suggested that Bri2 BRICHOS may have a role in cellular energy demands in the brain via glycolysis and mitochondrial oxidative phosphorylation and may play a role in mitochondrial homeostasis.
Collapse
Affiliation(s)
- Helene Tigro
- School of Natural Sciences and Health, Tallinn University, Tallinn, Estonia; Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Makoto Shimozawa
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Center for Alzheimer Research, Karolinska Institutet, Solna, Sweden
| | - Per Nilsson
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Center for Alzheimer Research, Karolinska Institutet, Solna, Sweden
| | - Alexey Lyashkov
- Biomedical Research Centre, National Institute on Aging, NIH, Baltimore, MD 21224, United States
| | - Mohammed Khadeer
- Biomedical Research Centre, National Institute on Aging, NIH, Baltimore, MD 21224, United States
| | - Ivar Järving
- Department of Chemistry and Biotechnology, Tallinn University of Technology, Tallinn, Estonia
| | - Luigi Ferrucci
- Biomedical Research Centre, National Institute on Aging, NIH, Baltimore, MD 21224, United States
| | - Ruth Shimmo
- School of Natural Sciences and Health, Tallinn University, Tallinn, Estonia
| | - Jan Johansson
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Ruin Moaddel
- Biomedical Research Centre, National Institute on Aging, NIH, Baltimore, MD 21224, United States.
| |
Collapse
|
11
|
García-Marín LM, Reyes-Pérez P, Diaz-Torres S, Medina-Rivera A, Martin NG, Mitchell BL, Rentería ME. Shared molecular genetic factors influence subcortical brain morphometry and Parkinson's disease risk. NPJ Parkinsons Dis 2023; 9:73. [PMID: 37164954 PMCID: PMC10172359 DOI: 10.1038/s41531-023-00515-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 04/28/2023] [Indexed: 05/12/2023] Open
Abstract
Parkinson's disease (PD) is a late-onset and genetically complex neurodegenerative disorder. Here we sought to identify genes and molecular pathways underlying the associations between PD and the volume of ten brain structures measured through magnetic resonance imaging (MRI) scans. We leveraged genome-wide genetic data from several cohorts, including the International Parkinson's Disease Genomics Consortium (IPDG), the UK Biobank, the Adolescent Brain Cognitive Development (ABCD) study, the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), the Enhancing Neuroimaging Genetics through Meta-Analyses (ENIGMA), and 23andMe. We observed significant positive genetic correlations between PD and intracranial and subcortical brain volumes. Genome-wide association studies (GWAS) - pairwise analyses identified 210 genomic segments with shared aetiology between PD and at least one of these brain structures. Pathway enrichment results highlight potential links with chronic inflammation, the hypothalamic-pituitary-adrenal pathway, mitophagy, disrupted vesicle-trafficking, calcium-dependent, and autophagic pathways. Investigations for putative causal genetic effects suggest that a larger putamen volume could influence PD risk, independently of the potential causal genetic effects of intracranial volume (ICV) on PD. Our findings suggest that genetic variants influencing larger intracranial and subcortical brain volumes, possibly during earlier stages of life, influence the risk of developing PD later in life.
Collapse
Affiliation(s)
- Luis M García-Marín
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
- Laboratorio Internacional de Investigación del Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México.
| | - Paula Reyes-Pérez
- Laboratorio Internacional de Investigación del Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Santiago Diaz-Torres
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Alejandra Medina-Rivera
- Laboratorio Internacional de Investigación del Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Nicholas G Martin
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Brittany L Mitchell
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Miguel E Rentería
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| |
Collapse
|
12
|
Reynolds RH, Wagen AZ, Lona-Durazo F, Scholz SW, Shoai M, Hardy J, Gagliano Taliun SA, Ryten M. Local genetic correlations exist among neurodegenerative and neuropsychiatric diseases. NPJ Parkinsons Dis 2023; 9:70. [PMID: 37117178 PMCID: PMC10147945 DOI: 10.1038/s41531-023-00504-1] [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: 03/16/2023] [Accepted: 03/27/2023] [Indexed: 04/30/2023] Open
Abstract
Genetic correlation ([Formula: see text]) between traits can offer valuable insight into underlying shared biological mechanisms. Neurodegenerative diseases overlap neuropathologically and often manifest comorbid neuropsychiatric symptoms. However, global [Formula: see text] analyses show minimal [Formula: see text] among neurodegenerative and neuropsychiatric diseases. Importantly, local [Formula: see text] s can exist in the absence of global relationships. To investigate this possibility, we applied LAVA, a tool for local [Formula: see text] analysis, to genome-wide association studies of 3 neurodegenerative diseases (Alzheimer's disease, Lewy body dementia and Parkinson's disease) and 3 neuropsychiatric disorders (bipolar disorder, major depressive disorder and schizophrenia). We identified several local [Formula: see text] s missed in global analyses, including between (i) all 3 neurodegenerative diseases and schizophrenia and (ii) Alzheimer's and Parkinson's disease. For those local [Formula: see text] s identified in genomic regions containing disease-implicated genes, such as SNCA, CLU and APOE, incorporation of expression quantitative trait loci identified genes that may drive genetic overlaps between diseases. Collectively, we demonstrate that complex genetic relationships exist among neurodegenerative and neuropsychiatric diseases, highlighting putative pleiotropic genomic regions and genes. These findings imply sharing of pathogenic processes and the potential existence of common therapeutic targets.
Collapse
Affiliation(s)
- Regina H Reynolds
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA.
| | - Aaron Z Wagen
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, London, UK
- Neurodegeneration Biology Laboratory, The Francis Crick Institute, London, UK
| | - Frida Lona-Durazo
- Montréal Heart Institute, Montréal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montréal, Canada
| | - Sonja W Scholz
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Maryam Shoai
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
- Department of Neurodegenerative Diseases, Queen Square Institute of Neurology, University College London, London, UK
- UK Dementia Research Institute, University College London, London, UK
| | - John Hardy
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
- Department of Neurodegenerative Diseases, Queen Square Institute of Neurology, University College London, London, UK
- UK Dementia Research Institute, University College London, London, UK
| | - Sarah A Gagliano Taliun
- Montréal Heart Institute, Montréal, QC, Canada
- Department of Medicine & Department of Neurosciences, Université de Montréal, Montréal, QC, Canada
| | - Mina Ryten
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA.
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK.
| |
Collapse
|
13
|
Genetic correlation and gene-based pleiotropy analysis for four major neurodegenerative diseases with summary statistics. Neurobiol Aging 2023; 124:117-128. [PMID: 36740554 DOI: 10.1016/j.neurobiolaging.2022.12.012] [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: 11/10/2021] [Revised: 03/25/2022] [Accepted: 12/27/2022] [Indexed: 01/02/2023]
Abstract
Recent genome-wide association studies suggested shared genetic components between neurodegenerative diseases. However, pleiotropic association patterns among them remain poorly understood. We here analyzed 4 major neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS), and found suggestively positive genetic correlation. We next implemented a gene-centric pleiotropy analysis with a powerful method called PLACO and detected 280 pleiotropic associations (226 unique genes) with these diseases. Functional analyses demonstrated that these genes were enriched in the pancreas, liver, heart, blood, brain, and muscle tissues; and that 42 pleiotropic genes exhibited drug-gene interactions with 341 drugs. Using Mendelian randomization, we discovered that AD and PD can increase the risk of developing ALS, and that AD and ALS can also increase the risk of developing FTD, respectively. Overall, this study provides in-depth insights into shared genetic components and causal relationship among the 4 major neurodegenerative diseases, indicating genetic overlap and causality commonly drive their co-occurrence. It also has important implications on the etiology understanding, drug development and therapeutic targets for neurodegenerative diseases.
Collapse
|
14
|
Sensi SL, Russo M, Tiraboschi P. Biomarkers of diagnosis, prognosis, pathogenesis, response to therapy: Convergence or divergence? Lessons from Alzheimer's disease and synucleinopathies. HANDBOOK OF CLINICAL NEUROLOGY 2023; 192:187-218. [PMID: 36796942 DOI: 10.1016/b978-0-323-85538-9.00015-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Alzheimer's disease (AD) is the most common disorder associated with cognitive impairment. Recent observations emphasize the pathogenic role of multiple factors inside and outside the central nervous system, supporting the notion that AD is a syndrome of many etiologies rather than a "heterogeneous" but ultimately unifying disease entity. Moreover, the defining pathology of amyloid and tau coexists with many others, such as α-synuclein, TDP-43, and others, as a rule, not an exception. Thus, an effort to shift our AD paradigm as an amyloidopathy must be reconsidered. Along with amyloid accumulation in its insoluble state, β-amyloid is becoming depleted in its soluble, normal states, as a result of biological, toxic, and infectious triggers, requiring a shift from convergence to divergence in our approach to neurodegeneration. These aspects are reflected-in vivo-by biomarkers, which have become increasingly strategic in dementia. Similarly, synucleinopathies are primarily characterized by abnormal deposition of misfolded α-synuclein in neurons and glial cells and, in the process, depleting the levels of the normal, soluble α-synuclein that the brain needs for many physiological functions. The soluble to insoluble conversion also affects other normal brain proteins, such as TDP-43 and tau, accumulating in their insoluble states in both AD and dementia with Lewy bodies (DLB). The two diseases have been distinguished by the differential burden and distribution of insoluble proteins, with neocortical phosphorylated tau deposition more typical of AD and neocortical α-synuclein deposition peculiar to DLB. We propose a reappraisal of the diagnostic approach to cognitive impairment from convergence (based on clinicopathologic criteria) to divergence (based on what differs across individuals affected) as a necessary step for the launch of precision medicine.
Collapse
Affiliation(s)
- Stefano L Sensi
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Molecular Neurology Unit, Center for Advanced Studies and Technology-CAST and ITAB Institute for Advanced Biotechnology, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.
| | - Mirella Russo
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Molecular Neurology Unit, Center for Advanced Studies and Technology-CAST and ITAB Institute for Advanced Biotechnology, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Pietro Tiraboschi
- Division of Neurology V-Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| |
Collapse
|
15
|
Zhang D, Shi Y, Yao J, Zhou L, Wei H, Liu J, Tong Q, Ma L, He H, Wu T. Free-Water Imaging of the Substantia Nigra in GBA Pathogenic Variant Carriers. Mov Disord 2023. [PMID: 36797645 DOI: 10.1002/mds.29356] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Pathogenic variants in the glucocerebrosidase gene (GBA) have been identified as the most common genetic risk factor for Parkinson's disease (PD). However, the features of substantia nigra damage in GBA pathogenic variant carriers remain unclear. OBJECTIVE We aimed to evaluate the microstructural changes in the substantia nigra in non-manifesting GBA pathogenic variant carriers (GBA-NMC) and PD patients with GBA pathogenic variant (GBA-PD) with free-water imaging. METHODS First, we compared free water values in the posterior substantia nigra between non-manifesting non-carriers (NMNC, n = 29), GBA-NMC (n = 26), and GBA-PD (n = 16). Then, free water values in the posterior substantia nigra were compared between GBA-PD and early- (n = 19) and late-onset (n = 40) idiopathic PD (iPD) patients. Furthermore, we examined whether the baseline free water values could predict the progressions of clinical symptoms. RESULTS The free water values in the posterior substantia nigra were significantly higher in the GBA-NMC and GBA-PD groups compared to NMNC, and were significantly increased in the GBA-PD group than both early- and late-onset iPD. Free water values in the posterior substantia nigra could predict the progression of anxiety and cognitive decline in GBA-NMC and GBA-PD groups. CONCLUSIONS We demonstrate that free water values are elevated in the substantia nigra and predict the development of non-motor symptoms in GBA-NMC and GBA-PD. Our findings demonstrate that a significant nigral impairment already exists in GBA-NMC, and nigral injury may be more severe in GBA-PD than in iPD. These results support that free-water imaging can as a potential early marker of substantia nigra damage. © 2023 International Parkinson and Movement Disorder Society.
Collapse
Affiliation(s)
- Dongling Zhang
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Parkinson's Disease Center, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Yuting Shi
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Junye Yao
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Liche Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Liu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiqi Tong
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Lingyan Ma
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Parkinson's Disease Center, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.,School of Physics, Zhejiang University, Hangzhou, China
| | - Tao Wu
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Parkinson's Disease Center, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| |
Collapse
|
16
|
Hong JY, Lee PH. Subjective Cognitive Complaints in Cognitively Normal Patients With Parkinson's Disease: A Systematic Review. J Mov Disord 2023; 16:1-12. [PMID: 36353806 PMCID: PMC9978265 DOI: 10.14802/jmd.22059] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 08/15/2022] [Indexed: 11/11/2022] Open
Abstract
Subjective cognitive complaints (SCCs) refer to self-perceived cognitive decline and are related to objective cognitive decline. SCCs in cognitively normal individuals are considered a preclinical sign of subsequent cognitive impairment due to Alzheimer's disease, and SCCs in cognitively normal patients with Parkinson's disease (PD) are also gaining attention. The aim of this review was to provide an overview of the current research on SCCs in cognitively normal patients with PD. A systematic search found a lack of consistency in the methodologies used to define and measure SCCs. Although the association between SCCs and objective cognitive performance in cognitively normal patients with PD is controversial, SCCs appear to be predictive of subsequent cognitive decline. These findings support the clinical value of SCCs in cognitively normal status in PD; however, further convincing evidence from biomarker studies is needed to provide a pathophysiological basis for these findings. Additionally, a consensus on the definition and assessment of SCCs is needed for further investigations.
Collapse
Affiliation(s)
- Jin Yong Hong
- Department of Neurology, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea,Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Korea,Corresponding author: Phil Hyu Lee, MD, PhD Department of Neurology, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea / Tel: +82-2-2228-1608 / Fax: +82-2-393-0705 / E-mail:
| |
Collapse
|
17
|
Mori H, Yoshino Y, Ueno M, Funahashi Y, Kumon H, Ozaki Y, Yamazaki K, Ochi S, Iga J, Ueno S. Blood MAPT expression and methylation status in Alzheimer's disease. PCN REPORTS : PSYCHIATRY AND CLINICAL NEUROSCIENCES 2022; 1:e65. [PMID: 38868661 PMCID: PMC11114303 DOI: 10.1002/pcn5.65] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 11/20/2022] [Accepted: 11/25/2022] [Indexed: 06/14/2024]
Abstract
Aim This study aimed to investigate the expression levels and methylation status of microtubule-associated protein tau (MAPT) in the blood of Alzheimer's disease (AD) patients and age- and sex-matched healthy controls. Methods Fifty AD outpatients and 50 healthy contorls were enrolled. Blood samples were collected for processing of complementary DNA and genomic DNA. MAPT messenger ribonucleic acid (mRNA) expression was analyzed by real-time quantitative polymerase chain reaction. The methylation rates of four cytosine-phosphate-guanine (CpG) sites in the upstream region of MAPT exon1 were evaluated by the pyrosequencing method. Results No significant differences in MAPT mRNA expression levels were found between AD and control subjects (AD 0.97 ± 0.49 vs. control 1.0 ± 0.64, p = 0.62). MAPT mRNA expression levels were not correlated with any other clinical characteristics or results of psychological tests. MAPT mRNA expression levels were significantly higher in AD subjects treated with acetylcholinesterase inhibitors (AchEIs) (n = 25) than in subjects not treated with AChEIs (n = 25) (unmedicated 0.83 ± 0.33 vs. medicated 1.12 ± 0.59, p = 0.049). The AD subjects did not differ from the control subjects in methylation rates at selected CpG sites. MAPT methylation status were not correlated with clinical characteristics, the results of psychological tests, or MAPT mRNA expression. Conclusion MAPT mRNA expression levels and methylation status in blood do not appear useful as biomarkers for AD or the examined CpG sites were not genetically significant for MAPT gene expression or AD pathology. However, AChEIs may alter MAPT mRNA expression. Further studies are needed to explore blood biomarkers that can discriminate AD patients from controls.
Collapse
Affiliation(s)
- Hiroaki Mori
- Department of Neuropsychiatry, Molecules and FunctionEhime University Graduate School of Medicine, ShitsukawaToonEhimeJapan
| | - Yuta Yoshino
- Department of Neuropsychiatry, Molecules and FunctionEhime University Graduate School of Medicine, ShitsukawaToonEhimeJapan
| | - Mariko Ueno
- Department of Neuropsychiatry, Molecules and FunctionEhime University Graduate School of Medicine, ShitsukawaToonEhimeJapan
| | - Yu Funahashi
- Department of Neuropsychiatry, Molecules and FunctionEhime University Graduate School of Medicine, ShitsukawaToonEhimeJapan
| | - Hiroshi Kumon
- Department of Neuropsychiatry, Molecules and FunctionEhime University Graduate School of Medicine, ShitsukawaToonEhimeJapan
| | - Yuki Ozaki
- Department of Neuropsychiatry, Molecules and FunctionEhime University Graduate School of Medicine, ShitsukawaToonEhimeJapan
| | - Kiyohiro Yamazaki
- Department of Neuropsychiatry, Molecules and FunctionEhime University Graduate School of Medicine, ShitsukawaToonEhimeJapan
| | - Shinichiro Ochi
- Department of Neuropsychiatry, Molecules and FunctionEhime University Graduate School of Medicine, ShitsukawaToonEhimeJapan
| | - Jun‐ichi Iga
- Department of Neuropsychiatry, Molecules and FunctionEhime University Graduate School of Medicine, ShitsukawaToonEhimeJapan
| | - Shu‐ichi Ueno
- Department of Neuropsychiatry, Molecules and FunctionEhime University Graduate School of Medicine, ShitsukawaToonEhimeJapan
| |
Collapse
|
18
|
Bowles KR, Pugh DA, Liu Y, Patel T, Renton AE, Bandres-Ciga S, Gan-Or Z, Heutink P, Siitonen A, Bertelsen S, Cherry JD, Karch CM, Frucht SJ, Kopell BH, Peter I, Park YJ, Charney A, Raj T, Crary JF, Goate AM. 17q21.31 sub-haplotypes underlying H1-associated risk for Parkinson's disease are associated with LRRC37A/2 expression in astrocytes. Mol Neurodegener 2022; 17:48. [PMID: 35841044 PMCID: PMC9284779 DOI: 10.1186/s13024-022-00551-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 06/21/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Parkinson's disease (PD) is genetically associated with the H1 haplotype of the MAPT 17q.21.31 locus, although the causal gene and variants underlying this association have not been identified. METHODS To better understand the genetic contribution of this region to PD and to identify novel mechanisms conferring risk for the disease, we fine-mapped the 17q21.31 locus by constructing discrete haplotype blocks from genetic data. We used digital PCR to assess copy number variation associated with PD-associated blocks, and used human brain postmortem RNA-seq data to identify candidate genes that were then further investigated using in vitro models and human brain tissue. RESULTS We identified three novel H1 sub-haplotype blocks across the 17q21.31 locus associated with PD risk. Protective sub-haplotypes were associated with increased LRRC37A/2 copy number and expression in human brain tissue. We found that LRRC37A/2 is a membrane-associated protein that plays a role in cellular migration, chemotaxis and astroglial inflammation. In human substantia nigra, LRRC37A/2 was primarily expressed in astrocytes, interacted directly with soluble α-synuclein, and co-localized with Lewy bodies in PD brain tissue. CONCLUSION These data indicate that a novel candidate gene, LRRC37A/2, contributes to the association between the 17q21.31 locus and PD via its interaction with α-synuclein and its effects on astrocytic function and inflammatory response. These data are the first to associate the genetic association at the 17q21.31 locus with PD pathology, and highlight the importance of variation at the 17q21.31 locus in the regulation of multiple genes other than MAPT and KANSL1, as well as its relevance to non-neuronal cell types.
Collapse
Affiliation(s)
- Kathryn R. Bowles
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Derian A. Pugh
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Yiyuan Liu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Tulsi Patel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Alan E. Renton
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Sara Bandres-Ciga
- Laboratory of Neurogenetics, National Institute On Aging, National Institutes of Health, Bethesda, MD USA
| | - Ziv Gan-Or
- Department of Human Genetics, McGill University, Montréal, Québec Canada
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montréal, Québec Canada
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec Canada
| | - Peter Heutink
- Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Ari Siitonen
- Institute of Clinical Medicine, Department of Neurology, University of Oulu, Oulu, Finland
- Department of Neurology and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Sarah Bertelsen
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Jonathan D. Cherry
- Alzheimer’s Disease and CTE Center, Boston University, Boston University School of Medicine, Boston, MA USA
- Department of Neurology, Boston University School of Medicine, Boston, MA USA
- VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA USA
- Department of Pathology and Laboratory Medicine, Boston University School of Medicine, Boston, MA USA
| | - Celeste M. Karch
- Department of Psychiatry, Washington University in St Louis, St. Louis, MO USA
| | - Steven J. Frucht
- Department of Neurology, Fresco Institute for Parkinson’s and Movement Disorders, New York University Langone, New York, NY USA
| | - Brian H. Kopell
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Center for Neuromodulation, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Inga Peter
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Y. J. Park
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | | | - Alexander Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Towfique Raj
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - John F. Crary
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - A. M. Goate
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| |
Collapse
|
19
|
Wang K, Lu Y, Morrow DF, Xiao D, Xu C. Associations of ARHGAP26 Polymorphisms with Alzheimer's Disease and Cardiovascular Disease. J Mol Neurosci 2022; 72:1085-1097. [PMID: 35171450 DOI: 10.1007/s12031-022-01972-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/10/2022] [Indexed: 02/03/2023]
Abstract
The Rho GTPase activating protein 26 (ARHGAP26) gene has been reported to be associated with neuropsychiatric diseases and neurodegenerative diseases including Parkinson's disease. We examined whether the ARHGAP26 gene is associated with Alzheimer's disease (AD) and/or cardiovascular disease (CVD). Multivariable logistic regression model was used to examine the associations of 154 single nucleotide polymorphisms (SNPs) within the ARHGAP26 gene with AD and CVD using the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) cohort. Fourteen SNPs were associated with AD (top SNP rs3776362 with p = 3.43 × 10-3), while 37 SNPs revealed associations with CVD (top SNP rs415235 with p = 2.06 × 10-4). Interestingly, 13 SNPs were associated with both AD and CVD. SNP rs3776362 was associated with CVD, Functional Activities Questionnaire (FAQ), and Clinical Dementia Rating Sum of Boxes (CDR-SB). A replication study using a Caribbean Hispanics sample showed that 17 SNPs revealed associations with AD, and 12 SNPs were associated with CVD. The third sample using a family-based study design showed that 9 SNPs were associated with AD, and 3 SNPs were associated with CVD. SNP rs6836509 within the ARHGAP10 gene (an important paralogon of ARHGAP26) was associated with AD and cerebrospinal fluid total tau (t-tau) level in the ADNI sample. Several SNPs were functionally important using the RegulomeDB, while a number of SNPs were associated with significant expression quantitative trait loci (eQTLs) using Genotype-Tissue Expression (GTEx) databases. In conclusion, genetic variants within ARHGAP26 were associated with AD and CVD. These findings add important new insights into the potentially shared pathogenesis of AD and CVD.
Collapse
Affiliation(s)
- Kesheng Wang
- Department of Family and Community Health, School of Nursing, Health Sciences Center, West Virginia University, Post Office Box 9600 - Office 6419, Morgantown, WV, 26506, USA.
| | - Yongke Lu
- Department of Biomedical Sciences, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, 25755, USA
| | - Deana F Morrow
- School of Social Work, West Virginia University, Morgantown, WV, 26506, USA
| | - Danqing Xiao
- Department of STEM, School of Arts and Sciences, Regis College, Weston, MA, 02493, USA
- McLean Imaging Center, McLean Hospital, MA, 02478, Belmont, USA
| | - Chun Xu
- Department of Health and Biomedical Sciences, College of Health Professions, University of Texas Rio Grande Valley, TX, 78520, Brownsville, USA.
| |
Collapse
|
20
|
Leveille E, Ross OA, Gan-Or Z. Tau and MAPT genetics in tauopathies and synucleinopathies. Parkinsonism Relat Disord 2021; 90:142-154. [PMID: 34593302 DOI: 10.1016/j.parkreldis.2021.09.008] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/25/2021] [Accepted: 09/09/2021] [Indexed: 10/20/2022]
Abstract
MAPT encodes the microtubule-associated protein tau, which is the main component of neurofibrillary tangles (NFTs) and found in other protein aggregates. These aggregates are among the pathological hallmarks of primary tauopathies such as frontotemporal dementia (FTD). Abnormal tau can also be observed in secondary tauopathies such as Alzheimer's disease (AD) and synucleinopathies such as Parkinson's disease (PD). On top of pathological findings, genetic data also links MAPT to these disorders. MAPT variations are a cause or risk factors for many tauopathies and synucleinopathies and are associated with certain clinical and pathological features in affected individuals. In addition to clinical, pathological, and genetic overlap, evidence also suggests that tau and alpha-synuclein may interact on the molecular level, and thus might collaborate in the neurodegenerative process. Understanding the role of MAPT variations in tauopathies and synucleinopathies is therefore essential to elucidate the role of tau in the pathogenesis and phenotype of those disorders, and ultimately to develop targeted therapies. In this review, we describe the role of MAPT genetic variations in tauopathies and synucleinopathies, several genotype-phenotype and pathological features, and discuss their implications for the classification and treatment of those disorders.
Collapse
Affiliation(s)
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA; Department of Clinical Genomics, Mayo Clinic, Jacksonville, FL, 32224, USA
| | - Ziv Gan-Or
- The Neuro (Montreal Neurological Institute-hospital), McGill University, Montréal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada; Department of Human Genetics, McGill University, Montréal, QC, Canada.
| |
Collapse
|
21
|
Lubben N, Ensink E, Coetzee GA, Labrie V. The enigma and implications of brain hemispheric asymmetry in neurodegenerative diseases. Brain Commun 2021; 3:fcab211. [PMID: 34557668 PMCID: PMC8454206 DOI: 10.1093/braincomms/fcab211] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/16/2021] [Accepted: 08/10/2021] [Indexed: 01/15/2023] Open
Abstract
The lateralization of the human brain may provide clues into the pathogenesis and progression of neurodegenerative diseases. Though differing in their presentation and underlying pathologies, neurodegenerative diseases are all devastating and share an intriguing theme of asymmetrical pathology and clinical symptoms. Parkinson’s disease, with its distinctive onset of motor symptoms on one side of the body, stands out in this regard, but a review of the literature reveals asymmetries in several other neurodegenerative diseases. Here, we review the lateralization of the structure and function of the healthy human brain and the common genetic and epigenetic patterns contributing to the development of asymmetry in health and disease. We specifically examine the role of asymmetry in Parkinson’s disease, Alzheimer’s disease, amyotrophic lateral sclerosis, and multiple sclerosis, and interrogate whether these imbalances may reveal meaningful clues about the origins of these diseases. We also propose several hypotheses for how lateralization may contribute to the distinctive and enigmatic features of asymmetry in neurodegenerative diseases, suggesting a role for asymmetry in the choroid plexus, neurochemistry, protein distribution, brain connectivity and the vagus nerve. Finally, we suggest how future studies may reveal novel insights into these diseases through the lens of asymmetry.
Collapse
Affiliation(s)
- Noah Lubben
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Elizabeth Ensink
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Gerhard A Coetzee
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI 49503, USA
| | - Viviane Labrie
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI 49503, USA
| |
Collapse
|
22
|
Cheng W, Frei O, van der Meer D, Wang Y, O’Connell KS, Chu Y, Bahrami S, Shadrin AA, Alnæs D, Hindley GFL, Lin A, Karadag N, Fan CC, Westlye LT, Kaufmann T, Molden E, Dale AM, Djurovic S, Smeland OB, Andreassen OA. Genetic Association Between Schizophrenia and Cortical Brain Surface Area and Thickness. JAMA Psychiatry 2021; 78:1020-1030. [PMID: 34160554 PMCID: PMC8223140 DOI: 10.1001/jamapsychiatry.2021.1435] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 04/28/2021] [Indexed: 01/03/2023]
Abstract
Importance Schizophrenia is a complex heritable disorder associated with many genetic variants, each with a small effect. While cortical differences between patients with schizophrenia and healthy controls are consistently reported, the underlying molecular mechanisms remain elusive. Objective To investigate the extent of shared genetic architecture between schizophrenia and brain cortical surface area (SA) and thickness (TH) and to identify shared genomic loci. Design, Setting, and Participants Independent genome-wide association study data on schizophrenia (Psychiatric Genomics Consortium and CLOZUK: n = 105 318) and SA and TH (UK Biobank: n = 33 735) were obtained. The extent of polygenic overlap was investigated using MiXeR. The specific shared genomic loci were identified by conditional/conjunctional false discovery rate analysis and were further examined in 3 independent cohorts. Data were collected from December 2019 to February 2021, and data analysis was performed from May 2020 to February 2021. Main Outcomes and Measures The primary outcomes were estimated fractions of polygenic overlap between schizophrenia, total SA, and average TH and a list of functionally characterized shared genomic loci. Results Based on genome-wide association study data from 139 053 participants, MiXeR estimated schizophrenia to be more polygenic (9703 single-nucleotide variants [SNVs]) than total SA (2101 SNVs) and average TH (1363 SNVs). Most SNVs associated with total SA (1966 of 2101 [93.6%]) and average TH (1322 of 1363 [97.0%]) may be associated with the development of schizophrenia. Subsequent conjunctional false discovery rate analysis identified 44 and 23 schizophrenia risk loci shared with total SA and average TH, respectively. The SNV associations of shared loci between schizophrenia and total SA revealed en masse concordant association between the discovery and independent cohorts. After removing high linkage disequilibrium regions, such as the major histocompatibility complex region, the shared loci were enriched in immunologic signature gene sets. Polygenic overlap and shared loci between schizophrenia and schizophrenia-associated regions of interest for SA (superior frontal and middle temporal gyri) and for TH (superior temporal, inferior temporal, and superior frontal gyri) were also identified. Conclusions and Relevance This study demonstrated shared genetic loci between cortical morphometry and schizophrenia, among which a subset are associated with immunity. These findings provide an insight into the complex genetic architecture and associated with schizophrenia.
Collapse
Affiliation(s)
- Weiqiu Cheng
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Yunpeng Wang
- Centre for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Oslo, Norway
| | - Kevin S. O’Connell
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Yunhan Chu
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A. Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F. L. Hindley
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King’s College London, London, United Kingdom
| | - Aihua Lin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Naz Karadag
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Chun-Chieh Fan
- Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla
- Center for Human Development, University of California, San Diego, La Jolla
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Espen Molden
- Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
- Department of Pharmacy, University of Oslo, Oslo, Norway
| | - Anders M. Dale
- Department of Radiology, University of California, San Diego, La Jolla
- Department of Psychiatry, University of California, San Diego, La Jolla
- Department of Neurosciences, University of California San Diego, La Jolla
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Olav B. Smeland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| |
Collapse
|
23
|
Kapoor M, Chao MJ, Johnson EC, Novikova G, Lai D, Meyers JL, Schulman J, Nurnberger JI, Porjesz B, Liu Y, Foroud T, Edenberg HJ, Marcora E, Agrawal A, Goate A. Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases. Nat Commun 2021; 12:5071. [PMID: 34417470 PMCID: PMC8379159 DOI: 10.1038/s41467-021-25392-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 08/03/2021] [Indexed: 11/27/2022] Open
Abstract
Identification of causal variants and genes underlying genome-wide association study (GWAS) loci is essential to understand the biology of alcohol use disorder (AUD) and drinks per week (DPW). Multi-omics integration approaches have shown potential for fine mapping complex loci to obtain biological insights to disease mechanisms. In this study, we use multi-omics approaches, to fine-map AUD and DPW associations at single SNP resolution to demonstrate that rs56030824 on chromosome 11 significantly reduces SPI1 mRNA expression in myeloid cells and lowers risk for AUD and DPW. Our analysis also identifies MAPT as a candidate causal gene specifically associated with DPW. Genes prioritized in this study show overlap with causal genes associated with neurodegenerative disorders. Multi-omics integration analyses highlight, genetic similarities and differences between alcohol intake and disordered drinking, suggesting molecular heterogeneity that might inform future targeted functional and cross-species studies.
Collapse
Affiliation(s)
- Manav Kapoor
- Departments of Genetics and Genomic Sciences and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Michael J Chao
- Departments of Genetics and Genomic Sciences and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Gloriia Novikova
- Departments of Genetics and Genomic Sciences and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jacquelyn L Meyers
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - Jessica Schulman
- Departments of Genetics and Genomic Sciences and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John I Nurnberger
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Bernice Porjesz
- Department of Psychiatry, State University of New York, Downstate Medical Center, Brooklyn, NY, USA
| | - Yunlong Liu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Howard J Edenberg
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Edoardo Marcora
- Departments of Genetics and Genomic Sciences and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Alison Goate
- Departments of Genetics and Genomic Sciences and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| |
Collapse
|
24
|
Swarup V, Chang TS, Duong DM, Dammer EB, Dai J, Lah JJ, Johnson ECB, Seyfried NT, Levey AI, Geschwind DH. Identification of Conserved Proteomic Networks in Neurodegenerative Dementia. Cell Rep 2021; 31:107807. [PMID: 32579933 PMCID: PMC8221021 DOI: 10.1016/j.celrep.2020.107807] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 04/27/2020] [Accepted: 06/03/2020] [Indexed: 12/11/2022] Open
Abstract
Data-driven analyses are increasingly valued in modern medicine. We integrate quantitative proteomics and transcriptomics from over 1,000 post-mortem brains from six cohorts representing Alzheimer’s disease (AD), asymptomatic AD, progressive supranuclear palsy (PSP), and control patients from the Accelerating Medicines Partnership – Alzheimer’s Disease consortium. We define robust co-expression trajectories related to disease progression, including early neuronal, microglial, astrocyte, and immune response modules, and later mRNA splicing and mitochondrial modules. The majority of, but not all, modules are conserved at the transcriptomic level, including module C3, which is only observed in proteome networks and enriched in mitogen-activated protein kinase (MAPK) signaling. Genetic risk enriches in modules changing early in disease and indicates that AD and PSP have distinct causal biological drivers at the pathway level, despite aspects of similar pathology, including synaptic loss and glial inflammatory changes. The conserved, high-confidence proteomic changes enriched in genetic risk represent targets for drug discovery. Swarup et al. use a multi-omic, multi-cohort approach to identify robust early and late proteomic changes in AD and other neurodegenerative dementias and find that genetic risk is differentially enriched across disorders. Shared co-expression modules showing consistent molecular alterations at multi-omic levels are ripe for future investigation as drug targets.
Collapse
Affiliation(s)
- Vivek Swarup
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Timothy S Chang
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Duc M Duong
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Eric B Dammer
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Jingting Dai
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA; Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA; Department of Neurology, Second Xiangya Hospital, Central South University, Changsha, China
| | - James J Lah
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Erik C B Johnson
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Nicholas T Seyfried
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA; Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Allan I Levey
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Daniel H Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA.
| |
Collapse
|
25
|
Liley J, Wallace C. Accurate error control in high-dimensional association testing using conditional false discovery rates. Biom J 2021; 63:1096-1130. [PMID: 33682201 PMCID: PMC7612315 DOI: 10.1002/bimj.201900254] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Revised: 12/05/2020] [Accepted: 12/30/2020] [Indexed: 01/13/2023]
Abstract
High-dimensional hypothesis testing is ubiquitous in the biomedical sciences, and informative covariates may be employed to improve power. The conditional false discovery rate (cFDR) is a widely used approach suited to the setting where the covariate is a set of p-values for the equivalent hypotheses for a second trait. Although related to the Benjamini–Hochberg procedure, it does not permit any easy control of type-1 error rate and existing methods are over-conservative. We propose a newmethod for type-1 error rate control based on identifyingmappings from the unit square to the unit interval defined by the estimated cFDR and splitting observations so that each map is independent of the observations it is used to test. We also propose an adjustment to the existing cFDR estimator which further improves power. We show by simulation that the new method more than doubles potential improvement in power over unconditional analyses compared to existing methods. We demonstrate our method on transcriptome-wide association studies and show that the method can be used in an iterative way, enabling the use of multiple covariates successively. Our methods substantially improve the power and applicability of cFDR analysis.
Collapse
Affiliation(s)
- James Liley
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.,Department of Medicine, Addenbrookes Hospital, University of Cambridge, Cambridge, UK
| | - Chris Wallace
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.,Department of Medicine, Addenbrookes Hospital, University of Cambridge, Cambridge, UK.,Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge, UK
| |
Collapse
|
26
|
Placek K, Benatar M, Wuu J, Rampersaud E, Hennessy L, Van Deerlin VM, Grossman M, Irwin DJ, Elman L, McCluskey L, Quinn C, Granit V, Statland JM, Burns TM, Ravits J, Swenson A, Katz J, Pioro EP, Jackson C, Caress J, So Y, Maiser S, Walk D, Lee EB, Trojanowski JQ, Cook P, Gee J, Sha J, Naj AC, Rademakers R, Chen W, Wu G, Paul Taylor J, McMillan CT. Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis. EMBO Mol Med 2021; 13:e12595. [PMID: 33270986 PMCID: PMC7799365 DOI: 10.15252/emmm.202012595] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 10/27/2020] [Accepted: 10/30/2020] [Indexed: 11/09/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a multi-system disease characterized primarily by progressive muscle weakness. Cognitive dysfunction is commonly observed in patients; however, factors influencing risk for cognitive dysfunction remain elusive. Using sparse canonical correlation analysis (sCCA), an unsupervised machine-learning technique, we observed that single nucleotide polymorphisms collectively associate with baseline cognitive performance in a large ALS patient cohort (N = 327) from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. We demonstrate that a polygenic risk score derived using sCCA relates to longitudinal cognitive decline in the same cohort and also to in vivo cortical thinning in the orbital frontal cortex, anterior cingulate cortex, lateral temporal cortex, premotor cortex, and hippocampus (N = 90) as well as post-mortem motor cortical neuronal loss (N = 87) in independent ALS cohorts from the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS.
Collapse
|
27
|
Placek K, Benatar M, Wuu J, Rampersaud E, Hennessy L, Van Deerlin VM, Grossman M, Irwin DJ, Elman L, McCluskey L, Quinn C, Granit V, Statland JM, Burns TM, Ravits J, Swenson A, Katz J, Pioro EP, Jackson C, Caress J, So Y, Maiser S, Walk D, Lee EB, Trojanowski JQ, Cook P, Gee J, Sha J, Naj AC, Rademakers R, Chen W, Wu G, Paul Taylor J, McMillan CT. Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis. EMBO Mol Med 2021. [PMID: 33270986 PMCID: PMC7799365 DOI: 10.15252/emmm.202012595|] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a multi-system disease characterized primarily by progressive muscle weakness. Cognitive dysfunction is commonly observed in patients; however, factors influencing risk for cognitive dysfunction remain elusive. Using sparse canonical correlation analysis (sCCA), an unsupervised machine-learning technique, we observed that single nucleotide polymorphisms collectively associate with baseline cognitive performance in a large ALS patient cohort (N = 327) from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. We demonstrate that a polygenic risk score derived using sCCA relates to longitudinal cognitive decline in the same cohort and also to in vivo cortical thinning in the orbital frontal cortex, anterior cingulate cortex, lateral temporal cortex, premotor cortex, and hippocampus (N = 90) as well as post-mortem motor cortical neuronal loss (N = 87) in independent ALS cohorts from the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS.
Collapse
Affiliation(s)
- Katerina Placek
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Michael Benatar
- Department of NeurologyLeonard M. Miller School of MedicineUniversity of MiamiMiamiFLUSA
| | - Joanne Wuu
- Department of NeurologyLeonard M. Miller School of MedicineUniversity of MiamiMiamiFLUSA
| | - Evadnie Rampersaud
- Center for Applied BioinformaticsSt. Jude Children’s Research HospitalMemphisTNUSA
| | - Laura Hennessy
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Vivianna M Van Deerlin
- Department of Pathology & Laboratory MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Murray Grossman
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - David J Irwin
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Lauren Elman
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Leo McCluskey
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Colin Quinn
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Volkan Granit
- Department of NeurologyLeonard M. Miller School of MedicineUniversity of MiamiMiamiFLUSA
| | - Jeffrey M Statland
- Department of NeurologyUniversity of Kansas Medical CenterKansas CityKSUSA
| | - Ted M Burns
- Department of NeurologyUniversity of Virginia Health SystemCharlottesvilleVAUSA
| | - John Ravits
- Department of NeurosciencesUniversity of California San DiegoSan DiegoCAUSA
| | | | - Jon Katz
- Forbes Norris ALS CenterCalifornia Pacific Medical CenterSan FranciscoCAUSA
| | - Erik P Pioro
- Department of NeurologyCleveland ClinicClevelandOHUSA
| | - Carlayne Jackson
- Department of NeurologyUniversity of Texas Health Science CenterSan AntonioTXUSA
| | - James Caress
- Department of NeurologyWake Forest University School of MedicineWinston‐SalemNCUSA
| | - Yuen So
- Department of NeurologyStanford University Medical CenterSan JoseCAUSA
| | - Samuel Maiser
- Department of NeurologyUniversity of Minnesota Medical CenterMinneapolisMNUSA
| | - David Walk
- Department of NeurologyUniversity of Minnesota Medical CenterMinneapolisMNUSA
| | - Edward B Lee
- Department of Pathology & Laboratory MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - John Q Trojanowski
- Department of Pathology & Laboratory MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Philip Cook
- Penn Image Computing Science Laboratory (PICSL)Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - James Gee
- Penn Image Computing Science Laboratory (PICSL)Department of RadiologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Jin Sha
- Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA,Penn Neurodegeneration Genomics CenterDepartment of Pathology and Laboratory MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Adam C Naj
- Department of Pathology & Laboratory MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA,Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA,Penn Neurodegeneration Genomics CenterDepartment of Pathology and Laboratory MedicineUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | | | | | - Wenan Chen
- Center for Applied BioinformaticsSt. Jude Children’s Research HospitalMemphisTNUSA
| | - Gang Wu
- Center for Applied BioinformaticsSt. Jude Children’s Research HospitalMemphisTNUSA
| | - J Paul Taylor
- Center for Applied BioinformaticsSt. Jude Children’s Research HospitalMemphisTNUSA,The Howard Hughes Medical InstituteChevy ChaseMSUSA
| | - Corey T McMillan
- Department of NeurologyUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| |
Collapse
|
28
|
Bell BJ, Malvankar MM, Tallon C, Slusher BS. Sowing the Seeds of Discovery: Tau-Propagation Models of Alzheimer's Disease. ACS Chem Neurosci 2020; 11:3499-3509. [PMID: 33050700 DOI: 10.1021/acschemneuro.0c00531] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
The propagation of pathological proteins throughout the brain is the primary physiological hallmark of the progression of Alzheimer's Disease (AD). A growing body of evidence indicates that hyperphosphorylated Tau proteins are spread transcellularly between neurons in a prionlike fashion, inducing misfolding and aggregation into neurofibrillary tangles which accumulate along specific connectivity pathways. Earlier transgenic rodent AD models did not capture this disease-relevant spread, and therefore, seeded Tau-propagation models have been developed. Here, mutant human Tau (as isolated protein or packaged into an adeno-associated virus (AAV) viral vector) is stereotaxically injected into select brain regions and its histopathological propagation to downstream neurons quantified. These models offer a faster and more direct mechanism to evaluate genetic components and therapeutic approaches which attenuate Tau spreading in vivo. Recently, these Tau-seeding models have revealed several new targets for AD drug discovery, including nSMase2, SIRT1, p300/CBP, LRP1, and TYROBP, as well as the potential therapeutics based on melatonin and chondroitinase ABC. Importantly, these Tau-propagation rodent models more closely phenocopy the progression of AD in humans and are therefore likely to improve preclinical studies and derisk future moves into clinical trials.
Collapse
Affiliation(s)
- Benjamin J. Bell
- Johns Hopkins Drug Discovery, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Medhinee M. Malvankar
- Johns Hopkins Drug Discovery, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Carolyn Tallon
- Johns Hopkins Drug Discovery, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Barbara S. Slusher
- Johns Hopkins Drug Discovery, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| |
Collapse
|
29
|
Shadrin AA, Mucha S, Ellinghaus D, Makarious MB, Blauwendraat C, Sreelatha AAK, Heras-Garvin A, Ding J, Hammer M, Foubert-Samier A, Meissner WG, Rascol O, Pavy-Le Traon A, Frei O, O'Connell KS, Bahrami S, Schreiber S, Lieb W, Müller-Nurasyid M, Schminke U, Homuth G, Schmidt CO, Nöthen MM, Hoffmann P, Gieger C, Wenning G, Gibbs JR, Franke A, Hardy J, Stefanova N, Gasser T, Singleton A, Houlden H, Scholz SW, Andreassen OA, Sharma M. Shared Genetics of Multiple System Atrophy and Inflammatory Bowel Disease. Mov Disord 2020; 36:449-459. [PMID: 33107653 PMCID: PMC8985479 DOI: 10.1002/mds.28338] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 09/01/2020] [Accepted: 09/21/2020] [Indexed: 11/22/2022] Open
Abstract
Background: Multiple system atrophy (MSA) is a rare neurodegenerative disease characterized by intracellular accumulations of α-synuclein and nerve cell loss in striatonigral and olivopontocerebellar structures. Epidemiological and clinical studies have reported potential involvement of autoimmune mechanisms in MSA pathogenesis. However, genetic etiology of this interaction remains unknown. We aimed to investigate genetic overlap between MSA and 7 autoimmune diseases and to identify shared genetic loci. Methods: Genome-wide association study summary statistics of MSA and 7 autoimmune diseases were combined in cross-trait conjunctional false discovery rate analysis to explore overlapping genetic background. Expression of selected candidate genes was compared in transgenic MSA mice and wild-type mice. Genetic variability of candidate genes was further investigated using independent whole-exome genotyping data from large cohorts of MSA and autoimmune disease patients and healthy controls. Results: We observed substantial polygenic overlap between MSA and inflammatory bowel disease and identified 3 shared genetic loci with leading variants upstream of the DENND1B and RSP04 genes, and in intron of the C7 gene. Further, the C7 gene showed significantly dysregulated expression in the degenerating midbrain of transgenic MSA mice compared with wild-type mice and had elevated burden of protein-coding variants in independent MSA and inflammatory bowel disease cohorts. Conclusion: Our study provides evidence of shared genetic etiology between MSA and inflammatory bowel disease with an important role of the C7 gene in both phenotypes, with the implication of immune and gut dysfunction in MSA pathophysiology.
Collapse
Affiliation(s)
- Alexey A Shadrin
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Sören Mucha
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Mary B Makarious
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and, Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Cornelis Blauwendraat
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Ashwin A K Sreelatha
- Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Tübingen, Germany
| | | | - Jinhui Ding
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Monia Hammer
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Alexandra Foubert-Samier
- Service de Neurologie, CRMR Atrophie Multisystématisée, CHU Bordeaux, Bordeaux, France.,Inserm, UMR1219, Bordeaux Population Health Research Center, Bordeaux University, ISPED, Bordeaux, France
| | - Wassilios G Meissner
- Service de Neurologie, CRMR Atrophie Multisystématisée, CHU Bordeaux, Bordeaux, France.,Univ. de Bordeaux, Institut des Maladies Neurodégénératives, UMR 5293, CNRS, Bordeaux, France
| | - Olivier Rascol
- Centre de Reference Maladie Rare Atrophie MultiSystématisée, Centre d'Investigation, Clinique CIC 1436, Services de Pharmacologie Clinique et Neurosciences, NeuroToul COEN Center, Toulouse, France.,Centre Hospitalo-Universitaire de Toulouse, 3, INSERM, Toulouse, France
| | - Anne Pavy-Le Traon
- Neurology Department, French Reference Centre for MSA, University Hospital of Toulouse and INSERM U 1048, Institute of Cardiovascular and Metabolic Diseases, Toulouse, France
| | - Oleksandr Frei
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kevin S O'Connell
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Shahram Bahrami
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Stefan Schreiber
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.,First Medical Department, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology and Biobank PopGen, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.,Chair of Genetic Epidemiology, IBE, Faculty of Medicine, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany.,Department of Internal Medicine I (Cardiology), Hospital of the Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
| | - Ulf Schminke
- Department of Neurology, University Medicine Greifswald, Greifswald, Germany
| | - Georg Homuth
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University Greifswald, Greifswald, Germany
| | - Carsten O Schmidt
- Institute for Community Medicine, Study of Health in Pomerania/KEF, University Medicine Greifswald, Greifswald, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - Per Hoffmann
- Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Gregor Wenning
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | | | - J Raphael Gibbs
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - John Hardy
- Rita Lila Weston Institute, University College London, London, UK
| | - Nadia Stefanova
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Thomas Gasser
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Andrew Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Henry Houlden
- Rita Lila Weston Institute, University College London, London, UK
| | - Sonja W Scholz
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and, Stroke, National Institutes of Health, Bethesda, Maryland, USA.,Department of Neurology, Johns Hopkins University Medical Center, Baltimore, Maryland, USA
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Manu Sharma
- Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Tübingen, Germany
| |
Collapse
|
30
|
Morabito S, Miyoshi E, Michael N, Swarup V. Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer's disease. Hum Mol Genet 2020; 29:2899-2919. [PMID: 32803238 PMCID: PMC7566321 DOI: 10.1093/hmg/ddaa182] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 07/10/2020] [Accepted: 07/27/2020] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is a devastating neurological disorder characterized by changes in cell-type proportions and consequently marked alterations of the transcriptome. Here we use a data-driven systems biology meta-analytical approach across three human AD cohorts, encompassing six cortical brain regions, and integrate with multi-scale datasets comprising of DNA methylation, histone acetylation, transcriptome- and genome-wide association studies and quantitative trait loci to further characterize the genetic architecture of AD. We perform co-expression network analysis across more than 1200 human brain samples, identifying robust AD-associated dysregulation of the transcriptome, unaltered in normal human aging. We assess the cell-type specificity of AD gene co-expression changes and estimate cell-type proportion changes in human AD by integrating co-expression modules with single-cell transcriptome data generated from 27 321 nuclei from human postmortem prefrontal cortical tissue. We also show that genetic variants of AD are enriched in a microglial AD-associated module and identify key transcription factors regulating co-expressed modules. Additionally, we validate our results in multiple published human AD gene expression datasets, which can be easily accessed using our online resource (https://swaruplab.bio.uci.edu/consensusAD).
Collapse
Affiliation(s)
- Samuel Morabito
- Mathematical, Computational and Systems Biology (MCSB) Program, University of California, Irvine, CA 92697, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA 92697, USA
| | - Emily Miyoshi
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA 92697, USA
| | - Neethu Michael
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA 92697, USA
| | - Vivek Swarup
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA 92697, USA
| |
Collapse
|
31
|
Ramanan VK, Wang X, Przybelski SA, Raghavan S, Heckman MG, Batzler A, Kosel ML, Hohman TJ, Knopman DS, Graff-Radford J, Lowe VJ, Mielke MM, Jack CR, Petersen RC, Ross OA, Vemuri P. Variants in PPP2R2B and IGF2BP3 are associated with higher tau deposition. Brain Commun 2020; 2:fcaa159. [PMID: 33426524 PMCID: PMC7780444 DOI: 10.1093/braincomms/fcaa159] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 07/29/2020] [Accepted: 08/24/2020] [Indexed: 12/18/2022] Open
Abstract
Tau deposition is a key biological feature of Alzheimer’s disease that is closely related to cognitive impairment. However, it remains poorly understood why certain individuals may be more susceptible to tau deposition while others are more resistant. The recent availability of in vivo assessment of tau burden through positron emission tomography provides an opportunity to test the hypothesis that common genetic variants may influence tau deposition. We performed a genome-wide association study of tau-positron emission tomography on a sample of 754 individuals over age 50 (mean age 72.4 years, 54.6% men, 87.6% cognitively unimpaired) from the population-based Mayo Clinic Study of Aging. Linear regression was performed to test nucleotide polymorphism associations with AV-1451 (18F-flortaucipir) tau-positron emission tomography burden in an Alzheimer’s-signature composite region of interest, using an additive genetic model and covarying for age, sex and genetic principal components. Genome-wide significant associations with higher tau were identified for rs76752255 (P = 9.91 × 10−9, β = 0.20) in the tau phosphorylation regulatory gene PPP2R2B (protein phosphatase 2 regulatory subunit B) and for rs117402302 (P = 4.00 × 10−8, β = 0.19) near IGF2BP3 (insulin-like growth factor 2 mRNA-binding protein 3). The PPP2R2B association remained genome-wide significant after additionally covarying for global amyloid burden and cerebrovascular disease risk, while the IGF2BP3 association was partially attenuated after accounting for amyloid load. In addition to these discoveries, three single nucleotide polymorphisms within MAPT (microtubule-associated protein tau) displayed nominal associations with tau-positron emission tomography burden, and the association of the APOE (apolipoprotein E) ɛ4 allele with tau-positron emission tomography was marginally nonsignificant (P = 0.06, β = 0.07). No associations with tau-positron emission tomography burden were identified for other single nucleotide polymorphisms associated with Alzheimer’s disease clinical diagnosis in prior large case–control studies. Our findings nominate PPP2R2B and IGF2BP3 as novel potential influences on tau pathology which warrant further functional characterization. Our data are also supportive of previous literature on the associations of MAPT genetic variation with tau, and more broadly supports the inference that tau accumulation may have a genetic architecture distinct from known Alzheimer’s susceptibility genes, which may have implications for improved risk stratification and therapeutic targeting.
Collapse
Affiliation(s)
- Vijay K Ramanan
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, MN 55905, USA
| | - Xuewei Wang
- Department of Health Sciences Research, Mayo Clinic-Minnesota, Rochester, MN 55905, USA
| | - Scott A Przybelski
- Department of Health Sciences Research, Mayo Clinic-Minnesota, Rochester, MN 55905, USA
| | | | - Michael G Heckman
- Division of Biomedical Statistics and Informatics, Mayo Clinic-Florida, Jacksonville, FL 32224, USA
| | - Anthony Batzler
- Department of Health Sciences Research, Mayo Clinic-Minnesota, Rochester, MN 55905, USA
| | - Matthew L Kosel
- Department of Health Sciences Research, Mayo Clinic-Minnesota, Rochester, MN 55905, USA
| | - Timothy J Hohman
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, MN 55905, USA
| | | | - Val J Lowe
- Department of Radiology, Mayo Clinic-Minnesota, Rochester, MN 55905, USA
| | - Michelle M Mielke
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, MN 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic-Minnesota, Rochester, MN 55905, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic-Minnesota, Rochester, MN 55905, USA
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic-Florida, Jacksonville, FL 32224, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic-Minnesota, Rochester, MN 55905, USA
| |
Collapse
|
32
|
Hofer E, Roshchupkin GV, Adams HHH, Knol MJ, Lin H, Li S, Zare H, Ahmad S, Armstrong NJ, Satizabal CL, Bernard M, Bis JC, Gillespie NA, Luciano M, Mishra A, Scholz M, Teumer A, Xia R, Jian X, Mosley TH, Saba Y, Pirpamer L, Seiler S, Becker JT, Carmichael O, Rotter JI, Psaty BM, Lopez OL, Amin N, van der Lee SJ, Yang Q, Himali JJ, Maillard P, Beiser AS, DeCarli C, Karama S, Lewis L, Harris M, Bastin ME, Deary IJ, Veronica Witte A, Beyer F, Loeffler M, Mather KA, Schofield PR, Thalamuthu A, Kwok JB, Wright MJ, Ames D, Trollor J, Jiang J, Brodaty H, Wen W, Vernooij MW, Hofman A, Uitterlinden AG, Niessen WJ, Wittfeld K, Bülow R, Völker U, Pausova Z, Bruce Pike G, Maingault S, Crivello F, Tzourio C, Amouyel P, Mazoyer B, Neale MC, Franz CE, Lyons MJ, Panizzon MS, Andreassen OA, Dale AM, Logue M, Grasby KL, Jahanshad N, Painter JN, Colodro-Conde L, Bralten J, Hibar DP, Lind PA, Pizzagalli F, Stein JL, Thompson PM, Medland SE, Sachdev PS, Kremen WS, Wardlaw JM, Villringer A, van Duijn CM, Grabe HJ, Longstreth WT, Fornage M, Paus T, Debette S, Ikram MA, Schmidt H, Schmidt R, Seshadri S. Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults. Nat Commun 2020; 11:4796. [PMID: 32963231 PMCID: PMC7508833 DOI: 10.1038/s41467-020-18367-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 08/20/2020] [Indexed: 12/22/2022] Open
Abstract
Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging. Cortex morphology varies with age, cognitive function, and in neurological and psychiatric diseases. Here the authors report 160 genome-wide significant associations with thickness, surface area and volume of the total cortex and 34 cortical regions from a GWAS meta-analysis in 22,824 adults.
Collapse
Affiliation(s)
- Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria.,Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | - Gennady V Roshchupkin
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Hieab H H Adams
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Maria J Knol
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Shuo Li
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Habil Zare
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, USA.,Department of Cell Systems & Anatomy, The University of Texas Health Science Center, San Antonio, TX, USA
| | - Shahzad Ahmad
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | | | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA.,QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Michelle Luciano
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Aniket Mishra
- University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, Bordeaux, France
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Rui Xia
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xueqiu Jian
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Thomas H Mosley
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Yasaman Saba
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria
| | - Lukas Pirpamer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Stephan Seiler
- Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology, University of California-Davis, Davis, CA, USA.,Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA, USA
| | - James T Becker
- Departments of Psychiatry, Neurology, and Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
| | - Oscar L Lopez
- Departments of Psychiatry, Neurology, and Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Najaf Amin
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jayandra J Himali
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Pauline Maillard
- Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology, University of California-Davis, Davis, CA, USA.,Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA, USA
| | - Alexa S Beiser
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.,Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Charles DeCarli
- Imaging of Dementia and Aging (IDeA) Laboratory, Department of Neurology, University of California-Davis, Davis, CA, USA.,Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA, USA
| | - Sherif Karama
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Lindsay Lewis
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Mat Harris
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Brain Research Imaging Centre, University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Brain Research Imaging Centre, University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - A Veronica Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Faculty of Medicine, CRC 1052 Obesity Mechanisms, University of Leipzig, Leipzig, Germany
| | - Frauke Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Faculty of Medicine, CRC 1052 Obesity Mechanisms, University of Leipzig, Leipzig, Germany
| | - Markus Loeffler
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.,LIFE Research Center for Civilization Diseases, University of Leipzig, Leipzig, Germany
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia.,Neuroscience Research Australia, Sydney, Australia
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, Australia.,School of Medical Sciences, University of New South Wales, Sydney, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - John B Kwok
- School of Medical Sciences, University of New South Wales, Sydney, Australia.,Brain and Mind Centre - The University of Sydney, Camperdown, NSW, Australia
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia.,Centre for Advanced Imaging, The University of Queensland, St Lucia, QLD, Australia
| | - David Ames
- National Ageing Research Institute, Royal Melbourne Hospital, Parkvill, VIC, Australia.,Academic Unit for Psychiatry of Old Age, University of Melbourne, St George's Hospital, Kew, VIC, Australia
| | - Julian Trollor
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia.,Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia.,Dementia Centre for Research Collaboration, University of New South Wales, Sydney, NSW, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Albert Hofman
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Katharina Wittfeld
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany.,Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Robin Bülow
- Institute for Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Zdenka Pausova
- Hospital for Sick Children, Toronto, ON, Canada.,Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - G Bruce Pike
- Departments of Radiology and Clinial Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Sophie Maingault
- Institut des Maladies Neurodégénratives UMR5293, CEA, CNRS, University of Bordeaux, Bordeaux, France
| | - Fabrice Crivello
- Institut des Maladies Neurodégénratives UMR5293, CEA, CNRS, University of Bordeaux, Bordeaux, France
| | - Christophe Tzourio
- University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, Bordeaux, France.,Pole de santé publique, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France
| | - Philippe Amouyel
- Centre Hospitalier Universitaire de Bordeaux, France; Inserm U1167, Lille, France.,Department of Epidemiology and Public Health, Pasteur Institute of Lille, Lille, France.,Department of Public Health, Lille University Hospital, Lille, France
| | - Bernard Mazoyer
- Institut des Maladies Neurodégénratives UMR5293, CEA, CNRS, University of Bordeaux, Bordeaux, France
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Anders M Dale
- Departments of Radiology and Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Mark Logue
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.,National Center for PTSD at Boston VA Healthcare System, Boston, MA, USA.,Department of Psychiatry and Department of Medicine-Biomedical Genetics Section, Boston University School of Medicine, Boston, MA, USA
| | - Katrina L Grasby
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Jodie N Painter
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Lucía Colodro-Conde
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Janita Bralten
- Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Derrek P Hibar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.,Neuroscience Biomarkers, Janssen Research and Development, LLC, San Diego, CA, USA
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Jason L Stein
- Department of Genetics & UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | | | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia.,Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW, Australia
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Joanna M Wardlaw
- Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.,Brain Research Imaging Centre, University of Edinburgh, Edinburgh, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Department of Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Day Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Hans J Grabe
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany.,Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - William T Longstreth
- Departments of Neurology and Epidemiology, University of Washington, Seattle, WA, USA
| | - Myriam Fornage
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Tomas Paus
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada.,Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Stephanie Debette
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA.,University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, Bordeaux, France.,CHU de Bordeaux, Department of Neurology, F-33000, Bordeaux, France
| | - M Arfan Ikram
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Helena Schmidt
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria.
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, USA. .,Department of Neurology, Boston University School of Medicine, Boston, MA, USA.
| |
Collapse
|
33
|
Wu Q, Chen Y, Gu Y, Fang S, Li W, Wang Q, Fang J, Cai C. Systems pharmacology-based approach to investigate the mechanisms of Danggui-Shaoyao-san prescription for treatment of Alzheimer's disease. BMC Complement Med Ther 2020; 20:282. [PMID: 32948180 PMCID: PMC7501700 DOI: 10.1186/s12906-020-03066-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 08/30/2020] [Indexed: 02/06/2023] Open
Abstract
Background Alzheimer’s disease (AD) is the most common cause of dementia in the elderly, characterized by a progressive and irreversible loss of memory and cognitive abilities. Currently, the prevention and treatment of AD still remains a huge challenge. As a traditional Chinese medicine (TCM) prescription, Danggui-Shaoyao-san decoction (DSS) has been demonstrated to be effective for alleviating AD symptoms in animal experiments and clinical applications. However, due to the complex components and biological actions, its underlying molecular mechanism and effective substances are not yet fully elucidated. Methods In this study, we firstly systematically reviewed and summarized the molecular effects of DSS against AD based on current literatures of in vivo studies. Furthermore, an integrated systems pharmacology framework was proposed to explore the novel anti-AD mechanisms of DSS and identify the main active components. We further developed a network-based predictive model for identifying the active anti-AD components of DSS by mapping the high-quality AD disease genes into the global drug-target network. Results We constructed a global drug-target network of DSS consisting 937 unique compounds and 490 targets by incorporating experimental and computationally predicted drug–target interactions (DTIs). Multi-level systems pharmacology analyses revealed that DSS may regulate multiple biological pathways related to AD pathogenesis, such as the oxidative stress and inflammatory reaction processes. We further conducted a network-based statistical model, drug-likeness analysis, human intestinal absorption (HIA) and blood-brain barrier (BBB) penetration prediction to uncover the key ani-AD ingredients in DSS. Finally, we highlighted 9 key ingredients and validated their synergistic role against AD through a subnetwork. Conclusion Overall, this study proposed an integrative systems pharmacology approach to disclose the therapeutic mechanisms of DSS against AD, which also provides novel in silico paradigm for investigating the effective substances of complex TCM prescription.
Collapse
Affiliation(s)
- Qihui Wu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, 570000, China.,Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510000, China
| | - Yunbo Chen
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510000, China
| | - Yong Gu
- Clinical Research Center, Hainan Provincial Hospital of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Haikou, 570000, China
| | - Shuhuan Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510000, China
| | - Weirong Li
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510000, China
| | - Qi Wang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510000, China.
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510000, China.
| | - Chuipu Cai
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510000, China. .,School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, 510000, China.
| |
Collapse
|
34
|
Walker RM, Bermingham ML, Vaher K, Morris SW, Clarke T, Bretherick AD, Zeng Y, Amador C, Rawlik K, Pandya K, Hayward C, Campbell A, Porteous DJ, McIntosh AM, Marioni RE, Evans KL. Epigenome-wide analyses identify DNA methylation signatures of dementia risk. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2020; 12:e12078. [PMID: 32789163 PMCID: PMC7416667 DOI: 10.1002/dad2.12078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 07/08/2020] [Accepted: 07/13/2020] [Indexed: 01/04/2023]
Abstract
INTRODUCTION Dementia pathogenesis begins years before clinical symptom onset, necessitating the understanding of premorbid risk mechanisms. Here we investigated potential pathogenic mechanisms by assessing DNA methylation associations with dementia risk factors in Alzheimer's disease (AD)-free participants. METHODS Associations between dementia risk measures (family history, AD genetic risk score [GRS], and dementia risk scores [combining lifestyle, demographic, and genetic factors]) and whole-blood DNA methylation were assessed in discovery and replication samples (n = ~400 to ~5000) from Generation Scotland. RESULTS AD genetic risk and two dementia risk scores were associated with differential methylation. The GRS associated predominantly with methylation differences in cis but also identified a genomic region implicated in Parkinson disease. Loci associated with dementia risk scores were enriched for those previously associated with body mass index and alcohol consumption. DISCUSSION Dementia risk measures show widespread association with blood-based methylation, generating several hypotheses for assessment by future studies.
Collapse
Affiliation(s)
- Rosie M. Walker
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Mairead L. Bermingham
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Kadi Vaher
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Stewart W. Morris
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Toni‐Kim Clarke
- Division of PsychiatryUniversity of EdinburghRoyal Edinburgh HospitalEdinburghUK
| | - Andrew D. Bretherick
- MRC Human Genetics UnitInstitute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Yanni Zeng
- Faculty of Forensic MedicineZhongshan School of MedicineSun Yat‐Sen University74 Zhongshan 2nd RoadGuangzhou510080China
| | - Carmen Amador
- MRC Human Genetics UnitInstitute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Konrad Rawlik
- Division of Genetics and GenomicsThe Roslin Institute and Royal (Dick) School of Veterinary StudiesUniversity of Edinburgh, Easter Bush, RoslinEdinburghUK
| | - Kalyani Pandya
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Caroline Hayward
- MRC Human Genetics UnitInstitute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Archie Campbell
- Generation ScotlandCentre for Genomic and Experimental MedicineInstitute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - David J. Porteous
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
- Generation ScotlandCentre for Genomic and Experimental MedicineInstitute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Andrew M. McIntosh
- MRC Centre for Reproductive HealthThe Queen's Medical Research InstituteEdinburgh Bioquarter47 Little France CrescentEdinburghEH16 4TJUK
| | - Riccardo E. Marioni
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Kathryn L. Evans
- Centre for Genomic and Experimental MedicineInstitute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| |
Collapse
|
35
|
Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis. Neuron 2020; 107:821-835.e12. [PMID: 32603655 DOI: 10.1016/j.neuron.2020.06.010] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 04/23/2020] [Accepted: 06/05/2020] [Indexed: 12/17/2022]
Abstract
A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerability, a key characteristic of the disease. Here, we present a framework integrating high-quality neuron-type-specific molecular profiles across the lifetime of the healthy mouse, which we generated using bacTRAP, with postmortem human functional genomics and quantitative genetics data. We demonstrate human-mouse conservation of cellular taxonomy at the molecular level for neurons vulnerable and resistant in AD, identify specific genes and pathways associated with AD neuropathology, and pinpoint a specific functional gene module underlying selective vulnerability, enriched in processes associated with axonal remodeling, and affected by amyloid accumulation and aging. We have made all cell-type-specific profiles and functional networks available at http://alz.princeton.edu. Overall, our study provides a molecular framework for understanding the complex interplay between Aβ, aging, and neurodegeneration within the most vulnerable neurons in AD.
Collapse
|
36
|
Henríquez G, Mendez L, Varela-Ramirez A, Guerrero E, Narayan M. Neuroprotective Effect of Brazilin on Amyloid β (25-35)-Induced Pathology in a Human Neuroblastoma Model. ACS OMEGA 2020; 5:13785-13792. [PMID: 32566844 PMCID: PMC7301549 DOI: 10.1021/acsomega.0c00396] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 05/18/2020] [Indexed: 05/06/2023]
Abstract
Until the recent past, the sole exemplar of proteins as infectious agents leading to neurodegenerative disorders remained the prion protein. Since then, the self-seeding mechanism characteristic of the prion protein has also been attributed to other neurodegenerative-disease-associated proteins, including amyloid-β (Aβ), tau, and α-synuclein (α-Syn). In model cell line studies, truncated Aβ, viz. amyloid beta (25-35), has been found to influence cellular homeostasis through its interactions with, and via, the disruption of key housekeeping machinery. Here, we demonstrate that the incubation of human neuroblastoma (SH-SY5Y) cell line with Brazilin ((6aS,11bR)-7,11b-dihydro-6H-indeno[2,1-c]chromene-3,6a,9,10-tetrol) prior to Aβ (25-35)-insult protected the cells from oxidative stress and apoptotic cell death. Furthermore, Brazilin mitigated Aβ-induced alterations in protein disulfide isomerase (PDI) and α-synuclein status, both of which are important biomarkers that report on Parkinson's pathogenesis. The results obtained in this study suggest that the tetrol is neuroprotective and helps resist Aβ-induced cross-pathology and amyloidogenic onset.
Collapse
Affiliation(s)
- Gabriela Henríquez
- Department
of Environmental Science & Engineering, The University of Texas at El Paso (UTEP), El Paso, Texas 79968, United States
| | - Lois Mendez
- Department of Chemistry and
Biochemistry, The University of Texas at
El Paso (UTEP), El Paso, Texas 79968, United
States
| | - Armando Varela-Ramirez
- Department
of Biological Sciences, Bioscience Research Building, Border Biomedical
Research Center, the Cellular Characterization and Biorepository Core
Facility, The University of Texas at El
Paso (UTEP), El Paso, Texas 79968, United
States
| | - Erick Guerrero
- Department of Chemistry and
Biochemistry, The University of Texas at
El Paso (UTEP), El Paso, Texas 79968, United
States
| | - Mahesh Narayan
- Department of Chemistry and
Biochemistry, The University of Texas at
El Paso (UTEP), El Paso, Texas 79968, United
States
| |
Collapse
|
37
|
Lu K, Yang K, Niyongabo E, Shu Z, Wang J, Chang K, Zou Q, Jiang J, Jia C, Liu B, Zhou X. Integrated network analysis of symptom clusters across disease conditions. J Biomed Inform 2020; 107:103482. [PMID: 32535270 DOI: 10.1016/j.jbi.2020.103482] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 05/18/2020] [Accepted: 06/08/2020] [Indexed: 10/24/2022]
Abstract
Identifying the symptom clusters (two or more related symptoms) with shared underlying molecular mechanisms has been a vital analysis task to promote the symptom science and precision health. Related studies have applied the clustering algorithms (e.g. k-means, latent class model) to detect the symptom clusters mostly from various kinds of clinical data. In addition, they focused on identifying the symptom clusters (SCs) for a specific disease, which also mainly concerned with the clinical regularities for symptom management. Here, we utilized a network-based clustering algorithm (i.e., BigCLAM) to obtain 208 typical SCs across disease conditions on a large-scale symptom network derived from integrated high-quality disease-symptom associations. Furthermore, we evaluated the underlying shared molecular mechanisms for SCs, i.e., shared genes, protein-protein interaction (PPI) and gene functional annotations using integrated networks and similarity measures. We found that the symptoms in the same SCs tend to share a higher degree of genes, PPIs and have higher functional homogeneities. In addition, we found that most SCs have related symptoms with shared underlying molecular mechanisms (e.g. enriched pathways) across different disease conditions. Our work demonstrated that the integrated network analysis method could be used for identifying robust SCs and investigate the molecular mechanisms of these SCs, which would be valuable for symptom science and precision health.
Collapse
Affiliation(s)
- Kezhi Lu
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Kuo Yang
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Edouard Niyongabo
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Zixin Shu
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Jingjing Wang
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Kai Chang
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Qunsheng Zou
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Jiyue Jiang
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Caiyan Jia
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Baoyan Liu
- Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| | - Xuezhong Zhou
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China; Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| |
Collapse
|
38
|
Bellou E, Stevenson-Hoare J, Escott-Price V. Polygenic risk and pleiotropy in neurodegenerative diseases. Neurobiol Dis 2020; 142:104953. [PMID: 32445791 PMCID: PMC7378564 DOI: 10.1016/j.nbd.2020.104953] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 05/12/2020] [Accepted: 05/18/2020] [Indexed: 12/12/2022] Open
Abstract
In this paper we explore the phenomenon of pleiotropy in neurodegenerative diseases, focusing on Alzheimer's disease (AD). We summarize the various techniques developed to investigate pleiotropy among traits, elaborating in the polygenic risk scores (PRS) analysis. PRS was designed to assess a cumulative effect of a large number of SNPs for association with a disease and, later for disease risk prediction. Since genetic predictions rely on heritability, we discuss SNP-based heritability from genome-wide association studies and its contribution to the prediction accuracy of PRS. We review work examining pleiotropy in neurodegenerative diseases and related phenotypes and biomarkers. We conclude that the exploitation of pleiotropy may aid in the identification of novel genes and provide further insights in the disease mechanisms, and along with PRS analysis, may be advantageous for precision medicine.
Collapse
|
39
|
Guerreiro R, Gibbons E, Tábuas-Pereira M, Kun-Rodrigues C, Santo GC, Bras J. Genetic architecture of common non-Alzheimer's disease dementias. Neurobiol Dis 2020; 142:104946. [PMID: 32439597 PMCID: PMC8207829 DOI: 10.1016/j.nbd.2020.104946] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 05/04/2020] [Accepted: 05/13/2020] [Indexed: 02/07/2023] Open
Abstract
Frontotemporal dementia (FTD), dementia with Lewy bodies (DLB) and vascular dementia (VaD) are the most common forms of dementia after Alzheimer’s disease (AD). The heterogeneity of these disorders and/or the clinical overlap with other diseases hinder the study of their genetic components. Even though Mendelian dementias are rare, the study of these forms of disease can have a significant impact in the lives of patients and families and have successfully brought to the fore many of the genes currently known to be involved in FTD and VaD, starting to give us a glimpse of the molecular mechanisms underlying these phenotypes. More recently, genome-wide association studies have also pointed to disease risk-associated loci. This has been particularly important for DLB where familial forms of disease are very rarely described. In this review we systematically describe the Mendelian and risk genes involved in these non-AD dementias in an effort to contribute to a better understanding of their genetic architecture, find differences and commonalities between different dementia phenotypes, and uncover areas that would benefit from more intense research endeavors.
Collapse
Affiliation(s)
- Rita Guerreiro
- Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA; Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI, USA.
| | - Elizabeth Gibbons
- Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Miguel Tábuas-Pereira
- Department of Neurology, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal; Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Celia Kun-Rodrigues
- Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | - Gustavo C Santo
- Department of Neurology, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal; Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Jose Bras
- Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA; Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI, USA
| |
Collapse
|
40
|
Cochran JN, Geier EG, Bonham LW, Newberry JS, Amaral MD, Thompson ML, Lasseigne BN, Karydas AM, Roberson ED, Cooper GM, Rabinovici GD, Miller BL, Myers RM, Yokoyama JS. Non-coding and Loss-of-Function Coding Variants in TET2 are Associated with Multiple Neurodegenerative Diseases. Am J Hum Genet 2020; 106:632-645. [PMID: 32330418 PMCID: PMC7212268 DOI: 10.1016/j.ajhg.2020.03.010] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 03/20/2020] [Indexed: 12/13/2022] Open
Abstract
We conducted genome sequencing to search for rare variation contributing to early-onset Alzheimer's disease (EOAD) and frontotemporal dementia (FTD). Discovery analysis was conducted on 435 cases and 671 controls of European ancestry. Burden testing for rare variation associated with disease was conducted using filters based on variant rarity (less than one in 10,000 or private), computational prediction of deleteriousness (CADD) (10 or 15 thresholds), and molecular function (protein loss-of-function [LoF] only, coding alteration only, or coding plus non-coding variants in experimentally predicted regulatory regions). Replication analysis was conducted on 16,434 independent cases and 15,587 independent controls. Rare variants in TET2 were enriched in the discovery combined EOAD and FTD cohort (p = 4.6 × 10-8, genome-wide corrected p = 0.0026). Most of these variants were canonical LoF or non-coding in predicted regulatory regions. This enrichment replicated across several cohorts of Alzheimer's disease (AD) and FTD (replication only p = 0.0029). The combined analysis odds ratio was 2.3 (95% confidence interval [CI] 1.6-3.4) for AD and FTD. The odds ratio for qualifying non-coding variants considered independently from coding variants was 3.7 (95% CI 1.7-9.4). For LoF variants, the combined odds ratio (for AD, FTD, and amyotrophic lateral sclerosis, which shares clinicopathological overlap with FTD) was 3.1 (95% CI 1.9-5.2). TET2 catalyzes DNA demethylation. Given well-defined changes in DNA methylation that occur during aging, rare variation in TET2 may confer risk for neurodegeneration by altering the homeostasis of key aging-related processes. Additionally, our study emphasizes the relevance of non-coding variation in genetic studies of complex disease.
Collapse
Affiliation(s)
- J Nicholas Cochran
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, United States
| | - Ethan G Geier
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, United States
| | - Luke W Bonham
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, United States
| | - J Scott Newberry
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, United States
| | - Michelle D Amaral
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, United States
| | - Michelle L Thompson
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, United States
| | - Brittany N Lasseigne
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, United States; Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL 35294, United States
| | - Anna M Karydas
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, United States
| | - Erik D Roberson
- Center for Neurodegeneration and Experimental Therapeutics, Alzheimer's Disease Center, Departments of Neurology and Neurobiology, University of Alabama at Birmingham, Birmingham, AL 35294, United States
| | - Gregory M Cooper
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, United States
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, United States; Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94158, United States
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, United States
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, United States
| | - Jennifer S Yokoyama
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, United States; Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94158, United States.
| |
Collapse
|
41
|
Beheshti I, Mishra S, Sone D, Khanna P, Matsuda H. T1-weighted MRI-driven Brain Age Estimation in Alzheimer's Disease and Parkinson's Disease. Aging Dis 2020; 11:618-628. [PMID: 32489706 PMCID: PMC7220281 DOI: 10.14336/ad.2019.0617] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 06/17/2019] [Indexed: 11/24/2022] Open
Abstract
Neuroimaging-driven brain age estimation has introduced a robust (reliable and heritable) biomarker for detecting and monitoring neurodegenerative diseases. Here, we computed and compared brain age in Alzheimer's disease (AD) and Parkinson's disease (PD) patients using an advanced machine learning procedure involving T1-weighted MRI scans and gray matter (GM) and white matter (WM) models. Brain age estimation frameworks were built using 839 healthy individuals and then the brain estimated age difference (Brain-EAD: chronological age subtracted from brain estimated age) was assessed in a large sample of PD patients (n = 160) and AD patients (n = 129), respectively. The mean Brain-EADs for GM were +9.29 ± 6.43 years for AD patients versus +1.50 ± 6.03 years for PD patients. For WM, the mean Brain-EADs were +8.85 ± 6.62 years for AD patients versus +2.47 ± 5.85 years for PD patients. In addition, PD patients showed a significantly higher WM Brain-EAD than GM Brain-EAD. In a direct comparison between PD and AD patients, we observed significantly higher Brain-EAD values in AD patients for both GM and WM. A comparison of the Brain-EAD between PD and AD patients revealed that AD patients may have a significantly "older-appearing" brain than PD patients.
Collapse
Affiliation(s)
- Iman Beheshti
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan.
| | - Shiwangi Mishra
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India.
| | - Daichi Sone
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan.
| | - Pritee Khanna
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India.
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan.
| |
Collapse
|
42
|
Das S, Zhang Z, Ang LC. Clinicopathological overlap of neurodegenerative diseases: A comprehensive review. J Clin Neurosci 2020; 78:30-33. [PMID: 32354648 DOI: 10.1016/j.jocn.2020.04.088] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 04/15/2020] [Indexed: 12/13/2022]
Abstract
Clinical and neuropathological overlap of two or more neurodegenerative diseases (ND) is not an uncommon occurrence yet is still underdiagnosed in clinical neurological and neuropathological. The authors present a clinicopathological overview of the current understanding of overlapping ND's with the hope that this review will encourage further studies that are required to investigate the effect of such overlaps on clinical presentations and how often clinical presentations raise the suspicion of multiple ND's. The authors suggest that as more patients with overlapping ND's come to light, traditional classification system of ND's may need to be modified.
Collapse
Affiliation(s)
- Sumit Das
- Department of Laboratory Medicine and Pathology (Neuropathology), University of Alberta Hospital, Edmonton, Canada; Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada.
| | - Zach Zhang
- Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Lee Cyn Ang
- Department of Pathology and Laboratory Medicine, Western University, London, Canada
| |
Collapse
|
43
|
Scheiblich H, Trombly M, Ramirez A, Heneka MT. Neuroimmune Connections in Aging and Neurodegenerative Diseases. Trends Immunol 2020; 41:300-312. [DOI: 10.1016/j.it.2020.02.002] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 02/07/2020] [Accepted: 02/07/2020] [Indexed: 11/26/2022]
|
44
|
Parkinson's disease treatment: past, present, and future. J Neural Transm (Vienna) 2020; 127:785-791. [PMID: 32172471 DOI: 10.1007/s00702-020-02167-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 03/03/2020] [Indexed: 12/15/2022]
Abstract
The substantial contributions of Dr. Gerald Stern to past and current treatments for Parkinson's disease patients are reviewed, which form the foundation for an evaluation of future options to control symptoms and halt progression of the disease. These opportunities will depend on a greater understanding of the relative contributions of the environment, genetic and epigenetic influences to disease onset, and promise to emerge as strategies for improving mitochondrial function, halting accumulation of synuclein and neuromelanin, in addition to refinement of stem cell and gene therapies. Such advances will be achieved through deployment of improved models for the disease.
Collapse
|
45
|
Montesanto A, Crocco P, Dato S, Geracitano S, Frangipane F, Colao R, Maletta R, Passarino G, Bruni AC, Rose G. Uncoupling protein 4 ( UCP4) gene variability in neurodegenerative disorders: further evidence of association in Frontotemporal dementia. Aging (Albany NY) 2019; 10:3283-3293. [PMID: 30425186 PMCID: PMC6286830 DOI: 10.18632/aging.101632] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 10/28/2018] [Indexed: 02/05/2023]
Abstract
Ongoing research suggests that mitochondrial dysfunction is a common hallmark in neurodegenerative diseases, pointing to mitochondrial uncoupling process as a critical player. We recently reported that rs9472817-C/G, an intronic variant of neuronal mitochondrial uncoupling protein-4 (UCP4/SLC25A27) gene affects the risk of late onset Alzheimer's disease (LOAD), and that the variant's effect is strongly dependent on APOE-ε4 status. Here, we extended our analysis to a cohort of 751 subjects including late-onset familial and sporadic cases of frontotemporal dementia (FTD; 213), Parkinson disease (PD;96), and 442 healthy controls. In all subgroups, carriers of APOE-ε4 allele were at higher risk of disease. Regarding the rs9472817, no association was detected in familial FTD and both subgroups of PD patients. In sporadic FTD, as in LOAD, we found that the C allele increased the risk of disease of about 1.51-fold in a dose-dependent manner (p=0.013) independently from that conferred by APOE-ε4. Expression quantitative trait loci (eQTL) data of different brain regions suggest that rs9472817 likely exerts its effect by a cis-regulatory mechanism involving modulation of UCP4. If validated, the involvement of UCP4 in both FTD and LOAD might indicate interesting shared etiological factors which might give future therapeutic clues.
Collapse
Affiliation(s)
- Alberto Montesanto
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy
| | - Paolina Crocco
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy
| | - Serena Dato
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy
| | - Silvana Geracitano
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy
| | | | - Rosanna Colao
- Regional Neurogenetic Centre, ASP CZ, Lamezia Terme, Italy
| | | | - Giuseppe Passarino
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy
| | - Amalia C Bruni
- Regional Neurogenetic Centre, ASP CZ, Lamezia Terme, Italy
| | - Giuseppina Rose
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy
| |
Collapse
|
46
|
The puzzle of preserved cognition in the oldest old. Neurol Sci 2019; 41:441-447. [PMID: 31713754 DOI: 10.1007/s10072-019-04111-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Accepted: 10/15/2019] [Indexed: 02/07/2023]
Abstract
Although epidemiological studies predict an exponential increase in the prevalence of dementia with age, recent studies have demonstrated that the oldest old are actually less frequently affected by dementia than the younger elderly. To explain this, I suggest a parallel between brain ageing and Alzheimer's disease (AD) and assume that theories concerning the brain's vulnerability to AD and its individual variability may also explain why some of the oldest old remain cognitively efficient. Some theories argue that AD is due to the continuing presence of the immature neurones vulnerable to amyloid beta protein (Aß) that are normally involved in brain development and then removed as a result of cell selection by the proteins associated with both brain development and AD. If a dysfunction in cell selection allows these immature neurones to survive, they degenerate early as a result of the neurotoxic action of Aß accumulation, which their mature counterparts can withstand. Consequently, age at the time of onset of AD and its clinical presentations depend on the number and location of such immature cells. I speculate that the same mechanism is responsible for the variability of normal brain ageing: the oldest old with well-preserved cognitive function are people genetically programmed for extreme ageing who have benefited from better cell selection during prenatal and neonatal life and therefore have fewer surviving neurones vulnerable to amyloid-promoted degeneration, whereas the process of early life cell selection was less successful in the oldest old who develop dementia.
Collapse
|
47
|
Rahul DR, Joseph Ponniah R. Language impairment in primary progressive aphasia and other neurodegenerative diseases. J Genet 2019; 98:95. [PMID: 31767822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Primary progressive aphasia (PPA) is a progressive neurodegenerative disease that disrupts the language capacity of an individual by selectively affecting the language network of brain. Although aphasic literature is replete with reports of brain damage responsible for various types of PPA, it does not provide a comprehensive understanding of whether PPA is an independent pathological condition or an atypical syndrome of neurodegenerative diseases (NDD). To address this ambiguity, we provide a detailed description of PPA, its variants and their brain anatomy. Subsequently, we unravel the relationship between PPA and NDDs like Alzheimer's, Parkinson's and Dyslexia. To substantiate the relationship further, we also provide a brief account of their genetic aetiology. In the final section, we offer an exhaustive approach towards the treatment of PPA by combining the existing language the rapies with clinical and pharmacological interventions.
Collapse
Affiliation(s)
- D R Rahul
- National Institute of Technology, Tiruchirappalli 620 015, India.
| | | |
Collapse
|
48
|
Karch CM, Wen N, Fan CC, Yokoyama JS, Kouri N, Ross OA, Höglinger G, Müller U, Ferrari R, Hardy J, Schellenberg GD, Sleiman PM, Momeni P, Hess CP, Miller BL, Sharma M, Van Deerlin V, Smeland OB, Andreassen OA, Dale AM, Desikan RS. Selective Genetic Overlap Between Amyotrophic Lateral Sclerosis and Diseases of the Frontotemporal Dementia Spectrum. JAMA Neurol 2019; 75:860-875. [PMID: 29630712 DOI: 10.1001/jamaneurol.2018.0372] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Importance Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder characterized by loss of upper and lower motor neurons. Although novel ALS genetic variants have been identified, the shared genetic risk between ALS and other neurodegenerative disorders remains poorly understood. Objectives To examine whether there are common genetic variants that determine the risk for ALS and other neurodegenerative diseases and to identify their functional pathways. Design, Setting, and Participants In this study conducted from December 1, 2016, to August 1, 2017, the genetic overlap between ALS, sporadic frontotemporal dementia (FTD), FTD with TDP-43 inclusions, Parkinson disease (PD), Alzheimer disease (AD), corticobasal degeneration (CBD), and progressive supranuclear palsy (PSP) were systematically investigated in 124 876 cases and controls. No participants were excluded from this study. Diagnoses were established using consensus criteria. Main Outcomes and Measures The primary outcomes were a list of novel loci and their functional pathways in ALS, FTD, PSP, and ALS mouse models. Results Among 124 876 cases and controls, genome-wide conjunction analyses of ALS, FTD, PD, AD, CBD, and PSP revealed significant genetic overlap between ALS and FTD at known ALS loci: rs13302855 and rs3849942 (nearest gene, C9orf72; P = .03 for rs13302855 and P = .005 for rs3849942) and rs4239633 (nearest gene, UNC13A; P = .03). Significant genetic overlap was also found between ALS and PSP at rs7224296, which tags the MAPT H1 haplotype (nearest gene, NSF; P = .045). Shared risk genes were enriched for pathways involving neuronal function and development. At a conditional FDR P < .05, 22 novel ALS polymorphisms were found, including rs538622 (nearest gene, ERGIC1; P = .03 for ALS and FTD), which modifies BNIP1 expression in human brains (35 of 137 females; mean age, 59 years; P = .001). BNIP1 expression was significantly reduced in spinal cord motor neurons from patients with ALS (4 controls: mean age, 60.5 years, mean [SE] value, 3984 [760.8] arbitrary units [AU]; 7 patients with ALS: mean age, 56 years, mean [SE] value, 1999 [274.1] AU; P = .02), in an ALS mouse model (mean [SE] value, 13.75 [0.09] AU for 2 SOD1 WT mice and 11.45 [0.03] AU for 2 SOD1 G93A mice; P = .002) and in brains of patients with PSP (80 controls: 39 females; mean age, 82 years, mean [SE] value, 6.8 [0.2] AU; 84 patients with PSP: 33 females, mean age 74 years, mean [SE] value, 6.8 [0.1] AU; β = -0.19; P = .009) or FTD (11 controls: 4 females; mean age, 67 years; mean [SE] value, 6.74 [0.05] AU; 17 patients with FTD: 10 females; mean age, 69 years; mean [SE] value, 6.53 [0.04] AU; P = .005). Conclusions and Relevance This study found novel genetic overlap between ALS and diseases of the FTD spectrum, that the MAPT H1 haplotype confers risk for ALS, and identified the mitophagy-associated, proapoptotic protein BNIP1 as an ALS risk gene. Together, these findings suggest that sporadic ALS may represent a selectively pleiotropic, polygenic disorder.
Collapse
Affiliation(s)
- Celeste M Karch
- Department of Psychiatry, Washington University in St Louis, St Louis, Missouri
| | - Natalie Wen
- Department of Psychiatry, Washington University in St Louis, St Louis, Missouri
| | - Chun C Fan
- Department of Cognitive Sciences, University of California, San Diego, La Jolla
| | - Jennifer S Yokoyama
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
| | - Naomi Kouri
- Department of Neuroscience, Mayo Clinic College of Medicine, Jacksonville, Florida
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic College of Medicine, Jacksonville, Florida
| | - Gunter Höglinger
- Department of Translational Neurodegeneration, German Center for Neurodegenerative Diseases, Munich, Germany.,Department of Neurology, Technical University of Munich, Munich Cluster for Systems Neurology SyNergy, Munich, Germany
| | - Ulrich Müller
- Institut for Humangenetik, Justus-Liebig-Universität, Giessen, Germany
| | - Raffaele Ferrari
- Department of Molecular Neuroscience, Institute of Neurology, University College London, London, United Kingdom
| | - John Hardy
- Department of Molecular Neuroscience, Institute of Neurology, University College London, London, United Kingdom
| | - Gerard D Schellenberg
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Patrick M Sleiman
- Center for Applied Genomics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,Division of Human Genetics, Abramson Research Center, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Parastoo Momeni
- Laboratory of Neurogenetics, Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock
| | - Christopher P Hess
- Neuroradiology Section, Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - Bruce L Miller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
| | - Manu Sharma
- Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Tübingen, Germany
| | - Vivianna Van Deerlin
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Olav B Smeland
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.,Department of Neurosciences, University of California, San Diego, La Jolla
| | - Anders M Dale
- Department of Cognitive Sciences, University of California, San Diego, La Jolla.,Department of Neurosciences and Radiology, University of California, San Diego, La Jolla
| | - Rahul S Desikan
- Neuroradiology Section, Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | | |
Collapse
|
49
|
Rahul DR, Joseph Ponniah R. Language impairment in primary progressive aphasia and other neurodegenerative diseases. J Genet 2019. [DOI: 10.1007/s12041-019-1139-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
|
50
|
Karki R, Kodamullil AT, Hoyt CT, Hofmann-Apitius M. Quantifying mechanisms in neurodegenerative diseases (NDDs) using candidate mechanism perturbation amplitude (CMPA) algorithm. BMC Bioinformatics 2019; 20:494. [PMID: 31604427 PMCID: PMC6788110 DOI: 10.1186/s12859-019-3101-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 09/16/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Literature derived knowledge assemblies have been used as an effective way of representing biological phenomenon and understanding disease etiology in systems biology. These include canonical pathway databases such as KEGG, Reactome and WikiPathways and disease specific network inventories such as causal biological networks database, PD map and NeuroMMSig. The represented knowledge in these resources delineates qualitative information focusing mainly on the causal relationships between biological entities. Genes, the major constituents of knowledge representations, tend to express differentially in different conditions such as cell types, brain regions and disease stages. A classical approach of interpreting a knowledge assembly is to explore gene expression patterns of the individual genes. However, an approach that enables quantification of the overall impact of differentially expressed genes in the corresponding network is still lacking. RESULTS Using the concept of heat diffusion, we have devised an algorithm that is able to calculate the magnitude of regulation of a biological network using expression datasets. We have demonstrated that molecular mechanisms specific to Alzheimer (AD) and Parkinson Disease (PD) regulate with different intensities across spatial and temporal resolutions. Our approach depicts that the mitochondrial dysfunction in PD is severe in cortex and advanced stages of PD patients. Similarly, we have shown that the intensity of aggregation of neurofibrillary tangles (NFTs) in AD increases as the disease progresses. This finding is in concordance with previous studies that explain the burden of NFTs in stages of AD. CONCLUSIONS This study is one of the first attempts that enable quantification of mechanisms represented as biological networks. We have been able to quantify the magnitude of regulation of a biological network and illustrate that the magnitudes are different across spatial and temporal resolution.
Collapse
Affiliation(s)
- Reagon Karki
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754, Sankt Augustin, Germany
- Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Endenicher Allee 19a, 53115, Bonn, Germany
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754, Sankt Augustin, Germany
- Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Endenicher Allee 19a, 53115, Bonn, Germany
| | - Charles Tapley Hoyt
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754, Sankt Augustin, Germany
- Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Endenicher Allee 19a, 53115, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53754, Sankt Augustin, Germany.
- Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Endenicher Allee 19a, 53115, Bonn, Germany.
| |
Collapse
|