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Adeoye T, Shah SI, Ullah G. Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease. Aging Dis 2024:AD.2024.0429. [PMID: 38913039 DOI: 10.14336/ad.2024.0429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 06/12/2024] [Indexed: 06/25/2024] Open
Abstract
Alzheimer's disease (AD) manifests as a complex systems pathology with intricate interplay among various genes and biological processes. Traditional differential gene expression (DEG) analysis, while commonly employed to characterize AD-driven perturbations, does not sufficiently capture the full spectrum of underlying biological processes. Utilizing single-nucleus RNA-sequencing data from postmortem brain samples across key regions-middle temporal gyrus, superior frontal gyrus, and entorhinal cortex-we provide a comprehensive systematic analysis of disrupted processes in AD. We go beyond the DEG-centric analysis by integrating pathway activity analysis with weighted gene co-expression patterns to comprehensively map gene interconnectivity, identifying region- and cell-type-specific drivers of biological processes associated with AD. Our analysis reveals profound modular heterogeneity in neurons and glia as well as extensive AD-related functional disruptions. Co-expression networks highlighted the extended involvement of astrocytes and microglia in biological processes beyond neuroinflammation, such as calcium homeostasis, glutamate regulation, lipid metabolism, vesicle-mediated transport, and TOR signaling. We find limited representation of DEGs within dysregulated pathways across neurons and glial cells, suggesting that differential gene expression alone may not adequately represent the disease complexity. Further dissection of inferred gene modules revealed distinct dynamics of hub DEGs in neurons versus glia, suggesting that DEGs exert more impact on neurons compared to glial cells in driving modular dysregulations underlying perturbed biological processes. Interestingly, we observe an overall downregulation of astrocyte and microglia modules across all brain regions in AD, indicating a prevailing trend of functional repression in glial cells across these regions. Notable genes from the CALM and HSP90 families emerged as hub genes across neuronal modules in all brain regions, suggesting conserved roles as drivers of synaptic dysfunction in AD. Our findings demonstrate the importance of an integrated, systems-oriented approach combining pathway and network analysis to comprehensively understand the cell-type-specific roles of genes in AD-related biological processes.
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Adeoye T, Shah SI, Ullah G. Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.15.585267. [PMID: 38559218 PMCID: PMC10980062 DOI: 10.1101/2024.03.15.585267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Alzheimer's disease (AD) manifests as a complex systems pathology with intricate interplay among various genes and biological processes. Traditional differential gene expression (DEG) analysis, while commonly employed to characterize AD-driven perturbations, does not sufficiently capture the full spectrum of underlying biological processes. Utilizing single-nucleus RNA-sequencing data from postmortem brain samples across key regions-middle temporal gyrus, superior frontal gyrus, and entorhinal cortex-we provide a comprehensive systematic analysis of disrupted processes in AD. We go beyond the DEG-centric analysis by integrating pathway activity analysis with weighted gene co-expression patterns to comprehensively map gene interconnectivity, identifying region- and cell-type-specific drivers of biological processes associated with AD. Our analysis reveals profound modular heterogeneity in neurons and glia as well as extensive AD-related functional disruptions. Co-expression networks highlighted the extended involvement of astrocytes and microglia in biological processes beyond neuroinflammation, such as calcium homeostasis, glutamate regulation, lipid metabolism, vesicle-mediated transport, and TOR signaling. We find limited representation of DEGs within dysregulated pathways across neurons and glial cells, indicating that differential gene expression alone may not adequately represent the disease complexity. Further dissection of inferred gene modules revealed distinct dynamics of hub DEGs in neurons versus glia, highlighting the differential impact of DEGs on neurons compared to glial cells in driving modular dysregulations underlying perturbed biological processes. Interestingly, we note an overall downregulation of both astrocyte and microglia modules in AD across all brain regions, suggesting a prevailing trend of functional repression in glial cells across these regions. Notable genes, including those of the CALM and HSP90 family genes emerged as hub genes across neuronal modules in all brain regions, indicating conserved roles as drivers of synaptic dysfunction in AD. Our findings demonstrate the importance of an integrated, systems-oriented approach combining pathway and network analysis for a comprehensive understanding of the cell-type-specific roles of genes in AD-related biological processes.
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Affiliation(s)
- Temitope Adeoye
- Department of Physics, University of South Florida, Tampa, FL 33620
| | - Syed I Shah
- Department of Physics, University of South Florida, Tampa, FL 33620
| | - Ghanim Ullah
- Department of Physics, University of South Florida, Tampa, FL 33620
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3
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Chakraborty S, Kahali B. Exome-wide analysis reveals role of LRP1 and additional novel loci in cognition. HGG ADVANCES 2023; 4:100208. [PMID: 37305557 PMCID: PMC10248556 DOI: 10.1016/j.xhgg.2023.100208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 05/16/2023] [Indexed: 06/13/2023] Open
Abstract
Cognitive functioning is heritable, with metabolic risk factors known to accelerate age-associated cognitive decline. Identifying genetic underpinnings of cognition is thus crucial. Here, we undertake single-variant and gene-based association analyses upon 6 neurocognitive phenotypes across 6 cognition domains in whole-exome sequencing data from 157,160 individuals of the UK Biobank cohort to expound the genetic architecture of human cognition. We report 20 independent loci associated with 5 cognitive domains while controlling for APOE isoform-carrier status and metabolic risk factors; 18 of which were not previously reported, and implicated genes relating to oxidative stress, synaptic plasticity and connectivity, and neuroinflammation. A subset of significant hits for cognition indicates mediating effects via metabolic traits. Some of these variants also exhibit pleiotropic effects on metabolic traits. We further identify previously unknown interactions of APOE variants with LRP1 (rs34949484 and others, suggestively significant), AMIGO1 (rs146766120; pAla25Thr, significant), and ITPR3 (rs111522866, significant), controlling for lipid and glycemic risks. Our gene-based analysis also suggests that APOC1 and LRP1 have plausible roles along shared pathways of amyloid beta (Aβ) and lipid and/or glucose metabolism in affecting complex processing speed and visual attention. In addition, we report pairwise suggestive interactions of variants harbored in these genes with APOE affecting visual attention. Our report based on this large-scale exome-wide study highlights the effects of neuronal genes, such as LRP1, AMIGO1, and other genomic loci, thus providing further evidence of the genetic underpinnings for cognition during aging.
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Affiliation(s)
- Shreya Chakraborty
- Centre for Brain Research, Indian Institute of Science, Bangalore, Karnataka 560012, India
- Interdisciplinary Mathematical Sciences, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Bratati Kahali
- Centre for Brain Research, Indian Institute of Science, Bangalore, Karnataka 560012, India
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4
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Marchese S, Cancelmo L, Diab O, Cahn L, Aaronson C, Daskalakis NP, Schaffer J, Horn SR, Johnson JS, Schechter C, Desarnaud F, Bierer LM, Makotkine I, Flory JD, Crane M, Moline JM, Udasin IG, Harrison DJ, Roussos P, Charney DS, Koenen KC, Southwick SM, Yehuda R, Pietrzak RH, Huckins LM, Feder A. Altered gene expression and PTSD symptom dimensions in World Trade Center responders. Mol Psychiatry 2022; 27:2225-2246. [PMID: 35177824 DOI: 10.1038/s41380-022-01457-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 11/16/2021] [Accepted: 01/18/2022] [Indexed: 11/09/2022]
Abstract
Despite experiencing a significant trauma, only a subset of World Trade Center (WTC) rescue and recovery workers developed posttraumatic stress disorder (PTSD). Identification of biomarkers is critical to the development of targeted interventions for treating disaster responders and potentially preventing the development of PTSD in this population. Analysis of gene expression from these individuals can help in identifying biomarkers of PTSD. We established a well-phenotyped sample of 371 WTC responders, recruited from a longitudinal WTC responder cohort using stratified random sampling, by obtaining blood, self-reported and clinical interview data. Using bulk RNA-sequencing from whole blood, we examined the association between gene expression and WTC-related PTSD symptom severity on (i) highest lifetime Clinician-Administered PTSD Scale (CAPS) score, (ii) past-month CAPS score, and (iii) PTSD symptom dimensions using a 5-factor model of re-experiencing, avoidance, emotional numbing, dysphoric arousal and anxious arousal symptoms. We corrected for sex, age, genotype-derived principal components and surrogate variables. Finally, we performed a meta-analysis with existing PTSD studies (total N = 1016), using case/control status as the predictor and correcting for these variables. We identified 66 genes significantly associated with total highest lifetime CAPS score (FDR-corrected p < 0.05), and 31 genes associated with total past-month CAPS score. Our more granular analyses of PTSD symptom dimensions identified additional genes that did not reach statistical significance in our analyses with total CAPS scores. In particular, we identified 82 genes significantly associated with lifetime anxious arousal symptoms. Several genes significantly associated with multiple PTSD symptom dimensions and total lifetime CAPS score (SERPINA1, RPS6KA1, and STAT3) have been previously associated with PTSD. Geneset enrichment of these findings has identified pathways significant in metabolism, immune signaling, other psychiatric disorders, neurological signaling, and cellular structure. Our meta-analysis revealed 10 genes that reached genome-wide significance, all of which were downregulated in cases compared to controls (CIRBP, TMSB10, FCGRT, CLIC1, RPS6KB2, HNRNPUL1, ALDOA, NACA, ZNF429 and COPE). Additionally, cellular deconvolution highlighted an enrichment in CD4 T cells and eosinophils in responders with PTSD compared to controls. The distinction in significant genes between total lifetime CAPS score and the anxious arousal symptom dimension of PTSD highlights a potential biological difference in the mechanism underlying the heterogeneity of the PTSD phenotype. Future studies should be clear about methods used to analyze PTSD status, as phenotypes based on PTSD symptom dimensions may yield different gene sets than combined CAPS score analysis. Potential biomarkers implicated from our meta-analysis may help improve therapeutic target development for PTSD.
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Affiliation(s)
- Shelby Marchese
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Leo Cancelmo
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Olivia Diab
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Leah Cahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Cindy Aaronson
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Nikolaos P Daskalakis
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Jamie Schaffer
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sarah R Horn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jessica S Johnson
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Clyde Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Frank Desarnaud
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Department of Psychiatry, James J. Peters Veterans Affairs Medical Center, Bronx, NY, USA
| | - Linda M Bierer
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Department of Psychiatry, James J. Peters Veterans Affairs Medical Center, Bronx, NY, USA
| | - Iouri Makotkine
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Department of Psychiatry, James J. Peters Veterans Affairs Medical Center, Bronx, NY, USA
| | - Janine D Flory
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Department of Psychiatry, James J. Peters Veterans Affairs Medical Center, Bronx, NY, USA
| | - Michael Crane
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jacqueline M Moline
- Department of Occupational Medicine, Epidemiology and Prevention, Zucker School of Medicine at Hofstra/Northwell, Great Neck, NY, USA
| | - Iris G Udasin
- Environmental and Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, NJ, USA
| | - Denise J Harrison
- Department of Medicine, Division of Pulmonary Critical Care and Sleep Medicine, NYU School of Medicine, New York, NY, USA
| | - Panos Roussos
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Mental Illness Research, Education and Clinical Centers, James J. Peters Department of Veterans Affairs Medical Center, Bronx, NY, 14068, USA
| | - Dennis S Charney
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Karestan C Koenen
- Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Boston, MA, USA.,Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA.,Harvard School of Public Health, Department of Epidemiology, Boston, MA, USA
| | - Steven M Southwick
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Rachel Yehuda
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.,Department of Psychiatry, James J. Peters Veterans Affairs Medical Center, Bronx, NY, USA
| | - Robert H Pietrzak
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,Department of Veterans Affairs National Center for Posttraumatic Stress Disorder, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Laura M Huckins
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. .,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. .,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. .,Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. .,Mental Illness Research, Education and Clinical Centers, James J. Peters Department of Veterans Affairs Medical Center, Bronx, NY, 14068, USA. .,Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| | - Adriana Feder
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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6
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Xiaoli L, Fengbin H, Shihui H, Xi N, Sheng L, Zhou W, Xueqin R, Jiafu W. Detection of genomic structure variants associated with wrinkled skin in Xiang pig by next generation sequencing. Aging (Albany NY) 2021; 13:24710-24739. [PMID: 34837693 PMCID: PMC8660620 DOI: 10.18632/aging.203711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 08/02/2021] [Indexed: 11/25/2022]
Abstract
Wrinkling is prominent manifestation of aging skin. A mutant phenotype characterized by systemic wrinkles and thickened skin was discovered in Xiang pig populations with incidence about 1-3%. The feature in histological structure was epidermal hyperplasia and thickening, collagen fibers disorder. To uncover genetic mechanisms for the mutant phenotype of Xiang pigs with systemic wrinkle (WXP), a genome-wide of structural variations (SVs) in WXP was described by next generation resequencing, taking Xiang pigs (XP) and European pigs (EUP) as compares. Total of 32,308 SVs were detected from three pig groups and 965 SVs were identified specifically from WXP, involving 481 protein-coding genes. These genes were mainly enriched in nuclear structure, ECM components and immunomodulatory pathways. According to gene function and enrichment analysis, we found that 65 candidate SVs in 59 protein genes were probably related with the systemic wrinkle of WXP. Of these, several genes are reported to be associate with aging, such as EIF4G2, NOLC1, XYLT1, FUT8, MDM2 and so on. The insertion/deletion and duplication variations of SVs in these genes resulted in the loss of stop-codon or frameshift mutation, and aberrant alternative splicing of transcripts. These genes are involved in cell lamin filament, intermediate filament cytoskeleton, supramolecular complex, cell differentiation and regulation of macromolecule metabolic process etc. Our study suggested that the loss of function or aberrant expression of these genes lead to structural disorder of nuclear and the extracellular matrix (ECM) in skin cells, which probably was the genetic mechanisms for the mutant phenotype of systemic skin wrinkle of Xiang pig.
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Affiliation(s)
- Liu Xiaoli
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservative and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Science and College of Animal Science, Guizhou University, Guiyang 550025, China
| | - Hu Fengbin
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservative and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Science and College of Animal Science, Guizhou University, Guiyang 550025, China
| | - Huang Shihui
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservative and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Science and College of Animal Science, Guizhou University, Guiyang 550025, China
| | - Niu Xi
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservative and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Science and College of Animal Science, Guizhou University, Guiyang 550025, China
| | - Li Sheng
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservative and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Science and College of Animal Science, Guizhou University, Guiyang 550025, China
| | - Wang Zhou
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservative and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Science and College of Animal Science, Guizhou University, Guiyang 550025, China
| | - Ran Xueqin
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservative and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Science and College of Animal Science, Guizhou University, Guiyang 550025, China
| | - Wang Jiafu
- Institute of Agro-Bioengineering, Key Laboratory of Plant Resource Conservative and Germplasm Innovation in Mountainous Region (Ministry of Education), College of Life Science and College of Animal Science, Guizhou University, Guiyang 550025, China
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7
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Sandal P, Jong CJ, Merrill RA, Song J, Strack S. Protein phosphatase 2A - structure, function and role in neurodevelopmental disorders. J Cell Sci 2021; 134:270819. [PMID: 34228795 DOI: 10.1242/jcs.248187] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Neurodevelopmental disorders (NDDs), including intellectual disability (ID), autism and schizophrenia, have high socioeconomic impact, yet poorly understood etiologies. A recent surge of large-scale genome or exome sequencing studies has identified a multitude of mostly de novo mutations in subunits of the protein phosphatase 2A (PP2A) holoenzyme that are strongly associated with NDDs. PP2A is responsible for at least 50% of total Ser/Thr dephosphorylation in most cell types and is predominantly found as trimeric holoenzymes composed of catalytic (C), scaffolding (A) and variable regulatory (B) subunits. PP2A can exist in nearly 100 different subunit combinations in mammalian cells, dictating distinct localizations, substrates and regulatory mechanisms. PP2A is well established as a regulator of cell division, growth, and differentiation, and the roles of PP2A in cancer and various neurodegenerative disorders, such as Alzheimer's disease, have been reviewed in detail. This Review summarizes and discusses recent reports on NDDs associated with mutations of PP2A subunits and PP2A-associated proteins. We also discuss the potential impact of these mutations on the structure and function of the PP2A holoenzymes and the etiology of NDDs.
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Affiliation(s)
- Priyanka Sandal
- Department of Neuroscience and Pharmacology, and Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa 52242, USA
| | - Chian Ju Jong
- Department of Neuroscience and Pharmacology, and Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa 52242, USA
| | - Ronald A Merrill
- Department of Neuroscience and Pharmacology, and Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa 52242, USA
| | - Jianing Song
- Department of Neuroscience and Pharmacology, and Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa 52242, USA
| | - Stefan Strack
- Department of Neuroscience and Pharmacology, and Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa 52242, USA
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8
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Tremblay-Mercier J, Madjar C, Das S, Pichet Binette A, Dyke SOM, Étienne P, Lafaille-Magnan ME, Remz J, Bellec P, Louis Collins D, Natasha Rajah M, Bohbot V, Leoutsakos JM, Iturria-Medina Y, Kat J, Hoge RD, Gauthier S, Tardif CL, Mallar Chakravarty M, Poline JB, Rosa-Neto P, Evans AC, Villeneuve S, Poirier J, Breitner JCS. Open science datasets from PREVENT-AD, a longitudinal cohort of pre-symptomatic Alzheimer's disease. Neuroimage Clin 2021; 31:102733. [PMID: 34192666 PMCID: PMC8254111 DOI: 10.1016/j.nicl.2021.102733] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 06/11/2021] [Accepted: 06/14/2021] [Indexed: 02/07/2023]
Abstract
To move Alzheimer Disease (AD) research forward it is essential to collect data from large cohorts, but also make such data available to the global research community. We describe the creation of an open science dataset from the PREVENT-AD (PResymptomatic EValuation of Experimental or Novel Treatments for AD) cohort, composed of cognitively unimpaired older individuals with a parental or multiple-sibling history of AD. From 2011 to 2017, 386 participants were enrolled (mean age 63 years old ± 5) for sustained investigation among whom 349 have retrospectively agreed to share their data openly. Repositories are findable through the unified interface of the Canadian Open Neuroscience Platform and contain up to five years of longitudinal imaging data, cerebral fluid biochemistry, neurosensory capacities, cognitive, genetic, and medical information. Imaging data can be accessed openly at https://openpreventad.loris.ca while most of the other information, sensitive by nature, is accessible by qualified researchers at https://registeredpreventad.loris.ca. In addition to being a living resource for continued data acquisition, PREVENT-AD offers opportunities to facilitate understanding of AD pathogenesis.
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Affiliation(s)
| | - Cécile Madjar
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - Samir Das
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - Alexa Pichet Binette
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill University, Montréal, QC, Canada.
| | - Stephanie O M Dyke
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, QC, Canada; McGill University, Montréal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - Pierre Étienne
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill University, Montréal, QC, Canada.
| | - Marie-Elyse Lafaille-Magnan
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill University, Montréal, QC, Canada; Centre for Child Development and Mental Health, Jewish General Hospital. Montréal, QC, Canada.
| | - Jordana Remz
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada.
| | - Pierre Bellec
- CRIUGM - Université de Montréal, Montréal, QC, Canada; Université de Montréal, Montréal, QC, Canada.
| | - D Louis Collins
- McGill University, Montréal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - M Natasha Rajah
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill University, Montréal, QC, Canada.
| | - Veronique Bohbot
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill University, Montréal, QC, Canada.
| | | | - Yasser Iturria-Medina
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, QC, Canada; McGill University, Montréal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - Justin Kat
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - Richard D Hoge
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, QC, Canada; McGill University, Montréal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montréal, QC, Canada; Université de Montréal, Montréal, QC, Canada.
| | - Serge Gauthier
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill University, Montréal, QC, Canada; McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC, Canada.
| | - Christine L Tardif
- McGill University, Montréal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - M Mallar Chakravarty
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill University, Montréal, QC, Canada.
| | - Jean-Baptiste Poline
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, QC, Canada; McGill University, Montréal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - Pedro Rosa-Neto
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill University, Montréal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montréal, QC, Canada; McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC, Canada.
| | - Alan C Evans
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, QC, Canada; McGill University, Montréal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - Sylvia Villeneuve
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montréal, QC, Canada; McGill University, Montréal, QC, Canada; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - Judes Poirier
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill University, Montréal, QC, Canada.
| | - John C S Breitner
- StoP-AD Centre, Douglas Mental Health Institute Research Centre, Montréal, QC, Canada; McGill University, Montréal, QC, Canada.
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9
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Drummond E, Pires G, MacMurray C, Askenazi M, Nayak S, Bourdon M, Safar J, Ueberheide B, Wisniewski T. Phosphorylated tau interactome in the human Alzheimer's disease brain. Brain 2021; 143:2803-2817. [PMID: 32812023 DOI: 10.1093/brain/awaa223] [Citation(s) in RCA: 162] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 05/05/2020] [Accepted: 05/22/2020] [Indexed: 01/07/2023] Open
Abstract
Accumulation of phosphorylated tau is a key pathological feature of Alzheimer's disease. Phosphorylated tau accumulation causes synaptic impairment, neuronal dysfunction and formation of neurofibrillary tangles. The pathological actions of phosphorylated tau are mediated by surrounding neuronal proteins; however, a comprehensive understanding of the proteins that phosphorylated tau interacts with in Alzheimer's disease is surprisingly limited. Therefore, the aim of this study was to determine the phosphorylated tau interactome. To this end, we used two complementary proteomics approaches: (i) quantitative proteomics was performed on neurofibrillary tangles microdissected from patients with advanced Alzheimer's disease; and (ii) affinity purification-mass spectrometry was used to identify which of these proteins specifically bound to phosphorylated tau. We identified 542 proteins in neurofibrillary tangles. This included the abundant detection of many proteins known to be present in neurofibrillary tangles such as tau, ubiquitin, neurofilament proteins and apolipoprotein E. Affinity purification-mass spectrometry confirmed that 75 proteins present in neurofibrillary tangles interacted with PHF1-immunoreactive phosphorylated tau. Twenty-nine of these proteins have been previously associated with phosphorylated tau, therefore validating our proteomic approach. More importantly, 34 proteins had previously been associated with total tau, but not yet linked directly to phosphorylated tau (e.g. synaptic protein VAMP2, vacuolar-ATPase subunit ATP6V0D1); therefore, we provide new evidence that they directly interact with phosphorylated tau in Alzheimer's disease. In addition, we also identified 12 novel proteins, not previously known to be physiologically or pathologically associated with tau (e.g. RNA binding protein HNRNPA1). Network analysis showed that the phosphorylated tau interactome was enriched in proteins involved in the protein ubiquitination pathway and phagosome maturation. Importantly, we were able to pinpoint specific proteins that phosphorylated tau interacts with in these pathways for the first time, therefore providing novel potential pathogenic mechanisms that can be explored in future studies. Combined, our results reveal new potential drug targets for the treatment of tauopathies and provide insight into how phosphorylated tau mediates its toxicity in Alzheimer's disease.
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Affiliation(s)
- Eleanor Drummond
- Brain and Mind Centre and Central Clinical School, Faculty of Medicine and Health, University of Sydney, Australia.,Centre for Cognitive Neurology, Department of Neurology, New York University School of Medicine, New York, NY, USA
| | - Geoffrey Pires
- Centre for Cognitive Neurology, Department of Neurology, New York University School of Medicine, New York, NY, USA.,Alzheimer's and Prion Diseases Team, Paris Brain Institute, CNRS, UMR 7225, INSERM 1127, Sorbonne University UM75, Paris, France
| | - Claire MacMurray
- Centre for Cognitive Neurology, Department of Neurology, New York University School of Medicine, New York, NY, USA
| | | | - Shruti Nayak
- Proteomics Laboratory, Division of Advanced Research Technologies, NYU School of Medicine, New York, NY, USA
| | - Marie Bourdon
- Centre for Cognitive Neurology, Department of Neurology, New York University School of Medicine, New York, NY, USA
| | - Jiri Safar
- Department of Pathology, Case Western Reserve University, Cleveland, OH, USA.,Department of Neurology, Case Western Reserve University, Cleveland, OH, USA
| | - Beatrix Ueberheide
- Biomedical Hosting LLC, USA.,Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY, USA
| | - Thomas Wisniewski
- Centre for Cognitive Neurology, Department of Neurology, New York University School of Medicine, New York, NY, USA.,Department of Psychiatry, New York University School of Medicine, New York, NY, USA
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10
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Xu GB, Guan PP, Wang P. Prostaglandin A1 Decreases the Phosphorylation of Tau by Activating Protein Phosphatase 2A via a Michael Addition Mechanism at Cysteine 377. Mol Neurobiol 2020; 58:1114-1127. [PMID: 33095414 DOI: 10.1007/s12035-020-02174-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 10/14/2020] [Indexed: 12/27/2022]
Abstract
Prostaglandin (PG) A1 is a metabolic product of cyclooxygenase 2 (COX-2) that is potentially involved in regulating the development and progression of Alzheimer's disease (AD). PGA1 is a cyclopentenone (cy) PG characterized by the presence of a chemically reactive α,β-unsaturated carbonyl. PGA1 is potentially involved in the regulation of multiple biological processes via Michael addition; however, the specific roles of PGA1 in AD remain unclear. TauP301S transgenic (Tg) mice were used as in vivo AD models, and neuroblastoma (N) 2a cells were used as an in vitro neuronal model. The PGA1-binding proteins were identified by HPLC-MS-MS after intracerebroventricular injection (i.c.v) of PGA1. Western blotting was used to determine tau phosphorylation in PGA1-treated Tg mice in the absence or in the presence of okadaic acid (OA), an inhibitor of protein phosphatase (PP) 2A. A combination of pull-down assay, immunoprecipitation, western blotting, and HPLC-MS-MS was used to determine that the PP2A scaffold subunit A alpha (PPP2R1A) is activated by the direct binding of PGA1 to cysteine 377. The effect of inhibiting tau hyperphosphorylation was tested in the Morris maze to determine the inhibitory effects of PGA1 on cognitive decline in tauP301S Tg mice. Incubation with N2a cells, pull-down assay, and mass spectrometry (MS) analysis revealed and indicated that PGA1 binds to more than 1000 proteins; some of these proteins are associated with AD and especially with tauopathies. Moreover, short-term administration of PGA1 in tauP301S Tg mice significantly decreased tau phosphorylation at Thr181, Ser202, and Ser404 in a dose-dependent manner. This effect was caused by the activation of PPP2R1A in tauP301S Tg mice. Importantly, PGA1 can form a Michael adduct with cysteine 377 of PPP2R1A, which is critical for the enzymatic activity of PP2A. Long-term treatment of tauP301S Tg mice with PGA1 activated PP2A and significantly reduced tau phosphorylation resulting in improvements in cognitive decline in tauP301S Tg mice. Our data provided new insight into the mechanisms of the ameliorating effects of PGA1 on cognitive decline in tauP301S Tg mice by activating PP2A via a mechanism involving the formation of a Michael adduct with cysteine 377 of PPP2R1A.
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Affiliation(s)
- Guo-Biao Xu
- College of Life and Health Sciences, Northeastern University, No. 3-11. Wenhua Road, Shenyang, 110819, People's Republic of China.,Liaoning Cheng Da Biotechnology Co., Ltd, Shenyang, 110179, People's Republic of China
| | - Pei-Pei Guan
- College of Life and Health Sciences, Northeastern University, No. 3-11. Wenhua Road, Shenyang, 110819, People's Republic of China
| | - Pu Wang
- College of Life and Health Sciences, Northeastern University, No. 3-11. Wenhua Road, Shenyang, 110819, People's Republic of China.
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