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Hannon E, Dempster EL, Davies JP, Chioza B, Blake GET, Burrage J, Policicchio S, Franklin A, Walker EM, Bamford RA, Schalkwyk LC, Mill J. Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles. BMC Biol 2024; 22:17. [PMID: 38273288 PMCID: PMC10809680 DOI: 10.1186/s12915-024-01827-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 01/11/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Due to interindividual variation in the cellular composition of the human cortex, it is essential that covariates that capture these differences are included in epigenome-wide association studies using bulk tissue. As experimentally derived cell counts are often unavailable, computational solutions have been adopted to estimate the proportion of different cell types using DNA methylation data. Here, we validate and profile the use of an expanded reference DNA methylation dataset incorporating two neuronal and three glial cell subtypes for quantifying the cellular composition of the human cortex. RESULTS We tested eight reference panels containing different combinations of neuronal- and glial cell types and characterised their performance in deconvoluting cell proportions from computationally reconstructed or empirically derived human cortex DNA methylation data. Our analyses demonstrate that while these novel brain deconvolution models produce accurate estimates of cellular proportions from profiles generated on postnatal human cortex samples, they are not appropriate for the use in prenatal cortex or cerebellum tissue samples. Applying our models to an extensive collection of empirical datasets, we show that glial cells are twice as abundant as neuronal cells in the human cortex and identify significant associations between increased Alzheimer's disease neuropathology and the proportion of specific cell types including a decrease in NeuNNeg/SOX10Neg nuclei and an increase of NeuNNeg/SOX10Pos nuclei. CONCLUSIONS Our novel deconvolution models produce accurate estimates for cell proportions in the human cortex. These models are available as a resource to the community enabling the control of cellular heterogeneity in epigenetic studies of brain disorders performed on bulk cortex tissue.
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Affiliation(s)
- Eilis Hannon
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK.
| | - Emma L Dempster
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Jonathan P Davies
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Barry Chioza
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Georgina E T Blake
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Joe Burrage
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Stefania Policicchio
- Italian Institute of Technology, Center for Human Technologies (CHT), Genova, Italy
| | - Alice Franklin
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Emma M Walker
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Rosemary A Bamford
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
| | - Leonard C Schalkwyk
- School of Life Sciences, University of Essex, Wivenhoe Park, Colchester, Essex, CO4 3SQ, UK
| | - Jonathan Mill
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Barrack Road, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, Devon, EX2 5DW, UK
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2
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Wang Y, Gorrie-Stone TJ, Grant OA, Andrayas AD, Zhai X, McDonald-Maier KD, Schalkwyk LC. InterpolatedXY: a two-step strategy to normalize DNA methylation microarray data avoiding sex bias. Bioinformatics 2022; 38:3950-3957. [PMID: 35771651 PMCID: PMC9364386 DOI: 10.1093/bioinformatics/btac436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 05/31/2022] [Accepted: 06/28/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Data normalization is an essential step to reduce technical variation within and between arrays. Due to the different karyotypes and the effects of X chromosome inactivation, females and males exhibit distinct methylation patterns on sex chromosomes; thus, it poses a significant challenge to normalize sex chromosome data without introducing bias. Currently, existing methods do not provide unbiased solutions to normalize sex chromosome data, usually, they just process autosomal and sex chromosomes indiscriminately. RESULTS Here, we demonstrate that ignoring this sex difference will lead to introducing artificial sex bias, especially for thousands of autosomal CpGs. We present a novel two-step strategy (interpolatedXY) to address this issue, which is applicable to all quantile-based normalization methods. By this new strategy, the autosomal CpGs are first normalized independently by conventional methods, such as funnorm or dasen; then the corrected methylation values of sex chromosome-linked CpGs are estimated as the weighted average of their nearest neighbors on autosomes. The proposed two-step strategy can also be applied to other non-quantile-based normalization methods, as well as other array-based data types. Moreover, we propose a useful concept: the sex explained fraction of variance, to quantitatively measure the normalization effect. AVAILABILITY AND IMPLEMENTATION The proposed methods are available by calling the function 'adjustedDasen' or 'adjustedFunnorm' in the latest wateRmelon package (https://github.com/schalkwyk/wateRmelon), with methods compatible with all the major workflows, including minfi. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yucheng Wang
- School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
| | | | - Olivia A Grant
- School of Life Sciences, University of Essex, Colchester CO4 3SQ, UK
| | - Alexandria D Andrayas
- Institute for Social and Economic Research, University of Essex, Colchester CO4 3SQ, UK
| | | | - Klaus D McDonald-Maier
- School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
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Flynn R, Washer S, Jeffries AR, Andrayas A, Shireby G, Kumari M, Schalkwyk LC, Mill J, Hannon E. Evaluation of nanopore sequencing for epigenetic epidemiology: a comparison with DNA methylation microarrays. Hum Mol Genet 2022; 31:3181-3190. [PMID: 35567415 PMCID: PMC9476619 DOI: 10.1093/hmg/ddac112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/25/2022] [Accepted: 05/05/2022] [Indexed: 11/14/2022] Open
Abstract
Most epigenetic epidemiology to date has utilized microarrays to identify positions in the genome where variation in DNA methylation is associated with environmental exposures or disease. However, these profile less than 3% of DNA methylation sites in the human genome, potentially missing affected loci and preventing the discovery of disrupted biological pathways. Third generation sequencing technologies, including Nanopore sequencing, have the potential to revolutionise the generation of epigenetic data, not only by providing genuine genome-wide coverage but profiling epigenetic modifications direct from native DNA. Here we assess the viability of using Nanopore sequencing for epidemiology by performing a comparison with DNA methylation quantified using the most comprehensive microarray available, the Illumina EPIC array. We implemented a CRISPR-Cas9 targeted sequencing approach in concert with Nanopore sequencing to profile DNA methylation in three genomic regions to attempt to rediscover genomic positions that existing technologies have shown are differentially methylated in tobacco smokers. Using Nanopore sequencing reads, DNA methylation was quantified at 1779 CpGs across three regions, providing a finer resolution of DNA methylation patterns compared to the EPIC array. The correlation of estimated levels of DNA methylation between platforms was high. Furthermore, we identified 12 CpGs where hypomethylation was significantly associated with smoking status, including 10 within the AHRR gene. In summary, Nanopore sequencing is a valid option for identifying genomic loci where large differences in DNAm are associated with a phenotype and has the potential to advance our understanding of the role differential methylation plays in the aetiology of complex disease.
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Affiliation(s)
- Robert Flynn
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom
| | - Sam Washer
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom.,Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Aaron R Jeffries
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom
| | - Alexandria Andrayas
- School of Biological Sciences, University of Essex, Colchester, CO4 3SQ, United Kingdom
| | - Gemma Shireby
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester CO3 3LG, United Kingdom
| | - Leonard C Schalkwyk
- School of Biological Sciences, University of Essex, Colchester, CO4 3SQ, United Kingdom
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom
| | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom
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4
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Min JL, Hemani G, Hannon E, Dekkers KF, Castillo-Fernandez J, Luijk R, Carnero-Montoro E, Lawson DJ, Burrows K, Suderman M, Bretherick AD, Richardson TG, Klughammer J, Iotchkova V, Sharp G, Al Khleifat A, Shatunov A, Iacoangeli A, McArdle WL, Ho KM, Kumar A, Söderhäll C, Soriano-Tárraga C, Giralt-Steinhauer E, Kazmi N, Mason D, McRae AF, Corcoran DL, Sugden K, Kasela S, Cardona A, Day FR, Cugliari G, Viberti C, Guarrera S, Lerro M, Gupta R, Bollepalli S, Mandaviya P, Zeng Y, Clarke TK, Walker RM, Schmoll V, Czamara D, Ruiz-Arenas C, Rezwan FI, Marioni RE, Lin T, Awaloff Y, Germain M, Aïssi D, Zwamborn R, van Eijk K, Dekker A, van Dongen J, Hottenga JJ, Willemsen G, Xu CJ, Barturen G, Català-Moll F, Kerick M, Wang C, Melton P, Elliott HR, Shin J, Bernard M, Yet I, Smart M, Gorrie-Stone T, Shaw C, Al Chalabi A, Ring SM, Pershagen G, Melén E, Jiménez-Conde J, Roquer J, Lawlor DA, Wright J, Martin NG, Montgomery GW, Moffitt TE, Poulton R, Esko T, Milani L, Metspalu A, Perry JRB, Ong KK, Wareham NJ, Matullo G, Sacerdote C, Panico S, Caspi A, Arseneault L, Gagnon F, Ollikainen M, Kaprio J, Felix JF, Rivadeneira F, Tiemeier H, van IJzendoorn MH, Uitterlinden AG, Jaddoe VWV, Haley C, McIntosh AM, Evans KL, Murray A, Räikkönen K, Lahti J, Nohr EA, Sørensen TIA, Hansen T, Morgen CS, Binder EB, Lucae S, Gonzalez JR, Bustamante M, Sunyer J, Holloway JW, Karmaus W, Zhang H, Deary IJ, Wray NR, Starr JM, Beekman M, van Heemst D, Slagboom PE, Morange PE, Trégouët DA, Veldink JH, Davies GE, de Geus EJC, Boomsma DI, Vonk JM, Brunekreef B, Koppelman GH, Alarcón-Riquelme ME, Huang RC, Pennell CE, van Meurs J, Ikram MA, Hughes AD, Tillin T, Chaturvedi N, Pausova Z, Paus T, Spector TD, Kumari M, Schalkwyk LC, Visscher PM, Davey Smith G, Bock C, Gaunt TR, Bell JT, Heijmans BT, Mill J, Relton CL. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation. Nat Genet 2021; 53:1311-1321. [PMID: 34493871 PMCID: PMC7612069 DOI: 10.1038/s41588-021-00923-x] [Citation(s) in RCA: 158] [Impact Index Per Article: 52.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 07/12/2021] [Indexed: 12/25/2022]
Abstract
Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.
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Affiliation(s)
- Josine L Min
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eilis Hannon
- University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Koen F Dekkers
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | | | - René Luijk
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Elena Carnero-Montoro
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Pfizer-University of Granada-Andalusian Government Center for Genomics and Oncological Research, Granada, Spain
| | - Daniel J Lawson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kimberley Burrows
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Matthew Suderman
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Andrew D Bretherick
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Johanna Klughammer
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | | | - Gemma Sharp
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
| | - Aleksey Shatunov
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
| | - Alfredo Iacoangeli
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- Department of Biostatistics and Health Informatics, King's College London, London, UK
| | - Wendy L McArdle
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Karen M Ho
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ashish Kumar
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Chronic Disease Epidemiology unit, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Cilla Söderhäll
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Carolina Soriano-Tárraga
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Eva Giralt-Steinhauer
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Nabila Kazmi
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford, UK
| | - Allan F McRae
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - David L Corcoran
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Karen Sugden
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Silva Kasela
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Alexia Cardona
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Felix R Day
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Giovanni Cugliari
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Clara Viberti
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Simonetta Guarrera
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Michael Lerro
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Richa Gupta
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Sailalitha Bollepalli
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Pooja Mandaviya
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Yanni Zeng
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Toni-Kim Clarke
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Vanessa Schmoll
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Darina Czamara
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Carlos Ruiz-Arenas
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Faisal I Rezwan
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Tian Lin
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Yvonne Awaloff
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Marine Germain
- INSERM UMR_S 1219, Bordeaux Population Health Center, University of Bordeaux, Bordeaux, France
| | - Dylan Aïssi
- Department of General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany
| | - Ramona Zwamborn
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Kristel van Eijk
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Annelot Dekker
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Cheng-Jian Xu
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, GRIAC Research Institute Groningen, Groningen, the Netherlands
- CiiM and TWINCORE, Hannover Medical School and Helmholtz Centre for Infection Research, Hannover, Germany
| | - Guillermo Barturen
- Pfizer-University of Granada-Andalusian Government Center for Genomics and Oncological Research, Granada, Spain
| | - Francesc Català-Moll
- Chromatin and Disease Group, Cancer Epigenetics and Biology Programme, Bellvitge Biomedical Research Institute, Barcelona, Spain
| | - Martin Kerick
- Instituto de Parasitología y Biomedicina López Neyra, CSIC, Granada, Spain
| | - Carol Wang
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
| | - Phillip Melton
- Menzies Institute for Medical Research, College of Health and Medicine, University of Tasmania, Hobart, Australia
- School of Global Population Health, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia
- School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Hannah R Elliott
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jean Shin
- The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Manon Bernard
- The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Idil Yet
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Bioinformatics, Institute of Health Sciences, Hacettepe University, Ankara, Turkey
| | - Melissa Smart
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | | | | | - Chris Shaw
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- Department of Neurology, King's College Hospital, London, UK
| | - Ammar Al Chalabi
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- Department of Neurology, King's College Hospital, London, UK
- United Kingdom Dementia Research Institute, King's College London, London, UK
| | - Susan M Ring
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Göran Pershagen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Erik Melén
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Jordi Jiménez-Conde
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Jaume Roquer
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford, UK
| | | | - Grant W Montgomery
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Terrie E Moffitt
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University Medical School, Durham, NC, USA
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Lili Milani
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - John R B Perry
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Ken K Ong
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Giuseppe Matullo
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Carlotta Sacerdote
- Italian Institute for Genomic Medicine, Turin, Italy
- Piemonte Centre for Cancer Prevention, Turin, Italy
| | - Salvatore Panico
- Dipartimento Di Medicina Clinica E Chirurgia, Federico II University, Naples, Italy
| | - Avshalom Caspi
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University Medical School, Durham, NC, USA
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Louise Arseneault
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - France Gagnon
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Miina Ollikainen
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry, Erasmus Medical Center, Rotterdam, the Netherlands
- Department of Social and Behavioral Science, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Marinus H van IJzendoorn
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Department of Clinical, Educational and Health Psychology, Division on Psychology and Language Sciences, Faculty of Brain Sciences, University College London, London, UK
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Chris Haley
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Alison Murray
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ellen A Nohr
- Research Unit for Gynaecology and Obstetrics, Institute of Clinical research, University of Southern Denmark, Odense, Denmark
- Centre of Women's, Family and Child Health, University of South-Eastern Norway, Kongsberg, Norway
| | - Thorkild I A Sørensen
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health (Section of Epidemiology), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Camilla S Morgen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Susanne Lucae
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Juan Ramon Gonzalez
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Mariona Bustamante
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
- Center for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Jordi Sunyer
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
- Hospital del Mar Medical Research Institute, Barcelona, Spain
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Wilfried Karmaus
- Division of Epidemiology, Biostatistics, and Environmental Health Sciences, School of Public Health, University of Memphis, Memphis, TN, USA
| | - Hongmei Zhang
- Division of Epidemiology, Biostatistics, and Environmental Health Sciences, School of Public Health, University of Memphis, Memphis, TN, USA
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Diana van Heemst
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | | | | | - Jan H Veldink
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Eco J C de Geus
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Judith M Vonk
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, GRIAC Research Institute Groningen, Groningen, the Netherlands
| | - Bert Brunekreef
- Institute for Risk Assessment Sciences, Universiteit Utrecht, Utrecht, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Gerard H Koppelman
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, GRIAC Research Institute Groningen, Groningen, the Netherlands
| | - Marta E Alarcón-Riquelme
- Pfizer-University of Granada-Andalusian Government Center for Genomics and Oncological Research, Granada, Spain
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Rae-Chi Huang
- Telethon Kids Institute, University of Western Australia, Perth, Australia
| | - Craig E Pennell
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | | | | | | | - Zdenka Pausova
- The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Tomas Paus
- Departments of Psychology and Psychiatry, University of Toronto, Toronto, Canada
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | | | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute of Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Jonathan Mill
- University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Caroline L Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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5
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Wang Y, Hannon E, Grant OA, Gorrie-Stone TJ, Kumari M, Mill J, Zhai X, McDonald-Maier KD, Schalkwyk LC. DNA methylation-based sex classifier to predict sex and identify sex chromosome aneuploidy. BMC Genomics 2021; 22:484. [PMID: 34182928 PMCID: PMC8240370 DOI: 10.1186/s12864-021-07675-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 05/05/2021] [Indexed: 03/17/2023] Open
Abstract
Background Sex is an important covariate of epigenome-wide association studies due to its strong influence on DNA methylation patterns across numerous genomic positions. Nevertheless, many samples on the Gene Expression Omnibus (GEO) frequently lack a sex annotation or are incorrectly labelled. Considering the influence that sex imposes on DNA methylation patterns, it is necessary to ensure that methods for filtering poor samples and checking of sex assignment are accurate and widely applicable. Results Here we presented a novel method to predict sex using only DNA methylation beta values, which can be readily applied to almost all DNA methylation datasets of different formats (raw IDATs or text files with only signal intensities) uploaded to GEO. We identified 4345 significantly (p<0.01) sex-associated CpG sites present on both 450K and EPIC arrays, and constructed a sex classifier based on the two first principal components of the DNA methylation data of sex-associated probes mapped on sex chromosomes. The proposed method is constructed using whole blood samples and exhibits good performance across a wide range of tissues. We further demonstrated that our method can be used to identify samples with sex chromosome aneuploidy, this function is validated by five Turner syndrome cases and one Klinefelter syndrome case. Conclusions This proposed sex classifier not only can be used for sex predictions but also applied to identify samples with sex chromosome aneuploidy, and it is freely and easily accessible by calling the ‘estimateSex’ function from the newest wateRmelon Bioconductor package (https://github.com/schalkwyk/wateRmelon). Supplementary Information The online version contains supplementary material available at (10.1186/s12864-021-07675-2).
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Affiliation(s)
- Yucheng Wang
- School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, UK
| | - Eilis Hannon
- Medical School, University of Exeter, Barrack Road, Exeter, UK
| | - Olivia A Grant
- School of Biological Sciences, University of Essex, Wivenhoe Park, Colchester, UK
| | | | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Wivenhoe Park, Colchester, UK
| | - Jonathan Mill
- Medical School, University of Exeter, Barrack Road, Exeter, UK
| | - Xiaojun Zhai
- School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, UK.
| | - Klaus D McDonald-Maier
- School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, UK
| | - Leonard C Schalkwyk
- School of Biological Sciences, University of Essex, Wivenhoe Park, Colchester, UK
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6
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Shireby GL, Davies JP, Francis PT, Burrage J, Walker EM, Neilson GWA, Dahir A, Thomas AJ, Love S, Smith RG, Lunnon K, Kumari M, Schalkwyk LC, Morgan K, Brookes K, Hannon E, Mill J. Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex. Brain 2021; 143:3763-3775. [PMID: 33300551 PMCID: PMC7805794 DOI: 10.1093/brain/awaa334] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 08/06/2020] [Accepted: 08/07/2020] [Indexed: 12/14/2022] Open
Abstract
Human DNA methylation data have been used to develop biomarkers of ageing, referred to as ‘epigenetic clocks’, which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into ‘training’ and ‘testing’ samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.
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Affiliation(s)
- Gemma L Shireby
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Jonathan P Davies
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Paul T Francis
- University of Exeter Medical School, University of Exeter, Exeter, UK.,Wolfson Centre for Age-Related Diseases, King's College London, London, UK
| | - Joe Burrage
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Emma M Walker
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Grant W A Neilson
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Aisha Dahir
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Alan J Thomas
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK
| | - Seth Love
- Dementia Research Group, Institute of Clinical Neurosciences, School of Clinical Sciences, University of Bristol, Bristol, UK
| | - Rebecca G Smith
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Katie Lunnon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | | | - Kevin Morgan
- Human Genetics, School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Keeley Brookes
- School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, UK
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7
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Åsenius F, Gorrie-Stone TJ, Brew A, Panchbhaya Y, Williamson E, Schalkwyk LC, Rakyan VK, Holland ML, Marzi SJ, Williams DJ. The DNA methylome of human sperm is distinct from blood with little evidence for tissue-consistent obesity associations. PLoS Genet 2020; 16:e1009035. [PMID: 33048947 PMCID: PMC7584170 DOI: 10.1371/journal.pgen.1009035] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 10/23/2020] [Accepted: 08/07/2020] [Indexed: 12/19/2022] Open
Abstract
Epidemiological research suggests that paternal obesity may increase the risk of fathering small for gestational age offspring. Studies in non-human mammals indicate that such associations could be mediated by DNA methylation changes in spermatozoa that influence offspring development in utero. Human obesity is associated with differential DNA methylation in peripheral blood. It is unclear, however, whether this differential DNA methylation is reflected in spermatozoa. We profiled genome-wide DNA methylation using the Illumina MethylationEPIC array in a cross-sectional study of matched human blood and sperm from lean (discovery n = 47; replication n = 21) and obese (n = 22) males to analyse tissue covariation of DNA methylation, and identify obesity-associated methylomic signatures. We found that DNA methylation signatures of human blood and spermatozoa are highly discordant, and methylation levels are correlated at only a minority of CpG sites (~1%). At the majority of these sites, DNA methylation appears to be influenced by genetic variation. Obesity-associated DNA methylation in blood was not generally reflected in spermatozoa, and obesity was not associated with altered covariation patterns or accelerated epigenetic ageing in the two tissues. However, one cross-tissue obesity-specific hypermethylated site (cg19357369; chr4:2429884; P = 8.95 × 10-8; 2% DNA methylation difference) was identified, warranting replication and further investigation. When compared to a wide range of human somatic tissue samples (n = 5,917), spermatozoa displayed differential DNA methylation across pathways enriched in transcriptional regulation. Overall, human sperm displays a unique DNA methylation profile that is highly discordant to, and practically uncorrelated with, that of matched peripheral blood. We observed that obesity was only nominally associated with differential DNA methylation in sperm, and therefore suggest that spermatozoal DNA methylation is an unlikely mediator of intergenerational effects of metabolic traits.
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Affiliation(s)
- Fredrika Åsenius
- UCL EGA Institute for Women’s Health, University College London, London, United Kingdom
| | | | - Ama Brew
- The Blizard Institute, Queen Mary University of London, London, United Kingdom
| | - Yasmin Panchbhaya
- UCL Genomics, Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Elizabeth Williamson
- Fertility & reproductive medicine laboratory, University College Hospital, London, United Kingdom
| | | | - Vardhman K. Rakyan
- The Blizard Institute, Queen Mary University of London, London, United Kingdom
| | - Michelle L. Holland
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King’s College London, London, United Kingdom
| | - Sarah J. Marzi
- UK Dementia Research Institute, Imperial College London, London, United Kingdom
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - David J. Williams
- UCL EGA Institute for Women’s Health, University College London, London, United Kingdom
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8
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El Khoury LY, Gorrie-Stone T, Smart M, Hughes A, Bao Y, Andrayas A, Burrage J, Hannon E, Kumari M, Mill J, Schalkwyk LC. Systematic underestimation of the epigenetic clock and age acceleration in older subjects. Genome Biol 2019; 20:283. [PMID: 31847916 PMCID: PMC6915902 DOI: 10.1186/s13059-019-1810-4] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 09/03/2019] [Indexed: 11/18/2022] Open
Abstract
Background The Horvath epigenetic clock is widely used. It predicts age quite well from 353 CpG sites in the DNA methylation profile in unknown samples and has been used to calculate “age acceleration” in various tissues and environments. Results The model systematically underestimates age in tissues from older people. This is seen in all examined tissues but most strongly in the cerebellum and is consistently observed in multiple datasets. Age acceleration is thus age-dependent, and this can lead to spurious associations. The current literature includes examples of association tests with age acceleration calculated in a wide variety of ways. Conclusions The concept of an epigenetic clock is compelling, but caution should be taken in interpreting associations with age acceleration. Association tests of age acceleration should include age as a covariate.
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Affiliation(s)
- Louis Y El Khoury
- School of Life Sciences, University of Essex, Colchester, UK.,Present Address: Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | | | - Melissa Smart
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | - Amanda Hughes
- MRC Integrative Epidemiology Unit - University of Bristol, Bristol, UK
| | - Yanchun Bao
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | | | - Joe Burrage
- Medical School, University of Exeter, Exeter, UK
| | - Eilis Hannon
- Medical School, University of Exeter, Exeter, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester, UK
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9
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Bell CG, Lowe R, Adams PD, Baccarelli AA, Beck S, Bell JT, Christensen BC, Gladyshev VN, Heijmans BT, Horvath S, Ideker T, Issa JPJ, Kelsey KT, Marioni RE, Reik W, Relton CL, Schalkwyk LC, Teschendorff AE, Wagner W, Zhang K, Rakyan VK. DNA methylation aging clocks: challenges and recommendations. Genome Biol 2019; 20:249. [PMID: 31767039 PMCID: PMC6876109 DOI: 10.1186/s13059-019-1824-y] [Citation(s) in RCA: 410] [Impact Index Per Article: 82.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 09/16/2019] [Indexed: 12/15/2022] Open
Abstract
Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual.
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Affiliation(s)
- Christopher G Bell
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| | - Robert Lowe
- The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| | - Peter D Adams
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA.
- Beatson Institute for Cancer Research and University of Glasgow, Glasgow, UK.
| | - Andrea A Baccarelli
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
| | - Stephan Beck
- Medical Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, London, UK.
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Department of Molecular and Systems Biology, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.
| | - Steve Horvath
- Department of Human Genetics, Gonda Research Center, David Geffen School of Medicine, Los Angeles, CA, USA.
- Department of Biostatistics, School of Public Health, University of California-Los Angeles, Los Angeles, CA, USA.
| | - Trey Ideker
- San Diego Center for Systems Biology, University of California-San Diego, San Diego, CA, USA.
| | - Jean-Pierre J Issa
- Fels Institute for Cancer Research, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA.
| | - Karl T Kelsey
- Department of Epidemiology, Brown University, Providence, RI, USA.
- Department of Pathology and Laboratory Medicine, Brown University, Providence, RI, USA.
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
| | - Wolf Reik
- Epigenetics Programme, The Babraham Institute, Cambridge, UK.
- The Wellcome Trust Sanger Institute, Cambridge, UK.
| | - Caroline L Relton
- Medical Research Council Integrative Epidemiology Unit (MRC IEU), School of Social and Community Medicine, University of Bristol, Bristol, UK.
| | | | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
- UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London, WC1E 6BT, UK.
| | - Wolfgang Wagner
- Helmholtz-Institute for Biomedical Engineering, Stem Cell Biology and Cellular Engineering, RWTH Aachen Faculty of Medicine, Aachen, Germany.
| | - Kang Zhang
- Faculty of Medicine, Macau University of Science and Technology, Taipa, Macau.
| | - Vardhman K Rakyan
- The Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
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10
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Saffari A, Arno M, Nasser E, Ronald A, Wong CCY, Schalkwyk LC, Mill J, Dudbridge F, Meaburn EL. RNA sequencing of identical twins discordant for autism reveals blood-based signatures implicating immune and transcriptional dysregulation. Mol Autism 2019; 10:38. [PMID: 31719968 PMCID: PMC6839145 DOI: 10.1186/s13229-019-0285-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 09/01/2019] [Indexed: 11/13/2022] Open
Abstract
Background A gap exists in our mechanistic understanding of how genetic and environmental risk factors converge at the molecular level to result in the emergence of autism symptoms. We compared blood-based gene expression signatures in identical twins concordant and discordant for autism spectrum condition (ASC) to differentiate genetic and environmentally driven transcription differences, and establish convergent evidence for biological mechanisms involved in ASC. Methods Genome-wide gene expression data were generated using RNA-seq on whole blood samples taken from 16 pairs of monozygotic (MZ) twins and seven twin pair members (39 individuals in total), who had been assessed for ASC and autism traits at age 12. Differential expression (DE) analyses were performed between (a) affected and unaffected subjects (N = 36) and (b) within discordant ASC MZ twin pairs (total N = 11) to identify environmental-driven DE. Gene set enrichment and pathway testing was performed on DE gene lists. Finally, an integrative analysis using DNA methylation data aimed to identify genes with consistent evidence for altered regulation in cis. Results In the discordant twin analysis, three genes showed evidence for DE at FDR < 10%: IGHG4, EVI2A and SNORD15B. In the case-control analysis, four DE genes were identified at FDR < 10% including IGHG4, PRR13P5, DEPDC1B, and ZNF501. We find enrichment for DE of genes curated in the SFARI human gene database. Pathways showing evidence of enrichment included those related to immune cell signalling and immune response, transcriptional control and cell cycle/proliferation. Integrative methylomic and transcriptomic analysis identified a number of genes showing suggestive evidence for cis dysregulation. Limitations Identical twins stably discordant for ASC are rare, and as such the sample size was limited and constrained to the use of peripheral blood tissue for transcriptomic and methylomic profiling. Given these primary limitations, we focused on transcript-level analysis. Conclusions Using a cohort of ASC discordant and concordant MZ twins, we add to the growing body of transcriptomic-based evidence for an immune-based component in the molecular aetiology of ASC. Whilst the sample size was limited, the study demonstrates the utility of the discordant MZ twin design combined with multi-omics integration for maximising the potential to identify disease-associated molecular signals.
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Affiliation(s)
- Ayden Saffari
- 1Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- 2Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, UK
| | - Matt Arno
- 3Edinburgh Genomics, University of Edinburgh, Edinburgh, Scotland UK
- 4King's Genomics Centre, King's College London, London, UK
| | - Eric Nasser
- 4King's Genomics Centre, King's College London, London, UK
| | - Angelica Ronald
- 2Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, UK
| | - Chloe C Y Wong
- 5Social Genetic and Developmental Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Jonathan Mill
- 7University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Frank Dudbridge
- 1Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- 8Department of Health Sciences, University of Leicester, Leicester, UK
| | - Emma L Meaburn
- 2Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, UK
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Wong CCY, Smith RG, Hannon E, Ramaswami G, Parikshak NN, Assary E, Troakes C, Poschmann J, Schalkwyk LC, Sun W, Prabhakar S, Geschwind DH, Mill J. Genome-wide DNA methylation profiling identifies convergent molecular signatures associated with idiopathic and syndromic autism in post-mortem human brain tissue. Hum Mol Genet 2019; 28:2201-2211. [PMID: 31220268 PMCID: PMC6602383 DOI: 10.1093/hmg/ddz052] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 03/04/2019] [Accepted: 03/04/2019] [Indexed: 12/13/2022] Open
Abstract
Autism spectrum disorder (ASD) encompasses a collection of complex neuropsychiatric disorders characterized by deficits in social functioning, communication and repetitive behaviour. Building on recent studies supporting a role for developmentally moderated regulatory genomic variation in the molecular aetiology of ASD, we quantified genome-wide patterns of DNA methylation in 223 post-mortem tissues samples isolated from three brain regions [prefrontal cortex, temporal cortex and cerebellum (CB)] dissected from 43 ASD patients and 38 non-psychiatric control donors. We identified widespread differences in DNA methylation associated with idiopathic ASD (iASD), with consistent signals in both cortical regions that were distinct to those observed in the CB. Individuals carrying a duplication on chromosome 15q (dup15q), representing a genetically defined subtype of ASD, were characterized by striking differences in DNA methylationacross a discrete domain spanning an imprinted gene cluster within the duplicated region. In addition to the dramatic cis-effects on DNA methylation observed in dup15q carriers, we identified convergent methylomic signatures associated with both iASD and dup15q, reflecting the findings from previous studies of gene expression and H3K27ac. Cortical co-methylation network analysis identified a number of co-methylated modules significantly associated with ASD that are enriched for genomic regions annotated to genes involved in the immune system, synaptic signalling and neuronal regulation. Our study represents the first systematic analysis of DNA methylation associated with ASD across multiple brain regions, providing novel evidence for convergent molecular signatures associated with both idiopathic and syndromic autism.
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Affiliation(s)
- Chloe C Y Wong
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, UK
| | - Rebecca G Smith
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Gokul Ramaswami
- Center for Autism Research and Treatment, and Program in Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Neelroop N Parikshak
- Center for Autism Research and Treatment, and Program in Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Elham Assary
- Department of Biological and Experimental Psychology, School of Biological and Chemical Sciences, Queen Mary University of London, London, UK
| | - Claire Troakes
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, UK
| | - Jeremie Poschmann
- Centre de Recherche en Transplantation et Immunologie, Institut de Transplantation Urologie Néphrologie (ITUN), CHU Nantes, Inserm, Université de Nantes, Nantes, France
| | | | - Wenjie Sun
- Computational and Systems Biology, Genome Institute of Singapore, Singapore
| | - Shyam Prabhakar
- Computational and Systems Biology, Genome Institute of Singapore, Singapore
| | - Daniel H Geschwind
- Center for Autism Research and Treatment, and Program in Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, UK
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12
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Gorrie-Stone TJ, Smart MC, Saffari A, Malki K, Hannon E, Burrage J, Mill J, Kumari M, Schalkwyk LC. Bigmelon: tools for analysing large DNA methylation datasets. Bioinformatics 2019; 35:981-986. [PMID: 30875430 PMCID: PMC6419913 DOI: 10.1093/bioinformatics/bty713] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Revised: 06/04/2018] [Accepted: 08/20/2018] [Indexed: 01/12/2023] Open
Abstract
MOTIVATION The datasets generated by DNA methylation analyses are getting bigger. With the release of the HumanMethylationEPIC micro-array and datasets containing thousands of samples, analyses of these large datasets using R are becoming impractical due to large memory requirements. As a result there is an increasing need for computationally efficient methodologies to perform meaningful analysis on high dimensional data. RESULTS Here we introduce the bigmelon R package, which provides a memory efficient workflow that enables users to perform the complex, large scale analyses required in epigenome wide association studies (EWAS) without the need for large RAM. Building on top of the CoreArray Genomic Data Structure file format and libraries packaged in the gdsfmt package, we provide a practical workflow that facilitates the reading-in, preprocessing, quality control and statistical analysis of DNA methylation data.We demonstrate the capabilities of the bigmelon package using a large dataset consisting of 1193 human blood samples from the Understanding Society: UK Household Longitudinal Study, assayed on the EPIC micro-array platform. AVAILABILITY AND IMPLEMENTATION The bigmelon package is available on Bioconductor (http://bioconductor.org/packages/bigmelon/). The Understanding Society dataset is available at https://www.understandingsociety.ac.uk/about/health/data upon request. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Melissa C Smart
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | - Ayden Saffari
- Department of Psychological Sciences, Birkbeck, University of London, London, UK
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- MRC Unit, The Gambia and MRC International Nutrition Group, London School of Hygiene and Tropical Medicine, London, UK
| | - Karim Malki
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Joe Burrage
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester, UK
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13
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O'Brien HE, Hannon E, Hill MJ, Toste CC, Robertson MJ, Morgan JE, McLaughlin G, Lewis CM, Schalkwyk LC, Hall LS, Pardiñas AF, Owen MJ, O'Donovan MC, Mill J, Bray NJ. Expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders. Genome Biol 2018; 19:194. [PMID: 30419947 PMCID: PMC6231252 DOI: 10.1186/s13059-018-1567-1] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Accepted: 10/18/2018] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Genetic influences on gene expression in the human fetal brain plausibly impact upon a variety of postnatal brain-related traits, including susceptibility to neuropsychiatric disorders. However, to date, there have been no studies that have mapped genome-wide expression quantitative trait loci (eQTL) specifically in the human prenatal brain. RESULTS We performed deep RNA sequencing and genome-wide genotyping on a unique collection of 120 human brains from the second trimester of gestation to provide the first eQTL dataset derived exclusively from the human fetal brain. We identify high confidence cis-acting eQTL at the individual transcript as well as whole gene level, including many mapping to a common inversion polymorphism on chromosome 17q21. Fetal brain eQTL are enriched among risk variants for postnatal conditions including attention deficit hyperactivity disorder, schizophrenia, and bipolar disorder. We further identify changes in gene expression within the prenatal brain that potentially mediate risk for neuropsychiatric traits, including increased expression of C4A in association with genetic risk for schizophrenia, increased expression of LRRC57 in association with genetic risk for bipolar disorder, and altered expression of multiple genes within the chromosome 17q21 inversion in association with variants influencing the personality trait of neuroticism. CONCLUSIONS We have mapped eQTL operating in the human fetal brain, providing evidence that these confer risk to certain neuropsychiatric disorders, and identifying gene expression changes that potentially mediate susceptibility to these conditions.
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Affiliation(s)
- Heath E O'Brien
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Matthew J Hill
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Carolina C Toste
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Matthew J Robertson
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Joanne E Morgan
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Gemma McLaughlin
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Cathryn M Lewis
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Lynsey S Hall
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Michael J Owen
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Michael C O'Donovan
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Nicholas J Bray
- MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University, Cardiff, UK.
- MRC Centre for Neuropsychiatric Genetics & Genomics, Cardiff University School of Medicine, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ, UK.
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14
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Hannon E, Gorrie-Stone TJ, Smart MC, Burrage J, Hughes A, Bao Y, Kumari M, Schalkwyk LC, Mill J. Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship between Methylomic Variation, Gene Expression, and Complex Traits. Am J Hum Genet 2018; 103:654-665. [PMID: 30401456 PMCID: PMC6217758 DOI: 10.1016/j.ajhg.2018.09.007] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 09/14/2018] [Indexed: 11/23/2022] Open
Abstract
Characterizing the complex relationship between genetic, epigenetic, and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. We undertook a comprehensive analysis of common genetic variation on DNA methylation (DNAm) by using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (p < 6.52 × 10-14) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified by previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits by using summary-data-based Mendelian randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression and thereby identify 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database as a resource to the research community.
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Affiliation(s)
- Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom
| | - Tyler J Gorrie-Stone
- School of Biological Sciences, University of Essex, Colchester, CO4 3SQ, United Kingdom
| | - Melissa C Smart
- Institute for Social and Economic Research, University of Essex, Colchester CO3 3LG, United Kingdom
| | - Joe Burrage
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom
| | - Amanda Hughes
- Institute for Social and Economic Research, University of Essex, Colchester CO3 3LG, United Kingdom
| | - Yanchun Bao
- Institute for Social and Economic Research, University of Essex, Colchester CO3 3LG, United Kingdom
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester CO3 3LG, United Kingdom
| | - Leonard C Schalkwyk
- School of Biological Sciences, University of Essex, Colchester, CO4 3SQ, United Kingdom
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom.
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15
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Marzi SJ, Leung SK, Ribarska T, Hannon E, Smith AR, Pishva E, Poschmann J, Moore K, Troakes C, Al-Sarraj S, Beck S, Newman S, Lunnon K, Schalkwyk LC, Mill J. A histone acetylome-wide association study of Alzheimer's disease identifies disease-associated H3K27ac differences in the entorhinal cortex. Nat Neurosci 2018; 21:1618-1627. [PMID: 30349106 DOI: 10.1038/s41593-018-0253-7] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 09/12/2018] [Indexed: 12/17/2022]
Abstract
We quantified genome-wide patterns of lysine H3K27 acetylation (H3K27ac) in entorhinal cortex samples from Alzheimer's disease (AD) cases and matched controls using chromatin immunoprecipitation and highly parallel sequencing. We observed widespread acetylomic variation associated with AD neuropathology, identifying 4,162 differential peaks (false discovery rate < 0.05) between AD cases and controls. Differentially acetylated peaks were enriched in disease-related biological pathways and included regions annotated to genes involved in the progression of amyloid-β and tau pathology (for example, APP, PSEN1, PSEN2, and MAPT), as well as regions containing variants associated with sporadic late-onset AD. Partitioned heritability analysis highlighted a highly significant enrichment of AD risk variants in entorhinal cortex H3K27ac peak regions. AD-associated variable H3K27ac was associated with transcriptional variation at proximal genes including CR1, GPR22, KMO, PIM3, PSEN1, and RGCC. In addition to identifying molecular pathways associated with AD neuropathology, we present a framework for genome-wide studies of histone modifications in complex disease.
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Affiliation(s)
- Sarah J Marzi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- The Blizard Institute, Queen Mary University of London, London, UK
| | - Szi Kay Leung
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | | | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Adam R Smith
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Ehsan Pishva
- University of Exeter Medical School, University of Exeter, Exeter, UK
- Department of Psychiatry and Neuropsychology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Jeremie Poschmann
- University of Exeter Medical School, University of Exeter, Exeter, UK
- Centre de Recherche en Transplantation et Immunologie, Inserm, Université de Nantes, Nantes, France
| | - Karen Moore
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Claire Troakes
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Safa Al-Sarraj
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Stephan Beck
- UCL Cancer Institute, University College London, London, UK
| | | | - Katie Lunnon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | | | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, UK.
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16
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Viana J, Hannon E, Dempster E, Pidsley R, Macdonald R, Knox O, Spiers H, Troakes C, Al-Saraj S, Turecki G, Schalkwyk LC, Mill J. Schizophrenia-associated methylomic variation: molecular signatures of disease and polygenic risk burden across multiple brain regions. Hum Mol Genet 2017; 26:210-225. [PMID: 28011714 PMCID: PMC5351932 DOI: 10.1093/hmg/ddw373] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Accepted: 10/26/2016] [Indexed: 01/29/2023] Open
Abstract
Genetic association studies provide evidence for a substantial polygenic component to schizophrenia, although the neurobiological mechanisms underlying the disorder remain largely undefined. Building on recent studies supporting a role for developmentally regulated epigenetic variation in the molecular aetiology of schizophrenia, this study aimed to identify epigenetic variation associated with both a diagnosis of schizophrenia and elevated polygenic risk burden for the disease across multiple brain regions. Genome-wide DNA methylation was quantified in 262 post-mortem brain samples, representing tissue from four brain regions (prefrontal cortex, striatum, hippocampus and cerebellum) from 41 schizophrenia patients and 47 controls. We identified multiple disease-associated and polygenic risk score-associated differentially methylated positions and regions, which are not enriched in genomic regions identified in genetic studies of schizophrenia and do not reflect direct genetic effects on DNA methylation. Our study represents the first analysis of epigenetic variation associated with schizophrenia across multiple brain regions and highlights the utility of polygenic risk scores for identifying molecular pathways associated with aetiological variation in complex disease.
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Affiliation(s)
- Joana Viana
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Emma Dempster
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Ruth Pidsley
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Ruby Macdonald
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Olivia Knox
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Helen Spiers
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Claire Troakes
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Safa Al-Saraj
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Gustavo Turecki
- Douglas Mental Health Institute, McGill University, Montreal, QC, Canada and
| | | | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, UK.,Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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17
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Spiers H, Hannon E, Schalkwyk LC, Bray NJ, Mill J. 5-hydroxymethylcytosine is highly dynamic across human fetal brain development. BMC Genomics 2017; 18:738. [PMID: 28923016 PMCID: PMC5604137 DOI: 10.1186/s12864-017-4091-x] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 08/25/2017] [Indexed: 12/30/2022] Open
Abstract
Background Epigenetic processes play a key role in orchestrating transcriptional regulation during the development of the human central nervous system. We previously described dynamic changes in DNA methylation (5mC) occurring during human fetal brain development, but other epigenetic processes operating during this period have not been extensively explored. Of particular interest is DNA hydroxymethylation (5hmC), a modification that is enriched in the human brain and hypothesized to play an important role in neuronal function, learning and memory. In this study, we quantify 5hmC across the genome of 71 human fetal brain samples spanning 23 to 184 days post-conception. Results We identify widespread changes in 5hmC occurring during human brain development, notable sex-differences in 5hmC in the fetal brain, and interactions between 5mC and 5hmC at specific sites. Finally, we identify loci where 5hmC in the fetal brain is associated with genetic variation. Conclusions This study represents the first systematic analysis of dynamic changes in 5hmC across human neurodevelopment and highlights the potential importance of this modification in the human brain. A searchable database of our fetal brain 5hmC data is available as a resource to the research community at http://www.epigenomicslab.com/online-data-resources. Electronic supplementary material The online version of this article doi:(10.1186/s12864-017-4091-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Helen Spiers
- Department of Physics, University of Oxford, Oxford, OX1 3RH, UK
| | - Eilis Hannon
- University of Exeter Medical School, RILD Building, Royal Devon and Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | | | - Nicholas J Bray
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, CF24 4HQ, UK
| | - Jonathan Mill
- University of Exeter Medical School, RILD Building, Royal Devon and Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK.
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18
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Janecka M, Haworth CM, Ronald A, Krapohl E, Happé F, Mill J, Schalkwyk LC, Fernandes C, Reichenberg A, Rijsdijk F. Paternal Age Alters Social Development in Offspring. J Am Acad Child Adolesc Psychiatry 2017; 56:383-390. [PMID: 28433087 PMCID: PMC5409803 DOI: 10.1016/j.jaac.2017.02.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Revised: 02/10/2017] [Accepted: 02/27/2017] [Indexed: 01/09/2023]
Abstract
OBJECTIVE Advanced paternal age (APA) at conception has been linked with autism and schizophrenia in offspring, neurodevelopmental disorders that affect social functioning. The current study explored the effects of paternal age on social development in the general population. METHOD We used multilevel growth modeling to investigate APA effects on socioemotional development from early childhood until adolescence, as measured by the Strengths and Difficulties Questionnaire (SDQ) in the Twins Early Development Study (TEDS) sample. We also investigated genetic and environmental underpinnings of the paternal age effects on development, using the Additive genetics, Common environment, unique Environment (ACE) and gene-environment (GxE) models. RESULTS In the general population, both very young and advanced paternal ages were associated with altered trajectory of social development (intercept: p = .01; slope: p = .03). No other behavioral domain was affected by either young or advanced age at fatherhood, suggesting specificity of paternal age effects. Increased importance of genetic factors in social development was recorded in the offspring of older but not very young fathers, suggesting distinct underpinnings of the paternal age effects at these two extremes. CONCLUSION Our findings highlight that the APA-related deficits that lead to autism and schizophrenia are likely continuously distributed in the population.
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Affiliation(s)
- Magdalena Janecka
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, King's College London, UK; Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York.
| | - Claire M.A. Haworth
- MRC Integrative Epidemiology Unit, School of Experimental Psychology and School of Social and Community Medicine, University of Bristol, UK
| | | | - Eva Krapohl
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, King's College London, UK
| | - Francesca Happé
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, King's College London, UK
| | - Jonathan Mill
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, King's College London, UK,University of Exeter Medical School, University of Exeter, Exeter, UK
| | | | - Cathy Fernandes
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, King's College London, UK
| | - Abraham Reichenberg
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York
| | - Frühling Rijsdijk
- Social, Genetic and Developmental Psychiatry (SGDP) Centre, King's College London, UK
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19
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Malki K, Tosto MG, Mouriño‐Talín H, Rodríguez‐Lorenzo S, Pain O, Jumhaboy I, Liu T, Parpas P, Newman S, Malykh A, Carboni L, Uher R, McGuffin P, Schalkwyk LC, Bryson K, Herbster M. Highly polygenic architecture of antidepressant treatment response: Comparative analysis of SSRI and NRI treatment in an animal model of depression. Am J Med Genet B Neuropsychiatr Genet 2017; 174:235-250. [PMID: 27696737 PMCID: PMC5434854 DOI: 10.1002/ajmg.b.32494] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 08/15/2016] [Indexed: 11/12/2022]
Abstract
Response to antidepressant (AD) treatment may be a more polygenic trait than previously hypothesized, with many genetic variants interacting in yet unclear ways. In this study we used methods that can automatically learn to detect patterns of statistical regularity from a sparsely distributed signal across hippocampal transcriptome measurements in a large-scale animal pharmacogenomic study to uncover genomic variations associated with AD. The study used four inbred mouse strains of both sexes, two drug treatments, and a control group (escitalopram, nortriptyline, and saline). Multi-class and binary classification using Machine Learning (ML) and regularization algorithms using iterative and univariate feature selection methods, including InfoGain, mRMR, ANOVA, and Chi Square, were used to uncover genomic markers associated with AD response. Relevant genes were selected based on Jaccard distance and carried forward for gene-network analysis. Linear association methods uncovered only one gene associated with drug treatment response. The implementation of ML algorithms, together with feature reduction methods, revealed a set of 204 genes associated with SSRI and 241 genes associated with NRI response. Although only 10% of genes overlapped across the two drugs, network analysis shows that both drugs modulated the CREB pathway, through different molecular mechanisms. Through careful implementation and optimisations, the algorithms detected a weak signal used to predict whether an animal was treated with nortriptyline (77%) or escitalopram (67%) on an independent testing set. The results from this study indicate that the molecular signature of AD treatment may include a much broader range of genomic markers than previously hypothesized, suggesting that response to medication may be as complex as the pathology. The search for biomarkers of antidepressant treatment response could therefore consider a higher number of genetic markers and their interactions. Through predominately different molecular targets and mechanisms of action, the two drugs modulate the same Creb1 pathway which plays a key role in neurotrophic responses and in inflammatory processes. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Karim Malki
- King's College LondonMRC SocialGenetic and Developmental Psychiatry Centre at the Institute of PsychiatryPsychology and Neuroscience (IOPPN)LondonUnited Kingdom
| | - Maria Grazia Tosto
- King's College LondonMRC SocialGenetic and Developmental Psychiatry Centre at the Institute of PsychiatryPsychology and Neuroscience (IOPPN)LondonUnited Kingdom,LCIBGTomsk State UniversityTomskRussia
| | | | | | - Oliver Pain
- BirkbeckUniversity of LondonUnited Kingdom,London School of Hygiene & Tropical MedicineUnited Kingdom
| | - Irfan Jumhaboy
- King's College LondonMRC SocialGenetic and Developmental Psychiatry Centre at the Institute of PsychiatryPsychology and Neuroscience (IOPPN)LondonUnited Kingdom
| | - Tina Liu
- Department of Computer Science Imperial College LondonUnited Kingdom
| | - Panos Parpas
- Department of Computer Science Imperial College LondonUnited Kingdom
| | - Stuart Newman
- King's College LondonMRC SocialGenetic and Developmental Psychiatry Centre at the Institute of PsychiatryPsychology and Neuroscience (IOPPN)LondonUnited Kingdom
| | | | - Lucia Carboni
- Department of Pharmacy and BiotechnologyAlma Mater Studiorum University of BolognaBolognaItaly
| | - Rudolf Uher
- King's College LondonMRC SocialGenetic and Developmental Psychiatry Centre at the Institute of PsychiatryPsychology and Neuroscience (IOPPN)LondonUnited Kingdom,Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada
| | - Peter McGuffin
- King's College LondonMRC SocialGenetic and Developmental Psychiatry Centre at the Institute of PsychiatryPsychology and Neuroscience (IOPPN)LondonUnited Kingdom
| | | | - Kevin Bryson
- Department of Computer ScienceUCLLondonUnited Kingdom
| | - Mark Herbster
- Department of Computer ScienceUCLLondonUnited Kingdom
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20
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Malki K, Du Rietz E, Crusio WE, Pain O, Paya-Cano J, Karadaghi RL, Sluyter F, de Boer SF, Sandnabba K, Schalkwyk LC, Asherson P, Tosto MG. Transcriptome analysis of genes and gene networks involved in aggressive behavior in mouse and zebrafish. Am J Med Genet B Neuropsychiatr Genet 2016; 171:827-38. [PMID: 27090961 DOI: 10.1002/ajmg.b.32451] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 04/01/2016] [Indexed: 01/01/2023]
Abstract
Despite moderate heritability estimates, the molecular architecture of aggressive behavior remains poorly characterized. This study compared gene expression profiles from a genetic mouse model of aggression with zebrafish, an animal model traditionally used to study aggression. A meta-analytic, cross-species approach was used to identify genomic variants associated with aggressive behavior. The Rankprod algorithm was used to evaluated mRNA differences from prefrontal cortex tissues of three sets of mouse lines (N = 18) selectively bred for low and high aggressive behavior (SAL/LAL, TA/TNA, and NC900/NC100). The same approach was used to evaluate mRNA differences in zebrafish (N = 12) exposed to aggressive or non-aggressive social encounters. Results were compared to uncover genes consistently implicated in aggression across both studies. Seventy-six genes were differentially expressed (PFP < 0.05) in aggressive compared to non-aggressive mice. Seventy genes were differentially expressed in zebrafish exposed to a fight encounter compared to isolated zebrafish. Seven genes (Fos, Dusp1, Hdac4, Ier2, Bdnf, Btg2, and Nr4a1) were differentially expressed across both species 5 of which belonging to a gene-network centred on the c-Fos gene hub. Network analysis revealed an association with the MAPK signaling cascade. In human studies HDAC4 haploinsufficiency is a key genetic mechanism associated with brachydactyly mental retardation syndrome (BDMR), which is associated with aggressive behaviors. Moreover, the HDAC4 receptor is a drug target for valproic acid, which is being employed as an effective pharmacological treatment for aggressive behavior in geriatric, psychiatric, and brain-injury patients. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Karim Malki
- King's College London, Social, Genetic and Developmental Psychiatry Centre (MRC), Institute of Psychiatry, Psychology and Neuroscience, United Kingdom
| | - Ebba Du Rietz
- King's College London, Social, Genetic and Developmental Psychiatry Centre (MRC), Institute of Psychiatry, Psychology and Neuroscience, United Kingdom
| | - Wim E Crusio
- University of Bordeaux, Aquitaine Institute for Cognitive and Integrative Neuroscience, Bordeaux, France.,CNRS, Aquitaine Institute for Cognitive and Integrative Neuroscience, Bordeaux, France
| | - Oliver Pain
- Centre of Brain and Cognitive Development, Birkbeck, University of London, United Kingdom.,Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Jose Paya-Cano
- King's College London, Social, Genetic and Developmental Psychiatry Centre (MRC), Institute of Psychiatry, Psychology and Neuroscience, United Kingdom
| | - Rezhaw L Karadaghi
- King's College London, Social, Genetic and Developmental Psychiatry Centre (MRC), Institute of Psychiatry, Psychology and Neuroscience, United Kingdom
| | - Frans Sluyter
- King's College London, Social, Genetic and Developmental Psychiatry Centre (MRC), Institute of Psychiatry, Psychology and Neuroscience, United Kingdom
| | - Sietse F de Boer
- Groningen Institute for Evolutionary LifeSciences (GELIFES), University of Groningen, Groningen, The Netherlands
| | - Kenneth Sandnabba
- Faculty of Arts, Psychology and Theology, Åbo Akademi University, Turku, Finland
| | - Leonard C Schalkwyk
- School of Biological Sciences, University of Essex, Colchester, United Kingdom
| | - Philip Asherson
- King's College London, Social, Genetic and Developmental Psychiatry Centre (MRC), Institute of Psychiatry, Psychology and Neuroscience, United Kingdom
| | - Maria Grazia Tosto
- King's College London, Social, Genetic and Developmental Psychiatry Centre (MRC), Institute of Psychiatry, Psychology and Neuroscience, United Kingdom.,Laboratory for Cognitive Investigations and Behavioural Genetics, Tomsk State University, Tomsk, Russia
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21
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Janecka M, Manduca A, Servadio M, Trezza V, Smith R, Mill J, Schalkwyk LC, Reichenberg A, Fernandes C. Effects of advanced paternal age on trajectories of social behavior in offspring. Genes Brain Behav 2016; 14:443-53. [PMID: 26096767 DOI: 10.1111/gbb.12227] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2015] [Revised: 06/06/2015] [Accepted: 06/16/2015] [Indexed: 12/15/2022]
Abstract
Our study is the first investigation of the effects of advanced paternal age (APA) on the developmental trajectory of social behavior in rodent offspring. Given the strong epidemiological association between APA and sexually dimorphic neurodevelopmental disorders that are characterized by abnormalities in social behavior (autism, schizophrenia), we assessed sociability in male and female inbred mice (C57BL/6J) across postnatal development (N = 104) in relation to paternal age. We found differences in early social behavior in both male and female offspring of older breeders, with differences in this social domain persisting into adulthood in males only. We showed that these social deficits were not present in the fathers of these offspring, confirming a de novo origin of an altered social trajectory in the offspring generation. Our results, highly novel in rodent research, support the epidemiological observations in humans and provide evidence for a causal link between APA, age-related changes in the paternal sperm DNA and neurodevelopmental disorders in their offspring.
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Affiliation(s)
- M Janecka
- Social, Genetic and Developmental Psychiatry MRC Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - A Manduca
- Department of Science, Roma Tre University, Rome, Italy
| | - M Servadio
- Department of Science, Roma Tre University, Rome, Italy
| | - V Trezza
- Department of Science, Roma Tre University, Rome, Italy
| | - R Smith
- Social, Genetic and Developmental Psychiatry MRC Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - J Mill
- Social, Genetic and Developmental Psychiatry MRC Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,University of Exeter Medical School, University of Exeter, Exeter, UK
| | - L C Schalkwyk
- Social, Genetic and Developmental Psychiatry MRC Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,School of Biological Sciences, University of Essex, Colchester, UK
| | - A Reichenberg
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - C Fernandes
- Social, Genetic and Developmental Psychiatry MRC Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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22
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Malki K, Tosto MG, Pain O, Sluyter F, Mineur YS, Crusio WE, de Boer S, Sandnabba KN, Kesserwani J, Robinson E, Schalkwyk LC, Asherson P. Comparative mRNA analysis of behavioral and genetic mouse models of aggression. Am J Med Genet B Neuropsychiatr Genet 2016; 171B:427-36. [PMID: 26888158 DOI: 10.1002/ajmg.b.32424] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 01/22/2016] [Indexed: 11/06/2022]
Abstract
Mouse models of aggression have traditionally compared strains, most notably BALB/cJ and C57BL/6. However, these strains were not designed to study aggression despite differences in aggression-related traits and distinct reactivity to stress. This study evaluated expression of genes differentially regulated in a stress (behavioral) mouse model of aggression with those from a recent genetic mouse model aggression. The study used a discovery-replication design using two independent mRNA studies from mouse brain tissue. The discovery study identified strain (BALB/cJ and C57BL/6J) × stress (chronic mild stress or control) interactions. Probe sets differentially regulated in the discovery set were intersected with those uncovered in the replication study, which evaluated differences between high and low aggressive animals from three strains specifically bred to study aggression. Network analysis was conducted on overlapping genes uncovered across both studies. A significant overlap was found with the genetic mouse study sharing 1,916 probe sets with the stress model. Fifty-one probe sets were found to be strongly dysregulated across both studies mapping to 50 known genes. Network analysis revealed two plausible pathways including one centered on the UBC gene hub which encodes ubiquitin, a protein well-known for protein degradation, and another on P38 MAPK. Findings from this study support the stress model of aggression, which showed remarkable molecular overlap with a genetic model. The study uncovered a set of candidate genes including the Erg2 gene, which has previously been implicated in different psychopathologies. The gene networks uncovered points at a Redox pathway as potentially being implicated in aggressive related behaviors.
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Affiliation(s)
- Karim Malki
- King's College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, London, United Kingdom
| | - Maria G Tosto
- King's College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, London, United Kingdom.,Laboratory for Cognitive Investigations and Behavioral Genetics, Tomsk State University, Tomsk, Russia
| | - Oliver Pain
- Centre for Brain and Cognitive Development, Birkbeck, University of London, London, United Kingdom.,Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Frans Sluyter
- King's College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, London, United Kingdom
| | - Yann S Mineur
- Department of Psychiatry, School of Medicine, Yale University, New Haven, Connecticut
| | - Wim E Crusio
- Aquitaine Institute for Cognitive and Integrative Neuroscience, University of Bordeaux, Bordeaux, France.,CNRS, Aquitaine Institute for Cognitive and Integrative Neuroscience, Bordeaux, France
| | - Sietse de Boer
- Groningen Institute for Evolutionary LifeSciences (GELIFES), University of Groningen, Groningen, The Netherlands
| | - Kenneth N Sandnabba
- Faculty of Arts, Psychology and Theology, Åbo Akademi University, Turku, Finland
| | - Jad Kesserwani
- King's College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, London, United Kingdom
| | - Edward Robinson
- King's College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, London, United Kingdom
| | - Leonard C Schalkwyk
- School of Biological Sciences, University of Essex, Colchester, United Kingdom
| | - Philip Asherson
- King's College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, London, United Kingdom
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23
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Marzi SJ, Meaburn EL, Dempster EL, Lunnon K, Paya-Cano JL, Smith RG, Volta M, Troakes C, Schalkwyk LC, Mill J. Tissue-specific patterns of allelically-skewed DNA methylation. Epigenetics 2016; 11:24-35. [PMID: 26786711 PMCID: PMC4846124 DOI: 10.1080/15592294.2015.1127479] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
While DNA methylation is usually thought to be symmetrical across both alleles, there are some notable exceptions. Genomic imprinting and X chromosome inactivation are two well-studied sources of allele-specific methylation (ASM), but recent research has indicated a more complex pattern in which genotypic variation can be associated with allelically-skewed DNA methylation in cis. Given the known heterogeneity of DNA methylation across tissues and cell types we explored inter- and intra-individual variation in ASM across several regions of the human brain and whole blood from multiple individuals. Consistent with previous studies, we find widespread ASM with > 4% of the ∼220,000 loci interrogated showing evidence of allelically-skewed DNA methylation. We identify ASM flanking known imprinted regions, and show that ASM sites are enriched in DNase I hypersensitivity sites and often located in an extended genomic context of intermediate DNA methylation. We also detect examples of genotype-driven ASM, some of which are tissue-specific. These findings contribute to our understanding of the nature of differential DNA methylation across tissues and have important implications for genetic studies of complex disease. As a resource to the community, ASM patterns across each of the tissues studied are available in a searchable online database: http://epigenetics.essex.ac.uk/ASMBrainBlood.
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Affiliation(s)
- Sarah J Marzi
- a Institute of Psychiatry, Psychology and Neuroscience, King's College London , London , UK
| | - Emma L Meaburn
- b Department of Psychological Sciences , Birkbeck, University of London , London , UK
| | - Emma L Dempster
- c University of Exeter Medical School, University of Exeter , Exeter , UK
| | - Katie Lunnon
- c University of Exeter Medical School, University of Exeter , Exeter , UK
| | - Jose L Paya-Cano
- a Institute of Psychiatry, Psychology and Neuroscience, King's College London , London , UK
| | - Rebecca G Smith
- c University of Exeter Medical School, University of Exeter , Exeter , UK
| | - Manuela Volta
- a Institute of Psychiatry, Psychology and Neuroscience, King's College London , London , UK
| | - Claire Troakes
- a Institute of Psychiatry, Psychology and Neuroscience, King's College London , London , UK
| | | | - Jonathan Mill
- a Institute of Psychiatry, Psychology and Neuroscience, King's College London , London , UK.,c University of Exeter Medical School, University of Exeter , Exeter , UK
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24
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Hannon E, Spiers H, Viana J, Pidsley R, Burrage J, Murphy TM, Troakes C, Turecki G, O'Donovan MC, Schalkwyk LC, Bray NJ, Mill J. Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Nat Neurosci 2015; 19:48-54. [PMID: 26619357 PMCID: PMC4714325 DOI: 10.1038/nn.4182] [Citation(s) in RCA: 242] [Impact Index Per Article: 26.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Accepted: 10/30/2015] [Indexed: 12/12/2022]
Abstract
We characterized DNA methylation quantitative trait loci (mQTLs) in a large collection (n = 166) of human fetal brain samples spanning 56-166 d post-conception, identifying >16,000 fetal brain mQTLs. Fetal brain mQTLs were primarily cis-acting, enriched in regulatory chromatin domains and transcription factor binding sites, and showed substantial overlap with genetic variants that were also associated with gene expression in the brain. Using tissue from three distinct regions of the adult brain (prefrontal cortex, striatum and cerebellum), we found that most fetal brain mQTLs were developmentally stable, although a subset was characterized by fetal-specific effects. Fetal brain mQTLs were enriched amongst risk loci identified in a recent large-scale genome-wide association study (GWAS) of schizophrenia, a severe psychiatric disorder with a hypothesized neurodevelopmental component. Finally, we found that mQTLs can be used to refine GWAS loci through the identification of discrete sites of variable fetal brain methylation associated with schizophrenia risk variants.
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Affiliation(s)
- Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Helen Spiers
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Joana Viana
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Ruth Pidsley
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Joe Burrage
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Therese M Murphy
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Claire Troakes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Gustavo Turecki
- Douglas Mental Health Institute, McGill University, Montreal, QC, Canada
| | - Michael C O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
| | | | - Nicholas J Bray
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
| | - Jonathan Mill
- University of Exeter Medical School, University of Exeter, Exeter, UK.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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25
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Pidsley R, Viana J, Hannon E, Spiers H, Troakes C, Al-Saraj S, Mechawar N, Turecki G, Schalkwyk LC, Bray NJ, Mill J. Methylomic profiling of human brain tissue supports a neurodevelopmental origin for schizophrenia. Genome Biol 2015; 15:483. [PMID: 25347937 PMCID: PMC4262979 DOI: 10.1186/s13059-014-0483-2] [Citation(s) in RCA: 109] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2014] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Schizophrenia is a severe neuropsychiatric disorder that is hypothesized to result from disturbances ine arly brain development. There is mounting evidence to support a role for developmentally regulated epigenetic variation in the molecular etiology of the disorder. Here, we describe a systematic study of schizophrenia-associated methylomic variation in the adult brain and its relationship to changes in DNA methylation across human fetal brain development. RESULTS We profile methylomic variation in matched prefrontal cortex and cerebellum brain tissue from schizophrenia patients and controls, identifying disease-associated differential DNA methylation at multiple loci,particularly in the prefrontal cortex, and confirming these differences in an independent set of adult brain samples.Our data reveal discrete modules of co-methylated loci associated with schizophrenia that are enriched for genes involved in neurodevelopmental processes and include loci implicated by genetic studies of the disorder. Methylomic data from human fetal cortex samples, spanning 23 to 184 days post-conception, indicates that schizophrenia-associated differentially methylated positions are significantly enriched for loci at which DNA methylation is dynamically altered during human fetal brain development. CONCLUSIONS Our data support the hypothesis that schizophrenia has an important early neurodevelopmental component, and suggest that epigenetic mechanisms may mediate these effects.
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Affiliation(s)
- Ruth Pidsley
- Institute of Psychiatry, King’s College London, London, SE5 8AF, UK
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26
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Malki K, Mineur YS, Tosto MG, Campbell J, Karia P, Jumabhoy I, Sluyter F, Crusio WE, Schalkwyk LC. Pervasive and opposing effects of Unpredictable Chronic Mild Stress (UCMS) on hippocampal gene expression in BALB/cJ and C57BL/6J mouse strains. BMC Genomics 2015; 16:262. [PMID: 25879669 PMCID: PMC4412144 DOI: 10.1186/s12864-015-1431-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2014] [Accepted: 03/03/2015] [Indexed: 01/03/2023] Open
Abstract
Background BALB/cJ is a strain susceptible to stress and extremely susceptible to a defective hedonic impact in response to chronic stressors. The strain offers much promise as an animal model for the study of stress related disorders. We present a comparative hippocampal gene expression study on the effects of unpredictable chronic mild stress on BALB/cJ and C57BL/6J mice. Affymetrix MOE 430 was used to measure hippocampal gene expression from 16 animals of two different strains (BALB/cJ and C57BL/6J) of both sexes and subjected to either unpredictable chronic mild stress (UCMS) or no stress. Differences were statistically evaluated through supervised and unsupervised linear modelling and using Weighted Gene Coexpression Network Analysis (WGCNA). In order to gain further understanding into mechanisms related to stress response, we cross-validated our results with a parallel study from the GENDEP project using WGCNA in a meta-analysis design. Results The effects of UCMS are visible through Principal Component Analysis which highlights the stress sensitivity of the BALB/cJ strain. A number of genes and gene networks related to stress response were uncovered including the Creb1 gene. WGCNA and pathway analysis revealed a gene network centered on Nfkb1. Results from the meta-analysis revealed a highly significant gene pathway centred on the Ubiquitin C (Ubc) gene. All pathways uncovered are associated with inflammation and immune response. Conclusions The study investigated the molecular mechanisms underlying the response to adverse environment in an animal model using a GxE design. Stress-related differences were visible at the genomic level through PCA analysis highlighting the high sensitivity of BALB/cJ animals to environmental stressors. Several candidate genes and gene networks reported are associated with inflammation and neurogenesis and could serve to inform candidate gene selection in human studies and provide additional insight into the pathology of Major Depressive Disorder. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1431-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Karim Malki
- MRC SGDP Centre, King's College London at the Institute of Psychiatry, PO80, DeCrespigny Park, London, UK.
| | - Yann S Mineur
- Present address: Department of Psychiatry, Yale School of Medicine, New Haven, USA. .,Brudnick Neuropsychiatric Research Institute, University of Massachusetts Medical School, Worcester, MA 01604, USA, USA.
| | - Maria Grazia Tosto
- MRC SGDP Centre, King's College London at the Institute of Psychiatry, PO80, DeCrespigny Park, London, UK. .,Department of Psychology, Tomsk State University, Tomsk, Russia.
| | | | - Priya Karia
- MRC SGDP Centre, King's College London at the Institute of Psychiatry, PO80, DeCrespigny Park, London, UK.
| | - Irfan Jumabhoy
- MRC SGDP Centre, King's College London at the Institute of Psychiatry, PO80, DeCrespigny Park, London, UK.
| | - Frans Sluyter
- MRC SGDP Centre, King's College London at the Institute of Psychiatry, PO80, DeCrespigny Park, London, UK.
| | - Wim E Crusio
- Present address: University of Bordeaux, Institute for Cognitive and Integrative Neuroscience (INCIA), Bordeaux, France. .,Brudnick Neuropsychiatric Research Institute, University of Massachusetts Medical School, Worcester, MA 01604, USA, USA.
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27
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Elliott G, Hong C, Xing X, Zhou X, Li D, Coarfa C, Bell RJ, Maire CL, Ligon KL, Sigaroudinia M, Gascard P, Tlsty TD, Harris RA, Schalkwyk LC, Bilenky M, Mill J, Farnham PJ, Kellis M, Marra MA, Milosavljevic A, Hirst M, Stormo GD, Wang T, Costello JF. Intermediate DNA methylation is a conserved signature of genome regulation. Nat Commun 2015; 6:6363. [PMID: 25691127 PMCID: PMC4333717 DOI: 10.1038/ncomms7363] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Accepted: 01/23/2015] [Indexed: 01/06/2023] Open
Abstract
The role of intermediate methylation states in DNA is unclear. Here, to comprehensively identify regions of intermediate methylation and their quantitative relationship with gene activity, we apply integrative and comparative epigenomics to 25 human primary cell and tissue samples. We report 18,452 intermediate methylation regions located near 36% of genes and enriched at enhancers, exons and DNase I hypersensitivity sites. Intermediate methylation regions average 57% methylation, are predominantly allele-independent and are conserved across individuals and between mouse and human, suggesting a conserved function. These regions have an intermediate level of active chromatin marks and their associated genes have intermediate transcriptional activity. Exonic intermediate methylation correlates with exon inclusion at a level between that of fully methylated and unmethylated exons, highlighting gene context-dependent functions. We conclude that intermediate DNA methylation is a conserved signature of gene regulation and exon usage.
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Affiliation(s)
- GiNell Elliott
- Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, Missouri 63108, USA
| | - Chibo Hong
- Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California 94115, USA
| | - Xiaoyun Xing
- Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, Missouri 63108, USA
| | - Xin Zhou
- Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, Missouri 63108, USA
| | - Daofeng Li
- Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, Missouri 63108, USA
| | - Cristian Coarfa
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Robert J.A. Bell
- Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California 94115, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, California 94158, USA
| | - Cecile L. Maire
- Department of Medical Oncology, Center for Molecular Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA
| | - Keith L. Ligon
- Department of Medical Oncology, Center for Molecular Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA
| | - Mahvash Sigaroudinia
- Department of Pathology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California 94115, USA
| | - Philippe Gascard
- Department of Pathology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California 94115, USA
| | - Thea D. Tlsty
- Department of Pathology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California 94115, USA
| | - R. Alan Harris
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | | | - Misha Bilenky
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 1L3, Canada
| | - Jonathan Mill
- Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, London WC2R 2LS, UK
- University of Exeter Medical School, Exeter University, St Luke's Campus, Exeter EX1 2LU, UK
| | - Peggy J. Farnham
- Department of Pharmacology and the Genome Center, University of California-Davis, Davis, California 95616, USA
| | - Manolis Kellis
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Marco A. Marra
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 1L3, Canada
| | - Aleksandar Milosavljevic
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Martin Hirst
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 1L3, Canada
- Centre for High-Throughput Biology and Department of Microbiology & Immunology, University of British Columbia, Vancouver, British Columbia, Canada V6T 1Z4
| | - Gary D. Stormo
- Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, Missouri 63108, USA
| | - Ting Wang
- Department of Genetics, Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, Missouri 63108, USA
| | - Joseph F. Costello
- Brain Tumor Research Center, Department of Neurosurgery, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California 94115, USA
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28
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Spiers H, Hannon E, Schalkwyk LC, Smith R, Wong CCY, O'Donovan MC, Bray NJ, Mill J. Methylomic trajectories across human fetal brain development. Genome Res 2015; 25:338-52. [PMID: 25650246 PMCID: PMC4352878 DOI: 10.1101/gr.180273.114] [Citation(s) in RCA: 195] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Abstract
Epigenetic processes play a key role in orchestrating transcriptional regulation during development. The importance of DNA methylation in fetal brain development is highlighted by the dynamic expression of de novo DNA methyltransferases during the perinatal period and neurodevelopmental deficits associated with mutations in the methyl-CpG binding protein 2 (MECP2) gene. However, our knowledge about the temporal changes to the epigenome during fetal brain development has, to date, been limited. We quantified genome-wide patterns of DNA methylation at ∼ 400,000 sites in 179 human fetal brain samples (100 male, 79 female) spanning 23 to 184 d post-conception. We identified highly significant changes in DNA methylation across fetal brain development at >7% of sites, with an enrichment of loci becoming hypomethylated with fetal age. Sites associated with developmental changes in DNA methylation during fetal brain development were significantly underrepresented in promoter regulatory regions but significantly overrepresented in regions flanking CpG islands (shores and shelves) and gene bodies. Highly significant differences in DNA methylation were observed between males and females at a number of autosomal sites, with a small number of regions showing sex-specific DNA methylation trajectories across brain development. Weighted gene comethylation network analysis (WGCNA) revealed discrete modules of comethylated loci associated with fetal age that are significantly enriched for genes involved in neurodevelopmental processes. This is, to our knowledge, the most extensive study of DNA methylation across human fetal brain development to date, confirming the prenatal period as a time of considerable epigenomic plasticity.
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Affiliation(s)
- Helen Spiers
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Eilis Hannon
- University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom
| | - Leonard C Schalkwyk
- School of Biological Sciences, University of Essex, Colchester CO4 3SQ, United Kingdom
| | - Rebecca Smith
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Chloe C Y Wong
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Michael C O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff CF24 4HQ, United Kingdom
| | - Nicholas J Bray
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Jonathan Mill
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom; University of Exeter Medical School, University of Exeter, Exeter EX2 5DW, United Kingdom;
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Lunnon K, Smith R, Hannon E, De Jager PL, Srivastava G, Volta M, Troakes C, Al-Sarraj S, Burrage J, Macdonald R, Condliffe D, Harries LW, Katsel P, Haroutunian V, Kaminsky Z, Joachim C, Powell J, Lovestone S, Bennett DA, Schalkwyk LC, Mill J. Methylomic profiling implicates cortical deregulation of ANK1 in Alzheimer's disease. Nat Neurosci 2014; 17:1164-70. [PMID: 25129077 PMCID: PMC4410018 DOI: 10.1038/nn.3782] [Citation(s) in RCA: 382] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2014] [Accepted: 07/07/2014] [Indexed: 02/08/2023]
Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disorder that is characterized by progressive neuropathology and cognitive decline. We performed a cross-tissue analysis of methylomic variation in AD using samples from four independent human post-mortem brain cohorts. We identified a differentially methylated region in the ankyrin 1 (ANK1) gene that was associated with neuropathology in the entorhinal cortex, a primary site of AD manifestation. This region was confirmed as being substantially hypermethylated in two other cortical regions (superior temporal gyrus and prefrontal cortex), but not in the cerebellum, a region largely protected from neurodegeneration in AD, or whole blood obtained pre-mortem from the same individuals. Neuropathology-associated ANK1 hypermethylation was subsequently confirmed in cortical samples from three independent brain cohorts. This study represents, to the best of our knowledge, the first epigenome-wide association study of AD employing a sequential replication design across multiple tissues and highlights the power of this approach for identifying methylomic variation associated with complex disease.
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Affiliation(s)
- Katie Lunnon
- University of Exeter Medical School, Exeter University, Exeter, UK
| | - Rebecca Smith
- Institute of Psychiatry, King's College London, London, UK
| | - Eilis Hannon
- University of Exeter Medical School, Exeter University, Exeter, UK
| | - Philip L De Jager
- 1] Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA. [2] Harvard Medical School, Boston, Massachusetts, USA. [3] Program in Medical and Population Genetics, Broad Institute, Cambridge, USA
| | - Gyan Srivastava
- 1] Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA. [2] Program in Medical and Population Genetics, Broad Institute, Cambridge, USA
| | - Manuela Volta
- Institute of Psychiatry, King's College London, London, UK
| | - Claire Troakes
- Institute of Psychiatry, King's College London, London, UK
| | - Safa Al-Sarraj
- Institute of Psychiatry, King's College London, London, UK
| | - Joe Burrage
- University of Exeter Medical School, Exeter University, Exeter, UK
| | - Ruby Macdonald
- University of Exeter Medical School, Exeter University, Exeter, UK
| | | | - Lorna W Harries
- University of Exeter Medical School, Exeter University, Exeter, UK
| | - Pavel Katsel
- Department of Psychiatry, The Icahn School of Medicine at Mount Sinai, New York, USA
| | - Vahram Haroutunian
- 1] Department of Psychiatry, The Icahn School of Medicine at Mount Sinai, New York, USA. [2] Department of Neuroscience, The Icahn School of Medicine at Mount Sinai, New York, USA. [3] JJ Peters Virginia Medical Center, Bronx, New York, USA
| | - Zachary Kaminsky
- 1] Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. [2] Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Catharine Joachim
- Department of Neuropathology, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - John Powell
- Institute of Psychiatry, King's College London, London, UK
| | - Simon Lovestone
- 1] Institute of Psychiatry, King's College London, London, UK. [2] Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | | | - Jonathan Mill
- 1] University of Exeter Medical School, Exeter University, Exeter, UK. [2] Institute of Psychiatry, King's College London, London, UK. [3]
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De Jager PL, Srivastava G, Lunnon K, Burgess J, Schalkwyk LC, Yu L, Eaton ML, Keenan BT, Ernst J, McCabe C, Tang A, Raj T, Replogle J, Brodeur W, Gabriel S, Chai HS, Younkin C, Younkin SG, Zou F, Szyf M, Epstein CB, Schneider JA, Bernstein BE, Meissner A, Ertekin-Taner N, Chibnik LB, Kellis M, Mill J, Bennett DA. Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci 2014; 17:1156-63. [PMID: 25129075 PMCID: PMC4292795 DOI: 10.1038/nn.3786] [Citation(s) in RCA: 612] [Impact Index Per Article: 61.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2014] [Accepted: 07/16/2014] [Indexed: 02/07/2023]
Abstract
Here, we leverage a unique collection of 708 prospectively collected autopsied brains to assess the methylation state of the brain's DNA in relation to Alzheimer's disease (AD). We find that the level of methylation at 71 of the 415,848 interrogated CpGs is significantly associated with the burden of AD pathology, including CpGs in the ABCA7 and BIN1 regions, which harbor known AD susceptibility variants. We validate 11 of the differentially methylated regions in an independent set of 117 subjects. Further, we functionally validate these CpG associations and identify the nearby genes whose RNA expression is altered in AD: ANK1, CDH23, DIP2A, RHBDF2, RPL13, RNF34, SERPINF1 and SERPINF2. Our analyses suggest that these DNA methylation changes may have a role in the onset of AD since (1) they are seen in presymptomatic subjects and (2) six of the validated genes connect to a known AD susceptibility gene network.
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Affiliation(s)
- Philip L De Jager
- 1] Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA. [2] Harvard Medical School, Boston, Massachusetts, USA. [3] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Gyan Srivastava
- 1] Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA. [2] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Katie Lunnon
- 1] University of Exeter Medical School, University of Exeter, Exeter, UK. [2] Institute of Psychiatry, King's College London, London, UK
| | - Jeremy Burgess
- 1] Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA. [2] Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Leonard C Schalkwyk
- 1] University of Exeter Medical School, University of Exeter, Exeter, UK. [2] Institute of Psychiatry, King's College London, London, UK
| | - Lei Yu
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Matthew L Eaton
- 1] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA. [2] Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Brendan T Keenan
- 1] Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA. [2] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Jason Ernst
- 1] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA. [2] Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Cristin McCabe
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Anna Tang
- Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Towfique Raj
- 1] Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA. [2] Harvard Medical School, Boston, Massachusetts, USA. [3] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Joseph Replogle
- 1] Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA. [2] Harvard Medical School, Boston, Massachusetts, USA. [3] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Wendy Brodeur
- Genetic Analysis Platform, Broad Institute, Cambridge, Massachusetts, USA
| | - Stacey Gabriel
- Genetic Analysis Platform, Broad Institute, Cambridge, Massachusetts, USA
| | - High S Chai
- 1] Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA. [2] Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Curtis Younkin
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Steven G Younkin
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Fanggeng Zou
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Moshe Szyf
- Department of Pharmacology and Therapeutics, McGill University, Montreal, Québec, Canada
| | | | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Bradley E Bernstein
- 1] Harvard Medical School, Boston, Massachusetts, USA. [2] Epigenomics Program, Broad Institute, Cambridge, Massachusetts, USA. [3] Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Alex Meissner
- 1] Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. [2] Epigenomics Program, Broad Institute, Cambridge, Massachusetts, USA. [3] Harvard Stem Cell Institute, Harvard University, Cambridge, Massachusetts, USA
| | - Nilufer Ertekin-Taner
- 1] Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA. [2] Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Lori B Chibnik
- 1] Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA. [2] Harvard Medical School, Boston, Massachusetts, USA. [3] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Manolis Kellis
- 1] Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA. [2] Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Jonathan Mill
- 1] University of Exeter Medical School, University of Exeter, Exeter, UK. [2] Institute of Psychiatry, King's College London, London, UK
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
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Malki K, Tosto MG, Jumabhoy I, Lourdusamy A, Sluyter F, Craig I, Uher R, McGuffin P, Schalkwyk LC. Integrative mouse and human mRNA studies using WGCNA nominates novel candidate genes involved in the pathogenesis of major depressive disorder. Pharmacogenomics 2014; 14:1979-90. [PMID: 24279853 DOI: 10.2217/pgs.13.154] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
AIM This study aims to identify novel genes associated with major depressive disorder and pharmacological treatment response using animal and human mRNA studies. MATERIALS & METHODS Weighted gene coexpression network analysis was used to uncover genes associated with stress factors in mice and to inform mRNA probe set selection in a post-mortem study of depression. RESULTS A total of 171 genes were found to be differentially regulated in response to both early and late stress protocols in a mouse study. Ten human genes, orthologous to mouse genes differentially expressed by stress, were also found to be dysregulated in depressed cases in a human post-mortem brain study from the Stanley Foundation Brain Collection. CONCLUSION Several novel genes associated with depression were uncovered, including NOVA1 and USP9X. Moreover, we found further evidence in support of hippocampal neurogenesis and peripheral inflammation in major depressive disorder.
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Affiliation(s)
- Karim Malki
- King's College London, MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, De Crespigny Park, Denmark Hill, London SE5 8AF, UK
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Powell TR, Powell-Smith G, Haddley K, Mcguffin P, Quinn J, Schalkwyk LC, Farmer AE, D'Souza UM. Mood-stabilizers differentially affect housekeeping gene expression in human cells. Int J Methods Psychiatr Res 2014; 23:279-88. [PMID: 24677680 PMCID: PMC6878232 DOI: 10.1002/mpr.1435] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2013] [Revised: 06/12/2013] [Accepted: 07/15/2013] [Indexed: 11/08/2022] Open
Abstract
Recent studies have revealed that antidepressants affect the expression of constitutively expressed "housekeeping genes" commonly used as normalizing reference genes in quantitative polymerase chain reaction (qPCR) experiments. There has yet to be an investigation however on the effects of mood-stabilizers on housekeeping gene stability. The current study utilized lymphoblastoid cell lines (LCLs) derived from patients with mood disorders to investigate the effects of a range of doses of lithium (0, 1, 2 and 5 mM) and sodium valproate (0, 0.06, 0.03 and 0.6 mM) on the stability of 12 housekeeping genes. RNA was extracted from LCLs and qPCR was used to generate cycle threshold (Ct ) values which were input into RefFinder analyses. The study revealed drug-specific effects on housekeeping gene stability. The most stable housekeeping genes in LCLs treated: acutely with sodium valproate were ACTB and RPL13A; acutely with lithium were GAPDH and ATP5B; chronically with lithium were ATP5B and CYC1. The stability of GAPDH and B2M were particularly affected by duration of lithium treatment. The study adds to a growing literature that the selection of appropriate housekeeping genes is important for the accurate normalization of target gene expression in experiments investigating the molecular effects of mood disorder pharmacotherapies.
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Affiliation(s)
- Timothy R Powell
- King's College London, MRC Social Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, London, UK
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Malki K, Keers R, Tosto MG, Lourdusamy A, Carboni L, Domenici E, Uher R, McGuffin P, Schalkwyk LC. The endogenous and reactive depression subtypes revisited: integrative animal and human studies implicate multiple distinct molecular mechanisms underlying major depressive disorder. BMC Med 2014; 12:73. [PMID: 24886127 PMCID: PMC4046519 DOI: 10.1186/1741-7015-12-73] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2013] [Accepted: 04/10/2014] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Traditional diagnoses of major depressive disorder (MDD) suggested that the presence or absence of stress prior to onset results in either 'reactive' or 'endogenous' subtypes of the disorder, respectively. Several lines of research suggest that the biological underpinnings of 'reactive' or 'endogenous' subtypes may also differ, resulting in differential response to treatment. We investigated this hypothesis by comparing the gene-expression profiles of three animal models of 'reactive' and 'endogenous' depression. We then translated these findings to clinical samples using a human post-mortem mRNA study. METHODS Affymetrix mouse whole-genome oligonucleotide arrays were used to measure gene expression from hippocampal tissues of 144 mice from the Genome-based Therapeutic Drugs for Depression (GENDEP) project. The study used four inbred mouse strains and two depressogenic 'stress' protocols (maternal separation and Unpredictable Chronic Mild Stress) to model 'reactive' depression. Stress-related mRNA differences in mouse were compared with a parallel mRNA study using Flinders Sensitive and Resistant rat lines as a model of 'endogenous' depression. Convergent genes differentially expressed across the animal studies were used to inform candidate gene selection in a human mRNA post-mortem case control study from the Stanley Brain Consortium. RESULTS In the mouse 'reactive' model, the expression of 350 genes changed in response to early stresses and 370 in response to late stresses. A minimal genetic overlap (less than 8.8%) was detected in response to both stress protocols, but 30% of these genes (21) were also differentially regulated in the 'endogenous' rat study. This overlap is significantly greater than expected by chance. The VAMP-2 gene, differentially expressed across the rodent studies, was also significantly altered in the human study after correcting for multiple testing. CONCLUSIONS Our results suggest that 'endogenous' and 'reactive' subtypes of depression are associated with largely distinct changes in gene-expression. However, they also suggest that the molecular signature of 'reactive' depression caused by early stressors differs considerably from that of 'reactive' depression caused by late stressors. A small set of genes was consistently dysregulated across each paradigm and in post-mortem brain tissue of depressed patients suggesting a final common pathway to the disorder. These genes included the VAMP-2 gene, which has previously been associated with Axis-I disorders including MDD, bipolar depression, schizophrenia and with antidepressant treatment response. We also discuss the implications of our findings for disease classification, personalized medicine and case-control studies of MDD.
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Affiliation(s)
- Karim Malki
- King’s College London, MRC Social, Genetic and Developmental Psychiatry Centre, at Institute of Psychiatry, SGDP Research Centre (PO80), De Crespigny Park, Denmark Hill, London SE5 8AF, UK
| | - Robert Keers
- King’s College London, MRC Social, Genetic and Developmental Psychiatry Centre, at Institute of Psychiatry, SGDP Research Centre (PO80), De Crespigny Park, Denmark Hill, London SE5 8AF, UK
| | - Maria Grazia Tosto
- King’s College London, MRC Social, Genetic and Developmental Psychiatry Centre, at Institute of Psychiatry, SGDP Research Centre (PO80), De Crespigny Park, Denmark Hill, London SE5 8AF, UK
- Department of Psychology, University of York, York, UK
| | | | - Lucia Carboni
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Enrico Domenici
- Center of Excellence for Drug Discovery in Neuroscience, GlaxoSmithKline Medicines Research Centre, Verona, Italy
- Current address: Pharma Research and Early Development, F. Hoffmann–La Roche, Basel, Switzerland
| | - Rudolf Uher
- King’s College London, MRC Social, Genetic and Developmental Psychiatry Centre, at Institute of Psychiatry, SGDP Research Centre (PO80), De Crespigny Park, Denmark Hill, London SE5 8AF, UK
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Peter McGuffin
- King’s College London, MRC Social, Genetic and Developmental Psychiatry Centre, at Institute of Psychiatry, SGDP Research Centre (PO80), De Crespigny Park, Denmark Hill, London SE5 8AF, UK
| | - Leonard C Schalkwyk
- King’s College London, MRC Social, Genetic and Developmental Psychiatry Centre, at Institute of Psychiatry, SGDP Research Centre (PO80), De Crespigny Park, Denmark Hill, London SE5 8AF, UK
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Smith RG, Fernandes C, Kember R, Schalkwyk LC, Buxbaum J, Reichenberg A, Mill J. Transcriptomic changes in the frontal cortex associated with paternal age. Mol Autism 2014; 5:24. [PMID: 24655730 PMCID: PMC3998024 DOI: 10.1186/2040-2392-5-24] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2013] [Accepted: 03/10/2014] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Advanced paternal age is robustly associated with several human neuropsychiatric disorders, particularly autism. The precise mechanism(s) mediating the paternal age effect are not known, but they are thought to involve the accumulation of de novo (epi)genomic alterations. In this study we investigate differences in the frontal cortex transcriptome in a mouse model of advanced paternal age. FINDINGS Transcriptomic profiling was undertaken for medial prefrontal cortex tissue dissected from the male offspring of young fathers (2 month old, 4 sires, n = 16 offspring) and old fathers (10 month old, 6 sires, n = 16 offspring) in a mouse model of advancing paternal age. We found a number of differentially expressed genes in the offspring of older fathers, many previously implicated in the aetiology of autism. Pathway analysis highlighted significant enrichment for changes in functional networks involved in inflammation and inflammatory disease, which are also implicated in autism. CONCLUSIONS We observed widespread alterations to the transcriptome associated with advanced paternal age with an enrichment of genes associated with inflammation, an interesting observation given previous evidence linking the immune system to several neuropsychiatric disorders including autism.
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Affiliation(s)
| | | | | | | | | | | | - Jonathan Mill
- Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK.
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Powell TR, McGuffin P, D'Souza UM, Cohen-Woods S, Hosang GM, Martin C, Matthews K, Day RK, Farmer AE, Tansey KE, Schalkwyk LC. Putative transcriptomic biomarkers in the inflammatory cytokine pathway differentiate major depressive disorder patients from control subjects and bipolar disorder patients. PLoS One 2014; 9:e91076. [PMID: 24618828 PMCID: PMC3949789 DOI: 10.1371/journal.pone.0091076] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2013] [Accepted: 02/06/2014] [Indexed: 12/12/2022] Open
Abstract
Mood disorders consist of two etiologically related, but distinctly treated illnesses, major depressive disorder (MDD) and bipolar disorder (BPD). These disorders share similarities in their clinical presentation, and thus show high rates of misdiagnosis. Recent research has revealed significant transcriptional differences within the inflammatory cytokine pathway between MDD patients and controls, and between BPD patients and controls, suggesting this pathway may possess important biomarker properties. This exploratory study attempts to identify disorder-specific transcriptional biomarkers within the inflammatory cytokine pathway, which can distinguish between control subjects, MDD patients and BPD patients. This is achieved using RNA extracted from subject blood and applying synthesized complementary DNA to quantitative PCR arrays containing primers for 87 inflammation-related genes. Initially, we use ANOVA to test for transcriptional differences in a 'discovery cohort' (total n = 90) and then we use t-tests to assess the reliability of any identified transcriptional differences in a 'validation cohort' (total n = 35). The two most robust and reliable biomarkers identified across both the discovery and validation cohort were Chemokine (C-C motif) ligand 24 (CCL24) which was consistently transcribed higher amongst MDD patients relative to controls and BPD patients, and C-C chemokine receptor type 6 (CCR6) which was consistently more lowly transcribed amongst MDD patients relative to controls. Results detailed here provide preliminary evidence that transcriptional measures within inflammation-related genes might be useful in aiding clinical diagnostic decision-making processes. Future research should aim to replicate findings detailed in this exploratory study in a larger medication-free sample and examine whether identified biomarkers could be used prospectively to aid clinical diagnosis.
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Affiliation(s)
- Timothy R. Powell
- King's College London, Institute of Psychiatry, MRC Social, Genetic and Developmental Psychiatry Centre, London, United Kingdom
| | - Peter McGuffin
- King's College London, Institute of Psychiatry, MRC Social, Genetic and Developmental Psychiatry Centre, London, United Kingdom
| | - Ursula M. D'Souza
- King's College London, Institute of Psychiatry, MRC Social, Genetic and Developmental Psychiatry Centre, London, United Kingdom
| | - Sarah Cohen-Woods
- King's College London, Institute of Psychiatry, MRC Social, Genetic and Developmental Psychiatry Centre, London, United Kingdom
- Discipline of Psychiatry, University of Adelaide, Adelaide, Australia
| | - Georgina M. Hosang
- King's College London, Institute of Psychiatry, MRC Social, Genetic and Developmental Psychiatry Centre, London, United Kingdom
| | - Charlotte Martin
- King's College London, Institute of Psychiatry, MRC Social, Genetic and Developmental Psychiatry Centre, London, United Kingdom
| | - Keith Matthews
- Division of Neuroscience, Ninewells Hospital and Medical School, Dundee, United Kingdom
| | - Richard K. Day
- Division of Neuroscience, Ninewells Hospital and Medical School, Dundee, United Kingdom
| | - Anne E. Farmer
- King's College London, Institute of Psychiatry, MRC Social, Genetic and Developmental Psychiatry Centre, London, United Kingdom
| | - Katherine E. Tansey
- King's College London, Institute of Psychiatry, MRC Social, Genetic and Developmental Psychiatry Centre, London, United Kingdom
| | - Leonard C. Schalkwyk
- King's College London, Institute of Psychiatry, MRC Social, Genetic and Developmental Psychiatry Centre, London, United Kingdom
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Abstract
Cytokines are pleotropic cell signaling proteins that, in addition to their role as inflammatory mediators, also affect neurotransmitter systems, brain functionality and mood. Here we explore the potential utility of cytokine biomarkers for major depressive disorder. Specifically, we explore how genetic, transcriptomic and proteomic information relating to the cytokines might act as biomarkers, aiding clinical diagnosis and treatment selection processes. We advise future studies to investigate whether cytokine biomarkers might differentiate major depressive disorder patients from other patient groups with overlapping clinical characteristics. Furthermore, we invite future pharmacogenetic studies to investigate whether early antidepressant-induced changes to cytokine mRNA or protein levels precede behavioral changes and act as longer-term predictors of clinical antidepressant response.
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Affiliation(s)
- Charlotte Martin
- MRC Social, Genetic & Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, King's College London, PO 80, Denmark Hill, London, SE5 8AF, UK.
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Tarantino LM, Schalkwyk LC. Introduction to mammalian genome special issue: genetics of behavior. Mamm Genome 2014. [DOI: 10.1007/s00335-013-9496-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Powell TR, Tansey KE, Breen G, Farmer AE, Craig IW, Uher R, McGuffin P, D'Souza UM, Schalkwyk LC. ATP-binding cassette sub-family F member 1 (ABCF1) is identified as a putative therapeutic target of escitalopram in the inflammatory cytokine pathway. J Psychopharmacol 2013; 27:609-15. [PMID: 23719290 DOI: 10.1177/0269881113490329] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The inflammatory cytokine pathway may be a potential therapeutic target for major depressive disorder (MDD). Previous reports suggest that antidepressants have anti-inflammatory properties and can cause a reduction in proinflammatory cytokines. Recent evidence suggests this might be mediated at the level of the transcriptome. The current study investigated the transcription of 86 genes in the inflammatory cytokine pathway both at baseline and after eight weeks of escitalopram treatment in MDD patients who were either clinical responders (n=25) or non-responders (n=21), using a subset of samples in the Genome-Based Therapeutic Drugs for Depression project (GENDEP). Changes in expression between baseline and eight weeks of treatment were assessed using two-tailed t-tests. To establish if any significant expression changes related to clinical response, the magnitude of the relative expression change between baseline and eight weeks of treatment was established and binary logistic regressions were used to compare differences between responders and non-responders. ATP-binding cassette sub-family F member 1 (ABCF1), a translational regulator of the inflammatory cytokine pathway showed a significant increase in expression after escitalopram treatment which was significantly greater in responders compared to non-responders, suggesting that ABCF1 may play a role in mediating antidepressant response.
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Affiliation(s)
- Timothy R Powell
- MRC Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK.
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Smith RG, Reichenberg A, Kember RL, Buxbaum JD, Schalkwyk LC, Fernandes C, Mill J. Advanced paternal age is associated with altered DNA methylation at brain-expressed imprinted loci in inbred mice: implications for neuropsychiatric disease. Mol Psychiatry 2013; 18:635-6. [PMID: 22733127 DOI: 10.1038/mp.2012.88] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Pidsley R, Y Wong CC, Volta M, Lunnon K, Mill J, Schalkwyk LC. A data-driven approach to preprocessing Illumina 450K methylation array data. BMC Genomics 2013; 14:293. [PMID: 23631413 PMCID: PMC3769145 DOI: 10.1186/1471-2164-14-293] [Citation(s) in RCA: 677] [Impact Index Per Article: 61.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Accepted: 04/04/2013] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND As the most stable and experimentally accessible epigenetic mark, DNA methylation is of great interest to the research community. The landscape of DNA methylation across tissues, through development and in disease pathogenesis is not yet well characterized. Thus there is a need for rapid and cost effective methods for assessing genome-wide levels of DNA methylation. The Illumina Infinium HumanMethylation450 (450K) BeadChip is a very useful addition to the available methods for DNA methylation analysis but its complex design, incorporating two different assay methods, requires careful consideration. Accordingly, several normalization schemes have been published. We have taken advantage of known DNA methylation patterns associated with genomic imprinting and X-chromosome inactivation (XCI), in addition to the performance of SNP genotyping assays present on the array, to derive three independent metrics which we use to test alternative schemes of correction and normalization. These metrics also have potential utility as quality scores for datasets. RESULTS The standard index of DNA methylation at any specific CpG site is β = M/(M + U + 100) where M and U are methylated and unmethylated signal intensities, respectively. Betas (βs) calculated from raw signal intensities (the default GenomeStudio behavior) perform well, but using 11 methylomic datasets we demonstrate that quantile normalization methods produce marked improvement, even in highly consistent data, by all three metrics. The commonly used procedure of normalizing betas is inferior to the separate normalization of M and U, and it is also advantageous to normalize Type I and Type II assays separately. More elaborate manipulation of quantiles proves to be counterproductive. CONCLUSIONS Careful selection of preprocessing steps can minimize variance and thus improve statistical power, especially for the detection of the small absolute DNA methylation changes likely associated with complex disease phenotypes. For the convenience of the research community we have created a user-friendly R software package called wateRmelon, downloadable from bioConductor, compatible with the existing methylumi, minfi and IMA packages, that allows others to utilize the same normalization methods and data quality tests on 450K data.
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Affiliation(s)
- Ruth Pidsley
- Social, Genetic and Developmental Psychiatry,Institute of Psychiatry, King's College London, De Crespigny Park, London, UK
| | - Chloe C Y Wong
- Social, Genetic and Developmental Psychiatry,Institute of Psychiatry, King's College London, De Crespigny Park, London, UK
| | - Manuela Volta
- Social, Genetic and Developmental Psychiatry,Institute of Psychiatry, King's College London, De Crespigny Park, London, UK
| | - Katie Lunnon
- Social, Genetic and Developmental Psychiatry,Institute of Psychiatry, King's College London, De Crespigny Park, London, UK
| | - Jonathan Mill
- Social, Genetic and Developmental Psychiatry,Institute of Psychiatry, King's College London, De Crespigny Park, London, UK
- University of Exeter Medical School, Exeter, UK
| | - Leonard C Schalkwyk
- Social, Genetic and Developmental Psychiatry,Institute of Psychiatry, King's College London, De Crespigny Park, London, UK
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Lill M, Kõks S, Soomets U, Schalkwyk LC, Fernandes C, Lutsar I, Taba P. Peripheral blood RNA gene expression profiling in patients with bacterial meningitis. Front Neurosci 2013; 7:33. [PMID: 23515576 PMCID: PMC3600829 DOI: 10.3389/fnins.2013.00033] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Accepted: 02/26/2013] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVES The aim of present study was to find genetic pathways activated during infection with bacterial meningitis (BM) and potentially influencing the course of the infection using genome-wide RNA expression profiling combined with pathway analysis and functional annotation of the differential transcription. METHODS We analyzed 21 patients with BM hospitalized in 2008. The control group consisted of 18 healthy subjects. The RNA was extracted from whole blood, globin mRNA was depleted and gene expression profiling was performed using GeneChip Human Gene 1.0 ST Arrays which can assess the transcription of 28,869 genes. Gene expression profile data were analyzed using Bioconductor packages and Bayesian modeling. Functional annotation of the enriched gene sets was used to define the altered genetic networks. We also analyzed whether gene expression profiles depend on the clinical course and outcome. In order to verify the microarray results, the expression levels of ten functionally relevant genes with high statistical significance (CD177, IL1R2, IL18R1, IL18RAP, OLFM4, TLR5, CPA3, FCER1A, IL5RA, and IL7R) were confirmed by quantitative real-time (qRT) PCR. RESULTS There were 8569 genes displaying differential expression at a significance level of p < 0.05. Following False Discovery Rate (FDR) correction, a total of 5500 genes remained significant at a p-value of < 0.01. Quantitative RT-PCR confirmed the differential expression in 10 selected genes. Functional annotation and network analysis indicated that most of the genes were related to activation of humoral and cellular immune responses (enrichment score 43). Those changes were found in both adults and in children with BM compared to the healthy controls. The gene expression profiles did not significantly depend on the clinical outcome, but there was a strong influence of the specific type of pathogen underlying BM. CONCLUSION This study demonstrates that there is a very strong activation of immune response at the transcriptional level during BM and that the type of pathogen influences this transcriptional activation.
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Affiliation(s)
- Margit Lill
- Department of Neurology and Neurosurgery, University of Tartu Tartu, Estonia
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Malki K, Campbell J, Davies M, Keers R, Uher R, Ward M, Paya-Cano J, Aitchinson KJ, Binder E, Sluyter F, Kuhn K, Selzer S, Craig I, McGuffin P, Schalkwyk LC. Pharmacoproteomic investigation into antidepressant response in two mouse inbred strains. Proteomics 2013; 12:2355-65. [PMID: 22696452 DOI: 10.1002/pmic.201100306] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In this study, we present a pharmacoproteomic investigation of response to antidepressants two inbred strains. Our aim was to uncover molecular mechanisms underlying antidepressant action and identify new biomarkers to determine therapeutic response to two antidepressants with proven efficacy in the treatment of depression but divergent mechanisms of action. Mice were treated with the pro-noradrenergic drug nortriptyline, the pro-serotonergic drug escitalopram or saline. Quantitative proteomic analyses were undertaken on hippocampal tissue from a study design that used two inbred mouse strains, two depressogenic protocols and a control condition, (maternal separation, chronic mild stress, control), two antidepressant drugs and two dosing protocols. The proteomic analysis was aimed at the identification of specific drug-response markers. Complementary approaches, 2DE and isobaric tandem mass tagging (TMT), were applied to the selected experimental groups. To investigate the relationship between proteomic profiles, depressogenic protocols and drug response, 2DE and TMT data sets were analysed using multivariate methods. The results highlighted significant strain- and stress-related differences across both 2DE and TMT data sets and identified the three gene products involved in serotonergic (PXBD5, YHWAB, SLC25A4) and one in noradrenergic antidepressant action (PXBD6).
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Affiliation(s)
- Karim Malki
- King's College London, MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, London, UK.
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Jeffries AR, Perfect LW, Ledderose J, Schalkwyk LC, Bray NJ, Mill J, Price J. Stochastic choice of allelic expression in human neural stem cells. Stem Cells 2013; 30:1938-47. [PMID: 22714879 DOI: 10.1002/stem.1155] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Monoallelic gene expression, such as genomic imprinting, is well described. Less well-characterized are genes undergoing stochastic monoallelic expression (MA), where specific clones of cells express just one allele at a given locus. We performed genome-wide allelic expression assessment of human clonal neural stem cells derived from cerebral cortex, striatum, and spinal cord, each with differing genotypes. We assayed three separate clonal lines from each donor, distinguishing stochastic MA from genotypic effects. Roughly 2% of genes showed evidence for autosomal MA, and in about half of these, allelic expression was stochastic between different clones. Many of these loci were known neurodevelopmental genes, such as OTX2 and OLIG2. Monoallelic genes also showed increased levels of DNA methylation compared to hypomethylated biallelic loci. Identified monoallelic gene loci showed altered chromatin signatures in fetal brain, suggesting an in vivo correlate of this phenomenon. We conclude that stochastic allelic expression is prevalent in neural stem cells, providing clonal diversity to developing tissues such as the human brain.
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Affiliation(s)
- Aaron R Jeffries
- King's College London, Institute of Psychiatry, Centre for the Cellular Basis of Behaviour, Department of Neuroscience, London, United Kingdom.
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Navarrete K, Pedroso I, De Jong S, Stefansson H, Steinberg S, Stefansson K, Ophoff RA, Schalkwyk LC, Collier DA. TCF4 (e2-2; ITF2): a schizophrenia-associated gene with pleiotropic effects on human disease. Am J Med Genet B Neuropsychiatr Genet 2013; 162B:1-16. [PMID: 23129290 DOI: 10.1002/ajmg.b.32109] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2012] [Accepted: 09/27/2012] [Indexed: 12/22/2022]
Abstract
Common SNPs in the transcription factor 4 (TCF4; ITF2, E2-2, SEF-2) gene, which encodes a basic Helix-Loop-Helix (bHLH) transcription factor, are associated with schizophrenia, conferring a small increase in risk. Other common SNPs in the gene are associated with the common eye disorder Fuch's corneal dystrophy, while rare, mostly de novo inactivating mutations cause Pitt-Hopkins syndrome. In this review, we present a systematic bioinformatics and literature review of the genomics, biological function and interactome of TCF4 in the context of schizophrenia. The TCF4 gene is present in all vertebrates, and although protein length varies, there is high conservation of primary sequence, including the DNA binding domain. Humans have a unique leucine-rich nuclear export signal. There are two main isoforms (A and B), as well as complex splicing generating many possible N-terminal amino acid sequences. TCF4 is highly expressed in the brain, where plays a role in neurodevelopment, interacting with class II bHLH transcription factors Math1, HASH1, and neuroD2. The Ca(2+) sensor protein calmodulin interacts with the DNA binding domain of TCF4, inhibiting transcriptional activation. It is also the target of microRNAs, including mir137, which is implicated in schizophrenia. The schizophrenia-associated SNPs are in linkage disequilibrium with common variants within putative DNA regulatory elements, suggesting that regulation of expression may underlie association with schizophrenia. Combined gene co-expression analyses and curated protein-protein interaction data provide a network involving TCF4 and other putative schizophrenia susceptibility genes. These findings suggest new opportunities for understanding the molecular basis of schizophrenia and other mental disorders.
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Affiliation(s)
- Katherinne Navarrete
- Social, Genetic and Developmental Psychiatry Centre, King's College London, Institute of Psychiatry, London, UK
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Pidsley R, Fernandes C, Viana J, Paya-Cano JL, Liu L, Smith RG, Schalkwyk LC, Mill J. DNA methylation at the Igf2/H19 imprinting control region is associated with cerebellum mass in outbred mice. Mol Brain 2012; 5:42. [PMID: 23216893 PMCID: PMC3541153 DOI: 10.1186/1756-6606-5-42] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2012] [Accepted: 12/02/2012] [Indexed: 11/10/2022] Open
Abstract
Background Insulin-like growth factor 2 (Igf2) is a paternally expressed imprinted gene regulating fetal growth, playing an integral role in the development of many tissues including the brain. The parent-of-origin specific expression of Igf2 is largely controlled by allele-specific DNA methylation at CTCF-binding sites in the imprinting control region (ICR), located immediately upstream of the neighboring H19 gene. Previously we reported evidence of a negative correlation between DNA methylation in this region and cerebellum weight in humans. Results We quantified cerebellar DNA methylation across all four CTCF binding sites spanning the murine Igf2/H19 ICR in an outbred population of Heterogeneous Stock (HS) mice (n = 48). We observe that DNA methylation at the second and third CTCF binding sites in the Igf2/H19 ICR shows a negative relationship with cerebellar mass, reflecting the association observed in human post-mortem cerebellum tissue. Conclusions Given the important role of the cerebellum in motor control and cognition, and the link between structural cerebellar abnormalities and neuropsychiatric phenotypes, the identification of epigenetic factors associated with cerebellum growth and development may provide important insights about the etiology of psychiatric disorders.
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Affiliation(s)
- Ruth Pidsley
- Institute of Psychiatry, King's College London, De Crespigny Park, Denmark Hill, London SE5 8AF, UK
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Davies MN, Volta M, Pidsley R, Lunnon K, Dixit A, Lovestone S, Coarfa C, Harris RA, Milosavljevic A, Troakes C, Al-Sarraj S, Dobson R, Schalkwyk LC, Mill J. Functional annotation of the human brain methylome identifies tissue-specific epigenetic variation across brain and blood. Genome Biol 2012; 13:R43. [PMID: 22703893 PMCID: PMC3446315 DOI: 10.1186/gb-2012-13-6-r43] [Citation(s) in RCA: 495] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2012] [Revised: 05/21/2012] [Accepted: 06/15/2012] [Indexed: 01/13/2023] Open
Abstract
Background Dynamic changes to the epigenome play a critical role in establishing and maintaining cellular phenotype during differentiation, but little is known about the normal methylomic differences that occur between functionally distinct areas of the brain. We characterized intra- and inter-individual methylomic variation across whole blood and multiple regions of the brain from multiple donors. Results Distinct tissue-specific patterns of DNA methylation were identified, with a highly significant over-representation of tissue-specific differentially methylated regions (TS-DMRs) observed at intragenic CpG islands and low CG density promoters. A large proportion of TS-DMRs were located near genes that are differentially expressed across brain regions. TS-DMRs were significantly enriched near genes involved in functional pathways related to neurodevelopment and neuronal differentiation, including BDNF, BMP4, CACNA1A, CACA1AF, EOMES, NGFR, NUMBL, PCDH9, SLIT1, SLITRK1 and SHANK3. Although between-tissue variation in DNA methylation was found to greatly exceed between-individual differences within any one tissue, we found that some inter-individual variation was reflected across brain and blood, indicating that peripheral tissues may have some utility in epidemiological studies of complex neurobiological phenotypes. Conclusions This study reinforces the importance of DNA methylation in regulating cellular phenotype across tissues, and highlights genomic patterns of epigenetic variation across functionally distinct regions of the brain, providing a resource for the epigenetics and neuroscience research communities.
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Affiliation(s)
- Matthew N Davies
- Institute of Psychiatry, King's College London, De Crespigny Park, London, UK
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47
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Keers R, Pedroso I, Breen G, Aitchison KJ, Nolan PM, Cichon S, Nöthen MM, Rietschel M, Schalkwyk LC, Fernandes C. Reduced anxiety and depression-like behaviours in the circadian period mutant mouse afterhours. PLoS One 2012; 7:e38263. [PMID: 22719873 PMCID: PMC3376117 DOI: 10.1371/journal.pone.0038263] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2012] [Accepted: 05/04/2012] [Indexed: 01/27/2023] Open
Abstract
Background Disruption of the circadian rhythm is a key feature of bipolar disorder. Variation in genes encoding components of the molecular circadian clock has been associated with increased risk of the disorder in clinical populations. Similarly in animal models, disruption of the circadian clock can result in altered mood and anxiety which resemble features of human mania; including hyperactivity, reduced anxiety and reduced depression-like behaviour. One such mutant, after hours (Afh), an ENU-derived mutant with a mutation in a recently identified circadian clock gene Fbxl3, results in a disturbed (long) circadian rhythm of approximately 27 hours. Methodology Anxiety, exploratory and depression-like behaviours were evaluated in Afh mice using the open-field, elevated plus maze, light-dark box, holeboard and forced swim test. To further validate findings for human mania, polymorphisms in the human homologue of FBXL3, genotyped by three genome wide case control studies, were tested for association with bipolar disorder. Principal Findings Afh mice showed reduced anxiety- and depression-like behaviour in all of the behavioural tests employed, and some evidence of increased locomotor activity in some tests. An analysis of three separate human data sets revealed a gene wide association between variation in FBXL3 and bipolar disorder (P = 0.009). Conclusions Our results are consistent with previous studies of mutants with extended circadian periods and suggest that disruption of FBXL3 is associated with mania-like behaviours in both mice and humans.
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Affiliation(s)
- Robert Keers
- MRC SGDP Centre, Institute of Psychiatry, King’s College London, London, United Kingdom
| | - Inti Pedroso
- MRC SGDP Centre, Institute of Psychiatry, King’s College London, London, United Kingdom
| | - Gerome Breen
- MRC SGDP Centre, Institute of Psychiatry, King’s College London, London, United Kingdom
| | - Kathy J. Aitchison
- MRC SGDP Centre, Institute of Psychiatry, King’s College London, London, United Kingdom
| | - Patrick M. Nolan
- MRC Mammalian Genetics Unit, Harwell, Didcot, Oxfordshire, United Kingdom
| | - Sven Cichon
- Institute of Neuroscience and Medicine (INM-1), Structural and Functional Organization of the Brain, Genomic Imaging, Research Center Juelich, Juelich, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
- Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - Markus M. Nöthen
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
- Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, University of Mannheim, Mannheim, Germany
| | - Leonard C. Schalkwyk
- MRC SGDP Centre, Institute of Psychiatry, King’s College London, London, United Kingdom
| | - Cathy Fernandes
- MRC SGDP Centre, Institute of Psychiatry, King’s College London, London, United Kingdom
- * E-mail:
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48
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Affiliation(s)
- Timothy R. Powell
- Social, Genetic and Developmental Psychiatry Centre, King's College London; London United Kingdom
| | - Cathy Fernandes
- Social, Genetic and Developmental Psychiatry Centre, King's College London; London United Kingdom
| | - Leonard C. Schalkwyk
- Social, Genetic and Developmental Psychiatry Centre, King's College London; London United Kingdom
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Sikk K, Kõks S, Soomets U, Schalkwyk LC, Fernandes C, Haldre S, Aquilonius SM, Taba P. Peripheral blood RNA expression profiling in illicit methcathinone users reveals effect on immune system. Front Genet 2012; 2:42. [PMID: 22303338 PMCID: PMC3268596 DOI: 10.3389/fgene.2011.00042] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2011] [Accepted: 06/22/2011] [Indexed: 02/05/2023] Open
Abstract
Methcathinone (ephedrone) is relatively easily accessible for abuse. Its users develop an extrapyramidal syndrome and it is not known if this is caused by methcathinone itself, by side-ingredients (manganese), or both. In the present study we aimed to clarify molecular mechanisms underlying this condition. We used microarrays to analyze whole-genome gene expression patterns of peripheral blood from 20 methcathinone users and 20 matched controls. Gene expression profile data were analyzed by Bayesian modeling and functional annotation. Of 28,869 genes on the microarrays, 326 showed statistically significant differential expression with FDR adjusted p-values below 0.05. Quantitative real-time PCR confirmed differential expression for the most of the genes selected for validation. Functional annotation and network analysis indicated activation of a gene network that included immunological disease, cellular movement, and cardiovascular disease functions (enrichment score 42). As HIV and HCV infections were confounding factors, we performed additional stratification of subjects. A similar functional activation of the “immunological disease” category was evident when we compared subjects according to injection status (past versus current users, balanced for HIV and HCV infection). However, this difference was not large therefore the major effect was related to the HIV status of the subjects. Mn–methcathinone abusers have blood RNA expression patterns that mostly reflect their HIV and HCV infections.
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Affiliation(s)
- Katrin Sikk
- Department of Neurology and Neurosurgery, University of Tartu Tartu, Estonia
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50
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Kõks S, Soomets U, Plaas M, Terasmaa A, Noormets K, Tillmann V, Vasar E, Fernandes C, Schalkwyk LC. Hypothalamic gene expression profile indicates a reduction in G protein signaling in the Wfs1 mutant mice. Physiol Genomics 2011; 43:1351-8. [PMID: 22028430 DOI: 10.1152/physiolgenomics.00117.2011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The Wfs1 gene codes for a protein with unknown function, but deficiency in this protein results in a range of neuropsychiatric and neuroendocrine syndromes. In the present study we aimed to find the functional networks influenced by Wfs1 in the hypothalamus. We performed gene expression profiling (Mouse Gene 1.0 ST Arrays) in Wfs1-deficient mice; 305 genes were differentially expressed with nominal P value<0.01. FDR (false discovery rate)-adjusted P values were significant (0.007) only for two genes: C4b (t=9.66) and Wfs1 (t=-9.03). However, several genes related to G protein signaling were very close to the FDR-adjusted significance level, such as Rgs4 (regulator of G protein signaling 4) that was downregulated (-0.34, t=-5.4) in Wfs1-deficient mice. Changes in Rgs4 and C4b expression were confirmed by QRT-PCR. In humans, Rgs4 is in the locus for bipolar disease (BPD), and its expression is downregulated in BPD. C4b is a gene related to the neurodegenerative diseases. Functional analysis including the entire data set revealed significant alterations in the canonical pathway "G protein-coupled receptor signaling." The gene expression profile in the hypothalami of the Wfs1 mutant mice was significantly similar to the profiles of following biological functions: psychological disorders, bipolar disorder, mood disorder. In conclusion, hypothalamic gene expression profile resembles with some molecular pathways functionally related to the clinical syndromes in the Wolfram syndrome patients.
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Affiliation(s)
- Sulev Kõks
- Department of Physiology, Centre of Translational Medicine, Institute of Technology, University of Tartu, Tartu, Estonia.
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