1
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Kuznetsov DV, Liu Y, Schowe AM, Czamara D, Instinske J, Pahnke CKL, Nöthen MM, Spinath FM, Binder EB, Diewald M, Forstner AJ, Kandler C, Mönkediek B. Genetic and environmental contributions to epigenetic aging across adolescence and young adulthood. Clin Epigenetics 2025; 17:78. [PMID: 40336042 PMCID: PMC12060359 DOI: 10.1186/s13148-025-01880-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Accepted: 04/09/2025] [Indexed: 05/09/2025] Open
Abstract
BACKGROUND Epigenetic aging estimators commonly track chronological and biological aging, quantifying its accumulation (i.e., epigenetic age acceleration) or speed (i.e., epigenetic aging pace). Their scores reflect a combination of inherent biological programming and the impact of environmental factors, which are suggested to vary at different life stages. The transition from adolescence to adulthood is an important period in this regard, marked by an increasing and, then, stabilizing epigenetic aging variance. Whether this pattern arises from environmental influences or genetic factors is still uncertain. This study delves into understanding the genetic and environmental contributions to variance in epigenetic aging across these developmental stages. Using twin modeling, we analyzed four estimators of epigenetic aging, namely Horvath Acceleration, PedBE Acceleration, GrimAge Acceleration, and DunedinPACE, based on saliva samples collected at two timepoints approximately 2.5 years apart from 976 twins of four birth cohorts (aged about 9.5, 15.5, 21.5, and 27.5 years at first and 12, 18, 24, and 30 years at second measurement occasion). RESULTS Half to two-thirds (50-68%) of the differences in epigenetic aging were due to unique environmental factors, indicating the role of life experiences and epigenetic drift, besides measurement error. The remaining variance was explained by genetic (Horvath Acceleration: 24%; GrimAge Acceleration: 32%; DunedinPACE: 47%) and shared environmental factors (Horvath Acceleration: 26%; PedBE Acceleration: 47%). The genetic and shared environmental factors represented the primary sources of stable differences in corresponding epigenetic aging estimators over 2.5 years. Age moderation analyses revealed that the variance due to individually unique environmental sources was smaller in younger than in older cohorts in epigenetic aging estimators trained on chronological age (Horvath Acceleration: 47-49%; PedBE Acceleration: 33-68%). The variance due to genetic contributions, in turn, potentially increased across age groups for epigenetic aging estimators trained in adult samples (Horvath Acceleration: 18-39%; GrimAge Acceleration: 24-43%; DunedinPACE: 42-57%). CONCLUSIONS Transition to adulthood is a period of the increasing variance in epigenetic aging. Both environmental and genetic factors contribute to this trend. The degree of environmental and genetic contributions can be partially explained by the design of epigenetic aging estimators.
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Affiliation(s)
- Dmitry V Kuznetsov
- Bielefeld University, Bielefeld, Germany.
- Center for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany.
| | - Yixuan Liu
- Bielefeld University, Bielefeld, Germany
| | - Alicia M Schowe
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
- Graduate School of Systemic Neuroscience, Ludwig Maximilian University, Munich, Germany
| | - Darina Czamara
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
| | | | - Charlotte K L Pahnke
- Institute of Human Genetics, School of Medicine and University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, School of Medicine and University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Frank M Spinath
- Department of Psychology, Saarland University, Saarbrücken, Germany
| | - Elisabeth B Binder
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
| | | | - Andreas J Forstner
- Institute of Human Genetics, School of Medicine and University Hospital Bonn, University of Bonn, Bonn, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - Christian Kandler
- Bielefeld University, Bielefeld, Germany
- University of Bremen, Bremen, Germany
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2
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Sinke L, Beekman M, Raz Y, Gehrmann T, Moustakas I, Boulinguiez A, Lakenberg N, Suchiman E, Bogaards FA, Bizzarri D, van den Akker EB, Waldenberger M, Butler‐Browne G, Trollet C, de Groot CPGM, Heijmans BT, Slagboom PE. Tissue-specific methylomic responses to a lifestyle intervention in older adults associate with metabolic and physiological health improvements. Aging Cell 2025; 24:e14431. [PMID: 39618079 PMCID: PMC11984676 DOI: 10.1111/acel.14431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2024] [Revised: 10/24/2024] [Accepted: 11/14/2024] [Indexed: 04/12/2025] Open
Abstract
Across the lifespan, diet and physical activity profiles substantially influence immunometabolic health. DNA methylation, as a tissue-specific marker sensitive to behavioral change, may mediate these effects through modulation of transcription factor binding and subsequent gene expression. Despite this, few human studies have profiled DNA methylation and gene expression simultaneously in multiple tissues or examined how molecular levels react and interact in response to lifestyle changes. The Growing Old Together (GOTO) study is a 13-week lifestyle intervention in older adults, which imparted health benefits to participants. Here, we characterize the DNA methylation response to this intervention at over 750 thousand CpGs in muscle, adipose, and blood. Differentially methylated sites are enriched for active chromatin states, located close to relevant transcription factor binding sites, and associated with changing expression of insulin sensitivity genes and health parameters. In addition, measures of biological age are consistently reduced, with decreases in grimAge associated with observed health improvements. Taken together, our results identify responsive molecular markers and demonstrate their potential to measure progression and finetune treatment of age-related risks and diseases.
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Affiliation(s)
- Lucy Sinke
- Molecular Epidemiology, Department of Biomedical Data SciencesLeiden University Medical CentreLeidenThe Netherlands
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data SciencesLeiden University Medical CentreLeidenThe Netherlands
| | - Yotam Raz
- Molecular Epidemiology, Department of Biomedical Data SciencesLeiden University Medical CentreLeidenThe Netherlands
| | - Thies Gehrmann
- Molecular Epidemiology, Department of Biomedical Data SciencesLeiden University Medical CentreLeidenThe Netherlands
- Department of Bioscience Engineering, Research Group Environmental Ecology and Applied MicrobiologyUniversity of AntwerpAntwerpBelgium
| | - Ioannis Moustakas
- Molecular Epidemiology, Department of Biomedical Data SciencesLeiden University Medical CentreLeidenThe Netherlands
- Sequencing Analysis Support Core, Department of Biomedical Data SciencesLeiden University Medical CenterLeidenThe Netherlands
| | - Alexis Boulinguiez
- Myology Center for Research, U974Sorbonne Université, INSERM, AIM, GH Pitié Salpêtrière Bat BabinskiParisFrance
| | - Nico Lakenberg
- Molecular Epidemiology, Department of Biomedical Data SciencesLeiden University Medical CentreLeidenThe Netherlands
| | - Eka Suchiman
- Molecular Epidemiology, Department of Biomedical Data SciencesLeiden University Medical CentreLeidenThe Netherlands
| | - Fatih A. Bogaards
- Molecular Epidemiology, Department of Biomedical Data SciencesLeiden University Medical CentreLeidenThe Netherlands
- Division of Human NutritionWageningen University and ResearchWageningenThe Netherlands
| | - Daniele Bizzarri
- Molecular Epidemiology, Department of Biomedical Data SciencesLeiden University Medical CentreLeidenThe Netherlands
- Delft Bioinformatics Lab, Pattern Recognition and BioinformaticsDelftThe Netherlands
| | - Erik B. van den Akker
- Molecular Epidemiology, Department of Biomedical Data SciencesLeiden University Medical CentreLeidenThe Netherlands
- Delft Bioinformatics Lab, Pattern Recognition and BioinformaticsDelftThe Netherlands
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of EpidemiologyHelmholtz Munich, German Research Center for Environmental HealthNeuherbergGermany
- German Center for Cardiovascular Research (DZHK)Partner Site Munich Heart AllianceMunichGermany
| | - Gillian Butler‐Browne
- Myology Center for Research, U974Sorbonne Université, INSERM, AIM, GH Pitié Salpêtrière Bat BabinskiParisFrance
| | - Capucine Trollet
- Myology Center for Research, U974Sorbonne Université, INSERM, AIM, GH Pitié Salpêtrière Bat BabinskiParisFrance
| | - C. P. G. M. de Groot
- Division of Human NutritionWageningen University and ResearchWageningenThe Netherlands
| | - Bastiaan T. Heijmans
- Molecular Epidemiology, Department of Biomedical Data SciencesLeiden University Medical CentreLeidenThe Netherlands
| | - P. Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data SciencesLeiden University Medical CentreLeidenThe Netherlands
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3
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Jones MJ, Konwar C, Asiimwe R, Dinh L, Razzaghian HR, de Goede O, MacIsaac JL, Morin AM, Kolsun KP, Gervin K, Lyle R, Ng RT, Koestler DC, Felix JF, Lavoie PM, Robinson WP, Mostafavi S, Kobor MS. DNA methylation differences between cord and adult white blood cells reflect postnatal immune cell maturation. Commun Biol 2025; 8:237. [PMID: 39953282 PMCID: PMC11828917 DOI: 10.1038/s42003-025-07661-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Accepted: 02/04/2025] [Indexed: 02/17/2025] Open
Abstract
Epigenetic modifications such as DNA methylation are both cell type and developmental age specific. Here, we show that the immunological maturation of blood cell types influences DNA methylation changes from naive cord blood to fully functional adult blood. Lymphoid cells in adult blood showed more variability than in cord blood suggesting an antigen-dependent maturation of DNA methylation in lymphoid cells throughout the lifespan. Fewer DNA methylation changes between cord and adult blood were observed in myeloid cells, particularly in monocytes, which demonstrated the least number of DNA methylation changes between cord and adult blood. We also noted differences in epigenetic ages by immune cell types within the same individuals, specifically in cord blood where monocytes were epigenetically oldest compared to the other cell types. In addition, we provide a publicly available resource to the community as an R Shiny web application to interactively explore epigenetic patterns between naive cord white blood cells and fully functional adult white blood cells for six immune cell types.
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Affiliation(s)
- Meaghan J Jones
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
- Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Chaini Konwar
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Rebecca Asiimwe
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Louie Dinh
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Hamid Reza Razzaghian
- BC Children's Hospital Research Institute, Vancouver, BC, Canada
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Olivia de Goede
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Julia L MacIsaac
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Alexander M Morin
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Kurt P Kolsun
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada
| | - Kristina Gervin
- Division of Clinical Neuroscience, Department of Research and Innovation, Oslo University Hospital, Oslo, Norway
| | - Robert Lyle
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Raymond T Ng
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Devin C Koestler
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA
| | - Janine F Felix
- 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
| | - Pascal M Lavoie
- BC Children's Hospital Research Institute, Vancouver, BC, Canada
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Wendy P Robinson
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Sara Mostafavi
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
- BC Children's Hospital Research Institute, Vancouver, BC, Canada
- Canadian Institute for Advanced Research, Toronto, ON, Canada
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Michael S Kobor
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.
- BC Children's Hospital Research Institute, Vancouver, BC, Canada.
- Canadian Institute for Advanced Research, Toronto, ON, Canada.
- Edwin S.H. Leong Centre for Healthy Aging, University of British Columbia, Vancouver, BC, Canada.
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4
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Kwa WT, Sim CK, Low A, Lee JWJ. A Comparison of Three Automated Nucleic Acid Extraction Systems for Human Stool Samples. Microorganisms 2024; 12:2417. [PMID: 39770620 PMCID: PMC11678849 DOI: 10.3390/microorganisms12122417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2024] [Revised: 11/16/2024] [Accepted: 11/19/2024] [Indexed: 01/11/2025] Open
Abstract
Automated nucleic acid extractors are useful instruments for the high-throughput processing of bio-samples and are expected to improve research throughput in addition to decreased inter-sample variability inherent to manual processing. We evaluated three commercial nucleic acid extractors Bioer GenePure Pro (Bioer Technology, Hangzhou, China), Maxwell RSC 16 (Promega Corporation, Madison, WI, USA), and KingFisher Apex (ThermoFisher Scientific, Waltham, MA, USA) based on their DNA yield, DNA purity, and 16S rRNA gene amplicon results using both human fecal samples and a mock community (ZymoBIOMICS Microbial Community Standard (Zymo Research Corp., Irvine, CA, USA)). Bead-beating provided incremental yield to effectively lyse and extract DNA from stool samples compared to lysis buffer alone. Differential abundance analysis and comparison of prevalent bacterial species revealed a greater representation of Gram-positive bacteria in samples subjected to mechanical lysis, regardless of sample type. All three commercial extractors had differences in terms of yield, inter-sample variability, and subsequent sequencing readouts, which we subsequently share in the paper and believe are significant considerations for all researchers undertaking human fecal microbiota research.
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Affiliation(s)
- Wit Thun Kwa
- Centre for Translational Medicine, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599, Singapore; (W.T.K.); (C.K.S.); (A.L.)
| | - Choon Kiat Sim
- Centre for Translational Medicine, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599, Singapore; (W.T.K.); (C.K.S.); (A.L.)
| | - Adrian Low
- Centre for Translational Medicine, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599, Singapore; (W.T.K.); (C.K.S.); (A.L.)
| | - Jonathan Wei Jie Lee
- Centre for Translational Medicine, Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599, Singapore; (W.T.K.); (C.K.S.); (A.L.)
- Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, E7, 15 Kent Ridge Crescent, Singapore 119276, Singapore
- Division of Gastroenterology & Hepatology, Department of Medicine, National University Hospital, Singapore 119074, Singapore
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5
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Czajka N, Northrup JM, Jones MJ, Shafer ABA. Epigenetic clocks, sex markers and age-class diagnostics in three harvested large mammals. Mol Ecol Resour 2024; 24:e13956. [PMID: 38553977 DOI: 10.1111/1755-0998.13956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/05/2024] [Accepted: 03/14/2024] [Indexed: 06/04/2024]
Abstract
The development of epigenetic clocks, or the DNA methylation-based inference of age, is an emerging tool for ageing in free ranging populations. In this study, we developed epigenetic clocks for three species of large mammals that are the focus of extensive management throughout their range in North America: white-tailed deer, black bear and mountain goat. We quantified differential DNA methylation patterns at over 30,000 cytosine-guanine sites (CpGs) from tissue samples of all three species (black bear n = 49; white-tailed deer n = 47; mountain goat n = 45). We used a penalized regression model (elastic net) to build explanatory (black bear r = .95; white-tailed deer r = .99; mountain goat r = .97) and robust (black bear Median Absolute Error or MAE = 1.33; white-tailed deer MAE = 0.29; mountain goat MAE = 0.61) models of age or clocks. We also characterized individual CpG sites within each species that demonstrated clear differences in methylation levels between age classes and sex, which can be used to develop a suite of accessible diagnostic markers. This tool has the potential to contribute to wildlife monitoring by providing easily obtainable representations of age structure in managed populations.
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Affiliation(s)
- Natalie Czajka
- Department of Environmental Life Sciences Graduate Program, Trent University, Peterborough, Ontario, Canada
| | - Joseph M Northrup
- Department of Environmental Life Sciences Graduate Program, Trent University, Peterborough, Ontario, Canada
- Wildlife Research and Monitoring Section, Ontario Ministry of Natural Resources and Forestry, Peterborough, Ontario, Canada
| | - Meaghan J Jones
- Department of Biochemistry and Medical Genetics, Children's Hospital Research Institute of Manitoba, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Aaron B A Shafer
- Department of Environmental Life Sciences Graduate Program, Trent University, Peterborough, Ontario, Canada
- Department of Forensic Science, Trent University, Peterborough, Ontario, Canada
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6
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Yusupov N, Roeh S, Sotillos Elliott L, Chang S, Loganathan S, Urbina-Treviño L, Fröhlich AS, Sauer S, Ködel M, Matosin N, Czamara D, Deussing JM, Binder EB. DNA methylation patterns of FKBP5 regulatory regions in brain and blood of humanized mice and humans. Mol Psychiatry 2024; 29:1510-1520. [PMID: 38317011 PMCID: PMC11189813 DOI: 10.1038/s41380-024-02430-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 12/19/2023] [Accepted: 01/11/2024] [Indexed: 02/07/2024]
Abstract
Humanized mouse models can be used to explore human gene regulatory elements (REs), which frequently lie in non-coding and less conserved genomic regions. Epigenetic modifications of gene REs, also in the context of gene x environment interactions, have not yet been explored in humanized mouse models. We applied high-accuracy measurement of DNA methylation (DNAm) via targeted bisulfite sequencing (HAM-TBS) to investigate DNAm in three tissues/brain regions (blood, prefrontal cortex and hippocampus) of mice carrying the human FK506-binding protein 5 (FKBP5) gene, an important candidate gene associated with stress-related psychiatric disorders. We explored DNAm in three functional intronic glucocorticoid-responsive elements (at introns 2, 5, and 7) of FKBP5 at baseline, in cases of differing genotype (rs1360780 single nucleotide polymorphism), and following application of the synthetic glucocorticoid dexamethasone. We compared DNAm patterns in the humanized mouse (N = 58) to those in human peripheral blood (N = 447 and N = 89) and human postmortem brain prefrontal cortex (N = 86). Overall, DNAm patterns in the humanized mouse model seem to recapitulate DNAm patterns observed in human tissue. At baseline, this was to a higher extent in brain tissue. The animal model also recapitulated effects of dexamethasone on DNAm, especially in peripheral blood and to a lesser extent effects of genotype on DNAm. The humanized mouse model could thus assist in reverse translation of human findings in psychiatry that involve genetic and epigenetic regulation in non-coding elements.
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Affiliation(s)
- Natan Yusupov
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Simone Roeh
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
| | - Laura Sotillos Elliott
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
- Molecular Neurogenetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Simon Chang
- Molecular Neurogenetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Srivaishnavi Loganathan
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
- Molecular Neurogenetics, Max Planck Institute of Psychiatry, Munich, Germany
| | | | - Anna S Fröhlich
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Susann Sauer
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
| | - Maik Ködel
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
| | - Natalie Matosin
- School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Darina Czamara
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
| | - Jan M Deussing
- Molecular Neurogenetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Elisabeth B Binder
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany.
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7
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Mohammed Y, Tran K, Carlsten C, Ryerson C, Wong A, Lee T, Cheng MP, Vinh DC, Lee TC, Winston BW, Sweet D, Boyd JH, Walley KR, Haljan G, McGeer A, Lamontagne F, Fowler R, Maslove D, Singer J, Patrick DM, Marshall JC, Murthy S, Jain F, Borchers CH, Goodlett DR, Levin A, Russell JA, ARBs CORONA I Consortium. Proteomic Evolution from Acute to Post-COVID-19 Conditions. J Proteome Res 2024; 23:52-70. [PMID: 38048423 PMCID: PMC10775146 DOI: 10.1021/acs.jproteome.3c00324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 10/30/2023] [Accepted: 11/13/2023] [Indexed: 12/06/2023]
Abstract
Many COVID-19 survivors have post-COVID-19 conditions, and females are at a higher risk. We sought to determine (1) how protein levels change from acute to post-COVID-19 conditions, (2) whether females have a plasma protein signature different from that of males, and (3) which biological pathways are associated with COVID-19 when compared to restrictive lung disease. We measured protein levels in 74 patients on the day of admission and at 3 and 6 months after diagnosis. We determined protein concentrations by multiple reaction monitoring (MRM) using a panel of 269 heavy-labeled peptides. The predicted forced vital capacity (FVC) and diffusing capacity of the lungs for carbon monoxide (DLCO) were measured by routine pulmonary function testing. Proteins associated with six key lipid-related pathways increased from admission to 3 and 6 months; conversely, proteins related to innate immune responses and vasoconstriction-related proteins decreased. Multiple biological functions were regulated differentially between females and males. Concentrations of eight proteins were associated with FVC, %, and they together had c-statistics of 0.751 (CI:0.732-0.779); similarly, concentrations of five proteins had c-statistics of 0.707 (CI:0.676-0.737) for DLCO, %. Lipid biology may drive evolution from acute to post-COVID-19 conditions, while activation of innate immunity and vascular regulation pathways decreased over that period. (ProteomeXchange identifiers: PXD041762, PXD029437).
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Affiliation(s)
- Yassene Mohammed
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Leiden 2333 ZA, The Netherlands
- UVic-Genome
BC Proteomics Centre, University of Victoria, Victoria V8Z 5N3, BC Canada
- Gerald
Bronfman Department of Oncology, McGill
University, Montreal, QC H3A 0G4, Canada
| | - Karen Tran
- Division
of General Internal Medicine, Vancouver
General Hospital and University of British Columbia, 2775 Laurel St, Vancouver, BC V5Z 1M9, Canada
| | - Chris Carlsten
- Division
of Respiratory Medicine, Vancouver General Hospital, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Christopher Ryerson
- Division
of Respiratory Medicine, Vancouver General Hospital, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Alyson Wong
- Division
of Respiratory Medicine, Vancouver General Hospital, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
| | - Terry Lee
- Centre for
Health Evaluation and Outcome Science (CHEOS), St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - Matthew P. Cheng
- Division
of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, PQ H4A 3J1, Canada
| | - Donald C. Vinh
- Division
of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, PQ H4A 3J1, Canada
| | - Todd C. Lee
- Division
of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, PQ H4A 3J1, Canada
| | - Brent W. Winston
- Departments
of Critical Care Medicine, Medicine and Biochemistry and Molecular
Biology, Foothills Medical Centre and University
of Calgary, 1403 29 Street
NW, Calgary, Alberta T2N 4N1, Canada
| | - David Sweet
- Division
of Critical Care Medicine, Vancouver General
Hospital, 2775 Laurel St, Vancouver, BC V5Z 1M9, Canada
| | - John H. Boyd
- Centre
for Heart Lung Innovation, St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
- Division of Critical Care Medicine, St.
Paul’s Hospital, University of British
Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - Keith R. Walley
- Centre
for Heart Lung Innovation, St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
- Division of Critical Care Medicine, St.
Paul’s Hospital, University of British
Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - Greg Haljan
- Department of Medicine, Surrey Memorial
Hospital, 13750 96th
Avenue, Surrey, BC V3V 1Z2, Canada
| | - Allison McGeer
- Mt. Sinai Hospital and University of Toronto, 600 University Avenue, Toronto, ON M5G 1X5, Canada
| | | | - Robert Fowler
- Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada
| | - David Maslove
- Department
of Critical Care, Kingston General Hospital
and Queen’s University, 76 Stuart Street, Kingston, ON K7L 2V7, Canada
| | - Joel Singer
- Centre for
Health Evaluation and Outcome Science (CHEOS), St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - David M. Patrick
- British Columbia Centre for Disease Control
(BCCDC) and University
of British Columbia, 655 West 12th Avenue, Vancouver, BC V5Z 4R4, Canada
| | - John C. Marshall
- Department of Surgery, St. Michael’s
Hospital, 30 Bond Street, Toronto, ON M5B
1W8, Canada
| | - Srinivas Murthy
- BC Children’s Hospital and University of British Columbia, 4500 Oak Street, Vancouver, BC V6H 3N1, Canada
| | - Fagun Jain
- Black Tusk Research Group, Vancouver, BC V6Z 2C7, Canada
| | - Christoph H. Borchers
- Segal Cancer Proteomics, Centre, Lady Davis
Institute
for Medical Research, McGill University, Montreal, QC H3T 1E2, Canada
- Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
- Division of Experimental Medicine, McGill
University, Montreal, QC H3T 1E2, Canada
- Department of Pathology, McGill
University, Montreal, QC H3T 1E2, Canada
| | - David R. Goodlett
- UVic-Genome
BC Proteomics Centre, University of Victoria, Victoria V8Z 5N3, BC Canada
| | - Adeera Levin
- Division of Nephrology, St.
Paul’s Hospital, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - James A. Russell
- Centre
for Heart Lung Innovation, St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
- Division of Critical Care Medicine, St.
Paul’s Hospital, University of British
Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
| | - ARBs CORONA I Consortium
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Leiden 2333 ZA, The Netherlands
- UVic-Genome
BC Proteomics Centre, University of Victoria, Victoria V8Z 5N3, BC Canada
- Gerald
Bronfman Department of Oncology, McGill
University, Montreal, QC H3A 0G4, Canada
- Division
of General Internal Medicine, Vancouver
General Hospital and University of British Columbia, 2775 Laurel St, Vancouver, BC V5Z 1M9, Canada
- Division
of Respiratory Medicine, Vancouver General Hospital, University of British Columbia, Vancouver, BC V5Z 1M9, Canada
- Centre for
Health Evaluation and Outcome Science (CHEOS), St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
- Division
of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, PQ H4A 3J1, Canada
- Departments
of Critical Care Medicine, Medicine and Biochemistry and Molecular
Biology, Foothills Medical Centre and University
of Calgary, 1403 29 Street
NW, Calgary, Alberta T2N 4N1, Canada
- Division
of Critical Care Medicine, Vancouver General
Hospital, 2775 Laurel St, Vancouver, BC V5Z 1M9, Canada
- Centre
for Heart Lung Innovation, St. Paul’s Hospital, University of British Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
- Division of Critical Care Medicine, St.
Paul’s Hospital, University of British
Columbia, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
- Department of Medicine, Surrey Memorial
Hospital, 13750 96th
Avenue, Surrey, BC V3V 1Z2, Canada
- Mt. Sinai Hospital and University of Toronto, 600 University Avenue, Toronto, ON M5G 1X5, Canada
- University of Sherbrooke, Sherbrooke, PQ J1K 2R1, Canada
- Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada
- Department
of Critical Care, Kingston General Hospital
and Queen’s University, 76 Stuart Street, Kingston, ON K7L 2V7, Canada
- British Columbia Centre for Disease Control
(BCCDC) and University
of British Columbia, 655 West 12th Avenue, Vancouver, BC V5Z 4R4, Canada
- Department of Surgery, St. Michael’s
Hospital, 30 Bond Street, Toronto, ON M5B
1W8, Canada
- BC Children’s Hospital and University of British Columbia, 4500 Oak Street, Vancouver, BC V6H 3N1, Canada
- Black Tusk Research Group, Vancouver, BC V6Z 2C7, Canada
- Segal Cancer Proteomics, Centre, Lady Davis
Institute
for Medical Research, McGill University, Montreal, QC H3T 1E2, Canada
- Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
- Division of Experimental Medicine, McGill
University, Montreal, QC H3T 1E2, Canada
- Department of Pathology, McGill
University, Montreal, QC H3T 1E2, Canada
- Division of Nephrology, St.
Paul’s Hospital, 1081 Burrard Street, Vancouver, BC V6Z 1Y6, Canada
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8
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Lu M, Jiang H, Wang R, An S, Wang J, Yu C. Injectiondesign: web service of plate design with optimized stratified block randomization for modern GC/LC-MS-based sample preparation. BMC Bioinformatics 2023; 24:489. [PMID: 38124029 PMCID: PMC10734102 DOI: 10.1186/s12859-023-05598-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Plate design is a necessary and time-consuming operation for GC/LC-MS-based sample preparation. The implementation of the inter-batch balancing algorithm and the intra-batch randomization algorithm can have a significant impact on the final results. For researchers without programming skills, a stable and efficient online service for plate design is necessary. RESULTS Here we describe InjectionDesign, a free online plate design service focused on GC/LC-MS-based multi-omics experiment design. It offers the ability to separate the position design from the sequence design, making the output more compatible with the requirements of a modern mass spectrometer-based laboratory. In addition, it has implemented an optimized block randomization algorithm, which can be better applied to sample stratification with block randomization for an unbalanced distribution. It is easy to use, with built-in support for common instrument models and quick export to a worksheet. CONCLUSIONS InjectionDesign is an open-source project based on Java. Researchers can get the source code for the project from Github: https://github.com/CSi-Studio/InjectionDesign . A free web service is also provided: http://www.injection.design .
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Affiliation(s)
- Miaoshan Lu
- Zhejiang University, Hangzhou, Zhejiang, China
- School of Engineering, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Hengxuan Jiang
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ruimin Wang
- School of Engineering, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Institute of Advanced Technology, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Fudan University, Shanghai, China
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Shaowei An
- School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Institute of Biology, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Fudan University, Shanghai, China
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jiawei Wang
- Carbon Silicon (Hangzhou) Biotechnology Co., Ltd, Hangzhou, China
| | - Changbin Yu
- Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
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9
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Felt JM, Yusupov N, Harrington KD, Fietz J, Zhang Z“Z, Sliwinski MJ, Ram N, O'Donnell KJ, BeCOME Working Group, Meaney MJ, Putnam FW, Noll JG, Binder EB, Shenk CE. Epigenetic age acceleration as a biomarker for impaired cognitive abilities in adulthood following early life adversity and psychiatric disorders. Neurobiol Stress 2023; 27:100577. [PMID: 37885906 PMCID: PMC10597797 DOI: 10.1016/j.ynstr.2023.100577] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/20/2023] [Accepted: 09/23/2023] [Indexed: 10/28/2023] Open
Abstract
Background Early life adversity and psychiatric disorders are associated with earlier declines in neurocognitive abilities during adulthood. These declines may be preceded by changes in biological aging, specifically epigenetic age acceleration, providing an opportunity to uncover genome-wide biomarkers that identify individuals most likely to benefit from early screening and prevention. Methods Five unique epigenetic age acceleration clocks derived from peripheral blood were examined in relation to latent variables of general and speeded cognitive abilities across two independent cohorts: 1) the Female Growth and Development Study (FGDS; n = 86), a 30-year prospective cohort study of substantiated child sexual abuse and non-abused controls, and 2) the Biological Classification of Mental Disorders study (BeCOME; n = 313), an adult community cohort established based on psychiatric disorders. Results A faster pace of biological aging (DunedinPoAm) was associated with lower general cognitive abilities in both cohorts and slower speeded abilities in the BeCOME cohort. Acceleration in the Horvath clock was significantly associated with slower speeded abilities in the BeCOME cohort but not the FGDS. Acceleration in the Hannum clock and the GrimAge clock were not significantly associated with either cognitive ability. Accelerated PhenoAge was associated with slower speeded abilities in the FGDS but not the BeCOME cohort. Conclusions The present results suggest that epigenetic age acceleration has the potential to serve as a biomarker for neurocognitive decline in adults with a history of early life adversity or psychiatric disorders. Estimates of epigenetic aging may identify adults at risk of cognitive decline that could benefit from early neurocognitive screening.
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Affiliation(s)
- John M. Felt
- Center for Healthy Aging, The Pennsylvania State University, United States
| | - Natan Yusupov
- Department Genes and Environment, Max Planck Institute of Psychiatry - Munich, Germany
- International Max Planck Research School for Translational Psychiatry, Max Planck Institute of Psychiatry, Germany
| | | | - Julia Fietz
- Department Genes and Environment, Max Planck Institute of Psychiatry - Munich, Germany
- International Max Planck Research School for Translational Psychiatry, Max Planck Institute of Psychiatry, Germany
| | | | - Martin J. Sliwinski
- Center for Healthy Aging, The Pennsylvania State University, United States
- Department of Human Development and Family Studies, The Pennsylvania State University, United States
| | - Nilam Ram
- Department of Communications, Stanford University, United States
- Department of Psychology, Stanford University, United States
| | - Kieran J. O'Donnell
- Child Study Center, Yale University, United States
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale University, United States
- The Douglas Hospital Research Centre, Department of Psychiatry, McGill University, Canada
- Child and Brain Development Program, Canadian Institute for Advanced Research, Canada
| | - BeCOME Working Group
- Department Genes and Environment, Max Planck Institute of Psychiatry - Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Michael J. Meaney
- The Douglas Hospital Research Centre, Department of Psychiatry, McGill University, Canada
- Child and Brain Development Program, Canadian Institute for Advanced Research, Canada
- Singapore Institute of Clinical Sciences, Singapore
| | - Frank W. Putnam
- Department of Psychiatry, University of North Carolina School of Medicine, United States
| | - Jennie G. Noll
- Department of Human Development and Family Studies, The Pennsylvania State University, United States
| | - Elisabeth B. Binder
- Department Genes and Environment, Max Planck Institute of Psychiatry - Munich, Germany
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, United States
| | - Chad E. Shenk
- Department of Human Development and Family Studies, The Pennsylvania State University, United States
- Department of Pediatrics, The Pennsylvania State University College of Medicine, United States
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10
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Onuzulu CD, Lee S, Basu S, Comte J, Hai Y, Hizon N, Chadha S, Fauni MS, Kahnamoui S, Xiang B, Halayko AJ, Dolinsky VW, Pascoe CD, Jones MJ. Early-life exposure to cigarette smoke primes lung function and DNA methylation changes at Cyp1a1 upon exposure later in life. Am J Physiol Lung Cell Mol Physiol 2023; 325:L552-L567. [PMID: 37642652 PMCID: PMC11068412 DOI: 10.1152/ajplung.00192.2023] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 08/31/2023] Open
Abstract
Prenatal and early-life exposure to cigarette smoke (CS) has repeatedly been shown to induce stable, long-term changes in DNA methylation (DNAm) in offspring. It has been hypothesized that these changes might be functionally related to the known outcomes of prenatal and early-life CS exposure, which include impaired lung development, altered lung function, and increased risk of asthma and wheeze. However, to date, few studies have examined DNAm changes induced by prenatal CS in tissues of the lung, and even fewer have attempted to examine the specific influences of prenatal versus early postnatal exposures. Here, we have established a mouse model of CS exposure which isolates the effects of prenatal and early postnatal CS exposures in early life. We have used this model to measure the effects of prenatal and/or postnatal CS exposures on lung function and immune cell infiltration as well as DNAm and expression of Cyp1a1, a candidate gene previously observed to demonstrate DNAm differences on CS exposure in humans. Our study revealed that exposure to CS prenatally and in the early postnatal period causes long-lasting differences in offspring lung function, gene expression, and lung Cyp1a1 DNAm, which wane over time but are reestablished on reexposure to CS in adulthood. This study creates a testable mouse model that can be used to investigate the effects of prenatal and early postnatal CS exposures and will contribute to the design of intervention strategies to mediate these detrimental effects.NEW & NOTEWORTHY Here, we isolated effects of prenatal from early postnatal cigarette smoke and showed that exposure to cigarette smoke early in life causes changes in offspring DNA methylation at Cyp1a1 that last through early adulthood but not into late adulthood. We also showed that smoking in adulthood reestablished these DNA methylation patterns at Cyp1a1, suggesting that a mechanism other than DNA methylation results in long-term memory associated with early-life cigarette smoke exposures at this gene.
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Affiliation(s)
- Chinonye Doris Onuzulu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Samantha Lee
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Sujata Basu
- Children's Hospital Research Institute of Manitoba, Winnipeg, Manitoba, Canada
| | - Jeannette Comte
- Children's Hospital Research Institute of Manitoba, Winnipeg, Manitoba, Canada
| | - Yan Hai
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Nikho Hizon
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Shivam Chadha
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Maria Shenna Fauni
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Shana Kahnamoui
- Children's Hospital Research Institute of Manitoba, Winnipeg, Manitoba, Canada
- Department of Physiology and Pathophysiology, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Bo Xiang
- Children's Hospital Research Institute of Manitoba, Winnipeg, Manitoba, Canada
- Department of Pharmacology and Therapeutics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Andrew J Halayko
- Children's Hospital Research Institute of Manitoba, Winnipeg, Manitoba, Canada
- Department of Physiology and Pathophysiology, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Vernon W Dolinsky
- Children's Hospital Research Institute of Manitoba, Winnipeg, Manitoba, Canada
- Department of Pharmacology and Therapeutics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Christopher D Pascoe
- Children's Hospital Research Institute of Manitoba, Winnipeg, Manitoba, Canada
- Department of Physiology and Pathophysiology, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Meaghan J Jones
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
- Children's Hospital Research Institute of Manitoba, Winnipeg, Manitoba, Canada
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11
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Yusupov N, van Doeselaar L, Röh S, Wiechmann T, Ködel M, Sauer S, Rex-Haffner M, Schmidt MV, Binder EB. Extensive evaluation of DNA methylation of functional elements in the murine Fkbp5 locus using high-accuracy DNA methylation measurement via targeted bisulfite sequencing. Eur J Neurosci 2023; 58:2662-2676. [PMID: 37414581 DOI: 10.1111/ejn.16078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/15/2023] [Accepted: 06/13/2023] [Indexed: 07/08/2023]
Abstract
FKBP5 is an important stress-regulatory gene implicated in stress-related psychiatric diseases. Single nucleotide polymorphisms of the FKBP5 gene were shown to interact with early life stress to alter the glucocorticoid-related stress response and moderate disease risk. Demethylation of cytosine-phosphate-guanine-dinucleotides (CpGs) in regulatory glucocorticoid-responsive elements was suggested to be the mediating epigenetic mechanism for long-term stress effects, but studies on Fkbp5 DNA methylation (DNAm) in rodents are so far limited. We evaluated the applicability of high-accuracy DNA methylation measurement via targeted bisulfite sequencing (HAM-TBS), a next-generation sequencing-based technology, to allow a more in-depth characterisation of the DNA methylation of the murine Fkbp5 locus in three different tissues (blood, frontal cortex and hippocampus). In this study, we not only increased the number of evaluated sites in previously described regulatory regions (in introns 1 and 5), but also extended the evaluation to novel, possibly relevant regulatory regions of the gene (in intron 8, the transcriptional start site, the proximal enhancer and CTCF-binding sites within the 5'UTR). We here describe the assessment of HAM-TBS assays for a panel of 157 CpGs with possible functional relevance in the murine Fkbp5 gene. DNAm profiles were tissue-specific, with lesser differences between the two brain regions than between the brain and blood. Moreover, we identified DNAm changes in the Fkbp5 locus after early life stress exposure in the frontal cortex and blood. Our findings indicate that HAM-TBS is a valuable tool for broader exploration of the DNAm of the murine Fkbp5 locus and its involvement in the stress response.
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Affiliation(s)
- Natan Yusupov
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry, Munich, Germany
| | - Lotte van Doeselaar
- International Max Planck Research School for Translational Psychiatry, Munich, Germany
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Simone Röh
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Tobias Wiechmann
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Maik Ködel
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Susann Sauer
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Monika Rex-Haffner
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Mathias V Schmidt
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
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12
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Yusupov N, Dieckmann L, Erhart M, Sauer S, Rex-Haffner M, Kopf-Beck J, Brückl TM, Czamara D, Binder EB. Transdiagnostic evaluation of epigenetic age acceleration and burden of psychiatric disorders. Neuropsychopharmacology 2023:10.1038/s41386-023-01579-3. [PMID: 37069357 PMCID: PMC10354057 DOI: 10.1038/s41386-023-01579-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/15/2023] [Accepted: 03/28/2023] [Indexed: 04/19/2023]
Abstract
Different psychiatric disorders as well as exposure to adverse life events have individually been associated with multiple age-related diseases and mortality. Age acceleration in different epigenetic clocks can serve as biomarker for such risk and could help to disentangle the interplay of psychiatric comorbidity and early adversity on age-related diseases and mortality. We evaluated five epigenetic clocks (Horvath, Hannum, PhenoAge, GrimAge and DunedinPoAm) in a transdiagnostic psychiatric sample using epigenome-wide DNA methylation data from peripheral blood of 429 subjects from two studies at the Max Planck Institute of Psychiatry. Burden of psychiatric disease, represented by a weighted score, was significantly associated with biological age acceleration as measured by GrimAge and DunedinPoAm (R2-adj. 0.22 and 0.33 for GrimAge and DunedinPoAm, respectively), but not the other investigated clocks. The relation of burden of psychiatric disease appeared independent of differences in socioeconomic status and medication. Our findings indicate that increased burden of psychiatric disease may associate with accelerated biological aging. This highlights the importance of medical management of patients with multiple psychiatric comorbidities and the potential usefulness of specific epigenetic clocks for early detection of risk and targeted intervention to reduce mortality in psychiatric patients.
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Affiliation(s)
- Natan Yusupov
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, 80804, Germany.
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany.
| | - Linda Dieckmann
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, 80804, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Mira Erhart
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, 80804, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Susann Sauer
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, 80804, Germany
| | - Monika Rex-Haffner
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, 80804, Germany
| | - Johannes Kopf-Beck
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, 80804, Germany
- Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Tanja M Brückl
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, 80804, Germany
| | - Darina Czamara
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, 80804, Germany
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, 80804, Germany
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13
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Edwards AD, Rueckert D, Smith SM, Abo Seada S, Alansary A, Almalbis J, Allsop J, Andersson J, Arichi T, Arulkumaran S, Bastiani M, Batalle D, Baxter L, Bozek J, Braithwaite E, Brandon J, Carney O, Chew A, Christiaens D, Chung R, Colford K, Cordero-Grande L, Counsell SJ, Cullen H, Cupitt J, Curtis C, Davidson A, Deprez M, Dillon L, Dimitrakopoulou K, Dimitrova R, Duff E, Falconer S, Farahibozorg SR, Fitzgibbon SP, Gao J, Gaspar A, Harper N, Harrison SJ, Hughes EJ, Hutter J, Jenkinson M, Jbabdi S, Jones E, Karolis V, Kyriakopoulou V, Lenz G, Makropoulos A, Malik S, Mason L, Mortari F, Nosarti C, Nunes RG, O’Keeffe C, O’Muircheartaigh J, Patel H, Passerat-Palmbach J, Pietsch M, Price AN, Robinson EC, Rutherford MA, Schuh A, Sotiropoulos S, Steinweg J, Teixeira RPAG, Tenev T, Tournier JD, Tusor N, Uus A, Vecchiato K, Williams LZJ, Wright R, Wurie J, Hajnal JV. The Developing Human Connectome Project Neonatal Data Release. Front Neurosci 2022; 16:886772. [PMID: 35677357 PMCID: PMC9169090 DOI: 10.3389/fnins.2022.886772] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/19/2022] [Indexed: 11/24/2022] Open
Abstract
The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance imaging (MRI) brain datasets from 1173 fetal and/or neonatal participants, together with collateral demographic, clinical, family, neurocognitive and genomic data from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed.
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Affiliation(s)
- A. David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
- Institute for AI and Informatics in Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Samy Abo Seada
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Amir Alansary
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Jennifer Almalbis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joanna Allsop
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Sophie Arulkumaran
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Luke Baxter
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Eleanor Braithwaite
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Jacqueline Brandon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Olivia Carney
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Raymond Chung
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Kathleen Colford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Serena J. Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Harriet Cullen
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King’s College London, London, United Kingdom
| | - John Cupitt
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Charles Curtis
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Alice Davidson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Louise Dillon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Konstantina Dimitrakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Translational Bioinformatics Platform, NIHR Biomedical Research Centre, Guy’s and St. Thomas’ NHS Foundation Trust and King’s College London, London, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Sean P. Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jianliang Gao
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Andreia Gaspar
- Institute for Systems and Robotics (ISR-Lisboa)/LaRSyS, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Nicholas Harper
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Sam J. Harrison
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Emer J. Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Emily Jones
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Vyacheslav Karolis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Antonios Makropoulos
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Shaihan Malik
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Luke Mason
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Rita G. Nunes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Institute for Systems and Robotics (ISR-Lisboa)/LaRSyS, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Camilla O’Keeffe
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Hamel Patel
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Maximillian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Anthony N. Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Emma C. Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Mary A. Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Stamatios Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Rui Pedro Azeredo Gomes Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Logan Z. J. Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Robert Wright
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Julia Wurie
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joseph V. Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
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