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Toro VD, Antonucci LA, Quarto T, Passiatore R, Fazio L, Ursini G, Chen Q, Masellis R, Torretta S, Sportelli L, Kikidis GC, Massari F, D'Ambrosio E, Rampino A, Pergola G, Weinberger DR, Bertolino A, Blasi G. The interaction between early life complications and a polygenic risk score for schizophrenia is associated with brain activity during emotion processing in healthy participants. Psychol Med 2024; 54:1876-1885. [PMID: 38305128 DOI: 10.1017/s0033291724000011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
BACKGROUND Previous evidence suggests that early life complications (ELCs) interact with polygenic risk for schizophrenia (SCZ) in increasing risk for the disease. However, no studies have investigated this interaction on neurobiological phenotypes. Among those, anomalous emotion-related brain activity has been reported in SCZ, even if evidence of its link with SCZ-related genetic risk is not solid. Indeed, it is possible this relationship is influenced by non-genetic risk factors. Thus, this study investigated the interaction between SCZ-related polygenic risk and ELCs on emotion-related brain activity. METHODS 169 healthy participants (HP) in a discovery and 113 HP in a replication sample underwent functional magnetic resonance imaging (fMRI) during emotion processing, were categorized for history of ELCs and genome-wide genotyped. Polygenic risk scores (PRSs) were computed using SCZ-associated variants considering the most recent genome-wide association study. Furthermore, 75 patients with SCZ also underwent fMRI during emotion processing to verify consistency of their brain activity patterns with those associated with risk factors for SCZ in HP. RESULTS Results in the discovery and replication samples indicated no effect of PRSs, but an interaction between PRS and ELCs in left ventrolateral prefrontal cortex (VLPFC), where the greater the activity, the greater PRS only in presence of ELCs. Moreover, SCZ had greater VLPFC response than HP. CONCLUSIONS These results suggest that emotion-related VLPFC response lies in the path from genetic and non-genetic risk factors to the clinical presentation of SCZ, and may implicate an updated concept of intermediate phenotype considering early non-genetic factors of risk for SCZ.
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Affiliation(s)
- Veronica Debora Toro
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- Department of Humanities, University of Foggia, Foggia, Italy
| | - Linda A Antonucci
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | - Tiziana Quarto
- Department of Humanities, University of Foggia, Foggia, Italy
| | - Roberta Passiatore
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | - Leonardo Fazio
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- Department of Medicine and Surgery, Libera Università Mediterranea "Giuseppe Degennaro", Bari, Italy
| | - Gianluca Ursini
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Rita Masellis
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- U.O.C. Psichiatria Universitaria, Azìenda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Silvia Torretta
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | - Leonardo Sportelli
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | - Gianluca Christos Kikidis
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | - Francesco Massari
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | - Enrico D'Ambrosio
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Antonio Rampino
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- U.O.C. Psichiatria Universitaria, Azìenda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Giulio Pergola
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Alessandro Bertolino
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- U.O.C. Psichiatria Universitaria, Azìenda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Giuseppe Blasi
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- U.O.C. Psichiatria Universitaria, Azìenda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
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Huuki-Myers LA, Spangler A, Eagles NJ, Montgomery KD, Kwon SH, Guo B, Grant-Peters M, Divecha HR, Tippani M, Sriworarat C, Nguyen AB, Ravichandran P, Tran MN, Seyedian A, Hyde TM, Kleinman JE, Battle A, Page SC, Ryten M, Hicks SC, Martinowich K, Collado-Torres L, Maynard KR. A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex. Science 2024; 384:eadh1938. [PMID: 38781370 DOI: 10.1126/science.adh1938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 12/06/2023] [Indexed: 05/25/2024]
Abstract
The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.
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Affiliation(s)
- Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Abby Spangler
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Nicholas J Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Kelsey D Montgomery
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Boyi Guo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Melissa Grant-Peters
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Heena R Divecha
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Chaichontat Sriworarat
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Annie B Nguyen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Prashanthi Ravichandran
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
| | - Matthew N Tran
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Arta Seyedian
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Stephanie C Page
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Mina Ryten
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London WC1N 1EH, UK
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Johns Hopkins Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
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3
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Pergola G, Rampino A, Sportelli L, Borcuk CJ, Passiatore R, Di Carlo P, Marakhovskaia A, Fazio L, Amoroso N, Castro MN, Domenici E, Gennarelli M, Khlghatyan J, Kikidis GC, Lella A, Magri C, Monaco A, Papalino M, Parihar M, Popolizio T, Quarto T, Romano R, Torretta S, Valsecchi P, Zunuer H, Blasi G, Dukart J, Beaulieu JM, Bertolino A. A miR-137-Related Biological Pathway of Risk for Schizophrenia Is Associated With Human Brain Emotion Processing. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:356-366. [PMID: 38000716 DOI: 10.1016/j.bpsc.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/04/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND miR-137 is a microRNA involved in brain development, regulating neurogenesis and neuronal maturation. Genome-wide association studies have implicated miR-137 in schizophrenia risk but do not explain its involvement in brain function and underlying biology. Polygenic risk for schizophrenia mediated by miR-137 targets is associated with working memory, although other evidence points to emotion processing. We characterized the functional brain correlates of miR-137 target genes associated with schizophrenia while disentangling previously reported associations of miR-137 targets with working memory and emotion processing. METHODS Using RNA sequencing data from postmortem prefrontal cortex (N = 522), we identified a coexpression gene set enriched for miR-137 targets and schizophrenia risk genes. We validated the relationship of this set to miR-137 in vitro by manipulating miR-137 expression in neuroblastoma cells. We translated this gene set into polygenic scores of coexpression prediction and associated them with functional magnetic resonance imaging activation in healthy volunteers (n1 = 214; n2 = 136; n3 = 2075; n4 = 1800) and with short-term treatment response in patients with schizophrenia (N = 427). RESULTS In 4652 human participants, we found that 1) schizophrenia risk genes were coexpressed in a biologically validated set enriched for miR-137 targets; 2) increased expression of miR-137 target risk genes was mediated by low prefrontal miR-137 expression; 3) alleles that predict greater gene set coexpression were associated with greater prefrontal activation during emotion processing in 3 independent healthy cohorts (n1, n2, n3) in interaction with age (n4); and 4) these alleles predicted less improvement in negative symptoms following antipsychotic treatment in patients with schizophrenia. CONCLUSIONS The functional translation of miR-137 target gene expression linked with schizophrenia involves the neural substrates of emotion processing.
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Affiliation(s)
- Giulio Pergola
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| | - Antonio Rampino
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy.
| | - Leonardo Sportelli
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
| | - Christopher James Borcuk
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
| | - Roberta Passiatore
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Institute of Neuroscience and Medicine, Brain & Behaviour, Research Centre Jülich, Jülich, Germany
| | - Pasquale Di Carlo
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | | | - Leonardo Fazio
- Department of Medicine and Surgery, Libera Università Mediterranea Giuseppe Degennaro, Casamassima, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy; Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Mariana Nair Castro
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Consejo Nacional de Investigaciones Científicas y Técnicas, Ciudad Autónoma de Buenos Aires, Argentina (MNC); Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Fleni-Consejo Nacional de Investigaciones Científicas y Técnicas Neurosciences Institute, Ciudad Autónoma de Buenos Aires, Argentina
| | - Enrico Domenici
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy; Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology, Rovereto, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy; Genetics Unit, Istituto di Ricovero e Cura a Carattere Sanitario Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Jivan Khlghatyan
- Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy; Department of Neuroscience, Novartis Institutes for Biomedical Research, Cambridge, Massachusetts
| | - Gianluca Christos Kikidis
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
| | - Annalisa Lella
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Chiara Magri
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy; Consejo Nacional de Investigaciones Científicas y Técnicas, Ciudad Autónoma de Buenos Aires, Argentina (MNC); Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Fleni-Consejo Nacional de Investigaciones Científicas y Técnicas Neurosciences Institute, Ciudad Autónoma de Buenos Aires, Argentina; Università degli Studi di Bari Aldo Moro, Dipartimento Interateneo di Fisica M. Merlin, Bari, Italy
| | - Marco Papalino
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Madhur Parihar
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
| | - Teresa Popolizio
- Istituto di Ricovero e Cura a Carattere Sanitario Istituto Centro San Giovanni di Dio Fatebenefratelli, Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Tiziana Quarto
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Department of Law, University of Foggia, Foggia, Italy
| | - Raffaella Romano
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Silvia Torretta
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Paolo Valsecchi
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy; Department of Mental Health and Addiction Services, Azienda Socio Sanitaria Territoriale Spedali Civili of Brescia, Brescia, Italy
| | - Hailiqiguli Zunuer
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Blasi
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain & Behaviour, Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - Alessandro Bertolino
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
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Chen J, Iraji A, Fu Z, Andrés-Camazón P, Thapaliya B, Liu J, Calhoun VD. Dynamic fusion of genomics and functional network connectivity in UK biobank reveals static and time-varying SNP manifolds. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24301013. [PMID: 38260328 PMCID: PMC10802663 DOI: 10.1101/2024.01.09.24301013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Many psychiatric and neurological disorders show significant heritability, indicating strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) measures functional temporal coupling between brain networks in a time-varying manner and has proven to identify disease-related changes in the brain. However, it remains largely unclear how genetic risk contributes to brain dysconnectivity that further manifests into clinical symptoms. The current work aimed to address this gap by proposing a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned SNP manifolds by linking static SNPs to dynamic functional information of the brain. The sliding window approach was utilized to estimate four dFNC states and compute subject-level state-specific dFNC features. Each state of dFNC features were then combined with 12946 SZ risk SNPs for jICA decomposition, resulting in four parallel fusions in 32861 European ancestry individuals within the UK Biobank cohort. The identified joint SNP-dFNC components were further validated for SZ relevance in an aggregated SZ cohort, and compared for across-state similarity to indicate level of dynamism. The results supported that dynamic fusion yielded "static" and "dynamic" components (i.e., high and low across-state similarity, respectively) for SNP and dFNC modalities. As expected, the SNP components presented a mixture of static and dynamic manifolds, with the latter largely driven by fusion with dFNC. We also showed that some of the dynamic SNP manifolds uniquely elicited by fusion with state-specific dFNC features complemented each other in terms of biological interpretation. This dynamic fusion framework thus allows expanding the SNP modality to manifolds in the time dimension, which provides a unique lens to elicit unique SNP correlates of dFNC otherwise unseen, promising additional insights on how genetic risk links to disease-related dysconnectivity.
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Affiliation(s)
- Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
| | - Pablo Andrés-Camazón
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - Bishal Thapaliya
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
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Jameei H, Rakesh D, Zalesky A, Cairns MJ, Reay WR, Wray NR, Di Biase MA. Linking Polygenic Risk of Schizophrenia to Variation in Magnetic Resonance Imaging Brain Measures: A Comprehensive Systematic Review. Schizophr Bull 2024; 50:32-46. [PMID: 37354489 PMCID: PMC10754175 DOI: 10.1093/schbul/sbad087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/26/2023]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia is highly heritable, with a polygenic effect of many genes conferring risk. Evidence on whether cumulative risk also predicts alterations in brain morphology and function is inconsistent. This systematic review examined evidence for schizophrenia polygenic risk score (sczPRS) associations with commonly used magnetic resonance imaging (MRI) measures. We expected consistent evidence to emerge for significant sczPRS associations with variation in structure and function, specifically in frontal, temporal, and insula cortices that are commonly implicated in schizophrenia pathophysiology. STUDY DESIGN In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched MEDLINE, Embase, and PsycINFO for peer-reviewed studies published between January 2013 and March 2022. Studies were screened against predetermined criteria and National Institutes of Health (NIH) quality assessment tools. STUDY RESULTS In total, 57 studies of T1-weighted structural, diffusion, and functional MRI were included (age range = 9-80 years, Nrange = 64-76 644). We observed moderate, albeit preliminary, evidence for higher sczPRS predicting global reductions in cortical thickness and widespread variation in functional connectivity, and to a lesser extent, region-specific reductions in frontal and temporal volume and thickness. Conversely, sczPRS does not predict whole-brain surface area or gray/white matter volume. Limited evidence emerged for sczPRS associations with diffusion tensor measures of white matter microstructure in a large community sample and smaller cohorts of children and young adults. These findings were broadly consistent across community and clinical populations. CONCLUSIONS Our review supports the hypothesis that schizophrenia is a disorder of disrupted within and between-region brain connectivity, and points to specific whole-brain and regional MRI metrics that may provide useful intermediate phenotypes.
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Affiliation(s)
- Hadis Jameei
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Divyangana Rakesh
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, VIC, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, VIC, Australia
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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6
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Passiatore R, Antonucci LA, DeRamus TP, Fazio L, Stolfa G, Sportelli L, Kikidis GC, Blasi G, Chen Q, Dukart J, Goldman AL, Mattay VS, Popolizio T, Rampino A, Sambataro F, Selvaggi P, Ulrich W, Weinberger DR, Bertolino A, Calhoun VD, Pergola G. Changes in patterns of age-related network connectivity are associated with risk for schizophrenia. Proc Natl Acad Sci U S A 2023; 120:e2221533120. [PMID: 37527347 PMCID: PMC10410767 DOI: 10.1073/pnas.2221533120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/24/2023] [Indexed: 08/03/2023] Open
Abstract
Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single-session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlates with genetic risk for SCZ and is detectable with MRI in young participants.
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Affiliation(s)
- Roberta Passiatore
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, 30303Atlanta, GA
- Institute of Neuroscience and Medicine, Brain and Behavior, Research Centre Jülich, 52428Jülich, Germany
| | - Linda A. Antonucci
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
| | - Thomas P. DeRamus
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, 30303Atlanta, GA
| | - Leonardo Fazio
- Department of Medicine and Surgery, Libera Università Mediterranea Giuseppe Degennaro, 70010Casamassima, Italy
| | - Giuseppe Stolfa
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
| | - Leonardo Sportelli
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
| | - Gianluca C. Kikidis
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
| | - Giuseppe Blasi
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Psychiatric Unit, University Hospital, 70124Bari, Italy
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain and Behavior, Research Centre Jülich, 52428Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225Düsseldorf, Germany
| | - Aaron L. Goldman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
| | - Venkata S. Mattay
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
- Department of Neurology and Radiology, Johns Hopkins Medical Campus, 21287Baltimore, MD
| | - Teresa Popolizio
- Neuroradiology Unit, Scientific Institute for Research, Hospitalization and Health Care, Casa Sollievo della Sofferenza, 71013San Giovanni Rotondo, Foggia, Italy
| | - Antonio Rampino
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Psychiatric Unit, University Hospital, 70124Bari, Italy
| | - Fabio Sambataro
- Section of Psychiatry, Department of Neuroscience, University of Padova, 35121Padua, Italy
| | - Pierluigi Selvaggi
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Psychiatric Unit, University Hospital, 70124Bari, Italy
| | - William Ulrich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
| | - Apulian Network on Risk for Psychosis
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Department of Mental Health, Azienda Sanitaria Locale Foggia, 71121Foggia, Italy
- Department of Clinical and Experimental Medicine, University of Foggia, 71122Foggia, Italy
- Department of Mental Health, Azienda Sanitaria Locale Barletta-Andria-Trani, 76123Andria, Italy
- Department of Mental Health, Azienda Sanitaria Locale Bari, 70132Bari, Italy
- Department of Mental Health, Azienda Sanitaria Locale Brindisi, 72100Brindisi, Italy
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
- Department of Neurology and Radiology, Johns Hopkins Medical Campus, 21287Baltimore, MD
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 21205Baltimore, MD
- Department of Neuroscience, Johns Hopkins University School of Medicine, 21287Baltimore, MD
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, 21287Baltimore, MD
| | - Alessandro Bertolino
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Psychiatric Unit, University Hospital, 70124Bari, Italy
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, 30303Atlanta, GA
| | - Giulio Pergola
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, 70124Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205Baltimore, MD
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 21205Baltimore, MD
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7
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Dimitriadis SI, Perry G, Lancaster TM, Tansey KE, Singh KD, Holmans P, Pocklington A, Davey Smith G, Zammit S, Hall J, O’Donovan MC, Owen MJ, Jones DK, Linden DE. Genetic risk for schizophrenia is associated with increased proportion of indirect connections in brain networks revealed by a semi-metric analysis: evidence from population sample stratified for polygenic risk. Cereb Cortex 2023; 33:2997-3011. [PMID: 35830871 PMCID: PMC10016061 DOI: 10.1093/cercor/bhac256] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/30/2022] [Accepted: 05/31/2022] [Indexed: 02/02/2023] Open
Abstract
Research studies based on tractography have revealed a prominent reduction of asymmetry in some key white-matter tracts in schizophrenia (SCZ). However, we know little about the influence of common genetic risk factors for SCZ on the efficiency of routing on structural brain networks (SBNs). Here, we use a novel recall-by-genotype approach, where we sample young adults from a population-based cohort (ALSPAC:N genotyped = 8,365) based on their burden of common SCZ risk alleles as defined by polygenic risk score (PRS). We compared 181 individuals at extremes of low (N = 91) or high (N = 90) SCZ-PRS under a robust diffusion MRI-based graph theoretical SBN framework. We applied a semi-metric analysis revealing higher SMR values for the high SCZ-PRS group compared with the low SCZ-PRS group in the left hemisphere. Furthermore, a hemispheric asymmetry index showed a higher leftward preponderance of indirect connections for the high SCZ-PRS group compared with the low SCZ-PRS group (PFDR < 0.05). These findings might indicate less efficient structural connectivity in the higher genetic risk group. This is the first study in a population-based sample that reveals differences in the efficiency of SBNs associated with common genetic risk variants for SCZ.
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Affiliation(s)
- S I Dimitriadis
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Neuroinformatics Group, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - G Perry
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - T M Lancaster
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Department of Psychology, Bath University, Claverton Down BA2 7AY, Bath, Wales, UK
| | - K E Tansey
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Queens Road BS8 1QU, Bristol, Wales, UK
| | - K D Singh
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - P Holmans
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - A Pocklington
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - G Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Queens Road BS8 1QU, Bristol, Wales, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road BS8 1NU, Bristol, Wales, UK
| | - S Zammit
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road BS8 1NU, Bristol, Wales, UK
| | - J Hall
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - M C O’Donovan
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - M J Owen
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - D K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - D E Linden
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road BS8 1NU, Bristol, Wales, UK
- School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Universiteitssingel 40 UNS40 6229 ER, Maastricht, The Netherlands
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8
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Huuki-Myers L, Spangler A, Eagles N, Montgomery KD, Kwon SH, Guo B, Grant-Peters M, Divecha HR, Tippani M, Sriworarat C, Nguyen AB, Ravichandran P, Tran MN, Seyedian A, Hyde TM, Kleinman JE, Battle A, Page SC, Ryten M, Hicks SC, Martinowich K, Collado-Torres L, Maynard KR. Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.15.528722. [PMID: 36824961 PMCID: PMC9949126 DOI: 10.1101/2023.02.15.528722] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/.
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Affiliation(s)
- Louise Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Abby Spangler
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Nick Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Kelsey D Montgomery
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Boyi Guo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Melissa Grant-Peters
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Heena R Divecha
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Chaichontat Sriworarat
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Annie B Nguyen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | | | - Matthew N Tran
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Arta Seyedian
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
| | - Stephanie C Page
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Mina Ryten
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
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9
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Zhao W, Zhang Q, Su Y, Chen X, Li X, Du B, Deng X, Ji F, Li J, Dong Q, Chen C, Li J. Effect of schizophrenia risk gene polymorphisms on cognitive and neural plasticity. Schizophr Res 2022; 248:173-179. [PMID: 36075127 DOI: 10.1016/j.schres.2022.08.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 07/13/2022] [Accepted: 08/20/2022] [Indexed: 11/24/2022]
Abstract
A recent Chinese genome-wide association study found evidence for 58 out of the 128 schizophrenia-associated variants previously discovered in Western samples by the Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC). However, the functional impact of these trans-ancestry genome-wide single-nucleotide polymorphisms (SNPs) is not clear. In the current study, we examined the roles of trans-ancestry SNPs in cognitive and neural plasticity. We first performed a behavioral study of 547 healthy volunteers, who received month-long working memory training, and working memory capability assessment both before and after the training. A separate sample of 101 subjects received the same training and received fMRI scans during a working memory task, both before and after the training. The behavioral study found a significant association between the polygenic risk score (PRS) and behavioral plasticity, with higher schizophrenia risk scores being linked to less plasticity. At the SNP level, rs36068923 showed a significant signal, with the risk allele being associated with less plasticity. The fMRI study further found that the PRS and rs36068923 polymorphism were associated with training-induced changes in striatal activation, with higher PRS and the risk allele of rs36068923 being linked to less brain plasticity. In sum, this study found that a high genetic risk for schizophrenia was associated with less plasticity at both behavioral and neural levels. These results provide new insights into the neural and cognitive mechanisms linking genes to schizophrenia.
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Affiliation(s)
- Wan Zhao
- School of Psychology, Nanjing Normal University, Nanjing 210097, Jiangsu, PR China
| | - Qiumei Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China; School of Public Health, Jining Medical University, Jining 272013, Shandong, PR China
| | - Yanyan Su
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Xiongying Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & the Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, School of Mental Health, Capital Medical University, Beijing 100088, PR China
| | - Xiaohong Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders & the Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, School of Mental Health, Capital Medical University, Beijing 100088, PR China
| | - Boqi Du
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Xiaoxiang Deng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Feng Ji
- School of Mental Health, Jining Medical University, Jining 272013, Shandong, PR China
| | - Jin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing 100190, PR China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing 100190, PR China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China
| | - Chuansheng Chen
- Department of Psychological Science, University of California, Irvine, CA 92697, United States
| | - Jun Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, PR China.
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10
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Rantala MJ, Luoto S, Borráz-León JI, Krams I. Schizophrenia: the new etiological synthesis. Neurosci Biobehav Rev 2022; 142:104894. [PMID: 36181926 DOI: 10.1016/j.neubiorev.2022.104894] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 08/25/2022] [Accepted: 09/25/2022] [Indexed: 10/31/2022]
Abstract
Schizophrenia has been an evolutionary paradox: it has high heritability, but it is associated with decreased reproductive success. The causal genetic variants underlying schizophrenia are thought to be under weak negative selection. To unravel this paradox, many evolutionary explanations have been suggested for schizophrenia. We critically discuss the constellation of evolutionary hypotheses for schizophrenia, highlighting the lack of empirical support for most existing evolutionary hypotheses-with the exception of the relatively well supported evolutionary mismatch hypothesis. It posits that evolutionarily novel features of contemporary environments, such as chronic stress, low-grade systemic inflammation, and gut dysbiosis, increase susceptibility to schizophrenia. Environmental factors such as microbial infections (e.g., Toxoplasma gondii) can better predict the onset of schizophrenia than polygenic risk scores. However, researchers have not been able to explain why only a small minority of infected people develop schizophrenia. The new etiological synthesis of schizophrenia indicates that an interaction between host genotype, microbe infection, and chronic stress causes schizophrenia, with neuroinflammation and gut dysbiosis mediating this etiological pathway. Instead of just alleviating symptoms with drugs, the parasite x genotype x stress model emphasizes that schizophrenia treatment should focus on detecting and treating possible underlying microbial infection(s), neuroinflammation, gut dysbiosis, and chronic stress.
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Affiliation(s)
- Markus J Rantala
- Department of Biology, University of Turku, FIN-20014 Turku, Finland.
| | - Severi Luoto
- School of Population Health, University of Auckland, 1023 Auckland, New Zealand
| | | | - Indrikis Krams
- Institute of Ecology and Earth Sciences, University of Tartu, 51014 Tartu, Estonia; Department of Zoology and Animal Ecology, Faculty of Biology, University of Latvia, 1004, Rīga, Latvia
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11
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Kang J, Jiao Z, Qin Y, Wang Y, Wang J, Jin L, Feng J, Wang F, Tang Y, Gong X. Associations between polygenic risk scores and amplitude of low-frequency fluctuation of inferior frontal gyrus in schizophrenia. J Psychiatr Res 2022; 147:4-12. [PMID: 34999338 DOI: 10.1016/j.jpsychires.2021.12.043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 12/14/2021] [Accepted: 12/20/2021] [Indexed: 10/19/2022]
Abstract
Schizophrenia (SCZ) is a serious and complex mental disorder with high heritability. Polygenic risk score (PRS) is a useful tool calculating the accumulating effects of multiple common genetic variants of schizophrenia. The amplitude of low-frequency fluctuation (ALFF) is an efficient index to reflect spontaneous, intrinsic neuronal activity. Aberrant ALFF of brain regions were reported in schizophrenia frequently, but the relationship between PRS and ALFF has not been studied. In the present study, we compared PRS and ALFF in 101 schizophrenia patients and 106 age-matched healthy controls to test their associations with schizophrenia. Then, the correlation of PRS with ALFF was measured to reveal the effect of polygenic risk on brain activity in schizophrenia. We found that schizophrenia patients showed significant differences in PRS and ALFF compared with controls. Twenty-six brain regions showed significant difference of ALFF between schizophrenia cases and controls, of which left inferior frontal gyrus, triangular part (IFGtriang.L) showed increased activity in schizophrenia. PRS-SCZ was positively correlated with ALFF in IFGtriang.L in 57 non-chronic patients. Genes involved in synaptic organization and transmission, especially in glutamatergic synapse, were highly enriched in PRS-SCZ genes, suggesting the dysfunction of synapses in schizophrenia. These results help to understand the molecular mechanism underlying schizophrenia and related brain dysfunction.
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Affiliation(s)
- Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Center for Mathematical Science, Fudan University, Shanghai, China
| | - Zeyu Jiao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Center for Mathematical Science, Fudan University, Shanghai, China
| | - Yue Qin
- School of Life Sciences, Fudan University, Shanghai, China
| | - Yi Wang
- School of Life Sciences, Fudan University, Shanghai, China
| | - Jiucun Wang
- School of Life Sciences, Fudan University, Shanghai, China; Human Phoneme Institute, Fudan University, Shanghai, China
| | - Li Jin
- School of Life Sciences, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Center for Mathematical Science, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
| | - Fei Wang
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, China.
| | - Xiaohong Gong
- School of Life Sciences, Fudan University, Shanghai, China.
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12
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Koch E, Nyberg L, Lundquist A, Kauppi K. Polygenic Risk for Schizophrenia Has Sex-Specific Effects on Brain Activity during Memory Processing in Healthy Individuals. Genes (Basel) 2022; 13:genes13030412. [PMID: 35327966 PMCID: PMC8950000 DOI: 10.3390/genes13030412] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/10/2022] [Accepted: 02/23/2022] [Indexed: 12/28/2022] Open
Abstract
Genetic risk for schizophrenia has a negative impact on memory and other cognitive abilities in unaffected individuals, and it was recently shown that this effect is specific to males. Using functional MRI, we investigated the effect of a polygenic risk score (PRS) for schizophrenia on brain activation during working memory and episodic memory in 351 unaffected participants (167 males and 184 females, 25–95 years), and specifically tested if any effect of PRS on brain activation is sex-specific. Schizophrenia PRS was significantly associated with decreased brain activation in the left dorsolateral prefrontal cortex (DLPFC) during working-memory manipulation and in the bilateral superior parietal lobule (SPL) during episodic-memory encoding and retrieval. A significant interaction effect between sex and PRS was seen in the bilateral SPL during episodic-memory encoding and retrieval, and sex-stratified analyses showed that the effect of PRS on SPL activation was male-specific. These results confirm previous findings of DLPFC inefficiency in schizophrenia, and highlight the SPL as another important genetic intermediate phenotype of the disease. The observed sex differences suggest that the previously shown male-specific effect of schizophrenia PRS on cognition translates into an additional corresponding effect on brain functioning.
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Affiliation(s)
- Elise Koch
- Department of Integrative Medical Biology, Umeå University, 901 87 Umeå, Sweden; (L.N.); (K.K.)
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden;
- Correspondence: ; Tel.: +46-90-786-50-00
| | - Lars Nyberg
- Department of Integrative Medical Biology, Umeå University, 901 87 Umeå, Sweden; (L.N.); (K.K.)
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden;
- Department of Radiation Sciences, Diagnostic Radiology, University Hospital, Umeå University, 901 87 Umeå, Sweden
| | - Anders Lundquist
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden;
- Department of Statistics, School of Business, Economics and Statistics, Umeå University, 901 87 Umeå, Sweden
| | - Karolina Kauppi
- Department of Integrative Medical Biology, Umeå University, 901 87 Umeå, Sweden; (L.N.); (K.K.)
- Umeå Center for Functional Brain Imaging, Umeå University, 901 87 Umeå, Sweden;
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels väg 12A, 171 65 Solna, Sweden
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13
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Lack of neural load modulation explains attention and working memory deficits in first-episode schizophrenia. Clin Neurophysiol 2022; 136:206-218. [DOI: 10.1016/j.clinph.2022.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 01/29/2022] [Accepted: 02/02/2022] [Indexed: 11/23/2022]
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14
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Childhood traumatic events and the dopaminergic theory of psychosis: A mini-review of studies investigating gene – environment interactions. CURRENT PSYCHOLOGY 2021. [DOI: 10.1007/s12144-021-02650-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Abstract
There is great body of evidence showing a relationship between childhood adversity and psychosis onset. Genetic factors moderate the association between childhood adversity and psychosis risk potentially by influencing biological and/or psychological reaction following exposure to adversity. In this review, we discuss studies identifying the specific genetic variants known to affect dopamine levels involved in this interaction. Our review shows that the catechol-O-methyltransferase (COMT), dopamine D2 receptor (DRD2), AKT1 gene play a key role in mediating the relationship between childhood adversity and development of psychosis. We have also found conflicting findings on the impact of dopamine genes on the relationship between childhood adversity and development of psychosis, suggesting that other genetic and environmental factors should be taken into account. We here discuss the implications of our findings and future directions.
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15
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Functional connectome-wide associations of schizophrenia polygenic risk. Mol Psychiatry 2021; 26:2553-2561. [PMID: 32127647 PMCID: PMC9557214 DOI: 10.1038/s41380-020-0699-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2019] [Revised: 02/15/2020] [Accepted: 02/20/2020] [Indexed: 01/29/2023]
Abstract
Schizophrenia is a highly heritable mental disorder characterized by functional dysconnectivity across the brain. However, the relationships between polygenic risk factors and connectome-wide neural mechanisms are unclear. Here, combining genetic and multiparadigm fMRI data of 623 healthy Caucasian adults drawn from the Human Connectome Project, we found that higher schizophrenia polygenic risk scores were significantly correlated with lower functional connectivity in a large-scale brain network primarily encompassing the visual system, default-mode system, and frontoparietal system. Such correlation was robustly observed across multiple fMRI paradigms, suggesting a brain-state-independent neural phenotype underlying individual genetic liability to schizophrenia. Moreover, using an independent clinical dataset acquired from the Consortium for Neuropsychiatric Phenomics, we further demonstrated that the connectivity of the identified network was reduced in patients with schizophrenia and significantly correlated with general cognitive ability. These findings provide the first evidence for connectome-wide associations of schizophrenia polygenic risk at the systems level and suggest that disrupted integration of sensori-cognitive information may be a hallmark of genetic effects on the brain that contributes to the pathogenesis of schizophrenia.
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16
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Meyers JL, Chorlian DB, Bigdeli TB, Johnson EC, Aliev F, Agrawal A, Almasy L, Anokhin A, Edenberg HJ, Foroud T, Goate A, Kamarajan C, Kinreich S, Nurnberger J, Pandey AK, Pandey G, Plawecki MH, Salvatore JE, Zhang J, Fanous A, Porjesz B. The association of polygenic risk for schizophrenia, bipolar disorder, and depression with neural connectivity in adolescents and young adults: examining developmental and sex differences. Transl Psychiatry 2021; 11:54. [PMID: 33446638 PMCID: PMC7809462 DOI: 10.1038/s41398-020-01185-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 12/15/2020] [Accepted: 12/17/2020] [Indexed: 02/03/2023] Open
Abstract
Neurodevelopmental abnormalities in neural connectivity have been long implicated in the etiology of schizophrenia (SCZ); however, it remains unclear whether these neural connectivity patterns are associated with genetic risk for SCZ in unaffected individuals (i.e., an absence of clinical features of SCZ or a family history of SCZ). We examine whether polygenic risk scores (PRS) for SCZ are associated with functional neural connectivity in adolescents and young adults without SCZ, whether this association is moderated by sex and age, and if similar associations are observed for genetically related neuropsychiatric PRS. One-thousand four-hundred twenty-six offspring from 913 families, unaffected with SCZ, were drawn from the Collaborative Study of the Genetics of Alcoholism (COGA) prospective cohort (median age at first interview = 15.6 (12-26), 51.6% female, 98.1% European American, 41% with a family history of alcohol dependence). Participants were followed longitudinally with resting-state EEG connectivity (i.e., coherence) assessed every two years. Higher SCZ PRS were associated with elevated theta (3-7 Hz) and alpha (7-12 Hz) EEG coherence. Associations differed by sex and age; the most robust associations were observed between PRS and parietal-occipital, central-parietal, and frontal-parietal alpha coherence among males between ages 15-19 (B: 0.15-0.21, p < 10-4). Significant associations among EEG coherence and Bipolar and Depression PRS were observed, but differed from SCZ PRS in terms of sex, age, and topography. Findings reveal that polygenic risk for SCZ is robustly associated with increased functional neural connectivity among young adults without a SCZ diagnosis. Striking differences were observed between men and women throughout development, mapping onto key periods of risk for the onset of psychotic illness and underlining the critical importance of examining sex differences in associations with neuropsychiatric PRS across development.
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Affiliation(s)
- J. L. Meyers
- grid.189747.40000 0000 9554 2494Department of Psychiatry, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY 11203 USA
| | - D. B. Chorlian
- grid.189747.40000 0000 9554 2494Department of Psychiatry, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY 11203 USA
| | - T. B. Bigdeli
- grid.189747.40000 0000 9554 2494Department of Psychiatry, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY 11203 USA
| | - E. C. Johnson
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - F. Aliev
- grid.224260.00000 0004 0458 8737Department of Psychology & College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA 23284 USA ,grid.440448.80000 0004 0384 3505Faculty of Business, Karabuk University, Karabuk, Turkey
| | - A. Agrawal
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - L. Almasy
- grid.25879.310000 0004 1936 8972Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - A. Anokhin
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110 USA
| | - H. J. Edenberg
- grid.257413.60000 0001 2287 3919Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202 USA ,grid.257413.60000 0001 2287 3919Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - T. Foroud
- grid.257413.60000 0001 2287 3919Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - A. Goate
- grid.59734.3c0000 0001 0670 2351Departments of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA ,grid.59734.3c0000 0001 0670 2351Departments of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - C. Kamarajan
- grid.189747.40000 0000 9554 2494Department of Psychiatry, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY 11203 USA
| | - S. Kinreich
- grid.189747.40000 0000 9554 2494Department of Psychiatry, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY 11203 USA
| | - J. Nurnberger
- grid.257413.60000 0001 2287 3919Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - A. K. Pandey
- grid.189747.40000 0000 9554 2494Department of Psychiatry, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY 11203 USA
| | - G. Pandey
- grid.189747.40000 0000 9554 2494Department of Psychiatry, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY 11203 USA
| | - M. H. Plawecki
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110 USA ,grid.257413.60000 0001 2287 3919Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - J. E. Salvatore
- grid.224260.00000 0004 0458 8737Department of Psychology & College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA 23284 USA ,grid.224260.00000 0004 0458 8737Virginia Institute of Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23284 USA
| | - J. Zhang
- grid.189747.40000 0000 9554 2494Department of Psychiatry, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY 11203 USA
| | - A. Fanous
- grid.189747.40000 0000 9554 2494Department of Psychiatry, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY 11203 USA
| | - B. Porjesz
- grid.189747.40000 0000 9554 2494Department of Psychiatry, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY 11203 USA
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17
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Chen J, Zang Z, Braun U, Schwarz K, Harneit A, Kremer T, Ma R, Schweiger J, Moessnang C, Geiger L, Cao H, Degenhardt F, Nöthen MM, Tost H, Meyer-Lindenberg A, Schwarz E. Association of a Reproducible Epigenetic Risk Profile for Schizophrenia With Brain Methylation and Function. JAMA Psychiatry 2020; 77:628-636. [PMID: 32049268 PMCID: PMC7042900 DOI: 10.1001/jamapsychiatry.2019.4792] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
IMPORTANCE Schizophrenia is a severe mental disorder in which epigenetic mechanisms may contribute to illness risk. Epigenetic profiles can be derived from blood cells, but to our knowledge, it is unknown whether these predict established brain alterations associated with schizophrenia. OBJECTIVE To identify an epigenetic signature (quantified as polymethylation score [PMS]) of schizophrenia using machine learning applied to genome-wide blood DNA-methylation data; evaluate whether differences in blood-derived PMS are mirrored in data from postmortem brain samples; test whether the PMS is associated with alterations of dorsolateral prefrontal cortex hippocampal (DLPFC-HC) connectivity during working memory in healthy controls (HC); explore the association between interactions between polygenic and epigenetic risk with DLPFC-HC connectivity; and test the specificity of the signature compared with other serious psychiatric disorders. DESIGN, SETTING, AND PARTICIPANTS In this case-control study conducted from 2008 to 2018 in sites in Germany, the United Kingdom, the United States, and Australia, blood DNA-methylation data from 2230 whole-blood samples from 6 independent cohorts comprising HC (1238 [55.5%]) and participants with schizophrenia (803 [36.0%]), bipolar disorder (39 [1.7%]), major depressive disorder 35 [1.6%]), and autism (27 [1.2%]), and first-degree relatives of all patient groups (88 [3.9%]) were analyzed. DNA-methylation data were further explored from 244 postmortem DLPFC samples from 136 HC and 108 patients with schizophrenia. Neuroimaging and genome-wide association data were available for 393 HC. The latter data was used to calculate a polygenic risk score (PRS) for schizophrenia. The data were analyzed in 2019. MAIN OUTCOMES AND MEASURES The accuracy of schizophrenia control classification based on machine learning using epigenetic data; association of schizophrenia PMS scores with DLPFC-HC connectivity; and association of the interaction between PRS and PMS with DLPFC-HC connectivity. RESULTS This study included 7488 participants (4395 men [58.7%]), of whom 3158 (2230 men [70.6%]) received a diagnosis of schizophrenia. The PMS signature was associated with schizophrenia across 3 independent data sets (area under the curve [AUC] from 0.69 to 0.78; P value from 0.049 to 1.24 × 10-7) and data from postmortem DLPFC samples (AUC = 0.63; P = 1.42 × 10-4), but not with major depressive disorder (AUC = 0.51; P = .16), autism (AUC = 0.53; P = .66), or bipolar disorder (AUC = 0.58; P = .21). Pathways contributing most to the classification included synaptic processes. Healthy controls with schizophrenia-like PMS showed significantly altered DLPFC-HC connectivity (validation methylation/magnetic resonance imaging, t < -3.81; P for familywise error, <.04; validation magnetic resonance imaging, t < -3.54; P for familywise error, <.02), mirroring the lack of functional decoupling in schizophrenia. There was no significant association of the interaction between PMS and PRS with DLPFC-HC connectivity (P > .19). CONCLUSIONS AND RELEVANCE We identified a reproducible blood DNA-methylation signature specific for schizophrenia that was correlated with altered functional DLPFC-HC coupling during working memory and mapped to methylation differences found in DLPFC postmortem samples. This indicates a possible epigenetic contribution to a schizophrenia intermediate phenotype and suggests that PMS could be of interest to be studied in the context of multimodal biomarkers for disease stratification and treatment personalization.
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Affiliation(s)
- Junfang Chen
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Zhenxiang Zang
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Urs Braun
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Kristina Schwarz
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Anais Harneit
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Thomas Kremer
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Ren Ma
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Janina Schweiger
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Carolin Moessnang
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lena Geiger
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Han Cao
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Franziska Degenhardt
- School of Medicine & University Hospital Bonn, Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - Markus M. Nöthen
- School of Medicine & University Hospital Bonn, Institute of Human Genetics, University of Bonn, Bonn, Germany,Life & Brain Center, Department of Genomics, University of Bonn, Bonn, Germany
| | - Heike Tost
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Emanuel Schwarz
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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18
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Liu S, Li A, Liu Y, Yan H, Wang M, Sun Y, Fan L, Song M, Xu K, Chen J, Chen Y, Wang H, Guo H, Wan P, Lv L, Yang Y, Li P, Lu L, Yan J, Wang H, Zhang H, Wu H, Ning Y, Zhang D, Jiang T, Liu B. Polygenic effects of schizophrenia on hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity. Br J Psychiatry 2020; 216:267-274. [PMID: 31169117 DOI: 10.1192/bjp.2019.127] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated. AIMS To investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia. METHOD Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant. RESULTS The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus-medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia. CONCLUSIONS These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.
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Affiliation(s)
- Shu Liu
- MSc Student, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences.,School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Ang Li
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China.,PhD Student, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
| | - Yong Liu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China.,Professor, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
| | - Hao Yan
- Associate Professor, Peking University Sixth Hospital, Institute of Mental Health.,Key Laboratory of Mental Health, Ministry of Health (Peking University), China
| | - Meng Wang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China.,PhD Student, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
| | - Yuqing Sun
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China.,PhD Student, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
| | - Lingzhong Fan
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China.,Professor, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
| | - Ming Song
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China.,Associate Professor, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
| | - Kaibin Xu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China.,PhD Student, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
| | - Jun Chen
- Associate Professor, Department of Radiology, Renmin Hospital of Wuhan University, China
| | - Yunchun Chen
- Associate Professor, Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, China
| | - Huaning Wang
- Associate Professor, Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, China
| | - Hua Guo
- Professor, Zhumadian Psychiatric Hospital, China
| | - Ping Wan
- Professor, Zhumadian Psychiatric Hospital, China
| | - Luxian Lv
- Professor, Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University.,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, China
| | - Yongfeng Yang
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, China.,Attending Doctor, Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University
| | - Peng Li
- Key Laboratory of Mental Health, Ministry of Health (Peking University), China.,Associate Professor, Peking University Sixth Hospital, Institute of Mental Health
| | - Lin Lu
- Key Laboratory of Mental Health, Ministry of Health (Peking University), China.,Professor, Peking University Sixth Hospital, Institute of Mental Health
| | - Jun Yan
- Key Laboratory of Mental Health, Ministry of Health (Peking University), China.,Professor, Peking University Sixth Hospital, Institute of Mental Health
| | - Huiling Wang
- Professor, Department of Radiology, Renmin Hospital of Wuhan University, China
| | - Hongxing Zhang
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, China.,Professor, Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University
| | - Huawang Wu
- Attending Doctor, Guangzhou Brain Hospital, The Affiliated Brain Hospital of Guangzhou Medical University, China
| | - Yuping Ning
- Professor, Guangzhou Brain Hospital, The Affiliated Brain Hospital of Guangzhou Medical University, China
| | - Dai Zhang
- Key Laboratory of Mental Health, Ministry of Health (Peking University), China.,Professor, Peking University Sixth Hospital, Institute of Mental Health
| | - Tianzi Jiang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China.,Professor, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
| | - Bing Liu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China.,Professor, Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
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19
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Nesic MJ, Stojkovic B, Maric NP. On the origin of schizophrenia: Testing evolutionary theories in the post-genomic era. Psychiatry Clin Neurosci 2019; 73:723-730. [PMID: 31525268 DOI: 10.1111/pcn.12933] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 09/10/2019] [Accepted: 09/12/2019] [Indexed: 12/13/2022]
Abstract
Considering the relatively high heritability of schizophrenia and the fact that it significantly reduces the reproductive fitness of affected individuals, it is not clear how the disorder is still maintained in human populations at a disproportionally high prevalence. Many theories propose that the disorder is a result of a trade-off between costs and benefits of the evolution of exclusively human adaptations. There have also been suggestions that schizophrenia risk alleles are accompanied with increase in fitness of affected persons or their relatives in both past and current social contexts. The discoveries of novel schizophrenia-related genes and the advancements in comparative genomics (especially comparisons of the human genome and the genomes of related species, such as chimpanzees and extinct hominids) have finally made certain evolutionary theories testable. In this paper, we review the current understanding of the genetics of schizophrenia, the basic principles of evolution that complement our understanding of the subject, and the latest genetic studies that examine long-standing evolutionary theories of schizophrenia using novel methodologies and data. We find that the origin of schizophrenia is complex and likely governed by different evolutionary mechanisms that are not mutually exclusive. Furthermore, the most recent evidence implies that schizophrenia cannot be comprehended as a trait that has elevated fitness in human evolutionary lineage, but has been a mildly deleterious by-product of specific patterns of the evolution of the human brain. In other words, novel findings do not support previous hypotheses stating that schizophrenia risk genes have an evolutionary advantage.
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Affiliation(s)
- Milica J Nesic
- Clinic for Psychiatry, Clinical Center of Serbia, Belgrade, Serbia
| | - Biljana Stojkovic
- Institute of Zoology, Faculty of Biology, University of Belgrade, Belgrade, Serbia.,Department of Evolutionary Biology, Institute for Biological Research 'Siniša Stanković', University of Belgrade, Belgrade, Serbia
| | - Nadja P Maric
- Clinic for Psychiatry, Clinical Center of Serbia, Belgrade, Serbia.,Faculty of Medicine, University of Belgrade, Belgrade, Serbia
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20
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Polygenic Scores for Neuropsychiatric Traits and White Matter Microstructure in the Pediatric Population. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:243-250. [DOI: 10.1016/j.bpsc.2018.07.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 07/08/2018] [Accepted: 07/09/2018] [Indexed: 12/31/2022]
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21
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Studying and modulating schizophrenia-associated dysfunctions of oligodendrocytes with patient-specific cell systems. NPJ SCHIZOPHRENIA 2018; 4:23. [PMID: 30451850 PMCID: PMC6242875 DOI: 10.1038/s41537-018-0066-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 10/30/2018] [Indexed: 02/07/2023]
Abstract
Postmortem studies in patients with schizophrenia (SCZ) have revealed deficits in myelination, abnormalities in myelin gene expression and altered numbers of oligodendrocytes in the brain. However, gaining mechanistic insight into oligodendrocyte (OL) dysfunction and its contribution to SCZ has been challenging because of technical hurdles. The advent of individual patient-derived human-induced pluripotent stem cells (hiPSCs), combined with the generation of in principle any neuronal and glial cell type, including OLs and oligodendrocyte precursor cells (OPCs), holds great potential for understanding the molecular basis of the aetiopathogenesis of genetically complex psychiatric diseases such as SCZ and could pave the way towards personalized medicine. The development of neuronal and glial co-culture systems now appears to enable the in vitro study of SCZ-relevant neurobiological endophenotypes, including OL dysfunction and myelination, with unprecedented construct validity. Nonetheless, the meaningful stratification of patients before the subsequent functional analyses of patient-derived cell systems still represents an important bottleneck. Here, to improve the predictive power of ex vivo disease modelling we propose using hiPSC technology to focus on representatives of patient subgroups stratified for genomic and/or phenomic features and neurobiological cell systems. Therefore, this review will outline the evidence for the involvement of OPCs/OLs in SCZ in the context of their proposed functions, including myelination and axon support, the implications for hiPSC-based cellular disease modelling and potential strategies for patient selection.
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22
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Abstract
Recent large-scale genomic studies have confirmed that schizophrenia is a polygenic syndrome and have implicated a number of biological pathways in its aetiology. Both common variants individually of small effect and rarer but more penetrant genetic variants have been shown to play a role in the pathogenesis of the disorder. No simple Mendelian forms of the condition have been identified, but progress has been made in stratifying risk on the basis of the polygenic burden of common variants individually of small effect, and the contribution of rarer variants of larger effect such as Copy Number Variants (CNVs). Pathway analysis of risk-associated variants has begun to identify specific biological processes implicated in risk for the disorder, including elements of the glutamatergic NMDA receptor complex and post synaptic density, voltage-gated calcium channels, targets of the Fragile X Mental Retardation Protein (FMRP targets) and immune pathways. Genetic studies have also been used to drive genomic imaging approaches to the investigation of brain markers associated with risk for the disorder. Genomic imaging approaches have been applied both to investigate the effect of polygenic risk and to study the impact of individual higher-penetrance variants such as CNVs. Both genomic and genomic imaging approaches offer potential for the stratification of patients and at-risk groups and the development of better biomarkers of risk and treatment response; however, further research is needed to integrate this work and realise the full potential of these approaches.
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