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Ji J, Chao H, Chen H, Liao J, Shi W, Ye Y, Wang T, You Y, Liu N, Ji J, Petretto E. Decoding frontotemporal and cell-type-specific vulnerabilities to neuropsychiatric disorders and psychoactive drugs. Open Biol 2024; 14:240063. [PMID: 38864245 DOI: 10.1098/rsob.240063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 04/29/2024] [Indexed: 06/13/2024] Open
Abstract
Frontotemporal lobe abnormalities are linked to neuropsychiatric disorders and cognition, but the role of cellular heterogeneity between temporal lobe (TL) and frontal lobe (FL) in the vulnerability to genetic risk factors remains to be elucidated. We integrated single-nucleus transcriptome analysis in 'fresh' human FL and TL with genetic susceptibility, gene dysregulation in neuropsychiatric disease and psychoactive drug response data. We show how intrinsic differences between TL and FL contribute to the vulnerability of specific cell types to both genetic risk factors and psychoactive drugs. Neuronal populations, specifically PVALB neurons, were most highly vulnerable to genetic risk factors for psychiatric disease. These psychiatric disease-associated genes were mostly upregulated in the TL, and dysregulated in the brain of patients with obsessive-compulsive disorder, bipolar disorder and schizophrenia. Among these genes, GRIN2A and SLC12A5, implicated in schizophrenia and bipolar disorder, were significantly upregulated in TL PVALB neurons and in psychiatric disease patients' brain. PVALB neurons from the TL were twofold more vulnerable to psychoactive drugs than to genetic risk factors, showing the influence and specificity of frontotemporal lobe differences on cell vulnerabilities. These studies provide a cell type resolved map of the impact of brain regional differences on cell type vulnerabilities in neuropsychiatric disorders.
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Affiliation(s)
- Jiatong Ji
- Institute for Big Data and Artificial Intelligence in Medicine, School of Science, China Pharmaceutical University (CPU), Nanjing, Jiangsu 211198, People's Republic of China
| | - Honglu Chao
- Department of Neurosurgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, People's Republic of China
| | - Huimei Chen
- Institute for Big Data and Artificial Intelligence in Medicine, School of Science, China Pharmaceutical University (CPU), Nanjing, Jiangsu 211198, People's Republic of China
- Duke-NUS Medical School, Singapore 169857, Singapore
| | - Jun Liao
- High Performance Computing Center, School of Science, China Pharmaceutical University (CPU), Nanjing, Jiangsu 211198, People's Republic of China
| | - Wenqian Shi
- Department of Neurosurgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, People's Republic of China
| | - Yangfan Ye
- Department of Neurosurgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, People's Republic of China
| | - Tian Wang
- Department of Neurosurgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, People's Republic of China
| | - Yongping You
- Department of Neurosurgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, People's Republic of China
| | - Ning Liu
- Department of Neurosurgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, People's Republic of China
| | - Jing Ji
- Department of Neurosurgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, People's Republic of China
- Department of Neurosurgery, The Affiliated Kizilsu Kirghiz Autonomous Prefecture People's Hospital of Nanjing Medical University, Xinjiang, Artux 845350, People's Republic of China
- Gusu School, Nanjing Medical University, Suzhou, Jiangsu 215006, People's Republic of China
| | - Enrico Petretto
- Institute for Big Data and Artificial Intelligence in Medicine, School of Science, China Pharmaceutical University (CPU), Nanjing, Jiangsu 211198, People's Republic of China
- Duke-NUS Medical School, Singapore 169857, Singapore
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2
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Castellini G, Merola GP, Baccaredda Boy O, Pecoraro V, Bozza B, Cassioli E, Rossi E, Bessi V, Sorbi S, Nacmias B, Ricca V. Emotional dysregulation, alexithymia and neuroticism: a systematic review on the genetic basis of a subset of psychological traits. Psychiatr Genet 2023; 33:79-101. [PMID: 36729042 PMCID: PMC10158611 DOI: 10.1097/ypg.0000000000000335] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 11/24/2022] [Indexed: 02/03/2023]
Abstract
Neuroticism, alexithymia and emotion dysregulation are key traits and known risk factors for several psychiatric conditions. In this systematic review, the aim is to evaluate the genetic contribution to these psychological phenotypes. A systematic review of articles found in PubMed was conducted. Search terms included 'genetic', 'GWAS', 'neuroticism', 'alexithymia' and 'emotion dysregulation'. Risk of bias was assessed utilizing the STREGA checklist. Two hundred two papers were selected from existing literature based on the inclusion and exclusion criteria. Among these, 27 were genome-wide studies and 175 were genetic association studies. Single gene association studies focused on selected groups of genes, mostly involved in neurotransmission, with conflicting results. GWAS studies on neuroticism, on the other hand, found several relevant and replicated intergenic and intronic loci affecting the expression and regulation of crucial and well-known genes (such as DRD2 and CRHR1). Mutations in genes coding for trascriptional factors were also found to be associated with neuroticism (DCC, XKR6, TCF4, RBFOX1), as well as a noncoding regulatory RNA (LINC00461). On the other hand, little GWAS data are available on alexythima and emotional dysregulation.
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Affiliation(s)
| | | | | | | | | | | | | | - Valentina Bessi
- Neurology Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Sandro Sorbi
- Neurology Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Benedetta Nacmias
- Neurology Unit, Department of Health Sciences, University of Florence, Florence, Italy
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3
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Mourad R. Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences. BMC Bioinformatics 2023; 24:186. [PMID: 37147561 PMCID: PMC10163727 DOI: 10.1186/s12859-023-05303-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 04/25/2023] [Indexed: 05/07/2023] Open
Abstract
MOTIVATION Genome-wide association studies have systematically identified thousands of single nucleotide polymorphisms (SNPs) associated with complex genetic diseases. However, the majority of those SNPs were found in non-coding genomic regions, preventing the understanding of the underlying causal mechanism. Predicting molecular processes based on the DNA sequence represents a promising approach to understand the role of those non-coding SNPs. Over the past years, deep learning was successfully applied to regulatory sequence prediction using supervised learning. Supervised learning required DNA sequences associated with functional data for training, whose amount is strongly limited by the finite size of the human genome. Conversely, the amount of mammalian DNA sequences is exponentially increasing due to ongoing large sequencing projects, but without functional data in most cases. RESULTS To alleviate the limitations of supervised learning, we propose a paradigm shift with semi-supervised learning, which does not only exploit labeled sequences (e.g. human genome with ChIP-seq experiment), but also unlabeled sequences available in much larger amounts (e.g. from other species without ChIP-seq experiment, such as chimpanzee). Our approach is flexible and can be plugged into any neural architecture including shallow and deep networks, and shows strong predictive performance improvements compared to supervised learning in most cases (up to [Formula: see text]). AVAILABILITY AND IMPLEMENTATION https://forgemia.inra.fr/raphael.mourad/deepgnn .
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Affiliation(s)
- Raphaël Mourad
- MIAT, INRAE, 31320, Castanet-Tolosan, France.
- University of Toulouse, UPS, 31062, Toulouse, France.
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4
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Omori NE, Malys MK, Woo G, Mansor L. Exploring the role of ketone bodies in the diagnosis and treatment of psychiatric disorders. Front Psychiatry 2023; 14:1142682. [PMID: 37139329 PMCID: PMC10149735 DOI: 10.3389/fpsyt.2023.1142682] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/28/2023] [Indexed: 05/05/2023] Open
Abstract
In recent times, advances in the field of metabolomics have shed greater light on the role of metabolic disturbances in neuropsychiatric conditions. The following review explores the role of ketone bodies and ketosis in both the diagnosis and treatment of three major psychiatric disorders: major depressive disorder, anxiety disorders, and schizophrenia. Distinction is made between the potential therapeutic effects of the ketogenic diet and exogenous ketone preparations, as exogenous ketones in particular offer a standardized, reproducible manner for inducing ketosis. Compelling associations between symptoms of mental distress and dysregulation in central nervous system ketone metabolism have been demonstrated in preclinical studies with putative neuroprotective effects of ketone bodies being elucidated, including effects on inflammasomes and the promotion of neurogenesis in the central nervous system. Despite emerging pre-clinical data, clinical research on ketone body effectiveness as a treatment option for psychiatric disorders remains lacking. This gap in understanding warrants further investigating, especially considering that safe and acceptable ways of inducing ketosis are readily available.
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Affiliation(s)
- Naomi Elyse Omori
- Health Via Modern Nutrition Inc. (H.V.M.N.), San Francisco, CA, United States
- *Correspondence: Naomi Elyse Omori,
| | - Mantas Kazimieras Malys
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, United Kingdom
| | - Geoffrey Woo
- Health Via Modern Nutrition Inc. (H.V.M.N.), San Francisco, CA, United States
| | - Latt Mansor
- Health Via Modern Nutrition Inc. (H.V.M.N.), San Francisco, CA, United States
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5
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Allesøe RL, Nudel R, Thompson WK, Wang Y, Nordentoft M, Børglum AD, Hougaard DM, Werge T, Rasmussen S, Benros ME. Deep learning-based integration of genetics with registry data for stratification of schizophrenia and depression. SCIENCE ADVANCES 2022; 8:eabi7293. [PMID: 35767618 PMCID: PMC9242585 DOI: 10.1126/sciadv.abi7293] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Currently, psychiatric diagnoses are, in contrast to most other medical fields, based on subjective symptoms and observable signs and call for new and improved diagnostics to provide the most optimal care. On the basis of a deep learning approach, we performed unsupervised patient stratification of 19,636 patients with depression [major depressive disorder (MDD)] and/or schizophrenia (SCZ) and 22,467 population controls from the iPSYCH2012 case cohort. We integrated data of disorder severity, history of mental disorders and disease comorbidities, genetics, and medical birth data. From this, we stratified the individuals in six and seven unique clusters for MDD and SCZ, respectively. When censoring data until diagnosis, we could predict MDD clusters with areas under the curve (AUCs) of 0.54 to 0.80 and SCZ clusters with AUCs of 0.71 to 0.86. Overall cases and controls could be predicted with an AUC of 0.81, illustrating the utility of data-driven subgrouping in psychiatry.
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Affiliation(s)
- Rosa Lundbye Allesøe
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ron Nudel
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
| | - Wesley K. Thompson
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, San Diego, CA, USA
| | - Yunpeng Wang
- Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Forskningsveien 3A, 0317 Oslo, Norway
| | - Merete Nordentoft
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anders D. Børglum
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Biomedicine, Aarhus University and Centre for Integrative Sequencing, iSEQ, Aarhus, Denmark
- Aarhus Genome Center, Aarhus, Denmark
| | - David M. Hougaard
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Thomas Werge
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Eriksen Benros
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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6
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Rajula HSR, Manchia M, Agarwal K, Akingbuwa WA, Allegrini AG, Diemer E, Doering S, Haan E, Jami ES, Karhunen V, Leone M, Schellhas L, Thompson A, van den Berg SM, Bergen SE, Kuja-Halkola R, Hammerschlag AR, Järvelin MR, Leval A, Lichtenstein P, Lundstrom S, Mauri M, Munafò MR, Myers D, Plomin R, Rimfeld K, Tiemeier H, Ystrom E, Fanos V, Bartels M, Middeldorp CM. Overview of CAPICE-Childhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe-an EU Marie Skłodowska-Curie International Training Network. Eur Child Adolesc Psychiatry 2022; 31:829-839. [PMID: 33474652 PMCID: PMC9142454 DOI: 10.1007/s00787-020-01713-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 12/21/2020] [Indexed: 01/30/2023]
Abstract
The Roadmap for Mental Health and Wellbeing Research in Europe (ROAMER) identified child and adolescent mental illness as a priority area for research. CAPICE (Childhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe) is a European Union (EU) funded training network aimed at investigating the causes of individual differences in common childhood and adolescent psychopathology, especially depression, anxiety, and attention deficit hyperactivity disorder. CAPICE brings together eight birth and childhood cohorts as well as other cohorts from the EArly Genetics and Life course Epidemiology (EAGLE) consortium, including twin cohorts, with unique longitudinal data on environmental exposures and mental health problems, and genetic data on participants. Here we describe the objectives, summarize the methodological approaches and initial results, and present the dissemination strategy of the CAPICE network. Besides identifying genetic and epigenetic variants associated with these phenotypes, analyses have been performed to shed light on the role of genetic factors and the interplay with the environment in influencing the persistence of symptoms across the lifespan. Data harmonization and building an advanced data catalogue are also part of the work plan. Findings will be disseminated to non-academic parties, in close collaboration with the Global Alliance of Mental Illness Advocacy Networks-Europe (GAMIAN-Europe).
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Affiliation(s)
- Hema Sekhar Reddy Rajula
- Neonatal Intensive Care Unit, Department of Surgical Sciences, AOU and University of Cagliari, Cagliari, Italy
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Science and Public Health, University of Cagliari, Cagliari, Italy.,Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Kratika Agarwal
- Department of Learning, Data Analytics and Technology, University of Twente, Enschede, The Netherlands
| | - Wonuola A Akingbuwa
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Andrea G Allegrini
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Elizabeth Diemer
- Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Sabrina Doering
- Centre for Ethics, Law and Mental Health (CELAM), Gillberg Neuropsychiatry Centre, University of Gothenburg, Gothenburg, Sweden
| | - Elis Haan
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,School of Psychological Science, University of Bristol, Bristol, UK
| | - Eshim S Jami
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Marica Leone
- Janssen Pharmaceutical, Global Commercial Strategy Organization, Stockholm, Sweden.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Laura Schellhas
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,School of Psychological Science, University of Bristol, Bristol, UK
| | - Ashley Thompson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Stéphanie M van den Berg
- Department of Learning, Data Analytics and Technology, University of Twente, Enschede, The Netherlands
| | - Sarah E Bergen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ralf Kuja-Halkola
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anke R Hammerschlag
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.,Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Service, Brisbane, Australia
| | - Marjo Riitta Järvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,Faculty of Medicine, Center for Life Course Health Research, University of Oulu, Oulun yliopisto, Oulu, Finland.,Biocenter Oulu, University of Oulu, Oulu, Finland.,Unit of Primary Health Care, Oulu University Hospital, Oulu, Finland.,Department of Life Sciences, College of Health and Life Sciences, Brunel University , London, UK
| | - Amy Leval
- Janssen Pharmaceutical, Global Commercial Strategy Organization, Stockholm, Sweden.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Sebastian Lundstrom
- Centre for Ethics, Law and Mental Health (CELAM), Gillberg Neuropsychiatry Centre, University of Gothenburg, Gothenburg, Sweden
| | | | - Marcus R Munafò
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.,School of Psychological Science, University of Bristol, Bristol, UK
| | - David Myers
- Janssen Pharmaceutical, Global Commercial Strategy Organization, Stockholm, Sweden
| | - Robert Plomin
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Kaili Rimfeld
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Henning Tiemeier
- Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Eivind Ystrom
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway.,Norwegian Institute of Public Health, Oslo, Norway.,Department of Pharmacy, University of Oslo, Oslo, Norway
| | - Vassilios Fanos
- Neonatal Intensive Care Unit, Department of Surgical Sciences, AOU and University of Cagliari, Cagliari, Italy
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Christel M Middeldorp
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. .,Child Health Research Centre, Level 6, Centre for Children's Health Research, University of Queensland, 62 Graham Street, South Brisbane, QLD, 4101, Australia. .,Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Service, Brisbane, Australia.
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7
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Saraiva LC, Cappi C, Simpson HB, Stein DJ, Viswanath B, van den Heuvel OA, Reddy YCJ, Miguel EC, Shavitt RG. Cutting-edge genetics in obsessive-compulsive disorder. Fac Rev 2020; 9:30. [PMID: 33659962 PMCID: PMC7886082 DOI: 10.12703/r/9-30] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
This article reviews recent advances in the genetics of obsessive-compulsive disorder (OCD). We cover work on the following: genome-wide association studies, whole-exome sequencing studies, copy number variation studies, gene expression, polygenic risk scores, gene–environment interaction, experimental animal systems, human cell models, imaging genetics, pharmacogenetics, and studies of endophenotypes. Findings from this work underscore the notion that the genetic architecture of OCD is highly complex and shared with other neuropsychiatric disorders. Also, the latest evidence points to the participation of gene networks involved in synaptic transmission, neurodevelopment, and the immune and inflammatory systems in this disorder. We conclude by highlighting that further study of the genetic architecture of OCD, a great part of which remains to be elucidated, could benefit the development of diagnostic and therapeutic approaches based on the biological basis of the disorder. Studies to date revealed that OCD is not a simple homogeneous entity, but rather that the underlying biological pathways are variable and heterogenous. We can expect that translation from bench to bedside, through continuous effort and collaborative work, will ultimately transform our understanding of what causes OCD and thus how best to treat it.
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Affiliation(s)
- Leonardo Cardoso Saraiva
- Department & Institute of Psychiatry, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Carolina Cappi
- Department & Institute of Psychiatry, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Helen Blair Simpson
- Columbia University Irving Medical Center, Columbia University, New York, NY, 10032, USA
- The New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Biju Viswanath
- Molecular Genetics Laboratory, National Institute of Mental Health & Neurosciences (NIMHANS); Accelerator Program for Discovery in Brain disorders using Stem cells (ADBS) Laboratory, NIMHANS, Bangalore, India
| | - Odile A van den Heuvel
- Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Department of Psychiatry, Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - YC Janardhan Reddy
- Obsessive-Compulsive Disorder (OCD) Clinic, Department of Psychiatry, NIMHANS, Bangalore, India
| | - Euripedes C Miguel
- Department & Institute of Psychiatry, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Roseli G Shavitt
- Department & Institute of Psychiatry, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
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8
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Skonieczna-Żydecka K, Jakubczyk K, Maciejewska-Markiewicz D, Janda K, Kaźmierczak-Siedlecka K, Kaczmarczyk M, Łoniewski I, Marlicz W. Gut Biofactory-Neurocompetent Metabolites within the Gastrointestinal Tract. A Scoping Review. Nutrients 2020; 12:E3369. [PMID: 33139656 PMCID: PMC7693392 DOI: 10.3390/nu12113369] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/27/2020] [Accepted: 10/29/2020] [Indexed: 12/12/2022] Open
Abstract
The gut microbiota have gained much scientific attention recently. Apart from unravelling the taxonomic data, we should understand how the altered microbiota structure corresponds to functions of this complex ecosystem. The metabolites of intestinal microorganisms, especially bacteria, exert pleiotropic effects on the human organism and contribute to the host systemic balance. These molecules play key roles in regulating immune and metabolic processes. A subset of them affect the gut brain axis signaling and balance the mental wellbeing. Neurotransmitters, short chain fatty acids, tryptophan catabolites, bile acids and phosphatidylcholine, choline, serotonin, and L-carnitine metabolites possess high neuroactive potential. A scoping literature search in PubMed/Embase was conducted up until 20 June 2020, using three major search terms "microbiota metabolites" AND "gut brain axis" AND "mental health". This review aimed to enhance our knowledge regarding the gut microbiota functional capacity, and support current and future attempts to create new compounds for future clinical interventions.
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Affiliation(s)
- Karolina Skonieczna-Żydecka
- Department of Human Nutrition and Metabolomics, Pomeranian Medical University in Szczecin, 71-460 Szczecin, Poland; (K.S.-Ż.); (K.J.); (D.M.-M.); (K.J.)
| | - Karolina Jakubczyk
- Department of Surgical Oncology, Medical University of Gdansk, Smoluchowskiego 17, 80-214 Gdańsk, Poland;
| | - Dominika Maciejewska-Markiewicz
- Department of Human Nutrition and Metabolomics, Pomeranian Medical University in Szczecin, 71-460 Szczecin, Poland; (K.S.-Ż.); (K.J.); (D.M.-M.); (K.J.)
| | - Katarzyna Janda
- Department of Human Nutrition and Metabolomics, Pomeranian Medical University in Szczecin, 71-460 Szczecin, Poland; (K.S.-Ż.); (K.J.); (D.M.-M.); (K.J.)
| | | | - Mariusz Kaczmarczyk
- Department of Clinical and Molecular Biochemistry, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland;
| | - Igor Łoniewski
- Department of Human Nutrition and Metabolomics, Pomeranian Medical University in Szczecin, 71-460 Szczecin, Poland; (K.S.-Ż.); (K.J.); (D.M.-M.); (K.J.)
| | - Wojciech Marlicz
- Department of Gastroenterology, Pomeranian Medical University, 71-252 Szczecin, Poland
- The Centre for Digestive Diseases Endoklinika, 70-535 Szczecin, Poland
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9
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Cochran AL, Nieser KJ, Forger DB, Zöllner S, McInnis MG. Gene-set Enrichment with Mathematical Biology (GEMB). Gigascience 2020; 9:giaa091. [PMID: 33034635 PMCID: PMC7546080 DOI: 10.1093/gigascience/giaa091] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 06/01/2020] [Accepted: 08/14/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Gene-set analyses measure the association between a disease of interest and a "set" of genes related to a biological pathway. These analyses often incorporate gene network properties to account for differential contributions of each gene. We extend this concept further-defining gene contributions based on biophysical properties-by leveraging mathematical models of biology to predict the effects of genetic perturbations on a particular downstream function. RESULTS We present a method that combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. We demonstrate in simulation how such a method can improve statistical power. To this effect, we identify a gene set, weighted by model-predicted contributions to intracellular calcium ion concentration, that is significantly related to bipolar disorder in a small dataset (P = 0.04; n = 544). We reproduce this finding using publicly available summary data from the Psychiatric Genomics Consortium (P = 1.7 × 10-4; n = 41,653). By contrast, an approach using a general calcium signaling pathway did not detect a significant association with bipolar disorder (P = 0.08). The weighted gene-set approach based on intracellular calcium ion concentration did not detect a significant relationship with schizophrenia (P = 0.09; n = 65,967) or major depression disorder (P = 0.30; n = 500,199). CONCLUSIONS Together, these findings show how incorporating math biology into gene-set analyses might help to identify biological functions that underlie certain polygenic disorders.
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Affiliation(s)
- Amy L Cochran
- Department of Math, University of Wisconsin–Madison, 480 Lincoln Drive, Madison, WI, 53706, USA
- Department of Population Health Sciences, University of Wisconsin–Madison, 610 Walnut Street, Madison, WI, 53726, USA
| | - Kenneth J Nieser
- Department of Population Health Sciences, University of Wisconsin–Madison, 610 Walnut Street, Madison, WI, 53726, USA
| | - Daniel B Forger
- Department of Mathematics, University of Michigan, 530 Church Street, Ann Arbor, MI, 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI, 48109, USA
| | - Sebastian Zöllner
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
- Department of Psychiatry, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
| | - Melvin G McInnis
- Department of Psychiatry, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
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10
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High polygenic burden is associated with blood DNA methylation changes in individuals with suicidal behavior. J Psychiatr Res 2020; 123:62-71. [PMID: 32036075 DOI: 10.1016/j.jpsychires.2020.01.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 01/06/2020] [Accepted: 01/24/2020] [Indexed: 12/27/2022]
Abstract
Suicidal behavior is result of the interaction of several contributors, including genetic and environmental factors. The integration of approaches considering the polygenic component of suicidal behavior, such as polygenic risk scores (PRS) and DNA methylation is promising for improving our understanding of the complex interplay between genetic and environmental factors in this behavior. The aim of this study was the evaluation of DNA methylation differences between individuals with high and low genetic burden for suicidality. The present study was divided into two phases. In the first phase, genotyping with the Psycharray chip was performed in a discovery sample of 568 Mexican individuals, of which 149 had suicidal behavior (64 individuals with suicidal ideation, 50 with suicide attempt and 35 with completed suicide). Then, a PRS analysis based on summary statistics from the Psychiatric Genomic Consortium was performed in the discovery sample. In a second phase, we evaluated DNA methylation differences between individuals with high and low genetic burden for suicidality in a sub-sample of the discovery sample (target sample) of 94 subjects. We identified 153 differentially methylated sites between individuals with low and high-PRS. Among genes mapped to differentially methylated sites, we found genes involved in neurodevelopment (CHD7, RFX4, KCNA1, PLCB1, PITX1, NUMBL) and ATP binding (KIF7, NUBP2, KIF6, ATP8B1, ATP11A, CLCN7, MYLK, MAP2K5). Our results suggest that genetic variants might increase the predisposition to epigenetic variations in genes involved in neurodevelopment. This study highlights the possible implication of polygenic burden in the alteration of epigenetic changes in suicidal behavior.
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11
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Bhattacherjee A, Djekidel MN, Chen R, Chen W, Tuesta LM, Zhang Y. Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction. Nat Commun 2019; 10:4169. [PMID: 31519873 PMCID: PMC6744514 DOI: 10.1038/s41467-019-12054-3] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 08/12/2019] [Indexed: 11/15/2022] Open
Abstract
Coordinated activity-induced transcriptional changes across multiple neuron subtypes of the prefrontal cortex (PFC) play a pivotal role in encoding and regulating major cognitive behaviors. Yet, the specific transcriptional programs in each neuron subtype remain unknown. Using single-cell RNA sequencing (scRNA-seq), here we comprehensively classify all unique cell subtypes in the PFC. We analyze transcriptional dynamics of each cell subtype under a naturally adaptive and an induced condition. Adaptive changes during adolescence (between P21 and P60), a highly dynamic phase of postnatal neuroplasticity, profoundly impacted transcription in each neuron subtype, including cell type-specific regulation of genes implicated in major neuropsychiatric disorders. On the other hand, an induced plasticity evoked by chronic cocaine addiction resulted in progressive transcriptional changes in multiple neuron subtypes and became most pronounced upon prolonged drug withdrawal. Our findings lay a foundation for understanding cell type-specific postnatal transcriptional dynamics under normal PFC function and in neuropsychiatric disease states.
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Affiliation(s)
- Aritra Bhattacherjee
- Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA, 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, 02115, USA
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Mohamed Nadhir Djekidel
- Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA, 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, 02115, USA
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Renchao Chen
- Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA, 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, 02115, USA
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Wenqiang Chen
- Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA, 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, 02115, USA
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Luis M Tuesta
- Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA, 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, 02115, USA
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Yi Zhang
- Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA, 02115, USA.
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, 02115, USA.
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children's Hospital, Boston, MA, 02115, USA.
- Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA.
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12
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Qualitative vs. Quantitative Methods in Psychiatric Research: Updated. Methods Mol Biol 2019. [PMID: 31273691 DOI: 10.1007/978-1-4939-9554-7_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Since the incipiency of psychiatry as a medical specialty, the "holy grail" has been neuroscience-based diagnostic system and treatment strategies, but this lofty, yet necessary, goal has eluded the greatest minds for centuries. Now, with advances in molecular genetics and resting-state neuroimaging, neuroscience-based diagnosis and treatment are now more possible than ever. However, clinical symptomatology, longitudinal course, and delimitation of illnesses (i.e., phenotypic classification) remain indispensable for responsible, reproducible, and meaningful use of these new methodologies.
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13
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Lee YC, Chao YL, Chang CE, Hsieh MH, Liu KT, Chen HC, Lu ML, Chen WY, Chen CH, Tsai MH, Lu TP, Huang MC, Kuo PH. Transcriptome Changes in Relation to Manic Episode. Front Psychiatry 2019; 10:280. [PMID: 31118907 PMCID: PMC6504680 DOI: 10.3389/fpsyt.2019.00280] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Accepted: 04/11/2019] [Indexed: 12/15/2022] Open
Abstract
Bipolar disorder (BD) is highly heritable and well known for its recurrent manic and depressive episodes. The present study focused on manic episode in BD patients and aimed to investigate state-specific transcriptome alterations between acute episode and remission, including messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and micro-RNAs (miRNAs), using microarray and RNA sequencing (RNA-Seq) platforms. BD patients were enrolled with clinical information, and peripheral blood samples collected at both acute and remission status spanning for at least 2 months were confirmed by follow-ups. Symptom severity was assessed by Young Mania Rating Scale. We enrolled six BD patients as the discovery samples and used the Affymetrix Human Transcriptome Array 2.0 to capture transcriptome data at the two time points. For replication, expression data from Gene Expression Omnibus that consisted of 11 BD patients were downloaded, and we performed a mega-analysis for microarray data of 17 patients. Moreover, we conducted RNA sequencing (RNA-Seq) in additional samples of 7 BD patients. To identify intraindividual differentially expressed genes (DEGs), we analyzed data using a linear model controlling for symptom severity. We found that noncoding genes were of majority among the top DEGs in microarray data. The expression fold change of coding genes among DEGs showed moderate to high correlations (∼0.5) across platforms. A number of lncRNAs and two miRNAs (MIR181B1 and MIR103A1) exhibited high levels of gene expression in the manic state. For coding genes, we reported that the taste function-related genes, including TAS2R5 and TAS2R3, may be mania state-specific markers. Additionally, four genes showed a nominal p-value of less than 0.05 in all our microarray data, mega-analysis, and RNA-Seq analysis. They were upregulated in the manic state and consisted of MS4A14, PYHIN1, UTRN, and DMXL2, and their gene expression patterns were further validated by quantitative real-time polymerase chain reaction (PCR) (qRT-PCR). We also performed weight gene coexpression network analysis to identify gene modules for manic episode. Genes in the mania-related modules were different from the susceptible loci of BD obtained from genome-wide association studies, and biological pathways in relation to these modules were mainly related to immune function, especially cytokine-cytokine receptor interaction. Results of the present study elucidated potential molecular targets and genomic networks that are involved in manic episode. Future studies are needed to further validate these biomarkers for their roles in the etiology of bipolar illness.
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Affiliation(s)
- Ya-Chin Lee
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yu-Lin Chao
- Department of Psychiatry, Buddhist Tzu Chi General Hospital and Tzu Chi University, Hualien, Taiwan
| | - Chiao-Erh Chang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Ming-Hsien Hsieh
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Kuan-Ting Liu
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Hsi-Chung Chen
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan
| | - Mong-Liang Lu
- Department of Psychiatry, Wang-Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wen-Yin Chen
- Department of Psychiatry, Taipei City Psychiatric Center, Taipei City Hospital, Taipei, Taiwan
| | - Chun-Hsin Chen
- Department of Psychiatry, Wang-Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Mong-Hsun Tsai
- Institute of Biotechnology, National Taiwan University, Taipei, Taiwan
| | - Tzu-Pin Lu
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Ming-Chyi Huang
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Psychiatry, Taipei City Psychiatric Center, Taipei City Hospital, Taipei, Taiwan
| | - Po-Hsiu Kuo
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Department of Public Health, National Taiwan University, Taipei, Taiwan
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14
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Wang W, Corominas R, Lin GN. De novo Mutations From Whole Exome Sequencing in Neurodevelopmental and Psychiatric Disorders: From Discovery to Application. Front Genet 2019; 10:258. [PMID: 31001316 PMCID: PMC6456656 DOI: 10.3389/fgene.2019.00258] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Accepted: 03/08/2019] [Indexed: 12/13/2022] Open
Abstract
Neurodevelopmental and psychiatric disorders are a highly disabling and heterogeneous group of developmental and mental disorders, resulting from complex interactions of genetic and environmental risk factors. The nature of multifactorial traits and the presence of comorbidity and polygenicity in these disorders present challenges in both disease risk identification and clinical diagnoses. The genetic component has been firmly established, but the identification of all the causative variants remains elusive. The development of next-generation sequencing, especially whole exome sequencing (WES), has greatly enriched our knowledge of the precise genetic alterations of human diseases, including brain-related disorders. In particular, the extensive usage of WES in research studies has uncovered the important contribution of de novo mutations (DNMs) to these disorders. Trio and quad familial WES are a particularly useful approach to discover DNMs. Here, we review the major WES studies in neurodevelopmental and psychiatric disorders and summarize how genes hit by discovered DNMs are shared among different disorders. Next, we discuss different integrative approaches utilized to interrogate DNMs and to identify biological pathways that may disrupt brain development and shed light on our understanding of the genetic architecture underlying these disorders. Lastly, we discuss the current state of the transition from WES research to its routine clinical application. This review will assist researchers and clinicians in the interpretation of variants obtained from WES studies, and highlights the need to develop consensus analytical protocols and validated lists of genes appropriate for clinical laboratory analysis, in order to reach the growing demands.
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Affiliation(s)
- Weidi Wang
- Shanghai Mental Health Center, School of Biomedical Engineering, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Roser Corominas
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras, Valencia, Spain
- Institut de Biomedicina de la Universitat de Barcelona, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
| | - Guan Ning Lin
- Shanghai Mental Health Center, School of Biomedical Engineering, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
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15
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Yin L, Cheung EFC, Chen RYL, Wong EHM, Sham PC, So HC. Leveraging genome-wide association and clinical data in revealing schizophrenia subgroups. J Psychiatr Res 2018; 106:106-117. [PMID: 30312963 DOI: 10.1016/j.jpsychires.2018.09.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 09/13/2018] [Accepted: 09/18/2018] [Indexed: 02/04/2023]
Abstract
Schizophrenia (SCZ) has long been recognized as a highly heterogeneous disorder. Patients differed in their clinical manifestations, prognosis, and underlying pathophysiologies. Here we presented and applied a framework for finding subtypes of SCZ utilizing genome-wide association study (GWAS) and clinical data. We postulated that genetic information may help stratify patient into useful subgroups, and incorporation of other clinical information and cognitive profiles will further improve patient subtyping. We conducted cluster analysis in 387 Hong Kong Chinese with SCZ. First we performed 'single-view' clustering using genetic or clinical data alone, then proceeded to 'multi-view' clustering (MVC) accounting for both types of information. We validated clustering results by assessing subgroup differences in various outcomes. We found significant differences in outcomes including treatment response, disease course and symptom severity (Simes overall p-value using MVC = 1.64E-9). Overall speaking, we identified three subgroups with good, intermediate and poor prognosis respectively. MVC generally out-performed single-view methods. The analysis was repeated for different sets of input SNPs, and stratified analysis of male and female patients, and the results remained largely robust. We also found significant enrichment for SCZ loci among the SNPs selected by the cluster algorithm. Numerous selected genes (e.g. NRG1, ERBB4, NRXN1, ANK3) and pathways (e.g. neuregulin-ErbB4 and calcium signaling) were implicated in SCZ or related pathophysiological processes. This is first study to combine both genetic and clinical data for subtyping SCZ, and to employ genome-wide SNP data in cluster analysis of a complex disease. This work points to a new way of GWAS analysis of translational potential.
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Affiliation(s)
- Liangying Yin
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong
| | - Eric Fuk-Chi Cheung
- Castle Peak Hospital, Hong Kong; Department of Psychiatry, University of Hong Kong, Hong Kong
| | | | | | - Pak-Chung Sham
- Department of Psychiatry, University of Hong Kong, Hong Kong; Centre for Genomic Sciences, University of Hong Kong, Hong Kong; State Key Laboratory for Cognitive and Brain Sciences, University of Hong Kong, Hong Kong
| | - Hon-Cheong So
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong; KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Zoology Institute of Zoology and the Chinese University of Hong Kong, China.
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16
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Beauchaine TP, Constantino JN, Hayden EP. Psychiatry and developmental psychopathology: Unifying themes and future directions. Compr Psychiatry 2018; 87:143-152. [PMID: 30415196 PMCID: PMC6296473 DOI: 10.1016/j.comppsych.2018.10.014] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Accepted: 10/30/2018] [Indexed: 02/06/2023] Open
Abstract
In the past 35 years, developmental psychopathology has grown into a flourishing discipline that shares a scientific agenda with contemporary psychiatry. In this editorial, which introduces the special issue, we describe the history of developmental psychopathology, including core principles that bridge allied disciplines. These include (1) emphasis on interdisciplinary research, (2) elucidation of multicausal pathways to seemingly single disorders (phenocopies), (3) description of divergent multifinal outcomes from common etiological start points (pathoplasticity), and (4) research conducted across multiple levels of analysis spanning genes to environments. Next, we discuss neurodevelopmental models of psychopathology, and provide selected examples. We emphasize differential neuromaturation of subcortical and cortical neural networks and connectivity, and how both acute and protracted environmental insults can compromise neural structure and function. To date, developmental psychopathology has placed greater emphasis than psychiatry on neuromaturational models of mental illness. However, this gap is closing rapidly as advances in technology render etiopathophysiologies of psychopathology more interrogable. We end with suggestions for future interdisciplinary research, including the need to evaluate measurement invariance across development, and to construct more valid assessment methods where indicated.
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Affiliation(s)
- Theodore P Beauchaine
- Department of Psychology, Nisonger Center for Excellence in Developmental Disabilities, The Ohio State University, United States of America.
| | - John N Constantino
- Departments of Psychiatry and Pediatrics, Washington University School of Medicine, United States of America
| | - Elizabeth P Hayden
- Department of Psychology, Brain and Mind Institute, Western University, Canada
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17
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Umlauf D, Mourad R. The 3D genome: From fundamental principles to disease and cancer. Semin Cell Dev Biol 2018; 90:128-137. [PMID: 30030142 DOI: 10.1016/j.semcdb.2018.07.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 07/03/2018] [Indexed: 12/18/2022]
Abstract
In higher eukaryotes, the three-dimensional (3D) organization of the genome is intimately related to numerous key biological functions including gene expression, DNA repair and DNA replication regulations. Alteration of this 3D organization is detrimental to the organism and can give rise to a broad range of diseases such as cancers. Here, we review recent advances in the field. We first describe how the genome is packed in 3D to form chromosome territories, compartments and domains. We also give an overview of the recent techniques that allow to map the genome in 3D up to the kilobase resolution. We then discuss potential mechanisms by which genome misfolding can affect proper gene expression by distal enhancers, and how the 3D genome influences the formation of genomic rearrangements.
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Affiliation(s)
- David Umlauf
- LBME, Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France.
| | - Raphaël Mourad
- LBCMCP, Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, 31062 Toulouse, France.
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18
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Heterozygosity for the Mood Disorder-Associated Variant Gln460Arg Alters P2X7 Receptor Function and Sleep Quality. J Neurosci 2017; 37:11688-11700. [PMID: 29079688 DOI: 10.1523/jneurosci.3487-16.2017] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Revised: 05/31/2017] [Indexed: 12/21/2022] Open
Abstract
A single nucleotide polymorphism substitution from glutamine (Gln, Q) to arginine (Arg, R) at codon 460 of the purinergic P2X7 receptor (P2X7R) has repeatedly been associated with mood disorders. The P2X7R-Gln460Arg variant per se is not compromised in its function. However, heterologous expression of P2X7R-Gln460Arg together with wild-type P2X7R has recently been demonstrated to impair receptor function. Here we show that this also applies to humanized mice coexpressing both human P2X7R variants. Primary hippocampal cells derived from heterozygous mice showed an attenuated calcium uptake upon agonist stimulation. While humanized mice were unaffected in their behavioral repertoire under basal housing conditions, mice that harbor both P2X7R variants showed alterations in their sleep quality resembling signs of a prodromal disease stage. Also healthy heterozygous human subjects showed mild changes in sleep parameters. These results indicate that heterozygosity for the wild-type P2X7R and its mood disorder-associated variant P2X7R-Gln460Arg represents a genetic risk factor, which is potentially able to convey susceptibility to mood disorders.SIGNIFICANCE STATEMENT Depression and bipolar disorder are the most common mood disorders. The P2X7 receptor (P2X7R) regulates many cellular functions. Its polymorphic variant Gln460Arg has repeatedly been associated with mood disorders. Genetically engineered mice, with human P2X7R, revealed that heterozygous mice (i.e., they coexpress the disease-associated Gln460Arg variant together with its normal version) have impaired receptor function and showed sleep disturbances. Human participants with the heterozygote genotype also had subtle alterations in their sleep profile. Our findings suggest that altered P2X7R function in heterozygote individuals disturbs sleep and might increase the risk for developing mood disorders.
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On the transdiagnostic nature of peripheral biomarkers in major psychiatric disorders: A systematic review. Neurosci Biobehav Rev 2017; 83:97-108. [PMID: 28986182 DOI: 10.1016/j.neubiorev.2017.10.001] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 08/31/2017] [Accepted: 10/01/2017] [Indexed: 12/20/2022]
Abstract
The search for biomarkers has been a leading endeavor in biological psychiatry. To analyze its evolution over the years, we performed a systematic review to evaluate (a) the most studied peripheral molecular markers in major psychiatric disorders, (b) the main features of studies proposing them as biomarkers and (c) whether their patterns of variation are similar across disorders. Of the six molecules most commonly studied as plasmatic markers of schizophrenia, major depressive disorder or bipolar disorder, five (BDNF, TNF-alpha, IL-6, C-reactive protein and cortisol) were the same across diagnoses. An analysis of this literature showed that, while 66% of studies compared patients and controls, only 34% were longitudinal, and only 10% presented a measure of diagnostic or prognostic efficacy. Meta-analyses showed variation in the levels of these molecules to be robust across studies, but similar among disorders, suggesting them to reflect transdiagnostic systemic consequences of psychiatric illness. Based on this, we discuss how current publication practices have led to research fragmentation across diagnoses, and suggest approaches to face this issue.
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20
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Coleman JR, Lester KJ, Keers R, Munafò MR, Breen G, Eley TC. Genome-wide association study of facial emotion recognition in children and association with polygenic risk for mental health disorders. Am J Med Genet B Neuropsychiatr Genet 2017; 174:701-711. [PMID: 28608620 PMCID: PMC5638097 DOI: 10.1002/ajmg.b.32558] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 05/12/2017] [Indexed: 11/12/2022]
Abstract
Emotion recognition is disrupted in many mental health disorders, which may reflect shared genetic aetiology between this trait and these disorders. We explored genetic influences on emotion recognition and the relationship between these influences and mental health phenotypes. Eight-year-old participants (n = 4,097) from the Avon Longitudinal Study of Parents and Children (ALSPAC) completed the Diagnostic Analysis of Non-Verbal Accuracy (DANVA) faces test. Genome-wide genotype data was available from the Illumina HumanHap550 Quad microarray. Genome-wide association studies were performed to assess associations with recognition of individual emotions and emotion in general. Exploratory polygenic risk scoring was performed using published genomic data for schizophrenia, bipolar disorder, depression, autism spectrum disorder, anorexia, and anxiety disorders. No individual genetic variants were identified at conventional levels of significance in any analysis although several loci were associated at a level suggestive of significance. SNP-chip heritability analyses did not identify a heritable component of variance for any phenotype. Polygenic scores were not associated with any phenotype. The effect sizes of variants influencing emotion recognition are likely to be small. Previous studies of emotion identification have yielded non-zero estimates of SNP-heritability. This discrepancy is likely due to differences in the measurement and analysis of the phenotype.
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Affiliation(s)
- Jonathan R.I. Coleman
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, MRC SocialGenetic and Developmental Psychiatry (SGDP) CentreLondonUK,National Institute for Health Research Biomedical Research CentreSouth London and Maudsley National Health Service TrustLondonUK
| | | | - Robert Keers
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, MRC SocialGenetic and Developmental Psychiatry (SGDP) CentreLondonUK,School of Biological and Chemical SciencesQueen Mary University of LondonLondonUK
| | - Marcus R. Munafò
- MRC Integrative Epidemiology UnitUniversity of BristolBristolUK,UK Centre for Tobacco and Alcohol Studies, School of Experimental PsychologyUniversity of BristolBristolUK
| | - Gerome Breen
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, MRC SocialGenetic and Developmental Psychiatry (SGDP) CentreLondonUK,National Institute for Health Research Biomedical Research CentreSouth London and Maudsley National Health Service TrustLondonUK
| | - Thalia C. Eley
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, MRC SocialGenetic and Developmental Psychiatry (SGDP) CentreLondonUK,National Institute for Health Research Biomedical Research CentreSouth London and Maudsley National Health Service TrustLondonUK
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Svirydzenka N, Ronzoni P, Dogra N. Meaning and barriers to quality care service provision in Child and Adolescent Mental Health Services: Qualitative study of stakeholder perspectives. BMC Health Serv Res 2017; 17:151. [PMID: 28219392 PMCID: PMC5319051 DOI: 10.1186/s12913-017-2080-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2015] [Accepted: 02/09/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Defining quality in health presents many challenges. The Institute of Medicine (IOM) defined quality clinical care as care that is equitable, timely, safe, efficient, effective and patient centred. However, it is not clear how different stakeholders within a child and adolescent mental health service (CAMHS) understand and/or apply this framework. This project aims to identify key stakeholders" understanding of the meaning of quality in the context of CAMHS. METHOD The study sample comprised of three groups: (i) patients and carers, (ii) CAMHS clinical staff, and (iii) commissioners (Total N = 24). Semi-structured interviews were used to collect data and thematic analysis was applied to explore participant's views on the meaning and measurement of quality and how these might reflect the IOM indicators and their relevance in CAMHS. RESULTS An initial barrier to implementing quality care in CAMHS was the difficulty and limited agreement in defining the meaning of quality care, its measurement and implementation for all participants. Clinical staff defined quality as personal values, a set of practical rules, or clinical discharge rates; while patients suggested being more involved in the decision-making process. Commissioners, while supportive of adequate safeguarding and patient satisfaction procedures, did not explicitly link their view on quality to commissioning guidelines. Identifying practical barriers to implementing quality care was easier for all interviewees and common themes included: lack of meaningful measures, recourses, accountability, and training. All interviewees considered the IOM six markers as comprehensive and relevant to CAMHS. CONCLUSIONS No respondent individually or within one stakeholder group identified more than a few of the indicators or barriers of a quality CAMHS service. However, the composite responses of the respondents enable us to develop a more complete picture of how to improve quality care in practice and guide future research in the area.
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Santoro ML, Moretti PN, Pellegrino R, Gadelha A, Abílio VC, Hayashi MAF, Belangero SI, Hakonarson H. A current snapshot of common genomic variants contribution in psychiatric disorders. Am J Med Genet B Neuropsychiatr Genet 2016; 171:997-1005. [PMID: 27486013 DOI: 10.1002/ajmg.b.32475] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2016] [Accepted: 07/06/2016] [Indexed: 12/22/2022]
Abstract
In the past decade, numerous advances were achieved in psychiatric genetics. Particularly, the genome wide association studies (GWAS) have contributed to uncovering new genes and pathways associated to psychiatric disorders (PDs). At the same time, with increasing sample sizes in the GWAS, the polygenic risk score (PRS) promoted an additional tool for identification and evaluation the genetic risk quantitatively in PDs. This concept review presents the state of the art GWAS analysis and PRS focusing on the genetic underpinnings of PDs. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Marcos L Santoro
- Interdisciplinary Laboratory of Clinical Neurosciences, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
- Department of Psychiatry, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
- Genetics Division, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
| | - Patricia N Moretti
- Interdisciplinary Laboratory of Clinical Neurosciences, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
- Department of Psychiatry, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
- Genetics Division, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
| | - Renata Pellegrino
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Ary Gadelha
- Interdisciplinary Laboratory of Clinical Neurosciences, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
- Department of Psychiatry, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
| | - Vanessa C Abílio
- Interdisciplinary Laboratory of Clinical Neurosciences, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
- Department of Psychiatry, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
- Department of Pharmacology, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
| | - Mirian A F Hayashi
- Interdisciplinary Laboratory of Clinical Neurosciences, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
- Department of Psychiatry, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
- Department of Pharmacology, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
| | - Sintia I Belangero
- Interdisciplinary Laboratory of Clinical Neurosciences, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
- Department of Psychiatry, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
- Genetics Division, Federal University of Sao Paulo (UNIFESP), Sao Paulo, SP, Brazil
| | - Hakon Hakonarson
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
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Olbrich S, van Dinteren R, Arns M. Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder. Neuropsychobiology 2016; 72:229-40. [PMID: 26901357 DOI: 10.1159/000437435] [Citation(s) in RCA: 105] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2015] [Accepted: 07/06/2015] [Indexed: 11/19/2022]
Abstract
Personalized medicine in psychiatry is in need of biomarkers that resemble central nervous system function at the level of neuronal activity. Electroencephalography (EEG) during sleep or resting-state conditions and event-related potentials (ERPs) have not only been used to discriminate patients from healthy subjects, but also for the prediction of treatment outcome in various psychiatric diseases, yielding information about tailored therapy approaches for an individual. This review focuses on baseline EEG markers for two psychiatric conditions, namely major depressive disorder and attention deficit hyperactivity disorder. It covers potential biomarkers from EEG sleep research and vigilance regulation, paroxysmal EEG patterns and epileptiform discharges, quantitative EEG features within the EEG main frequency bands, connectivity markers and ERP components that might help to identify favourable treatment outcome. Further, the various markers are discussed in the context of their potential clinical value and as research domain criteria, before giving an outline for future studies that are needed to pave the way to an electrophysiological biomarker-based personalized medicine.
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Jiang W, Xue JH, Yu W. What is the probability of replicating a statistically significant association in genome-wide association studies? Brief Bioinform 2016; 18:928-939. [DOI: 10.1093/bib/bbw091] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Indexed: 11/14/2022] Open
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Svirydzenka N, Aitken J, Dogra N. Research and partnerships with schools. Soc Psychiatry Psychiatr Epidemiol 2016; 51:1203-9. [PMID: 27194140 DOI: 10.1007/s00127-016-1224-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2015] [Accepted: 04/16/2016] [Indexed: 11/30/2022]
Abstract
PURPOSE Despite the quantity of research on child and adolescent mental health being done in schools, little output has focused on the practical aspects of recruiting schools and students into a study. Furthermore, there is limited knowledge on how to develop and sustain productive and mutually beneficial partnerships with schools after the project finishes. METHODS A large study examining prevalence of mental health problems in young people involving nine schools is used as an example for the procedure of recruitment and carrying out a research project, while developing and sustaining partnerships with schools. RESULTS While recruiting the schools, a three-stage model was developed that corresponded closely to the school's needs and existing demands. The suggested procedure for the study, thus, closely reflected the varying existing cultures of participating schools. Partnerships, developed as a result of the project, were used in developing further projects and interventions for promoting good mental health in schools. CONCLUSIONS Rather than a blanket research recruitment and procedural approach with an end to school involvement at the end of the project, the paper advocates for a deeper understanding of the schools' internal culture for improved recruitment and study outcomes. Developed partnerships, when sustained past the completion of research, prove to be a useful tool in applying the findings in promoting good mental health in schools and continuing research further.
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Affiliation(s)
- Nadzeya Svirydzenka
- Faculty of Health and Life Sciences, De Montfort University, The Gateway, Leicester, LE1 9BH, UK.
| | | | - Nisha Dogra
- Department of Psychology, Greenwood Institute of Child Health, University of Leicester, Leicester, UK
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Waldman ID, Lilienfeld SO. Thinking About Data, Research Methods, and Statistical Analyses: Commentary on Sijtsma's (2014) "Playing with Data". PSYCHOMETRIKA 2016; 81:16-26. [PMID: 25820981 DOI: 10.1007/s11336-015-9447-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
We comment on Sijtsma's (2014) thought-provoking essay on how to minimize questionable research practices (QRPs) in psychology. We agree with Sijtsma that proactive measures to decrease the risk of QRPs will ultimately be more productive than efforts to target individual researchers and their work. In particular, we concur that encouraging researchers to make their data and research materials public is the best institutional antidote against QRPs, although we are concerned that Sijtsma's proposal to delegate more responsibility to statistical and methodological consultants could inadvertently reinforce the dichotomy between the substantive and statistical aspects of research. We also discuss sources of false-positive findings and replication failures in psychological research, and outline potential remedies for these problems. We conclude that replicability is the best metric of the minimization of QRPs and their adverse effects on psychological research.
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Affiliation(s)
- Irwin D Waldman
- Department of Psychology, Emory University, 475 PAIS Building, 36 Eagle Row, Atlanta, GA, 30322, USA.
| | - Scott O Lilienfeld
- Department of Psychology, Emory University, 475 PAIS Building, 36 Eagle Row, Atlanta, GA, 30322, USA.
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Knowles EEM, Kent JW, McKay DR, Sprooten E, Mathias SR, Curran JE, Carless MA, de Almeida MAA, Harald HHG, Dyer TD, Olvera RL, Fox PT, Duggirala R, Almasy L, Blangero J, Glahn DC. Genome-wide linkage on chromosome 10q26 for a dimensional scale of major depression. J Affect Disord 2016; 191:123-31. [PMID: 26655122 PMCID: PMC4715913 DOI: 10.1016/j.jad.2015.11.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Revised: 10/27/2015] [Accepted: 11/09/2015] [Indexed: 12/28/2022]
Abstract
Major depressive disorder (MDD) is a common and potentially life-threatening mood disorder. Identifying genetic markers for depression might provide reliable indicators of depression risk, which would, in turn, substantially improve detection, enabling earlier and more effective treatment. The aim of this study was to identify rare variants for depression, modeled as a continuous trait, using linkage and post-hoc association analysis. The sample comprised 1221 Mexican-American individuals from extended pedigrees. A single dimensional scale of MDD was derived using confirmatory factor analysis applied to all items from the Past Major Depressive Episode section of the Mini-International Neuropsychiatric Interview. Scores on this scale of depression were subjected to linkage analysis followed by QTL region-specific association analysis. Linkage analysis revealed a single genome-wide significant QTL (LOD=3.43) on 10q26.13, QTL-specific association analysis conducted in the entire sample revealed a suggestive variant within an intron of the gene LHPP (rs11245316, p=7.8×10(-04); LD-adjusted Bonferroni-corrected p=8.6×10(-05)). This region of the genome has previously been implicated in the etiology of MDD; the present study extends our understanding of the involvement of this region by highlighting a putative gene of interest (LHPP).
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Affiliation(s)
- Emma E M Knowles
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA.
| | - Jack W Kent
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - D Reese McKay
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Emma Sprooten
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Samuel R Mathias
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Joanne E Curran
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center at San Antonio & University of Texas of the Rio Grande Valley, Brownsville, TX, United States
| | - Melanie A Carless
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Marcio A A de Almeida
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center at San Antonio & University of Texas of the Rio Grande Valley, Brownsville, TX, United States
| | - H H Goring Harald
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center at San Antonio & University of Texas of the Rio Grande Valley, Brownsville, TX, United States
| | - Tom D Dyer
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center at San Antonio & University of Texas of the Rio Grande Valley, Brownsville, TX, United States
| | - Rene L Olvera
- Department of Psychiatry, University of Texas Health Science Center San Antonio, Texas Center San Antonio, San Antonio, TX, United States
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center San Antonio, San Antonio, TX, United States; South Texas Veterans' Healthcare System, 7400 Merton Minter, San Antonio, TX 78229, USA
| | - Ravi Duggirala
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center at San Antonio & University of Texas of the Rio Grande Valley, Brownsville, TX, United States
| | - Laura Almasy
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center at San Antonio & University of Texas of the Rio Grande Valley, Brownsville, TX, United States
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Health Science Center at San Antonio & University of Texas of the Rio Grande Valley, Brownsville, TX, United States
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
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28
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Won E, Ham BJ. Imaging genetics studies on monoaminergic genes in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2016; 64:311-9. [PMID: 25828849 DOI: 10.1016/j.pnpbp.2015.03.014] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 03/17/2015] [Accepted: 03/20/2015] [Indexed: 12/28/2022]
Abstract
Although depression is the leading cause of disability worldwide, current understanding of the neurobiology of depression has failed to be translated into clinical practice. Major depressive disorder (MDD) pathogenesis is considered to be significantly influenced by multiple risk genes, however genetic effects are not simply expressed at a behavioral level. Therefore the concept of endophenotype has been applied in psychiatric genetics. Imaging genetics applies anatomical or functional imaging technologies as phenotypic assays to evaluate genetic variation and their impact on behavior. This paper attempts to provide a comprehensive review of available imaging genetics studies, including reports on genetic variants that have most frequently been linked to MDD, such as the monoaminergic genes (serotonin transporter gene, monoamine oxidase A gene, tryptophan hydroxylase-2 gene, serotonin receptor 1A gene and catechol-O-methyl transferase gene), with regard to key structures involved in emotion processing, such as the hippocampus, amygdala, anterior cingulate cortex and orbitofrontal cortex.
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Affiliation(s)
- Eunsoo Won
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
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Yoo H. Genetics of Autism Spectrum Disorder: Current Status and Possible Clinical Applications. Exp Neurobiol 2015; 24:257-72. [PMID: 26713075 PMCID: PMC4688327 DOI: 10.5607/en.2015.24.4.257] [Citation(s) in RCA: 96] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Revised: 12/02/2015] [Accepted: 12/02/2015] [Indexed: 12/18/2022] Open
Abstract
Autism spectrum disorder (ASD) is one of the most complex behavioral disorders with a strong genetic influence. The objectives of this article are to review the current status of genetic research in ASD, and to provide information regarding the potential candidate genes, mutations, and genetic loci possibly related to pathogenesis in ASD. Investigations on monogenic causes of ASD, candidate genes among common variants, rare de novo mutations, and copy number variations are reviewed. The current possible clinical applications of the genetic knowledge and their future possibilities are highlighted.
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Affiliation(s)
- Heejeong Yoo
- Department of Psychiatry, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam 13620, Korea
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30
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Logue MW, Amstadter AB, Baker DG, Duncan L, Koenen KC, Liberzon I, Miller MW, Morey RA, Nievergelt CM, Ressler KJ, Smith AK, Smoller JW, Stein MB, Sumner JA, Uddin M. The Psychiatric Genomics Consortium Posttraumatic Stress Disorder Workgroup: Posttraumatic Stress Disorder Enters the Age of Large-Scale Genomic Collaboration. Neuropsychopharmacology 2015; 40:2287-97. [PMID: 25904361 PMCID: PMC4538342 DOI: 10.1038/npp.2015.118] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Revised: 03/10/2015] [Accepted: 03/25/2015] [Indexed: 11/09/2022]
Abstract
The development of posttraumatic stress disorder (PTSD) is influenced by genetic factors. Although there have been some replicated candidates, the identification of risk variants for PTSD has lagged behind genetic research of other psychiatric disorders such as schizophrenia, autism, and bipolar disorder. Psychiatric genetics has moved beyond examination of specific candidate genes in favor of the genome-wide association study (GWAS) strategy of very large numbers of samples, which allows for the discovery of previously unsuspected genes and molecular pathways. The successes of genetic studies of schizophrenia and bipolar disorder have been aided by the formation of a large-scale GWAS consortium: the Psychiatric Genomics Consortium (PGC). In contrast, only a handful of GWAS of PTSD have appeared in the literature to date. Here we describe the formation of a group dedicated to large-scale study of PTSD genetics: the PGC-PTSD. The PGC-PTSD faces challenges related to the contingency on trauma exposure and the large degree of ancestral genetic diversity within and across participating studies. Using the PGC analysis pipeline supplemented by analyses tailored to address these challenges, we anticipate that our first large-scale GWAS of PTSD will comprise over 10 000 cases and 30 000 trauma-exposed controls. Following in the footsteps of our PGC forerunners, this collaboration-of a scope that is unprecedented in the field of traumatic stress-will lead the search for replicable genetic associations and new insights into the biological underpinnings of PTSD.
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Affiliation(s)
- Mark W Logue
- Research, VA Boston Healthcare System, Boston, MA, USA
- Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA
- Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ananda B Amstadter
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Dewleen G Baker
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- VA San Diego Healthcare System, VA Center of Excellence for Stress and Mental Health (CESAMH), La Jolla, CA, USA
| | - Laramie Duncan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Israel Liberzon
- Department of Psychiatry, University of Michigan, Ann Arbor, MI
- Veterans Affairs Ann Arbor Health System, Ann Arbor, MI, USA
| | - Mark W Miller
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Rajendra A Morey
- Duke-UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
- Mental Illness Research Education and Clinical Center for Post Deployment Mental Health, Durham VA Medical Center, Durham, NC, USA
| | - Caroline M Nievergelt
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- VA San Diego Healthcare System, VA Center of Excellence for Stress and Mental Health (CESAMH), La Jolla, CA, USA
| | - Kerry J Ressler
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
- Center for Behavioral Neuroscience, Yerkes National Primate Research Center, Atlanta, GA, USA
- Howard Hughes Medical Institute, Bethesda, MD, USA
| | - Alicia K Smith
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Jordan W Smoller
- Center of Human Genetics Research, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Murray B Stein
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Jennifer A Sumner
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Monica Uddin
- Carl R Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, USA
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Landin-Romero R, McKenna PJ, Salgado-Pineda P, Sarró S, Aguirre C, Sarri C, Compte A, Bosque C, Blanch J, Salvador R, Pomarol-Clotet E. Failure of deactivation in the default mode network: a trait marker for schizophrenia? Psychol Med 2015; 45:1315-1325. [PMID: 25331916 DOI: 10.1017/s0033291714002426] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Functional imaging studies in relatives of schizophrenic patients have had inconsistent findings, particularly with respect to altered dorsolateral prefrontal cortex activation. Some recent studies have also suggested that failure of deactivation may be seen. METHOD A total of 28 patients with schizophrenia, 28 of their siblings and 56 healthy controls underwent functional magnetic resonance imaging during performance of the n-back working memory task. An analysis of variance was fitted to individual whole-brain maps from each set of patient-relative-matched pair of controls. Clusters of significant difference among the groups were then used as regions of interest to compare mean activations and deactivations among the groups. RESULTS In all, five clusters of significant differences were found. The schizophrenic patients, but not the relatives, showed reduced activation compared with the controls in the lateral frontal cortex bilaterally, the left basal ganglia and the cerebellum. In contrast, both the patients and the relatives showed significant failure of deactivation compared with the healthy controls in the medial frontal cortex, with the relatives also showing less failure than the patients. Failure of deactivation was not associated with schizotypy scores or presence of psychotic-like experiences in the relatives. CONCLUSIONS Both schizophrenic patients and their relatives show altered task-related deactivation in the medial frontal cortex. This in turn suggests that default mode network dysfunction may function as a trait marker for schizophrenia.
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Affiliation(s)
| | - P J McKenna
- FIDMAG Germanes Hospitalàries,Barcelona,Spain
| | | | - S Sarró
- FIDMAG Germanes Hospitalàries,Barcelona,Spain
| | - C Aguirre
- FIDMAG Germanes Hospitalàries,Barcelona,Spain
| | - C Sarri
- FIDMAG Germanes Hospitalàries,Barcelona,Spain
| | - A Compte
- FIDMAG Germanes Hospitalàries,Barcelona,Spain
| | - C Bosque
- FIDMAG Germanes Hospitalàries,Barcelona,Spain
| | - J Blanch
- Hospital Sant Joan de Déu Infantil,Barcelona,Spain
| | - R Salvador
- FIDMAG Germanes Hospitalàries,Barcelona,Spain
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Genetics of Attention Deficit Hyperactivity Disorder (ADHD): Recent Updates and Future Prospects. CURRENT DEVELOPMENTAL DISORDERS REPORTS 2014. [DOI: 10.1007/s40474-013-0004-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Levine ME, Crimmins EM, Prescott CA, Phillips D, Arpawong TE, Lee J. A polygenic risk score associated with measures of depressive symptoms among older adults. BIODEMOGRAPHY AND SOCIAL BIOLOGY 2014; 60:199-211. [PMID: 25343367 PMCID: PMC4298361 DOI: 10.1080/19485565.2014.952705] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
It has been suggested that depression is a polygenic trait, arising from the influences of multiple loci with small individual effects. The aim of this study is to generate a polygenic risk score (PRS) to examine the association between genetic variation and depressive symptoms. Our analytic sample included N = 10,091 participants aged 50 and older from the Health and Retirement Study (HRS). Depressive symptoms were measured by Center for Epidemiological Studies-Depression scale (CESD) scores assessed on up to nine occasions across 18 years. We conducted a genome-wide association analysis for a discovery set (n = 7,000) and used the top 11 single-nucleotide polymorphisms, all with p < 10(-5) to generate a weighted PRS for our replication sample (n = 3,091). Results showed that the PRS was significantly associated with mean CESD score in the replication sample (β = .08, p = .002). The R(2) change for the inclusion of the PRS was .003. Using a multinomial logistic regression model, we also examined the association between genetic risk and chronicity of high (4+) CESD scores. We found that a one-standard-deviation increase in PRS was associated with a 36 percent increase in the odds of having chronically high CESD scores relative to never having had high CESD scores. Our findings are consistent with depression being a polygenic trait and suggest that the cumulative influence of multiple variants increases an individual's susceptibility for chronically experiencing high levels of depressive symptoms.
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Affiliation(s)
- Morgan E Levine
- a Davis School of Gerontology , University of Southern California , Los Angeles , California , USA
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Alawieh A, Sabra Z, Nokkari A, El-Assaad A, Mondello S, Zaraket F, Fadlallah B, Kobeissy FH. Bioinformatics approach to understanding interacting pathways in neuropsychiatric disorders. Methods Mol Biol 2014; 1168:157-172. [PMID: 24870135 DOI: 10.1007/978-1-4939-0847-9_9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Bioinformatics-based applications have been incorporated into several medical disciplines, including cancer, neuroscience, and recently psychiatry. Both the increasing interest in the molecular aspect of neuropsychiatry and the availability of high-throughput discovery and analysis tools have encouraged the incorporation of bioinformatics and neurosystems biology techniques into psychiatry and neuroscience research. As applied to neuropsychiatry, systems biology involves the acquisition and processing of high-throughput datasets to infer new information. A major component in bioinformatics output is pathway analysis that provides an insight into and prediction of possible underlying pathogenic processes which may help understand disease pathogenesis. In addition, this analysis serves as a tool to identify potential biomarkers implicated in these disorders. In this chapter, we summarize the different tools and algorithms used in pathway analysis along with their applications to the different layers of molecular investigations, from genomics to proteomics.
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Affiliation(s)
- Ali Alawieh
- Department of Neurosciences, Medical University of South Carolina, Charleston, SC, USA
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