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Radulescu E, Chen Q, Pergola G, Di Carlo P, Han S, Shin JH, Hyde TM, Kleinman JE, Weinberger DR. Investigating trait variability of gene co-expression network architecture in brain by controlling for genomic risk of schizophrenia. PLoS Genet 2023; 19:e1010989. [PMID: 37831723 PMCID: PMC10599557 DOI: 10.1371/journal.pgen.1010989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 10/25/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023] Open
Abstract
The effect of schizophrenia (SCZ) genetic risk on gene expression in brain remains elusive. A popular approach to this problem has been the application of gene co-expression network algorithms (e.g., WGCNA). To improve reliability with this method it is critical to remove unwanted sources of variance while also preserving biological signals of interest. In this WCGNA study of RNA-Seq data from postmortem prefrontal cortex (78 neurotypical donors, EUR ancestry), we tested the effects of SCZ genetic risk on co-expression networks. Specifically, we implemented a novel design in which gene expression was adjusted by linear regression models to preserve or remove variance explained by biological signal of interest (GWAS genomic scores for SCZ risk-(GS-SCZ), and genomic scores- GS of height (GS-Ht) as a negative control), while removing variance explained by covariates of non-interest. We calculated co-expression networks from adjusted expression (GS-SCZ and GS-Ht preserved or removed), and consensus between them (representative of a "background" network free of genomic scores effects). We then tested the overlap between GS-SCZ preserved modules and background networks reasoning that modules with reduced overlap would be most affected by GS-SCZ biology. Additionally, we tested these modules for convergence of SCZ risk (i.e., enrichment in PGC3 SCZ GWAS priority genes, enrichment in SCZ risk heritability and relevant biological ontologies. Our results highlight key aspects of GS-SCZ effects on brain co-expression networks, specifically: 1) preserving/removing SCZ genetic risk alters the co-expression modules; 2) biological pathways enriched in modules affected by GS-SCZ implicate processes of transcription, translation and metabolism that converge to influence synaptic transmission; 3) priority PGC3 SCZ GWAS genes and SCZ risk heritability are enriched in modules associated with GS-SCZ effects. Overall, our results indicate that gene co-expression networks that selectively integrate information about genetic risk can reveal novel combinations of biological pathways involved in schizophrenia.
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Affiliation(s)
- Eugenia Radulescu
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland United States of America
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland United States of America
| | - Giulio Pergola
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland United States of America
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Pasquale Di Carlo
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland United States of America
| | - Shizhong Han
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland United States of America
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland United States of America
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland United States of America
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland United States of America
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland United States of America
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, United States of America
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2
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Sreedasyam A, Plott C, Hossain MS, Lovell J, Grimwood J, Jenkins J, Daum C, Barry K, Carlson J, Shu S, Phillips J, Amirebrahimi M, Zane M, Wang M, Goodstein D, Haas F, Hiss M, Perroud PF, Jawdy S, Yang Y, Hu R, Johnson J, Kropat J, Gallaher S, Lipzen A, Shakirov E, Weng X, Torres-Jerez I, Weers B, Conde D, Pappas M, Liu L, Muchlinski A, Jiang H, Shyu C, Huang P, Sebastian J, Laiben C, Medlin A, Carey S, Carrell A, Chen JG, Perales M, Swaminathan K, Allona I, Grattapaglia D, Cooper E, Tholl D, Vogel J, Weston DJ, Yang X, Brutnell T, Kellogg E, Baxter I, Udvardi M, Tang Y, Mockler T, Juenger T, Mullet J, Rensing S, Tuskan G, Merchant S, Stacey G, Schmutz J. JGI Plant Gene Atlas: an updateable transcriptome resource to improve functional gene descriptions across the plant kingdom. Nucleic Acids Res 2023; 51:8383-8401. [PMID: 37526283 PMCID: PMC10484672 DOI: 10.1093/nar/gkad616] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 06/21/2023] [Accepted: 07/11/2023] [Indexed: 08/02/2023] Open
Abstract
Gene functional descriptions offer a crucial line of evidence for candidate genes underlying trait variation. Conversely, plant responses to environmental cues represent important resources to decipher gene function and subsequently provide molecular targets for plant improvement through gene editing. However, biological roles of large proportions of genes across the plant phylogeny are poorly annotated. Here we describe the Joint Genome Institute (JGI) Plant Gene Atlas, an updateable data resource consisting of transcript abundance assays spanning 18 diverse species. To integrate across these diverse genotypes, we analyzed expression profiles, built gene clusters that exhibited tissue/condition specific expression, and tested for transcriptional response to environmental queues. We discovered extensive phylogenetically constrained and condition-specific expression profiles for genes without any previously documented functional annotation. Such conserved expression patterns and tightly co-expressed gene clusters let us assign expression derived additional biological information to 64 495 genes with otherwise unknown functions. The ever-expanding Gene Atlas resource is available at JGI Plant Gene Atlas (https://plantgeneatlas.jgi.doe.gov) and Phytozome (https://phytozome.jgi.doe.gov/), providing bulk access to data and user-specified queries of gene sets. Combined, these web interfaces let users access differentially expressed genes, track orthologs across the Gene Atlas plants, graphically represent co-expressed genes, and visualize gene ontology and pathway enrichments.
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Affiliation(s)
| | | | - Md Shakhawat Hossain
- Division of Plant Science and Technology, C.S. Bond Life Science Center, University of Missouri, Columbia, MO, USA
| | - John T Lovell
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jane Grimwood
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Jerry W Jenkins
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Christopher Daum
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Kerrie Barry
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Joseph Carlson
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Shengqiang Shu
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jeremy Phillips
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Mojgan Amirebrahimi
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Matthew Zane
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Mei Wang
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - David Goodstein
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Fabian B Haas
- Plant Cell Biology, Faculty of Biology, University of Marburg, Karl-von-Frisch-Str, Marburg, Germany
| | - Manuel Hiss
- Plant Cell Biology, Faculty of Biology, University of Marburg, Karl-von-Frisch-Str, Marburg, Germany
| | - Pierre-François Perroud
- Plant Cell Biology, Faculty of Biology, University of Marburg, Karl-von-Frisch-Str, Marburg, Germany
| | - Sara S Jawdy
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Yongil Yang
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Rongbin Hu
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jenifer Johnson
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Janette Kropat
- Department of Chemistry and Biochemistry and Institute for Genomics and Proteomics, University of California, Los Angeles, CA, USA
| | - Sean D Gallaher
- Department of Chemistry and Biochemistry and Institute for Genomics and Proteomics, University of California, Los Angeles, CA, USA
| | - Anna Lipzen
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Eugene V Shakirov
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
| | - Xiaoyu Weng
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
| | | | - Brock Weers
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, USA
| | - Daniel Conde
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
| | - Marilia R Pappas
- Laboratório de Genética Vegetal, EMBRAPA Recursos Genéticos e Biotecnologia, EPQB Final W5 Norte, Brasília, Brazil
| | - Lifeng Liu
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Andrew Muchlinski
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA
| | - Hui Jiang
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Christine Shyu
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Pu Huang
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Jose Sebastian
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Carol Laiben
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Alyssa Medlin
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Sankalpi Carey
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Alyssa A Carrell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Jin-Gui Chen
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Mariano Perales
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Isabel Allona
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA-CSIC), Madrid, Spain
- Departamento de Biotecnología-Biología Vegetal, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
| | - Dario Grattapaglia
- Laboratório de Genética Vegetal, EMBRAPA Recursos Genéticos e Biotecnologia, EPQB Final W5 Norte, Brasília, Brazil
| | | | - Dorothea Tholl
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA
| | - John P Vogel
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - David J Weston
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Xiaohan Yang
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | | | | | - Ivan Baxter
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | | | | | - Todd C Mockler
- Donald Danforth Plant Science Center, St. Louis, MO, USA
| | - Thomas E Juenger
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
| | - John Mullet
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, USA
| | - Stefan A Rensing
- Plant Cell Biology, Faculty of Biology, University of Marburg, Karl-von-Frisch-Str, Marburg, Germany
| | - Gerald A Tuskan
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Sabeeha S Merchant
- Department of Chemistry and Biochemistry and Institute for Genomics and Proteomics, University of California, Los Angeles, CA, USA
| | - Gary Stacey
- Division of Plant Science and Technology, C.S. Bond Life Science Center, University of Missouri, Columbia, MO, USA
| | - Jeremy Schmutz
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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3
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Anantha J, Wilson FE, McCarthy E, Morales-Prieto N, Mazzocchi M, Collins LM, Sullivan AM, O'Keeffe GW. A combined proteomics and bioinformatics analysis of ZNHIT1-interacting proteins reveals a significant enrichment in proteins associated with mitochondrial function. J Chem Neuroanat 2023; 131:102288. [PMID: 37178741 DOI: 10.1016/j.jchemneu.2023.102288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 05/05/2023] [Accepted: 05/10/2023] [Indexed: 05/15/2023]
Abstract
Adenosine 5'-triphosphate (ATP) is the principal source of cellular energy, which is essential for neuronal health and maintenance. Parkinson's disease (PD) and other neurodegenerative disorders are characterised by impairments in mitochondrial function and reductions in cellular ATP levels. Thus there is a need to better understand the biology of intracellular regulators of ATP production, in order to inform the development of new neuroprotective therapies for diseases such as PD. One such regulator is Zinc finger HIT-domain containing protein 1 (ZNHIT1). ZNHIT1 is an evolutionarily-conserved component of a chromatin-remodelling complex, which has been recently shown to increase cellular ATP production in SH-SY5Y cells and to protect against impairments in mitochondrial function caused by alpha-synuclein, a protein which is integral to PD pathophysiology. This effect of ZNHIT1 on cellular ATP production is thought to be due to increased expression of genes associated with mitochondrial function, but it is also possible that ZNHIT1 regulates mitochondrial function by binding to mitochondrial proteins. To examine this question, we performed a combined proteomics and bioinformatics analysis to identify ZNHIT1-interacting proteins in SH-SY5Y cells. We report that ZNHIT1-interacting proteins are significantly enriched in multiple functional categories, including mitochondrial transport, ATP synthesis and ATP-dependent activity. Furthermore we also report that the correlation between ZNHIT1 and dopaminergic markers is reduced in the PD brain. These data suggest that the reported beneficial effects of ZNHIT1 on ATP production may be mediated, at least in part, by its direct interaction with mitochondrial proteins and suggest that potential alterations in ZNHIT1 in PD may contribute to the known impairments in ATP generation in midbrain dopaminergic neurons in PD.
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Affiliation(s)
- Jayanth Anantha
- Department of Anatomy & Neuroscience, University College Cork (UCC), Cork, Ireland
| | - Fionnuala E Wilson
- Department of Anatomy & Neuroscience, University College Cork (UCC), Cork, Ireland
| | - Erin McCarthy
- Department of Anatomy & Neuroscience, University College Cork (UCC), Cork, Ireland
| | | | - Martina Mazzocchi
- Department of Anatomy & Neuroscience, University College Cork (UCC), Cork, Ireland
| | - Louise M Collins
- Department of Anatomy & Neuroscience, University College Cork (UCC), Cork, Ireland; Parkinson's Disease Research Cluster (PDRC), University College Cork, Cork, Ireland
| | - Aideen M Sullivan
- Department of Anatomy & Neuroscience, University College Cork (UCC), Cork, Ireland; Parkinson's Disease Research Cluster (PDRC), University College Cork, Cork, Ireland; APC Microbiome Ireland, University College Cork, Cork, Ireland.
| | - Gerard W O'Keeffe
- Department of Anatomy & Neuroscience, University College Cork (UCC), Cork, Ireland; Parkinson's Disease Research Cluster (PDRC), University College Cork, Cork, Ireland; APC Microbiome Ireland, University College Cork, Cork, Ireland.
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4
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Bharadhwaj VS, Mubeen S, Sargsyan A, Jose GM, Geissler S, Hofmann-Apitius M, Domingo-Fernández D, Kodamullil AT. Integrative analysis to identify shared mechanisms between schizophrenia and bipolar disorder and their comorbidities. Prog Neuropsychopharmacol Biol Psychiatry 2023; 122:110688. [PMID: 36462601 DOI: 10.1016/j.pnpbp.2022.110688] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 11/04/2022] [Accepted: 11/27/2022] [Indexed: 12/04/2022]
Abstract
Schizophrenia and bipolar disorder are characterized by highly similar neuropsychological signatures, implying shared neurobiological mechanisms between these two disorders. These disorders also have comorbidities, such as type 2 diabetes mellitus (T2DM). To date, an understanding of the mechanisms that mediate the link between these two disorders remains incomplete. In this work, we identify and investigate shared patterns across multiple schizophrenia, bipolar disorder and T2DM gene expression datasets through multiple strategies. Firstly, we investigate dysregulation patterns at the gene-level and compare our findings against disease-specific knowledge graphs (KGs). Secondly, we analyze the concordance of co-expression patterns across datasets to identify disease-specific as well as common pathways. Thirdly, we examine enriched pathways across datasets and disorders to identify common biological mechanisms between them. Lastly, we investigate the correspondence of shared genetic variants between these two disorders and T2DM as well as the disease-specific KGs. In conclusion, our work reveals several shared candidate genes and pathways, particularly those related to the immune system, such as TNF signaling pathway, IL-17 signaling pathway and NF-kappa B signaling pathway and nervous system, such as dopaminergic synapse and GABAergic synapse, which we propose mediate the link between schizophrenia and bipolar disorder and its shared comorbidity, T2DM.
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Affiliation(s)
- Vinay Srinivas Bharadhwaj
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany; Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany.
| | - Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany; Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany; Fraunhofer Center for Machine Learning, Germany
| | - Astghik Sargsyan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany
| | - Geena Mariya Jose
- Causality Biomodels, Kinfra Hi-Tech Park, Kalamassery, Cochin, Kerala 683503, India
| | | | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany; Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany; Fraunhofer Center for Machine Learning, Germany; Enveda Biosciences, Boulder, CO, 80301, USA
| | - Alpha Tom Kodamullil
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin 53757, Germany; Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115 Bonn, Germany; Causality Biomodels, Kinfra Hi-Tech Park, Kalamassery, Cochin, Kerala 683503, India
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5
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Andreou D, Steen NE, Jørgensen KN, Smelror RE, Wedervang-Resell K, Nerland S, Westlye LT, Nærland T, Myhre AM, Joa I, Reitan SMK, Vaaler A, Morken G, Bøen E, Elvsåshagen T, Boye B, Malt UF, Aukrust P, Skrede S, Kroken RA, Johnsen E, Djurovic S, Andreassen OA, Ueland T, Agartz I. Lower circulating neuron-specific enolase concentrations in adults and adolescents with severe mental illness. Psychol Med 2023; 53:1479-1488. [PMID: 35387700 PMCID: PMC10009386 DOI: 10.1017/s0033291721003056] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/05/2021] [Accepted: 07/13/2021] [Indexed: 01/01/2023]
Abstract
BACKGROUND Both neurodegenerative and neurodevelopmental abnormalities have been suggested to be part of the etiopathology of severe mental illness (SMI). Neuron-specific enolase (NSE), mainly located in the neuronal cytoplasm, may indicate the process as it is upregulated after neuronal injury while a switch from non-neuronal enolase to NSE occurs during neuronal maturation. METHODS We included 1132 adult patients with SMI [schizophrenia (SZ) or bipolar spectrum disorders], 903 adult healthy controls (HC), 32 adolescent patients with SMI and 67 adolescent HC. Plasma NSE concentrations were measured by enzyme immunoassay. For 842 adults and 85 adolescents, we used total grey matter volume (TGMV) based on T1-weighted magnetic resonance images processed in FreeSurfer v6.0. We explored NSE case-control differences in adults and adolescents separately. To investigate whether putative case-control differences in NSE were TGMV-dependent we controlled for TGMV. RESULTS We found significantly lower NSE concentrations in both adult (p < 0.001) and adolescent patients with SMI (p = 0.007) compared to HC. The results remained significant after controlling for TGMV. Among adults, both patients with SZ spectrum (p < 0.001) and bipolar spectrum disorders (p = 0.005) had lower NSE than HC. In both patient subgroups, lower NSE levels were associated with increased symptom severity. Among adults (p < 0.001) and adolescents (p = 0.040), females had lower NSE concentrations than males. CONCLUSION We found lower NSE concentrations in adult and adolescent patients with SMI compared to HC. The results suggest the lack of progressive neuronal injury, and may reflect abnormal neuronal maturation. This provides further support of a neurodevelopmental rather than a neurodegenerative mechanism in SMI.
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Affiliation(s)
- Dimitrios Andreou
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Nils Eiel Steen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway
| | - Kjetil Nordbø Jørgensen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Runar Elle Smelror
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Kirsten Wedervang-Resell
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Child and Adolescent Mental Health Research Unit, Division of Mental Health and Addiction, Department of Research and Innovation, Oslo University Hospital, Oslo, Norway
| | - Stener Nerland
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Lars T. Westlye
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Terje Nærland
- K.G. Jebsen Center for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- NevSom, Department of Rare Disorders, Oslo University Hospital, Oslo, Norway
| | - Anne Margrethe Myhre
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Department of Research and Innovation, Oslo University Hospital, Oslo, Norway
| | - Inge Joa
- TIPS – Network for Clinical Research in Psychosis, Stavanger University Hospital, Stavanger, Norway
- Faculty of Health, Network for Medical Sciences, University of Stavanger, Stavanger, Norway
| | - Solveig Merete Klæbo Reitan
- Faculty of Medicine and Health Sciences, Department of Mental Health, NTNU, Trondheim, Norway
- St Olavs Hospital, Department of Mental Health, Trondheim, Norway
| | - Arne Vaaler
- Faculty of Medicine and Health Sciences, Department of Mental Health, NTNU, Trondheim, Norway
- St Olavs Hospital, Department of Mental Health, Trondheim, Norway
| | - Gunnar Morken
- Faculty of Medicine and Health Sciences, Department of Mental Health, NTNU, Trondheim, Norway
- St Olavs Hospital, Department of Mental Health, Trondheim, Norway
| | - Erlend Bøen
- Psychosomatic and C-L Psychiatry, Adult, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Torbjørn Elvsåshagen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway
- Department of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Birgitte Boye
- Psychosomatic and C-L Psychiatry, Adult, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Behavioural Medicine, University of Oslo, Oslo, Norway
| | - Ulrik Fredrik Malt
- Department of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pål Aukrust
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Research Institute of Internal Medicine, Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Silje Skrede
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway
| | - Rune Andreas Kroken
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Haukeland University Hospital, Bergen, Norway
| | - Erik Johnsen
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Haukeland University Hospital, Bergen, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- Department of Clinical Science, Norwegian Centre for Mental Disorders Research (NORMENT), University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway
| | - Thor Ueland
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- K.G. Jebsen Thrombosis Research and Expertise Center, University of Tromsø, Tromsø, Norway
- Research Institute of Internal Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
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6
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Snelleksz M, Rossell SL, Gibbons A, Nithianantharajah J, Dean B. Evidence that the frontal pole has a significant role in the pathophysiology of schizophrenia. Psychiatry Res 2022; 317:114850. [PMID: 36174274 DOI: 10.1016/j.psychres.2022.114850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/07/2022] [Accepted: 09/11/2022] [Indexed: 01/04/2023]
Abstract
Different regions of the cortex have been implicated in the pathophysiology of schizophrenia. Recently published data suggested there are many more changes in gene expression in the frontal pole (Brodmann's Area (BA) 10) compared to the dorsolateral prefrontal cortex (BA 9) and the anterior cingulate cortex (BA 33) from patients with schizophrenia. These data argued that the frontal pole is significantly affected by the pathophysiology of schizophrenia. The frontal pole is a region necessary for higher cognitive functions and is highly interconnected with many other brain regions. In this review we summarise the growing body of evidence to support the hypothesis that a dysfunctional frontal pole, due at least in part to its widespread effects on brain function, is making an important contribution to the pathophysiology of schizophrenia. We detail the many structural, cellular and molecular abnormalities in the frontal pole from people with schizophrenia and present findings that argue the symptoms of schizophrenia are closely linked to dysfunction in this critical brain region.
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Affiliation(s)
- Megan Snelleksz
- Synaptic Biology and Cognition Laboratory, The Florey Institute for Neuroscience and Mental Health, Parkville, Victoria, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Susan L Rossell
- Centre for Mental Health, School of Health Sciences, Swinburne University, Melbourne, Victoria, Australia; Department of Psychiatry, St Vincent's Hospital, Melbourne, Victoria, Australia
| | - Andrew Gibbons
- The Department of Psychiatry, Monash University, Clayton, Victoria, Australia
| | - Jess Nithianantharajah
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Brian Dean
- Synaptic Biology and Cognition Laboratory, The Florey Institute for Neuroscience and Mental Health, Parkville, Victoria, Australia; The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia.
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Dean B, Thomas EHX, Bozaoglu K, Tan EJ, Van Rheenen TE, Neill E, Sumner PJ, Carruthers SP, Scarr E, Rossell SL, Gurvich C. Evidence that a working memory cognitive phenotype within schizophrenia has a unique underlying biology. Psychiatry Res 2022; 317:114873. [PMID: 36252418 DOI: 10.1016/j.psychres.2022.114873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/27/2022] [Accepted: 09/30/2022] [Indexed: 01/05/2023]
Abstract
It is suggested studying phenotypes within the syndrome of schizophrenia will accelerate understanding the complex molecular pathology of the disorder. Supporting this hypothesis, we have identified a sub-group within schizophrenia with impaired working memory (WM) and have used Affymetrix™ Human Exon 1.0 ST Arrays to compare their blood RNA levels (n=16) to a group of with intact WM (n=18). Levels of 72 RNAs were higher in blood from patients with impaired WM, 11 of which have proven links to the maintenance of different aspects of working memory (cognition). Overall, changed gene expression in those with impaired WM could be linked to cognition through glutamatergic activity, olfaction, immunity, inflammation as well as energy and metabolism. Our data gives preliminary support to the hypotheses that there is a working memory deficit phenotype within the syndrome of schizophrenia with has a biological underpinning. In addition, our data raises the possibility that a larger study could show that the specific changes in gene expression we have identified could prove to be the biomarkers needed to develop a blood test to identify those with impaired WM; a significant step toward allowing the use of personalised medicine directed toward improving their impaired working memory.
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Affiliation(s)
- Brian Dean
- The Molecular Psychiatry Laboratory, The Florey Institute for Neuroscience and Mental Health, Parkville, Victoria, Australia.
| | - Elizabeth H X Thomas
- Monash Alfred Psychiatry Research Centre, Central Clinical School, Monash University and The Alfred Hospital, Melbourne, Victoria, Australia
| | - Kiymet Bozaoglu
- The Murdoch Children's Research Institute, Parkville, Victoria, Australia; Department of Paediatrics, The University of Melbourne, Victoria, Australia
| | - Eric J Tan
- Centre for Mental Health, Swinburne University of Technology, Hawthorne, Victoria, Australia; Department of Psychiatry, St Vincent's Hospital, Fitzroy, Victoria, Australia
| | - Tamsyn E Van Rheenen
- Centre for Mental Health, Swinburne University of Technology, Hawthorne, Victoria, Australia; Melbourne Neuropsychiatry Centre, University of Melbourne, Parkville, Victoria, Australia
| | - Erica Neill
- Centre for Mental Health, Swinburne University of Technology, Hawthorne, Victoria, Australia; Department of Psychiatry, St Vincent's Hospital, Fitzroy, Victoria, Australia
| | - Philip J Sumner
- Centre for Mental Health, Swinburne University of Technology, Hawthorne, Victoria, Australia
| | - Sean P Carruthers
- Centre for Mental Health, Swinburne University of Technology, Hawthorne, Victoria, Australia
| | - Elizabeth Scarr
- Melbourne Veterinary School, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, Victoria, Australia
| | - Susan L Rossell
- Centre for Mental Health, Swinburne University of Technology, Hawthorne, Victoria, Australia; Department of Psychiatry, St Vincent's Hospital, Fitzroy, Victoria, Australia
| | - Caroline Gurvich
- Monash Alfred Psychiatry Research Centre, Central Clinical School, Monash University and The Alfred Hospital, Melbourne, Victoria, Australia
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Gupta C, Chandrashekar P, Jin T, He C, Khullar S, Chang Q, Wang D. Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases. J Neurodev Disord 2022; 14:28. [PMID: 35501679 PMCID: PMC9059371 DOI: 10.1186/s11689-022-09438-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 04/07/2022] [Indexed: 12/31/2022] Open
Abstract
Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the "big data" revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.
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Affiliation(s)
- Chirag Gupta
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Pramod Chandrashekar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Ting Jin
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Chenfeng He
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Saniya Khullar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Qiang Chang
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Neurology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Daifeng Wang
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA. .,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA. .,Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA.
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Identification of Key Modules and Genes Associated with Major Depressive Disorder in Adolescents. Genes (Basel) 2022; 13:genes13030464. [PMID: 35328018 PMCID: PMC8949287 DOI: 10.3390/genes13030464] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 02/26/2022] [Accepted: 03/02/2022] [Indexed: 12/25/2022] Open
Abstract
Major depressive disorder (MDD) is a leading cause of disability worldwide. Adolescence is a crucial period for the occurrence and development of depression. There are essential distinctions between adolescent and adult depression patients, and the etiology of depressive disorder is unclear. The interactions of multiple genes in a co-expression network are likely to be involved in the physiopathology of MDD. In the present study, RNA-Seq data of mRNA were acquired from the peripheral blood of MDD in adolescents and healthy control (HC) subjects. Co-expression modules were constructed via weighted gene co-expression network analysis (WGCNA) to investigate the relationships between the underlying modules and MDD in adolescents. In the combined MDD and HC groups, the dynamic tree cutting method was utilized to assign genes to modules through hierarchical clustering. Moreover, functional enrichment analysis was conducted on those co-expression genes from interested modules. The results showed that eight modules were constructed by WGCNA. The blue module was significantly associated with MDD after multiple comparison adjustment. Several Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with stress and inflammation were identified in this module, including histone methylation, apoptosis, NF-kappa β signaling pathway, and TNF signaling pathway. Five genes related to inflammation, immunity, and the nervous system were identified as hub genes: CNTNAP3, IL1RAP, MEGF9, UBE2W, and UBE2D1. All of these findings supported that MDD was associated with stress, inflammation, and immune responses, helping us to obtain a better understanding of the internal molecular mechanism and to explore biomarkers for the diagnosis or treatment of depression in adolescents.
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Gene Co-expression Analysis of the Human Substantia Nigra Identifies ZNHIT1 as an SNCA Co-expressed Gene that Protects Against α-Synuclein-Induced Impairments in Neurite Growth and Mitochondrial Dysfunction in SH-SY5Y Cells. Mol Neurobiol 2022; 59:2745-2757. [PMID: 35175558 PMCID: PMC9016026 DOI: 10.1007/s12035-022-02768-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 02/03/2022] [Indexed: 11/17/2022]
Abstract
Parkinson’s disease (PD) is neurodegenerative disorder with the pathological hallmarks of progressive degeneration of midbrain dopaminergic neurons from the substantia nigra (SN), and accumulation and spread of inclusions of aggregated α-synuclein (α-Syn). Since current PD therapies do not prevent neurodegeneration, there is a need to identify therapeutic targets that can prevent α-Syn-induced reductions in neuronal survival and neurite growth. We hypothesised that genes that are normally co-expressed with the α-Syn gene (SNCA), and whose co-expression pattern is lost in PD, may be important for protecting against α-Syn-induced dopaminergic degeneration, since broken correlations can be used as an index of functional misregulation. Gene co-expression analysis of the human SN showed that nuclear zinc finger HIT-type containing 1 (ZNHIT1) is co-expressed with SNCA and that this co-expression pattern is lost in PD. Overexpression of ZNHIT1 was found to increase deposition of the H2A.Z histone variant in SH-SY5Y cells, to promote neurite growth and to prevent α-Syn-induced reductions in neurite growth and cell viability. Analysis of ZNHIT1 co-expressed genes showed significant enrichment in genes associated with mitochondrial function. In agreement, bioenergetic state analysis of mitochondrial function revealed that ZNHIT1 increased cellular ATP synthesis. Furthermore, α-Syn-induced impairments in basal respiration, maximal respiration and spare respiratory capacity were not seen in ZNHIT1-overexpressing cells. These data show that ZNHIT1 can protect against α-Syn-induced degeneration and mitochondrial dysfunction, which rationalises further investigation of ZNHIT1 as a therapeutic target for PD.
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Andreou D, Jørgensen KN, Nerland S, Smelror RE, Wedervang-Resell K, Johannessen CH, Myhre AM, Andreassen OA, Blennow K, Zetterberg H, Agartz I. Lower plasma total tau in adolescent psychosis: Involvement of the orbitofrontal cortex. J Psychiatr Res 2021; 144:255-261. [PMID: 34700214 DOI: 10.1016/j.jpsychires.2021.10.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 10/16/2021] [Accepted: 10/19/2021] [Indexed: 10/20/2022]
Abstract
Schizophrenia is thought to be a neurodevelopmental disorder with neuronal migration, differentiation and maturation disturbances. Tau is a microtubule-associated protein with a crucial role in these processes. Lower circulating tau levels have been reported in adults with schizophrenia, but this association has not been investigated in adolescent psychosis. We aimed to test the hypotheses that a) adolescents with early-onset psychosis (EOP; age of onset <18 years) display lower plasma tau concentrations compared to healthy controls, and b) among patients with psychosis, tau levels are linked to structural brain measures associated with the microtubule-associated tau (MAPT) gene and psychosis. We included 37 adolescent patients with EOP (mean age 16.4 years) and 59 adolescent healthy controls (mean age 16.2 years). We investigated putative patient-control differences in plasma total tau concentrations measured by a Single molecule array (Simoa) immunoassay. We explored the correlations between tau and selected structural brain measures based on T1-weighted MRI scans processed in FreeSurfer v6.0. We found significantly lower plasma tau concentrations in patients compared to healthy controls (p = 0.017, partial eta-squared = 0.061). Tau was not associated with antipsychotic use or the antipsychotic dosage. Among patients but not healthy controls, tau levels were positively correlated with the cortical orbitofrontal surface area (p = 0.013, R-squared = 0.24). The results are suggestive of a tau-related neurodevelopmental disturbance in adolescent psychosis.
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Affiliation(s)
- Dimitrios Andreou
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden.
| | - Kjetil Nordbø Jørgensen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Stener Nerland
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Runar Elle Smelror
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Kirsten Wedervang-Resell
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Child and Adolescent Mental Health Research Unit, Department of Research and Innovation, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Cecilie Haggag Johannessen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anne Margrethe Myhre
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Research and Innovation, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK; UK Dementia Research Institute at UCL, London, UK
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
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12
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Davarinejad O, Najafi S, Zhaleh H, Golmohammadi F, Radmehr F, Alikhani M, Moghadam RH, Rahmati Y. MiR-574-5P, miR-1827, and miR-4429 as Potential Biomarkers for Schizophrenia. J Mol Neurosci 2021; 72:226-238. [PMID: 34811713 DOI: 10.1007/s12031-021-01945-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/06/2021] [Indexed: 01/02/2023]
Abstract
Schizophrenia is a severe chronic debilitating disorder with millions of affected individuals. Diagnosis is based on clinical presentations, which are made when the progressive disease has appeared. Early diagnosis may help improve the clinical outcomes and response to treatments. Lack of a reliable molecular diagnostic invokes the identification of novel biomarkers. To elucidate the molecular basis of the disease, in this study we used two mRNA expression arrays, including GSE93987 and GSE38485, and one miRNA array, GSE54914, and meta-analysis was conducted for evaluation of mRNA expression arrays via metaDE package. Using WGCNA package, we performed network analysis for both mRNA expression arrays separately. Then, we constructed protein-protein interaction network for significant modules. Limma package was employed to analyze the miRNA array for identification of dysregulated miRNAs (DEMs). Using genes of significant modules and DEMs, a mRNA-miRNA network was constructed and hub genes and miRNAs were identified. To confirm the dysregulated genes, expression values were evaluated through available datasets including GSE62333, GSE93987, and GSE38485. The ability of the detected hub miRNAs to discriminate schizophrenia from healthy controls was evaluated by assessing the receiver-operating curve. Finally, the expression levels of genes and miRNAs were evaluated in 40 schizophrenia patients compared with healthy controls via Real-Time PCR. The results confirmed dysregulation of hsa-miR-574-5P, hsa-miR-1827, hsa-miR-4429, CREBRF, ARPP19, TGFBR2, and YWHAZ in blood samples of schizophrenia patients. In conclusion, three miRNAs including hsa-miR-574-5P, hsa-miR-1827, and hsa-miR-4429 are suggested as potential biomarkers for diagnosis of schizophrenia.
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Affiliation(s)
- Omran Davarinejad
- Clinical Research Development Center, Imam Khomeini and Mohammad Kermanshahi and Farabi Hospitals, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Sajad Najafi
- Student Research Committee, Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hossein Zhaleh
- Substance Abuse Prevention Research Center, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Farzaneh Golmohammadi
- Clinical Research Development Center, Imam Khomeini and Mohammad Kermanshahi and Farabi Hospitals, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Farnaz Radmehr
- Clinical Research Development Center, Imam Khomeini and Mohammad Kermanshahi and Farabi Hospitals, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mostafa Alikhani
- Clinical Research Development Center, Imam Khomeini and Mohammad Kermanshahi and Farabi Hospitals, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Reza Heidari Moghadam
- Cardiovascular Research Center, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Yazdan Rahmati
- Clinical Research Development Center, Imam Khomeini and Mohammad Kermanshahi and Farabi Hospitals, Kermanshah University of Medical Sciences, Kermanshah, Iran.
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13
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Lin D, Chen J, Duan K, Perrone-Bizzozero N, Sui J, Calhoun V, Liu J. Network modules linking expression and methylation in prefrontal cortex of schizophrenia. Epigenetics 2021; 16:876-893. [PMID: 33079616 PMCID: PMC8331039 DOI: 10.1080/15592294.2020.1827718] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 08/13/2020] [Accepted: 08/21/2020] [Indexed: 02/07/2023] Open
Abstract
Tremendous work has demonstrated the critical roles of genetics, epigenetics as well as their interplay in brain transcriptional regulations in the pathology of schizophrenia (SZ). There is great success currently in the dissection of the genetic components underlying risk-conferring transcriptomic networks. However, the study of regulating effect of epigenetics in the etiopathogenesis of SZ still faces many challenges. In this work, we investigated DNA methylation and gene expression from the dorsolateral prefrontal cortex (DLPFC) region of schizophrenia patients and healthy controls using weighted correlation network approach. We identified and replicated two expression and two methylation modules significantly associated with SZ. Among them, one pair of expression and methylation modules were significantly overlapped in the module genes which were significantly enriched in astrocyte-associated functional pathways, and specifically expressed in astrocytes. Another two linked expression-methylation module pairs were involved ageing process with module genes mostly related to oligodendrocyte development and myelination, and specifically expressed in oligodendrocytes. Further examination of underlying quantitative trait loci (QTLs) showed significant enrichment in genetic risk of most psychiatric disorders for expression QTLs but not for methylation QTLs. These results support the coherence between methylation and gene expression at the network level, and suggest a combinatorial effect of genetics and epigenetics in regulating gene expression networks specific to glia cells in relation to SZ and ageing process.
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Affiliation(s)
- Dongdong Lin
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, USA
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, USA
| | - Kuaikuai Duan
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
| | - Nora Perrone-Bizzozero
- Department of Neurosciences, University of New Mexico, Albuquerque, NM, USA
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, USA
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
- Department of Psychology, Georgia State University, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, USA
- Department of Computer Science, Georgia State University, Atlanta, USA
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14
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Harris BD, Crow M, Fischer S, Gillis J. Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain. Cell Syst 2021; 12:748-756.e3. [PMID: 34015329 PMCID: PMC8298279 DOI: 10.1016/j.cels.2021.04.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/11/2021] [Accepted: 04/23/2021] [Indexed: 12/27/2022]
Abstract
Gene-gene relationships are commonly measured via the co-variation of gene expression across samples, also known as gene co-expression. Because shared expression patterns are thought to reflect shared function, co-expression networks describe functional relationships between genes, including co-regulation. However, the heterogeneity of cell types in bulk RNA-seq samples creates connections in co-expression networks that potentially obscure co-regulatory modules. The brain initiative cell census network (BICCN) single-cell RNA sequencing (scRNA-seq) datasets provide an unparalleled opportunity to understand how gene-gene relationships shape cell identity. Comparison of the BICCN data (500,000 cells/nuclei across 7 BICCN datasets) with that of bulk RNA-seq networks (2,000 mouse brain samples across 52 studies) reveals a consistent topology reflecting a shared co-regulatory signal. Differential signals between broad cell classes persist in driving variation at finer levels, indicating that convergent regulatory processes affect cell phenotype at multiple scales.
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Affiliation(s)
- Benjamin D Harris
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Cold Spring Harbor School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Megan Crow
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Stephan Fischer
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jesse Gillis
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Cold Spring Harbor School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
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15
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Zuo Y, Wei D, Zhu C, Naveed O, Hong W, Yang X. Unveiling the Pathogenesis of Psychiatric Disorders Using Network Models. Genes (Basel) 2021; 12:1101. [PMID: 34356117 PMCID: PMC8304351 DOI: 10.3390/genes12071101] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/15/2021] [Accepted: 07/16/2021] [Indexed: 01/13/2023] Open
Abstract
Psychiatric disorders are complex brain disorders with a high degree of genetic heterogeneity, affecting millions of people worldwide. Despite advances in psychiatric genetics, the underlying pathogenic mechanisms of psychiatric disorders are still largely elusive, which impedes the development of novel rational therapies. There has been accumulating evidence suggesting that the genetics of complex disorders can be viewed through an omnigenic lens, which involves contextualizing genes in highly interconnected networks. Thus, applying network-based multi-omics integration methods could cast new light on the pathophysiology of psychiatric disorders. In this review, we first provide an overview of the recent advances in psychiatric genetics and highlight gaps in translating molecular associations into mechanistic insights. We then present an overview of network methodologies and review previous applications of network methods in the study of psychiatric disorders. Lastly, we describe the potential of such methodologies within a multi-tissue, multi-omics approach, and summarize the future directions in adopting diverse network approaches.
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Affiliation(s)
- Yanning Zuo
- Department of Biological Chemistry, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA; (Y.Z.); (D.W.); (W.H.)
- Department of Neurobiology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Department of Integrative Biology and Physiology, University of California at Los Angeles, Los Angeles, CA 90095, USA; (C.Z.); (O.N.)
| | - Don Wei
- Department of Biological Chemistry, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA; (Y.Z.); (D.W.); (W.H.)
- Department of Neurobiology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Semel Institute, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Carissa Zhu
- Department of Integrative Biology and Physiology, University of California at Los Angeles, Los Angeles, CA 90095, USA; (C.Z.); (O.N.)
| | - Ormina Naveed
- Department of Integrative Biology and Physiology, University of California at Los Angeles, Los Angeles, CA 90095, USA; (C.Z.); (O.N.)
| | - Weizhe Hong
- Department of Biological Chemistry, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA; (Y.Z.); (D.W.); (W.H.)
- Department of Neurobiology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Brain Research Institute, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California at Los Angeles, Los Angeles, CA 90095, USA; (C.Z.); (O.N.)
- Brain Research Institute, University of California at Los Angeles, Los Angeles, CA 90095, USA
- Institute for Quantitative and Computational Biosciences, University of California at Los Angeles, Los Angeles, CA 90095, USA
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16
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Sefik E, Purcell RH, Walker EF, Bassell GJ, Mulle JG. Convergent and distributed effects of the 3q29 deletion on the human neural transcriptome. Transl Psychiatry 2021; 11:357. [PMID: 34131099 PMCID: PMC8206125 DOI: 10.1038/s41398-021-01435-2] [Citation(s) in RCA: 9] [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: 10/07/2020] [Revised: 04/29/2021] [Accepted: 05/07/2021] [Indexed: 12/13/2022] Open
Abstract
The 3q29 deletion (3q29Del) confers high risk for schizophrenia and other neurodevelopmental and psychiatric disorders. However, no single gene in this interval is definitively associated with disease, prompting the hypothesis that neuropsychiatric sequelae emerge upon loss of multiple functionally-connected genes. 3q29 genes are unevenly annotated and the impact of 3q29Del on the human neural transcriptome is unknown. To systematically formulate unbiased hypotheses about molecular mechanisms linking 3q29Del to neuropsychiatric illness, we conducted a systems-level network analysis of the non-pathological adult human cortical transcriptome and generated evidence-based predictions that relate 3q29 genes to novel functions and disease associations. The 21 protein-coding genes located in the interval segregated into seven clusters of highly co-expressed genes, demonstrating both convergent and distributed effects of 3q29Del across the interrogated transcriptomic landscape. Pathway analysis of these clusters indicated involvement in nervous-system functions, including synaptic signaling and organization, as well as core cellular functions, including transcriptional regulation, posttranslational modifications, chromatin remodeling, and mitochondrial metabolism. Top network-neighbors of 3q29 genes showed significant overlap with known schizophrenia, autism, and intellectual disability-risk genes, suggesting that 3q29Del biology is relevant to idiopathic disease. Leveraging "guilt by association", we propose nine 3q29 genes, including one hub gene, as prioritized drivers of neuropsychiatric risk. These results provide testable hypotheses for experimental analysis on causal drivers and mechanisms of the largest known genetic risk factor for schizophrenia and highlight the study of normal function in non-pathological postmortem tissue to further our understanding of psychiatric genetics, especially for rare syndromes like 3q29Del, where access to neural tissue from carriers is unavailable or limited.
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Affiliation(s)
- Esra Sefik
- grid.189967.80000 0001 0941 6502Department of Human Genetics, Emory University School of Medicine, Atlanta, GA USA ,grid.189967.80000 0001 0941 6502Department of Psychology, Emory University, Atlanta, GA USA
| | - Ryan H. Purcell
- grid.189967.80000 0001 0941 6502Department of Cell Biology, Emory University School of Medicine, Atlanta, GA USA ,grid.189967.80000 0001 0941 6502Laboratory of Translational Cell Biology, Emory University School of Medicine, Atlanta, GA USA
| | | | - Elaine F. Walker
- grid.189967.80000 0001 0941 6502Department of Psychology, Emory University, Atlanta, GA USA
| | - Gary J. Bassell
- grid.189967.80000 0001 0941 6502Department of Cell Biology, Emory University School of Medicine, Atlanta, GA USA ,grid.189967.80000 0001 0941 6502Laboratory of Translational Cell Biology, Emory University School of Medicine, Atlanta, GA USA
| | - Jennifer G. Mulle
- grid.189967.80000 0001 0941 6502Department of Human Genetics, Emory University School of Medicine, Atlanta, GA USA ,grid.189967.80000 0001 0941 6502Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA USA
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17
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Andreou D, Jørgensen KN, Wortinger LA, Engen K, Vaskinn A, Ueland T, Yolken RH, Andreassen OA, Agartz I. Cytomegalovirus infection and IQ in patients with severe mental illness and healthy individuals. Psychiatry Res 2021; 300:113929. [PMID: 33866186 DOI: 10.1016/j.psychres.2021.113929] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 04/05/2021] [Indexed: 11/26/2022]
Abstract
Cytomegalovirus (CMV) infection in immunocompetent adults is usually asymptomatic, but results in lifelong latency. Infection occurring congenitally or in immunodeficiency can lead to cognitive impairment. We aimed to investigate the associations between CMV exposure and intelligence quotient (IQ) in patients with schizophrenia spectrum disorders (SZS), bipolar spectrum disorders (BDS) and healthy controls (HC). CMV immunoglobulin G antibody concentrations were measured by immunoassay and expressed as dichotomous measures (seropositive/CMV+ vs. seronegative/CMV-). Based on a significant CMV-by-diagnosis-by-sex interaction on IQ, we investigated main and interaction effects of CMV and sex on IQ in each diagnostic category. Significant CMV-by-sex interactions were found in patient groups. In SZS, CMV+ female patients (n = 50) had significantly lower IQ than CMV- female patients (n = 33), whereas CMV+ (n = 48) and CMV- (n = 45) male patients did not differ in IQ. In BDS, CMV+ (n = 49) and CMV- (n = 37) female patients did not differ in IQ, whereas CMV+ male patients (n = 33) had significantly higher IQ than CMV- male patients (n = 32). Among HC, CMV+ (n = 138) and CMV- (n = 118) male participants as well as CMV+ (n = 125) and CMV- (n = 93) female participants did not differ in IQ. Our findings suggest that CMV exposure may affect IQ in patients with severe mental illness but not HC.
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Affiliation(s)
- Dimitrios Andreou
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden; 1st Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.
| | - Kjetil Nordbø Jørgensen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Laura A Wortinger
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Kristine Engen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Anja Vaskinn
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Psychosis Research Section, Oslo University Hospital, Oslo, Norway
| | - Torill Ueland
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Psychosis Research Section, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Robert H Yolken
- Stanley Division of Developmental Neurovirology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
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18
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Integrative network analyses of transcriptomics data reveal potential drug targets for acute radiation syndrome. Sci Rep 2021; 11:5585. [PMID: 33692493 PMCID: PMC7946886 DOI: 10.1038/s41598-021-85044-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 02/17/2021] [Indexed: 11/25/2022] Open
Abstract
Recent political unrest has highlighted the importance of understanding the short- and long-term effects of gamma-radiation exposure on human health and survivability. In this regard, effective treatment for acute radiation syndrome (ARS) is a necessity in cases of nuclear disasters. Here, we propose 20 therapeutic targets for ARS identified using a systematic approach that integrates gene coexpression networks obtained under radiation treatment in humans and mice, drug databases, disease-gene association, radiation-induced differential gene expression, and literature mining. By selecting gene targets with existing drugs, we identified potential candidates for drug repurposing. Eight of these genes (BRD4, NFKBIA, CDKN1A, TFPI, MMP9, CBR1, ZAP70, IDH3B) were confirmed through literature to have shown radioprotective effect upon perturbation. This study provided a new perspective for the treatment of ARS using systems-level gene associations integrated with multiple biological information. The identified genes might provide high confidence drug target candidates for potential drug repurposing for ARS.
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19
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Anantha J, Goulding SR, Wyatt SL, Concannon RM, Collins LM, Sullivan AM, O'Keeffe GW. STRAP and NME1 Mediate the Neurite Growth-Promoting Effects of the Neurotrophic Factor GDF5. iScience 2020; 23:101457. [PMID: 32853992 PMCID: PMC7452236 DOI: 10.1016/j.isci.2020.101457] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 07/17/2020] [Accepted: 08/10/2020] [Indexed: 12/23/2022] Open
Abstract
Loss of midbrain dopaminergic (mDA) neurons and their axons is central to Parkinson's disease (PD). Growth differentiation factor (GDF)5 is a potential neurotrophic factor for PD therapy. However, the molecular mediators of its neurotrophic action are unknown. Our proteomics analysis shows that GDF5 increases the expression of serine threonine receptor-associated protein kinase (STRAP) and nucleoside diphosphate kinase (NME)1 in the SH-SY5Y neuronal cell line. GDF5 overexpression increased NME1 expression in adult rat brain in vivo. NME and STRAP mRNAs are expressed in developing and adult rodent midbrain. Expression of both STRAP and NME1 is necessary and sufficient for the promotion of neurite growth in SH-SY5Y cells by GDF5. NME1 treatment increased neurite growth in both SH-SY5Y cells and cultured mDA neurons. Expression patterns of NME and STRAP are altered in PD midbrain. NME1 and STRAP are thus key mediators of GDF5's neurotrophic effects, rationalizing their future study as therapeutic targets for PD.
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Affiliation(s)
- Jayanth Anantha
- Department of Anatomy & Neuroscience, University College Cork (UCC), Cork, Ireland
| | - Susan R. Goulding
- Department of Anatomy & Neuroscience, University College Cork (UCC), Cork, Ireland
- Department of Biological Sciences, Cork Institute of Technology, Cork, Ireland
| | - Sean L. Wyatt
- School of Biosciences, Cardiff University, Museum Avenue, Cardiff, UK
| | - Ruth M. Concannon
- Department of Anatomy & Neuroscience, University College Cork (UCC), Cork, Ireland
| | - Louise M. Collins
- Department of Anatomy & Neuroscience, University College Cork (UCC), Cork, Ireland
- Department of Physiology, UCC, Cork, Ireland
| | - Aideen M. Sullivan
- Department of Anatomy & Neuroscience, University College Cork (UCC), Cork, Ireland
- APC Microbiome Ireland, UCC, Cork, Ireland
- Cork Neuroscience Centre, UCC, Cork, Ireland
| | - Gerard W. O'Keeffe
- Department of Anatomy & Neuroscience, University College Cork (UCC), Cork, Ireland
- APC Microbiome Ireland, UCC, Cork, Ireland
- Cork Neuroscience Centre, UCC, Cork, Ireland
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20
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Association of distinct type 1 bone morphogenetic protein receptors with different molecular pathways and survival outcomes in neuroblastoma. Neuronal Signal 2020; 4:NS20200006. [PMID: 32714600 PMCID: PMC7366490 DOI: 10.1042/ns20200006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/14/2020] [Accepted: 04/15/2020] [Indexed: 12/21/2022] Open
Abstract
Neuroblastoma (NB) is a paediatric cancer that arises in the sympathetic nervous system. Patients with stage 4 tumours have poor outcomes and 20% of high-risk cases have MYCN amplification. The bone morphogenetic proteins (BMPs) play roles in sympathetic neuritogenesis, by signalling through bone morphogenetic protein receptor (BMPR)2 and either BMPR1A or BMPR1B. Alterations in BMPR2 expression have been reported in NB; it is unknown if the expression of BMPR1A or BMPR1B is altered. We report lower BMPR2 and BMPR1B, and higher BMPR1A, expression in stage 4 and in MYCN-amplified NB. Kaplan–Meier plots showed that high BMPR2 or BMPR1B expression was linked to better survival, while high BMPR1A was linked to worse survival. Gene ontology enrichment and pathway analyses revealed that BMPR2 and BMPR1B co-expressed genes were enriched in those associated with NB differentiation. BMPR1A co-expressed genes were enriched in those associated with cell proliferation. Moreover, the correlation between BMPR2 and BMPR1A was strengthened, while the correlation between BMPR2 and BMPR1B was lost, in MYCN-amplified NB. This suggested that differentiation should decrease BMPR1A and increase BMPR1B expression. In agreement, nerve growth factor treatment of cultured sympathetic neurons decreased Bmpr1a expression and increased Bmpr1b expression. Overexpression of dominant negative BMPR1B, treatment with a BMPR1B inhibitor and treatment with GDF5, which signals via BMPR1B, showed that BMPR1B signalling is required for optimal neuritogenesis in NB cells, suggesting that loss of BMPR1B may alter neuritogenesis. The present study shows that expression of distinct BMPRs is associated with different survival outcomes in NB.
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21
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Chowdhury HA, Bhattacharyya DK, Kalita JK. (Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1154-1173. [PMID: 30668502 DOI: 10.1109/tcbb.2019.2893170] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.
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22
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Powell SK, O'Shea CP, Shannon SR, Akbarian S, Brennand KJ. Investigation of Schizophrenia with Human Induced Pluripotent Stem Cells. ADVANCES IN NEUROBIOLOGY 2020; 25:155-206. [PMID: 32578147 DOI: 10.1007/978-3-030-45493-7_6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Schizophrenia is a chronic and severe neuropsychiatric condition manifested by cognitive, emotional, affective, perceptual, and behavioral abnormalities. Despite decades of research, the biological substrates driving the signs and symptoms of the disorder remain elusive, thus hampering progress in the development of treatments aimed at disease etiologies. The recent emergence of human induced pluripotent stem cell (hiPSC)-based models has provided the field with a highly innovative approach to generate, study, and manipulate living neural tissue derived from patients, making possible the exploration of fundamental roles of genes and early-life stressors in disease-relevant cell types. Here, we begin with a brief overview of the clinical, epidemiological, and genetic aspects of the condition, with a focus on schizophrenia as a neurodevelopmental disorder. We then highlight relevant technical advancements in hiPSC models and assess novel findings attained using hiPSC-based approaches and their implications for disease biology and treatment innovation. We close with a critical appraisal of the developments necessary for both further expanding knowledge of schizophrenia and the translation of new insights into therapeutic innovations.
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Affiliation(s)
- Samuel K Powell
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Callan P O'Shea
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sara Rose Shannon
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Schahram Akbarian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kristen J Brennand
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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23
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Tunca C, Şeker T, Akçimen F, Coşkun C, Bayraktar E, Palvadeau R, Zor S, Koçoğlu C, Kartal E, Şen NE, Hamzeiy H, Özoğuz Erimiş A, Norman U, Karakahya O, Olgun G, Akgün T, Durmuş H, Şahin E, Çakar A, Başar Gürsoy E, Babacan Yıldız G, İşak B, Uluç K, Hanağası H, Bilgiç B, Turgut N, Aysal F, Ertaş M, Boz C, Kotan D, İdrisoğlu H, Soysal A, Uzun Adatepe N, Akalın MA, Koç F, Tan E, Oflazer P, Deymeer F, Taştan Ö, Çiçek AE, Kavak E, Parman Y, Başak AN. Revisiting the complex architecture of ALS in Turkey: Expanding genotypes, shared phenotypes, molecular networks, and a public variant database. Hum Mutat 2020; 41:e7-e45. [PMID: 32579787 DOI: 10.1002/humu.24055] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 05/05/2020] [Accepted: 05/18/2020] [Indexed: 12/11/2022]
Abstract
The last decade has proven that amyotrophic lateral sclerosis (ALS) is clinically and genetically heterogeneous, and that the genetic component in sporadic cases might be stronger than expected. This study investigates 1,200 patients to revisit ALS in the ethnically heterogeneous yet inbred Turkish population. Familial ALS (fALS) accounts for 20% of our cases. The rates of consanguinity are 30% in fALS and 23% in sporadic ALS (sALS). Major ALS genes explained the disease cause in only 35% of fALS, as compared with ~70% in Europe and North America. Whole exome sequencing resulted in a discovery rate of 42% (53/127). Whole genome analyses in 623 sALS cases and 142 population controls, sequenced within Project MinE, revealed well-established fALS gene variants, solidifying the concept of incomplete penetrance in ALS. Genome-wide association studies (GWAS) with whole genome sequencing data did not indicate a new risk locus. Coupling GWAS with a coexpression network of disease-associated candidates, points to a significant enrichment for cell cycle- and division-related genes. Within this network, literature text-mining highlights DECR1, ATL1, HDAC2, GEMIN4, and HNRNPA3 as important genes. Finally, information on ALS-related gene variants in the Turkish cohort sequenced within Project MinE was compiled in the GeNDAL variant browser (www.gendal.org).
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Affiliation(s)
- Ceren Tunca
- Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (NDAL), Research Center for Translational Medicine (KUTTAM), Koç University School of Medicine, Istanbul, Turkey.,Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (NDAL), Department of Molecular Biology and Genetics, Boğaziçi University, Istanbul, Turkey
| | - Tuncay Şeker
- Genomize Inc., Boğaziçi University Technology Development Region, Istanbul, Turkey
| | - Fulya Akçimen
- Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (NDAL), Department of Molecular Biology and Genetics, Boğaziçi University, Istanbul, Turkey
| | - Cemre Coşkun
- Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (NDAL), Department of Molecular Biology and Genetics, Boğaziçi University, Istanbul, Turkey
| | - Elif Bayraktar
- Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (NDAL), Research Center for Translational Medicine (KUTTAM), Koç University School of Medicine, Istanbul, Turkey
| | - Robin Palvadeau
- Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (NDAL), Research Center for Translational Medicine (KUTTAM), Koç University School of Medicine, Istanbul, Turkey
| | - Seyit Zor
- Genomize Inc., Boğaziçi University Technology Development Region, Istanbul, Turkey
| | - Cemile Koçoğlu
- Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (NDAL), Department of Molecular Biology and Genetics, Boğaziçi University, Istanbul, Turkey
| | - Ece Kartal
- Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (NDAL), Department of Molecular Biology and Genetics, Boğaziçi University, Istanbul, Turkey
| | - Nesli Ece Şen
- Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (NDAL), Department of Molecular Biology and Genetics, Boğaziçi University, Istanbul, Turkey
| | - Hamid Hamzeiy
- Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (NDAL), Department of Molecular Biology and Genetics, Boğaziçi University, Istanbul, Turkey
| | - Aslıhan Özoğuz Erimiş
- Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (NDAL), Department of Molecular Biology and Genetics, Boğaziçi University, Istanbul, Turkey
| | - Utku Norman
- Department of Computer Engineering, Bilkent University, Ankara, Turkey
| | - Oğuzhan Karakahya
- Department of Computer Engineering, Bilkent University, Ankara, Turkey
| | - Gülden Olgun
- Department of Computer Engineering, Bilkent University, Ankara, Turkey
| | - Tahsin Akgün
- Department of Anesthesiology and Reanimation, American Hospital, Istanbul, Turkey
| | - Hacer Durmuş
- Department of Neurology, Istanbul Medical School, Istanbul University, Istanbul, Turkey
| | - Erdi Şahin
- Department of Neurology, Istanbul Medical School, Istanbul University, Istanbul, Turkey
| | - Arman Çakar
- Department of Neurology, Istanbul Medical School, Istanbul University, Istanbul, Turkey
| | - Esra Başar Gürsoy
- Department of Neurology, Faculty of Medicine, Bezmialem Vakıf University, Istanbul, Turkey
| | - Gülsen Babacan Yıldız
- Department of Neurology, Faculty of Medicine, Bezmialem Vakıf University, Istanbul, Turkey
| | - Barış İşak
- Department of Neurology, Marmara University School of Medicine, Istanbul, Turkey
| | - Kayıhan Uluç
- Department of Neurology, Marmara University School of Medicine, Istanbul, Turkey
| | - Haşmet Hanağası
- Department of Neurology, Istanbul Medical School, Istanbul University, Istanbul, Turkey
| | - Başar Bilgiç
- Department of Neurology, Istanbul Medical School, Istanbul University, Istanbul, Turkey
| | - Nilda Turgut
- Department of Neurology, Namık Kemal University School of Medicine, Tekirdağ, Turkey
| | - Fikret Aysal
- Department of Neurology, Medipol University School of Medicine, Istanbul, Turkey
| | - Mustafa Ertaş
- Department of Neurology, Istanbul Medical School, Istanbul University, Istanbul, Turkey
| | - Cavit Boz
- Department of Neurology, Karadeniz Technical University School of Medicine, Trabzon, Turkey
| | - Dilcan Kotan
- Department of Neurology, Faculty of Medicine, Sakarya University, Sakarya, Turkey
| | - Halil İdrisoğlu
- Department of Neurology, Istanbul Medical School, Istanbul University, Istanbul, Turkey
| | - Aysun Soysal
- Department of Neurology, Bakırköy Research and Training Hospital for Neurologic and Psychiatric Diseases, Istanbul, Turkey
| | - Nurten Uzun Adatepe
- Department of Neurology, Cerrahpaşa Medical School, Istanbul University-Cerrahpaşa, Istanbul, Turkey
| | - Mehmet Ali Akalın
- Department of Neurology, Cerrahpaşa Medical School, Istanbul University-Cerrahpaşa, Istanbul, Turkey
| | - Filiz Koç
- Department of Neurology, Çukurova University Medical School, Adana, Turkey
| | - Ersin Tan
- Department of Neurology, Hacettepe University Medical School, Ankara, Turkey
| | - Piraye Oflazer
- Department of Neurology, Istanbul Medical School, Istanbul University, Istanbul, Turkey
| | - Feza Deymeer
- Department of Neurology, Istanbul Medical School, Istanbul University, Istanbul, Turkey
| | - Öznur Taştan
- Department of Computer Science and Engineering, Sabancı University, Istanbul, Turkey
| | - A Ercüment Çiçek
- Department of Computer Engineering, Bilkent University, Ankara, Turkey.,Department of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Erşen Kavak
- Genomize Inc., Boğaziçi University Technology Development Region, Istanbul, Turkey
| | - Yeşim Parman
- Department of Neurology, Istanbul Medical School, Istanbul University, Istanbul, Turkey
| | - A Nazlı Başak
- Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (NDAL), Research Center for Translational Medicine (KUTTAM), Koç University School of Medicine, Istanbul, Turkey.,Suna and İnan Kıraç Foundation, Neurodegeneration Research Laboratory (NDAL), Department of Molecular Biology and Genetics, Boğaziçi University, Istanbul, Turkey
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24
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Identification and prioritization of gene sets associated with schizophrenia risk by co-expression network analysis in human brain. Mol Psychiatry 2020; 25:791-804. [PMID: 30478419 DOI: 10.1038/s41380-018-0304-1] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2018] [Revised: 08/07/2018] [Accepted: 10/29/2018] [Indexed: 12/13/2022]
Abstract
Schizophrenia polygenic risk is plausibly manifested by complex transcriptional dysregulation in the brain, involving networks of co-expressed and functionally related genes. The main purpose of this study was to identify and prioritize co-expressed gene sets in a hierarchical manner, based on the strength of the relationships with clinical diagnosis and with polygenic risk for schizophrenia. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to RNA-quality-adjusted DLPFC RNA-Seq data from the LIBD Postmortem Human Brain Repository (90 controls, 74 schizophrenia cases; all Caucasians) to construct co-expression networks and detect "modules" of co-expressed genes. After multiple internal and external validation procedures, modules of selected interest were tested for enrichment in biological ontologies, for association with schizophrenia polygenic risk scores (PRSs) and with diagnosis, and also for enrichment in genes within the significant GWAS loci reported by the Psychiatric Genomic Consortium (PGC2). The association between schizophrenia genetic signals and modules of co-expression converged on one module showing not only a significant association with both diagnosis and PRS but also significant overlap with 36 PGC2 loci genes, deemed the strongest candidates for drug targets. This module contained many genes involved in synaptic signaling and neuroplasticity. Fifty-three PGC2 genes were in modules associated only with diagnosis and 59 in modules unrelated to diagnosis or PRS. Our study highlights complex relationships between gene co-expression networks in the brain and clinical state and polygenic risk for SCZ and provides a strategy for using this information in selecting and prioritizing potentially targetable gene sets for therapeutic drug development.
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25
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Tenenbaum JD, Bhuvaneshwar K, Gagliardi JP, Fultz Hollis K, Jia P, Ma L, Nagarajan R, Rakesh G, Subbian V, Visweswaran S, Zhao Z, Rozenblit L. Translational bioinformatics in mental health: open access data sources and computational biomarker discovery. Brief Bioinform 2019; 20:842-856. [PMID: 29186302 PMCID: PMC6585382 DOI: 10.1093/bib/bbx157] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 10/24/2017] [Indexed: 12/12/2022] Open
Abstract
Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data.
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Affiliation(s)
- Jessica D Tenenbaum
- Department of Biostatistics and Bioinformatics at the Duke University School of Medicine
| | | | | | - Kate Fultz Hollis
- Department of Biomedical Informatics and Clinical Epidemiology at Oregon Health and Science University
| | - Peilin Jia
- University of Texas Health Science Center at Houston
| | - Liang Ma
- Bioinformatics and Systems Medicine Laboratory (BSML), Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston
| | | | | | - Vignesh Subbian
- Department of Biomedical Engineering and the Department of Systems and Industrial Engineering at the University of Arizona
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Smith MR, Readhead B, Dudley JT, Morishita H. Critical period plasticity-related transcriptional aberrations in schizophrenia and bipolar disorder. Schizophr Res 2019; 207:12-21. [PMID: 30442475 PMCID: PMC6591017 DOI: 10.1016/j.schres.2018.10.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Revised: 10/17/2018] [Accepted: 10/22/2018] [Indexed: 10/27/2022]
Abstract
Childhood critical periods of experience-dependent plasticity are essential for the development of environmentally appropriate behavior and cognition. Disruption of critical periods can alter development of normal function and confer risk for neurodevelopmental disorders. While genes and their expression relevant to neurodevelopment are associated with schizophrenia, the molecular relationship between schizophrenia and critical periods has not been assessed systematically. Here, we apply a transcriptome-based bioinformatics approach to assess whether genes associated with the human critical period for visual cortex plasticity, a well-studied model of cortical critical periods, are aberrantly expressed in schizophrenia and bipolar disorder. Across two dozen datasets encompassing 522 cases and 374 controls, we find that the majority show aberrations in expression of genes associated with the critical period. We observed both hyper- and hypo-critical period plasticity phenotypes at the transcriptome level, which partially mapped to drug candidates that reverse the disorder signatures in silico. Our findings indicate plasticity aberrations in schizophrenia and their treatment may need to be considered in the context of subpopulations with elevated and others reduced plasticity. Future work should leverage ongoing consortia RNA-sequencing efforts to tease out the sources of plasticity-related transcriptional aberrations seen in schizophrenia, including true biological heterogeneity, interaction between normal development/aging and the disorder, and medication history. Our study also urges innovation towards direct assessment of visual cortex plasticity in humans with schizophrenia to precisely deconstruct the role of plasticity in this disorder.
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Affiliation(s)
- Milo R. Smith
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA
| | - Ben Readhead
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA; Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA.
| | - Joel T. Dudley
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA,Correspondence to: J. T. Dudley, One Gustave L. Levy Place, New York, NY 10029, USA., (M.R. Smith), (B. Readhead), (J.T. Dudley), (H. Morishita)
| | - Hirofumi Morishita
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA; Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA; Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, New York, NY 10029, USA.
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Sabunciyan S. Gene Expression Profiles Associated with Brain Aging are Altered in Schizophrenia. Sci Rep 2019; 9:5896. [PMID: 30976116 PMCID: PMC6459977 DOI: 10.1038/s41598-019-42308-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 03/27/2019] [Indexed: 11/08/2022] Open
Abstract
Existence of aging associated transcriptional differences in the schizophrenia brain was investigated in RNA sequencing data from 610 postmortem Dorso-Lateral Pre-Frontal Cortex (DLPFC) samples in the CommondMind Consortium (CMC) and the psychENCODE cohorts. This analysis discovered that the trajectory of gene expression changes that occur during brain aging differed between schizophrenia cases and unaffected controls. Mainly, the identified gene expression differences between the diagnosis groups shrank in magnitude following 60 years of age. A differential expression analysis restricted to the 40 to 60 year age group identified 556 statistically significant loci that replicated and had highly consistent gene expression fold changes in the two cohorts. An interaction between age and diagnosis in the wider psychENCODE cohort was also detected. Gene set enrichment analysis discovered disruptions in mitochondria, RNA splicing and phosphoprotein gene pathways. The identified differentially expressed genes in the two cohorts were also significantly enriched in genomic regions associated with schizophrenia although no enrichment was observed for differentially expressed genes identified in the 40 to 60 year age group. This work implicates disruptions to the normal brain aging processes in the pathology of schizophrenia and demonstrates the need for age stratification in schizophrenia postmortem brain gene expression studies.
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Affiliation(s)
- Sarven Sabunciyan
- Department of Pediatrics, Johns Hopkins University, Baltimore, MD, 21287, USA.
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Pearl JR, Colantuoni C, Bergey DE, Funk CC, Shannon P, Basu B, Casella AM, Oshone RT, Hood L, Price ND, Ament SA. Genome-Scale Transcriptional Regulatory Network Models of Psychiatric and Neurodegenerative Disorders. Cell Syst 2019; 8:122-135.e7. [DOI: 10.1016/j.cels.2019.01.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 10/19/2018] [Accepted: 01/14/2019] [Indexed: 12/23/2022]
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Kim S, Jo Y, Webster MJ, Lee D. Shared co-expression networks in frontal cortex of the normal aged brain and schizophrenia. Schizophr Res 2019; 204:253-261. [PMID: 30224231 DOI: 10.1016/j.schres.2018.09.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 08/17/2018] [Accepted: 09/11/2018] [Indexed: 11/25/2022]
Abstract
Previous studies on the brain of people with schizophrenia have identified structural changes and gene expression changes, suggesting that brain aging maybe accelerated in people with schizophrenia. To better characterize gene expression profiles in schizophrenia and in the aged population we constructed co-expression networks using RNA-Seq data from frontal cortex. The first data set analysed was from 62 subjects with schizophrenia and 51 unaffected controls ranging in age from 19 to 63 years. The second separate data set was from normal control individuals ranging in age from 29 to 106 years. In the first data set, we found two co-expression modules significantly associated with schizophrenia. One was a downregulated co-expression module enriched for neuron function related genes and the other was an upregulated immune/inflammation-related module. In the second data set of normal individuals, we found seven co-expression modules significantly correlated with age. A comparison of the co-expression modules from the two data sets revealed a significant consensus in nodes associated with schizophrenia and those associated with normal aging. The results indicate that a co-expression module related to neuronal function is downregulated and an immune/inflammation related co-expression module is upregulated, and associated with cells of the blood vessels, in both schizophrenia and in normal aging. This finding adds further support to the hypothesis that there may be accelerated brain aging in schizophrenia.
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Affiliation(s)
- Sanghyeon Kim
- Stanley Brain Research Laboratory, Stanley Medical Research Institute, 9800 Medical Center Drive, Rockville, MD 20850, United States of America.
| | - Yousang Jo
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea
| | - Maree J Webster
- Stanley Brain Research Laboratory, Stanley Medical Research Institute, 9800 Medical Center Drive, Rockville, MD 20850, United States of America
| | - Doheon Lee
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea.
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Batushansky A, Matsuzaki S, Newhardt MF, West MS, Griffin TM, Humphries KM. GC-MS metabolic profiling reveals fructose-2,6-bisphosphate regulates branched chain amino acid metabolism in the heart during fasting. Metabolomics 2019; 15:18. [PMID: 30830475 PMCID: PMC6478396 DOI: 10.1007/s11306-019-1478-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 01/16/2019] [Indexed: 12/24/2022]
Abstract
INTRODUCTION As an insulin sensitive tissue, the heart decreases glucose usage during fasting. This response is mediated, in part, by decreasing phosphofructokinase-2 (PFK-2) activity and levels of its product fructose-2,6-bisphosphate. However, the importance of fructose-2,6-bisphosphate in the fasting response on other metabolic pathways has not been evaluated. OBJECTIVES The goal of this study is to determine how sustaining cardiac fructose-2,6-bisphosphate levels during fasting affects the metabolomic profile. METHODS Control and transgenic mice expressing a constitutively active form of PFK-2 (GlycoHi) were subjected to either 12-h fasting or regular feeding. Animals (n = 4 per group) were used for whole-heart extraction, followed by gas chromatography-mass spectrometry metabolic profiling and multivariate data analysis. RESULTS Principal component analysis displayed differences between Control and GlycoHi groups under both fasting and fed conditions while a clear response to fasting was observed only for Control animals. However, pathway analysis revealed that these smaller changes in the GlycoHi group were significantly associated with branched-chain amino acid (BCAA) metabolism (~ 40% increase in all BCAAs). Correlation network analysis demonstrated clear differences in response to fasting between Control and GlycoHi groups amongst most parameters. Notably, fasting caused an increase in network density in the Control group from 0.12 to 0.14 while the GlycoHi group responded oppositely (0.17-0.15). CONCLUSIONS Elevated cardiac PFK-2 activity during fasting selectively increases BCAAs levels and decreases global changes in metabolism.
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Affiliation(s)
- Albert Batushansky
- Aging and Metabolism Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104, US
| | - Satoshi Matsuzaki
- Aging and Metabolism Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104, US
| | - Maria F Newhardt
- Aging and Metabolism Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104, US
- Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, US
| | - Melinda S West
- Aging and Metabolism Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104, US
| | - Timothy M Griffin
- Aging and Metabolism Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104, US
- Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, US
- Department of Physiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, US
| | - Kenneth M Humphries
- Aging and Metabolism Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, 73104, US.
- Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, US.
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Chen C, Meng Q, Xia Y, Ding C, Wang L, Dai R, Cheng L, Gunaratne P, Gibbs RA, Min S, Coarfa C, Reid JG, Zhang C, Jiao C, Jiang Y, Giase G, Thomas A, Fitzgerald D, Brunetti T, Shieh A, Xia C, Wang Y, Wang Y, Badner JA, Gershon ES, White KP, Liu C. The transcription factor POU3F2 regulates a gene coexpression network in brain tissue from patients with psychiatric disorders. Sci Transl Med 2018; 10:eaat8178. [PMID: 30545964 PMCID: PMC6494100 DOI: 10.1126/scitranslmed.aat8178] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 07/26/2018] [Accepted: 11/07/2018] [Indexed: 12/22/2022]
Abstract
Schizophrenia and bipolar disorder are complex psychiatric diseases with risks contributed by multiple genes. Dysregulation of gene expression has been implicated in these disorders, but little is known about such dysregulation in the human brain. We analyzed three transcriptome datasets from 394 postmortem brain tissue samples from patients with schizophrenia or bipolar disorder or from healthy control individuals without a known history of psychiatric disease. We built genome-wide coexpression networks that included microRNAs (miRNAs). We identified a coexpression network module that was differentially expressed in the brain tissue from patients compared to healthy control individuals. This module contained genes that were principally involved in glial and neural cell genesis and glial cell differentiation, and included schizophrenia risk genes carrying rare variants. This module included five miRNAs and 545 mRNAs, with six transcription factors serving as hub genes in this module. We found that the most connected transcription factor gene POU3F2, also identified on a genome-wide association study for bipolar disorder, could regulate the miRNA hsa-miR-320e and other putative target mRNAs. These regulatory relationships were replicated using PsychENCODE/BrainGVEX datasets and validated by knockdown and overexpression experiments in SH-SY5Y cells and human neural progenitor cells in vitro. Thus, we identified a brain gene expression module that was enriched for rare coding variants in genes associated with schizophrenia and that contained the putative bipolar disorder risk gene POU3F2 The transcription factor POU3F2 may be a key regulator of gene expression in this disease-associated gene coexpression module.
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Affiliation(s)
- Chao Chen
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Qingtuan Meng
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Yan Xia
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Chaodong Ding
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Le Wang
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Child Health Institute of New Jersey, Department of Neuroscience, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Rujia Dai
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Lijun Cheng
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Preethi Gunaratne
- Department of Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Shishi Min
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Cristian Coarfa
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Jeffrey G Reid
- Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Chunling Zhang
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Chuan Jiao
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Yi Jiang
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
| | - Gina Giase
- School of Public Health, University of Illinois at Chicago, Chicago, IL, USA
| | - Amber Thomas
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Dominic Fitzgerald
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Tonya Brunetti
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Annie Shieh
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Cuihua Xia
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Yongjun Wang
- The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yunpeng Wang
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- LifeSpan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Oslo, Norway
| | - Judith A Badner
- Department of Psychiatry, Rush University Medical Center, Chicago, IL, USA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, USA
| | - Kevin P White
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
- Tempus Labs Inc., Chicago, IL, USA
| | - Chunyu Liu
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China.
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- Department of Psychology, Shaanxi Normal University, Xi'an, China
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Dopamine perturbation of gene co-expression networks reveals differential response in schizophrenia for translational machinery. Transl Psychiatry 2018; 8:278. [PMID: 30546022 PMCID: PMC6293320 DOI: 10.1038/s41398-018-0325-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 11/13/2018] [Indexed: 12/02/2022] Open
Abstract
The dopaminergic hypothesis of schizophrenia (SZ) postulates that positive symptoms of SZ, in particular psychosis, are due to disturbed neurotransmission via the dopamine (DA) receptor D2 (DRD2). However, DA is a reactive molecule that yields various oxidative species, and thus has important non-receptor-mediated effects, with empirical evidence of cellular toxicity and neurodegeneration. Here we examine non-receptor-mediated effects of DA on gene co-expression networks and its potential role in SZ pathology. Transcriptomic profiles were measured by RNA-seq in B-cell transformed lymphoblastoid cell lines from 514 SZ cases and 690 controls, both before and after exposure to DA ex vivo (100 μM). Gene co-expression modules were identified using Weighted Gene Co-expression Network Analysis for both baseline and DA-stimulated conditions, with each module characterized for biological function and tested for association with SZ status and SNPs from a genome-wide panel. We identified seven co-expression modules under baseline, of which six were preserved in DA-stimulated data. One module shows significantly increased association with SZ after DA perturbation (baseline: P = 0.023; DA-stimulated: P = 7.8 × 10-5; ΔAIC = -10.5) and is highly enriched for genes related to ribosomal proteins and translation (FDR = 4 × 10-141), mitochondrial oxidative phosphorylation, and neurodegeneration. SNP association testing revealed tentative QTLs underlying module co-expression, notably at FASTKD2 (top P = 2.8 × 10-6), a gene involved in mitochondrial translation. These results substantiate the role of translational machinery in SZ pathogenesis, providing insights into a possible dopaminergic mechanism disrupting mitochondrial function, and demonstrates the utility of disease-relevant functional perturbation in the study of complex genetic etiologies.
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Toker L, Mancarci BO, Tripathy S, Pavlidis P. Transcriptomic Evidence for Alterations in Astrocytes and Parvalbumin Interneurons in Subjects With Bipolar Disorder and Schizophrenia. Biol Psychiatry 2018; 84:787-796. [PMID: 30177255 PMCID: PMC6226343 DOI: 10.1016/j.biopsych.2018.07.010] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 07/05/2018] [Accepted: 07/06/2018] [Indexed: 11/26/2022]
Abstract
BACKGROUND High-throughput expression analyses of postmortem brain tissue have been widely used to study bipolar disorder and schizophrenia. However, despite the extensive efforts, no consensus has emerged as to the functional interpretation of the findings. We hypothesized that incorporating information on cell type-specific expression would provide new insights. METHODS We reanalyzed 15 publicly available bulk tissue expression datasets on schizophrenia and bipolar disorder, representing various brain regions from eight different cohorts of subjects (unique subjects: 332 control, 129 bipolar disorder, 341 schizophrenia). We studied changes in the expression profiles of cell type marker genes and evaluated whether these expression profiles could serve as surrogates for relative abundance of their corresponding cells. RESULTS In both bipolar disorder and schizophrenia, we consistently observed an increase in the expression profiles of cortical astrocytes and a decrease in the expression profiles of fast-spiking parvalbumin interneurons. No changes in astrocyte expression profiles were observed in subcortical regions. Furthermore, we found that many of the genes previously identified as differentially expressed in schizophrenia are highly correlated with the expression profiles of astrocytes or fast-spiking parvalbumin interneurons. CONCLUSIONS Our results indicate convergence of transcriptome studies of schizophrenia and bipolar disorder on changes in cortical astrocytes and fast-spiking parvalbumin interneurons, providing a unified interpretation of numerous studies. We suggest that these changes can be attributed to alterations in the relative abundance of the cells and are important for understanding the pathophysiology of the disorders.
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Affiliation(s)
- Lilah Toker
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada; Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Burak Ogan Mancarci
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada; Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada; Graduate Program in Bioinformatics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Shreejoy Tripathy
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada; Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Paul Pavlidis
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada; Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada.
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Castillo-Morales A, Monzón-Sandoval J, Urrutia AO, Gutiérrez H. Postmitotic cell longevity-associated genes: a transcriptional signature of postmitotic maintenance in neural tissues. Neurobiol Aging 2018; 74:147-160. [PMID: 30448614 DOI: 10.1016/j.neurobiolaging.2018.10.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 10/03/2018] [Accepted: 10/11/2018] [Indexed: 12/24/2022]
Abstract
Different cell types have different postmitotic maintenance requirements. Nerve cells, however, are unique in this respect as they need to survive and preserve their functional complexity for the entire lifetime of the organism, and failure at any level of their supporting mechanisms leads to a wide range of neurodegenerative conditions. Whether these differences across tissues arise from the activation of distinct cell type-specific maintenance mechanisms or the differential activation of a common molecular repertoire is not known. To identify the transcriptional signature of postmitotic cellular longevity (PMCL), we compared whole-genome transcriptome data from human tissues ranging in longevity from 120 days to over 70 years and found a set of 81 genes whose expression levels are closely associated with increased cell longevity. Using expression data from 10 independent sources, we found that these genes are more highly coexpressed in longer-living tissues and are enriched in specific biological processes and transcription factor targets compared with randomly selected gene samples. Crucially, we found that PMCL-associated genes are downregulated in the cerebral cortex and substantia nigra of patients with Alzheimer's and Parkinson's disease, respectively, as well as Hutchinson-Gilford progeria-derived fibroblasts, and that this downregulation is specifically linked to their underlying association with cellular longevity. Moreover, we found that sexually dimorphic brain expression of PMCL-associated genes reflects sexual differences in lifespan in humans and macaques. Taken together, our results suggest that PMCL-associated genes are part of a generalized machinery of postmitotic maintenance and functional stability in both neural and non-neural cells and support the notion of a common molecular repertoire differentially engaged in different cell types with different survival requirements.
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Affiliation(s)
- Atahualpa Castillo-Morales
- School of Life Sciences, University of Lincoln, Lincoln, UK; Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - Jimena Monzón-Sandoval
- School of Life Sciences, University of Lincoln, Lincoln, UK; Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK
| | - Araxi O Urrutia
- Milner Centre for Evolution, Department of Biology and Biochemistry, University of Bath, Bath, UK; Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico.
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Le TT, Savitz J, Suzuki H, Misaki M, Teague TK, White BC, Marino JH, Wiley G, Gaffney PM, Drevets WC, McKinney BA, Bodurka J. Identification and replication of RNA-Seq gene network modules associated with depression severity. Transl Psychiatry 2018; 8:180. [PMID: 30185774 PMCID: PMC6125582 DOI: 10.1038/s41398-018-0234-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 06/21/2018] [Accepted: 07/14/2018] [Indexed: 01/08/2023] Open
Abstract
Genomic variation underlying major depressive disorder (MDD) likely involves the interaction and regulation of multiple genes in a network. Data-driven co-expression network module inference has the potential to account for variation within regulatory networks, reduce the dimensionality of RNA-Seq data, and detect significant gene-expression modules associated with depression severity. We performed an RNA-Seq gene co-expression network analysis of mRNA data obtained from the peripheral blood mononuclear cells of unmedicated MDD (n = 78) and healthy control (n = 79) subjects. Across the combined MDD and HC groups, we assigned genes into modules using hierarchical clustering with a dynamic tree cut method and projected the expression data onto a lower-dimensional module space by computing the single-sample gene set enrichment score of each module. We tested the single-sample scores of each module for association with levels of depression severity measured by the Montgomery-Åsberg Depression Scale (MADRS). Independent of MDD status, we identified 23 gene modules from the co-expression network. Two modules were significantly associated with the MADRS score after multiple comparison adjustment (adjusted p = 0.009, 0.028 at 0.05 FDR threshold), and one of these modules replicated in a previous RNA-Seq study of MDD (p = 0.03). The two MADRS-associated modules contain genes previously implicated in mood disorders and show enrichment of apoptosis and B cell receptor signaling. The genes in these modules show a correlation between network centrality and univariate association with depression, suggesting that intramodular hub genes are more likely to be related to MDD compared to other genes in a module.
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Affiliation(s)
- Trang T Le
- Department of Mathematics, The University of Tulsa, Tulsa, OK, USA
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Jonathan Savitz
- Laureate Institute for Brain Research, Tulsa, OK, USA
- School of Community Medicine, University of Tulsa, Tulsa, OK, USA
| | - Hideo Suzuki
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Department of Educational Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - T Kent Teague
- Departments of Surgery and Psychiatry, University of Oklahoma School of Community Medicine, Tulsa, OK, USA
- Department of Pharmaceutical Sciences, University of Oklahoma College of Pharmacy, Tulsa, OK, USA
- Department of Biochemistry and Microbiology, Oklahoma State University Center for the Health Sciences, Tulsa, OK, USA
| | - Bill C White
- Tandy School of Computer Sciences, The University of Tulsa, Tulsa, OK, USA
| | - Julie H Marino
- Department of Surgery, Integrative Immunology Center, University of Oklahoma School of Community Medicine, Tulsa, OK, USA
| | - Graham Wiley
- Arthritis and Clinical Immunology Research Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Patrick M Gaffney
- Arthritis and Clinical Immunology Research Program, Division of Genomics and Data Sciences, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Wayne C Drevets
- Janssen Research & Development, LLC, Johnson & Johnson, Inc, Titusville, NJ, USA
| | - Brett A McKinney
- Department of Mathematics, The University of Tulsa, Tulsa, OK, USA.
- Tandy School of Computer Sciences, The University of Tulsa, Tulsa, OK, USA.
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, USA.
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA.
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van Dam S, Võsa U, van der Graaf A, Franke L, de Magalhães JP. Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform 2018; 19:575-592. [PMID: 28077403 PMCID: PMC6054162 DOI: 10.1093/bib/bbw139] [Citation(s) in RCA: 431] [Impact Index Per Article: 71.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 12/01/2016] [Indexed: 01/06/2023] Open
Abstract
Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.
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Affiliation(s)
- Sipko van Dam
- Department of Genetics, UMCG HPC CB50, RB Groningen, Netherlands
| | - Urmo Võsa
- Department of Genetics, UMCG HPC CB50, RB Groningen, Netherlands
| | | | - Lude Franke
- Department of Genetics, UMCG HPC CB50, RB Groningen, Netherlands
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Jo Y, Kim S, Lee D. Identification of common coexpression modules based on quantitative network comparison. BMC Bioinformatics 2018; 19:213. [PMID: 29897320 PMCID: PMC5998758 DOI: 10.1186/s12859-018-2193-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Finding common molecular interactions from different samples is essential work to understanding diseases and other biological processes. Coexpression networks and their modules directly reflect sample-specific interactions among genes. Therefore, identification of common coexpression network or modules may reveal the molecular mechanism of complex disease or the relationship between biological processes. However, there has been no quantitative network comparison method for coexpression networks and we examined previous methods for other networks that cannot be applied to coexpression network. Therefore, we aimed to propose quantitative comparison methods for coexpression networks and to find common biological mechanisms between Huntington’s disease and brain aging by the new method. Results We proposed two similarity measures for quantitative comparison of coexpression networks. Then, we performed experiments using known coexpression networks. We showed the validity of two measures and evaluated threshold values for similar coexpression network pairs from experiments. Using these similarity measures and thresholds, we quantitatively measured the similarity between disease-specific and aging-related coexpression modules and found similar Huntington’s disease-aging coexpression module pairs. Conclusions We identified similar Huntington’s disease-aging coexpression module pairs and found that these modules are related to brain development, cell death, and immune response. It suggests that up-regulated cell signalling related cell death and immune/ inflammation response may be the common molecular mechanisms in the pathophysiology of HD and normal brain aging in the frontal cortex. Electronic supplementary material The online version of this article (10.1186/s12859-018-2193-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yousang Jo
- Bio-Synergy Research Center, Daejeon, 34141, South Korea.,Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Sanghyeon Kim
- Brain Research Laboratory, Stanley Medical Research Institute, Rockville, MD, 20850, USA.
| | - Doheon Lee
- Bio-Synergy Research Center, Daejeon, 34141, South Korea. .,Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea.
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Couto CMV, Comin CH, Costa LDF. Effects of threshold on the topology of gene co-expression networks. MOLECULAR BIOSYSTEMS 2018; 13:2024-2035. [PMID: 28770908 DOI: 10.1039/c7mb00101k] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Several developments regarding the analysis of gene co-expression profiles using complex network theory have been reported recently. Such approaches usually start with the construction of an unweighted gene co-expression network, therefore requiring the selection of a suitable threshold defining which pairs of vertices will be connected. We aimed at addressing such an important problem by suggesting and comparing five different approaches for threshold selection. Each of the methods considers a respective biologically-motivated criterion for electing a potentially suitable threshold. A set of 21 microarray experiments from different biological groups was used to investigate the effect of applying the five proposed criteria to several biological situations. For each experiment, we used the Pearson correlation coefficient to measure the relationship between each gene pair, and the resulting weight matrices were thresholded considering several values, generating respective adjacency matrices (co-expression networks). Each of the five proposed criteria was then applied in order to select the respective threshold value. The effects of these thresholding approaches on the topology of the resulting networks were compared by using several measurements, and we verified that, depending on the database, the impact on the topological properties can be large. However, a group of databases was verified to be similarly affected by most of the considered criteria. Based on such results, it can be suggested that when the generated networks present similar measurements, the thresholding method can be chosen with greater freedom. If the generated networks are markedly different, the thresholding method that better suits the interests of each specific research study represents a reasonable choice.
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Scarr E, Udawela M, Thomas EA, Dean B. Changed gene expression in subjects with schizophrenia and low cortical muscarinic M1 receptors predicts disrupted upstream pathways interacting with that receptor. Mol Psychiatry 2018; 23:295-303. [PMID: 27801890 PMCID: PMC5794886 DOI: 10.1038/mp.2016.195] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Revised: 08/24/2016] [Accepted: 08/26/2016] [Indexed: 12/14/2022]
Abstract
We tested the hypothesis that, compared with subjects with no history of psychiatric illness (controls), changes in gene expression in the dorsolateral prefrontal cortex from two subgroups of subjects with schizophrenia, one with a marked deficit in muscarinic M1 receptors (muscarinic receptor-deficit schizophrenia (MRDS)), would identify different biochemical pathways that would be affected by their aetiologies. Hence, we measured levels of cortical (Brodmann area 9) mRNA in 15 MRDS subjects, 15 subjects with schizophrenia but without a deficit in muscarinic M1 receptors (non-MRDS) and 15 controls using Affymetrix Exon 1.0 ST arrays. Levels of mRNA for 65 genes were significantly different in the cortex of subjects with MRDS and predicted changes in pathways involved in cellular movement and cell-to-cell signalling. Levels of mRNA for 45 genes were significantly different in non-MRDS and predicted changes in pathways involved in cellular growth and proliferation as well as cellular function and maintenance. Changes in gene expression also predicted effects on pathways involved in amino acid metabolism, molecular transport and small-molecule biochemistry in both MRDS and non-MRDS. Overall, our data argue a prominent role for glial function in MRDS and neurodevelopment in non-MRDS. Finally, the interactions of gene with altered levels of mRNA in the cortex of subjects with MRDS suggest many of their affects will be upstream of the muscarinic M1 receptor. Our study gives new insight into the molecular pathways affected in the cortex of subjects with MRDS and supports the notion that studying subgroups within the syndrome of schizophrenia is worthwhile.
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Affiliation(s)
- E Scarr
- Molecular Psychiatry Laboratory, Florey Institute for Neuroscience and Mental Health, Parkville, VIC, Australia,CRC for Mental Health, Carlton, VIC, Australia,Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
| | - M Udawela
- Molecular Psychiatry Laboratory, Florey Institute for Neuroscience and Mental Health, Parkville, VIC, Australia,CRC for Mental Health, Carlton, VIC, Australia
| | - E A Thomas
- Department of Molecular and Cellular Neuroscience, The Scripps Research Institute, La Jolla, CA, Australia
| | - B Dean
- Molecular Psychiatry Laboratory, Florey Institute for Neuroscience and Mental Health, Parkville, VIC, Australia,CRC for Mental Health, Carlton, VIC, Australia,Molecular Psychiatry Laboratory, Florey Institute for Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC 3052, Australia. E-mail:
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Izadi F, Soheilifar MH. Exploring Potential Biomarkers Underlying Pathogenesis of Alzheimer's Disease by Differential Co-expression Analysis. Avicenna J Med Biotechnol 2018; 10:233-241. [PMID: 30555656 PMCID: PMC6252023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Alzheimer's Disease (AD) is the most common form of dementia in the elderly. Due to the facts that biological causes of AD are complex in addition to increasing rates of AD worldwide, a deeper understanding of AD etiology is required for AD treatment and diagnosis. METHODS To identify molecular pathological alterations in AD brains, GSE36980 series containing microarray data samples from temporal cortex, frontal cortex and hippocampus were downloaded from Gene Expression Omnibus (GEO) database and valid gene symbols were subjected to building a gene co-expression network by a bioinformatics tool known as differential regulation from differential co-expression (DCGL) software package. Then, a network-driven integrative analysis was performed to find significant genes and underlying biological terms. RESULTS A total of 17088 unique genes were parsed into three independent differential co-expression networks. As a result, a small number of differentially co-regulated genes mostly in frontal and hippocampus lobs were detected as potential biomarkers related to AD brains. Ultimately differentially co-regulated genes were enriched in biological terms including response to lipid and fatty acid and pathways mainly signaling pathway such as G-protein signaling pathway and glutamate receptor groups II and III. By conducting co-expression analysis, our study identified multiple genes that may play an important role in the pathogenesis of AD. CONCLUSION The study aimed to provide a systematic understanding of the potential relationships among these genes and it is hoped that it could aid in AD biomarker discovery.
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Affiliation(s)
- Fereshteh Izadi
- Department of Genetics, Evolution and Environment, Darwin Building, University College London (UCL), London, UK,Corresponding author: Fereshteh Izadi, PhD, Department of Genetics, Evolution and Environment, Darwin Building, University College London (UCL), Gower Street, London WC1E 6BT, UK, Tel: +44 7846280861, E-mail:
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Deconvolution of Transcriptional Networks in Post-Traumatic Stress Disorder Uncovers Master Regulators Driving Innate Immune System Function. Sci Rep 2017; 7:14486. [PMID: 29101382 PMCID: PMC5670244 DOI: 10.1038/s41598-017-15221-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 10/23/2017] [Indexed: 01/05/2023] Open
Abstract
Post-Traumatic Stress Disorder (PTSD) is a psychiatric disorder that develops in individuals experiencing a shocking incident, but the underlying disease susceptibility gene networks remain poorly understood. Breen et al. conducted a Weighted Gene Co-expression Network Analysis on PTSD, and identified a dysregulated innate immune module associated with PTSD development. To further identify the Master Regulators (MRs) driving the network function, here we deconvoluted the transcriptional networks on the same datasets using ARACNe (Algorithm for Reconstruction of Accurate Cellular Networks) followed by protein activity analysis. We successfully identified several MRs including SOX3, TNFAIP3, TRAFD1, POU3F3, STAT2, and PML that govern the expression of a large collection of genes. Transcription factor binding site enrichment analysis verified the binding of these MRs to their predicted targets. Notably, the sub-networks regulated by TNFAIP3, TRAFD1 and PML are involved in innate immune response, suggesting that these MRs may correlate with the innate immune module identified by Breen et al. These findings were replicated in an independent dataset generated on expression microarrays. In conclusion, our analysis corroborated previous findings that innate immunity may be involved in the progression of PTSD, yet also identified candidate MRs driving the disease progression in the innate immunity pathways.
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Uddin R, Singh SM. Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment. Front Syst Neurosci 2017; 11:75. [PMID: 29066959 PMCID: PMC5641338 DOI: 10.3389/fnsys.2017.00075] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 09/22/2017] [Indexed: 01/06/2023] Open
Abstract
As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in “learning and memory” related functions and pathways. Subsequent differential network analysis of this “learning and memory” module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they provide a new insight and generate new hypotheses into the molecular mechanisms responsible for age associated learning impairment, including spatial learning.
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Affiliation(s)
- Raihan Uddin
- Department of Biology, University of Western Ontario, London, ON, Canada
| | - Shiva M Singh
- Department of Biology, University of Western Ontario, London, ON, Canada
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Warden AS, Mayfield RD. Gene expression profiling in the human alcoholic brain. Neuropharmacology 2017; 122:161-174. [PMID: 28254370 DOI: 10.1016/j.neuropharm.2017.02.017] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 02/13/2017] [Accepted: 02/17/2017] [Indexed: 01/12/2023]
Abstract
Long-term alcohol use causes widespread changes in gene expression in the human brain. Aberrant gene expression changes likely contribute to the progression from occasional alcohol use to alcohol use disorder (including alcohol dependence). Transcriptome studies have identified individual gene candidates that are linked to alcohol-dependence phenotypes. The use of bioinformatics techniques to examine expression datasets has provided novel systems-level approaches to transcriptome profiling in human postmortem brain. These analytical advances, along with recent developments in next-generation sequencing technology, have been instrumental in detecting both known and novel coding and non-coding RNAs, alternative splicing events, and cell-type specific changes that may contribute to alcohol-related pathologies. This review offers an integrated perspective on alcohol-responsive transcriptional changes in the human brain underlying the regulatory gene networks that contribute to alcohol dependence. This article is part of the Special Issue entitled "Alcoholism".
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Affiliation(s)
- Anna S Warden
- Institute for Neuroscience, The University of Texas at Austin, 1 University Station, C7000, Austin, TX 78712, USA; Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, 2500 Speedway, A4800, Austin, TX 78712, USA
| | - R Dayne Mayfield
- Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, 2500 Speedway, A4800, Austin, TX 78712, USA.
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Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion. BMC SYSTEMS BIOLOGY 2016. [DOI: 10.1186/s12918-016-0366-0 order by 8029-- awyx] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Lin M, Pedrosa E, Hrabovsky A, Chen J, Puliafito BR, Gilbert SR, Zheng D, Lachman HM. Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion. BMC SYSTEMS BIOLOGY 2016. [DOI: 10.1186/s12918-016-0366-0 and 1880=1880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion. BMC SYSTEMS BIOLOGY 2016. [DOI: 10.1186/s12918-016-0366-0 order by 8029-- #] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Lin M, Pedrosa E, Hrabovsky A, Chen J, Puliafito BR, Gilbert SR, Zheng D, Lachman HM. Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion. BMC SYSTEMS BIOLOGY 2016. [DOI: 10.1186/s12918-016-0366-0 order by 8029-- -] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Lin M, Pedrosa E, Hrabovsky A, Chen J, Puliafito BR, Gilbert SR, Zheng D, Lachman HM. Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion. BMC SYSTEMS BIOLOGY 2016. [DOI: 10.1186/s12918-016-0366-0 order by 1-- -] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Lin M, Pedrosa E, Hrabovsky A, Chen J, Puliafito BR, Gilbert SR, Zheng D, Lachman HM. Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion. BMC SYSTEMS BIOLOGY 2016. [DOI: 10.1186/s12918-016-0366-0 order by 1-- gadu] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Lin M, Pedrosa E, Hrabovsky A, Chen J, Puliafito BR, Gilbert SR, Zheng D, Lachman HM. Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion. BMC SYSTEMS BIOLOGY 2016. [DOI: 10.1186/s12918-016-0366-0 order by 1-- #] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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