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Pushparaj PN, Kalamegam G, Wali Sait KH, Rasool M. Decoding the Role of Astrocytes in the Entorhinal Cortex in Alzheimer’s Disease Using High-Dimensional Single-Nucleus RNA Sequencing Data and Next-Generation Knowledge Discovery Methodologies: Focus on Drugs and Natural Product Remedies for Dementia. Front Pharmacol 2022; 12:720170. [PMID: 35295737 PMCID: PMC8918735 DOI: 10.3389/fphar.2021.720170] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 12/10/2021] [Indexed: 12/14/2022] Open
Abstract
Introduction: Alzheimer’s disease (AD) is a major cause of the development of cognitive decline and dementia. AD and associated dementias (ADRD) are the major contributors to the enormous burden of morbidity and mortality worldwide. To date, there are no robust therapies to alleviate or cure this debilitating disease. Most drug treatments focus on restoring the normal function of neurons and the cells that cause inflammation, such as microglia in the brain. However, the role of astrocytes, the brain’s housekeeping cells, in the development of AD and the initiation of dementia is still not well understood. Objective: To decipher the role of astrocytes in the entorhinal cortex of AD patients using single nuclear RNA sequencing (snRNASeq) datasets from the Single Cell RNA-seq Database for Alzheimer’s Disease (scREAD). The datasets were originally derived from astrocytes, isolated from the entorhinal cortex of AD brain and healthy brain to decipher disease-specific signaling pathways as well as drugs and natural products that reverse AD-specific signatures in astrocytes. Methods: We used snRNASeq datasets from the scREAD database originally derived from astrocytes isolated from the entorhinal cortex of AD and healthy brains from the Gene Expression Omnibus (GEO) (GSE138852 and GSE147528) and analyzed them using next-generation knowledge discovery (NGKD) platforms. scREAD is a user-friendly open-source interface available at https://bmbls.bmi.osumc.edu/scread/that enables more discovery-oriented strategies. snRNASeq data and metadata can also be visualized and downloaded via an interactive web application at adsn.ddnetbio.com. Differentially expressed genes (DEGs) for each snRNASeq dataset were analyzed using iPathwayGuide to compare and derive disease-specific pathways, gene ontologies, and in silico predictions of drugs and natural products that regulate AD -specific signatures in astrocytes. In addition, DEGs were analyzed using the L1000FWD and L1000CDS2 signature search programming interfaces (APIs) to identify additional drugs and natural products that mimic or reverse AD-specific gene signatures in astrocytes. Results: We found that PI3K/AKT signaling, Wnt signaling, neuroactive ligand-receptor interaction pathways, neurodegeneration pathways, etc. were significantly impaired in astrocytes from the entorhinal cortex of AD patients. Biological processes such as glutamate receptor signaling pathway, regulation of synapse organization, cell-cell adhesion via plasma membrane adhesion molecules, and chylomicrons were negatively enriched in the astrocytes from the entorhinal cortex of AD patients. Gene sets involved in cellular components such as postsynaptic membrane, synaptic membrane, postsynapse, and synapse part were negatively enriched (p < 0.01). Moreover, molecular functions such as glutamate receptor activity, neurotransmitter receptor activity, and extracellular ligand-gated ion channels were negatively regulated in the astrocytes of the entorhinal cortex of AD patients (p < 0.01). Moreover, the application of NGKD platforms revealed that antirheumatic drugs, vitamin-E, emetine, narciclasine, cephaeline, trichostatin A, withaferin A, dasatinib, etc. can potentially reverse gene signatures associated with AD. Conclusions: The present study highlights an innovative approach to use NGKD platforms to find unique disease-associated signaling pathways and specific synthetic drugs and natural products that can potentially reverse AD and ADRD-associated gene signatures.
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Affiliation(s)
- Peter Natesan Pushparaj
- Center of Excellence in Genomic Medicine Research, Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
- Center for Transdisciplinary Research, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, India
- *Correspondence: Peter Natesan Pushparaj, ; Mahmood Rasool,
| | - Gauthaman Kalamegam
- Center for Transdisciplinary Research, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, India
| | - Khalid Hussain Wali Sait
- Department of Obstetrics and Gynaecology, King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mahmood Rasool
- Center of Excellence in Genomic Medicine Research, Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
- *Correspondence: Peter Natesan Pushparaj, ; Mahmood Rasool,
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Kozell LB, Lockwood D, Darakjian P, Edmunds S, Shepherdson K, Buck KJ, Hitzemann R. RNA-Seq Analysis of Genetic and Transcriptome Network Effects of Dual-Trait Selection for Ethanol Preference and Withdrawal Using SOT and NOT Genetic Models. Alcohol Clin Exp Res 2020; 44:820-830. [PMID: 32090358 PMCID: PMC7169974 DOI: 10.1111/acer.14312] [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: 06/05/2019] [Accepted: 02/13/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Genetic factors significantly affect alcohol consumption and vulnerability to withdrawal. Furthermore, some genetic models showing predisposition to severe withdrawal are also predisposed to low ethanol (EtOH) consumption and vice versa, even when tested independently in naïve animals. METHODS Beginning with a C57BL/6J × DBA/2J F2 intercross founder population, animals were simultaneously selectively bred for both high alcohol consumption and low acute withdrawal (SOT line), or vice versa (NOT line). Using randomly chosen fourth selected generation (S4) mice (N = 18-22/sex/line), RNA-Seq was employed to assess genome-wide gene expression in ventral striatum. The MegaMUGA array was used to detect genome-wide genotypic differences. Differential gene expression and the weighted gene co-expression network analysis were implemented as described elsewhere (Genes Brain Behav 16, 2017, 462). RESULTS The new selection of the SOT and NOT lines was similar to that reported previously (Alcohol Clin Exp Res 38, 2014, 2915). One thousand eight hundred and sixteen transcripts were detected as differentially expressed between the lines. For genes more highly expressed in the SOT line, there was enrichment in genes associated with cell adhesion, synapse organization, and postsynaptic membrane. The genes with a cell adhesion annotation included 23 protocadherins, Mpdz and Dlg2. Genes with a postsynaptic membrane annotation included Gabrb3, Gphn, Grid1, Grin2b, Grin2c, and Grm3. The genes more highly expressed in the NOT line were enriched in a network module (red) with annotations associated with mitochondrial function. Several of these genes were module hub nodes, and these included Nedd8, Guk1, Elof1, Ndufa8, and Atp6v1f. CONCLUSIONS Marked effects of selection on gene expression were detected. The NOT line was characterized by higher expression of hub nodes associated with mitochondrial function. Genes more highly expressed in the SOT aligned with previous findings, for example, Colville and colleagues (Genes Brain Behav 16, 2017, 462) that both high EtOH preference and consumption are associated with effects on cell adhesion and glutamate synaptic plasticity.
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Affiliation(s)
- Laura B Kozell
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
| | - Denesa Lockwood
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
| | - Priscila Darakjian
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
| | - Stephanie Edmunds
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
| | - Karen Shepherdson
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
| | - Kari J Buck
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
| | - Robert Hitzemann
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
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8
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Iancu OD, Colville AM, Wilmot B, Searles R, Darakjian P, Zheng C, McWeeney S, Kawane S, Crabbe JC, Metten P, Oberbeck D, Hitzemann R. Gender-Specific Effects of Selection for Drinking in the Dark on the Network Roles of Coding and Noncoding RNAs. Alcohol Clin Exp Res 2018; 42:1454-1465. [PMID: 29786871 DOI: 10.1111/acer.13777] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 05/10/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND Transcriptional differences between heterogeneous stock mice and high drinking-in-the-dark selected mouse lines have previously been described based on microarray technology coupled with network-based analysis. The network changes were reproducible in 2 independent selections and largely confined to 2 distinct network modules; in contrast, differential expression appeared more specific to each selected line. This study extends these results by utilizing RNA-Seq technology, allowing evaluation of the relationship between genetic risk and transcription of noncoding RNA (ncRNA); we additionally evaluate sex-specific transcriptional effects of selection. METHODS Naïve mice (N = 24/group and sex) were utilized for gene expression analysis in the ventral striatum; the transcriptome was sequenced with the Illumina HiSeq platform. Differential gene expression and the weighted gene co-expression network analysis were implemented largely as described elsewhere, resulting in the identification of genes that change expression level or (co)variance structure. RESULTS Across both sexes, we detect selection effects on the extracellular matrix and synaptic signaling, although the identity of individual genes varies. A majority of nc RNAs cluster in a single module of relatively low density in both the male and female network. The most strongly differentially expressed transcript in both sexes was Gm22513, a small nuclear RNA with unknown function. Associated with selection, we also found a number of network hubs that change edge strength and connectivity. At the individual gene level, there are many sex-specific effects; however, at the annotation level, results are more concordant. CONCLUSIONS In addition to demonstrating sex-specific effects of selection on the transcriptome, the data point to the involvement of extracellular matrix genes as being associated with the binge drinking phenotype.
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Affiliation(s)
- Ovidiu Dan Iancu
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
| | - Alex M Colville
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
| | - Beth Wilmot
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
| | - Robert Searles
- Integrated Genomics Laboratory, Oregon Health & Science University, Portland, Oregon
| | - Priscila Darakjian
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
| | - Christina Zheng
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon.,Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon
| | - Shannon McWeeney
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
| | - Sunita Kawane
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon
| | - John C Crabbe
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon.,VA Portland Health Care System , Portland, Oregon
| | - Pamela Metten
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon.,VA Portland Health Care System , Portland, Oregon
| | - Denesa Oberbeck
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
| | - Robert Hitzemann
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon
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12
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Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, Ruderfer DM, Oh EC, Topol A, Shah HR, Klei LL, Kramer R, Pinto D, Gümüş ZH, Cicek AE, Dang KK, Browne A, Lu C, Xie L, Readhead B, Stahl EA, Xiao J, Parvizi M, Hamamsy T, Fullard JF, Wang YC, Mahajan MC, Derry JMJ, Dudley JT, Hemby SE, Logsdon BA, Talbot K, Raj T, Bennett DA, De Jager PL, Zhu J, Zhang B, Sullivan PF, Chess A, Purcell SM, Shinobu LA, Mangravite LM, Toyoshiba H, Gur RE, Hahn CG, Lewis DA, Haroutunian V, Peters MA, Lipska BK, Buxbaum JD, Schadt EE, Hirai K, Roeder K, Brennand KJ, Katsanis N, Domenici E, Devlin B, Sklar P. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci 2016; 19:1442-1453. [PMID: 27668389 PMCID: PMC5083142 DOI: 10.1038/nn.4399] [Citation(s) in RCA: 724] [Impact Index Per Article: 90.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Accepted: 09/01/2016] [Indexed: 12/15/2022]
Abstract
Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ∼20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.
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Affiliation(s)
- Menachem Fromer
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Panos Roussos
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Psychiatry, JJ Peters Virginia Medical Center, Bronx, New York, USA
| | | | - Jessica S Johnson
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - David H Kavanagh
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Douglas M Ruderfer
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Edwin C Oh
- Center for Human Disease Modeling, Duke University, Durham, North Carolina, USA
- Department of Neurology, Duke University, Durham, North Carolina, USA
| | - Aaron Topol
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Hardik R Shah
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lambertus L Klei
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Robin Kramer
- Human Brain Collection Core, National Institutes of Health, NIMH, Bethesda, Maryland, USA
| | - Dalila Pinto
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Zeynep H Gümüş
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - A Ercument Cicek
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Kristen K Dang
- Systems Biology, Sage Bionetworks, Seattle, Washington, USA
| | - Andrew Browne
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Cong Lu
- Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Lu Xie
- Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Ben Readhead
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eli A Stahl
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jianqiu Xiao
- Center for Human Disease Modeling, Duke University, Durham, North Carolina, USA
| | - Mahsa Parvizi
- Center for Human Disease Modeling, Duke University, Durham, North Carolina, USA
| | - Tymor Hamamsy
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - John F Fullard
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ying-Chih Wang
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Milind C Mahajan
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Joel T Dudley
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Scott E Hemby
- Department of Basic Pharmaceutical Sciences, Fred Wilson School of Pharmacy, High Point University, High Point, North Carolina, USA
| | | | - Konrad Talbot
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Towfique Raj
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Philip L De Jager
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Departments of Neurology and Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jun Zhu
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Bin Zhang
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Patrick F Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Andrew Chess
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Shaun M Purcell
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Leslie A Shinobu
- CNS Drug Discovery Unit, Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa, Japan
| | | | - Hiroyoshi Toyoshiba
- Integrated Technology Research Laboratories, Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa, Japan
| | - Raquel E Gur
- Neuropsychiatry Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Chang-Gyu Hahn
- Neuropsychiatric Signaling Program, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Lewis
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Vahram Haroutunian
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Psychiatry, JJ Peters Virginia Medical Center, Bronx, New York, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mette A Peters
- Systems Biology, Sage Bionetworks, Seattle, Washington, USA
| | - Barbara K Lipska
- Human Brain Collection Core, National Institutes of Health, NIMH, Bethesda, Maryland, USA
| | - Joseph D Buxbaum
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eric E Schadt
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Keisuke Hirai
- CNS Drug Discovery Unit, Pharmaceutical Research Division, Takeda Pharmaceutical Company Limited, Fujisawa, Kanagawa, Japan
| | - Kathryn Roeder
- Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
- Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Kristen J Brennand
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Nicholas Katsanis
- Center for Human Disease Modeling, Duke University, Durham, North Carolina, USA
- Department of Cell Biology and Pediatrics, Duke University, Durham, North Carolina, USA
| | - Enrico Domenici
- Laboratory of Neurogenomic Biomarkers, Centre for Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Pamela Sklar
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Genomics and Multiscale Biology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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