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Courchesne E, Taluja V, Nazari S, Aamodt CM, Pierce K, Duan K, Stophaeros S, Lopez L, Barnes CC, Troxel J, Campbell K, Wang T, Hoekzema K, Eichler EE, Nani JV, Pontes W, Sanchez SS, Lombardo MV, de Souza JS, Hayashi MAF, Muotri AR. Embryonic origin of two ASD subtypes of social symptom severity: the larger the brain cortical organoid size, the more severe the social symptoms. Mol Autism 2024; 15:22. [PMID: 38790065 PMCID: PMC11127428 DOI: 10.1186/s13229-024-00602-8] [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: 12/05/2023] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Social affective and communication symptoms are central to autism spectrum disorder (ASD), yet their severity differs across toddlers: Some toddlers with ASD display improving abilities across early ages and develop good social and language skills, while others with "profound" autism have persistently low social, language and cognitive skills and require lifelong care. The biological origins of these opposite ASD social severity subtypes and developmental trajectories are not known. METHODS Because ASD involves early brain overgrowth and excess neurons, we measured size and growth in 4910 embryonic-stage brain cortical organoids (BCOs) from a total of 10 toddlers with ASD and 6 controls (averaging 196 individual BCOs measured/subject). In a 2021 batch, we measured BCOs from 10 ASD and 5 controls. In a 2022 batch, we tested replicability of BCO size and growth effects by generating and measuring an independent batch of BCOs from 6 ASD and 4 control subjects. BCO size was analyzed within the context of our large, one-of-a-kind social symptom, social attention, social brain and social and language psychometric normative datasets ranging from N = 266 to N = 1902 toddlers. BCO growth rates were examined by measuring size changes between 1- and 2-months of organoid development. Neurogenesis markers at 2-months were examined at the cellular level. At the molecular level, we measured activity and expression of Ndel1; Ndel1 is a prime target for cell cycle-activated kinases; known to regulate cell cycle, proliferation, neurogenesis, and growth; and known to be involved in neuropsychiatric conditions. RESULTS At the BCO level, analyses showed BCO size was significantly enlarged by 39% and 41% in ASD in the 2021 and 2022 batches. The larger the embryonic BCO size, the more severe the ASD social symptoms. Correlations between BCO size and social symptoms were r = 0.719 in the 2021 batch and r = 0. 873 in the replication 2022 batch. ASD BCOs grew at an accelerated rate nearly 3 times faster than controls. At the cell level, the two largest ASD BCOs had accelerated neurogenesis. At the molecular level, Ndel1 activity was highly correlated with the growth rate and size of BCOs. Two BCO subtypes were found in ASD toddlers: Those in one subtype had very enlarged BCO size with accelerated rate of growth and neurogenesis; a profound autism clinical phenotype displaying severe social symptoms, reduced social attention, reduced cognitive, very low language and social IQ; and substantially altered growth in specific cortical social, language and sensory regions. Those in a second subtype had milder BCO enlargement and milder social, attention, cognitive, language and cortical differences. LIMITATIONS Larger samples of ASD toddler-derived BCO and clinical phenotypes may reveal additional ASD embryonic subtypes. CONCLUSIONS By embryogenesis, the biological bases of two subtypes of ASD social and brain development-profound autism and mild autism-are already present and measurable and involve dysregulated cell proliferation and accelerated neurogenesis and growth. The larger the embryonic BCO size in ASD, the more severe the toddler's social symptoms and the more reduced the social attention, language ability, and IQ, and the more atypical the growth of social and language brain regions.
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Affiliation(s)
- Eric Courchesne
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA.
| | - Vani Taluja
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Sanaz Nazari
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Caitlin M Aamodt
- Department of Pediatrics and Department of Molecular and Cellular Medicine, University of California, San Diego, Gilman Drive, La Jolla, CA, 92093, USA
| | - Karen Pierce
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Kuaikuai Duan
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Sunny Stophaeros
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Linda Lopez
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Cynthia Carter Barnes
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Jaden Troxel
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Kathleen Campbell
- Autism Center of Excellence, Department of Neurosciences, University of California, San Diego, 8110 La Jolla Shores Dr., La Jolla, CA, 92037, USA
| | - Tianyun Wang
- Department of Medical Genetics, Center for Medical Genetics, Peking University Health Science Center, Beijing, 100191, China
- Neuroscience Research Institute, Peking University, Key Laboratory for Neuroscience, Ministry of Education of China and National Health Commission of China, Beijing, 100191, China
| | - Kendra Hoekzema
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
| | - Evan E Eichler
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, 98195, USA
| | - Joao V Nani
- Department of Pediatrics and Department of Molecular and Cellular Medicine, University of California, San Diego, Gilman Drive, La Jolla, CA, 92093, USA
- Department of Pharmacology, Escola Paulista de Medicina (EPM), Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil
| | - Wirla Pontes
- Department of Pediatrics and Department of Molecular and Cellular Medicine, University of California, San Diego, Gilman Drive, La Jolla, CA, 92093, USA
| | - Sandra Sanchez Sanchez
- Department of Pediatrics and Department of Molecular and Cellular Medicine, University of California, San Diego, Gilman Drive, La Jolla, CA, 92093, USA
| | - Michael V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Janaina S de Souza
- Department of Pediatrics and Department of Molecular and Cellular Medicine, University of California, San Diego, Gilman Drive, La Jolla, CA, 92093, USA
| | - Mirian A F Hayashi
- Department of Pharmacology, Escola Paulista de Medicina (EPM), Universidade Federal de São Paulo (UNIFESP), São Paulo, SP, Brazil
| | - Alysson R Muotri
- Department of Pediatrics and Department of Molecular and Cellular Medicine, University of California, San Diego, Gilman Drive, La Jolla, CA, 92093, USA.
- Rady Children's Hospital, Center for Academic Research and Training in Anthropogeny (CARTA), Archealization Center (ArchC), Kavli Institute for Brain and Mind, La Jolla, CA, USA.
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2
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Patowary A, Zhang P, Jops C, Vuong CK, Ge X, Hou K, Kim M, Gong N, Margolis M, Vo D, Wang X, Liu C, Pasaniuc B, Li JJ, Gandal MJ, de la Torre-Ubieta L. Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms. Science 2024; 384:eadh7688. [PMID: 38781356 DOI: 10.1126/science.adh7688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 03/13/2024] [Indexed: 05/25/2024]
Abstract
RNA splicing is highly prevalent in the brain and has strong links to neuropsychiatric disorders; yet, the role of cell type-specific splicing and transcript-isoform diversity during human brain development has not been systematically investigated. In this work, we leveraged single-molecule long-read sequencing to deeply profile the full-length transcriptome of the germinal zone and cortical plate regions of the developing human neocortex at tissue and single-cell resolution. We identified 214,516 distinct isoforms, of which 72.6% were novel (not previously annotated in Gencode version 33), and uncovered a substantial contribution of transcript-isoform diversity-regulated by RNA binding proteins-in defining cellular identity in the developing neocortex. We leveraged this comprehensive isoform-centric gene annotation to reprioritize thousands of rare de novo risk variants and elucidate genetic risk mechanisms for neuropsychiatric disorders.
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Affiliation(s)
- Ashok Patowary
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
- Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Pan Zhang
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
- Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Connor Jops
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute at Penn Med and the Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Celine K Vuong
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
- Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Xinzhou Ge
- Department of Statistics, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Minsoo Kim
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Naihua Gong
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael Margolis
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Daniel Vo
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute at Penn Med and the Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Xusheng Wang
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38103, USA
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY 13210, USA
- Center for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China
| | - Bogdan Pasaniuc
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jingyi Jessica Li
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Statistics, University of California Los Angeles, Los Angeles, CA 90095, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Biostatistics, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Michael J Gandal
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
- Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute at Penn Med and the Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Luis de la Torre-Ubieta
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
- Intellectual and Developmental Disabilities Research Center, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
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3
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Pratt HE, Andrews G, Shedd N, Phalke N, Li T, Pampari A, Jensen M, Wen C, Consortium P, Gandal MJ, Geschwind DH, Gerstein M, Moore J, Kundaje A, Colubri A, Weng Z. Using a comprehensive atlas and predictive models to reveal the complexity and evolution of brain-active regulatory elements. SCIENCE ADVANCES 2024; 10:eadj4452. [PMID: 38781344 PMCID: PMC11114231 DOI: 10.1126/sciadv.adj4452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 04/25/2024] [Indexed: 05/25/2024]
Abstract
Most genetic variants associated with psychiatric disorders are located in noncoding regions of the genome. To investigate their functional implications, we integrate epigenetic data from the PsychENCODE Consortium and other published sources to construct a comprehensive atlas of candidate brain cis-regulatory elements. Using deep learning, we model these elements' sequence syntax and predict how binding sites for lineage-specific transcription factors contribute to cell type-specific gene regulation in various types of glia and neurons. The elements' evolutionary history suggests that new regulatory information in the brain emerges primarily via smaller sequence mutations within conserved mammalian elements rather than entirely new human- or primate-specific sequences. However, primate-specific candidate elements, particularly those active during fetal brain development and in excitatory neurons and astrocytes, are implicated in the heritability of brain-related human traits. Additionally, we introduce PsychSCREEN, a web-based platform offering interactive visualization of PsychENCODE-generated genetic and epigenetic data from diverse brain cell types in individuals with psychiatric disorders and healthy controls.
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Affiliation(s)
- Henry E. Pratt
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Gregory Andrews
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Tongxin Li
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
- Khoury College of Computer Science, Northeastern University, Boston, MA 02115, USA
| | - Anusri Pampari
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Matthew Jensen
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Cindy Wen
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | | | - Michael J. Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Daniel H. Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Andrés Colubri
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
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4
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Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. Science 2024; 384:eadi5199. [PMID: 38781369 DOI: 10.1126/science.adi5199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 04/05/2024] [Indexed: 05/25/2024]
Abstract
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
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Affiliation(s)
- Prashant S Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jason J Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Matthew Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Chirag Gupta
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Cagatay Dursun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yuhang Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Zhiyuan Chu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA 92697, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA 92697, USA
| | - Yunyang Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Xiao Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Tanima Chatterjee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | | | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael Gancz
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona 08028, Spain
| | - Sophia Gaynor-Gillett
- Tempus Labs, Chicago, IL 60654, USA
- Department of Biology, Cornell College, Mount Vernon, IA 52314, USA
| | - Jennifer Grundman
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Natalie Hawken
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Ella Henry
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Ao Huang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Ting Jin
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | | | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA 90095, USA
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Junhao Liu
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Shaojie Ma
- Department of Neuroscience, Yale University, New Haven, CT 06510, USA
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | | | - Eric Nguyen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Henry Pratt
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | | | - Tiernon R Riesenmy
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | | | | | - Rosemarie Terwilliger
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | | | - Brie Wamsley
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Gaoyuan Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yan Xia
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Andrew C Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Suchen Zheng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University, New Haven, CT 06510, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Kevin P White
- Yong Loo Lin School of Medicine, National University of Singapore, 117597 Singapore
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06520, USA
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT 06520, USA
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5
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Yap CX, Vo DD, Heffel MG, Bhattacharya A, Wen C, Yang Y, Kemper KE, Zeng J, Zheng Z, Zhu Z, Hannon E, Vellame DS, Franklin A, Caggiano C, Wamsley B, Geschwind DH, Zaitlen N, Gusev A, Pasaniuc B, Mill J, Luo C, Gandal MJ. Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics. SCIENCE ADVANCES 2024; 10:eadn7655. [PMID: 38781333 PMCID: PMC11114225 DOI: 10.1126/sciadv.adn7655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/14/2024] [Indexed: 05/25/2024]
Abstract
Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit (P2RX5 and TRPV3) and excitatory neurons (DPY30 and MEMO1). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.
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Affiliation(s)
- Chloe X. Yap
- Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel D. Vo
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Lifespan Brain Institute at Penn Medicine and The Children’s Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew G. Heffel
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Institute for Data Science in Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cindy Wen
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Yuanhao Yang
- Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Kathryn E. Kemper
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Jian Zeng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Zhihong Zhu
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- The National Centre for Register-based Research, Aarhus University, Denmark
| | - Eilis Hannon
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Dorothea Seiler Vellame
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Alice Franklin
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Christa Caggiano
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | - Brie Wamsley
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel H. Geschwind
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA
| | - Noah Zaitlen
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham & Women’s Hospital, Boston, MA, USA
- Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Bogdan Pasaniuc
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jonathan Mill
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Michael J. Gandal
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Lifespan Brain Institute at Penn Medicine and The Children’s Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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6
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Wamsley B, Bicks L, Cheng Y, Kawaguchi R, Quintero D, Margolis M, Grundman J, Liu J, Xiao S, Hawken N, Mazariegos S, Geschwind DH. Molecular cascades and cell type-specific signatures in ASD revealed by single-cell genomics. Science 2024; 384:eadh2602. [PMID: 38781372 DOI: 10.1126/science.adh2602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 02/28/2024] [Indexed: 05/25/2024]
Abstract
Genomic profiling in postmortem brain from autistic individuals has consistently revealed convergent molecular changes. What drives these changes and how they relate to genetic susceptibility in this complex condition are not well understood. We performed deep single-nucleus RNA sequencing (snRNA-seq) to examine cell composition and transcriptomics, identifying dysregulation of cell type-specific gene regulatory networks (GRNs) in autism spectrum disorder (ASD), which we corroborated using single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) and spatial transcriptomics. Transcriptomic changes were primarily cell type specific, involving multiple cell types, most prominently interhemispheric and callosal-projecting neurons, interneurons within superficial laminae, and distinct glial reactive states involving oligodendrocytes, microglia, and astrocytes. Autism-associated GRN drivers and their targets were enriched in rare and common genetic risk variants, connecting autism genetic susceptibility and cellular and circuit alterations in the human brain.
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Affiliation(s)
- Brie Wamsley
- Program in Neurobehavioral Genetics and Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michael Margolis
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jennifer Grundman
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Natalie Hawken
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Daniel H Geschwind
- Program in Neurobehavioral Genetics and Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
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7
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Xie X, Zhou R, Fang Z, Zhang Y, Wang Q, Liu X. Seeing beyond words: Visualizing autism spectrum disorder biomarker insights. Heliyon 2024; 10:e30420. [PMID: 38694128 PMCID: PMC11061761 DOI: 10.1016/j.heliyon.2024.e30420] [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: 10/07/2023] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/04/2024] Open
Abstract
Objective This study employs bibliometric and visual analysis to elucidate global research trends in Autism Spectrum Disorder (ASD) biomarkers, identify critical research focal points, and discuss the potential integration of diverse biomarker modalities for precise ASD assessment. Methods A comprehensive bibliometric analysis was conducted using data from the Web of Science Core Collection database until December 31, 2022. Visualization tools, including R, VOSviewer, CiteSpace, and gCLUTO, were utilized to examine collaborative networks, co-citation patterns, and keyword associations among countries, institutions, authors, journals, documents, and keywords. Results ASD biomarker research emerged in 2004, accumulating a corpus of 4348 documents by December 31, 2022. The United States, with 1574 publications and an H-index of 213, emerged as the most prolific and influential country. The University of California, Davis, contributed significantly with 346 publications and an H-index of 69, making it the leading institution. Concerning journals, the Journal of Autism and Developmental Disorders, Autism Research, and PLOS ONE were the top three publishers of ASD biomarker-related articles among a total of 1140 academic journals. Co-citation and keyword analyses revealed research hotspots in genetics, imaging, oxidative stress, neuroinflammation, gut microbiota, and eye tracking. Emerging topics included "DNA methylation," "eye tracking," "metabolomics," and "resting-state fMRI." Conclusion The field of ASD biomarker research is dynamically evolving. Future endeavors should prioritize individual stratification, methodological standardization, the harmonious integration of biomarker modalities, and longitudinal studies to advance the precision of ASD diagnosis and treatment.
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Affiliation(s)
- Xinyue Xie
- The First Affiliated Hospital of Henan University of Chinese Medicine, Pediatrics Hospital, Zhengzhou, Henan, 450000, China
- Henan University of Chinese Medicine, School of Pediatrics, Zhengzhou, Henan, 450046, China
| | - Rongyi Zhou
- The First Affiliated Hospital of Henan University of Chinese Medicine, Pediatrics Hospital, Zhengzhou, Henan, 450000, China
- Henan University of Chinese Medicine, School of Pediatrics, Zhengzhou, Henan, 450046, China
| | - Zihan Fang
- Henan University of Chinese Medicine, School of Pediatrics, Zhengzhou, Henan, 450046, China
| | - Yongting Zhang
- The First Affiliated Hospital of Henan University of Chinese Medicine, Pediatrics Hospital, Zhengzhou, Henan, 450000, China
- Henan University of Chinese Medicine, School of Pediatrics, Zhengzhou, Henan, 450046, China
| | - Qirong Wang
- Henan University of Chinese Medicine, School of Pediatrics, Zhengzhou, Henan, 450046, China
| | - Xiaomian Liu
- Henan University of Chinese Medicine, School of Medicine, Zhengzhou, Henan, 450046, China
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8
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Mandelli V, Severino I, Eyler L, Pierce K, Courchesne E, Lombardo MV. A 3D approach to understanding heterogeneity in early developing autisms. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.08.24307039. [PMID: 38766085 PMCID: PMC11100949 DOI: 10.1101/2024.05.08.24307039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology. Using relatively large (n=615) publicly available data from early developing (24-68 months) standardized clinical tests tapping LIMA features, we show that stability-based relative cluster validation analysis can identify two robust and replicable clusters in the autism population with high levels of generalization accuracy (98%). These clusters can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression. This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance.
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9
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Meng J, Zhang L, Zhang YW. Microglial Dysfunction in Autism Spectrum Disorder. Neuroscientist 2024:10738584241252576. [PMID: 38712859 DOI: 10.1177/10738584241252576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder with onset in childhood. The molecular mechanisms underlying ASD have not yet been elucidated completely. Evidence has emerged to support a link between microglial dysfunction and the etiology of ASD. This review summarizes current research on microglial dysfunction in neuroinflammation and synaptic pruning, which are associated with altered transcriptomes and autophagy in ASD. Dysbiosis of gut microbiota in ASD and its correlation with microglial dysfunction are also addressed.
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Affiliation(s)
- Jian Meng
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Lingliang Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Yun-Wu Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
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10
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Fatemi SH, Eschenlauer A, Aman J, Folsom TD, Chekouo T. Quantitative proteomics of dorsolateral prefrontal cortex reveals an early pattern of synaptic dysmaturation in children with idiopathic autism. Cereb Cortex 2024; 34:161-171. [PMID: 38696595 DOI: 10.1093/cercor/bhae044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 01/23/2024] [Indexed: 05/04/2024] Open
Abstract
Autism spectrum disorder (ASD) is a developmental disorder with a rising prevalence and unknown etiology presenting with deficits in cognition and abnormal behavior. We hypothesized that the investigation of the synaptic component of prefrontal cortex may provide proteomic signatures that may identify the biological underpinnings of cognitive deficits in childhood ASD. Subcellular fractions of synaptosomes from prefrontal cortices of age-, brain area-, and postmortem-interval-matched samples from children and adults with idiopathic ASD vs. controls were subjected to HPLC-tandem mass spectrometry. Analysis of data revealed the enrichment of ASD risk genes that participate in slow maturation of the postsynaptic density (PSD) structure and function during early brain development. Proteomic analysis revealed down regulation of PSD-related proteins including AMPA and NMDA receptors, GRM3, DLG4, olfactomedins, Shank1-3, Homer1, CaMK2α, NRXN1, NLGN2, Drebrin1, ARHGAP32, and Dock9 in children with autism (FDR-adjusted P < 0.05). In contrast, PSD-related alterations were less severe or unchanged in adult individuals with ASD. Network analyses revealed glutamate receptor abnormalities. Overall, the proteomic data support the concept that idiopathic autism is a synaptopathy involving PSD-related ASD risk genes. Interruption in evolutionarily conserved slow maturation of the PSD complex in prefrontal cortex may lead to the development of ASD in a susceptible individual.
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Affiliation(s)
- S Hossein Fatemi
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, 420 Delaware Street SE, Minneapolis, MN 55455, USA
| | - Arthur Eschenlauer
- Minnesota Supercomputing Institute, 599 Walter Library, 117 Pleasant Street, Minneapolis, MN 55455, USA
| | - Justin Aman
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, 420 Delaware Street SE, Minneapolis, MN 55455, USA
| | - Timothy D Folsom
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, USA
- Masonic Cancer Center, University of Minnesota Medical School, Minneapolis, MN 55455, USA
- Center for Genome Engineering, University of Minnesota Medical School, Minneapolis, MN 55455, USA
- Stem Cell Institute, University of Minnesota Medical School, Minneapolis, MN 55455, USA
| | - Thierry Chekouo
- University of Minnesota School of Public Health, Minneapolis, MN 55455, USA
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11
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Dear R, Wagstyl K, Seidlitz J, Markello RD, Arnatkevičiūtė A, Anderson KM, Bethlehem RAI, Raznahan A, Bullmore ET, Vértes PE. Cortical gene expression architecture links healthy neurodevelopment to the imaging, transcriptomics and genetics of autism and schizophrenia. Nat Neurosci 2024:10.1038/s41593-024-01624-4. [PMID: 38649755 DOI: 10.1038/s41593-024-01624-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 03/18/2024] [Indexed: 04/25/2024]
Abstract
Human brain organization involves the coordinated expression of thousands of genes. For example, the first principal component (C1) of cortical transcription identifies a hierarchy from sensorimotor to association regions. In this study, optimized processing of the Allen Human Brain Atlas revealed two new components of cortical gene expression architecture, C2 and C3, which are distinctively enriched for neuronal, metabolic and immune processes, specific cell types and cytoarchitectonics, and genetic variants associated with intelligence. Using additional datasets (PsychENCODE, Allen Cell Atlas and BrainSpan), we found that C1-C3 represent generalizable transcriptional programs that are coordinated within cells and differentially phased during fetal and postnatal development. Autism spectrum disorder and schizophrenia were specifically associated with C1/C2 and C3, respectively, across neuroimaging, differential expression and genome-wide association studies. Evidence converged especially in support of C3 as a normative transcriptional program for adolescent brain development, which can lead to atypical supragranular cortical connectivity in people at high genetic risk for schizophrenia.
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Affiliation(s)
- Richard Dear
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | | | - Jakob Seidlitz
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Ross D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Aurina Arnatkevičiūtė
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | | | | | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, USA
| | | | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
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12
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Gu Y, Maria-Stauffer E, Bedford SA, Romero-Garcia R, Grove J, Børglum AD, Martin H, Baron-Cohen S, Bethlehem RA, Warrier V. Polygenic scores for autism are associated with neurite density in adults and children from the general population. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.10.24305539. [PMID: 38645251 PMCID: PMC11030520 DOI: 10.1101/2024.04.10.24305539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Genetic variants linked to autism are thought to change cognition and behaviour by altering the structure and function of the brain. Although a substantial body of literature has identified structural brain differences in autism, it is unknown whether autism-associated common genetic variants are linked to changes in cortical macro- and micro-structure. We investigated this using neuroimaging and genetic data from adults (UK Biobank, N = 31,748) and children (ABCD, N = 4,928). Using polygenic scores and genetic correlations we observe a robust negative association between common variants for autism and a magnetic resonance imaging derived phenotype for neurite density (intracellular volume fraction) in the general population. This result is consistent across both children and adults, in both the cortex and in white matter tracts, and confirmed using polygenic scores and genetic correlations. There were no sex differences in this association. Mendelian randomisation analyses provide no evidence for a causal relationship between autism and intracellular volume fraction, although this should be revisited using better powered instruments. Overall, this study provides evidence for shared common variant genetics between autism and cortical neurite density.
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Affiliation(s)
- Yuanjun Gu
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
| | | | - Saashi A. Bedford
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
| | | | | | - Rafael Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH
- Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla (IBiS), HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, 41013, Sevilla, Spain, 41013
| | - Jakob Grove
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, 8210, Denmark
- Center for Genomics and Personalized Medicine (CGPM), Aarhus University, Aarhus, 8000, Denmark
- Department of Biomedicine (Human Genetics) and iSEQ Center, Aarhus University, Aarhus, 8000, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark, 8000
| | - Anders D. Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, 8210, Denmark
- Center for Genomics and Personalized Medicine (CGPM), Aarhus University, Aarhus, 8000, Denmark
- Department of Biomedicine (Human Genetics) and iSEQ Center, Aarhus University, Aarhus, 8000, Denmark
| | - Hilary Martin
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
| | | | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
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13
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Alvino FG, Gini S, Minetti A, Pagani M, Sastre-Yagüe D, Barsotti N, De Guzman E, Schleifer C, Stuefer A, Kushan L, Montani C, Galbusera A, Papaleo F, Lombardo MV, Pasqualetti M, Bearden CE, Gozzi A. Synaptic-dependent developmental dysconnectivity in 22q11.2 deletion syndrome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.29.587339. [PMID: 38585897 PMCID: PMC10996624 DOI: 10.1101/2024.03.29.587339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Chromosome 22q11.2 deletion is among the strongest known genetic risk factors for neuropsychiatric disorders, including autism and schizophrenia. Brain imaging studies have reported disrupted large-scale functional connectivity in people with 22q11 deletion syndrome (22q11DS). However, the significance and biological determinants of these functional alterations remain unclear. Here, we use a cross-species design to investigate the developmental trajectory and neural underpinnings of brain dysconnectivity in 22q11DS. We find that LgDel mice, an established mouse model of 22q11DS, exhibit age-specific patterns of functional MRI (fMRI) dysconnectivity, with widespread fMRI hyper-connectivity in juvenile mice reverting to focal hippocampal hypoconnectivity over puberty. These fMRI connectivity alterations are mirrored by co-occurring developmental alterations in dendritic spine density, and are both transiently normalized by developmental GSK3β inhibition, suggesting a synaptic origin for this phenomenon. Notably, analogous hyper- to hypoconnectivity reconfiguration occurs also in human 22q11DS, where it affects hippocampal and cortical regions spatially enriched for synaptic genes that interact with GSK3β, and autism-relevant transcripts. Functional dysconnectivity in somatomotor components of this network is predictive of age-dependent social alterations in 22q11.2 deletion carriers. Taken together, these findings suggest that synaptic-related mechanisms underlie developmentally mediated functional dysconnectivity in 22q11DS.
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Affiliation(s)
- F G Alvino
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| | - S Gini
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - A Minetti
- Department of Biology, Unit of Cell and Developmental Biology, University of Pisa, Pisa, Italy
| | - M Pagani
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- IMT School for Advanced Studies, Lucca, Italy
| | - D Sastre-Yagüe
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - N Barsotti
- Centro per l'Integrazione della Strumentazione Scientifica dell'Universita di Pisa (CISUP), Pisa, Italy
| | - E De Guzman
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| | - C Schleifer
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, California
| | - A Stuefer
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - L Kushan
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, California
| | - C Montani
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| | - A Galbusera
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
| | - F Papaleo
- Genetics of Cognition Laboratory, Neuroscience area, Istituto Italiano di Tecnologia, Genova, Italy
| | - M V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - M Pasqualetti
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
- Centro per l'Integrazione della Strumentazione Scientifica dell'Universita di Pisa (CISUP), Pisa, Italy
| | - C E Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, California
| | - A Gozzi
- Functional Neuroimaging Laboratory, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems @UniTn, Rovereto, Italy
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14
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Wang M, Wang Z, Zhao D, Yu Y, Wei F. Periodontitis causally affects the brain cortical structure: A Mendelian randomization study. J Periodontal Res 2024; 59:381-386. [PMID: 38059384 DOI: 10.1111/jre.13222] [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: 09/12/2023] [Revised: 11/15/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023]
Abstract
OBJECTIVE To estimate whether genetically proxied periodontitis causally impacts the brain cortical structure using Mendelian randomization (MR). BACKGROUND Periodontitis is one of the most prevalent inflammatory conditions globally, and emerging evidence has indicated its influences on distal organs, including the brain, whose disorders are always accompanied by magnetic resonance imaging (MRI)-identified brain cortical changes. However, to date, no available evidence has revealed the association between periodontitis and brain cortical structures. METHODS The instrumental variables (IVs) were adopted from previous genome-wide association study (GWAS) studies and meta-analyses of GWAS studies of periodontitis from 1844 to 5266 cases and 8255 to 12 515 controls. IVs were linked to GWAS summary data of 51 665 patients from the ENIGMA Consortium, assessing the impacts of genetically proxied periodontitis on the surficial area (SA) or the cortical thickness (TH) of the global and 34 MRI-identified functional regions of the brain. Inverse-variance weighted was used as the primary estimate; the MR pleiotropy residual sum and outlier (MR-PRESSO), the MR-Egger intercept test, and leave-one-out analyses were used to examine the potential horizontal pleiotropy. RESULTS Genetically proxied periodontitis affects the SA of the medial orbitofrontal cortex, the lateral orbitofrontal cortex, the inferior temporal cortex, the entorhinal cortex, and the temporal pole, as well as the TH of the entorhinal. No pleiotropy was detected. CONCLUSIONS Periodontitis causally influences the brain cortical structures, implying the existence of a periodontal tissue-brain axis.
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Affiliation(s)
- Mengqiao Wang
- Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Jinan, China
| | - Ziyao Wang
- Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Jinan, China
| | - Delu Zhao
- Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Jinan, China
| | - Yajie Yu
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital/Institute of Mental Health, The Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
| | - Fulan Wei
- Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Jinan, China
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15
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Shen Y, Shao M, Hao ZZ, Huang M, Xu N, Liu S. Multimodal Nature of the Single-cell Primate Brain Atlas: Morphology, Transcriptome, Electrophysiology, and Connectivity. Neurosci Bull 2024; 40:517-532. [PMID: 38194157 PMCID: PMC11003949 DOI: 10.1007/s12264-023-01160-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 09/23/2023] [Indexed: 01/10/2024] Open
Abstract
Primates exhibit complex brain structures that augment cognitive function. The neocortex fulfills high-cognitive functions through billions of connected neurons. These neurons have distinct transcriptomic, morphological, and electrophysiological properties, and their connectivity principles vary. These features endow the primate brain atlas with a multimodal nature. The recent integration of next-generation sequencing with modified patch-clamp techniques is revolutionizing the way to census the primate neocortex, enabling a multimodal neuronal atlas to be established in great detail: (1) single-cell/single-nucleus RNA-seq technology establishes high-throughput transcriptomic references, covering all major transcriptomic cell types; (2) patch-seq links the morphological and electrophysiological features to the transcriptomic reference; (3) multicell patch-clamp delineates the principles of local connectivity. Here, we review the applications of these technologies in the primate neocortex and discuss the current advances and tentative gaps for a comprehensive understanding of the primate neocortex.
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Affiliation(s)
- Yuhui Shen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Mingting Shao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Zhao-Zhe Hao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Mengyao Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Nana Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Sheng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China.
- Guangdong Province Key Laboratory of Brain Function and Disease, Guangzhou, 510080, China.
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16
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Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585576. [PMID: 38562822 PMCID: PMC10983939 DOI: 10.1101/2024.03.18.585576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
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Affiliation(s)
- Prashant S Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jason J Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Matthew Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Chirag Gupta
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Cagatay Dursun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yuhang Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Zhiyuan Chu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA, 92697, USA
| | - Yunyang Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Xiao Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Tanima Chatterjee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Department of Opthalmology, Perlman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | | | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Michael Gancz
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona, 08028, Spain
| | - Sophia Gaynor-Gillett
- Tempus Labs, Inc., Chicago, IL, 60654, USA
- Department of Biology, Cornell College, Mount Vernon, IA, 52314, USA
| | - Jennifer Grundman
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Natalie Hawken
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Ella Henry
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Ao Huang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Ting Jin
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | | | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Junhao Liu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Shaojie Ma
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Michael Margolis
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | | | - Eric Nguyen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Henry Pratt
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | | | - Tiernon R Riesenmy
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Manman Shi
- Tempus Labs, Inc., Chicago, IL, 60654, USA
| | | | - Rosemarie Terwilliger
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
| | | | - Brie Wamsley
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Gaoyuan Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yan Xia
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Andrew C Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Suchen Zheng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA, 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98195, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Kevin P White
- Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06520, USA
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT, 06520, USA
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17
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Lu X, Ni P, Suarez-Meade P, Ma Y, Forrest EN, Wang G, Wang Y, Quiñones-Hinojosa A, Gerstein M, Jiang YH. Transcriptional Determinism and Stochasticity Contribute to the Complexity of Autism Associated SHANK Family Genes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585480. [PMID: 38562714 PMCID: PMC10983920 DOI: 10.1101/2024.03.18.585480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Precision of transcription is critical because transcriptional dysregulation is disease causing. Traditional methods of transcriptional profiling are inadequate to elucidate the full spectrum of the transcriptome, particularly for longer and less abundant mRNAs. SHANK3 is one of the most common autism causative genes. Twenty-four Shank3 mutant animal lines have been developed for autism modeling. However, their preclinical validity has been questioned due to incomplete Shank3 transcript structure. We applied an integrative approach combining cDNA-capture and long-read sequencing to profile the SHANK3 transcriptome in human and mice. We unexpectedly discovered an extremely complex SHANK3 transcriptome. Specific SHANK3 transcripts were altered in Shank3 mutant mice and postmortem brains tissues from individuals with ASD. The enhanced SHANK3 transcriptome significantly improved the detection rate for potential deleterious variants from genomics studies of neuropsychiatric disorders. Our findings suggest the stochastic transcription of genome associated with SHANK family genes.
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Affiliation(s)
- Xiaona Lu
- Department of Genetics, Yale University School of Medicine New Haven, CT, 06520 USA
| | - Pengyu Ni
- Biomedical Informatics & Data Science, Yale University School of Medicine New Haven, CT, 06520 USA
| | | | - Yu Ma
- Department of Neurology, Children’s Hospital of Fudan University, Shanghai, 201102 China
| | | | - Guilin Wang
- Yale Center for Genome Analysis, Yale University School of Medicine New Haven, CT, 06520 USA
| | - Yi Wang
- Department of Neurology, Children’s Hospital of Fudan University, Shanghai, 201102 China
| | | | - Mark Gerstein
- Biomedical Informatics & Data Science, Yale University School of Medicine New Haven, CT, 06520 USA
- Yale Center for Genome Analysis, Yale University School of Medicine New Haven, CT, 06520 USA
| | - Yong-hui Jiang
- Department of Genetics, Yale University School of Medicine New Haven, CT, 06520 USA
- Neuroscienc, Yale University School of Medicine New Haven, CT, 06520 USA
- Pediatrics, Yale University School of Medicine New Haven, CT, 06520 USA
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18
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Lin KZ, Qiu Y, Roeder K. eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings. BMC Bioinformatics 2024; 25:113. [PMID: 38486150 PMCID: PMC10941434 DOI: 10.1186/s12859-024-05724-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/28/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes. RESULTS We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression. CONCLUSIONS eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.
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Affiliation(s)
- Kevin Z Lin
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
| | - Yixuan Qiu
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, People's Republic of China
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
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19
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Yen A, Chen X, Skinner DD, Leti F, Crosby M, Hoisington-Lopez J, Wu Y, Chen J, Mitra RD, Dougherty JD. MYT1L deficiency impairs excitatory neuron trajectory during cortical development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583632. [PMID: 38496654 PMCID: PMC10942489 DOI: 10.1101/2024.03.06.583632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Mutations that reduce the function of MYT1L, a neuron-specific transcription factor, are associated with a syndromic neurodevelopmental disorder. Furthermore, MYT1L is routinely used as a proneural factor in fibroblast-to-neuron transdifferentiation. MYT1L has been hypothesized to play a role in the trajectory of neuronal specification and subtype specific maturation, but this hypothesis has not been directly tested, nor is it clear which neuron types are most impacted by MYT1L loss. In this study, we profiled 313,335 nuclei from the forebrains of wild-type and MYT1L-deficient mice at two developmental stages: E14 at the peak of neurogenesis and P21, when neurogenesis is complete, to examine the role of MYT1L levels in the trajectory of neuronal development. We found that MYT1L deficiency significantly disrupted the relative proportion of cortical excitatory neurons at E14 and P21. Significant changes in gene expression were largely concentrated in excitatory neurons, suggesting that transcriptional effects of MYT1L deficiency are largely due to disruption of neuronal maturation programs. Most effects on gene expression were cell autonomous and persistent through development. In addition, while MYT1L can both activate and repress gene expression, the repressive effects were most sensitive to haploinsufficiency, and thus more likely mediate MYT1L syndrome. These findings illuminate the intricate role of MYT1L in orchestrating gene expression dynamics during neuronal development, providing insights into the molecular underpinnings of MYT1L syndrome.
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Affiliation(s)
- Allen Yen
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Xuhua Chen
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, Saint Louis, MO, USA
| | | | | | - MariaLynn Crosby
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, Saint Louis, MO, USA
- DNA Sequencing and Innovation Lab, Washington University School of Medicine, Saint Louis, MO
| | - Jessica Hoisington-Lopez
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, Saint Louis, MO, USA
- DNA Sequencing and Innovation Lab, Washington University School of Medicine, Saint Louis, MO
| | - Yizhe Wu
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jiayang Chen
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robi D. Mitra
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
- Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Joseph D. Dougherty
- Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
- Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine, Saint Louis, MO, USA
- Lead contact
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20
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Lin KZ, Qiu Y, Roeder K. eSVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.22.568369. [PMID: 38045428 PMCID: PMC10690270 DOI: 10.1101/2023.11.22.568369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Background Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes. Results We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression. Conclusions eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.
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Affiliation(s)
- Kevin Z Lin
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Yixuan Qiu
- School of Statistics & Management, Shanghai University of Finance and Economics, Shanghai,People's Republic of China
| | - Kathryn Roeder
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
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21
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Khodosevich K, Dragicevic K, Howes O. Drug targeting in psychiatric disorders - how to overcome the loss in translation? Nat Rev Drug Discov 2024; 23:218-231. [PMID: 38114612 DOI: 10.1038/s41573-023-00847-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2023] [Indexed: 12/21/2023]
Abstract
In spite of major efforts and investment in development of psychiatric drugs, many clinical trials have failed in recent decades, and clinicians still prescribe drugs that were discovered many years ago. Although multiple reasons have been discussed for the drug development deadlock, we focus here on one of the major possible biological reasons: differences between the characteristics of drug targets in preclinical models and the corresponding targets in patients. Importantly, based on technological advances in single-cell analysis, we propose here a framework for the use of available and newly emerging knowledge from single-cell and spatial omics studies to evaluate and potentially improve the translational predictivity of preclinical models before commencing preclinical and, in particular, clinical studies. We believe that these recommendations will improve preclinical models and the ability to assess drugs in clinical trials, reducing failure rates in expensive late-stage trials and ultimately benefitting psychiatric drug discovery and development.
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Affiliation(s)
- Konstantin Khodosevich
- Biotech Research and Innovation Centre, Faculty of Health, University of Copenhagen, Copenhagen, Denmark.
| | - Katarina Dragicevic
- Biotech Research and Innovation Centre, Faculty of Health, University of Copenhagen, Copenhagen, Denmark
| | - Oliver Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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22
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Zhuang H, Liang Z, Ma G, Qureshi A, Ran X, Feng C, Liu X, Yan X, Shen L. Autism spectrum disorder: pathogenesis, biomarker, and intervention therapy. MedComm (Beijing) 2024; 5:e497. [PMID: 38434761 PMCID: PMC10908366 DOI: 10.1002/mco2.497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 01/27/2024] [Accepted: 01/30/2024] [Indexed: 03/05/2024] Open
Abstract
Autism spectrum disorder (ASD) has become a common neurodevelopmental disorder. The heterogeneity of ASD poses great challenges for its research and clinical translation. On the basis of reviewing the heterogeneity of ASD, this review systematically summarized the current status and progress of pathogenesis, diagnostic markers, and interventions for ASD. We provided an overview of the ASD molecular mechanisms identified by multi-omics studies and convergent mechanism in different genetic backgrounds. The comorbidities, mechanisms associated with important physiological and metabolic abnormalities (i.e., inflammation, immunity, oxidative stress, and mitochondrial dysfunction), and gut microbial disorder in ASD were reviewed. The non-targeted omics and targeting studies of diagnostic markers for ASD were also reviewed. Moreover, we summarized the progress and methods of behavioral and educational interventions, intervention methods related to technological devices, and research on medical interventions and potential drug targets. This review highlighted the application of high-throughput omics methods in ASD research and emphasized the importance of seeking homogeneity from heterogeneity and exploring the convergence of disease mechanisms, biomarkers, and intervention approaches, and proposes that taking into account individuality and commonality may be the key to achieve accurate diagnosis and treatment of ASD.
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Affiliation(s)
- Hongbin Zhuang
- College of Life Science and OceanographyShenzhen UniversityShenzhenP. R. China
| | - Zhiyuan Liang
- College of Life Science and OceanographyShenzhen UniversityShenzhenP. R. China
| | - Guanwei Ma
- College of Life Science and OceanographyShenzhen UniversityShenzhenP. R. China
| | - Ayesha Qureshi
- College of Life Science and OceanographyShenzhen UniversityShenzhenP. R. China
| | - Xiaoqian Ran
- College of Life Science and OceanographyShenzhen UniversityShenzhenP. R. China
| | - Chengyun Feng
- Maternal and Child Health Hospital of BaoanShenzhenP. R. China
| | - Xukun Liu
- College of Life Science and OceanographyShenzhen UniversityShenzhenP. R. China
| | - Xi Yan
- College of Life Science and OceanographyShenzhen UniversityShenzhenP. R. China
| | - Liming Shen
- College of Life Science and OceanographyShenzhen UniversityShenzhenP. R. China
- Shenzhen‐Hong Kong Institute of Brain Science‐Shenzhen Fundamental Research InstitutionsShenzhenP. R. China
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23
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Al-Saei ANJM, Nour-Eldine W, Rajpoot K, Arshad N, Al-Shammari AR, Kamal M, Akil AAS, Fakhro KA, Thornalley PJ, Rabbani N. Validation of plasma protein glycation and oxidation biomarkers for the diagnosis of autism. Mol Psychiatry 2024; 29:653-659. [PMID: 38135754 DOI: 10.1038/s41380-023-02357-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/22/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023]
Abstract
Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder in children. It is currently diagnosed by behaviour-based assessments made by observation and interview. In 2018 we reported a discovery study of a blood biomarker diagnostic test for ASD based on a combination of four plasma protein glycation and oxidation adducts. The test had 88% accuracy in children 5-12 years old. Herein, we present an international multicenter clinical validation study (N = 478) with application of similar biomarkers to a wider age range of 1.5-12 years old children. Three hundred and eleven children with ASD (247 male, 64 female; age 5.2 ± 3.0 years) and 167 children with typical development (94 male, 73 female; 4.9 ± 2.4 years) were recruited for this study at Sidra Medicine and Hamad Medical Corporation hospitals, Qatar, and Hospital Regional Universitario de Málaga, Spain. For subjects 5-12 years old, the diagnostic algorithm with features, advanced glycation endproducts (AGEs)-Nε-carboxymethyl-lysine (CML), Nω-carboxymethylarginine (CMA) and 3-deoxyglucosone-derived hydroimidazolone (3DG-H), and oxidative damage marker, o,o'-dityrosine (DT), age and gender had accuracy 83% (CI 79 - 89%), sensitivity 94% (CI 90-98%), specificity 67% (CI 57-76%) and area-under-the-curve of receiver operating characteristic plot (AUROC) 0.87 (CI 0.84-0.90). Inclusion of additional plasma protein glycation and oxidation adducts increased the specificity to 74%. An algorithm with 12 plasma protein glycation and oxidation adduct features was optimum for children of 1.5-12 years old: accuracy 74% (CI 70-79%), sensitivity 75% (CI 63-87%), specificity 74% (CI 58-90%) and AUROC 0.79 (CI 0.74-0.84). We conclude that ASD diagnosis may be supported using an algorithm with features of plasma protein CML, CMA, 3DG-H and DT in 5-12 years-old children, and an algorithm with additional features applicable for ASD screening in younger children. ASD severity, as assessed by ADOS-2 score, correlated positively with plasma protein glycation adducts derived from methylglyoxal, hydroimidazolone MG-H1 and Nε(1-carboxyethyl)lysine (CEL). The successful validation herein may indicate that the algorithm modifiable features are mechanistic risk markers linking ASD to increased lipid peroxidation, neuronal plasticity and proteotoxic stress.
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Affiliation(s)
| | - Wared Nour-Eldine
- Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, PO Box 34110, Doha, Qatar
| | - Kashif Rajpoot
- University of Birmingham Dubai, Dubai International Academic City, PO Box 341799, Dubai, UAE
| | - Noman Arshad
- BIOMISA Laboratory, Department of Computer & Software Engineering, National University of Science & Technology (NUST), Islamabad, Pakistan
| | - Abeer R Al-Shammari
- Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, PO Box 34110, Doha, Qatar
| | - Madeeha Kamal
- College of Medicine, QU Health, Qatar University, PO Box 2713, Doha, Qatar
- Department of Pediatrics, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- Department of Genetic Medicine, Weill Cornell Medical College, Doha, P.O. Box 24144, Doha, Qatar
| | - Ammira Al-Shabeeb Akil
- Precision Medicine in Diabetes Prevention Laboratory, Population Genetics, Sidra Medicine, P.O. Box 26999, Doha, Qatar
| | - Khalid A Fakhro
- Department of Genetic Medicine, Weill Cornell Medical College, Doha, P.O. Box 24144, Doha, Qatar
- Precision Medicine in Diabetes Prevention Laboratory, Population Genetics, Sidra Medicine, P.O. Box 26999, Doha, Qatar
- Laboratory of Genomic Medicine-Precision Medicine Program, Sidra Medicine, P.O. Box 26999, Doha, Qatar
| | - Paul J Thornalley
- Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, PO Box 34110, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, P.O. Box 34110, Doha, Qatar
| | - Naila Rabbani
- College of Medicine, QU Health, Qatar University, PO Box 2713, Doha, Qatar.
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24
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Nakamura T, Ueda J, Mizuno S, Honda K, Kazuno AA, Yamamoto H, Hara T, Takata A. Topologically associating domains define the impact of de novo promoter variants on autism spectrum disorder risk. CELL GENOMICS 2024; 4:100488. [PMID: 38280381 PMCID: PMC10879036 DOI: 10.1016/j.xgen.2024.100488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/24/2023] [Accepted: 01/02/2024] [Indexed: 01/29/2024]
Abstract
Whole-genome sequencing (WGS) studies of autism spectrum disorder (ASD) have demonstrated the roles of rare promoter de novo variants (DNVs). However, most promoter DNVs in ASD are not located immediately upstream of known ASD genes. In this study analyzing WGS data of 5,044 ASD probands, 4,095 unaffected siblings, and their parents, we show that promoter DNVs within topologically associating domains (TADs) containing ASD genes are significantly and specifically associated with ASD. An analysis considering TADs as functional units identified specific TADs enriched for promoter DNVs in ASD and indicated that common variants in these regions also confer ASD heritability. Experimental validation using human induced pluripotent stem cells (iPSCs) showed that likely deleterious promoter DNVs in ASD can influence multiple genes within the same TAD, resulting in overall dysregulation of ASD-associated genes. These results highlight the importance of TADs and gene-regulatory mechanisms in better understanding the genetic architecture of ASD.
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Affiliation(s)
- Takumi Nakamura
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Junko Ueda
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.
| | - Shota Mizuno
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Kurara Honda
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - An-A Kazuno
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Hirona Yamamoto
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Tomonori Hara
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Department of Organ Anatomy, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8575, Japan
| | - Atsushi Takata
- Laboratory for Molecular Pathology of Psychiatric Disorders, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Research Institute for Diseases of Old Age, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan.
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Faust TE, Lee YH, O’Connor CD, Boyle MA, Gunner G, Badimon A, Ayata P, Schaefer A, Schafer DP. Microglia-astrocyte crosstalk regulates synapse remodeling via Wnt signaling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.08.579178. [PMID: 38370645 PMCID: PMC10871360 DOI: 10.1101/2024.02.08.579178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Astrocytes and microglia are emerging key regulators of activity-dependent synapse remodeling that engulf and remove synapses in response to changes in neural activity. Yet, the degree to which these cells communicate to coordinate this process remains an open question. Here, we use whisker removal in postnatal mice to induce activity-dependent synapse removal in the barrel cortex. We show that astrocytes do not engulf synapses in this paradigm. Instead, astrocytes reduce their contact with synapses prior to microglia-mediated synapse engulfment. We further show that reduced astrocyte-contact with synapses is dependent on microglial CX3CL1-CX3CR1 signaling and release of Wnts from microglia following whisker removal. These results demonstrate an activity-dependent mechanism by which microglia instruct astrocyte-synapse interactions, which then provides a permissive environment for microglia to remove synapses. We further show that this mechanism is critical to remodel synapses in a changing sensory environment and this signaling is upregulated in several disease contexts.
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Affiliation(s)
- Travis E. Faust
- Department of Neurobiology, Brudnick Neuropsychiatric Research Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Yi-Han Lee
- Department of Neurobiology, Brudnick Neuropsychiatric Research Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
- These authors contributed equally
| | - Ciara D. O’Connor
- Department of Neurobiology, Brudnick Neuropsychiatric Research Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
- Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada K7L 3N6
- These authors contributed equally
| | - Margaret A. Boyle
- Department of Neurobiology, Brudnick Neuropsychiatric Research Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
- These authors contributed equally
| | - Georgia Gunner
- Department of Neurobiology, Brudnick Neuropsychiatric Research Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | - Ana Badimon
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Patricia and John Rosenwald Laboratory of Neurobiology and Genetics, The Rockefeller University, New York, NY 10065, USA
| | - Pinar Ayata
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Neuroscience Initiative, Advanced Science Research Center, The City University of New York Graduate Center, New York, NY 10031, USA
| | - Anne Schaefer
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Max Planck Institute for Biology of Ageing, 50931 Cologne, Germany
| | - Dorothy P. Schafer
- Department of Neurobiology, Brudnick Neuropsychiatric Research Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
- Lead contact
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Wang M, Wang Z, Yu Y, Zhao D, Shen Z, Wei F. From teeth to brain: dental caries causally affects the cortical thickness of the banks of the superior temporal sulcus. BMC Oral Health 2024; 24:124. [PMID: 38263072 PMCID: PMC10807149 DOI: 10.1186/s12903-024-03899-2] [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: 11/16/2023] [Accepted: 01/15/2024] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVES Dental caries is one of the most prevalent oral diseases and causes of tooth loss. Cross-sectional studies observed epidemiological associations between dental caries and brain degeneration disorders, while it is unknown whether dental caries causally affect the cerebral structures. This study tested whether genetically proxied DMFS (the sum of Decayed, Missing, and Filled tooth Surfaces) causally impacts the brain cortical structure using Mendelian randomization (MR). METHODS The summary-level GWAS meta-analysis data from the GLIDE consortium were used for DMFS, including 26,792 participants. ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) consortium GWAS summary data of 51,665 patients were used for brain structure. This study estimated the causal effects of DMFS on the surface area (SA) and thickness (TH) of the global cortex and functional cortical regions accessed by magnetic resonance imaging (MRI). Inverse-variance weighted (IVW) was used as the primary estimate, the MR pleiotropy residual sum and outlier (MR-PRESSO), the MR-Egger intercept test, and leave-one-out analyses were used to examine the potential horizontal pleiotropy. RESULTS Genetically proxied DMFS decreases the TH of the banks of the superior temporal sulcus (BANSSTS) with or without global weighted (weighted, β = - 0.0277 mm, 95% CI: - 0.0470 mm to - 0.0085 mm, P = 0.0047; unweighted, β = - 0.0311 mm, 95% CI: - 0.0609 mm to - 0.0012 mm, P = 0.0412). The causal associations were robust in various sensitivity analyses. CONCLUSIONS Dental caries causally decrease the cerebral cortical thickness of the BANKSSTS, a cerebral cortical region crucial for language-related functions, and is the most affected brain region in Alzheimer's disease. This investigation provides the first evidence that dental caries causally affects brain structure, proving the existence of teeth-brain axes. This study also suggested that clinicians should highlight the causal effects of dental caries on brain disorders during the diagnosis and treatments, the cortical thickness of BANKSSTS is a promising diagnostic measurement for dental caries-related brain degeneration.
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Affiliation(s)
- Mengqiao Wang
- Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Research Center of Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, China
| | - Ziyao Wang
- Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Research Center of Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, China
| | - Yajie Yu
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital/Institute of Mental Health, The Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, 100191, China
| | - Delu Zhao
- Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Research Center of Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, China
| | - Zhiyuan Shen
- Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Research Center of Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, China
| | - Fulan Wei
- Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Research Center of Dental Materials and Oral Tissue Regeneration & Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, China.
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27
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Luo Y, Wang Z. The Impact of Microglia on Neurodevelopment and Brain Function in Autism. Biomedicines 2024; 12:210. [PMID: 38255315 PMCID: PMC10813633 DOI: 10.3390/biomedicines12010210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/29/2023] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
Microglia, as one of the main types of glial cells in the central nervous system (CNS), are widely distributed throughout the brain and spinal cord. The normal number and function of microglia are very important for maintaining homeostasis in the CNS. In recent years, scientists have paid widespread attention to the role of microglia in the CNS. Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental disorder, and patients with ASD have severe deficits in behavior, social skills, and communication. Most previous studies on ASD have focused on neuronal pathological changes, such as increased cell proliferation, accelerated neuronal differentiation, impaired synaptic development, and reduced neuronal spontaneous and synchronous activity. Currently, more and more research has found that microglia, as immune cells, can promote neurogenesis and synaptic pruning to maintain CNS homeostasis. They can usually reduce unnecessary synaptic connections early in life. Some researchers have proposed that many pathological phenotypes of ASD may be caused by microglial abnormalities. Based on this, we summarize recent research on microglia in ASD, focusing on the function of microglia and neurodevelopmental abnormalities. We aim to clarify the essential factors influenced by microglia in ASD and explore the possibility of microglia-related pathways as potential research targets for ASD.
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Affiliation(s)
- Yuyi Luo
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China;
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
| | - Zhengbo Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China;
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
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Flotho M, Amand J, Hirsch P, Grandke F, Wyss-Coray T, Keller A, Kern F. ZEBRA: a hierarchically integrated gene expression atlas of the murine and human brain at single-cell resolution. Nucleic Acids Res 2024; 52:D1089-D1096. [PMID: 37941147 PMCID: PMC10767845 DOI: 10.1093/nar/gkad990] [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: 08/14/2023] [Revised: 10/02/2023] [Accepted: 10/16/2023] [Indexed: 11/10/2023] Open
Abstract
The molecular causes and mechanisms of neurodegenerative diseases remain poorly understood. A growing number of single-cell studies have implicated various neural, glial, and immune cell subtypes to affect the mammalian central nervous system in many age-related disorders. Integrating this body of transcriptomic evidence into a comprehensive and reproducible framework poses several computational challenges. Here, we introduce ZEBRA, a large single-cell and single-nucleus RNA-seq database. ZEBRA integrates and normalizes gene expression and metadata from 33 studies, encompassing 4.2 million human and mouse brain cells sampled from 39 brain regions. It incorporates samples from patients with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease, and Multiple sclerosis, as well as samples from relevant mouse models. We employed scVI, a deep probabilistic auto-encoder model, to integrate the samples and curated both cell and sample metadata for downstream analysis. ZEBRA allows for cell-type and disease-specific markers to be explored and compared between sample conditions and brain regions, a cell composition analysis, and gene-wise feature mappings. Our comprehensive molecular database facilitates the generation of data-driven hypotheses, enhancing our understanding of mammalian brain function during aging and disease. The data sets, along with an interactive database are freely available at https://www.ccb.uni-saarland.de/zebra.
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Affiliation(s)
- Matthias Flotho
- Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, Saarland University Campus, 66123 Saarbrücken, Germany
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Jérémy Amand
- Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, Saarland University Campus, 66123 Saarbrücken, Germany
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Pascal Hirsch
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Friederike Grandke
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Tony Wyss-Coray
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
- The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
| | - Andreas Keller
- Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, Saarland University Campus, 66123 Saarbrücken, Germany
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Fabian Kern
- Helmholtz-Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, Saarland University Campus, 66123 Saarbrücken, Germany
- Clinical Bioinformatics, Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
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29
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Sun N, Teyssier N, Wang B, Drake S, Seyler M, Zaltsman Y, Everitt A, Teerikorpi N, Willsey HR, Goodarzi H, Tian R, Kampmann M, Willsey AJ. Autism genes converge on microtubule biology and RNA-binding proteins during excitatory neurogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.22.573108. [PMID: 38187634 PMCID: PMC10769323 DOI: 10.1101/2023.12.22.573108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Recent studies have identified over one hundred high-confidence (hc) autism spectrum disorder (ASD) genes. Systems biological and functional analyses on smaller subsets of these genes have consistently implicated excitatory neurogenesis. However, the extent to which the broader set of hcASD genes are involved in this process has not been explored systematically nor have the biological pathways underlying this convergence been identified. Here, we leveraged CROP-Seq to repress 87 hcASD genes in a human in vitro model of cortical neurogenesis. We identified 17 hcASD genes whose repression significantly alters developmental trajectory and results in a common cellular state characterized by disruptions in proliferation, differentiation, cell cycle, microtubule biology, and RNA-binding proteins (RBPs). We also characterized over 3,000 differentially expressed genes, 286 of which had expression profiles correlated with changes in developmental trajectory. Overall, we uncovered transcriptional disruptions downstream of hcASD gene perturbations, correlated these disruptions with distinct differentiation phenotypes, and reinforced neurogenesis, microtubule biology, and RBPs as convergent points of disruption in ASD.
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30
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Buch AM, Liston C. Gene-brain-behavior mechanisms underlying autism spectrum disorder: implications for precision psychiatry. Neuropsychopharmacology 2024; 49:343-344. [PMID: 37717083 PMCID: PMC10700301 DOI: 10.1038/s41386-023-01722-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/18/2023]
Affiliation(s)
- Amanda M Buch
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY, 10021, USA.
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, 413 East 69th Street, Box 240, New York, NY, 10021, USA.
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31
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Fan G, Ma J, Ma R, Suo M, Chen Y, Zhang S, Zeng Y, Chen Y. Microglia Modulate Neurodevelopment in Autism Spectrum Disorder and Schizophrenia. Int J Mol Sci 2023; 24:17297. [PMID: 38139124 PMCID: PMC10743577 DOI: 10.3390/ijms242417297] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023] Open
Abstract
Neurodevelopmental disorders (NDDs) include various neurological disorders with high genetic heterogeneity, characterized by delayed or impaired cognition, communication, adaptive behavior, and psychomotor skills. These disorders result in significant morbidity for children, thus burdening families and healthcare/educational systems. However, there is a lack of early diagnosis and effective therapies. Therefore, a more connected approach is required to explore these disorders. Microglia, the primary phagocytic cells within the central nervous system, are crucial in regulating neuronal viability, influencing synaptic dynamics, and determining neurodevelopmental outcomes. Although the neurobiological basis of autism spectrum disorder (ASD) and schizophrenia (SZ) has attracted attention in recent decades, the role of microglia in ASD and SZ remains unclear and requires further discussion. In this review, the important and frequently multifaceted roles that microglia play during neurodevelopment are meticulously emphasized and potential microglial mechanisms that might be involved in conditions such as ASD and SZ are postulated. It is of utmost importance to acquire a comprehensive understanding of the complexities of the interplay between microglia and neurons to design effective, targeted therapeutic strategies to mitigate the effects of NDDs.
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Affiliation(s)
| | | | | | | | | | | | - Yan Zeng
- Brain Science and Advanced Technology Institute, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Yushan Chen
- Brain Science and Advanced Technology Institute, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan 430065, China
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32
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Bedford SA, Lai MC, Lombardo MV, Chakrabarti B, Ruigrok A, Suckling J, Anagnostou E, Lerch JP, Taylor M, Nicolson R, Stelios G, Crosbie J, Schachar R, Kelley E, Jones J, Arnold PD, Courchesne E, Pierce K, Eyler LT, Campbell K, Barnes CC, Seidlitz J, Alexander-Bloch AF, Bullmore ET, Baron-Cohen S, Bethlehem RA. Brain-charting autism and attention deficit hyperactivity disorder reveals distinct and overlapping neurobiology. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.06.23299587. [PMID: 38106166 PMCID: PMC10723556 DOI: 10.1101/2023.12.06.23299587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background Autism and attention deficit hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology. Despite overlapping presentation and sex-biased prevalence, autism and ADHD are rarely studied together, and sex differences are often overlooked. Normative modelling provides a unified framework for studying age-specific and sex-specific divergences in neurodivergent brain development. Methods Here we use normative modelling and a large, multi-site neuroimaging dataset to characterise cortical anatomy associated with autism and ADHD, benchmarked against models of typical brain development based on a sample of over 75,000 individuals. We also examined sex and age differences, relationship with autistic traits, and explored the co-occurrence of autism and ADHD (autism+ADHD). Results We observed robust neuroanatomical signatures of both autism and ADHD. Overall, autistic individuals showed greater cortical thickness and volume localised to the superior temporal cortex, whereas individuals with ADHD showed more global effects of cortical thickness increases but lower cortical volume and surface area across much of the cortex. The autism+ADHD group displayed a unique pattern of widespread increases in cortical thickness, and certain decreases in surface area. We also found evidence that sex modulates the neuroanatomy of autism but not ADHD, and an age-by-diagnosis interaction for ADHD only. Conclusions These results indicate distinct cortical differences in autism and ADHD that are differentially impacted by age, sex, and potentially unique patterns related to their co-occurrence.
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Affiliation(s)
- Saashi A. Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei 100229, Taiwan
| | - Michael V. Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Bhismadev Chakrabarti
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Centre for Autism, School of Psychology and Clinical Language Sciences, University of Reading, Reading RG6 6ES, UK
| | - Amber Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Evdokia Anagnostou
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Department of Pediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Jason P. Lerch
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Margot Taylor
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Rob Nicolson
- Department of Psychiatry, University of Western Ontario, London, Ontario, Canada
| | | | - Jennifer Crosbie
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Russell Schachar
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Elizabeth Kelley
- Department of Psychology, Queen’s University, Kingston, ON K7L 3N6 Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, ON K7L 3N6 Canada
- Department of Psychiatry, Queen’s University, Kingston, ON K7L 3N6 Canada
| | - Jessica Jones
- Department of Psychology, Queen’s University, Kingston, ON K7L 3N6 Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, ON K7L 3N6 Canada
- Department of Psychiatry, Queen’s University, Kingston, ON K7L 3N6 Canada
| | - Paul D. Arnold
- The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Departments of Psychiatry and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Eric Courchesne
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Karen Pierce
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Lisa T. Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
| | - Kathleen Campbell
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Cynthia Carter Barnes
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - Edward T. Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Cambridge Lifetime Autism Spectrum Service (CLASS), Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Richard A.I. Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
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Wang J, Yu J, Wang M, Zhang L, Yang K, Du X, Wu J, Wang X, Li F, Qiu Z. Discovery and Validation of Novel Genes in a Large Chinese Autism Spectrum Disorder Cohort. Biol Psychiatry 2023; 94:792-803. [PMID: 37393044 DOI: 10.1016/j.biopsych.2023.06.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 06/02/2023] [Accepted: 06/20/2023] [Indexed: 07/03/2023]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a neurodevelopmental disorder that causes impairments in social communication and stereotypical behaviors, often accompanied by developmental delay or intellectual disability. A growing body of evidence suggests that ASD is highly heritable, and genetic studies have defined numerous risk genes. However, most studies have been conducted with individuals of European and Hispanic ancestry, and there is a lack of genetic analyses of ASD in the East Asian population. METHODS We performed whole-exome sequencing on 772 Chinese ASD trios and combined the data with a previous study of 369 Chinese ASD trios, identifying de novo variants in 1141 ASD trios. We used single-cell RNA sequencing analysis to identify the cell types in which ASD-related genes were enriched. In addition, we validated the function of a candidate high-functioning autism gene in mouse models using genetic approaches. RESULTS Our findings showed that ASD without developmental delay or intellectual disability carried fewer disruptive de novo variants than ASD with developmental delay or intellectual disability. Moreover, we identified 9 novel ASD candidate genes that were not present in the current ASD gene database. We further validated one such novel ASD candidate gene, SLC35G1, by showing that mice harboring a heterozygous deletion of Slc35g1 exhibited defects in interactive social behaviors. CONCLUSIONS Our work nominates novel ASD candidate genes and emphasizes the importance of genome-wide genetic studies with ASD cohorts of different ancestries to reveal the comprehensive genetic architecture of ASD.
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Affiliation(s)
- Jincheng Wang
- Department of Developmental and Behavioural Pediatric & Child Primary Care, Brain and Behavioural Research Unit of Shanghai Institute for Pediatric Research, Institute of Autism, and MOE-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Juehua Yu
- Department of Developmental and Behavioural Pediatric & Child Primary Care, Brain and Behavioural Research Unit of Shanghai Institute for Pediatric Research, Institute of Autism, and MOE-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mengdi Wang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Lingli Zhang
- Department of Developmental and Behavioural Pediatric & Child Primary Care, Brain and Behavioural Research Unit of Shanghai Institute for Pediatric Research, Institute of Autism, and MOE-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kan Yang
- Department of Developmental and Behavioural Pediatric & Child Primary Care, Brain and Behavioural Research Unit of Shanghai Institute for Pediatric Research, Institute of Autism, and MOE-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiujuan Du
- Department of Developmental and Behavioural Pediatric & Child Primary Care, Brain and Behavioural Research Unit of Shanghai Institute for Pediatric Research, Institute of Autism, and MOE-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jinyu Wu
- Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiaoqun Wang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Fei Li
- Department of Developmental and Behavioural Pediatric & Child Primary Care, Brain and Behavioural Research Unit of Shanghai Institute for Pediatric Research, Institute of Autism, and MOE-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Zilong Qiu
- Department of Developmental and Behavioural Pediatric & Child Primary Care, Brain and Behavioural Research Unit of Shanghai Institute for Pediatric Research, Institute of Autism, and MOE-Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China; Songjiang Research Institute, Songjiang District Central Hospital, and Institute of Autism, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Clinical Neuroscience Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
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Matuleviciute R, Akinluyi ET, Muntslag TAO, Dewing JM, Long KR, Vernon AC, Tremblay ME, Menassa DA. Microglial contribution to the pathology of neurodevelopmental disorders in humans. Acta Neuropathol 2023; 146:663-683. [PMID: 37656188 PMCID: PMC10564830 DOI: 10.1007/s00401-023-02629-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 08/25/2023] [Accepted: 08/26/2023] [Indexed: 09/02/2023]
Abstract
Microglia are the brain's resident macrophages, which guide various developmental processes crucial for brain maturation, activity, and plasticity. Microglial progenitors enter the telencephalic wall by the 4th postconceptional week and colonise the fetal brain in a manner that spatiotemporally tracks key neurodevelopmental processes in humans. However, much of what we know about how microglia shape neurodevelopment comes from rodent studies. Multiple differences exist between human and rodent microglia warranting further focus on the human condition, particularly as microglia are emerging as critically involved in the pathological signature of various cognitive and neurodevelopmental disorders. In this article, we review the evidence supporting microglial involvement in basic neurodevelopmental processes by focusing on the human species. We next concur on the neuropathological evidence demonstrating whether and how microglia contribute to the aetiology of two neurodevelopmental disorders: autism spectrum conditions and schizophrenia. Next, we highlight how recent technologies have revolutionised our understanding of microglial biology with a focus on how these tools can help us elucidate at unprecedented resolution the links between microglia and neurodevelopmental disorders. We conclude by reviewing which current treatment approaches have shown most promise towards targeting microglia in neurodevelopmental disorders and suggest novel avenues for future consideration.
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Affiliation(s)
- Rugile Matuleviciute
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Elizabeth T Akinluyi
- Division of Medical Sciences, University of Victoria, Victoria, Canada
- Department of Pharmacology and Therapeutics, Afe Babalola University, Ado Ekiti, Nigeria
| | - Tim A O Muntslag
- Princess Maxima Centre for Paediatric Oncology, Utrecht, The Netherlands
| | | | - Katherine R Long
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Anthony C Vernon
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - David A Menassa
- Department of Neuropathology & The Queen's College, University of Oxford, Oxford, UK.
- Department of Women's and Children's Health, Karolinska Institutet, Solna, Sweden.
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35
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Konopka G, Bhaduri A. Functional genomics and systems biology in human neuroscience. Nature 2023; 623:274-282. [PMID: 37938705 DOI: 10.1038/s41586-023-06686-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 09/27/2023] [Indexed: 11/09/2023]
Abstract
Neuroscience research has entered a phase of key discoveries in the realm of neurogenomics owing to strong financial and intellectual support for resource building and tool development. The previous challenge of tissue heterogeneity has been met with the application of techniques that can profile individual cells at scale. Moreover, the ability to perturb genes, gene regulatory elements and neuronal activity in a cell-type-specific manner has been integrated with gene expression studies to uncover the functional underpinnings of the genome at a systems level. Although these insights have necessarily been grounded in model systems, we now have the opportunity to apply these approaches in humans and in human tissue, thanks to advances in human genetics, brain imaging and tissue collection. We acknowledge that there will probably always be limits to the extent to which we can apply the genomic tools developed in model systems to human neuroscience; however, as we describe in this Perspective, the neuroscience field is now primed with an optimal foundation for tackling this ambitious challenge. The application of systems-level network analyses to these datasets will facilitate a deeper appreciation of human neurogenomics that cannot otherwise be achieved from directly observable phenomena.
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Affiliation(s)
- Genevieve Konopka
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA.
- Peter O'Donnell Jr Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA.
| | - Aparna Bhaduri
- Department of Biological Chemistry, University of California, Los Angeles, CA, USA.
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36
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Farsi Z, Nicolella A, Simmons SK, Aryal S, Shepard N, Brenner K, Lin S, Herzog L, Moran SP, Stalnaker KJ, Shin W, Gazestani V, Song BJ, Bonanno K, Keshishian H, Carr SA, Pan JQ, Macosko EZ, Datta SR, Dejanovic B, Kim E, Levin JZ, Sheng M. Brain-region-specific changes in neurons and glia and dysregulation of dopamine signaling in Grin2a mutant mice. Neuron 2023; 111:3378-3396.e9. [PMID: 37657442 DOI: 10.1016/j.neuron.2023.08.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 05/19/2023] [Accepted: 08/04/2023] [Indexed: 09/03/2023]
Abstract
A genetically valid animal model could transform our understanding of schizophrenia (SCZ) disease mechanisms. Rare heterozygous loss-of-function (LoF) mutations in GRIN2A, encoding a subunit of the NMDA receptor, greatly increase the risk of SCZ. By transcriptomic, proteomic, and behavioral analyses, we report that heterozygous Grin2a mutant mice show (1) large-scale gene expression changes across multiple brain regions and in neuronal (excitatory and inhibitory) and non-neuronal cells (astrocytes and oligodendrocytes), (2) evidence of hypoactivity in the prefrontal cortex (PFC) and hyperactivity in the hippocampus and striatum, (3) an elevated dopamine signaling in the striatum and hypersensitivity to amphetamine-induced hyperlocomotion (AIH), (4) altered cholesterol biosynthesis in astrocytes, (5) a reduction in glutamatergic receptor signaling proteins in the synapse, and (6) an aberrant locomotor pattern opposite of that induced by antipsychotic drugs. These findings reveal potential pathophysiologic mechanisms, provide support for both the "hypo-glutamate" and "hyper-dopamine" hypotheses of SCZ, and underscore the utility of Grin2a-deficient mice as a genetic model of SCZ.
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Affiliation(s)
- Zohreh Farsi
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Ally Nicolella
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sean K Simmons
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sameer Aryal
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nate Shepard
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kira Brenner
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sherry Lin
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Linnea Herzog
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sean P Moran
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Katherine J Stalnaker
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wangyong Shin
- Center for Synaptic Brain Dysfunctions, Institute for Basic Science, Daejeon, South Korea
| | - Vahid Gazestani
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bryan J Song
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kevin Bonanno
- Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hasmik Keshishian
- Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Steven A Carr
- Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jen Q Pan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Evan Z Macosko
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA
| | | | - Borislav Dejanovic
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Eunjoon Kim
- Center for Synaptic Brain Dysfunctions, Institute for Basic Science, Daejeon, South Korea; Department of Biological Sciences, Korea Advanced Institute for Science and Technology, Daejeon, South Korea
| | - Joshua Z Levin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Morgan Sheng
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Sha Z, Warrier V, Bethlehem RA, Schultz LM, Merikangas A, Sun KY, Gur RC, Gur RE, Shinohara RT, Seidlitz J, Almasy L, Andreassen OA, Alexander-Bloch AF. The overlapping genetic architecture of psychiatric disorders and cortical brain structure. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.05.561040. [PMID: 37873315 PMCID: PMC10592957 DOI: 10.1101/2023.10.05.561040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Both psychiatric vulnerability and cortical structure are shaped by the cumulative effect of common genetic variants across the genome. However, the shared genetic underpinnings between psychiatric disorders and brain structural phenotypes, such as thickness and surface area of the cerebral cortex, remains elusive. In this study, we employed pleiotropy-informed conjunctional false discovery rate analysis to investigate shared loci across genome-wide association scans of regional cortical thickness, surface area, and seven psychiatric disorders in approximately 700,000 individuals of European ancestry. Aggregating regional measures, we identified 50 genetic loci shared between psychiatric disorders and surface area, as well as 26 genetic loci shared with cortical thickness. Risk alleles exhibited bidirectional effects on both cortical thickness and surface area, such that some risk alleles for each disorder increased regional brain size while other risk alleles decreased regional brain size. Due to bidirectional effects, in many cases we observed extensive pleiotropy between an imaging phenotype and a psychiatric disorder even in the absence of a significant genetic correlation between them. The impact of genetic risk for psychiatric disorders on regional brain structure did exhibit a consistent pattern across highly comorbid psychiatric disorders, with 80% of the genetic loci shared across multiple disorders displaying consistent directions of effect. Cortical patterning of genetic overlap revealed a hierarchical genetic architecture, with the association cortex and sensorimotor cortex representing two extremes of shared genetic influence on psychiatric disorders and brain structural variation. Integrating multi-scale functional annotations and transcriptomic profiles, we observed that shared genetic loci were enriched in active genomic regions, converged on neurobiological and metabolic pathways, and showed differential expression in postmortem brain tissue from individuals with psychiatric disorders. Cumulatively, these findings provide a significant advance in our understanding of the overlapping polygenic architecture between psychopathology and cortical brain structure.
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Affiliation(s)
- Zhiqiang Sha
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | | | - Laura M. Schultz
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Alison Merikangas
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kevin Y. Sun
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, 19104, USA
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA, 19104, USA
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
- Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, Perelman School of Medicine, United States
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
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Zhu H, Guan A, Liu J, Peng L, Zhang Z, Wang S. Noteworthy perspectives on microglia in neuropsychiatric disorders. J Neuroinflammation 2023; 20:223. [PMID: 37794488 PMCID: PMC10548593 DOI: 10.1186/s12974-023-02901-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 09/22/2023] [Indexed: 10/06/2023] Open
Abstract
Microglia are so versatile that they not only provide immune surveillance for central nervous system, but participate in neural circuitry development, brain blood vessels formation, blood-brain barrier architecture, and intriguingly, the regulation of emotions and behaviors. Microglia have a profound impact on neuronal survival, brain wiring and synaptic plasticity. As professional phagocytic cells in the brain, they remove dead cell debris and neurotoxic agents via an elaborate mechanism. The functional profile of microglia varies considerately depending on age, gender, disease context and other internal or external environmental factors. Numerous studies have demonstrated a pivotal involvement of microglia in neuropsychiatric disorders, including negative affection, social deficit, compulsive behavior, fear memory, pain and other symptoms associated with major depression disorder, anxiety disorder, autism spectrum disorder and schizophrenia. In this review, we summarized the latest discoveries regarding microglial ontogeny, cell subtypes or state spectrum, biological functions and mechanistic underpinnings of emotional and behavioral disorders. Furthermore, we highlight the potential of microglia-targeted therapies of neuropsychiatric disorders, and propose outstanding questions to be addressed in future research of human microglia.
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Affiliation(s)
- Hongrui Zhu
- Department of Anesthesiology, First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, Anhui, China.
| | - Ao Guan
- School of Medicine, Xiamen University, Xiamen, 361102, China
| | - Jiayuan Liu
- Department of Anesthesiology, First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, Anhui, China
| | - Li Peng
- Department of Anesthesiology, First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, Anhui, China
| | - Zhi Zhang
- Department of Anesthesiology, First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, Anhui, China.
- Hefei National Laboratory for Physical Sciences at the Microscale, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, Anhui, China.
| | - Sheng Wang
- Department of Anesthesiology, First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, Anhui, China.
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Matrone C, Ferretti G. Semaphorin 3A influences neuronal processes that are altered in patients with autism spectrum disorder: Potential diagnostic and therapeutic implications. Neurosci Biobehav Rev 2023; 153:105338. [PMID: 37524141 DOI: 10.1016/j.neubiorev.2023.105338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/02/2023]
Abstract
Autism spectrum disorder (ASD) is a pervasive disorder that most frequently manifests in early childhood and lasts for their entire lifespan. Several behavioural traits characterise the phenotype of patients with ASD, including difficulties in reciprocal social communication as well as compulsive/repetitive stereotyped verbal and non-verbal behaviours. Although multiple hypotheses have been proposed to explain the aetiology of ASD and many resources have been used to improve our understanding of ASD, several aspects remain largely unexplored. Class 3 semaphorins (SEMA3) are secreted proteins involved in the organisation of structural and functional connectivity in the brain that regulate synaptic and dendritic development. Alterations in brain connectivity and aberrant neuronal development have been described in some patients with ASD. Mutations and polymorphisms in SEMA3A and alterations in its receptors and signalling have been associated with some neurological disorders such as schizophrenia and epilepsy, which are comorbidities in ASD, but also with ASD itself. In addition, SEMA3A is a key regulator of the immune response and neuroinflammatory processes, which have been found to be dysregulated in mothers of children who develop ASD and in affected patients. In this review, we highlight neurodevelopmental-related processes in which SEMA3A is involved, which are altered in ASD, and provide a viewpoint emphasising the development of strategies targeting changes in the SEMA3A signal to identify patterns of anomalies distinctive of ASD or to predict the prognosis of affected patients.
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Affiliation(s)
- Carmela Matrone
- Division of Pharmacology, Department of Neuroscience, School of Medicine, University of Naples "Federico II", Via Pansini 5, 80131 Naples, Italy.
| | - Gabriella Ferretti
- Division of Pharmacology, Department of Neuroscience, School of Medicine, University of Naples "Federico II", Via Pansini 5, 80131 Naples, Italy
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40
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Falck-Ytter T, Bussu G. The sensory-first account of autism. Neurosci Biobehav Rev 2023; 153:105405. [PMID: 37742990 DOI: 10.1016/j.neubiorev.2023.105405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/18/2023] [Accepted: 09/21/2023] [Indexed: 09/26/2023]
Affiliation(s)
- Terje Falck-Ytter
- Development and Neurodiversity Lab, Department of Psychology, Uppsala University, Uppsala, Sweden; Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Giorgia Bussu
- Development and Neurodiversity Lab, Department of Psychology, Uppsala University, Uppsala, Sweden.
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Perez Y, Velmeshev D, Wang L, White M, Siebert C, Baltazar J, Dutton NG, Wang S, Haeussler M, Chamberlain S, Kriegstein A. Single cell analysis of dup15q syndrome reveals developmental and postnatal molecular changes in autism. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.22.559056. [PMID: 37790331 PMCID: PMC10543006 DOI: 10.1101/2023.09.22.559056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Duplication 15q (dup15q) syndrome is the most common genetic cause of autism spectrum disorder (ASD). Due to a higher genetic and phenotypic homogeneity compared to idiopathic autism, dup15q syndrome provides a well-defined setting to investigate ASD mechanisms. Previous bulk gene expression studies identified shared molecular changes in ASD. However, how cell type specific changes compare across different autism subtypes and how they change during development is largely unknown. In this study, we used single cell and single nucleus mRNA sequencing of dup15q cortical organoids from patient iPSCs, as well as post-mortem patient brain samples. We find cell-type specific dysregulated programs that underlie dup15q pathogenesis, which we validate by spatial resolved transcriptomics using brain tissue samples. We find degraded identity and vulnerability of deep-layer neurons in fetal stage organoids and highlight increased molecular burden of postmortem upper-layer neurons implicated in synaptic signaling, a finding shared between idiopathic ASD and dup15q syndrome. Gene co-expression network analysis of organoid and postmortem excitatory neurons uncovers modules enriched with autism risk genes. Organoid developmental modules were involved in transcription regulation via chromatin remodeling, while postmortem modules were associated with synaptic transmission and plasticity. The findings reveal a shifting landscape of ASD cellular vulnerability during brain development.
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Affiliation(s)
- Yonatan Perez
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Dmitry Velmeshev
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
- Current address: Bryan Research Building, Duke University, Durham, NC27710, USA
| | - Li Wang
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Matthew White
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Clara Siebert
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jennifer Baltazar
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Natalia Garcia Dutton
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Shaohui Wang
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
| | | | - Stormy Chamberlain
- Departments of Genetics and Genome Sciences and Pediatrics, Connecticut Children's Medical Center, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, CT 06030-6403, USA
| | - Arnold Kriegstein
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94158, USA
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Nano PR, Fazzari E, Azizad D, Nguyen CV, Wang S, Kan RL, Wick B, Haeussler M, Bhaduri A. A Meta-Atlas of the Developing Human Cortex Identifies Modules Driving Cell Subtype Specification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557406. [PMID: 37745597 PMCID: PMC10515829 DOI: 10.1101/2023.09.12.557406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Human brain development requires the generation of hundreds of diverse cell types, a process targeted by recent single-cell transcriptomic profiling efforts. Through a meta-analysis of seven of these published datasets, we have generated 225 meta-modules - gene co-expression networks that can describe mechanisms underlying cortical development. Several meta-modules have potential roles in both establishing and refining cortical cell type identities, and we validated their spatiotemporal expression in primary human cortical tissues. These include meta-module 20, associated with FEZF2+ deep layer neurons. Half of meta-module 20 genes are putative FEZF2 targets, including TSHZ3, a transcription factor associated with neurodevelopmental disorders. Human cortical organoid experiments validated that both factors are necessary for deep layer neuron specification. Importantly, subtle manipulations of these factors drive slight changes in meta-module activity that cascade into strong differences in cell fate - demonstrating how of our meta-atlas can engender further mechanistic analyses of cortical fate specification.
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Monday HR, Wang HC, Feldman DE. Circuit-level theories for sensory dysfunction in autism: convergence across mouse models. Front Neurol 2023; 14:1254297. [PMID: 37745660 PMCID: PMC10513044 DOI: 10.3389/fneur.2023.1254297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 08/14/2023] [Indexed: 09/26/2023] Open
Abstract
Individuals with autism spectrum disorder (ASD) exhibit a diverse range of behavioral features and genetic backgrounds, but whether different genetic forms of autism involve convergent pathophysiology of brain function is unknown. Here, we analyze evidence for convergent deficits in neural circuit function across multiple transgenic mouse models of ASD. We focus on sensory areas of neocortex, where circuit differences may underlie atypical sensory processing, a central feature of autism. Many distinct circuit-level theories for ASD have been proposed, including increased excitation-inhibition (E-I) ratio and hyperexcitability, hypofunction of parvalbumin (PV) interneuron circuits, impaired homeostatic plasticity, degraded sensory coding, and others. We review these theories and assess the degree of convergence across ASD mouse models for each. Behaviorally, our analysis reveals that innate sensory detection behavior is heightened and sensory discrimination behavior is impaired across many ASD models. Neurophysiologically, PV hypofunction and increased E-I ratio are prevalent but only rarely generate hyperexcitability and excess spiking. Instead, sensory tuning and other aspects of neural coding are commonly degraded and may explain impaired discrimination behavior. Two distinct phenotypic clusters with opposing neural circuit signatures are evident across mouse models. Such clustering could suggest physiological subtypes of autism, which may facilitate the development of tailored therapeutic approaches.
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Affiliation(s)
- Hannah R. Monday
- Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | | | - Daniel E. Feldman
- Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
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Jourdon A, Wu F, Mariani J, Capauto D, Norton S, Tomasini L, Amiri A, Suvakov M, Schreiner JD, Jang Y, Panda A, Nguyen CK, Cummings EM, Han G, Powell K, Szekely A, McPartland JC, Pelphrey K, Chawarska K, Ventola P, Abyzov A, Vaccarino FM. Modeling idiopathic autism in forebrain organoids reveals an imbalance of excitatory cortical neuron subtypes during early neurogenesis. Nat Neurosci 2023; 26:1505-1515. [PMID: 37563294 PMCID: PMC10573709 DOI: 10.1038/s41593-023-01399-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/30/2023] [Indexed: 08/12/2023]
Abstract
Idiopathic autism spectrum disorder (ASD) is highly heterogeneous, and it remains unclear how convergent biological processes in affected individuals may give rise to symptoms. Here, using cortical organoids and single-cell transcriptomics, we modeled alterations in the forebrain development between boys with idiopathic ASD and their unaffected fathers in 13 families. Transcriptomic changes suggest that ASD pathogenesis in macrocephalic and normocephalic probands involves an opposite disruption of the balance between excitatory neurons of the dorsal cortical plate and other lineages such as early-generated neurons from the putative preplate. The imbalance stemmed from divergent expression of transcription factors driving cell fate during early cortical development. While we did not find genomic variants in probands that explained the observed transcriptomic alterations, a significant overlap between altered transcripts and reported ASD risk genes affected by rare variants suggests a degree of gene convergence between rare forms of ASD and the developmental transcriptome in idiopathic ASD.
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Affiliation(s)
- Alexandre Jourdon
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Feinan Wu
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Jessica Mariani
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Davide Capauto
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Scott Norton
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Livia Tomasini
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Anahita Amiri
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Milovan Suvakov
- Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jeremy D Schreiner
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Yeongjun Jang
- Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Arijit Panda
- Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA
| | - Cindy Khanh Nguyen
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Elise M Cummings
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Gloria Han
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Kelly Powell
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Anna Szekely
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - James C McPartland
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Kevin Pelphrey
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
- Brain Institute, Department of Neurology, University of Virginia School of Medicine, Charlottesville, VA, USA
| | | | - Pamela Ventola
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Alexej Abyzov
- Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
| | - Flora M Vaccarino
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA.
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA.
- Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA.
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Fass SB, Mulvey B, Yang W, Selmanovic D, Chaturvedi S, Tycksen E, Weiss LA, Dougherty JD. Relationship between sex biases in gene expression and sex biases in autism and Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.29.23294773. [PMID: 37693465 PMCID: PMC10491382 DOI: 10.1101/2023.08.29.23294773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Sex differences in the brain may play an important role in sex-differential prevalence of neuropsychiatric conditions. In order to understand the transcriptional basis of sex differences, we analyzed multiple, large-scale, human postmortem brain RNA-seq datasets using both within-region and pan-regional frameworks. We find evidence of sex-biased transcription in many autosomal genes, some of which provide evidence for pathways and cell population differences between chromosomally male and female individuals. These analyses also highlight regional differences in the extent of sex-differential gene expression. We observe an increase in specific neuronal transcripts in male brains and an increase in immune and glial function-related transcripts in female brains. Integration with single-cell data suggests this corresponds to sex differences in cellular states rather than cell abundance. Integration with case-control gene expression studies suggests a female molecular predisposition towards Alzheimer's disease, a female-biased disease. Autism, a male-biased diagnosis, does not exhibit a male predisposition pattern in our analysis. Finally, we provide region specific analyses of sex differences in brain gene expression to enable additional studies at the interface of gene expression and diagnostic differences.
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Affiliation(s)
- Stuart B Fass
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
| | - Bernard Mulvey
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Lieber Institute for Brain Development, 855 North Wolfe St. Ste 300, Baltimore, MD 21205, USA
| | - Wei Yang
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Din Selmanovic
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
| | - Sneha Chaturvedi
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
| | - Eric Tycksen
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Lauren A Weiss
- Institute for Human Genetics, University of California, San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA 94143
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA 94143
- Weill Institute for Neurosciences, University of California, San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA 94143
| | - Joseph D Dougherty
- Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
- Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis MO, 63110, USA
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Sznajder L, Khan M, Tadross M, Ciesiołka A, Nutter C, Taylor K, Pearson C, Sobczak K, Lewis M, Swanson M, Yuen R. Autistic traits in myotonic dystrophy type 1 due to MBNL inhibition and RNA mis-splicing. RESEARCH SQUARE 2023:rs.3.rs-3221704. [PMID: 37645891 PMCID: PMC10462192 DOI: 10.21203/rs.3.rs-3221704/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Tandem repeat expansions are enriched in autism spectrum disorder, including CTG expansion in the DMPK gene that underlines myotonic muscular dystrophy type 1. Although the clinical connection of autism to myotonic dystrophy is corroborated, the molecular links remained unknown. Here, we show a mechanistic path of autism via repeat expansion in myotonic dystrophy. We found that inhibition of muscleblind-like (MBNL) splicing factors by expanded CUG RNAs alerts the splicing of autism-risk genes during brain development especially a class of autism-relevant microexons. To provide in vivo evidence that the CTG expansion and MBNL inhibition axis leads to the presentation of autistic traits, we demonstrate that CTG expansion and MBNL-null mouse models recapitulate autism-relevant mis-splicing profiles and demonstrate social deficits. Our findings indicate that DMPK CTG expansion-associated autism arises from developmental mis-splicing. Understanding this pathomechanistic connection provides an opportunity for greater in-depth investigations of mechanistic threads in autism.
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Cuppens T, Shatto J, Mangnier L, Kumar AA, Ng ACH, Kaur M, Bui TA, Leclercq M, Droit A, Dunham I, Bolduc FV. Sex difference contributes to phenotypic diversity in individuals with neurodevelopmental disorders. Front Pediatr 2023; 11:1172154. [PMID: 37609366 PMCID: PMC10441218 DOI: 10.3389/fped.2023.1172154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 07/20/2023] [Indexed: 08/24/2023] Open
Abstract
Objective Gain a better understanding of sex-specific differences in individuals with global developmental delay (GDD), with a focus on phenotypes and genotypes. Methods Using the Deciphering Developmental Disorders (DDD) dataset, we extracted phenotypic information from 6,588 individuals with GDD and then identified statistically significant variations in phenotypes and genotypes based on sex. We compared genes with pathogenic variants between sex and then performed gene network and molecular function enrichment analysis and gene expression profiling between sex. Finally, we contrasted individuals with autism as an associated condition. Results We identified significantly differentially expressed phenotypes in males vs. females individuals with GDD. Autism and macrocephaly were significantly more common in males whereas microcephaly and stereotypies were more common in females. Importantly, 66% of GDD genes with pathogenic variants overlapped between both sexes. In the cohort, males presented with only slightly increased X-linked genes (9% vs. 8%, respectively). Individuals from both sexes harbored a similar number of pathogenic variants overall (3) but females presented with a significantly higher load for GDD genes with high intolerance to loss of function. Sex difference in gene expression correlated with genes identified in a sex specific manner. While we identified sex-specific GDD gene mutations, their pathways overlapped. Interestingly, individuals with GDD but also co-morbid autism phenotypes, we observed distinct mutation load, pathways and phenotypic presentation. Conclusion Our study shows for the first time that males and females with GDD present with significantly different phenotypes. Moreover, while most GDD genes overlapped, some genes were found uniquely in each sex. Surprisingly they shared similar molecular functions. Sorting genes by predicted tolerance to loss of function (pLI) led to identifying an increased mutation load in females with GDD, suggesting potentially a tolerance to GDD genes of higher pLI compared to overall GDD genes. Finally, we show that considering associated conditions (for instance autism) may influence the genomic underpinning found in individuals with GDD and highlight the importance of comprehensive phenotyping.
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Affiliation(s)
- Tania Cuppens
- Centre de Recherche du CHU de Québec-Université Laval, Département de Médecine Moléculaire de L'Université Laval, Québec, QC, Canada
| | - Julie Shatto
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
| | - Loïc Mangnier
- Centre de Recherche du CHU de Québec-Université Laval, Département de Médecine Moléculaire de L'Université Laval, Québec, QC, Canada
| | - Ajay A. Kumar
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI); Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Andy Cheuk-Him Ng
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
| | - Manpreet Kaur
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
| | - Truong An Bui
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
| | - Mickael Leclercq
- Centre de Recherche du CHU de Québec-Université Laval, Département de Médecine Moléculaire de L'Université Laval, Québec, QC, Canada
| | - Arnaud Droit
- Centre de Recherche du CHU de Québec-Université Laval, Département de Médecine Moléculaire de L'Université Laval, Québec, QC, Canada
| | - Ian Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI); Wellcome Genome Campus, Cambridgeshire, United Kingdom
| | - Francois V. Bolduc
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
- Department of Medical Genetics, University of Alberta, Edmonton, AB, Canada
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
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Walter TJ, Suter RK, Ayad NG. An overview of human single-cell RNA sequencing studies in neurobiological disease. Neurobiol Dis 2023; 184:106201. [PMID: 37321420 PMCID: PMC10470823 DOI: 10.1016/j.nbd.2023.106201] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 05/09/2023] [Accepted: 06/12/2023] [Indexed: 06/17/2023] Open
Abstract
Neurobiological disorders are highly prevalent medical conditions that contribute to significant morbidity and mortality. Single-cell RNA sequencing (scRNA-seq) is a technique that measures gene expression in individual cells. In this review, we survey scRNA-seq studies of tissues from patients suffering from neurobiological disease. This includes postmortem human brains and organoids derived from peripheral cells. We highlight a range of conditions, including epilepsy, cognitive disorders, substance use disorders, and mood disorders. These findings provide new insights into neurobiological disease in multiple ways, including discovering novel cell types or subtypes involved in disease, proposing new pathophysiological mechanisms, uncovering novel drug targets, or identifying potential biomarkers. We discuss the quality of these findings and suggest potential future directions and areas open for additional research, including studies of non-cortical brain regions and additional conditions such as anxiety disorders, mood disorders, and sleeping disorders. We argue that additional scRNA-seq of tissues from patients suffering from neurobiological disease could advance our understanding and treatment of these conditions.
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Affiliation(s)
- T Jordan Walter
- Georgetown University, Lombardi Comprehensive Cancer Center, 3970 Reservoir Rd NW, Washington D.C. 20007, USA.
| | - Robert K Suter
- Georgetown University, Lombardi Comprehensive Cancer Center, 3970 Reservoir Rd NW, Washington D.C. 20007, USA
| | - Nagi G Ayad
- Georgetown University, Lombardi Comprehensive Cancer Center, 3970 Reservoir Rd NW, Washington D.C. 20007, USA
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49
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Di Biase MA. The Search for Unitary Mechanisms in Psychiatric Illness. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:588-589. [PMID: 37286290 DOI: 10.1016/j.bpsc.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 04/07/2023] [Indexed: 06/09/2023]
Affiliation(s)
- Maria A Di Biase
- Departments of Psychiatry and Anatomy and Physiology, The University of Melbourne, Melbourne, Australia; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
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50
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Rukh S, Meechan DW, Maynard TM, Lamantia AS. Out of Line or Altered States? Neural Progenitors as a Target in a Polygenic Neurodevelopmental Disorder. Dev Neurosci 2023; 46:1-21. [PMID: 37231803 DOI: 10.1159/000530898] [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: 02/15/2023] [Accepted: 04/19/2023] [Indexed: 05/27/2023] Open
Abstract
The genesis of a mature complement of neurons is thought to require, at least in part, precursor cell lineages in which neural progenitors have distinct identities recognized by exclusive expression of one or a few molecular markers. Nevertheless, limited progenitor types distinguished by specific markers and lineal progression through such subclasses cannot easily yield the magnitude of neuronal diversity in most regions of the nervous system. The late Verne Caviness, to whom this edition of Developmental Neuroscience is dedicated, recognized this mismatch. In his pioneering work on the histogenesis of the cerebral cortex, he acknowledged the additional flexibility required to generate multiple classes of cortical projection and interneurons. This flexibility may be accomplished by establishing cell states in which levels rather than binary expression or repression of individual genes vary across each progenitor's shared transcriptome. Such states may reflect local, stochastic signaling via soluble factors or coincidence of cell surface ligand/receptor pairs in subsets of neighboring progenitors. This probabilistic, rather than determined, signaling could modify transcription levels via multiple pathways within an apparently uniform population of progenitors. Progenitor states, therefore, rather than lineal relationships between types may underlie the generation of neuronal diversity in most regions of the nervous system. Moreover, mechanisms that influence variation required for flexible progenitor states may be targets for pathological changes in a broad range of neurodevelopmental disorders, especially those with polygenic origins.
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Affiliation(s)
- Shah Rukh
- Fralin Biomedical Research Institute, Virginia Tech Carilion School of Medicine, Roanoke, Virginia, USA
| | - Daniel W Meechan
- Fralin Biomedical Research Institute, Virginia Tech Carilion School of Medicine, Roanoke, Virginia, USA
| | - Thomas M Maynard
- Fralin Biomedical Research Institute, Virginia Tech Carilion School of Medicine, Roanoke, Virginia, USA
| | - Anthony-Samuel Lamantia
- Fralin Biomedical Research Institute, Virginia Tech Carilion School of Medicine, Roanoke, Virginia, USA
- Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia, USA
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