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Salemi M, Schillaci FA, Lanza G, Marchese G, Salluzzo MG, Cordella A, Caniglia S, Bruccheri MG, Truda A, Greco D, Ferri R, Romano C. Transcriptome Study in Sicilian Patients with Autism Spectrum Disorder. Biomedicines 2024; 12:1402. [PMID: 39061976 PMCID: PMC11274004 DOI: 10.3390/biomedicines12071402] [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: 04/24/2024] [Revised: 06/12/2024] [Accepted: 06/14/2024] [Indexed: 07/28/2024] Open
Abstract
ASD is a complex condition primarily rooted in genetics, although influenced by environmental, prenatal, and perinatal risk factors, ultimately leading to genetic and epigenetic alterations. These mechanisms may manifest as inflammatory, oxidative stress, hypoxic, or ischemic damage. To elucidate potential variances in gene expression in ASD, a transcriptome analysis of peripheral blood mononuclear cells was conducted via RNA-seq on 12 ASD patients and 13 healthy controls, all of Sicilian ancestry to minimize environmental confounds. A total of 733 different statistically significant genes were identified between the two cohorts. Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) terms were employed to explore the pathways influenced by differentially expressed mRNAs. GSEA revealed GO pathways strongly associated with ASD, namely the GO Biological Process term "Response to Oxygen-Containing Compound". Additionally, the GO Cellular Component pathway "Mitochondrion" stood out among other pathways, with differentially expressed genes predominantly affiliated with this specific pathway, implicating the involvement of different mitochondrial functions in ASD. Among the differentially expressed genes, FPR2 was particularly highlighted, belonging to three GO pathways. FPR2 can modulate pro-inflammatory responses, with its intracellular cascades triggering the activation of several kinases, thus suggesting its potential utility as a biomarker of pro-inflammatory processes in ASD.
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Affiliation(s)
- Michele Salemi
- Oasi Research Institute—IRCCS, 94018 Troina, Italy; (F.A.S.); (G.L.); (M.G.S.); (S.C.); (M.G.B.); (D.G.); (R.F.); (C.R.)
| | - Francesca A. Schillaci
- Oasi Research Institute—IRCCS, 94018 Troina, Italy; (F.A.S.); (G.L.); (M.G.S.); (S.C.); (M.G.B.); (D.G.); (R.F.); (C.R.)
| | - Giuseppe Lanza
- Oasi Research Institute—IRCCS, 94018 Troina, Italy; (F.A.S.); (G.L.); (M.G.S.); (S.C.); (M.G.B.); (D.G.); (R.F.); (C.R.)
- Department of Surgery and Medical—Surgical Specialties, University of Catania, 95124 Catania, Italy
| | - Giovanna Marchese
- Genomix4Life S.r.l., 84081 Baronissi, Italy; (G.M.); (A.C.); (A.T.)
- Genome Research Center for Health—CRGS, 84081 Baronissi, Italy
| | - Maria Grazia Salluzzo
- Oasi Research Institute—IRCCS, 94018 Troina, Italy; (F.A.S.); (G.L.); (M.G.S.); (S.C.); (M.G.B.); (D.G.); (R.F.); (C.R.)
| | - Angela Cordella
- Genomix4Life S.r.l., 84081 Baronissi, Italy; (G.M.); (A.C.); (A.T.)
- Genome Research Center for Health—CRGS, 84081 Baronissi, Italy
| | - Salvatore Caniglia
- Oasi Research Institute—IRCCS, 94018 Troina, Italy; (F.A.S.); (G.L.); (M.G.S.); (S.C.); (M.G.B.); (D.G.); (R.F.); (C.R.)
| | - Maria Grazia Bruccheri
- Oasi Research Institute—IRCCS, 94018 Troina, Italy; (F.A.S.); (G.L.); (M.G.S.); (S.C.); (M.G.B.); (D.G.); (R.F.); (C.R.)
| | - Anna Truda
- Genomix4Life S.r.l., 84081 Baronissi, Italy; (G.M.); (A.C.); (A.T.)
- Genome Research Center for Health—CRGS, 84081 Baronissi, Italy
| | - Donatella Greco
- Oasi Research Institute—IRCCS, 94018 Troina, Italy; (F.A.S.); (G.L.); (M.G.S.); (S.C.); (M.G.B.); (D.G.); (R.F.); (C.R.)
| | - Raffaele Ferri
- Oasi Research Institute—IRCCS, 94018 Troina, Italy; (F.A.S.); (G.L.); (M.G.S.); (S.C.); (M.G.B.); (D.G.); (R.F.); (C.R.)
| | - Corrado Romano
- Oasi Research Institute—IRCCS, 94018 Troina, Italy; (F.A.S.); (G.L.); (M.G.S.); (S.C.); (M.G.B.); (D.G.); (R.F.); (C.R.)
- Department of Biomedical and Biotechnological Sciences, University of Catania, 95124 Catania, Italy
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Nautiyal H, Jaiswar A, Jha PK, Dwivedi S. Exploring key genes and pathways associated with sex differences in autism spectrum disorder: integrated bioinformatic analysis. Mamm Genome 2024; 35:280-295. [PMID: 38594551 DOI: 10.1007/s00335-024-10036-5] [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: 12/06/2023] [Accepted: 02/20/2024] [Indexed: 04/11/2024]
Abstract
Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder marked by functional abnormalities in brain that causes social and linguistic difficulties. The incidence of ASD is more prevalent in males compared to females, but the underlying mechanism, as well as molecular indications for identifying sex-specific differences in ASD symptoms remain unknown. Thus, impacting the development of personalized strategy towards pharmacotherapy of ASD. The current study employs an integrated bioinformatic approach to investigate the genes and pathways uniquely associated with sex specific differences in autistic individuals. Based on microarray dataset (GSE6575) extracted from the gene expression omnibus, the dysregulated genes between the autistic and the neurotypical individuals for both sexes were identified. Gene set enrichment analysis was performed to ascertain biological activities linked to the dysregulated genes. Protein-protein interaction network analysis was carried out to identify hub genes. The identified hub genes were examined to determine their functions and involvement in the associated pathways using Enrichr. Additionally, hub genes were validated from autism-associated databases and the potential small molecules targeting the hub genes were identified. The present study utilized whole blood transcriptomic gene expression analysis data and identified 2211 and 958 differentially expressed unique genes in males and females respectively. The functional enrichment analysis revealed that male hub genes were functionally associated with RNA polymerase II mediated transcriptional regulation whereas female hub genes were involved in intracellular signal transduction and cell migration. The top male hub genes exhibited functional enrichment in tyrosine kinase signalling pathway. The pathway enrichment analysis of male hub genes indicates the enrichment of papillomavirus infection. Female hub genes were enriched in androgen receptor signalling pathway and functionally enriched in focal adhesion specific excision repair. Identified drug like candidates targeting these genes may serve as a potential sex specific therapeutics. Wortmannin for males, 5-Fluorouracil for females had the highest scores. Targeted and sex-specific pharmacotherapies may be created for the management of ASD. The current investigation identifies sex-specific molecular signatures derived from whole blood which may serve as a potential peripheral sex-specific biomarkers for ASD. The study also uncovers the possible pharmacological interventions against the selected genes/pathway, providing support in development of therapeutic strategies to mitigate ASD. However, experimental proofs on biological systems are warranted.
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Affiliation(s)
- Himani Nautiyal
- Department of Pharmaceutical Sciences, School of Health Sciences and Technology, UPES, Dehradun, 248001, India
| | - Akanksha Jaiswar
- Laboratory of Human Disease Multiomics, Mossakowski Medical Research Institute Polish Academy of Sciences, Warsaw, Poland
| | - Prabhash Kumar Jha
- Center for Excellence in Vascular Biology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shubham Dwivedi
- Department of Pharmaceutical Sciences, School of Health Sciences and Technology, UPES, Dehradun, 248001, India.
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Quinn TP, Hess JL, Marshe VS, Barnett MM, Hauschild AC, Maciukiewicz M, Elsheikh SSM, Men X, Schwarz E, Trakadis YJ, Breen MS, Barnett EJ, Zhang-James Y, Ahsen ME, Cao H, Chen J, Hou J, Salekin A, Lin PI, Nicodemus KK, Meyer-Lindenberg A, Bichindaritz I, Faraone SV, Cairns MJ, Pandey G, Müller DJ, Glatt SJ. A primer on the use of machine learning to distil knowledge from data in biological psychiatry. Mol Psychiatry 2024; 29:387-401. [PMID: 38177352 PMCID: PMC11228968 DOI: 10.1038/s41380-023-02334-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/21/2023] [Accepted: 11/17/2023] [Indexed: 01/06/2024]
Abstract
Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.
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Affiliation(s)
- Thomas P Quinn
- Applied Artificial Intelligence Institute (A2I2), Burwood, VIC, 3125, Australia
| | - Jonathan L Hess
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Victoria S Marshe
- Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Michelle M Barnett
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Anne-Christin Hauschild
- Department of Medical Informatics, Medical University Center Göttingen, Göttingen, Lower Saxony, 37075, Germany
| | - Malgorzata Maciukiewicz
- Hospital Zurich, University of Zurich, Zurich, 8091, Switzerland
- Department of Rheumatology and Immunology, University Hospital Bern, Bern, 3010, Switzerland
- Department for Biomedical Research (DBMR), University of Bern, Bern, 3010, Switzerland
| | - Samar S M Elsheikh
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Xiaoyu Men
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, M5S 1A1, Canada
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Yannis J Trakadis
- Department Human Genetics, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada
| | - Michael S Breen
- Psychiatry, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric J Barnett
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Mehmet Eren Ahsen
- Department of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
- Department of Biomedical and Translational Sciences, Carle-Illinois School of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
| | - Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Jiahui Hou
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Asif Salekin
- Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, 13244, USA
| | - Ping-I Lin
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, 2052, Australia
- Mental Health Research Unit, South Western Sydney Local Health District, Liverpool, NSW, 2170, Australia
| | | | - Andreas Meyer-Lindenberg
- Clinical Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Isabelle Bichindaritz
- Biomedical and Health Informatics/Computer Science Department, State University of New York at Oswego, Oswego, NY, 13126, USA
- Intelligent Bio Systems Lab, State University of New York at Oswego, Oswego, NY, 13126, USA
| | - Stephen V Faraone
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniel J Müller
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Würzburg, 97080, Germany
| | - Stephen J Glatt
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Public Health and Preventive Medicine, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
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Nishikawa-Shimono R, Kuwabara M, Fujisaki S, Matsuda D, Endo M, Kamitani M, Futamura A, Nomura Y, Yamaguchi-Sasaki T, Yabuuchi T, Yamaguchi C, Tanaka-Yamamoto N, Satake S, Abe-Sato K, Funayama K, Sakata M, Takahashi S, Hirano K, Fukunaga T, Uozumi Y, Kato S, Tamura Y, Nakamori T, Mima M, Mishima-Tsumagari C, Nozawa D, Imai Y, Asami T. Discovery of novel indole derivatives as potent and selective inhibitors of proMMP-9 activation. Bioorg Med Chem Lett 2024; 97:129541. [PMID: 37952596 DOI: 10.1016/j.bmcl.2023.129541] [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: 08/24/2023] [Revised: 10/18/2023] [Accepted: 11/03/2023] [Indexed: 11/14/2023]
Abstract
Matrix metalloproteinase-9 (MMP-9) is a secreted zinc-dependent endopeptidase that degrades the extracellular matrix and basement membrane of neurons, and then contributes to synaptic plasticity by remodeling the extracellular matrix. Inhibition of MMP-9 activity has therapeutic potential for neurodegenerative diseases such as fragile X syndrome. This paper reports the molecular design, synthesis, and in vitro studies of novel indole derivatives as inhibitors of proMMP-9 activation. High-throughput screening (HTS) of our internal compound library and subsequent merging of hit compounds 1 and 2 provided compound 4 as a bona-fide lead. X-ray structure-based design and subsequent lead optimization led to the discovery of compound 33, a highly potent and selective inhibitor of proMMP-9 activation.
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Affiliation(s)
- Rie Nishikawa-Shimono
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Motoi Kuwabara
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Sho Fujisaki
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Daisuke Matsuda
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan.
| | - Mayumi Endo
- Discovery Technologies Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Masafumi Kamitani
- Discovery Technologies Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Aya Futamura
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Yusaku Nomura
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Toru Yamaguchi-Sasaki
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Tetsuya Yabuuchi
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Chitose Yamaguchi
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Nozomi Tanaka-Yamamoto
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Shunya Satake
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Kumi Abe-Sato
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Kosuke Funayama
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Mayumi Sakata
- Discovery Technologies Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Shinji Takahashi
- Pharmacology Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Koga Hirano
- Pharmacology Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Takuya Fukunaga
- Discovery Technologies Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Yoriko Uozumi
- Discovery Technologies Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Sayaka Kato
- Discovery Technologies Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Yunoshin Tamura
- Discovery Technologies Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Tomoaki Nakamori
- Discovery Technologies Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Masashi Mima
- Discovery Technologies Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Chiemi Mishima-Tsumagari
- Discovery Technologies Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Dai Nozawa
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Yudai Imai
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan
| | - Taiji Asami
- Medicinal Chemistry Laboratories, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama 331-9530, Japan.
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Goh CJ, Kwon HJ, Kim Y, Jung S, Park J, Lee IK, Park BR, Kim MJ, Kim MJ, Lee MS. Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach. Diagnostics (Basel) 2023; 14:84. [PMID: 38201393 PMCID: PMC10871075 DOI: 10.3390/diagnostics14010084] [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: 10/17/2023] [Revised: 12/26/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Copy number variation (CNV) is a primary source of structural variation in the human genome, leading to several disorders. Therefore, analyzing neonatal CNVs is crucial for managing CNV-related chromosomal disabilities. However, genomic waves can hinder accurate CNV analysis. To mitigate the influences of the waves, we adopted a machine learning approach and developed a new method that uses a modified log R ratio instead of the commonly used log R ratio. Validation results using samples with known CNVs demonstrated the superior performance of our method. We analyzed a total of 16,046 Korean newborn samples using the new method and identified CNVs related to 39 genetic disorders were identified in 342 cases. The most frequently detected CNV-related disorder was Joubert syndrome 4. The accuracy of our method was further confirmed by analyzing a subset of the detected results using NGS and comparing them with our results. The utilization of a genome-wide single nucleotide polymorphism array with wave offset was shown to be a powerful method for identifying CNVs in neonatal cases. The accurate screening and the ability to identify various disease susceptibilities offered by our new method could facilitate the identification of CNV-associated chromosomal disease etiologies.
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Affiliation(s)
- Chul Jun Goh
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Hyuk-Jung Kwon
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
- Department of Computer Science and Engineering, Incheon National University (INU), Incheon 22012, Republic of Korea
| | - Yoonhee Kim
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Seunghee Jung
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Jiwoo Park
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Isaac Kise Lee
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
- Department of Computer Science and Engineering, Incheon National University (INU), Incheon 22012, Republic of Korea
- NGENI Foundation, San Diego, CA 92127, USA
| | - Bo-Ram Park
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Myeong-Ji Kim
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
| | - Min-Jeong Kim
- Diagnomics, Inc., 5795 Kearny Villa Rd., San Diego, CA 92123, USA;
| | - Min-Seob Lee
- Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea; (C.J.G.); (H.-J.K.); (Y.K.); (S.J.); (J.P.); (I.K.L.); (B.-R.P.); (M.-J.K.)
- Diagnomics, Inc., 5795 Kearny Villa Rd., San Diego, CA 92123, USA;
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Angelakis A, Soulioti I, Filippakis M. Diagnosis of acute myeloid leukaemia on microarray gene expression data using categorical gradient boosted trees. Heliyon 2023; 9:e20530. [PMID: 37860531 PMCID: PMC10582309 DOI: 10.1016/j.heliyon.2023.e20530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/21/2023] Open
Abstract
We define an iterative method for dimensionality reduction using categorical gradient boosted trees and Shapley values and created four machine learning models which potentially could be used as diagnostic tests for acute myeloid leukaemia (AML). For the final Catboost model we use a dataset of 2177 individuals using as features 16 probe sets and the age in order to classify if someone has AML or is healthy. The dataset is multicentric and consists of data from 27 organizations, 25 cities, 15 countries and 4 continents. The performance of our last model is specificity: 0.9909, sensitivity: 0.9985, F1-score: 0.9976 and its ROC-AUC: 0.9962 using ten fold cross validation. On an inference dataset the perormance is: specificity: 0.9909, sensitivity: 0.9969, F1-score: 0.9969 and its ROC-AUC: 0.9939. To the best of our knowledge the performance of our model is the best one in the literature, as regards the diagnosis of AML using similar or not data. Moreover, there has not been any bibliographic reference which associates AML or any other type of cancer with the 16 probe sets we used as features in our final model.
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Affiliation(s)
- Athanasios Angelakis
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, Amsterdam Public Health Research Institute, University of Amsterdam Data Science Center, Netherlands
| | - Ioanna Soulioti
- Department of Biology, National and Kapodistrian University of Athens, Greece
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7
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Wu C, Guo D. Identification of Two Flip-Over Genes in Grass Family as Potential Signature of C4 Photosynthesis Evolution. Int J Mol Sci 2023; 24:14165. [PMID: 37762466 PMCID: PMC10531853 DOI: 10.3390/ijms241814165] [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: 07/09/2023] [Revised: 09/05/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
In flowering plants, C4 photosynthesis is superior to C3 type in carbon fixation efficiency and adaptation to extreme environmental conditions, but the mechanisms behind the assembly of C4 machinery remain elusive. This study attempts to dissect the evolutionary divergence from C3 to C4 photosynthesis in five photosynthetic model plants from the grass family, using a combined comparative transcriptomics and deep learning technology. By examining and comparing gene expression levels in bundle sheath and mesophyll cells of five model plants, we identified 16 differentially expressed signature genes showing cell-specific expression patterns in C3 and C4 plants. Among them, two showed distinctively opposite cell-specific expression patterns in C3 vs. C4 plants (named as FOGs). The in silico physicochemical analysis of the two FOGs illustrated that C3 homologous proteins of LHCA6 had low and stable pI values of ~6, while the pI values of LHCA6 homologs increased drastically in C4 plants Setaria viridis (7), Zea mays (8), and Sorghum bicolor (over 9), suggesting this protein may have different functions in C3 and C4 plants. Interestingly, based on pairwise protein sequence/structure similarities between each homologous FOG protein, one FOG PGRL1A showed local inconsistency between sequence similarity and structure similarity. To find more examples of the evolutionary characteristics of FOG proteins, we investigated the protein sequence/structure similarities of other FOGs (transcription factors) and found that FOG proteins have diversified incompatibility between sequence and structure similarities during grass family evolution. This raised an interesting question as to whether the sequence similarity is related to structure similarity during C4 photosynthesis evolution.
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Affiliation(s)
| | - Dianjing Guo
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China;
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8
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Abi-Dargham A, Moeller SJ, Ali F, DeLorenzo C, Domschke K, Horga G, Jutla A, Kotov R, Paulus MP, Rubio JM, Sanacora G, Veenstra-VanderWeele J, Krystal JH. Candidate biomarkers in psychiatric disorders: state of the field. World Psychiatry 2023; 22:236-262. [PMID: 37159365 PMCID: PMC10168176 DOI: 10.1002/wps.21078] [Citation(s) in RCA: 51] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2023] [Indexed: 05/11/2023] Open
Abstract
The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.
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Affiliation(s)
- Anissa Abi-Dargham
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Scott J Moeller
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Farzana Ali
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Basics in Neuromodulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Amandeep Jutla
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Roman Kotov
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | | | - Jose M Rubio
- Zucker School of Medicine at Hofstra-Northwell, Hempstead, NY, USA
- Feinstein Institute for Medical Research - Northwell, Manhasset, NY, USA
- Zucker Hillside Hospital - Northwell Health, Glen Oaks, NY, USA
| | - Gerard Sanacora
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jeremy Veenstra-VanderWeele
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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9
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Fajarda O, Almeida JR, Duarte-Pereira S, Silva RM, Oliveira JL. Methodology to identify a gene expression signature by merging microarray datasets. Comput Biol Med 2023; 159:106867. [PMID: 37060770 DOI: 10.1016/j.compbiomed.2023.106867] [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: 10/19/2022] [Revised: 03/01/2023] [Accepted: 03/30/2023] [Indexed: 04/17/2023]
Abstract
A vast number of microarray datasets have been produced as a way to identify differentially expressed genes and gene expression signatures. A better understanding of these biological processes can help in the diagnosis and prognosis of diseases, as well as in the therapeutic response to drugs. However, most of the available datasets are composed of a reduced number of samples, leading to low statistical, predictive and generalization power. One way to overcome this problem is by merging several microarray datasets into a single dataset, which is typically a challenging task. Statistical methods or supervised machine learning algorithms are usually used to determine gene expression signatures. Nevertheless, statistical methods require an arbitrary threshold to be defined, and supervised machine learning methods can be ineffective when applied to high-dimensional datasets like microarrays. We propose a methodology to identify gene expression signatures by merging microarray datasets. This methodology uses statistical methods to obtain several sets of differentially expressed genes and uses supervised machine learning algorithms to select the gene expression signature. This methodology was validated using two distinct research applications: one using heart failure and the other using autism spectrum disorder microarray datasets. For the first, we obtained a gene expression signature composed of 117 genes, with a classification accuracy of approximately 98%. For the second use case, we obtained a gene expression signature composed of 79 genes, with a classification accuracy of approximately 82%. This methodology was implemented in R language and is available, under the MIT licence, at https://github.com/bioinformatics-ua/MicroGES.
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Affiliation(s)
- Olga Fajarda
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal.
| | - João Rafael Almeida
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal; Department of Computation, University of A Coruña, A Coruña, Spain.
| | - Sara Duarte-Pereira
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal; Department of Medical Sciences and iBiMED-Institute of Biomedicine, University of Aveiro, Aveiro, Portugal.
| | - Raquel M Silva
- Universidade Católica Portuguesa, Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Viseu, Portugal.
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10
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Hess JL, Quinn TP, Zhang C, Hearn GC, Chen S, Kong SW, Cairns M, Tsuang MT, Faraone SV, Glatt SJ. BrainGENIE: The Brain Gene Expression and Network Imputation Engine. Transl Psychiatry 2023; 13:98. [PMID: 36949060 PMCID: PMC10033657 DOI: 10.1038/s41398-023-02390-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/24/2023] Open
Abstract
In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood-brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947-11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.
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Affiliation(s)
- Jonathan L Hess
- Department of Psychiatry & Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Thomas P Quinn
- Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia
| | - Chunling Zhang
- Department of Neuroscience & Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Gentry C Hearn
- Department of Neuroscience & Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Samuel Chen
- Department of Psychiatry & Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Murray Cairns
- School of Biomedical Sciences & Pharmacy, Faculty of Health, The University of Newcastle, New South Wales, Callaghan, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, Australia
- Centre for Brain & Mental Health Research, The University of Newcastle, Callaghan, Australia
| | - Ming T Tsuang
- Center for Behavioral Genomics, Department of Psychiatry, Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA
- Harvard Institute of Psychiatric Epidemiology and Genetics, Boston, MA, USA
| | - Stephen V Faraone
- Department of Psychiatry & Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
- Department of Neuroscience & Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Stephen J Glatt
- Department of Psychiatry & Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA.
- Department of Neuroscience & Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA.
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11
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Bao B, Zahiri J, Gazestani VH, Lopez L, Xiao Y, Kim R, Wen TH, Chiang AWT, Nalabolu S, Pierce K, Robasky K, Wang T, Hoekzema K, Eichler EE, Lewis NE, Courchesne E. A predictive ensemble classifier for the gene expression diagnosis of ASD at ages 1 to 4 years. Mol Psychiatry 2023; 28:822-833. [PMID: 36266569 PMCID: PMC9908553 DOI: 10.1038/s41380-022-01826-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 09/13/2022] [Accepted: 09/27/2022] [Indexed: 11/09/2022]
Abstract
Autism Spectrum Disorder (ASD) diagnosis remains behavior-based and the median age of diagnosis is ~52 months, nearly 5 years after its first-trimester origin. Accurate and clinically-translatable early-age diagnostics do not exist due to ASD genetic and clinical heterogeneity. Here we collected clinical, diagnostic, and leukocyte RNA data from 240 ASD and typically developing (TD) toddlers (175 toddlers for training and 65 for test). To identify gene expression ASD diagnostic classifiers, we developed 42,840 models composed of 3570 gene expression feature selection sets and 12 classification methods. We found that 742 models had AUC-ROC ≥ 0.8 on both Training and Test sets. Weighted Bayesian model averaging of these 742 models yielded an ensemble classifier model with accurate performance in Training and Test gene expression datasets with ASD diagnostic classification AUC-ROC scores of 85-89% and AUC-PR scores of 84-92%. ASD toddlers with ensemble scores above and below the overall ASD ensemble mean of 0.723 (on a scale of 0 to 1) had similar diagnostic and psychometric scores, but those below this ASD ensemble mean had more prenatal risk events than TD toddlers. Ensemble model feature genes were involved in cell cycle, inflammation/immune response, transcriptional gene regulation, cytokine response, and PI3K-AKT, RAS and Wnt signaling pathways. We additionally collected targeted DNA sequencing smMIPs data on a subset of ASD risk genes from 217 of the 240 ASD and TD toddlers. This DNA sequencing found about the same percentage of SFARI Level 1 and 2 ASD risk gene mutations in TD (12 of 105) as in ASD (13 of 112) toddlers, and classification based only on the presence of mutation in these risk genes performed at a chance level of 49%. By contrast, the leukocyte ensemble gene expression classifier correctly diagnostically classified 88% of TD and ASD toddlers with ASD risk gene mutations. Our ensemble ASD gene expression classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; out-performs a risk gene mutation classifier; and has potential for clinical translation.
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Affiliation(s)
- Bokan Bao
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Javad Zahiri
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Vahid H Gazestani
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Linda Lopez
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Yaqiong Xiao
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Raphael Kim
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Teresa H Wen
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Austin W T Chiang
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Srinivasa Nalabolu
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Karen Pierce
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Kimberly Robasky
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, US
- School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Carolina Health and Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Tianyun Wang
- Department of Medical Genetics, Center for Medical Genetics, Peking University Health Science Center, 100191, Beijing, China
- Neuroscience Research Institute, Peking University; Key Laboratory for Neuroscience, Ministry of Education of China & National Health Commission of China, 100191, Beijing, 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
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
| | - Eric Courchesne
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA.
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12
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Li H, Xu Y, Li W, Zhang L, Zhang X, Li B, Chen Y, Wang X, Zhu C. Novel insights into the immune cell landscape and gene signatures in autism spectrum disorder by bioinformatics and clinical analysis. Front Immunol 2023; 13:1082950. [PMID: 36761165 PMCID: PMC9905846 DOI: 10.3389/fimmu.2022.1082950] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 12/28/2022] [Indexed: 01/26/2023] Open
Abstract
The pathogenesis of autism spectrum disorder (ASD) is not well understood, especially in terms of immunity and inflammation, and there are currently no early diagnostic or treatment methods. In this study, we obtained six existing Gene Expression Omnibus transcriptome datasets from the blood of ASD patients. We performed functional enrichment analysis, PPI analysis, CIBERSORT algorithm, and Spearman correlation analysis, with a focus on expression profiling in hub genes and immune cells. We validated that monocytes and nonclassical monocytes were upregulated in the ASD group using peripheral blood (30 children with ASD and 30 age and sex-matched typically developing children) using flow cytometry. The receiver operating characteristic curves (PSMC4 and ALAS2) and analysis stratified by ASD severity (LIlRB1 and CD69) showed that they had predictive value using the "training" and verification groups. Three immune cell types - monocytes, M2 macrophages, and activated dendritic cells - had different degrees of correlation with 15 identified hub genes. In addition, we analyzed the miRNA-mRNA network and agents-gene interactions using miRNA databases (starBase and miRDB) and the DSigDB database. Two miRNAs (miR-342-3p and miR-1321) and 23 agents were linked with ASD. These findings suggest that dysregulation of the immune system may contribute to ASD development, especially dysregulation of monocytes and monocyte-derived cells. ASD-related hub genes may serve as potential predictors for ASD, and the potential ASD-related miRNAs and agents identified here may open up new strategies for the prevention and treatment of ASD.
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Affiliation(s)
- Hongwei Li
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yiran Xu
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China,National Health Council (NHC) Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Zhengzhou, China
| | - Wenhua Li
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lingling Zhang
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoli Zhang
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingbing Li
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yiwen Chen
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaoyang Wang
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China,Centre of Perinatal Medicine and Health, Institute of Clinical Science, University of Gothenburg, Gothenburg, Sweden
| | - Changlian Zhu
- Henan Key Laboratory of Child Brain Injury and Henan Pediatric Clinical Research Center, Institute of Neuroscience and the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China,Center for Brain Repair and Rehabilitation, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden,*Correspondence: Changlian Zhu, ;;
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13
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Rastegari M, Salehi N, Zare-Mirakabad F. Biomarker prediction in autism spectrum disorder using a network-based approach. BMC Med Genomics 2023; 16:12. [PMID: 36691005 PMCID: PMC9869547 DOI: 10.1186/s12920-023-01439-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 01/12/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Autism is a neurodevelopmental disorder that is usually diagnosed in early childhood. Timely diagnosis and early initiation of treatments such as behavioral therapy are important in autistic people. Discovering critical genes and regulators in this disorder can lead to early diagnosis. Since the contribution of miRNAs along their targets can lead us to a better understanding of autism, we propose a framework containing two steps for gene and miRNA discovery. METHODS The first step, called the FA_gene algorithm, finds a small set of genes involved in autism. This algorithm uses the WGCNA package to construct a co-expression network for control samples and seek modules of genes that are not reproducible in the corresponding co-expression network for autistic samples. Then, the protein-protein interaction network is constructed for genes in the non-reproducible modules and a small set of genes that may have potential roles in autism is selected based on this network. The second step, named the DMN_miRNA algorithm, detects the minimum number of miRNAs related to autism. To do this, DMN_miRNA defines an extended Set Cover algorithm over the mRNA-miRNA network, consisting of the selected genes and corresponding miRNA regulators. RESULTS In the first step of the framework, the FA_gene algorithm finds a set of important genes; TP53, TNF, MAPK3, ACTB, TLR7, LCK, RAC2, EEF2, CAT, ZAP70, CD19, RPLP0, CDKN1A, CCL2, CDK4, CCL5, CTSD, CD4, RACK1, CD74; using co-expression and protein-protein interaction networks. In the second step, the DMN_miRNA algorithm extracts critical miRNAs, hsa-mir-155-5p, hsa-mir-17-5p, hsa-mir-181a-5p, hsa-mir-18a-5p, and hsa-mir-92a-1-5p, as signature regulators for autism using important genes and mRNA-miRNA network. The importance of these key genes and miRNAs is confirmed by previous studies and enrichment analysis. CONCLUSION This study suggests FA_gene and DMN_miRNA algorithms for biomarker discovery, which lead us to a list of important players in ASD with potential roles in the nervous system or neurological disorders that can be experimentally investigated as candidates for ASD diagnostic tests.
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Affiliation(s)
- Maryam Rastegari
- Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran, Polytechnic), 424, Hafez Ave, P.O. Box: 15875-4413, Tehran, Iran
| | - Najmeh Salehi
- School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | - Fatemeh Zare-Mirakabad
- Department of Mathematics and Computer Science, Amirkabir University of Technology (Tehran, Polytechnic), 424, Hafez Ave, P.O. Box: 15875-4413, Tehran, Iran.
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14
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Balasubramanian R, Vinod PK. Inferring miRNA sponge modules across major neuropsychiatric disorders. Front Mol Neurosci 2022; 15:1009662. [PMID: 36385761 PMCID: PMC9650411 DOI: 10.3389/fnmol.2022.1009662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/05/2022] [Indexed: 12/01/2022] Open
Abstract
The role of non-coding RNAs in neuropsychiatric disorders (NPDs) is an emerging field of study. The long non-coding RNAs (lncRNAs) are shown to sponge the microRNAs (miRNAs) from interacting with their target mRNAs. Investigating the sponge activity of lncRNAs in NPDs will provide further insights into biological mechanisms and help identify disease biomarkers. In this study, a large-scale inference of the lncRNA-related miRNA sponge network of pan-neuropsychiatric disorders, including autism spectrum disorder (ASD), schizophrenia (SCZ), and bipolar disorder (BD), was carried out using brain transcriptomic (RNA-Seq) data. The candidate miRNA sponge modules were identified based on the co-expression pattern of non-coding RNAs, sharing of miRNA binding sites, and sensitivity canonical correlation. miRNA sponge modules are associated with chemical synaptic transmission, nervous system development, metabolism, immune system response, ribosomes, and pathways in cancer. The identified modules showed similar and distinct gene expression patterns depending on the neuropsychiatric condition. The preservation of miRNA sponge modules was shown in the independent brain and blood-transcriptomic datasets of NPDs. We also identified miRNA sponging lncRNAs that may be potential diagnostic biomarkers for NPDs. Our study provides a comprehensive resource on miRNA sponging in NPDs.
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15
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Matta J, Dobrino D, Yeboah D, Howard S, EL-Manzalawy Y, Obafemi-Ajayi T. Connecting phenotype to genotype: PheWAS-inspired analysis of autism spectrum disorder. Front Hum Neurosci 2022; 16:960991. [PMID: 36310845 PMCID: PMC9605200 DOI: 10.3389/fnhum.2022.960991] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 09/14/2022] [Indexed: 04/13/2024] Open
Abstract
Autism Spectrum Disorder (ASD) is extremely heterogeneous clinically and genetically. There is a pressing need for a better understanding of the heterogeneity of ASD based on scientifically rigorous approaches centered on systematic evaluation of the clinical and research utility of both phenotype and genotype markers. This paper presents a holistic PheWAS-inspired method to identify meaningful associations between ASD phenotypes and genotypes. We generate two types of phenotype-phenotype (p-p) graphs: a direct graph that utilizes only phenotype data, and an indirect graph that incorporates genotype as well as phenotype data. We introduce a novel methodology for fusing the direct and indirect p-p networks in which the genotype data is incorporated into the phenotype data in varying degrees. The hypothesis is that the heterogeneity of ASD can be distinguished by clustering the p-p graph. The obtained graphs are clustered using network-oriented clustering techniques, and results are evaluated. The most promising clusterings are subsequently analyzed for biological and domain-based relevance. Clusters obtained delineated different aspects of ASD, including differentiating ASD-specific symptoms, cognitive, adaptive, language and communication functions, and behavioral problems. Some of the important genes associated with the clusters have previous known associations to ASD. We found that clusters based on integrated genetic and phenotype data were more effective at identifying relevant genes than clusters constructed from phenotype information alone. These genes included five with suggestive evidence of ASD association and one known to be a strong candidate.
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Affiliation(s)
- John Matta
- Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL, United States
| | - Daniel Dobrino
- Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL, United States
| | - Dacosta Yeboah
- Department of Computer Science, Missouri State University, Springfield, MO, United States
| | - Swade Howard
- Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL, United States
| | - Yasser EL-Manzalawy
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, United States
| | - Tayo Obafemi-Ajayi
- Engineering Program, Missouri State University, Springfield, MO, United States
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16
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Voinsky I, Zoabi Y, Shomron N, Harel M, Cassuto H, Tam J, Rose S, Scheck AC, Karim MA, Frye RE, Aran A, Gurwitz D. Blood RNA Sequencing Indicates Upregulated BATF2 and LY6E and Downregulated ISG15 and MT2A Expression in Children with Autism Spectrum Disorder. Int J Mol Sci 2022; 23:ijms23179843. [PMID: 36077244 PMCID: PMC9456089 DOI: 10.3390/ijms23179843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 08/24/2022] [Accepted: 08/28/2022] [Indexed: 11/16/2022] Open
Abstract
Mutations in over 100 genes are implicated in autism spectrum disorder (ASD). DNA SNPs, CNVs, and epigenomic modifications also contribute to ASD. Transcriptomics analysis of blood samples may offer clues for pathways dysregulated in ASD. To expand and validate published findings of RNA-sequencing (RNA-seq) studies, we performed RNA-seq of whole blood samples from an Israeli discovery cohort of eight children with ASD compared with nine age- and sex-matched neurotypical children. This revealed 10 genes with differential expression. Using quantitative real-time PCR, we compared RNAs from whole blood samples of 73 Israeli and American children with ASD and 26 matched neurotypical children for the 10 dysregulated genes detected by RNA-seq. This revealed higher expression levels of the pro-inflammatory transcripts BATF2 and LY6E and lower expression levels of the anti-inflammatory transcripts ISG15 and MT2A in the ASD compared to neurotypical children. BATF2 was recently reported as upregulated in blood samples of Japanese adults with ASD. Our findings support an involvement of these genes in ASD phenotypes, independent of age and ethnicity. Upregulation of BATF2 and downregulation of ISG15 and MT2A were reported to reduce cancer risk. Implications of the dysregulated genes for pro-inflammatory phenotypes, immunity, and cancer risk in ASD are discussed.
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Affiliation(s)
- Irena Voinsky
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yazeed Zoabi
- Department of Cell and Developmental Biology, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
| | - Noam Shomron
- Department of Cell and Developmental Biology, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Moria Harel
- Shaare Zedek Medical Center, Jerusalem 91031, Israel
| | | | - Joseph Tam
- Obesity and Metabolism Laboratory, Institute for Drug Research, School of Pharmacy, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 91120, Israel
| | - Shannon Rose
- Department of Pediatrics, University of Arkansas for Medical Sciences and Arkansas Children’s Research Institute, Little Rock, AR 72205, USA
| | - Adrienne C. Scheck
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
- Department of Child Health, University of Arizona College of Medicine-Phoenix, Phoenix, AZ 85004, USA
| | - Mohammad A. Karim
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
- Department of Child Health, University of Arizona College of Medicine-Phoenix, Phoenix, AZ 85004, USA
| | - Richard E. Frye
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
- Department of Child Health, University of Arizona College of Medicine-Phoenix, Phoenix, AZ 85004, USA
- Rossignol Medical Center, Phoenix, AZ 85050, USA
| | - Adi Aran
- Shaare Zedek Medical Center, Jerusalem 91031, Israel
- Obesity and Metabolism Laboratory, Institute for Drug Research, School of Pharmacy, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 91120, Israel
- Correspondence: (A.A.); (D.G.)
| | - David Gurwitz
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
- Correspondence: (A.A.); (D.G.)
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17
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Hughes HK, Rowland ME, Onore CE, Rogers S, Ciernia AV, Ashwood P. Dysregulated gene expression associated with inflammatory and translation pathways in activated monocytes from children with autism spectrum disorder. Transl Psychiatry 2022; 12:39. [PMID: 35082275 PMCID: PMC8791942 DOI: 10.1038/s41398-021-01766-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 11/17/2021] [Accepted: 12/07/2021] [Indexed: 01/26/2023] Open
Abstract
Autism spectrum disorder (ASD) is a complex developmental disorder characterized by deficits in social interactions, communication, and stereotypical behaviors. Immune dysfunction is a common co-morbidity seen in ASD, with innate immune activation seen both in the brain and periphery. We previously identified significant differences in peripheral monocyte cytokine responses after stimulation with lipoteichoic acid (LTA) and lipopolysaccharide (LPS), which activate toll-like receptors (TLR)-2 and 4 respectively. However, an unbiased examination of monocyte gene expression in response to these stimulants had not yet been performed. To identify how TLR activation impacts gene expression in ASD monocytes, we isolated peripheral blood monocytes from 26 children diagnosed with autistic disorder (AD) or pervasive developmental disorder-not otherwise specified (PDDNOS) and 22 typically developing (TD) children and cultured them with LTA or LPS for 24 h, then performed RNA sequencing. Activation of both TLR2 and TLR4 induced expression of immune genes, with a subset that were differentially regulated in AD compared to TD samples. In response to LPS, monocytes from AD children showed a unique increase in KEGG pathways and GO terms that include key immune regulator genes. In contrast, monocytes from TD children showed a consistent decrease in expression of genes associated with translation in response to TLR stimulation. This decrease was not observed in AD or PDDNOS monocytes, suggesting a failure to properly downregulate a prolonged immune response in monocytes from children with ASD. As monocytes are involved in early orchestration of the immune response, our findings will help elucidate the mechanisms regulating immune dysfunction in ASD.
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Affiliation(s)
- Heather K Hughes
- Department of Medical Microbiology and Immunology, School of Medicine, University of California, Davis, CA, USA
- M.I.N.D. Institute, University of California, Davis, CA, USA
| | - Megan E Rowland
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
| | - Charity E Onore
- Department of Medical Microbiology and Immunology, School of Medicine, University of California, Davis, CA, USA
- M.I.N.D. Institute, University of California, Davis, CA, USA
| | - Sally Rogers
- M.I.N.D. Institute, University of California, Davis, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, CA, USA
| | - Annie Vogel Ciernia
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
| | - Paul Ashwood
- Department of Medical Microbiology and Immunology, School of Medicine, University of California, Davis, CA, USA.
- M.I.N.D. Institute, University of California, Davis, CA, USA.
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18
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Artificial Intelligence for Autism Spectrum Disorders. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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19
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Beversdorf DQ, Anagnostou E, Hardan A, Wang P, Erickson CA, Frazier TW, Veenstra-VanderWeele J. Editorial: Precision medicine approaches for heterogeneous conditions such as autism spectrum disorders (The need for a biomarker exploration phase in clinical trials - Phase 2m). Front Psychiatry 2022; 13:1079006. [PMID: 36741580 PMCID: PMC9893852 DOI: 10.3389/fpsyt.2022.1079006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/13/2022] [Indexed: 01/20/2023] Open
Affiliation(s)
- David Q Beversdorf
- Departments of Radiology, Neurology, and Psychological Sciences, William and Nancy Thompson Endowed Chair in Radiology, University of Missouri, Columbia, MO, United States
| | - Evdokia Anagnostou
- Holland Bloorview Kids Rehabilitation Hospital, University of Toronto, Toronto, ON, Canada
| | - Antonio Hardan
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
| | - Paul Wang
- Clinical Research Associates LLC, Simons Foundation, Department of Pediatrics, Yale University School of Medicine, New Haven, CT, United States
| | - Craig A Erickson
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Thomas W Frazier
- Department of Psychology, John Carroll University, University Heights, OH, United States.,Department of Pediatrics, State University of New York Upstate Medical University, Syracuse, NY, United States
| | - Jeremy Veenstra-VanderWeele
- Departments of Psychiatry and Pediatrics, New York State Psychiatric Institute, Columbia University, New York, NY, United States.,NewYork-Presbyterian Center for Autism and the Developing Brain, New York, NY, United States
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20
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Dean DD, Agarwal S, Muthuswamy S, Asim A. Brain exosomes as minuscule information hub for Autism Spectrum Disorder. Expert Rev Mol Diagn 2021; 21:1323-1331. [PMID: 34720032 DOI: 10.1080/14737159.2021.2000395] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Autism spectrum disorder (ASD) is a neurodevelopmental disorder initiating in the first three years of life. Early initiation of management therapies can significantly improve the health and quality of life of ASD subjects. Thus, indicating the need for suitable biomarkers for the early identification of ASD. Various biological domains were investigated in the quest for reliable biomarkers. However, most biomarkers are in the preliminary stage, and clinical validation is yet to be defined. Exosome based research gained momentum in various Central Nervous System disorders for biomarker identification. However, the utility and prospect of exosomes in ASD is still underexplored. AREAS COVERED In the present review, we summarized the biomarker discovery current status and the future of brain-specific exosomes in understanding pathophysiology and its potential as a biomarker. The studies reviewed herein were identified via systematic search (dated: June 2021) of PubMed using variations related to autism (ASD OR autism OR Autism spectrum disorder) AND exosomes AND/OR biomarkers. EXPERT OPINION As exosomess are highly relevant in brain disorders like ASD, direct access to brain tissue for molecular assessment is ethically impossible. Thus investigating the brain-derived exosomes would undoubtedly answer many unsolved aspects of the pathogenesis and provide reliable biomarkers.
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Affiliation(s)
- Deepika Delsa Dean
- Deptartment of Medical Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences (Sgpgims), Lucknow, India
| | - Sarita Agarwal
- Deptartment of Medical Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences (Sgpgims), Lucknow, India
| | | | - Ambreen Asim
- Deptartment of Medical Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences (Sgpgims), Lucknow, India
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21
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Matta J, Dobrino D, Howard S, Yeboah D, Kopel J, El-Manzalawy Y, Obafemi-Ajayi T. A PheWAS Model of Autism Spectrum Disorder. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2110-2114. [PMID: 34891705 DOI: 10.1109/embc46164.2021.9629533] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Children with Autism Spectrum Disorder (ASD) exhibit a wide diversity in type, number, and severity of social deficits as well as communicative and cognitive difficulties. It is a challenge to categorize the phenotypes of a particular ASD patient with their unique genetic variants. There is a need for a better understanding of the connections between genotype information and the phenotypes to sort out the heterogeneity of ASD. In this study, single nucleotide polymorphism (SNP) and phenotype data obtained from a simplex ASD sample are combined using a PheWAS-inspired approach to construct a phenotype-phenotype network. The network is clustered, yielding groups of etiologically related phenotypes. These clusters are analyzed to identify relevant genes associated with each set of phenotypes. The results identified multiple discriminant SNPs associated with varied phenotype clusters such as ASD aberrant behavior (self-injury, compulsiveness and hyperactivity), as well as IQ and language skills. Overall, these SNPs were linked to 22 significant genes. An extensive literature search revealed that eight of these are known to have strong evidence of association with ASD. The others have been linked to related disorders such as mental conditions, cognition, and social functioning.Clinical relevance- This study further informs on connections between certain groups of ASD phenotypes and their unique genetic variants. Such insight regarding the heterogeneity of ASD would support clinicians to advance more tailored interventions and improve outcomes for ASD patients.
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22
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Chung MK, Smith MR, Lin Y, Walker DI, Jones D, Patel CJ, Kong SW. Plasma metabolomics of autism spectrum disorder and influence of shared components in proband families. EXPOSOME 2021; 1:osab004. [PMID: 35028569 PMCID: PMC8739333 DOI: 10.1093/exposome/osab004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 09/24/2021] [Accepted: 09/29/2021] [Indexed: 11/25/2022]
Abstract
Prevalence of autism spectrum disorder (ASD) has been increasing in the United States in the past decades. The exact mechanisms remain enigmatic, and diagnosis of the disease still relies primarily on assessment of behavior. We first used a case-control design (75 idiopathic cases and 29 controls, enrolled at Boston Children's Hospital from 2007-2012) to identify plasma biomarkers of ASD through a metabolome-wide association study approach. Then we leveraged a family-based design (31 families) to investigate the influence of shared genetic and environmental components on the autism-associated features. Using untargeted high-resolution mass spectrometry metabolomics platforms, we detected 19 184 features. Of these, 191 were associated with ASD (false discovery rate < 0.05). We putatively annotated 30 features that had an odds ratio (OR) between <0.01 and 5.84. An identified endogenous metabolite, O-phosphotyrosine, was associated with an extremely low autism odds (OR 0.17; 95% confidence interval 0.06-0.39). We also found that glutathione metabolism was associated with ASD (P = 0.048). Correlations of the significant features between proband and parents were low (median = 0.09). Of the 30 annotated features, the median correlations within families (proband-parents) were -0.15 and 0.24 for the endogenous and exogenous metabolites, respectively. We hypothesize that, without feature identification, family-based correlation analysis of autism-associated features can be an alternative way to assist the prioritization of potentially diagnostic features. A panel of ASD diagnostic metabolic markers with high specificity could be derived upon further studies.
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Affiliation(s)
- Ming Kei Chung
- Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Matthew Ryan Smith
- Division of Pulmonary Medicine, Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA, USA
| | - Yufei Lin
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA
| | - Douglas I Walker
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dean Jones
- Division of Pulmonary Medicine, Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, GA, USA
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Harvard University, Boston, MA, USA
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23
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Napoli E, Flores A, Mansuri Y, Hagerman RJ, Giulivi C. Sulforaphane improves mitochondrial metabolism in fibroblasts from patients with fragile X-associated tremor and ataxia syndrome. Neurobiol Dis 2021; 157:105427. [PMID: 34153466 PMCID: PMC8475276 DOI: 10.1016/j.nbd.2021.105427] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 06/10/2021] [Accepted: 06/16/2021] [Indexed: 02/09/2023] Open
Abstract
CGG expansions between 55 and 200 in the 5'-untranslated region of the fragile-X mental retardation gene (FMR1) increase the risk of developing the late-onset debilitating neuromuscular disease Fragile X-Associated Tremor/Ataxia Syndrome (FXTAS). While the science behind this mutation, as a paradigm for RNA-mediated nucleotide triplet repeat expansion diseases, has progressed rapidly, no treatment has proven effective at delaying the onset or decreasing morbidity, especially at later stages of the disease. Here, we demonstrated the beneficial effect of the phytochemical sulforaphane (SFN), exerted through NRF2-dependent and independent manner, on pathways relevant to brain function, bioenergetics, unfolded protein response, proteosome, antioxidant defenses, and iron metabolism in fibroblasts from FXTAS-affected subjects at all disease stages. This study paves the way for future clinical studies with SFN in the treatment of FXTAS, substantiated by the established use of this agent in clinical trials of diseases with NRF2 dysregulation and in which age is the leading risk factor.
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Affiliation(s)
- Eleonora Napoli
- Department of Molecular Biosciences, School of Veterinary Medicine, University of California, Davis, CA 95616
| | - Amanda Flores
- Department of Molecular Biosciences, School of Veterinary Medicine, University of California, Davis, CA 95616;,Department of Biochemistry, Medical Sciences Campus, University of Puerto Rico, San Juan, Puerto Rico
| | - Yasmeen Mansuri
- Department of Molecular Biosciences, School of Veterinary Medicine, University of California, Davis, CA 95616
| | - Randi J. Hagerman
- Department of Pediatrics, University of California Davis Medical Center, Sacramento, CA;,Medical Investigations of Neurodevelopmental Disorders (M.I.N.D.) Institute, University of California Davis, CA 95817
| | - Cecilia Giulivi
- Department of Molecular Biosciences, School of Veterinary Medicine, University of California, Davis, CA 95616, United States of America; Medical Investigations of Neurodevelopmental Disorders (M.I.N.D.) Institute, University of California Davis, CA 95817, USA.
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24
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Schmidt RJ, Liang D, Busgang SA, Curtin P, Giulivi C. Maternal Plasma Metabolic Profile Demarcates a Role for Neuroinflammation in Non-Typical Development of Children. Metabolites 2021; 11:metabo11080545. [PMID: 34436486 PMCID: PMC8400060 DOI: 10.3390/metabo11080545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 11/16/2022] Open
Abstract
Maternal and cord plasma metabolomics were used to elucidate biological pathways associated with increased diagnosis risk for autism spectrum disorders (ASD). Metabolome-wide associations were assessed in both maternal and umbilical cord plasma in relation to diagnoses of ASD and other non-typical development (Non-TD) compared to typical development (TD) in the Markers of Autism risk in Babies: Learning Early Signs (MARBLES) cohort study of children born to mothers who already have at least one child with ASD. Analyses were stratified by sample matrix type, machine mode, and annotation confidence level. Dimensionality reduction techniques were used [i.e, principal component analysis (PCA) and random subset weighted quantile sum regression (WQSRS)] to minimize the high multiple comparison burden. With WQSRS, a metabolite mixture obtained from the negative mode of maternal plasma decreased the odds of Non-TD compared to TD. These metabolites, all related to the prostaglandin pathway, underscored the relevance of neuroinflammation status. No other significant findings were observed. Dimensionality reduction strategies provided confirming evidence that a set of maternal plasma metabolites are important in distinguishing Non-TD compared to TD diagnosis. A lower risk for Non-TD was linked to anti-inflammatory elements, thereby linking neuroinflammation to detrimental brain function consistent with studies ranging from neurodevelopment to neurodegeneration.
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Affiliation(s)
- Rebecca J. Schmidt
- Department of Public Health Sciences, School of Medicine, University of California Davis, Davis, CA 95616, USA;
- Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, School of Medicine, University of California Davis, Sacramento, CA 95817, USA
| | - Donghai Liang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA;
| | - Stefanie A. Busgang
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (S.A.B.); (P.C.)
| | - Paul Curtin
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (S.A.B.); (P.C.)
| | - Cecilia Giulivi
- Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, School of Medicine, University of California Davis, Sacramento, CA 95817, USA
- Department of Molecular Biosciences, School of Veterinary Medicine, University of California Davis, Davis, CA 95616, USA
- Correspondence:
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25
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Somekh J. Model-based pathway enrichment analysis applied to the TGF-beta regulation of autophagy in autism. J Biomed Inform 2021; 118:103781. [PMID: 33839306 DOI: 10.1016/j.jbi.2021.103781] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 03/23/2021] [Accepted: 04/05/2021] [Indexed: 10/21/2022]
Abstract
To differentiate between conditions of health and disease, current pathway enrichment analysis methods detect the differential expression of distinct biological pathways. System-level model-driven approaches, however, are lacking. Here we present a new methodology that uses a dynamic model to suggest a unified subsystem to better differentiate between diseased and healthy conditions. Our methodology includes the following steps: 1) detecting connections between relevant differentially expressed pathways; 2) construction of a unified in silico model, a stochastic Petri net model that links these distinct pathways; 3) model execution to predict subsystem activation; and 4) enrichment analysis of the predicted subsystem. We apply our approach to the TGF-beta regulation of the autophagy system implicated in autism. Our model was constructed manually, based on the literature, to predict, using model simulation, the TGF-beta-to-autophagy active subsystem and downstream gene expression changes associated with TGF-beta, which go beyond the individual findings derived from literature. We evaluated the in silico predicted subsystem and found it to be co-expressed in the normative whole blood human gene expression data. Finally, we show our subsystem's gene set to be significantly differentially expressed in two independent datasets of blood samples of ASD (autistic spectrum disorders) individuals as opposed to controls. Our study demonstrates that dynamic pathway unification can define a new refined subsystem that can significantly differentiate between disease conditions.
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Affiliation(s)
- Judith Somekh
- Department of Information Systems, University of Haifa, Haifa 3498838, Israel.
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26
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Hammill C, Lerch JP, Taylor MJ, Ameis SH, Chakravarty MM, Szatmari P, Anagnostou E, Lai MC. Quantitative and Qualitative Sex Modulations in the Brain Anatomy of Autism. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:898-909. [PMID: 33713843 DOI: 10.1016/j.bpsc.2021.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 02/27/2021] [Accepted: 03/01/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Sex-based neurobiological heterogeneity in autism is poorly understood. Research is disproportionately biased to males, leading to an unwarranted presumption that autism neurobiology is the same across sexes. Previous neuroimaging studies using amalgamated multicenter datasets to increase autistic female samples are characterized by large statistical noise. METHODS We used a better-powered dataset of 1183 scans of 839 individuals-299 (467 scans) autistic males, 74 (102 scans) autistic females, 240 (334 scans) control males, and 226 (280 scans) control females-to test two whole-brain models of overall/global sex modulations on autism neuroanatomy, by summary measures computed across the brain: the local magnitude model, in which the same brain regions/circuitries are involved across sexes but effect sizes are larger in females, indicating quantitative sex modulation; and spatial dissimilarity model, in which the neuroanatomy differs spatially between sexes, indicating qualitative sex modulation. The male and female autism groups were matched on age, IQ, and autism symptoms. Autism brain features were defined by comparisons with same-sex control individuals. RESULTS Across five metrics (cortical thickness, surface area, volume, mean absolute curvature, and subcortical volume), we found no evidence supporting the local magnitude model. We found indicators supporting the spatial dissimilarity model on cortical mean absolute curvature and subcortical volume, but not on other metrics. CONCLUSIONS The overall/global autism neuroanatomy in females and males does not simply differ quantitatively in the same brain regions/circuitries. They may differ qualitatively in spatial involvement in cortical curvature and subcortical volume. The neuroanatomy of autism may be partly sex specific. Sex stratification to inform autism preclinical/clinical research is needed to identify sex-informed neurodevelopmental targets.
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Affiliation(s)
| | - Jason P Lerch
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Margot J Taylor
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada
| | - Stephanie H Ameis
- Department of Psychiatry, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Peter Szatmari
- Department of Psychiatry, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Evdokia Anagnostou
- Holland Bloorview Kids Rehabilitation Hospital and Department of Paediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Meng-Chuan Lai
- Department of Psychiatry, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.
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27
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Garbulowski M, Smolinska K, Diamanti K, Pan G, Maqbool K, Feuk L, Komorowski J. Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder. Front Genet 2021; 12:618277. [PMID: 33719335 PMCID: PMC7946989 DOI: 10.3389/fgene.2021.618277] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 01/12/2021] [Indexed: 01/16/2023] Open
Abstract
Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between EMC4 and TMEM30A, which may suggest a co-regulation between these genes. The present study demonstrates the potential of applying interpretable machine learning in bioinformatics analyses. Although the proposed methodology was designed for transcriptomics data, it can be applied to other omics disciplines.
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Affiliation(s)
- Mateusz Garbulowski
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Karolina Smolinska
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Klev Diamanti
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Gang Pan
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Khurram Maqbool
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Lars Feuk
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Jan Komorowski
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden.,Swedish Collegium for Advanced Study, Uppsala, Sweden.,Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland.,Washington National Primate Research Center, Seattle, WA, United States
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28
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Hewitson L, Mathews JA, Devlin M, Schutte C, Lee J, German DC. Blood biomarker discovery for autism spectrum disorder: A proteomic analysis. PLoS One 2021; 16:e0246581. [PMID: 33626076 PMCID: PMC7904196 DOI: 10.1371/journal.pone.0246581] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 01/22/2021] [Indexed: 12/12/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and social interaction and restricted, repetitive patterns of behavior, interests, or activities. Given the lack of specific pharmacological therapy for ASD and the clinical heterogeneity of the disorder, current biomarker research efforts are geared mainly toward identifying markers for determining ASD risk or for assisting with a diagnosis. A wide range of putative biological markers for ASD is currently being investigated. Proteomic analyses indicate that the levels of many proteins in plasma/serum are altered in ASD, suggesting that a panel of proteins may provide a blood biomarker for ASD. Serum samples from 76 boys with ASD and 78 typically developing (TD) boys, 18 months-8 years of age, were analyzed to identify possible early biological markers for ASD. Proteomic analysis of serum was performed using SomaLogic’s SOMAScanTM assay 1.3K platform. A total of 1,125 proteins were analyzed. There were 86 downregulated proteins and 52 upregulated proteins in ASD (FDR < 0.05). Combining three different algorithms, we found a panel of 9 proteins that identified ASD with an area under the curve (AUC) = 0.8599±0.0640, with specificity and sensitivity of 0.8217±0.1178 and 0.835±0.1176, respectively. All 9 proteins were significantly different in ASD compared with TD boys, and were significantly correlated with ASD severity as measured by ADOS total scores. Using machine learning methods, a panel of serum proteins was identified that may be useful as a blood biomarker for ASD in boys. Further verification of the protein biomarker panel with independent test sets is warranted.
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Affiliation(s)
- Laura Hewitson
- The Johnson Center for Child Health and Development, Austin, TX, United States of America
| | - Jeremy A Mathews
- Departments of Mathematical Sciences and Biological Sciences, Bioinformatics & Computational Biology Program, University of Texas at Dallas, Dallas, TX, United States of America
| | - Morgan Devlin
- The Johnson Center for Child Health and Development, Austin, TX, United States of America
| | - Claire Schutte
- The Johnson Center for Child Health and Development, Austin, TX, United States of America
| | - Jeon Lee
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Dwight C German
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, United States of America
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29
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Guan J, Wang Y, Lin Y, Yin Q, Zhuang Y, Ji G. Cell Type-Specific Predictive Models Perform Prioritization of Genes and Gene Sets Associated With Autism. Front Genet 2021; 11:628539. [PMID: 33519924 PMCID: PMC7844401 DOI: 10.3389/fgene.2020.628539] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 12/16/2020] [Indexed: 12/13/2022] Open
Abstract
Bulk transcriptomic analyses of autism spectrum disorder (ASD) have revealed dysregulated pathways, while the brain cell type-specific molecular pathology of ASD still needs to be studied. Machine learning-based studies can be conducted for ASD, prioritizing high-confidence gene candidates and promoting the design of effective interventions. Using human brain nucleus gene expression of ASD and controls, we construct cell type-specific predictive models for ASD based on individual genes and gene sets, respectively, to screen cell type-specific ASD-associated genes and gene sets. These two kinds of predictive models can predict the diagnosis of a nucleus with known cell type. Then, we construct a multi-label predictive model for predicting the cell type and diagnosis of a nucleus at the same time. Our findings suggest that layer 2/3 and layer 4 excitatory neurons, layer 5/6 cortico-cortical projection neurons, parvalbumin interneurons, and protoplasmic astrocytes are preferentially affected in ASD. The functions of genes with predictive power for ASD are different and the top important genes are distinct across different cells, highlighting the cell-type heterogeneity of ASD. The constructed predictive models can promote the diagnosis of ASD, and the prioritized cell type-specific ASD-associated genes and gene sets may be used as potential biomarkers of ASD.
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Affiliation(s)
- Jinting Guan
- Department of Automation, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yang Wang
- Department of Automation, Xiamen University, Xiamen, China
| | - Yiping Lin
- Department of Automation, Xiamen University, Xiamen, China
| | - Qingyang Yin
- Department of Automation, Xiamen University, Xiamen, China
| | - Yibo Zhuang
- Xiamen YLZ Yihui Technology Co., Ltd., Xiamen, China
| | - Guoli Ji
- Department of Automation, Xiamen University, Xiamen, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.,Innovation Center for Cell Signaling Network, Xiamen University, Xiamen, China
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30
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Xie X, Li L, Wu H, Hou F, Chen Y, Xue Q, Zhou Y, Zhang J, Gong J, Song R. Comprehensive Integrative Analyses Identify TIGD5 rs75547282 as a Risk Variant for Autism Spectrum Disorder. Autism Res 2021; 14:631-644. [PMID: 33393181 DOI: 10.1002/aur.2466] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 12/16/2020] [Accepted: 12/21/2020] [Indexed: 12/12/2022]
Abstract
Although recent genome-wide association studies have identified risk loci that strongly associates with autism spectrum disorder (ASD), how to pinpoint the causal genes remains a challenge. We aimed to pinpoint the potential causal genes and explore the possible susceptibility and mechanism. A convergent functional genomics (CFG) method was used to prioritize the candidate genes by combining lines of evidence, including Sherlock analysis, spatio-temporal expression patterns, expression analysis, protein-protein interactions, co-expression and association with brain structure. A higher score in the CFG approach suggested that more evidence supported this gene as an ASD risk gene. We screened genes with higher CFG scores for candidate functional single nucleotide polymorphisms (SNPs). A genotyping experiment (602 ASD children and 604 healthy sex-matched children) and the dual-luciferase reporter gene assay were followed to validate the effects of SNPs. We identified three genes (MAPT, ZNF285, and TIGD5) as candidate causal genes using the CFG approach. The genotyping experiment showed that TIGD5 rs75547282 was associated with an increased risk of ASD under the dominant model (OR = 1.37, 95% CI = 1.09-1.72, P = 0.006) though the statistical power was limited (5.2%). The T allele of rs75547282 activated the expression of TIGD5 compared with the C allele in the dual-luciferase reporter assay. Our study indicates that such comprehensive integrative analyses may be an effective way to explore promising ASD susceptibility variants and needs to be further investigated in future research. Genotyping experiments should, however, be based on a larger population sample to increase statistical power. LAY SUMMARY: We set out to pinpoint the potential causal genes of ASD and explore the possible susceptibility and mechanism by combining lines of evidence from different analyses. Our results show that TIGD5 rs75547282 is associated with the risk of ASD in the Han Chinese population. In addition, a similar framework to seek promising ASD risk variants could be further investigated in future research Autism Res 2021, 14: 631-644. © 2021 International Society for Autism Research and Wiley Periodicals LLC.
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Affiliation(s)
- Xinyan Xie
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li Li
- Maternity and Children Health Care Hospital of Luohu District, Shenzhen, China
| | - Hao Wu
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fang Hou
- Maternity and Children Health Care Hospital of Luohu District, Shenzhen, China
| | - Yanlin Chen
- Maternity and Children Health Care Hospital of Luohu District, Shenzhen, China
| | - Qi Xue
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Zhou
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiajia Zhang
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA
| | - Jianhua Gong
- Maternity and Children Health Care Hospital of Luohu District, Shenzhen, China
| | - Ranran Song
- Department of Maternal and Child Health and MOE (Ministry of Education) Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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31
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Johnson CL, Jazan E, Kong SW, Pennell KD. A two-step gas chromatography-tandem mass spectrometry method for measurement of multiple environmental pollutants in human plasma. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:3266-3279. [PMID: 32914305 PMCID: PMC7790997 DOI: 10.1007/s11356-020-10702-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 09/01/2020] [Indexed: 06/11/2023]
Abstract
Individuals are exposed to a wide variety of chemicals over their lifetime, yet current understanding of mixture toxicology is still limited. We present a two-step analytical method using a gas chromatograph-triple quadrupole mass spectrometer that requires less than 1 mL of sample. The method is applied to 183 plasma samples from a study population of children with autism spectrum disorder, their parents, and unrelated neurotypical children. We selected 156 environmental chemical compounds and ruled out chemicals with detection rates less than 20% of our study cohort (n = 61), as well as ones not amenable to the selected extraction and analytical methods (n = 34). The targeted method then focused on remaining chemicals (n = 61) plus 8 additional polychlorinated biphenyls (PCBs). Persistent pollutants, such as p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE) and PCB congeners 118 and 180, were detected at high frequencies and several previously unreported chemicals, including 2,4,6-trichlorophenol, isosafrole, and hexachlorobutadiene, were frequently detected in our study cohort. This work highlights the benefits of employing a multi-step analytical method in exposure studies and demonstrates the efficacy of such methods for reporting novel information on previously unstudied pollutant exposures.
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Affiliation(s)
- Caitlin L Johnson
- Department of Civil and Environmental Engineering, Tufts University, Medford, MA, 02155, USA
| | - Elisa Jazan
- Department of Civil and Environmental Engineering, Tufts University, Medford, MA, 02155, USA
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02115, USA.
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA.
| | - Kurt D Pennell
- School of Engineering, Brown University, Box D, 184 Hope Street, Providence, RI, 02912, USA.
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32
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Artificial Intelligence for Autism Spectrum Disorders. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_249-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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33
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Xiong C, Sun S, Jiang W, Ma L, Zhang J. ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder. Front Genet 2020; 11:562971. [PMID: 33173536 PMCID: PMC7591752 DOI: 10.3389/fgene.2020.562971] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 08/31/2020] [Indexed: 12/14/2022] Open
Abstract
Autism spectrum disorder (ASD) is a class of neurodevelopmental disorders characterized by genetic and environmental risk factors. The pathogenesis of ASD has a strong genetic basis, consisting of rare de novo or inherited variants among a variety of multiple molecules. Previous studies have shown that microRNAs (miRNAs) are involved in neurogenesis and brain development and are closely associated with the pathogenesis of ASD. However, the regulatory mechanisms of miRNAs in ASD are largely unclear. In this work, we present a stepwise method, ASDmiR, for the identification of underlying pathogenic genes, networks, and modules associated with ASD. First, we conduct a comparison study on 12 miRNA target prediction methods by using the matched miRNA, lncRNA, and mRNA expression data in ASD. In terms of the number of experimentally confirmed miRNA-target interactions predicted by each method, we choose the best method for identifying miRNA-target regulatory network. Based on the miRNA-target interaction network identified by the best method, we further infer miRNA-target regulatory bicliques or modules. In addition, by integrating high-confidence miRNA-target interactions and gene expression data, we identify three types of networks, including lncRNA-lncRNA, lncRNA-mRNA, and mRNA-mRNA related miRNA sponge interaction networks. To reveal the community of miRNA sponges, we further infer miRNA sponge modules from the identified miRNA sponge interaction network. Functional analysis results show that the identified hub genes, as well as miRNA-associated networks and modules, are closely linked with ASD. ASDmiR is freely available at https://github.com/chenchenxiong/ASDmiR.
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Affiliation(s)
- Chenchen Xiong
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
| | - Shaoping Sun
- Department of Medical Engineering, People's Hospital of Yuxi City, Yuxi, China
| | - Weili Jiang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
| | - Lei Ma
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
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34
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Miron O, Delgado RE, Delgado CF, Simpson EA, Yu KH, Gutierrez A, Zeng G, Gerstenberger JN, Kohane IS. Prolonged Auditory Brainstem Response in Universal Hearing Screening of Newborns with Autism Spectrum Disorder. Autism Res 2020; 14:46-52. [PMID: 33140578 PMCID: PMC7894135 DOI: 10.1002/aur.2422] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 10/09/2020] [Accepted: 10/12/2020] [Indexed: 12/21/2022]
Abstract
Previous studies report prolonged auditory brainstem response (ABR) in children and adults with autism spectrum disorder (ASD). Despite its promise as a biomarker, it is unclear whether healthy newborns who later develop ASD also show ABR abnormalities. In the current study, we extracted ABR data on 139,154 newborns from their Universal Newborn Hearing Screening, including 321 newborns who were later diagnosed with ASD. We found that the ASD newborns had significant prolongations of their ABR phase and V‐negative latency compared with the non‐ASD newborns. Newborns in the ASD group also exhibited greater variance in their latencies compared to previous studies in older ASD samples, likely due in part to the low intensity of the ABR stimulus. These findings suggest that newborns display neurophysiological variation associated with ASD at birth. Future studies with higher‐intensity stimulus ABRs may allow more accurate predictions of ASD risk, which could augment the universal ABR test that currently screens millions of newborns worldwide. Lay Summary Children with autism spectrum disorder (ASD) have slow brain responses to sounds. We examined these brain responses from newborns' hearing tests and found that newborns who were later diagnosed with autism also had slower brain responses to sounds. Future studies might use these findings to better predict autism risk, with a hearing test that is already used on millions of newborns worldwide.
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Affiliation(s)
- Oren Miron
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Rafael E Delgado
- Intelligent Hearing Systems, Miami, Florida, USA.,Department of Biomedical Engineering, University of Miami, Coral Gables, Florida, USA
| | | | | | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Anibal Gutierrez
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | - Guangyu Zeng
- Department of Psychology, University of Miami, Coral Gables, Florida, USA
| | | | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
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35
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Karunakaran KB, Chaparala S, Lo CW, Ganapathiraju MK. Cilia interactome with predicted protein-protein interactions reveals connections to Alzheimer's disease, aging and other neuropsychiatric processes. Sci Rep 2020; 10:15629. [PMID: 32973177 PMCID: PMC7515907 DOI: 10.1038/s41598-020-72024-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 08/10/2020] [Indexed: 12/12/2022] Open
Abstract
Cilia are dynamic microtubule-based organelles present on the surface of many eukaryotic cell types and can be motile or non-motile primary cilia. Cilia defects underlie a growing list of human disorders, collectively called ciliopathies, with overlapping phenotypes such as developmental delays and cognitive and memory deficits. Consistent with this, cilia play an important role in brain development, particularly in neurogenesis and neuronal migration. These findings suggest that a deeper systems-level understanding of how ciliary proteins function together may provide new mechanistic insights into the molecular etiologies of nervous system defects. Towards this end, we performed a protein-protein interaction (PPI) network analysis of known intraflagellar transport, BBSome, transition zone, ciliary membrane and motile cilia proteins. Known PPIs of ciliary proteins were assembled from online databases. Novel PPIs were predicted for each ciliary protein using a computational method we developed, called High-precision PPI Prediction (HiPPIP) model. The resulting cilia "interactome" consists of 165 ciliary proteins, 1,011 known PPIs, and 765 novel PPIs. The cilia interactome revealed interconnections between ciliary proteins, and their relation to several pathways related to neuropsychiatric processes, and to drug targets. Approximately 184 genes in the cilia interactome are targeted by 548 currently approved drugs, of which 103 are used to treat various diseases of nervous system origin. Taken together, the cilia interactome presented here provides novel insights into the relationship between ciliary protein dysfunction and neuropsychiatric disorders, for e.g. interconnections of Alzheimer's disease, aging and cilia genes. These results provide the framework for the rational design of new therapeutic agents for treatment of ciliopathies and neuropsychiatric disorders.
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Affiliation(s)
- Kalyani B Karunakaran
- Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India
| | - Srilakshmi Chaparala
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
- Health Sciences Library System, University of Pittsburgh, Pittsburgh, PA, USA
| | - Cecilia W Lo
- Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Madhavi K Ganapathiraju
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
- Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA.
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36
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Chen J, Cao H, Kaufmann T, Westlye LT, Tost H, Meyer-Lindenberg A, Schwarz E. Identification of Reproducible BCL11A Alterations in Schizophrenia Through Individual-Level Prediction of Coexpression. Schizophr Bull 2020; 46:1165-1171. [PMID: 32232389 PMCID: PMC7505190 DOI: 10.1093/schbul/sbaa047] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Previous studies have provided evidence for an alteration of genetic coexpression in schizophrenia (SCZ). However, such analyses have thus far lacked biological specificity for individual genes, which may be critical for identifying illness-relevant effects. Therefore, we applied machine learning to identify gene-specific coexpression differences at the individual subject level and compared these between individuals with SCZ, bipolar disorder, major depressive disorder (MDD), autism spectrum disorder (ASD), and healthy controls. Utilizing transcriptome-wide gene expression data from 21 independent datasets, comprising a total of 9509 participants, we identified a reproducible decrease of BCL11A coexpression across 4 SCZ datasets that showed diagnostic specificity for SCZ when compared with ASD and MDD. We further demonstrate that individual-level coexpression differences can be combined in multivariate coexpression scores that show reproducible illness classification across independent datasets in SCZ and ASD. This study demonstrates that machine learning can capture gene-specific coexpression differences at the individual subject level for SCZ and identify novel biomarker candidates.
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Affiliation(s)
- Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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37
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Integrated Systems Analysis Explores Dysfunctional Molecular Modules and Regulatory Factors in Children with Autism Spectrum Disorder. J Mol Neurosci 2020; 71:358-368. [PMID: 32653993 DOI: 10.1007/s12031-020-01658-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 07/03/2020] [Indexed: 12/22/2022]
Abstract
Autism spectrum disorder (ASD) is a genetic neurodevelopmental disorder involving multiple genes that occurs in early childhood, and a number of risk genes have been reported in previous studies. However, the molecular mechanism of the polygenic regulation leading to pathological changes in ASD remains unclear. First, we identified 8 dysregulated gene coexpression modules by analyzing blood transcriptome data from 96 children with ASD and 42 controls. These modules are rich in ASD risk genes and function related to metabolism, immunity, neurodevelopment, and signaling. The regulatory factors of each module including microRNA (miRNA) and transcription factors (TFs) were subsequently predicted based on transcriptional and posttranscriptional regulation. We identified a set of miRNAs that regulate metabolic and immune modules, as well as transcription factors that cause dysregulation of the modules, and we constructed a coregulatory network between the regulatory factors and modules. Our work reveals dysfunctional modules in children with ASD, elucidates the role of miRNA and transcription factor dysregulation in the pathophysiology of ASD, and helps us to further understand the underlying molecular mechanism of ASD.
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38
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Vargason T, Grivas G, Hollowood-Jones KL, Hahn J. Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements. Semin Pediatr Neurol 2020; 34:100803. [PMID: 32446437 PMCID: PMC7248126 DOI: 10.1016/j.spen.2020.100803] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.
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Affiliation(s)
- Troy Vargason
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Genevieve Grivas
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Kathryn L Hollowood-Jones
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Juergen Hahn
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY; Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY.
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39
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Jakubosky D, Smith EN, D'Antonio M, Jan Bonder M, Young Greenwald WW, D'Antonio-Chronowska A, Matsui H, Stegle O, Montgomery SB, DeBoever C, Frazer KA. Discovery and quality analysis of a comprehensive set of structural variants and short tandem repeats. Nat Commun 2020; 11:2928. [PMID: 32522985 PMCID: PMC7287045 DOI: 10.1038/s41467-020-16481-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 05/05/2020] [Indexed: 02/07/2023] Open
Abstract
Structural variants (SVs) and short tandem repeats (STRs) are important sources of genetic diversity but are not routinely analyzed in genetic studies because they are difficult to accurately identify and genotype. Because SVs and STRs range in size and type, it is necessary to apply multiple algorithms that incorporate different types of evidence from sequencing data and employ complex filtering strategies to discover a comprehensive set of high-quality and reproducible variants. Here we assemble a set of 719 deep whole genome sequencing (WGS) samples (mean 42×) from 477 distinct individuals which we use to discover and genotype a wide spectrum of SV and STR variants using five algorithms. We use 177 unique pairs of genetic replicates to identify factors that affect variant call reproducibility and develop a systematic filtering strategy to create of one of the most complete and well characterized maps of SVs and STRs to date.
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Affiliation(s)
- David Jakubosky
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, 92093-0419, USA
- Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093-0419, USA
| | - Erin N Smith
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Matteo D'Antonio
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Marc Jan Bonder
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - William W Young Greenwald
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, USA
| | | | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center, Heidelberg, Germany
| | - Stephen B Montgomery
- Department of Pathology, Stanford University, Stanford, CA, 94305, USA
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA
| | - Christopher DeBoever
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Kelly A Frazer
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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Jakubosky D, D'Antonio M, Bonder MJ, Smail C, Donovan MKR, Young Greenwald WW, Matsui H, D'Antonio-Chronowska A, Stegle O, Smith EN, Montgomery SB, DeBoever C, Frazer KA. Properties of structural variants and short tandem repeats associated with gene expression and complex traits. Nat Commun 2020; 11:2927. [PMID: 32522982 PMCID: PMC7286898 DOI: 10.1038/s41467-020-16482-4] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 05/05/2020] [Indexed: 12/14/2022] Open
Abstract
Structural variants (SVs) and short tandem repeats (STRs) comprise a broad group of diverse DNA variants which vastly differ in their sizes and distributions across the genome. Here, we identify genomic features of SV classes and STRs that are associated with gene expression and complex traits, including their locations relative to eGenes, likelihood of being associated with multiple eGenes, associated eGene types (e.g., coding, noncoding, level of evolutionary constraint), effect sizes, linkage disequilibrium with tagging single nucleotide variants used in GWAS, and likelihood of being associated with GWAS traits. We identify a set of high-impact SVs/STRs associated with the expression of three or more eGenes via chromatin loops and show that they are highly enriched for being associated with GWAS traits. Our study provides insights into the genomic properties of structural variant classes and short tandem repeats that are associated with gene expression and human traits.
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Affiliation(s)
- David Jakubosky
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, 92093-0419, USA
- Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093-0419, USA
| | - Matteo D'Antonio
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Marc Jan Bonder
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Craig Smail
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Pathology, Stanford University, Stanford, California, 94305, USA
| | - Margaret K R Donovan
- Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093-0419, USA
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, USA
| | - William W Young Greenwald
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, USA
| | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | | | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center, Heidelberg, Germany
| | - Erin N Smith
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Stephen B Montgomery
- Department of Pathology, Stanford University, Stanford, California, 94305, USA
- Department of Genetics, Stanford University, Stanford, California, 94305, USA
| | - Christopher DeBoever
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Kelly A Frazer
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
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Courchesne E, Gazestani VH, Lewis NE. Prenatal Origins of ASD: The When, What, and How of ASD Development. Trends Neurosci 2020; 43:326-342. [PMID: 32353336 PMCID: PMC7373219 DOI: 10.1016/j.tins.2020.03.005] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 01/28/2020] [Accepted: 03/04/2020] [Indexed: 02/08/2023]
Abstract
Autism spectrum disorder (ASD) is a largely heritable, multistage prenatal disorder that impacts a child's ability to perceive and react to social information. Most ASD risk genes are expressed prenatally in many ASD-relevant brain regions and fall into two categories: broadly expressed regulatory genes that are expressed in the brain and other organs, and brain-specific genes. In trimesters one to three (Epoch-1), one set of broadly expressed (the majority) and brain-specific risk genes disrupts cell proliferation, neurogenesis, migration, and cell fate, while in trimester three and early postnatally (Epoch-2) another set (the majority being brain specific) disrupts neurite outgrowth, synaptogenesis, and the 'wiring' of the cortex. A proposed model is that upstream, highly interconnected regulatory ASD gene mutations disrupt transcriptional programs or signaling pathways resulting in dysregulation of downstream processes such as proliferation, neurogenesis, synaptogenesis, and neural activity. Dysregulation of signaling pathways is correlated with ASD social symptom severity. Since the majority of ASD risk genes are broadly expressed, many ASD individuals may benefit by being treated as having a broader medical disorder. An important future direction is the noninvasive study of ASD cell biology.
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Affiliation(s)
- Eric Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA 92093, USA; Autism Center of Excellence, University of California, San Diego, San Diego, CA 92037, USA.
| | - Vahid H Gazestani
- Department of Neuroscience, University of California, San Diego, San Diego, CA 92093, USA; Autism Center of Excellence, University of California, San Diego, San Diego, CA 92037, USA; Department of Pediatrics, University of California, San Diego, San Diego, CA 92093, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, San Diego, CA 92093, USA; Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA
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Transcriptome signatures from discordant sibling pairs reveal changes in peripheral blood immune cell composition in Autism Spectrum Disorder. Transl Psychiatry 2020; 10:106. [PMID: 32291385 PMCID: PMC7156413 DOI: 10.1038/s41398-020-0778-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 02/14/2020] [Accepted: 02/26/2020] [Indexed: 12/22/2022] Open
Abstract
Notwithstanding several research efforts in the past years, robust and replicable molecular signatures for autism spectrum disorders from peripheral blood remain elusive. The available literature on blood transcriptome in ASD suggests that through accurate experimental design it is possible to extract important information on the disease pathophysiology at the peripheral level. Here we exploit the availability of a resource for molecular biomarkers in ASD, the Italian Autism Network (ITAN) collection, for the investigation of transcriptomic signatures in ASD based on a discordant sibling pair design. Whole blood samples from 75 discordant sibling pairs selected from the ITAN network where submitted to RNASeq analysis and data analyzed by complementary approaches. Overall, differences in gene expression between affected and unaffected siblings were small. In order to assess the contribution of differences in the relative proportion of blood cells between discordant siblings, we have applied two different cell deconvolution algorithms, showing that the observed molecular signatures mainly reflect changes in peripheral blood immune cell composition, in particular NK cells. The results obtained by the cell deconvolution approach are supported by the analysis performed by WGCNA. Our report describes the largest differential gene expression profiling in peripheral blood of ASD subjects and controls conducted by RNASeq. The observed signatures are consistent with the hypothesis of immune alterations in autism and an increased risk of developing autism in subjects exposed to prenatal infections or stress. Our study also points to a potential role of NMUR1, HMGB3, and PTPRN2 in ASD.
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Network Structure Analysis Identifying Key Genes of Autism and Its Mechanism. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:3753080. [PMID: 32273901 PMCID: PMC7125446 DOI: 10.1155/2020/3753080] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 11/04/2019] [Accepted: 02/22/2020] [Indexed: 01/16/2023]
Abstract
Identifying the key genes of autism is of great significance for understanding its pathogenesis and improving the clinical level of medicine. In this paper, we use the structural parameters (average degree) of gene correlation networks to identify genes related to autism and study its pathogenesis. Based on the gene expression profiles of 82 autistic patients (the experimental group, E) and 64 healthy persons (the control group, C) in NCBI database, spearman correlation networks are established, and their average degrees under different thresholds are analyzed. It is found that average degrees of C and E are basically separable at the full thresholds. This indicates that there is a clear difference between the network structures of C and E, and it also suggests that this difference is related to the mechanism of disease. By annotating and enrichment analysis of the first 20 genes (MD-Gs) with significant difference in the average degree, we find that they are significantly related to gland development, cardiovascular development, and embryogenesis of nervous system, which support the results in Alter et al.'s original research. In addition, FIGF and CSF3 may play an important role in the mechanism of autism.
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Transcriptomic Analysis Reveals Abnormal Expression of Prion Disease Gene Pathway in Brains from Patients with Autism Spectrum Disorders. Brain Sci 2020; 10:brainsci10040200. [PMID: 32235346 PMCID: PMC7226514 DOI: 10.3390/brainsci10040200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 03/25/2020] [Accepted: 03/26/2020] [Indexed: 12/22/2022] Open
Abstract
The role of infections in the pathogenesis of autism spectrum disorder (ASD) is still controversial. In this study, we aimed to evaluate markers of infections and immune activation in ASD by performing a meta-analysis of publicly available whole-genome transcriptomic datasets of brain samples from autistic patients and otherwise normal people. Among the differentially expressed genes, no significant enrichment was observed for infectious diseases previously associated with ASD, including herpes simplex virus-1 (HSV-1), cytomegalovirus and Epstein–Barr virus in brain samples, nor was it found in peripheral blood from ASD patients. Interestingly, a significant number of genes belonging to the “prion diseases” pathway were found to be modulated in our ASD brain meta-analysis. Overall, our data do not support an association between infection and ASD. However, the data do provide support for the involvement of pathways related to other neurodegenerative diseases and give input to uncover novel pathogenetic mechanisms underlying ASD.
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Cheng W, Zhou S, Zhou J, Wang X. Identification of a robust non-coding RNA signature in diagnosing autism spectrum disorder by cross-validation of microarray data from peripheral blood samples. Medicine (Baltimore) 2020; 99:e19484. [PMID: 32176083 PMCID: PMC7220435 DOI: 10.1097/md.0000000000019484] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Novel molecular signatures are needed to improve the early and accurate diagnosis of autism spectrum disorder (ASD), and indicate physicians to provide timely intervention. This study aimed to identify a robust blood non-coding RNA (ncRNA) signature in diagnosing ASD. One hundred eighty six blood samples in the microarray dataset were randomly divided into the training set (n = 112) and validation set (n = 72). Then, the microarray probe expression profile was re-annotated into the expression profile of 4143 ncRNAs though probe sequence mapping. In the training set, least absolute shrinkage and selection operator (LASSO) penalized generalized linear model was adopted to identify the 20-ncRNA signature, and a diagnostic score was calculated for each sample according to the ncRNA expression levels and the model coefficients. The score demonstrated an excellent diagnostic ability for ASD in the training set (area under receiver operating characteristic curve [AUC] = 0.96), validation set (AUC = 0.97) and the overall (AUC = 0.96). Moreover, the blood samples of 23 ASD patients and 23 age- and gender-matched controls were collected as the external validation set, in which the signature also showed a good diagnostic ability for ASD (AUC = 0.96). In subgroup analysis, the signature was also robust when considering the potential confounders of sex, age, and disease subtypes. In comparison with a 55-gene signature deriving from the same dataset, the ncRNA signature showed an obviously better diagnostic ability (AUC: 0.96 vs 0.68, P < .001). In conclusion, this study identified a robust blood ncRNA signature in diagnosing ASD, which might help improve the diagnostic accuracy for ASD in clinical practice.
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Aldosary M, Al-Bakheet A, Al-Dhalaan H, Almass R, Alsagob M, Al-Younes B, AlQuait L, Mustafa OM, Bulbul M, Rahbeeni Z, Alfadhel M, Chedrawi A, Al-Hassnan Z, AlDosari M, Al-Zaidan H, Al-Muhaizea MA, AlSayed MD, Salih MA, AlShammari M, Faiyaz-Ul-Haque M, Chishti MA, Al-Harazi O, Al-Odaib A, Kaya N, Colak D. Rett Syndrome, a Neurodevelopmental Disorder, Whole-Transcriptome, and Mitochondrial Genome Multiomics Analyses Identify Novel Variations and Disease Pathways. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2020; 24:160-171. [PMID: 32105570 DOI: 10.1089/omi.2019.0192] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Rett syndrome (RTT) is a severe neurodevelopmental disorder reported worldwide in diverse populations. RTT is diagnosed primarily in females, with clinical findings manifesting early in life. Despite the variable rates across populations, RTT has an estimated prevalence of ∼1 in 10,000 live female births. Among 215 Saudi Arabian patients with neurodevelopmental and autism spectrum disorders, we identified 33 patients with RTT who were subsequently examined by genome-wide transcriptome and mitochondrial genome variations. To the best of our knowledge, this is the first in-depth molecular and multiomics analyses of a large cohort of Saudi RTT cases with a view to informing the underlying mechanisms of this disease that impact many patients and families worldwide. The patients were unrelated, except for 2 affected sisters, and comprised of 25 classic and eight atypical RTT cases. The cases were screened for methyl-CpG binding protein 2 (MECP2), CDKL5, FOXG1, NTNG1, and mitochondrial DNA (mtDNA) variants, as well as copy number variations (CNVs) using a genome-wide experimental strategy. We found that 15 patients (13 classic and two atypical RTT) have MECP2 mutations, 2 of which were novel variants. Two patients had novel FOXG1 and CDKL5 variants (both atypical RTT). Whole mtDNA sequencing of the patients who were MECP2 negative revealed two novel mtDNA variants in two classic RTT patients. Importantly, the whole-transcriptome analysis of our RTT patients' blood and further comparison with previous expression profiling of brain tissue from patients with RTT revealed 77 significantly dysregulated genes. The gene ontology and interaction network analysis indicated potentially critical roles of MAPK9, NDUFA5, ATR, SMARCA5, RPL23, SRSF3, and mitochondrial dysfunction, oxidative stress response and MAPK signaling pathways in the pathogenesis of RTT genes. This study expands our knowledge on RTT disease networks and pathways as well as presents novel mutations and mtDNA alterations in RTT in a population sample that was not previously studied.
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Affiliation(s)
- Mazhor Aldosary
- Department of Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - AlBandary Al-Bakheet
- Department of Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Hesham Al-Dhalaan
- Department of Neuroscience, and King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Rawan Almass
- Department of Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Maysoon Alsagob
- Department of Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Banan Al-Younes
- Department of Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Laila AlQuait
- Department of Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Osama Mufid Mustafa
- Department of Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Mustafa Bulbul
- Department of Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Zuhair Rahbeeni
- Department of Medical Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Majid Alfadhel
- King Abdullah International Medical Research Centre, King Saud bin Abdulaziz University for Health Sciences, Genetics Division, Department of Pediatrics, King Abdullah Specialized Children Hospital, Riyadh, Saudi Arabia
| | - Aziza Chedrawi
- Department of Neuroscience, and King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Zuhair Al-Hassnan
- Department of Medical Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Mohammed AlDosari
- Center for Pediatric Neurosciences, Cleveland Clinic, Cleveland, Ohio
| | - Hamad Al-Zaidan
- Department of Medical Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Mohammad A Al-Muhaizea
- Department of Neuroscience, and King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Moeenaldeen D AlSayed
- Department of Medical Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Mustafa A Salih
- Division of Pediatric Neurology, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Mai AlShammari
- Department of Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | | | - Mohammad Azhar Chishti
- Department of Biochemistry, King Khalid Hospital, King Saud University, Riyadh, Saudi Arabia
| | - Olfat Al-Harazi
- Department of Biostatistics, Epidemiology, and Scientific Computing, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Ali Al-Odaib
- Department of Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Namik Kaya
- Department of Genetics, King Faisal Specialist Hospital & Research Center, Riyadh, Saudi Arabia
| | - Dilek Colak
- Department of Biostatistics, Epidemiology, and Scientific Computing, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
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Chen HY, Maher BJ. Lost in Translation: Cul3-Dependent Pathological Mechanisms in Psychiatric Disorders. Neuron 2020; 105:398-399. [PMID: 32027827 DOI: 10.1016/j.neuron.2020.01.010] [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] [Indexed: 10/25/2022]
Abstract
In this issue of Neuron, Dong et al. (2020) finds that deficiency of the psychiatric risk gene Cul3, which encodes an E3 ubiquitin ligase, leads to an upregulation of Cap-dependent protein translation. The resulting imbalance in protein synthesis and degradation is found to disrupt glutamatergic transmission and excitability in networks that underlie sociability and anxiety.
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Affiliation(s)
- Huei-Ying Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Brady J Maher
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA; The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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48
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Conrad D, Wilker S, Schneider A, Karabatsiakis A, Pfeiffer A, Kolassa S, Freytag V, Vukojevic V, Vogler C, Milnik A, Papassotiropoulos A, J.‐F. de Quervain D, Elbert T, Kolassa I. Integrated genetic, epigenetic, and gene set enrichment analyses identify NOTCH as a potential mediator for PTSD risk after trauma: Results from two independent African cohorts. Psychophysiology 2020; 57:e13288. [PMID: 30328613 PMCID: PMC7379258 DOI: 10.1111/psyp.13288] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Revised: 08/14/2018] [Accepted: 08/17/2018] [Indexed: 12/17/2022]
Abstract
The risk of developing posttraumatic stress disorder (PTSD) increases with the number of traumatic event types experienced (trauma load) in interaction with other psychobiological risk factors. The NOTCH (neurogenic locus notch homolog proteins) signaling pathway, consisting of four different trans-membrane receptor proteins (NOTCH1-4), constitutes an evolutionarily well-conserved intercellular communication pathway (involved, e.g., in cell-cell interaction, inflammatory signaling, and learning processes). Its association with fear memory consolidation makes it an interesting candidate for PTSD research. We tested for significant associations of common genetic variants of NOTCH1-4 (investigated by microarray) and genomic methylation of saliva-derived DNA with lifetime PTSD risk in independent cohorts from Northern Uganda (N1 = 924) and Rwanda (N2 = 371), and investigated whether NOTCH-related gene sets were enriched for associations with lifetime PTSD risk. We found associations of lifetime PTSD risk with single nucleotide polymorphism (SNP) rs2074621 (NOTCH3) (puncorrected = 0.04) in both cohorts, and with methylation of CpG site cg17519949 (NOTCH3) (puncorrected = 0.05) in Rwandans. Yet, none of the (epi-)genetic associations survived multiple testing correction. Gene set enrichment analyses revealed enrichment for associations of two NOTCH pathways with lifetime PTSD risk in Ugandans: NOTCH binding (pcorrected = 0.003) and NOTCH receptor processing (pcorrected = 0.01). The environmental factor trauma load was significant in all analyses (all p < 0.001). Our integrated methodological approach suggests NOTCH as a possible mediator of PTSD risk after trauma. The results require replication, and the precise underlying pathophysiological mechanisms should be illuminated in future studies.
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Affiliation(s)
- Daniela Conrad
- Clinical Psychology and NeuropsychologyUniversity of KonstanzKonstanzGermany
- Clinical & Biological Psychology, Institute of Psychology and EducationUlm UniversityUlmGermany
| | - Sarah Wilker
- Clinical & Biological Psychology, Institute of Psychology and EducationUlm UniversityUlmGermany
| | - Anna Schneider
- Clinical & Biological Psychology, Institute of Psychology and EducationUlm UniversityUlmGermany
| | - Alexander Karabatsiakis
- Clinical & Biological Psychology, Institute of Psychology and EducationUlm UniversityUlmGermany
| | - Anett Pfeiffer
- Clinical Psychology and NeuropsychologyUniversity of KonstanzKonstanzGermany
| | | | - Virginie Freytag
- Division of Molecular NeuroscienceUniversity of BaselBaselSwitzerland
- Transfaculty Research Platform Molecular and Cognitive NeurosciencesUniversity of BaselBaselSwitzerland
| | - Vanja Vukojevic
- Division of Molecular NeuroscienceUniversity of BaselBaselSwitzerland
- Transfaculty Research Platform Molecular and Cognitive NeurosciencesUniversity of BaselBaselSwitzerland
- Department Biozentrum, Life Sciences Training FacilityUniversity of BaselBaselSwitzerland
- Psychiatric University ClinicsUniversity of BaselBaselSwitzerland
| | - Christian Vogler
- Division of Molecular NeuroscienceUniversity of BaselBaselSwitzerland
- Transfaculty Research Platform Molecular and Cognitive NeurosciencesUniversity of BaselBaselSwitzerland
- Psychiatric University ClinicsUniversity of BaselBaselSwitzerland
| | - Annette Milnik
- Division of Molecular NeuroscienceUniversity of BaselBaselSwitzerland
- Transfaculty Research Platform Molecular and Cognitive NeurosciencesUniversity of BaselBaselSwitzerland
- Psychiatric University ClinicsUniversity of BaselBaselSwitzerland
| | - Andreas Papassotiropoulos
- Division of Molecular NeuroscienceUniversity of BaselBaselSwitzerland
- Transfaculty Research Platform Molecular and Cognitive NeurosciencesUniversity of BaselBaselSwitzerland
- Department Biozentrum, Life Sciences Training FacilityUniversity of BaselBaselSwitzerland
- Psychiatric University ClinicsUniversity of BaselBaselSwitzerland
| | - Dominique J.‐F. de Quervain
- Transfaculty Research Platform Molecular and Cognitive NeurosciencesUniversity of BaselBaselSwitzerland
- Psychiatric University ClinicsUniversity of BaselBaselSwitzerland
- Division of Cognitive NeuroscienceUniversity of BaselBaselSwitzerland
| | - Thomas Elbert
- Clinical Psychology and NeuropsychologyUniversity of KonstanzKonstanzGermany
| | - Iris‐Tatjana Kolassa
- Clinical & Biological Psychology, Institute of Psychology and EducationUlm UniversityUlmGermany
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49
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Zhou J, Hu Q, Wang X, Cheng W, Pan C, Xing X. Development and validation of a novel and robust blood small nuclear RNA signature in diagnosing autism spectrum disorder. Medicine (Baltimore) 2019; 98:e17858. [PMID: 31702648 PMCID: PMC6855622 DOI: 10.1097/md.0000000000017858] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Reliable molecular signatures are needed to improve the early and accurate diagnosis of autism spectrum disorder (ASD), and indicate physicians to provide timely intervention. This study aimed to identify a robust blood small nuclear RNA (snRNA) signature in diagnosing ASD. 186 blood samples in the microarray dataset were randomly divided into the training set (n = 112) and validation set (n = 72). Then, the microarray probe expression profiles were re-annotated into the expression profiles of 1253 snRNAs though probe sequence mapping. In the training set, least absolute shrinkage and selection operator (LASSO) penalized generalized linear model was adopted to identify the 9-snRNA signature (RNU1-16P, RNU6-1031P, RNU6-258P, RNU6-335P, RNU6-485P, RNU6-549P, RNU6-98P, RNU6ATAC26P, and RNVU1-15), and a diagnostic score was calculated for each sample according to the snRNA expression levels and the model coefficients. The score demonstrated a good diagnostic ability for ASD in the training set (area under receiver operating characteristic curve (AUC) = 0.90), validation set (AUC = 0.87), and the overall (AUC = 0.88). Moreover, the blood samples of 23 ASD patients and 23 age- and gender-matched controls were collected as the external validation set, in which the signature also showed a good diagnostic ability for ASD (AUC = 0.88). In subgroup analysis, the signature was robust when considering the confounders of gender, age, and disease subtypes, and displayed a significantly better performance among the female and younger cases (P = .039; P = .002). In comparison with a 55-gene signature deriving from the same dataset, the snRNA signature showed a better diagnostic ability (AUC: 0.88 vs 0.80, P = .049). In conclusion, this study identified a novel and robust blood snRNA signature in diagnosing ASD, which might help improve the diagnostic accuracy for ASD in clinical practice. Nevertheless, a large-scale prospective study was needed to validate our results.
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Mordaunt CE, Park BY, Bakulski KM, Feinberg JI, Croen LA, Ladd-Acosta C, Newschaffer CJ, Volk HE, Ozonoff S, Hertz-Picciotto I, LaSalle JM, Schmidt RJ, Fallin MD. A meta-analysis of two high-risk prospective cohort studies reveals autism-specific transcriptional changes to chromatin, autoimmune, and environmental response genes in umbilical cord blood. Mol Autism 2019; 10:36. [PMID: 31673306 PMCID: PMC6814108 DOI: 10.1186/s13229-019-0287-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 09/08/2019] [Indexed: 12/17/2022] Open
Abstract
Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects more than 1% of children in the USA. ASD risk is thought to arise from both genetic and environmental factors, with the perinatal period as a critical window. Understanding early transcriptional changes in ASD would assist in clarifying disease pathogenesis and identifying biomarkers. However, little is known about umbilical cord blood gene expression profiles in babies later diagnosed with ASD compared to non-typically developing and non-ASD (Non-TD) or typically developing (TD) children. Methods Genome-wide transcript levels were measured by Affymetrix Human Gene 2.0 array in RNA from cord blood samples from both the Markers of Autism Risk in Babies-Learning Early Signs (MARBLES) and the Early Autism Risk Longitudinal Investigation (EARLI) high-risk pregnancy cohorts that enroll younger siblings of a child previously diagnosed with ASD. Younger siblings were diagnosed based on assessments at 36 months, and 59 ASD, 92 Non-TD, and 120 TD subjects were included. Using both differential expression analysis and weighted gene correlation network analysis, gene expression between ASD and TD, and between Non-TD and TD, was compared within each study and via meta-analysis. Results While cord blood gene expression differences comparing either ASD or Non-TD to TD did not reach genome-wide significance, 172 genes were nominally differentially expressed between ASD and TD cord blood (log2(fold change) > 0.1, p < 0.01). These genes were significantly enriched for functions in xenobiotic metabolism, chromatin regulation, and systemic lupus erythematosus (FDR q < 0.05). In contrast, 66 genes were nominally differentially expressed between Non-TD and TD, including 8 genes that were also differentially expressed in ASD. Gene coexpression modules were significantly correlated with demographic factors and cell type proportions. Limitations ASD-associated gene expression differences identified in this study are subtle, as cord blood is not the main affected tissue, it is composed of many cell types, and ASD is a heterogeneous disorder. Conclusions This is the first study to identify gene expression differences in cord blood specific to ASD through a meta-analysis across two prospective pregnancy cohorts. The enriched gene pathways support involvement of environmental, immune, and epigenetic mechanisms in ASD etiology.
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Affiliation(s)
- Charles E Mordaunt
- 1Department of Medical Microbiology and Immunology, Genome Center, and MIND Institute, University of California, Davis, CA USA
| | - Bo Y Park
- 2Department of Public Health, California State University, Fullerton, CA USA
| | - Kelly M Bakulski
- 3Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI USA
| | - Jason I Feinberg
- 4Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD USA
| | - Lisa A Croen
- 5Division of Research and Autism Research Program, Kaiser Permanente Northern California, Oakland, CA USA
| | | | - Craig J Newschaffer
- 6Department of Biobehavioral Health, College of Health and Human Development, Pennsylvania State University, University Park, PA USA
| | - Heather E Volk
- 4Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD USA
| | - Sally Ozonoff
- 7Psychiatry and Behavioral Sciences and MIND Institute, University of California, Davis, CA USA
| | - Irva Hertz-Picciotto
- 8Department of Public Health Sciences and MIND Institute, University of California, Davis, CA USA
| | - Janine M LaSalle
- 1Department of Medical Microbiology and Immunology, Genome Center, and MIND Institute, University of California, Davis, CA USA
| | - Rebecca J Schmidt
- 8Department of Public Health Sciences and MIND Institute, University of California, Davis, CA USA
| | - M Daniele Fallin
- 4Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD USA
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