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Huang H, Luo J, Qi Y, Wu Y, Qi J, Yan X, Xu G, He F, Zheng Y. Comprehensive analysis of circRNA expression profile and circRNA-miRNA-mRNA network susceptibility to very early-onset schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:70. [PMID: 37816766 PMCID: PMC10564922 DOI: 10.1038/s41537-023-00399-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 09/22/2023] [Indexed: 10/12/2023]
Abstract
To explore the potential role of circular RNAs (circRNAs) in children developing very early-onset schizophrenia (VEOS). Total RNA was extracted from the plasma samples of 10 VEOS patients and eight healthy controls. Expression profiles of circRNAs, micro RNAs (miRNAs), and messenger RNAs (mRNAs) were analyzed using RNA-seq. The interaction networks between miRNAs and targets were predicted using the miRanda tool. A differentially expressed circRNA-miRNA-mRNA (ceRNA) network was further constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the target mRNAs in the ceRNA network were performed to predict the potential functions of their host genes. The patient group and the control group were also compared on the regulatory patterns of circRNAs on mRNAs. 1934 circRNAs were identified from the samples and reported for the first time in schizophrenia. The circRNA expression levels were lower in the VEOS group than in the healthy control group, and 1889 circRNAs were expressed only in the control group. Differential expression analysis (i.e., log2fold change > 1.5, p 0.05) identified 235 circRNAs (1 up-regulated, 234 down-regulated), 11 miRNAs (7 up-regulated, 4 down-regulated), and 2,308 mRNAs (1906 up-regulated, 402 down-regulated) respectively. In VEOS, a ceRNA network with 10 down-regulated circRNA targets, 6 up-regulated miRNAs, and 47 down-regulated mRNAs was constructed. The target genes were involved in the membrane, the signal transduction, and the cytoskeleton and transport pathways. Finally, different expression correlation patterns of circRNA and mRNA in the network were observed between the patient group and the control group. The current research is the first to reveal the differentially expressed circRNAs in the plasma of VEOS patients. A circRNA-miRNA-mRNA network was also conducted in this study. It may be implied that the circRNAs in this network are potential diagnostic biomarkers for VEOS and they play an important role in the onset and development of VEOS symptoms.
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Affiliation(s)
- Huanhuan Huang
- National Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical University, Beijing, People's Republic of China
| | - Jie Luo
- National Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical University, Beijing, People's Republic of China
| | - Yanjie Qi
- National Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical University, Beijing, People's Republic of China
| | - Yuanzhen Wu
- National Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical University, Beijing, People's Republic of China
| | - Junhui Qi
- National Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical University, Beijing, People's Republic of China
| | - Xiuping Yan
- National Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical University, Beijing, People's Republic of China
| | - Gaoyang Xu
- National Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical University, Beijing, People's Republic of China
| | - Fan He
- National Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical University, Beijing, People's Republic of China.
| | - Yi Zheng
- National Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders Capital Medical University, Beijing, People's Republic of China.
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2
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Wong TS, Li G, Li S, Gao W, Chen G, Gan S, Zhang M, Li H, Wu S, Du Y. G protein-coupled receptors in neurodegenerative diseases and psychiatric disorders. Signal Transduct Target Ther 2023; 8:177. [PMID: 37137892 PMCID: PMC10154768 DOI: 10.1038/s41392-023-01427-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 02/17/2023] [Accepted: 03/30/2023] [Indexed: 05/05/2023] Open
Abstract
Neuropsychiatric disorders are multifactorial disorders with diverse aetiological factors. Identifying treatment targets is challenging because the diseases are resulting from heterogeneous biological, genetic, and environmental factors. Nevertheless, the increasing understanding of G protein-coupled receptor (GPCR) opens a new possibility in drug discovery. Harnessing our knowledge of molecular mechanisms and structural information of GPCRs will be advantageous for developing effective drugs. This review provides an overview of the role of GPCRs in various neurodegenerative and psychiatric diseases. Besides, we highlight the emerging opportunities of novel GPCR targets and address recent progress in GPCR drug development.
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Affiliation(s)
- Thian-Sze Wong
- Kobilka Institute of Innovative Drug Discovery, Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, 518172, Shenzhen, Guangdong, China
- School of Medicine, Tsinghua University, 100084, Beijing, China
| | - Guangzhi Li
- Institute of Urology, The Affiliated Luohu Hospital of Shenzhen University, Shenzhen University, 518000, Shenzhen, Guangdong, China
| | - Shiliang Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 200237, Shanghai, China
- Innovation Center for AI and Drug Discovery, East China Normal University, 200062, Shanghai, China
| | - Wei Gao
- Kobilka Institute of Innovative Drug Discovery, Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, 518172, Shenzhen, Guangdong, China
- Innovation Center for AI and Drug Discovery, East China Normal University, 200062, Shanghai, China
| | - Geng Chen
- Kobilka Institute of Innovative Drug Discovery, Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, 518172, Shenzhen, Guangdong, China
| | - Shiyi Gan
- Kobilka Institute of Innovative Drug Discovery, Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, 518172, Shenzhen, Guangdong, China
| | - Manzhan Zhang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 200237, Shanghai, China
- Innovation Center for AI and Drug Discovery, East China Normal University, 200062, Shanghai, China
| | - Honglin Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 200237, Shanghai, China.
- Innovation Center for AI and Drug Discovery, East China Normal University, 200062, Shanghai, China.
| | - Song Wu
- Institute of Urology, The Affiliated Luohu Hospital of Shenzhen University, Shenzhen University, 518000, Shenzhen, Guangdong, China.
- Department of Urology, South China Hospital, Health Science Center, Shenzhen University, 518116, Shenzhen, Guangdong, China.
| | - Yang Du
- Kobilka Institute of Innovative Drug Discovery, Shenzhen Key Laboratory of Steroid Drug Discovery and Development, School of Medicine, The Chinese University of Hong Kong, 518172, Shenzhen, Guangdong, China.
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3
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Matar E, Ehgoetz Martens KA, Phillips JR, Wainstein G, Halliday GM, Lewis SJG, Shine JM. Dynamic network impairments underlie cognitive fluctuations in Lewy body dementia. NPJ Parkinsons Dis 2022; 8:16. [PMID: 35177652 PMCID: PMC8854384 DOI: 10.1038/s41531-022-00279-x] [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: 06/13/2021] [Accepted: 01/12/2022] [Indexed: 11/10/2022] Open
Abstract
Cognitive fluctuations are a characteristic and distressing disturbance of attention and consciousness seen in patients with Dementia with Lewy bodies and Parkinson's disease dementia. It has been proposed that fluctuations result from disruption of key neuromodulatory systems supporting states of attention and wakefulness which are normally characterised by temporally variable and highly integrated functional network architectures. In this study, patients with DLB (n = 25) and age-matched controls (n = 49) were assessed using dynamic resting state fMRI. A dynamic network signature of reduced temporal variability and integration was identified in DLB patients compared to controls. Reduced temporal variability correlated significantly with fluctuation-related measures using a sustained attention task. A less integrated (more segregated) functional network architecture was seen in DLB patients compared to the control group, with regions of reduced integration observed across dorsal and ventral attention, sensorimotor, visual, cingulo-opercular and cingulo-parietal networks. Reduced network integration correlated positively with subjective and objective measures of fluctuations. Regions of reduced integration and unstable regional assignments significantly matched areas of expression of specific classes of noradrenergic and cholinergic receptors across the cerebral cortex. Correlating topological measures with maps of neurotransmitter/neuromodulator receptor gene expression, we found that regions of reduced integration and unstable modular assignments correlated significantly with the pattern of expression of subclasses of noradrenergic and cholinergic receptors across the cerebral cortex. Altogether, these findings demonstrate that cognitive fluctuations are associated with an imaging signature of dynamic network impairment linked to specific neurotransmitters/neuromodulators within the ascending arousal system, highlighting novel potential diagnostic and therapeutic approaches for this troubling symptom.
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Affiliation(s)
- Elie Matar
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia. .,Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia. .,Forefront Research Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.
| | - Kaylena A Ehgoetz Martens
- Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada
| | - Joseph R Phillips
- Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,School of Social Sciences and Psychology, Western Sydney University, Sydney, NSW, Australia
| | - Gabriel Wainstein
- Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Centro de Investigaciones Médicas, Pontifical Catholic University of Chile, Santiago, Chile
| | - Glenda M Halliday
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.,Forefront Research Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Simon J G Lewis
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.,Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - James M Shine
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.,Forefront Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Forefront Research Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
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4
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Yu Z, Du M, Lu L. A Novel 16-Genes Signature Scoring System as Prognostic Model to Evaluate Survival Risk in Patients with Glioblastoma. Biomedicines 2022; 10:biomedicines10020317. [PMID: 35203526 PMCID: PMC8869708 DOI: 10.3390/biomedicines10020317] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 01/20/2022] [Accepted: 01/21/2022] [Indexed: 12/15/2022] Open
Abstract
Previous studies have found that gene expression levels are associated with prognosis and some genes can be used to predict the survival risk of glioblastoma (GBM) patients. However, most of them just built the survival-related gene signature, and personal survival risk can be evaluated only in group. This study aimed to find the prognostic survival related genes of GBM, and construct survival risk prediction model, which can be used to evaluate survival risk by individual. We collected gene expression data and clinical information from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Cox regression analysis and LASSO-cox regression analysis were performed to get survival-related genes and establish the overall survival prediction model. The ROC curve and Kaplan Meier analysis were used to evaluate the prediction ability of the model in training set and two independent cohorts. We also analyzed the biological functions of survival-related genes by GO and KEGG enrichment analysis. We identified 99 genes associated with overall survival and selected 16 genes (IGFBP2, GPRASP1, C1R, CHRM3, CLSTN2, NELL1, SEZ6L2, NMB, ICAM5, HPCAL4, SNAP91, PCSK1N, PGBD5, INA, UCHL1 and LHX6) to establish the survival risk prediction model. Multivariate Cox regression analysis indicted that the risk score could predict overall survival independent of age and gender. ROC analyses showed that our model was more robust than four existing signatures. The sixteen genes can also be potential transcriptional biomarkers and the model can assist doctors on clinical decision-making and personalized treatment of GBM patients.
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5
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Guan F, Ni T, Zhu W, Williams LK, Cui LB, Li M, Tubbs J, Sham PC, Gui H. Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction. Mol Psychiatry 2022; 27:113-126. [PMID: 34193973 PMCID: PMC11018294 DOI: 10.1038/s41380-021-01201-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 06/15/2021] [Accepted: 06/17/2021] [Indexed: 02/06/2023]
Abstract
Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.
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Affiliation(s)
- Fanglin Guan
- Department of Forensic Psychiatry, School of Medicine & Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Tong Ni
- Department of Forensic Psychiatry, School of Medicine & Forensics, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Weili Zhu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, China
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Long-Biao Cui
- Department of Clinical Psychology, School of Medical Psychology, Air Force Medical University, Xi'an, Shaanxi, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Justin Tubbs
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Pak-Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong SAR, China.
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China.
| | - Hongsheng Gui
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA.
- Behavioral Health Services, Henry Ford Health System, Detroit, MI, USA.
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6
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Zhao SW, Xu X, Wang XY, Yan TC, Cao Y, Yan QH, Chen K, Jin YC, Zhang YH, Yin H, Cui LB. Shaping the Trans-Scale Properties of Schizophrenia via Cerebral Alterations on Magnetic Resonance Imaging and Single-Nucleotide Polymorphisms of Coding and Non-Coding Regions. Front Hum Neurosci 2021; 15:720239. [PMID: 34566604 PMCID: PMC8458928 DOI: 10.3389/fnhum.2021.720239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 08/05/2021] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia is a complex mental illness with genetic heterogeneity, which is often accompanied by alterations in brain structure and function. The neurobiological mechanism of schizophrenia associated with heredity remains unknown. Recently, the development of trans-scale and multi-omics methods that integrate gene and imaging information sheds new light on the nature of schizophrenia. In this article, we summarized the results of brain structural and functional changes related to the specific single-nucleotide polymorphisms (SNPs) in the past decade, and the SNPs were divided into non-coding regions and coding regions, respectively. It is hoped that the relationship between SNPs and cerebral alterations can be displayed more clearly and intuitively, so as to provide fresh approaches for the discovery of potential biomarkers and the development of clinical accurate individualized treatment decision-making.
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Affiliation(s)
- Shu-Wan Zhao
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China.,Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Xian Xu
- Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xian-Yang Wang
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Tian-Cai Yan
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Yang Cao
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Qing-Hong Yan
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Kun Chen
- Department of Anatomy and K. K. Leung Brain Research Centre, Fourth Military Medical University, Xi'an, China
| | - Yin-Chuan Jin
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China
| | - Ya-Hong Zhang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Long-Biao Cui
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China.,Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China
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7
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Xi C, Liu ZN, Yang J, Zhang W, Deng MJ, Pan YZ, Cheng YQ, Pu WD. Schizophrenia patients and their healthy siblings share decreased prefronto-thalamic connectivity but not increased sensorimotor-thalamic connectivity. Schizophr Res 2020; 222:354-361. [PMID: 32507372 DOI: 10.1016/j.schres.2020.04.033] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 04/12/2020] [Accepted: 04/26/2020] [Indexed: 12/12/2022]
Abstract
The pattern of decreased prefronto-thalamic connectivity and increased sensorimotor-thalamic connectivity has been consistently documented in schizophrenia. However, whether this thalamo-cortical abnormality pattern is of genetic predisposition remains unknown. The present study for the first time aimed to investigate the common and distinct characteristics of this circuit in schizophrenia patients and their unaffected siblings who share half of the patient's genotype. Totally 293 participants were recruited into this study including 94 patients with schizophrenia, 96 their healthy siblings, and 103 healthy controls scanned using gradient-echo echo-planar imaging at rest. By using a fine-grained atlas of thalamus with 16 sub-regions, we mapped the thalamocortical network in three groups. Decreased thalamo-prefronto-cerebellar connectivity was shared between schizophrenia and their healthy siblings, but increased sensorimotor-thalamic connectivity was only found in schizophrenia. The shared thalamo-prefronto-cerebellar dysconnectivity showed an impressively gradient reduction pattern in patients and siblings comparing to controls: higher in the controls, lower in the patients and intermediate in the siblings. Anatomically, the decreased thalamic connectivity mostly centered on the pre-frontal thalamic subregions locating at the mediodorsal nucleus, while the increased functional connectivity with sensorimotor cortices was only observed in the caudal temporal thalamic subregion anchoring at the dorsal and ventral lateral nuclei. Moreover, both decreased thalamo-prefronto-cerebellar connectivity and increased sensorimotor-thalamic connectivity were related to clinical symptoms in patients. Our findings extend the evidence that the decreased thalamo-prefronto-cerebellar connectivity may be related to the high genetic risk in schizophrenia, while increased sensorimotor-thalamic connectivity potentially represents a neural biomarker for this severe mental disorder.
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Affiliation(s)
- Chang Xi
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha 410011, China; Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China; Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, China; The China National Clinical Research Center for Mental Health Disorders, Changsha, China
| | - Zhe-Ning Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China; Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, China; The China National Clinical Research Center for Mental Health Disorders, Changsha, China
| | - Jie Yang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China; Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, China; The China National Clinical Research Center for Mental Health Disorders, Changsha, China
| | - Wen Zhang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China; Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, China; The China National Clinical Research Center for Mental Health Disorders, Changsha, China
| | - Meng-Jie Deng
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China; Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, China; The China National Clinical Research Center for Mental Health Disorders, Changsha, China
| | - Yun-Zhi Pan
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China; Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, China; The China National Clinical Research Center for Mental Health Disorders, Changsha, China
| | - Yu-Qi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Wei-Dan Pu
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha 410011, China; The China National Clinical Research Center for Mental Health Disorders, Changsha, China.
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8
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Wu C, Pan W. Integration of methylation QTL and enhancer-target gene maps with schizophrenia GWAS summary results identifies novel genes. Bioinformatics 2020; 35:3576-3583. [PMID: 30850848 DOI: 10.1093/bioinformatics/btz161] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 02/04/2019] [Accepted: 03/05/2019] [Indexed: 01/06/2023] Open
Abstract
MOTIVATION Most trait-associated genetic variants identified in genome-wide association studies (GWASs) are located in non-coding regions of the genome and thought to act through their regulatory roles. RESULTS To account for enriched association signals in DNA regulatory elements, we propose a novel and general gene-based association testing strategy that integrates enhancer-target gene pairs and methylation quantitative trait locus data with GWAS summary results; it aims to both boost statistical power for new discoveries and enhance mechanistic interpretability of any new discovery. By reanalyzing two large-scale schizophrenia GWAS summary datasets, we demonstrate that the proposed method could identify some significant and novel genes (containing no genome-wide significant SNPs nearby) that would have been missed by other competing approaches, including the standard and some integrative gene-based association methods, such as one incorporating enhancer-target gene pairs and one integrating expression quantitative trait loci. AVAILABILITY AND IMPLEMENTATION Software: wuchong.org/egmethyl.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chong Wu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
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9
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A regulatory variant of CHRM3 is associated with cannabis-induced hallucinations in European Americans. Transl Psychiatry 2019; 9:309. [PMID: 31740666 PMCID: PMC6861240 DOI: 10.1038/s41398-019-0639-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 07/01/2019] [Accepted: 08/11/2019] [Indexed: 11/08/2022] Open
Abstract
Cannabis, the most widely used illicit drug, can induce hallucinations. Our understanding of the biology of cannabis-induced hallucinations (Ca-HL) is limited. We used the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA) to identify cannabis-induced hallucinations (Ca-HL) among long-term cannabis users (used cannabis ≥1 year and ≥100 times). A genome-wide association study (GWAS) was conducted by analyzing European Americans (EAs) and African Americans (AAs) in Yale-Penn 1 and 2 cohorts individually, then meta-analyzing the two cohorts within population. In the meta-analysis of Yale-Penn EAs (n = 1917), one genome-wide significant (GWS) signal emerged at the CHRM3 locus, represented by rs115455482 (P = 1.66 × 10-10), rs74722579 (P = 2.81 × 10-9), and rs1938228 (P = 1.57 × 10-8); signals were GWS in Yale-Penn 1 EAs (n = 1092) and nominally significant in Yale-Penn 2 EAs (n = 825). Two SNPs, rs115455482 and rs74722579, were available from the Collaborative Study on the Genetics of Alcoholism data (COGA; 3630 long-term cannabis users). The signals did not replicate, but when meta-analyzing Yale-Penn and COGA EAs, the two SNPs' association signals were increased (meta-P-values 1.32 × 10-10 and 2.60 × 10-9, respectively; n = 4291). There were no significant findings in AAs, but in the AA meta-analysis (n = 3624), nominal significance was seen for rs74722579. The rs115455482*T risk allele was associated with lower CHRM3 expression in the thalamus. CHRM3 was co-expressed with three psychosis risk genes (GABAG2, CHRNA4, and HRH3) in the thalamus and other human brain tissues and mouse GABAergic neurons. This work provides strong evidence for the association of CHRM3 with Ca-HL and provides insight into the potential involvement of thalamus for this trait.
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10
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Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI. J Digit Imaging 2019; 31:252-261. [PMID: 28924878 DOI: 10.1007/s10278-017-0020-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic subjects from normal controls using a publicly available functional MRI (fMRI) data set. Global and local parameters of functional connectivity were extracted for classification. We found decreased global and local network connectivity in subjects with schizophrenia, particularly in the anterior right cingulate cortex, the superior right temporal region, and the inferior left parietal region as compared to healthy subjects. Using support vector machine and 10-fold cross-validation, nine features reached 92.1% prediction accuracy, respectively. Our results suggest that there are significant differences between control and schizophrenic subjects based on regional brain activity detected with fMRI.
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11
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Mohandoss AA, Thavarajah R. Salivary Flow Alteration in Patients Undergoing Treatment for Schizophrenia: Disease-Drug-Target Gene/Protein Association Study for Side-effects. J Oral Biol Craniofac Res 2019; 9:286-293. [PMID: 31289718 PMCID: PMC6593211 DOI: 10.1016/j.jobcr.2019.06.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 06/14/2019] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Salivary flow alteration (SA), is a known unwarranted effect of schizophrenic medications. It manifest either as reduced (xerostomia) or increased (sialorrhea) SA, among treated schizophrenic patients. It is believed that the SA is due to action of the drugs/disease process involving the muscarinic receptor-3 to process acetyl choline, the common neurotransmitter. The genetic mediation behind the SA in such patients remains largely unexplored. We aimed to address the same by using curated literary databases to identify such relationship, if any existed. MATERIAL AND METHODS Curated databases of Gene-Disease Association, www.DisGeNet.org and www.networkanalyst.ca were effectively used to identify the probable genes, strength of association and the drug-genes pathway that could be possibly be involved. The genes associated with schizophrenia and SA were analyzed in detail. Protein-Protein interaction (PPI) network proven experimentally in humans were used to identify the missing or unreported links. RESULTS In all 28 genes associated with schizophrenia were linked to SA. The genetic network of schizophrenia and xerostomia involved FGFR2 gene prominently and network module was statistically significant (P = 9.87*10-8) was achieved that had xerostomia as a node, while schizophrenia (P = 0.025) had statistical significance. Sialorrhea had no statistical significance (P = 0.555). When schizophrenia and sialorrhea connections were analyzed for genetic interaction, only gene GCH1 emerged. On combining the three disease entities, the association of TAC1 gene with sialorrhea was also identified. Using PPI, the coordination of CHRM3, TAC1 and GPRASP1 gene were identified. This network involved several genes that has significant influence on calcium signaling pathway (P = 7.74*10-16), cholingeric synapse(P = 6 × 10-4), salivary secretion(P = 4.38*10-3), endocytosis(P = 8.23*10-4), TGFβ signaling pathway(P = 0.0031), gap junction (P = 4.08*10-3) and glutamergic synapse(P = 6.4*10-3). The involvement of G-receptor signaling protein product, GNAQ was established. DISCUSSION AND CONCLUSION The possible genetic pathway of SA in schizophrenic patients are discussed in light of pharmacotherapeutics. Using the knowledge effectively would help to increase the quality of life of schizophrenic besides increasing the understanding to use saliva as a biomarker of prognosis of schizophrenia and its drug effects.
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Affiliation(s)
- Anusa Arunachalam Mohandoss
- Dept of Psychiatry, Shri Satya Sai Medical College and Research Institute, Affiliated to Shri Balaji Vidyapeeth, Ammapettai, Kanchipuram, India
| | - Rooban Thavarajah
- Marundeeshwara Oral Pathology Services and Analytics, B-1, Mistral Apartments, Wipro Street, Shollinganallur, Chennai, 600 119, India
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Cheng X, Yang Q, Liu J, Ye J, Xiao H, Zhang G, Pan Y, Li X, Hao R, Li Y. Constitutional 763.3 Kb chromosome 1q43 duplication encompassing only CHRM3 gene identified by next generation sequencing (NGS) in a child with intellectual disability. Mol Cytogenet 2019; 12:16. [PMID: 31019551 PMCID: PMC6472087 DOI: 10.1186/s13039-019-0427-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 03/26/2019] [Indexed: 01/08/2023] Open
Abstract
Background Deletion or duplication on the distal portion of the long arm of chromosome 1 result in complex and highly variable clinical phenotype including. intellectual disability and autism. Case presentation We report on a patient with intellectual disability and a 763.3 Kb duplication on 1q43 that includes only CHRM3, which was detected by next generation sequencing (NGS). The patient presented with intellectual disability, developmental delay, autistic behavior, limited or no speech, social withdrawal, self-injurious, feeding difficulties, strabismus, short stature, hand anomalie, and no seizures, anxiety, or mood swings, and clinodactyly. Conclusions We propose that CHRM3 is the critical gene responsible for the common characteristics in the cases with 1q43 duplication and deletion.
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Affiliation(s)
- Xiaofei Cheng
- 1Department of Obstetrics and Gynecology, the First Hospital of Huhhot City, Inner Mongolia, China
| | - Qifang Yang
- 1Department of Obstetrics and Gynecology, the First Hospital of Huhhot City, Inner Mongolia, China
| | - Jun Liu
- 2Department of Radiology, the First Hospital of Huhhot City, Inner Mongolia, China
| | - Juan Ye
- 1Department of Obstetrics and Gynecology, the First Hospital of Huhhot City, Inner Mongolia, China
| | - Huiying Xiao
- 1Department of Obstetrics and Gynecology, the First Hospital of Huhhot City, Inner Mongolia, China
| | - Gaimei Zhang
- 1Department of Obstetrics and Gynecology, the First Hospital of Huhhot City, Inner Mongolia, China
| | - Yuanyuan Pan
- 1Department of Obstetrics and Gynecology, the First Hospital of Huhhot City, Inner Mongolia, China
| | - Xia Li
- 1Department of Obstetrics and Gynecology, the First Hospital of Huhhot City, Inner Mongolia, China
| | - Ruifeng Hao
- 1Department of Obstetrics and Gynecology, the First Hospital of Huhhot City, Inner Mongolia, China
| | - Yinfeng Li
- 1Department of Obstetrics and Gynecology, the First Hospital of Huhhot City, Inner Mongolia, China
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Eszlari N, Millinghoffer A, Petschner P, Gonda X, Baksa D, Pulay AJ, Réthelyi JM, Breen G, Deakin JFW, Antal P, Bagdy G, Juhasz G. Genome-wide association analysis reveals KCTD12 and miR-383-binding genes in the background of rumination. Transl Psychiatry 2019; 9:119. [PMID: 30886212 PMCID: PMC6423133 DOI: 10.1038/s41398-019-0454-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 01/31/2019] [Accepted: 02/13/2019] [Indexed: 12/12/2022] Open
Abstract
Ruminative response style is a passive and repetitive way of responding to stress, associated with several disorders. Although twin and candidate gene studies have proven the genetic underpinnings of rumination, no genome-wide association study (GWAS) has been conducted yet. We performed a GWAS on ruminative response style and its two subtypes, brooding and reflection, among 1758 European adults recruited in the general population of Budapest, Hungary, and Manchester, United Kingdom. We evaluated single-nucleotide polymorphism (SNP)-based, gene-based and gene set-based tests, together with inferences on genes regulated by our most significant SNPs. While no genome-wide significant hit emerged at the SNP level, the association of rumination survived correction for multiple testing with KCTD12 at the gene level, and with the set of genes binding miR-383 at the gene set level. SNP-level results were concordant between the Budapest and Manchester subsamples for all three rumination phenotypes. SNP-level results and their links to brain expression levels based on external databases supported the role of KCTD12, SRGAP3, and SETD5 in rumination, CDH12 in brooding, and DPYSL5, MAPRE3, KCNK3, ATXN7L3B, and TPH2 in reflection, among others. The relatively low sample size is a limitation of our study. Results of the first GWAS on rumination identified genes previously implicated in psychiatric disorders underscoring the transdiagnostic nature of rumination, and pointed to the possible role of the dorsolateral prefrontal cortex, hippocampus, and cerebellum in this cognitive process.
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Affiliation(s)
- Nora Eszlari
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary. .,NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary.
| | - Andras Millinghoffer
- 0000 0001 0942 9821grid.11804.3cNAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary ,0000 0001 2180 0451grid.6759.dDepartment of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Peter Petschner
- 0000 0001 0942 9821grid.11804.3cDepartment of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary ,0000 0001 0942 9821grid.11804.3cMTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary
| | - Xenia Gonda
- 0000 0001 0942 9821grid.11804.3cNAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary ,0000 0001 0942 9821grid.11804.3cMTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary ,0000 0001 0942 9821grid.11804.3cDepartment of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Daniel Baksa
- 0000 0001 0942 9821grid.11804.3cDepartment of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary ,0000 0001 0942 9821grid.11804.3cSE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Attila J. Pulay
- 0000 0001 0942 9821grid.11804.3cDepartment of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - János M. Réthelyi
- 0000 0001 0942 9821grid.11804.3cDepartment of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary ,0000 0001 0942 9821grid.11804.3cNAP2 Molecular Psychiatry Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Gerome Breen
- 0000 0001 2322 6764grid.13097.3cSocial, Genetic and Developmental Psychiatry Centre, King’s College London, London, UK
| | - John Francis William Deakin
- 0000000121662407grid.5379.8Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK ,0000 0004 0417 0074grid.462482.eManchester Academic Health Sciences Centre, Manchester, UK ,0000 0004 0430 6955grid.450837.dGreater Manchester Mental Health NHS Foundation Trust, Prestwich, Manchester, M25 3BL UK
| | - Peter Antal
- 0000 0001 2180 0451grid.6759.dDepartment of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Gyorgy Bagdy
- 0000 0001 0942 9821grid.11804.3cDepartment of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary ,0000 0001 0942 9821grid.11804.3cNAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary ,0000 0001 0942 9821grid.11804.3cMTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary
| | - Gabriella Juhasz
- 0000 0001 0942 9821grid.11804.3cDepartment of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary ,0000 0001 0942 9821grid.11804.3cNAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary ,0000 0001 0942 9821grid.11804.3cMTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary ,0000 0001 0942 9821grid.11804.3cSE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary ,0000000121662407grid.5379.8Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK ,0000 0004 0417 0074grid.462482.eManchester Academic Health Sciences Centre, Manchester, UK
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GWAS and systems biology analysis of depressive symptoms among smokers from the COPDGene cohort. J Affect Disord 2019; 243:16-22. [PMID: 30219690 PMCID: PMC6186181 DOI: 10.1016/j.jad.2018.09.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 07/24/2018] [Accepted: 09/06/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND Large sample GWAS is needed to identify genetic factors associated with depression. This study used genome-wide genotypic and phenotypic data from the COPDGene study to identify genetic risk factors for depression. METHODS Data were from 9716 COPDGene subjects with ≥10 pack-year history. Depression was defined as antidepressant use and/or a HADS depression subscale score ≥8. Non-Hispanic White (6576) and African-American (3140) subsets were analyzed. A GWAS pipeline identified SNPs associated with depression in each group. Network analysis software analyzed gene interactions through common biological pathways, genetic interactions, and tissue-specific gene expression. RESULTS The mean age was 59.4 years (SD 9.0) with 46.5% female subjects. Depression was in 24.7% of the NHW group (1622) and 12.5% of the AA group (391). No SNPs had genome-wide significance. One of the top SNPs, rs12036147 (p = 1.28 × 10-6), is near CHRM3. Another SNP was near MDGA2 (rs17118176, p = 3.52 × 10-6). Top genes formed networks for synaptic transmission with a statistically significant level of more co-expression in brain than other tissues, particularly in the basal ganglia (p = 1.00 × 10-4). LIMITATIONS Limitations included a depression definition based on antidepressant use and a limited HADS score subgroup, which could increase false negatives in depressed patients not on antidepressants. Antidepressants used for smoking cessation in non-depressed patients could lead to false positives. CONCLUSIONS Systems biology analysis identified statistically significant pathways whereby multiple genes influence depression. The gene set pathway analysis and COPDGene data can help investigate depression in future studies.
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The common variants implicated in microstructural abnormality of first episode and drug-naïve patients with schizophrenia. Sci Rep 2017; 7:11750. [PMID: 28924203 PMCID: PMC5603592 DOI: 10.1038/s41598-017-10507-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Accepted: 08/09/2017] [Indexed: 02/05/2023] Open
Abstract
Both post-mortem and neuroimaging studies have identified abnormal white matter (WM) microstructure in patients with schizophrenia. However, its genetic underpinnings and relevant biological pathways remain unclear. In order to unravel the genes and the pathways associated with abnormal WM microstructure in schizophrenia, we recruited 100 first-episode, drug-naïve patients with schizophrenia and 140 matched healthy controls to conduct genome-wide association analysis of fractional anisotropy (FA) value measured using diffusing tensor imaging (DTI), followed by multivariate association study and pathway enrichment analysis. The results showed that one intergenic SNP (rs11901793), which is 20 kb upstream of CXCR7 gene on chromosome 2, was associated with the total mean FA values with genome-wide significance (p = 4.37 × 10−8), and multivariate association analysis identified a strong association between one region-specific SNP (rs10509852), 400 kb upstream of SORCS1 gene on chromosome 10, and the global trait of abnormal WM microstructure (p = 1.89 × 10−7). Furthermore, one pathway that is involved in cell cycle regulation, REACTOME_CHROMOSOME _MAINTENANCE, was significantly enriched by the genes that were identified in our study (p = 1.54 × 10−17). In summary, our study provides suggestive evidence that abnormal WM microstructure in schizophrenia is associated with genes that are likely involved in diverse biological signals and cell-cycle regulation although further replication in a larger independent sample is needed.
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