1
|
Thomaidis GV, Papadimitriou K, Michos S, Chartampilas E, Tsamardinos I. A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning. IBRO Neurosci Rep 2023; 15:77-89. [PMID: 38025660 PMCID: PMC10668096 DOI: 10.1016/j.ibneur.2023.06.008] [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: 01/06/2023] [Revised: 05/19/2023] [Accepted: 06/29/2023] [Indexed: 12/01/2023] Open
Abstract
Background Transcriptomic profile differences between patients with bipolar disorder and healthy controls can be identified using machine learning and can provide information about the potential role of the cerebellum in the pathogenesis of bipolar disorder.With this aim, user-friendly, fully automated machine learning algorithms can achieve extremely high classification scores and disease-related predictive biosignature identification, in short time frames and scaled down to small datasets. Method A fully automated machine learning platform, based on the most suitable algorithm selection and relevant set of hyper-parameter values, was applied on a preprocessed transcriptomics dataset, in order to produce a model for biosignature selection and to classify subjects into groups of patients and controls. The parent GEO datasets were originally produced from the cerebellar and parietal lobe tissue of deceased bipolar patients and healthy controls, using Affymetrix Human Gene 1.0 ST Array. Results Patients and controls were classified into two separate groups, with no close-to-the-boundary cases, and this classification was based on the cerebellar transcriptomic biosignature of 25 features (genes), with Area Under Curve 0.929 and Average Precision 0.955. The biosignature includes both genes connected before to bipolar disorder, depression, psychosis or epilepsy, as well as genes not linked before with any psychiatric disease. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed participation of 4 identified features in 6 pathways which have also been associated with bipolar disorder. Conclusion Automated machine learning (AutoML) managed to identify accurately 25 genes that can jointly - in a multivariate-fashion - separate bipolar patients from healthy controls with high predictive power. The discovered features lead to new biological insights. Machine Learning (ML) analysis considers the features in combination (in contrast to standard differential expression analysis), removing both irrelevant as well as redundant markers, and thus, focusing to biological interpretation.
Collapse
Affiliation(s)
- Georgios V. Thomaidis
- Greek National Health System, Psychiatric Department, Katerini General Hospital, Katerini, Greece
| | - Konstantinos Papadimitriou
- Greek National Health System, G. Papanikolaou General Hospital, Organizational Unit - Psychiatric Hospital of Thessaloniki, Thessaloniki, Greece
| | | | - Evangelos Chartampilas
- Laboratory of Radiology, AHEPA General Hospital, University of Thessaloniki, Thessaloniki, Greece
| | | |
Collapse
|
2
|
Deng Q, Gupta A, Jeon H, Nam JH, Yilmaz AS, Chang W, Pietrzak M, Li L, Kim HJ, Chung D. graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data. Front Genet 2023; 14:1079198. [PMID: 37501720 PMCID: PMC10370274 DOI: 10.3389/fgene.2023.1079198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 06/21/2023] [Indexed: 07/29/2023] Open
Abstract
Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Next, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus, along with the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood-related epigenetic marks, especially B cells and regulatory T cells, across multiple diseases. Psychiatric disorders were enriched for brain-related epigenetic marks, especially the prefrontal cortex and the inferior temporal lobe for bipolar disorder and schizophrenia, respectively. In addition, the pleiotropy between bipolar disorder and schizophrenia was also detected. Finally, we found that GGPA 2.0 is robust to the use of irrelevant and/or incorrect functional annotations. These results demonstrate that GGPA 2.0 can be a powerful tool to identify genetic variants associated with each phenotype or those shared across multiple phenotypes, while also promoting an understanding of functional mechanisms underlying the associated variants.
Collapse
Affiliation(s)
- Qiaolan Deng
- The Interdisciplinary PhD Program in Biostatistics, The Ohio State University, Columbus, OH, United States
| | - Arkobrato Gupta
- The Interdisciplinary PhD Program in Biostatistics, The Ohio State University, Columbus, OH, United States
| | - Hyeongseon Jeon
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States
| | - Jin Hyun Nam
- Division of Big Data Science, Korea University Sejong Campus, Sejong, Republic of Korea
| | - Ayse Selen Yilmaz
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Won Chang
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, United States
| | - Maciej Pietrzak
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Lang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Hang J. Kim
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, United States
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States
| |
Collapse
|
3
|
Papageorgiou L, Kalospyrou E, Papakonstantinou E, Diakou I, Pierouli K, Dragoumani K, Bacopoulou F, Chrousos GP, Exarchos TP, Vlamos P, Eliopoulos E, Vlachakis D. DRDs and Brain-Derived Neurotrophic Factor Share a Common Therapeutic Ground: A Novel Bioinformatic Approach Sheds New Light Toward Pharmacological Treatment of Cognitive and Behavioral Disorders. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1424:97-115. [PMID: 37486484 DOI: 10.1007/978-3-031-31982-2_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Cognitive and behavioral disorders are subgroups of mental health disorders. Both cognitive and behavioral disorders can occur in people of different ages, genders, and social backgrounds, and they can cause serious physical, mental, or social problems. The risk factors for these diseases are numerous, with a range from genetic and epigenetic factors to physical factors. In most cases, the appearance of such a disorder in an individual is a combination of his genetic profile and environmental stimuli. To date, researchers have not been able to identify the specific causes of these disorders, and as such, there is urgent need for innovative study approaches. The aim of the present study was to identify the genetic factors which seem to be more directly responsible for the occurrence of a cognitive and/or behavioral disorder. More specifically, through bioinformatics tools and software as well as analytical methods such as systemic data and text mining, semantic analysis, and scoring functions, we extracted the most relevant single nucleotide polymorphisms (SNPs) and genes connected to these disorders. All the extracted SNPs were filtered, annotated, classified, and evaluated in order to create the "genomic grammar" of these diseases. The identified SNPs guided the search for top suspected genetic factors, dopamine receptors D and neurotrophic factor BDNF, for which regulatory networks were built. The identification of the "genomic grammar" and underlying factors connected to cognitive and behavioral disorders can aid in the successful disease profiling and the establishment of novel pharmacological targets and provide the basis for personalized medicine, which takes into account the patient's genetic background as well as epigenetic factors.
Collapse
Affiliation(s)
- Louis Papageorgiou
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Efstathia Kalospyrou
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Eleni Papakonstantinou
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Io Diakou
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Katerina Pierouli
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Konstantina Dragoumani
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Flora Bacopoulou
- University Research Institute of Maternal and Child Health & Precision Medicine, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece
| | - George P Chrousos
- University Research Institute of Maternal and Child Health & Precision Medicine, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece
| | - Themis P Exarchos
- Department of Informatics, Bioinformatics & Human Electrophysiology Laboratory, Ionian University, Corfu, Greece
| | - Panagiotis Vlamos
- Department of Informatics, Bioinformatics & Human Electrophysiology Laboratory, Ionian University, Corfu, Greece
| | - Elias Eliopoulos
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Dimitrios Vlachakis
- Department of Biotechnology, Laboratory of Genetics, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece.
- University Research Institute of Maternal and Child Health & Precision Medicine, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece.
- Division of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.
| |
Collapse
|
4
|
Bioinformatics and Network-based Approaches for Determining Pathways, Signature Molecules, and Drug Substances connected to Genetic Basis of Schizophrenia etiology. Brain Res 2022; 1785:147889. [PMID: 35339428 DOI: 10.1016/j.brainres.2022.147889] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/28/2022] [Accepted: 03/21/2022] [Indexed: 12/12/2022]
Abstract
Knowledge of heterogeneous etiology and pathophysiology of schizophrenia (SZP) is reasonably inadequate and non-deterministic due to its inherent complexity and underlying vast dynamics related to genetic mechanisms. The evolution of large-scale transcriptome-wide datasets and subsequent development of relevant, robust technologies for their analyses show promises toward elucidating the genetic basis of disease pathogenesis, its early risk prediction, and predicting drug molecule targets for therapeutic intervention. In this research, we have scrutinized the genetic basis of SZP through functional annotation and network-based system biology approaches. We have determined 96 overlapping differentially expressed genes (DEGs) from 2 microarray datasets and subsequently identified their interconnecting networks to reveal transcriptome signatures like hub proteins (FYN, RAD51, SOCS3, XIAP, AKAP13, PIK3C2A, CBX5, GATA3, EIF3K, and CDKN2B), transcription factors and miRNAs. In addition, we have employed gene set enrichment to highlight significant gene ontology (e.g., positive regulation of microglial cell activation) and relevant pathways (such as axon guidance and focal adhesion) interconnected to the genes associated with SZP. Finally, we have suggested candidate drug substances like Luteolin HL60 UP as a possible therapeutic target based on these key molecular signatures.
Collapse
|
5
|
Engelke R, Ouanes S, Ghuloum S, Chamali R, Kiwan N, Sarwath H, Schmidt F, Suhre K, Al-Amin H. Proteomic Analysis of Plasma Markers in Patients Maintained on Antipsychotics: Comparison to Patients Off Antipsychotics and Normal Controls. Front Psychiatry 2022; 13:809071. [PMID: 35546954 PMCID: PMC9081931 DOI: 10.3389/fpsyt.2022.809071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Schizophrenia (SZ) and bipolar disorder (BD) share many features: overlap in mood and psychotic symptoms, common genetic predisposition, treatment with antipsychotics (APs), and similar metabolic comorbidities. The pathophysiology of both is still not well defined, and no biomarkers can be used clinically for diagnosis and management. This study aimed to assess the plasma proteomics profile of patients with SZ and BD maintained on APs compared to those who had been off APs for 6 months and to healthy controls (HCs). METHODS We analyzed the data using functional enrichment, random forest modeling to identify potential biomarkers, and multivariate regression for the associations with metabolic abnormalities. RESULTS We identified several proteins known to play roles in the differentiation of the nervous system like NTRK2, CNTN1, ROBO2, and PLXNC1, which were downregulated in AP-free SZ and BD patients but were "normalized" in those on APs. Other proteins (like NCAM1 and TNFRSF17) were "normal" in AP-free patients but downregulated in patients on APs, suggesting that these changes are related to medication's effects. We found significant enrichment of proteins involved in neuronal plasticity, mainly in SZ patients on APs. Most of the proteins associated with metabolic abnormalities were more related to APs use than having SZ or BD. The biomarkers identification showed specific and sensitive results for schizophrenia, where two proteins (PRL and MRC2) produced adequate results. CONCLUSIONS Our results confirmed the utility of blood samples to identify protein signatures and mechanisms involved in the pathophysiology and treatment of SZ and BD.
Collapse
Affiliation(s)
- Rudolf Engelke
- Proteomics Core, Research Department, Weill Cornell Medicine in Qatar, Doha, Qatar
| | - Sami Ouanes
- Psychiatry Department, Hamad Medical Corporation, Doha, Qatar
| | - Suhaila Ghuloum
- Psychiatry Department, Hamad Medical Corporation, Doha, Qatar
| | - Rifka Chamali
- Psychiatry Department, Weill Cornell Medicine, Doha, Qatar
| | - Nancy Kiwan
- Psychiatry Department, Weill Cornell Medicine, Doha, Qatar
| | - Hina Sarwath
- Proteomics Core, Research Department, Weill Cornell Medicine in Qatar, Doha, Qatar
| | - Frank Schmidt
- Proteomics Core, Research Department, Weill Cornell Medicine in Qatar, Doha, Qatar
| | - Karsten Suhre
- Bioinformatics Core, Research Department, Weill Cornell Medicine in Qatar, Doha, Qatar
| | - Hassen Al-Amin
- Psychiatry Department, Weill Cornell Medicine, Doha, Qatar
| |
Collapse
|
6
|
Oku S, Feng H, Connor S, Toledo A, Zhang P, Zhang Y, Thoumine O, Zhang C, Craig AM. Alternative splicing at neuroligin site A regulates glycan interaction and synaptogenic activity. eLife 2020; 9:58668. [PMID: 32915137 PMCID: PMC7486126 DOI: 10.7554/elife.58668] [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: 05/07/2020] [Accepted: 08/31/2020] [Indexed: 01/18/2023] Open
Abstract
Post-transcriptional mechanisms regulating cell surface synaptic organizing complexes that control the properties of connections in brain circuits are poorly understood. Alternative splicing regulates the prototypical synaptic organizing complex, neuroligin-neurexin. In contrast to the well-studied neuroligin splice site B, little is known about splice site A. We discovered that inclusion of the positively charged A1 insert in mouse neuroligin-1 increases its binding to heparan sulphate, a modification on neurexin. The A1 insert increases neurexin recruitment, presynaptic differentiation, and synaptic transmission mediated by neuroligin-1. We propose that the A1 insert could be a target for alleviating the consequences of deleterious NLGN1/3 mutations, supported by assays with the autism-linked neuroligin-1-P89L mutant. An enrichment of neuroligin-1 A1 in GABAergic neuron types suggests a role in synchrony of cortical circuits. Altogether, these data reveal an unusual mode by which neuroligin splicing controls synapse development through protein-glycan interaction and identify it as a potential therapeutic target.
Collapse
Affiliation(s)
- Shinichiro Oku
- Djavad Mowafaghian Centre for Brain Health and Department of Psychiatry, University of British Columbia, Vancouver, Canada
| | - Huijuan Feng
- Departments of Systems Biology and Biochemistry and Molecular Biophysics, Center for Motor Neuron Biology and Disease, Columbia University, New York, United States
| | - Steven Connor
- Djavad Mowafaghian Centre for Brain Health and Department of Psychiatry, University of British Columbia, Vancouver, Canada.,Department of Biology, York University, Toronto, Canada
| | - Andrea Toledo
- Interdisciplinary Institute for Neuroscience UMR 5297, CNRS and University of Bordeaux, Bordeaux, France
| | - Peng Zhang
- Djavad Mowafaghian Centre for Brain Health and Department of Psychiatry, University of British Columbia, Vancouver, Canada
| | - Yue Zhang
- Djavad Mowafaghian Centre for Brain Health and Department of Psychiatry, University of British Columbia, Vancouver, Canada
| | - Olivier Thoumine
- Interdisciplinary Institute for Neuroscience UMR 5297, CNRS and University of Bordeaux, Bordeaux, France
| | - Chaolin Zhang
- Departments of Systems Biology and Biochemistry and Molecular Biophysics, Center for Motor Neuron Biology and Disease, Columbia University, New York, United States
| | - Ann Marie Craig
- Djavad Mowafaghian Centre for Brain Health and Department of Psychiatry, University of British Columbia, Vancouver, Canada
| |
Collapse
|
7
|
Bregin A, Kaare M, Jagomäe T, Karis K, Singh K, Laugus K, Innos J, Leidmaa E, Heinla I, Visnapuu T, Oja EM, Kõiv K, Lilleväli K, Harro J, Philips MA, Vasar E. Expression and impact of Lsamp neural adhesion molecule in the serotonergic neurotransmission system. Pharmacol Biochem Behav 2020; 198:173017. [PMID: 32828972 DOI: 10.1016/j.pbb.2020.173017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 08/15/2020] [Accepted: 08/17/2020] [Indexed: 12/22/2022]
Abstract
Limbic system associated membrane protein (Lsamp) is a neural adhesion protein which has been recently found to be differentially expressed between serotonergic neuron subtypes. We have previously shown elevated serotonin (5-HT) turnover rate in Lsamp-deficient mice. The purpose of the current study was to elucidate the role of Lsamp in serotonergic neurotransmission. Chronic (18 days) administration of serotonin reuptake inhibitor (SSRI) escitalopram (10 mg/kg) significantly increased general activity in wild-type mice in the open field and protected exploration in Lsamp-/- mice in the elevated-plus maze. An important psychopathology-related endophenotype, elevated 5-HT turnover in the brain of Lsamp-deficient mice, was reproduced in the saline group. Escitalopram restored the elevated 5-HT turnover of Lsamp-deficient mice to a level comparable with their wild-type littermates, suggesting that high 5-HT turnover in mutants is mediated by the increased activity of serotonin transporter (SERT protein encoded by Slc6a4 gene). The baseline level of Slc6a4 transcript was not changed in Lsamp-deficient mice, however, our immunohistochemical analysis showed partial co-expression of Lsamp with both SERT and Tph2 proteins in raphe. Overactivity of SERT in Lsamp-/- mice is further supported by significant elevation of Maoa transcript and increase of DOPAC, another Mao A product, specifically in the raphe. Again, elevation of DOPAC was reduced to the level of wild-type by chronic SSRI treatment. The activity of Lsamp gene promoters varied in 5-HT producing nuclei: both Lsamp 1a and 1b promoters were active in the dorsal raphe; most of the expression in the median raphe was from 1b promoter, whereas Lsamp 1a promoter was almost exclusively active in the caudal subgroup of raphe nuclei. We suggest that Lsamp may have an impact on the integrity of serotonergic synapses, which is possibly the neurochemical basis of the anxiety- and sociability-related phenotype in Lsamp-deficient mice.
Collapse
Affiliation(s)
- Aleksandr Bregin
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Maria Kaare
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Toomas Jagomäe
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Karina Karis
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Katyayani Singh
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Karita Laugus
- Division of Neuropsychopharmacology, Department of Psychology, Estonian Centre of Behavioural and Health Sciences, University of Tartu, Estonia
| | - Jürgen Innos
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Este Leidmaa
- Institute of Molecular Psychiatry, Medical Faculty, University of Bonn, Bonn, Germany
| | - Indrek Heinla
- Department of Psychology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Tanel Visnapuu
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Eva-Maria Oja
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Kadri Kõiv
- Division of Neuropsychopharmacology, Department of Psychology, Estonian Centre of Behavioural and Health Sciences, University of Tartu, Estonia
| | - Kersti Lilleväli
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Jaanus Harro
- Division of Neuropsychopharmacology, Department of Psychology, Estonian Centre of Behavioural and Health Sciences, University of Tartu, Estonia
| | - Mari-Anne Philips
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, Tartu, Estonia.
| | - Eero Vasar
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, 19 Ravila Street, 50411 Tartu, Estonia; Centre of Excellence in Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| |
Collapse
|
8
|
Squillario M, Abate G, Tomasi F, Tozzo V, Barla A, Uberti D. A telescope GWAS analysis strategy, based on SNPs-genes-pathways ensamble and on multivariate algorithms, to characterize late onset Alzheimer's disease. Sci Rep 2020; 10:12063. [PMID: 32694537 PMCID: PMC7374579 DOI: 10.1038/s41598-020-67699-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 06/10/2020] [Indexed: 12/18/2022] Open
Abstract
Genome–wide association studies (GWAS) have revealed a plethora of putative susceptibility genes for Alzheimer’s disease (AD), with the sole exception of APOE gene unequivocally validated in independent study. Considering that the etiology of complex diseases like AD could depend on functional multiple genes interaction network, here we proposed an alternative GWAS analysis strategy based on (i) multivariate methods and on a (ii) telescope approach, in order to guarantee the identification of correlated variables, and reveal their connections at three biological connected levels. Specifically as multivariate methods, we employed two machine learning algorithms and a genetic association test and we considered SNPs, Genes and Pathways features in the analysis of two public GWAS dataset (ADNI-1 and ADNI-2). For each dataset and for each feature we addressed two binary classifications tasks: cases vs. controls and the low vs. high risk of developing AD considering the allelic status of APOEe4. This complex strategy allowed the identification of SNPs, genes and pathways lists statistically robust and meaningful from the biological viewpoint. Among the results, we confirm the involvement of TOMM40 gene in AD and we propose GRM7 as a novel gene significantly associated with AD.
Collapse
Affiliation(s)
| | - Giulia Abate
- Department of Molecular and Translational Medicine, University of Brescia, 25123, Brescia, Italy
| | | | | | | | - Daniela Uberti
- Department of Molecular and Translational Medicine, University of Brescia, 25123, Brescia, Italy
| | | |
Collapse
|
9
|
Clark SL, Hattab MW, Chan RF, Shabalin AA, Han LKM, Zhao M, Smit JH, Jansen R, Milaneschi Y, Xie LY, van Grootheest G, Penninx BWJH, Aberg KA, van den Oord EJCG. A methylation study of long-term depression risk. Mol Psychiatry 2020; 25:1334-1343. [PMID: 31501512 PMCID: PMC7061076 DOI: 10.1038/s41380-019-0516-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 03/11/2019] [Accepted: 07/22/2019] [Indexed: 12/20/2022]
Abstract
Recurrent and chronic major depressive disorder (MDD) accounts for a substantial part of the disease burden because this course is most prevalent and typically requires long-term treatment. We associated blood DNA methylation profiles from 581 MDD patients at baseline with MDD status 6 years later. A resampling approach showed a highly significant association between methylation profiles in blood at baseline and future disease status (P = 2.0 × 10-16). Top MWAS results were enriched specific pathways, overlapped with genes found in GWAS of MDD disease status, autoimmune disease and inflammation, and co-localized with eQTLS and (genic enhancers of) of transcription sites in brain and blood. Many of these findings remained significant after correction for multiple testing. The major themes emerging were cellular responses to stress and signaling mechanisms linked to immune cell migration and inflammation. This suggests that an immune signature of treatment-resistant depression is already present at baseline. We also created a methylation risk score (MRS) to predict MDD status 6 years later. The AUC of our MRS was 0.724 and higher than risk scores created using a set of five putative MDD biomarkers, genome-wide SNP data, and 27 clinical, demographic and lifestyle variables. Although further studies are needed to examine the generalizability to different patient populations, these results suggest that methylation profiles in blood may present a promising avenue to support clinical decision making by providing empirical information about the likelihood MDD is chronic or will recur in the future.
Collapse
Affiliation(s)
- Shaunna L Clark
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Mohammad W Hattab
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Robin F Chan
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Andrey A Shabalin
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Laura KM Han
- Department of Psychiatry, VU University Medical Center / GGZ inGeest, Amsterdam, the Netherlands 1081 HV
| | - Min Zhao
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Johannes H Smit
- Department of Psychiatry, VU University Medical Center / GGZ inGeest, Amsterdam, the Netherlands 1081 HV
| | - Rick Jansen
- Department of Psychiatry, VU University Medical Center / GGZ inGeest, Amsterdam, the Netherlands 1081 HV
| | - Yuri Milaneschi
- Department of Psychiatry, VU University Medical Center / GGZ inGeest, Amsterdam, the Netherlands 1081 HV
| | - Lin Ying Xie
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Gerard van Grootheest
- Department of Psychiatry, VU University Medical Center / GGZ inGeest, Amsterdam, the Netherlands 1081 HV
| | - Brenda WJH Penninx
- Department of Psychiatry, VU University Medical Center / GGZ inGeest, Amsterdam, the Netherlands 1081 HV
| | - Karolina A Aberg
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Edwin JCG van den Oord
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA
| |
Collapse
|
10
|
Zhang Z, Chen G. A logical relationship for schizophrenia, bipolar, and major depressive disorder. Part 1: Evidence from chromosome 1 high density association screen. J Comp Neurol 2020; 528:2620-2635. [PMID: 32266715 DOI: 10.1002/cne.24921] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 03/28/2020] [Accepted: 03/30/2020] [Indexed: 12/16/2022]
Abstract
Familial clustering of schizophrenia (SCZ), bipolar disorder (BPD), and major depressive disorder (MDD) was investigated systematically (Aukes et al., Genetics in Medicine, 2012, 14, 338-341) and any two or even three of these disorders could coexist in some families. Furthermore, evidence from symptomatology and psychopharmacology also imply the existence of intrinsic connections between these three major psychiatric disorders. A total of 71,445 SNPs on chromosome 1 were genotyped on 119 SCZ, 253 BPD (type-I), 177 MDD cases and 1000 controls and further validated in 986 SCZ patients in the population of Shandong province of China. Outstanding psychosis genes are systematically revealed( ATP1A4, ELTD1, FAM5C, HHAT, KIF26B, LMX1A, NEGR1, NFIA, NR5A2, NTNG1, PAPPA2, PDE4B, PEX14, RYR2, SYT6, TGFBR3, TTLL7, and USH2A). Unexpectedly, flanking genes for up to 97.09% of the associated SNPs were also replicated in an enlarged cohort of 986 SCZ patients. From the perspective of etiological rather than clinical psychiatry, bipolar, and major depressive disorder could be subtypes of schizophrenia. Meanwhile, the varied clinical feature and prognosis might be the result of interaction of genetics and epigenetics, for example, irreversible or reversible shut down, and over or insufficient expression of certain genes, which may gives other aspects of these severe mental disorders.
Collapse
Affiliation(s)
- Zhihua Zhang
- Shandong Mental Health Center, Jinan, Shandong, China
| | - Gang Chen
- Department of Medical Genetics, Institute of Basic Medicine, Shandong Academy of Medical Sciences, Jinan, Shandong, China
| |
Collapse
|
11
|
Liang W, Yu H, Su Y, Lu T, Yan H, Yue W, Zhang D. Variants of GRM7 as risk factor and response to antipsychotic therapy in schizophrenia. Transl Psychiatry 2020; 10:83. [PMID: 32127521 PMCID: PMC7054263 DOI: 10.1038/s41398-020-0763-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 01/07/2020] [Accepted: 02/12/2020] [Indexed: 11/09/2022] Open
Abstract
Genome-wide association study (GWAS) has determined the metabotropic glutamate receptor 7 (GRM7) gene as potential locus for schizophrenia risk variants; However, the relationship between the GRM7 variants and the risk of schizophrenia is still uncertain, and there are significant individual variations in response to the antipsychotic drugs. In order to identify susceptible gene and drug-response-related markers, 2413 subjects in our research were chosen for determining drug-response-related markers in schizophrenia. The rs1516569 variant (OR = 0.95, P < 3.47 × 10-4) was a significant risk factor, and a single-nucleotide polymorphism of GRM7 gene- rs9883258 (OR = 0.84, P = 2.18 × 10-3) has been determined as potential biomarkers for therapeutic responses of seven commonly used antipsychotic drugs (aripiprazole, haloperidol, olanzapine, perphenazine, quetiapine, risperidone and ziprasidone) in Chinese Han population; Significant associations with treatment response for several single-nucleotide polymorphisms in every antipsychotic drugs, such as rs779746 (OR = 1.39, P = 0.03), rs480409 (OR = 0.73, P = 0.04), rs78137319 (OR = 3.09, P = 0.04), rs1154370 (OR = 1.51, P = 0.006) have been identified in our study. Hence our research elucidates that GRM7 variants play the critical role of predicting the risk of schizophrenia and antipsychotic effect of seven common drugs.
Collapse
Affiliation(s)
- Wei Liang
- grid.459847.30000 0004 1798 0615Institute of Mental Health, Peking University Sixth Hospital, 100191 Beijing, China ,grid.453135.50000 0004 1769 3691NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University Sixth Hospital), 100191 Beijing, China
| | - Hao Yu
- grid.449428.70000 0004 1797 7280Shandong Collaborative Innovation Center for Diagnosis, Treatment and Behavioral Interventions of Mental Disorders, Department of Psychiatry, Jining Medical University, 272067 Jining, Shandong China
| | - Yi Su
- grid.459847.30000 0004 1798 0615Institute of Mental Health, Peking University Sixth Hospital, 100191 Beijing, China ,grid.453135.50000 0004 1769 3691NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University Sixth Hospital), 100191 Beijing, China
| | - Tianlan Lu
- grid.459847.30000 0004 1798 0615Institute of Mental Health, Peking University Sixth Hospital, 100191 Beijing, China ,grid.453135.50000 0004 1769 3691NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University Sixth Hospital), 100191 Beijing, China
| | - Hao Yan
- grid.459847.30000 0004 1798 0615Institute of Mental Health, Peking University Sixth Hospital, 100191 Beijing, China ,grid.453135.50000 0004 1769 3691NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University Sixth Hospital), 100191 Beijing, China
| | - Weihua Yue
- Institute of Mental Health, Peking University Sixth Hospital, 100191, Beijing, China. .,NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University Sixth Hospital), 100191, Beijing, China. .,PKU-IDG/McGovern Institute for Brain Research, Peking University, 100871, Beijing, China. .,Chinese Academy of Medical Sciences Research Unit (No. 2018RU006), Peking University, 100191, Beijing, China.
| | - Dai Zhang
- Institute of Mental Health, Peking University Sixth Hospital, 100191, Beijing, China. .,NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders and Key Laboratory of Mental Health, Ministry of Health (Peking University Sixth Hospital), 100191, Beijing, China. .,PKU-IDG/McGovern Institute for Brain Research, Peking University, 100871, Beijing, China.
| |
Collapse
|
12
|
Zhou Y, Li Z, Wang Y, Huang H, Chen W, Dong L, Wu J, Chen J, Miao Y, Qi L, Zhang S, Lang X, Zhang X. Prevalence and clinical correlates of psychotic depression in first-episode and drug-naïve outpatients with major depressive disorder in a Chinese Han population. J Affect Disord 2020; 263:500-506. [PMID: 31759662 DOI: 10.1016/j.jad.2019.10.051] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 09/11/2019] [Accepted: 10/28/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND Few systemic studies of psychotic depression (PD) have been conducted in first-episode and drug-naïve (FEDN) patients. This study was to examine the prevalence of PD and its clinical correlates in a large sample of FEDN outpatients with major depressive disorder (MDD) in a Chinese Han population, which to our best knowledge has not been studied. METHODS In this cross-sectional study, a total of 1718 FEDN MDD outpatients were recruited. All patients were rated on the positive symptom subscale of the Positive and Negative Syndrome Scale (PANSS) for psychotic symptoms, the 17-item Hamilton Depression Rating Scale (HAMD-17) for depression and Hamilton Anxiety Rating Scale (HAMA) for anxiety. RESULTS The prevalence of PD was 10.0% (171/1718) in MDD patients. Compared to the non-PD patients, PD patients had significantly older age, lower educational levels, higher anxiety symptom score and were more likely to attempt suicide. Further logistic regression analysis indicated that PD was associated with older age, suicide attempt and comorbid anxiety (all p < 0.05). Multiple regression analyses showed that both HAMD and HAMA total scores were significantly associated with PANSS positive symptom subscale score. LIMITATIONS Our cross-sectional study design does not show a direct causal relationship between psychiatric symptoms and related factors in patients with MDD. CONCLUSIONS Our results suggest a high prevalence of PD in MDD patients in the acute early stage in a Chinese Han population, which is associated with demographic variables and clinical symptoms.
Collapse
Affiliation(s)
- Yongjie Zhou
- Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China
| | - Zezhi Li
- Department of Neurology, Ren Ji Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
| | - Yijun Wang
- Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China
| | - Huiteng Huang
- Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China
| | - Wencai Chen
- Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China
| | - Ling Dong
- Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China
| | - Jiang Wu
- Wuhan Youfu Hospital, Wuhan, China
| | - Jing Chen
- Wuhan Youfu Hospital, Wuhan, China; Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China
| | - Ying Miao
- Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China
| | - Ling Qi
- Wuhan Youfu Hospital, Wuhan, China; School of Health Science and Nursing, Wuhan Polytechnic University, Wuhan, 430023, China
| | - Shufang Zhang
- Wuhan Youfu Hospital, Wuhan, China; Research Center for Psychological and Health Sciences, China University of Geosciences, Wuhan, China; Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China
| | - Xiaoe Lang
- Department of Psychiatry, The First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi Province, China
| | - Xiangyang Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
| |
Collapse
|
13
|
A Novel Schizophrenia Diagnostic Model Based on Statistically Significant Changes in Gene Methylation in Specific Brain Regions. BIOMED RESEARCH INTERNATIONAL 2020; 2020:8047146. [PMID: 32104705 PMCID: PMC7037884 DOI: 10.1155/2020/8047146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 10/28/2019] [Accepted: 11/15/2019] [Indexed: 12/23/2022]
Abstract
Objective The present study identified methylation patterns of schizophrenia- (SCZ-) related genes in different brain regions and used them to construct a novel DNA methylation-based SCZ diagnostic model. Methods Four DNA methylation datasets representing different brain regions were downloaded from the Gene Expression Omnibus. The common differentially methylated genes (CDMGs) in all datasets were identified to perform functional enrichment analysis. The differential methylation sites of 10 CDMGs involved in the largest numbers of neurological or psychiatric-related biological processes were used to construct a DNA methylation-based diagnostic model for SCZ in the respective datasets. Results A total of 849 CDMGs were identified in the four datasets, but the methylation sites as well as degree of methylation differed across the brain regions. Functional enrichment analysis showed CDMGs were significantly involved in biological processes associated with neuronal axon development, intercellular adhesion, and cell morphology changes and, specifically, in PI3K-Akt, AMPK, and MAPK signaling pathways. Four DNA methylation-based classifiers for diagnosing SCZ were constructed in the four datasets, respectively. The sample recognition efficiency of the classifiers showed an area under the receiver operating characteristic curve of 1.00 in three datasets and >0.9 in one dataset. Conclusion DNA methylation patterns in SCZ vary across different brain regions, which may be a useful epigenetic characteristic for diagnosing SCZ. Our novel model based on SCZ-gene methylation shows promising diagnostic power.
Collapse
|
14
|
Yang QX, Wang YX, Li FC, Zhang S, Luo YC, Li Y, Tang J, Li B, Chen YZ, Xue WW, Zhu F. Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence-based method of enhanced reproducibility. CNS Neurosci Ther 2019; 25:1054-1063. [PMID: 31350824 PMCID: PMC6698965 DOI: 10.1111/cns.13196] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 06/27/2019] [Accepted: 07/03/2019] [Indexed: 12/15/2022] Open
Abstract
Aims As one of the most fundamental questions in modern science, “what causes schizophrenia (SZ)” remains a profound mystery due to the absence of objective gene markers. The reproducibility of the gene signatures identified by independent studies is found to be extremely low due to the incapability of available feature selection methods and the lack of measurement on validating signatures’ robustness. These irreproducible results have significantly limited our understanding of the etiology of SZ. Methods In this study, a new feature selection strategy was developed, and a comprehensive analysis was then conducted to ensure a reliable signature discovery. Particularly, the new strategy (a) combined multiple randomized sampling with consensus scoring and (b) assessed gene ranking consistency among different datasets, and a comprehensive analysis among nine independent studies was conducted. Results Based on a first‐ever evaluation of methods’ reproducibility that was cross‐validated by nine independent studies, the newly developed strategy was found to be superior to the traditional ones. As a result, 33 genes were consistently identified from multiple datasets by the new strategy as differentially expressed, which might facilitate our understanding of the mechanism underlying the etiology of SZ. Conclusion A new strategy capable of enhancing the reproducibility of feature selection in current SZ research was successfully constructed and validated. A group of candidate genes identified in this study should be considered as great potential for revealing the etiology of SZ.
Collapse
Affiliation(s)
- Qing-Xia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Yun-Xia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Feng-Cheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Song Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yong-Chao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yi Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Bo Li
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Yu-Zong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore, Singapore
| | - Wei-Wei Xue
- School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences, Chongqing University, Chongqing, China
| |
Collapse
|
15
|
Yang Q, Li B, Tang J, Cui X, Wang Y, Li X, Hu J, Chen Y, Xue W, Lou Y, Qiu Y, Zhu F. Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data. Brief Bioinform 2019; 21:1058-1068. [DOI: 10.1093/bib/bbz049] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 03/11/2019] [Accepted: 03/30/2019] [Indexed: 12/16/2022] Open
Abstract
Abstract
The etiology of schizophrenia (SCZ) is regarded as one of the most fundamental puzzles in current medical research, and its diagnosis is limited by the lack of objective molecular criteria. Although plenty of studies were conducted, SCZ gene signatures identified by these independent studies are found highly inconsistent. As one of the most important factors contributing to this inconsistency, the feature selection methods used currently do not fully consider the reproducibility among the signatures discovered from different datasets. Therefore, it is crucial to develop new bioinformatics tools of novel strategy for ensuring a stable discovery of gene signature for SCZ. In this study, a novel feature selection strategy (1) integrating repeated random sampling with consensus scoring and (2) evaluating the consistency of gene rank among different datasets was constructed. By systematically assessing the identified SCZ signature comprising 135 differentially expressed genes, this newly constructed strategy demonstrated significantly enhanced stability and better differentiating ability compared with the feature selection methods popular in current SCZ research. Based on a first-ever assessment on methods’ reproducibility cross-validated by independent datasets from three representative studies, the new strategy stood out among the popular methods by showing superior stability and differentiating ability. Finally, 2 novel and 17 previously reported transcription factors were identified and showed great potential in revealing the etiology of SCZ. In sum, the SCZ signature identified in this study would provide valuable clues for discovering diagnostic molecules and potential targets for SCZ.
Collapse
Affiliation(s)
- Qingxia Yang
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Bo Li
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jing Tang
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Xuejiao Cui
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yunxia Wang
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Xiaofeng Li
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jie Hu
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yuzong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, and Center for Computational Science and Engineering, National University of Singapore, Singapore, Singapore
| | - Weiwei Xue
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yunqing Qiu
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Feng Zhu
- Innovative Drug Research and Bioinformatics Group, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| |
Collapse
|
16
|
Abstract
Synapse formation is mediated by a surprisingly large number and wide variety of genes encoding many different protein classes. One of the families increasingly implicated in synapse wiring is the immunoglobulin superfamily (IgSF). IgSF molecules are by definition any protein containing at least one Ig-like domain, making this family one of the most common protein classes encoded by the genome. Here, we review the emerging roles for IgSF molecules in synapse formation specifically in the vertebrate brain, focusing on examples from three classes of IgSF members: ( a) cell adhesion molecules, ( b) signaling molecules, and ( c) immune molecules expressed in the brain. The critical roles for IgSF members in regulating synapse formation may explain their extensive involvement in neuropsychiatric and neurodevelopmental disorders. Solving the IgSF code for synapse formation may reveal multiple new targets for rescuing IgSF-mediated deficits in synapse formation and, eventually, new treatments for psychiatric disorders caused by altered IgSF-induced synapse wiring.
Collapse
Affiliation(s)
- Scott Cameron
- Center for Neuroscience, University of California, Davis, California 95618, USA; ,
| | | |
Collapse
|
17
|
Tang J, Chen X, Cai B, Chen G. A logical relationship for schizophrenia, bipolar, and major depressive disorder. Part 4: Evidence from chromosome 4 high-density association screen. J Comp Neurol 2018; 527:392-405. [DOI: 10.1002/cne.24543] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 06/27/2018] [Accepted: 06/28/2018] [Indexed: 12/19/2022]
Affiliation(s)
- Jian Tang
- Department of Radiology; Qianfo Hill Campus Hospital of Shandong University; Jinan 250061 Shandong People's Republic of China
| | - Xing Chen
- Department of Medical Genetics, Institute of Basic Medicine; Shandong Academy of Medical Sciences; Jinan Shandong People's Republic of China
| | - Bin Cai
- CapitalBio corporation, 18 Life Science Parkway, Changping District; Beijing People's Republic of China
| | - Gang Chen
- Department of Medical Genetics, Institute of Basic Medicine; Shandong Academy of Medical Sciences; Jinan Shandong People's Republic of China
| |
Collapse
|
18
|
Karis K, Eskla KL, Kaare M, Täht K, Tuusov J, Visnapuu T, Innos J, Jayaram M, Timmusk T, Weickert CS, Väli M, Vasar E, Philips MA. Altered Expression Profile of IgLON Family of Neural Cell Adhesion Molecules in the Dorsolateral Prefrontal Cortex of Schizophrenic Patients. Front Mol Neurosci 2018; 11:8. [PMID: 29434535 PMCID: PMC5797424 DOI: 10.3389/fnmol.2018.00008] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 01/08/2018] [Indexed: 01/03/2023] Open
Abstract
Neural adhesion proteins are crucial in the development and maintenance of functional neural connectivity. Growing evidence suggests that the IgLON family of neural adhesion molecules LSAMP, NTM, NEGR1, and OPCML are important candidates in forming the susceptibility to schizophrenia (SCZ). IgLON proteins have been shown to be involved in neurite outgrowth, synaptic plasticity and neuronal connectivity, all of which have been shown to be altered in the brains of patients with the diagnosis of schizophrenia. Here we optimized custom 5'-isoform-specific TaqMan gene-expression analysis for the transcripts of human IgLON genes to study the expression of IgLONs in the dorsolateral prefrontal cortex (DLPFC) of schizophrenic patients (n = 36) and control subjects (n = 36). Uniform 5'-region and a single promoter was confirmed for the human NEGR1 gene by in silico analysis. IgLON5, a recently described family member, was also included in the study. We detected significantly elevated levels of the NEGR1 transcript (1.33-fold increase) and the NTM 1b isoform transcript (1.47-fold increase) in the DLPFC of schizophrenia patients compared to healthy controls. Consequent protein analysis performed in male subjects confirmed the increase in NEGR1 protein content both in patients with the paranoid subtype and in patients with other subtypes. In-group analysis of patients revealed that lower expression of certain IgLON transcripts, mostly LSAMP 1a and 1b, could be related with concurrent depressive endophenotype in schizophrenic patients. Additionally, our study cohort provides further evidence that cannabis use may be a relevant risk factor associated with suicidal behaviors in psychotic patients. In conclusion, we provide clinical evidence of increased expression levels of particular IgLON family members in the DLPFC of schizophrenic patients. We propose that alterations in the expression profile of IgLON neural adhesion molecules are associated with brain circuit disorganization in neuropsychiatric disorders, such as schizophrenia. In the light of previously published data, we suggest that increased level of NEGR1 in the frontal cortex may serve as molecular marker for a wider spectrum of psychiatric conditions.
Collapse
Affiliation(s)
- Karina Karis
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Kattri-Liis Eskla
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Maria Kaare
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Karin Täht
- Institute of Psychology, University of Tartu, Tartu, Estonia
| | - Jana Tuusov
- Department of Pathological Anatomy and Forensic Medicine, University of Tartu, Tartu, Estonia.,Estonian Forensic Science Institute, Tallinn, Estonia
| | - Tanel Visnapuu
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Jürgen Innos
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Mohan Jayaram
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Tõnis Timmusk
- Department of Chemistry and Biotechnology, Tallinn University of Technology, Tallinn, Estonia
| | - Cynthia S Weickert
- Faculty of Medicine, School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Schizophrenia Research Institute, Neuroscience Research Australia, Randwick, NSW, Australia
| | - Marika Väli
- Department of Pathological Anatomy and Forensic Medicine, University of Tartu, Tartu, Estonia.,Estonian Forensic Science Institute, Tallinn, Estonia
| | - Eero Vasar
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| | - Mari-Anne Philips
- Department of Physiology, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.,Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia
| |
Collapse
|