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Jang T, Kaul M. Immune, RNA, and Neurocognitive Genetic Networks in Bipolar Disorder Subtypes: A Transcriptomic Meta-Analysis. RESEARCH SQUARE 2024:rs.3.rs-3508951. [PMID: 38313297 PMCID: PMC10836095 DOI: 10.21203/rs.3.rs-3508951/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Background Little is known about the pathogenesis of Bipolar Disorder, and even less is known about the genetic differences between its subtypes. Bipolar Disorder is classified into different subtypes, which present different symptoms and lifetime courses. While genetic studies have been conducted in Bipolar Disorder, most examined the gene expression of only Bipolar Disorder Type 1. Studies that include Bipolar Disorder Type 1 and Bipolar Disorder Type 2 often fail to differentiate them into separate conditions. Few large transcriptomic meta-analyses in Bipolar Disorder have been conducted to identify genetic pathways. Thus, using publicly available data sets we aim here to uncover significant differential gene expression that allows distinguishing Type 1 and Type 2 Bipolar Disorders, as well as find patterns in Bipolar Disorder as a whole. Methods We analyze 17 different gene expression data sets from different tissue in Bipolar Disorder using GEO2R and manual analysis, of which 15 contained significant differential gene expression results. We use STRING and Cytoscape to examine Gene Ontology to find significantly affected genetic pathways. We identify hub genes using cytoHubba, a plugin in Cytoscape. We find genes common to data sets of the same material or subtype. Results 12 out of 15 data sets are enriched for immune system and RNA related pathways. 9 out of 15 data sets are enriched for neurocognitive and metal ion related GO terms. Analysis of Bipolar Disorder Type 1 vs Bipolar Disorder Type 2 revealed most differentially expressed genes were related to immune function, especially cytokines. Terms related to synaptic signaling and neurotransmitter secretion were found in down-regulated GO terms while terms related to neuron apoptosis and death were up-regulated. We identify the gene SNCA as a potential biomarker for overall Bipolar Disorder diagnosis due to its prevalence in our data sets. Conclusions The immune system and RNA related pathways are significantly enriched across the Bipolar Disorder data sets. The role of these pathways is likely more critically important to the function of Bipolar Disorder than currently understood. Further studies should clearly label the subtype of Bipolar Disorder used in their research and more effort needs to be undertaken to collect samples from Cyclothymic Disorder and Bipolar Disorder Type 2.
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Affiliation(s)
- Tyler Jang
- University of California, Riverside, Graduate Program of Genetics, Genomics, and Bioinformatics, Riverside, 92507, USA
| | - Marcus Kaul
- University of California, Riverside, Graduate Program of Genetics, Genomics, and Bioinformatics, Riverside, 92507, USA
- University of California, Riverside, Department of Biomedical Sciences, Riverside, 92507, USA
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2
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Angelakis A, Soulioti I, Filippakis M. Diagnosis of acute myeloid leukaemia on microarray gene expression data using categorical gradient boosted trees. Heliyon 2023; 9:e20530. [PMID: 37860531 PMCID: PMC10582309 DOI: 10.1016/j.heliyon.2023.e20530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/21/2023] Open
Abstract
We define an iterative method for dimensionality reduction using categorical gradient boosted trees and Shapley values and created four machine learning models which potentially could be used as diagnostic tests for acute myeloid leukaemia (AML). For the final Catboost model we use a dataset of 2177 individuals using as features 16 probe sets and the age in order to classify if someone has AML or is healthy. The dataset is multicentric and consists of data from 27 organizations, 25 cities, 15 countries and 4 continents. The performance of our last model is specificity: 0.9909, sensitivity: 0.9985, F1-score: 0.9976 and its ROC-AUC: 0.9962 using ten fold cross validation. On an inference dataset the perormance is: specificity: 0.9909, sensitivity: 0.9969, F1-score: 0.9969 and its ROC-AUC: 0.9939. To the best of our knowledge the performance of our model is the best one in the literature, as regards the diagnosis of AML using similar or not data. Moreover, there has not been any bibliographic reference which associates AML or any other type of cancer with the 16 probe sets we used as features in our final model.
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Affiliation(s)
- Athanasios Angelakis
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, Amsterdam Public Health Research Institute, University of Amsterdam Data Science Center, Netherlands
| | - Ioanna Soulioti
- Department of Biology, National and Kapodistrian University of Athens, Greece
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3
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Hess JL, Quinn TP, Zhang C, Hearn GC, Chen S, Kong SW, Cairns M, Tsuang MT, Faraone SV, Glatt SJ. BrainGENIE: The Brain Gene Expression and Network Imputation Engine. Transl Psychiatry 2023; 13:98. [PMID: 36949060 PMCID: PMC10033657 DOI: 10.1038/s41398-023-02390-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/24/2023] Open
Abstract
In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood-brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947-11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.
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Affiliation(s)
- Jonathan L Hess
- Department of Psychiatry & Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Thomas P Quinn
- Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia
| | - Chunling Zhang
- Department of Neuroscience & Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Gentry C Hearn
- Department of Neuroscience & Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Samuel Chen
- Department of Psychiatry & Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Murray Cairns
- School of Biomedical Sciences & Pharmacy, Faculty of Health, The University of Newcastle, New South Wales, Callaghan, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, Australia
- Centre for Brain & Mental Health Research, The University of Newcastle, Callaghan, Australia
| | - Ming T Tsuang
- Center for Behavioral Genomics, Department of Psychiatry, Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA
- Harvard Institute of Psychiatric Epidemiology and Genetics, Boston, MA, USA
| | - Stephen V Faraone
- Department of Psychiatry & Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
- Department of Neuroscience & Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA
| | - Stephen J Glatt
- Department of Psychiatry & Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA.
- Department of Neuroscience & Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, USA.
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4
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Han M, Yuan L, Huang Y, Wang G, Du C, Wang Q, Zhang G. Integrated co-expression network analysis uncovers novel tissue-specific genes in major depressive disorder and bipolar disorder. Front Psychiatry 2022; 13:980315. [PMID: 36081461 PMCID: PMC9445988 DOI: 10.3389/fpsyt.2022.980315] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Tissue-specific gene expression has been found to be associated with multiple complex diseases including cancer, metabolic disease, aging, etc. However, few studies of brain-tissue-specific gene expression patterns have been reported, especially in psychiatric disorders. In this study, we performed joint analysis on large-scale transcriptome multi-tissue data to investigate tissue-specific expression patterns in major depressive disorder (MDD) and bipolar disorder (BP). We established the strategies of identifying tissues-specific modules, annotated pathways for elucidating biological functions of tissues, and tissue-specific genes based on weighted gene co-expression network analysis (WGCNA) and robust rank aggregation (RRA) with transcriptional profiling data from different human tissues and genome wide association study (GWAS) data, which have been expanded into overlapping tissue-specific modules and genes sharing with MDD and BP. Nine tissue-specific modules were identified and distributed across the four tissues in the MDD and six modules in the BP. In general, the annotated biological functions of differentially expressed genes (DEGs) in blood were mainly involved in MDD and BP progression through immune response, while those in the brain were in neuron and neuroendocrine response. Tissue-specific genes of the prefrontal cortex (PFC) in MDD-, such as IGFBP2 and HTR1A, were involved in disease-related functions, such as response to glucocorticoid, taste transduction, and tissue-specific genes of PFC in BP-, such as CHRM5 and LTB4R2, were involved in neuroactive ligand-receptor interaction. We also found PFC tissue-specific genes including SST and CRHBP were shared in MDD-BP, SST was enriched in neuroactive ligand-receptor interaction, and CRHBP shown was related to the regulation of hormone secretion and hormone transport.
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Affiliation(s)
- Mengyao Han
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.,CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Liyun Yuan
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yuwei Huang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Guiying Wang
- Shanghai Key Laboratory of Signaling and Disease Research, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, National Stem Cell Translational Resource Center, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Changsheng Du
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Qingzhong Wang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Shanghai Key Laboratory of Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Guoqing Zhang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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5
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Increased levels of TAR DNA-binding protein 43 in the hippocampus of subjects with bipolar disorder: a postmortem study. J Neural Transm (Vienna) 2022; 129:95-103. [PMID: 34966974 PMCID: PMC9169569 DOI: 10.1007/s00702-021-02455-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/16/2021] [Indexed: 01/03/2023]
Abstract
Bipolar disorder shares symptoms and pathological pathways with other neurodegenerative diseases, including frontotemporal dementia (FTD). Since TAR DNA-binding protein 43 (TDP-43) is a neuropathological marker of frontotemporal dementia and it is involved in synaptic transmission, we explored the role of TDP-43 as a molecular feature of bipolar disorder (BD). Homogenates were acquired from frozen hippocampus of postmortem brains of bipolar disorder subjects. TDP-43 levels were quantified using an ELISA-sandwich method and compared between the postmortem brains of bipolar disorder subjects and age-matched control group. We found higher levels of TDP-43 protein in the hippocampus of BD (n = 15) subjects, when compared to controls (n = 15). We did not find associations of TDP-43 with age at death, postmortem interval, or age of disease onset. Our results suggest that protein TDP-43 may be potentially implicated in behavioral abnormalities seen in BD. Further investigation is needed to validate these findings and to examine the role of this protein during the disease course and mood states.
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6
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Song W, Wang W, Liu Z, Cai W, Yu S, Zhao M, Lin GN. A Comprehensive Evaluation of Cross-Omics Blood-Based Biomarkers for Neuropsychiatric Disorders. J Pers Med 2021; 11:1247. [PMID: 34945719 PMCID: PMC8703948 DOI: 10.3390/jpm11121247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 12/03/2022] Open
Abstract
The identification of peripheral multi-omics biomarkers of brain disorders has long been hindered by insufficient sample size and confounder influence. This study aimed to compare biomarker potential for different molecules and diseases. We leveraged summary statistics of five blood quantitative trait loci studies (N = 1980 to 22,609) and genome-wide association studies (N = 9725 to 500,199) from 14 different brain disorders, such as Schizophrenia (SCZ) and Alzheimer's Disease (AD). We applied summary-based and two-sample Mendelian Randomization to estimate the associations between blood molecules and brain disorders. We identified 524 RNA, 807 methylation sites, 29 proteins, seven cytokines, and 22 metabolites having a significant association with at least one of 14 brain disorders. Simulation analyses indicated that a cross-omics combination of biomarkers had better performance for most disorders, and different disorders could associate with different omics. We identified an 11-methylation-site model for SCZ diagnosis (Area Under Curve, AUC = 0.74) by analyzing selected candidate markers in published datasets (total N = 6098). Moreover, we constructed an 18-methylation-sites model that could predict the prognosis of elders with mild cognitive impairment (hazard ratio = 2.32). We provided an association landscape between blood cross-omic biomarkers and 14 brain disorders as well as a suggestion guide for future clinical discovery and application.
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Affiliation(s)
- Weichen Song
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
| | - Weidi Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
| | - Zhe Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
| | - Wenxiang Cai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
| | - Shunying Yu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, China; (S.Y.); (M.Z.)
| | - Min Zhao
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, China; (S.Y.); (M.Z.)
| | - Guan Ning Lin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
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7
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Moni MA, Lin PI, Quinn JMW, Eapen V. COVID-19 patient transcriptomic and genomic profiling reveals comorbidity interactions with psychiatric disorders. Transl Psychiatry 2021; 11:160. [PMID: 33723208 PMCID: PMC7957287 DOI: 10.1038/s41398-020-01151-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 11/28/2020] [Accepted: 12/07/2020] [Indexed: 12/16/2022] Open
Abstract
Psychiatric symptoms are seen in some COVID-19 patients, as direct or indirect sequelae, but it is unclear whether SARS-CoV-2 infection interacts with underlying neuronal or psychiatric susceptibilities. Such interactions might arise from COVID-19 immune responses, from infection of neurons themselves or may reflect social-psychological causes. To clarify this we sought the key gene expression pathways altered in COVID-19 also affected in bipolar disorder, post-traumatic stress disorder (PTSD) and schizophrenia, since this may identify pathways of interaction that could be treatment targets. We performed large scale comparisons of whole transcriptome data and immune factor transcript data in peripheral blood mononuclear cells (PBMC) from COVID-19 patients and patients with psychiatric disorders. We also analysed genome-wide association study (GWAS) data for symptomatic COVID-19 patients, comparing GWAS and whole-genome sequence data from patients with bipolar disorder, PTSD and schizophrenia patients. These studies revealed altered signalling and ontology pathways shared by COVID-19 patients and the three psychiatric disorders. Finally, co-expression and network analyses identified gene clusters common to the conditions. COVID-19 patients had peripheral blood immune system profiles that overlapped with those of patients with psychiatric conditions. From the pathways identified, PTSD profiles were the most highly correlated with COVID-19, perhaps consistent with stress-immune system interactions seen in PTSD. We also revealed common inflammatory pathways that may exacerbate psychiatric disorders, which may support the usage of anti-inflammatory medications in these patients. It also highlights the potential clinical application of multi-level dataset studies in difficult-to-treat psychiatric disorders in this COVID-19 pandemic.
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Affiliation(s)
- Mohammad Ali Moni
- Faculty of Medicine, School of Psychiatry, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Ping-I Lin
- Faculty of Medicine, School of Psychiatry, University of New South Wales, Sydney, NSW, 2052, Australia
- South Western Sydney Area Health Service, Sydney, NSW, 2170, Australia
| | - Julian M W Quinn
- The Garvan Institute of Medical Research, Healthy Ageing Theme, Darlinghurst, NSW, 2010, Australia
- Division of Surgery and Anesthesia, Royal North Shore Hospital SERT Institute, St Leonards, NSW, 2065, Australia
| | - Valsamma Eapen
- Faculty of Medicine, School of Psychiatry, University of New South Wales, Sydney, NSW, 2052, Australia.
- South Western Sydney Area Health Service, Sydney, NSW, 2170, Australia.
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8
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Shen H, Liu T, Cui J, Borole P, Benjamin A, Kording K, Issadore D. A web-based automated machine learning platform to analyze liquid biopsy data. LAB ON A CHIP 2020; 20:2166-2174. [PMID: 32420563 DOI: 10.1039/d0lc00096e] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Liquid biopsy (LB) technologies continue to improve in sensitivity, specificity, and multiplexing and can measure an ever growing library of disease biomarkers. However, clinical interpretation of the increasingly large sets of data these technologies generate remains a challenge. Machine learning is a popular approach to discover and detect signatures of disease. However, limited machine learning expertise in the LB field has kept the discipline from fully leveraging these tools and risks improper analyses and irreproducible results. In this paper, we develop a web-based automated machine learning tool tailored specifically for LB, where machine learning models can be built without the user's input. We also incorporate a differential privacy algorithm, designed to limit the effects of overfitting that can arise from users iteratively developing a panel with feedback from our platform. We validate our approach by performing a meta-analysis on 11 published LB datasets, and found that we had similar or better performance compared to those reported in the literature. Moreover, we show that our platform's performance improved when incorporating information from prior LB datasets, suggesting that this approach can continue to improve with increased access to LB data. Finally, we show that by using our platform the results achieved in the literature can be matched using 40% of the number of subjects in the training set, potentially reducing study cost and time. This self-improving and overfitting-resistant automatic machine learning platform provides a new standard that can be used to validate machine learning works in the LB field.
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Affiliation(s)
- Hanfei Shen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Hess JL, Tylee DS, Barve R, de Jong S, Ophoff RA, Kumarasinghe N, Tooney P, Schall U, Gardiner E, Beveridge NJ, Scott RJ, Yasawardene S, Perera A, Mendis J, Carr V, Kelly B, Cairns M, Tsuang MT, Glatt SJ. Transcriptomic abnormalities in peripheral blood in bipolar disorder, and discrimination of the major psychoses. Schizophr Res 2020; 217:124-135. [PMID: 31391148 PMCID: PMC6997041 DOI: 10.1016/j.schres.2019.07.036] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 07/20/2019] [Accepted: 07/23/2019] [Indexed: 02/07/2023]
Abstract
We performed a transcriptome-wide meta-analysis and gene co-expression network analysis to identify genes and gene networks dysregulated in the peripheral blood of bipolar disorder (BD) cases relative to unaffected comparison subjects, and determined the specificity of the transcriptomic signatures of BD and schizophrenia (SZ). Nineteen genes and 4 gene modules were significantly differentially expressed in BD cases. Thirteen gene modules were shown to be differentially expressed in a combined case-group of BD and SZ subjects called "major psychosis", including genes biologically linked to apoptosis, reactive oxygen, chromatin remodeling, and immune signaling. No modules were differentially expressed between BD and SZ cases. Machine-learning classifiers trained to separate diagnostic classes based solely on gene expression profiles could distinguish BD cases from unaffected comparison subjects with an area under the curve (AUC) of 0.724, as well as BD cases from SZ cases with AUC = 0.677 in withheld test samples. We introduced a novel and straightforward method called "polytranscript risk scoring" that could distinguish BD cases from unaffected subjects (AUC = 0.672) and SZ cases (AUC = 0.607) significantly better than expected by chance. Taken together, our results highlighted gene expression alterations common to BD and SZ that involve biological processes of inflammation, oxidative stress, apoptosis, and chromatin regulation, and highlight disorder-specific changes in gene expression that discriminate the major psychoses.
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Affiliation(s)
- Jonathan L Hess
- Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab), Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Daniel S Tylee
- Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab), Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Rahul Barve
- Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab), Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Simone de Jong
- MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Roel A Ophoff
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Behavior, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, CA, USA; Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Nishantha Kumarasinghe
- School of Medicine & Public Health, The University of Newcastle, Callaghan, Newcastle, Australia.; Department of Anatomy, Faculty of Medical Sciences, University of Sri Jayawardenepura, Nugegoda, Sri Lanka; Faculty of Medicine, Sir John Kotelawala Defence University, Ratmalana, Sri Lanka
| | - Paul Tooney
- School of Biomedical Sciences & Pharmacy, Faculty of Health, The University of Newcastle, New South Wales, Australia; Hunter Medical Research Institute, Newcastle, Australia
| | - Ulrich Schall
- School of Medicine & Public Health, The University of Newcastle, Callaghan, Newcastle, Australia.; Priority Centre for Brain & Mental Health Research, The University of Newcastle, Callaghan, Newcastle, Australia
| | - Erin Gardiner
- School of Biomedical Sciences & Pharmacy, Faculty of Health, The University of Newcastle, New South Wales, Australia; Priority Centre for Brain & Mental Health Research, The University of Newcastle, Callaghan, Newcastle, Australia
| | - Natalie Jane Beveridge
- School of Medicine & Public Health, The University of Newcastle, Callaghan, Newcastle, Australia.; Hunter Medical Research Institute, Newcastle, Australia; Priority Centre for Brain & Mental Health Research, The University of Newcastle, Callaghan, Newcastle, Australia
| | - Rodney J Scott
- School of Biomedical Sciences & Pharmacy, Faculty of Health, The University of Newcastle, New South Wales, Australia; Hunter Medical Research Institute, Newcastle, Australia
| | - Surangi Yasawardene
- Department of Anatomy, Faculty of Medical Sciences, University of Sri Jayawardenepura, Nugegoda, Sri Lanka
| | - Antionette Perera
- Department of Anatomy, Faculty of Medical Sciences, University of Sri Jayawardenepura, Nugegoda, Sri Lanka
| | - Jayan Mendis
- Department of Anatomy, Faculty of Medical Sciences, University of Sri Jayawardenepura, Nugegoda, Sri Lanka
| | - Vaughan Carr
- School of Psychiatry, University of New South Wales, Kensington, New South Wales, Australia
| | - Brian Kelly
- School of Medicine & Public Health, The University of Newcastle, Callaghan, Newcastle, Australia.; Hunter Medical Research Institute, Newcastle, Australia; Priority Centre for Brain & Mental Health Research, The University of Newcastle, Callaghan, Newcastle, Australia
| | - Murray Cairns
- School of Biomedical Sciences & Pharmacy, Faculty of Health, The University of Newcastle, New South Wales, Australia; Hunter Medical Research Institute, Newcastle, Australia; Priority Centre for Brain & Mental Health Research, The University of Newcastle, Callaghan, Newcastle, Australia
| | - Ming T Tsuang
- Center for Behavioral Genomics, Department of Psychiatry, Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA; Harvard Institute of Psychiatric Epidemiology and Genetics, Boston, USA
| | - Stephen J Glatt
- Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab), Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA.
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Patel H, Iniesta R, Stahl D, Dobson RJ, Newhouse SJ. Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer's Disease. J Alzheimers Dis 2020; 74:545-561. [PMID: 32065794 PMCID: PMC7175937 DOI: 10.3233/jad-191163] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/13/2020] [Indexed: 11/15/2022]
Abstract
BACKGROUND The typical approach to identify blood-derived gene expression signatures as a biomarker for Alzheimer's disease (AD) have relied on training classification models using AD and healthy controls only. This may inadvertently result in the identification of markers for general illness rather than being disease-specific. OBJECTIVE Investigate whether incorporating additional related disorders in the classification model development process can lead to the discovery of an AD-specific gene expression signature. METHODS Two types of XGBoost classification models were developed. The first used 160 AD and 127 healthy controls and the second used the same 160 AD with 6,318 upsampled mixed controls consisting of Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, bipolar disorder, schizophrenia, coronary artery disease, rheumatoid arthritis, chronic obstructive pulmonary disease, and cognitively healthy subjects. Both classification models were evaluated in an independent cohort consisting of 127 AD and 687 mixed controls. RESULTS The AD versus healthy control models resulted in an average 48.7% sensitivity (95% CI = 34.7-64.6), 41.9% specificity (95% CI = 26.8-54.3), 13.6% PPV (95% CI = 9.9-18.5), and 81.1% NPV (95% CI = 73.3-87.7). In contrast, the mixed control models resulted in an average of 40.8% sensitivity (95% CI = 27.5-52.0), 95.3% specificity (95% CI = 93.3-97.1), 61.4% PPV (95% CI = 53.8-69.6), and 89.7% NPV (95% CI = 87.8-91.4). CONCLUSIONS This early work demonstrates the value of incorporating additional related disorders into the classification model developmental process, which can result in models with improved ability to distinguish AD from a heterogeneous aging population. However, further improvement to the sensitivity of the test is still required.
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Affiliation(s)
- Hamel Patel
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
| | - Raquel Iniesta
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Daniel Stahl
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Richard J.B. Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Stephen J. Newhouse
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
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11
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A multimodal attempt to follow-up linkage regions using RNA expression, SNPs and CpG methylation in schizophrenia and bipolar disorder kindreds. Eur J Hum Genet 2019; 28:499-507. [PMID: 31695175 DOI: 10.1038/s41431-019-0526-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 08/20/2019] [Accepted: 09/27/2019] [Indexed: 11/09/2022] Open
Abstract
The complexity of schizophrenia (SZ) and bipolar disorder (BD) has slowed down progress in understanding their genetic roots. Alternative genomic approaches are needed to bypass these difficulties. We attempted a multimodal approach to follow-up on reported linkage findings in SZ and BD from the Eastern Quebec kindreds in chromosomes 3q21, 4p34, 6p22, 8p21, 8p11, 13q11-q14, 15q13, 16p12, and 18q21. First, in 498 subjects, we measured RNA expression (47 K Illumina chips) in SZ and BD patients that we compared with their non-affected relatives (NARs) to identify, for each chromosomal region, genes showing the most significant differences in expression. Second, we performed SNP genotyping (700 K Illumina chips) and cis-eQTN analysis. Third, we measured DNA methylation on genes with RNA expression differences or eQTNs. We found a significant overexpression of the gene ITGB5 at 3q25 in SZ and BD after multiple testing p value adjustment. SPCS3 gene at 4q34, and FZD3 gene at 8p21, contained significant eQTNs after multiple testing corrections, while ITGB5 provided suggestive results. Methylation in associated genes did not explain the expression differences between patients and NARs. Our multimodal approach involving RNA expression, dense SNP genotyping and eQTN analyses, restricted to chromosomal regions having shown linkage, lowered the multiple testing burden and allowed for a deeper examination of candidate genes in SZ or BD.
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12
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Multi-study reanalysis of 2,213 acute myeloid leukemia patients reveals age- and sex-dependent gene expression signatures. Sci Rep 2019; 9:12413. [PMID: 31455838 PMCID: PMC6712049 DOI: 10.1038/s41598-019-48872-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 08/14/2019] [Indexed: 11/19/2022] Open
Abstract
In 2019 it is estimated that more than 21,000 new acute myeloid leukemia (AML) patients will be diagnosed in the United States, and nearly 11,000 are expected to die from the disease. AML is primarily diagnosed among the elderly (median 68 years old at diagnosis). Prognoses have significantly improved for younger patients, but as much as 70% of patients over 60 years old will die within a year of diagnosis. In this study, we conducted a reanalysis of 2,213 acute myeloid leukemia patients compared to 548 healthy individuals, using curated publicly available microarray gene expression data. We carried out an analysis of normalized batch corrected data, using a linear model that included considerations for disease, age, sex, and tissue. We identified 974 differentially expressed probe sets and 4 significant pathways associated with AML. Additionally, we identified 375 age- and 70 sex-related probe set expression signatures relevant to AML. Finally, we trained a k nearest neighbors model to classify AML and healthy subjects with 90.9% accuracy. Our findings provide a new reanalysis of public datasets, that enabled the identification of new gene sets relevant to AML that can potentially be used in future experiments and possible stratified disease diagnostics.
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13
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Patel H, Dobson RJ, Newhouse SJ. A Meta-Analysis of Alzheimer's Disease Brain Transcriptomic Data. J Alzheimers Dis 2019; 68:1635-1656. [PMID: 30909231 PMCID: PMC6484273 DOI: 10.3233/jad-181085] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/11/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND Microarray technologies have identified imbalances in the expression of specific genes and biological pathways in Alzheimer's disease (AD) brains. However, there is a lack of reproducibility across individual AD studies, and many related neurodegenerative and mental health disorders exhibit similar perturbations. OBJECTIVE Meta-analyze publicly available transcriptomic data from multiple brain-related disorders to identify robust transcriptomic changes specific to AD brains. METHODS Twenty-two AD, eight schizophrenia, five bipolar disorder, four Huntington's disease, two major depressive disorder, and one Parkinson's disease dataset totaling 2,667 samples and mapping to four different brain regions (temporal lobe, frontal lobe, parietal lobe, and cerebellum) were analyzed. Differential expression analysis was performed independently in each dataset, followed by meta-analysis using a combining p-value method known as Adaptively Weighted with One-sided Correction. RESULTS Meta-analysis identified 323, 435, 1,023, and 828 differentially expressed genes specific to the AD temporal lobe, frontal lobe, parietal lobe, and cerebellum brain regions, respectively. Seven of these genes were consistently perturbed across all AD brain regions with SPCS1 gene expression pattern replicating in RNA-Seq data. A further nineteen genes were perturbed specifically in AD brain regions affected by both plaques and tangles, suggesting possible involvement in AD neuropathology. In addition, biological pathways involved in the "metabolism of proteins" and viral components were significantly enriched across AD brains. CONCLUSION This study identified transcriptomic changes specific to AD brains, which could make a significant contribution toward the understanding of AD disease mechanisms and may also provide new therapeutic targets.
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Affiliation(s)
- Hamel Patel
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) and Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
| | - Richard J.B. Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) and Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Stephen J. Newhouse
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) and Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
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Golightly NP, Bell A, Bischoff AI, Hollingsworth PD, Piccolo SR. Curated compendium of human transcriptional biomarker data. Sci Data 2018; 5:180066. [PMID: 29664470 PMCID: PMC5903354 DOI: 10.1038/sdata.2018.66] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Accepted: 02/22/2018] [Indexed: 12/25/2022] Open
Abstract
One important use of genome-wide transcriptional profiles is to identify relationships between transcription levels and patient outcomes. These translational insights can guide the development of biomarkers for clinical application. Data from thousands of translational-biomarker studies have been deposited in public repositories, enabling reuse. However, data-reuse efforts require considerable time and expertise because transcriptional data are generated using heterogeneous profiling technologies, preprocessed using diverse normalization procedures, and annotated in non-standard ways. To address this problem, we curated 45 publicly available, translational-biomarker datasets from a variety of human diseases. To increase the data's utility, we reprocessed the raw expression data using a uniform computational pipeline, addressed quality-control problems, mapped the clinical annotations to a controlled vocabulary, and prepared consistently structured, analysis-ready data files. These data, along with scripts we used to prepare the data, are available in a public repository. We believe these data will be particularly useful to researchers seeking to perform benchmarking studies—for example, to compare and optimize machine-learning algorithms' ability to predict biomedical outcomes.
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Affiliation(s)
| | - Avery Bell
- Department of Biology, Brigham Young University, Provo, Utah 84602, USA
| | - Anna I Bischoff
- Department of Biology, Brigham Young University, Provo, Utah 84602, USA
| | - Parker D Hollingsworth
- Department of Biology, Brigham Young University, Provo, Utah 84602, USA.,Northeast Ohio Medical University, Rootstown, Ohio 44272, USA
| | - Stephen R Piccolo
- Department of Biology, Brigham Young University, Provo, Utah 84602, USA.,Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah 84602, USA
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15
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Roles of NUCKS1 in Diseases: Susceptibility, Potential Biomarker, and Regulatory Mechanisms. BIOMED RESEARCH INTERNATIONAL 2018; 2018:7969068. [PMID: 29619377 PMCID: PMC5830027 DOI: 10.1155/2018/7969068] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 12/31/2017] [Indexed: 12/16/2022]
Abstract
Nuclear casein kinase and cyclin-dependent kinase substrate 1 (NUCKS1) is a 27 kD chromosomal, highly conserved, and vertebrate-specific protein. NUCKS1 gene encodes a nuclear protein and the conserved regions of NUCKS1 contain several consensus phosphorylation sites for casein kinase II (CK2) and cyclin-dependent kinases (Cdk) and a basic DNA-binding domain. NUCKS1 is similar to the high mobility group (HMG) family which dominates chromatin remodeling and regulates gene transcription. Meanwhile, NUCKS1 is a RAD51 associated protein 1 (RAD51AP1) paralog that is significant for homologous recombination (HR) and genome stability and also a transcriptional regulator of the insulin signaling components. NUCKS1 plays an important role in DNA damage response and metabolism, participates in inflammatory immune response, and correlates with microRNA. Although there is still not enough functional information on NUCKS1, evidences suggest that NUCKS1 can be used as the biomarker of several cancers. This review summarizes the latest research on NUCKS1 about its susceptibility in diseases, expression levels, and regulatory mechanisms as well as the possible functions in reference to diseases.
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16
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Chen X, Long F, Cai B, Chen X, Chen G. A novel relationship for schizophrenia, bipolar and major depressive disorder Part 3: Evidence from chromosome 3 high density association screen. J Comp Neurol 2017; 526:59-79. [PMID: 28856687 DOI: 10.1002/cne.24311] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Revised: 07/29/2017] [Accepted: 07/31/2017] [Indexed: 12/30/2022]
Abstract
Familial clustering of schizophrenia (SCZ), bipolar disorder (BPD), and major depressive disorder (MDD) was systematically reported (Aukes et al, Genet Med 2012, 14, 338-341) and convergent evidence from genetics, symptomatology, and psychopharmacology imply that there are intrinsic connections between these three major psychiatric disorders, for example, any two or even three of these disorders could co-exist in some families. A total of 60, 838 single-nucleotide polymorphisms (SNPs) on chromosome 3 were genotyped by Affymetrix Genome-Wide Human SNP array 6.0 on 119 SCZ, 253 BPD (type-I), 177 MDD patients and 1,000 controls. The population of Shandong province was formed in 14 century and believed that it belongs to homogenous population. Associated SNPs were systematically revealed and outstanding susceptibility genes (CADPS, GRM7,KALRN, LSAMP, NLGN1, PRICKLE2, ROBO2) were identified. Unexpectedly, flanking genes for the associated SNPs distinctive for BPD and/or MDD were replicated in an enlarged cohort of 986 SCZ patients. The evidence from this chromosome 3 analysis supports the notion that both of bipolar and MDD might be subtypes of schizophrenia rather than independent disease entity. Also, a similar finding was detected on chromosome 5, 6, 7, and 8 (Chen et al. Am J Transl Res 2017;9 (5):2473-2491; Curr Mol Med 2016;16(9):840-854; Behav Brain Res 2015;293:241-251; Mol Neurobiol 2016. doi: 10.1007/s12035-016-0102-1). Furthermore, PRICKLE2 play an important role in the pathogenesis of three major psychoses in this population.
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Affiliation(s)
- Xing Chen
- Department of Medical Genetics, Institute of Basic Medicine, Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Feng Long
- Department of Medical Genetics, Institute of Basic Medicine, Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Bin Cai
- CapitalBio corporation, Beijing, People's Republic of China
| | - Xiaohong Chen
- CapitalBio corporation, 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
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17
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Wang J, Qu S, Wang W, Guo L, Zhang K, Chang S, Wang J. A combined analysis of genome-wide expression profiling of bipolar disorder in human prefrontal cortex. J Psychiatr Res 2016; 82:23-9. [PMID: 27459029 DOI: 10.1016/j.jpsychires.2016.07.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Revised: 07/12/2016] [Accepted: 07/15/2016] [Indexed: 01/29/2023]
Abstract
Numbers of gene expression profiling studies of bipolar disorder have been published. Besides different array chips and tissues, variety of the data processes in different cohorts aggravated the inconsistency of results of these genome-wide gene expression profiling studies. By searching the gene expression databases, we obtained six data sets for prefrontal cortex (PFC) of bipolar disorder with raw data and combinable platforms. We used standardized pre-processing and quality control procedures to analyze each data set separately and then combined them into a large gene expression matrix with 101 bipolar disorder subjects and 106 controls. A standard linear mixed-effects model was used to calculate the differentially expressed genes (DEGs). Multiple levels of sensitivity analyses and cross validation with genetic data were conducted. Functional and network analyses were carried out on basis of the DEGs. In the result, we identified 198 unique differentially expressed genes in the PFC of bipolar disorder and control. Among them, 115 DEGs were robust to at least three leave-one-out tests or different pre-processing methods; 51 DEGs were validated with genetic association signals. Pathway enrichment analysis showed these DEGs were related with regulation of neurological system, cell death and apoptosis, and several basic binding processes. Protein-protein interaction network further identified one key hub gene. We have contributed the most comprehensive integrated analysis of bipolar disorder expression profiling studies in PFC to date. The DEGs, especially those with multiple validations, may denote a common signature of bipolar disorder and contribute to the pathogenesis of disease.
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Affiliation(s)
- Jinglu Wang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Susu Qu
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Weixiao Wang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Liyuan Guo
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Kunlin Zhang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Suhua Chang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
| | - Jing Wang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
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18
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Gene expression alterations related to mania and psychosis in peripheral blood of patients with a first episode of psychosis. Transl Psychiatry 2016; 6:e908. [PMID: 27701407 PMCID: PMC5315542 DOI: 10.1038/tp.2016.159] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Revised: 06/23/2016] [Accepted: 07/12/2016] [Indexed: 01/11/2023] Open
Abstract
Psychotic disorders affect ~3% of the general population and are among the most severe forms of mental diseases. In early stages of psychosis, clinical aspects may be difficult to distinguish from one another. Undifferentiated psychopathology at the first-episode of psychosis (FEP) highlights the need for biomarkers that can improve and refine differential diagnosis. We investigated gene expression differences between patients with FEP-schizophrenia spectrum (SCZ; N=53) or FEP-Mania (BD; N=16) and healthy controls (N=73). We also verified whether gene expression was correlated to severity of psychotic, manic, depressive symptoms and/or functional impairment. All participants were antipsychotic-naive. After the psychiatric interview, blood samples were collected and the expression of 12 psychotic-disorder-related genes was evaluated by quantitative PCR. AKT1 and DICER1 expression levels were higher in BD patients compared with that in SCZ patients and healthy controls, suggesting that expression of these genes is associated more specifically to manic features. Furthermore, MBP and NDEL1 expression levels were higher in SCZ and BD patients than in healthy controls, indicating that these genes are psychosis related (independent of diagnosis). No correlation was found between gene expression and severity of symptoms or functional impairment. Our findings suggest that genes related to neurodevelopment are altered in psychotic disorders, and some might support the differential diagnosis between schizophrenia and bipolar disorder, with a potential impact on the treatment of these disorders.
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19
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Karpiński P, Frydecka D, Sąsiadek MM, Misiak B. Reduced number of peripheral natural killer cells in schizophrenia but not in bipolar disorder. Brain Behav Immun 2016; 54:194-200. [PMID: 26872421 DOI: 10.1016/j.bbi.2016.02.005] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 01/26/2016] [Accepted: 02/08/2016] [Indexed: 12/27/2022] Open
Abstract
Overwhelming evidence indicates that subthreshold inflammatory state might be implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BPD). It has been reported that both groups of patients might be characterized by abnormal lymphocyte counts. However, little is known about alterations in lymphocyte proportions that may differentiate SCZ and BPD patients. Therefore, in this study we investigated blood cell proportions quantified by means of microarray expression deconvolution using publicly available data from SCZ and BPD patients. We found significantly lower counts of natural killer (NK) cells in drug-naïve and medicated SCZ patients compared to healthy controls across all datasets. In one dataset from SCZ patients, there were no significant differences in the number of NK cells between acutely relapsed and remitted SCZ patients. No significant difference in the number of NK cells between BPD patients and healthy controls was observed in all datasets. Our results indicate that SCZ patients, but not BPD patients, might be characterized by reduced counts of NK cells. Future studies looking at lymphocyte counts in SCZ should combine the analysis of data obtained using computational deconvolution and flow cytometry techniques.
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Affiliation(s)
- Paweł Karpiński
- Department of Genetics, Wroclaw Medical University, 1 Marcinkowski Street, 50-368 Wroclaw, Poland
| | - Dorota Frydecka
- Department of Psychiatry, Wroclaw Medical University, 10 Pasteur Street, 50-367 Wroclaw, Poland
| | - Maria M Sąsiadek
- Department of Genetics, Wroclaw Medical University, 1 Marcinkowski Street, 50-368 Wroclaw, Poland
| | - Błażej Misiak
- Department of Genetics, Wroclaw Medical University, 1 Marcinkowski Street, 50-368 Wroclaw, Poland; Department of Psychiatry, Wroclaw Medical University, 10 Pasteur Street, 50-367 Wroclaw, Poland.
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20
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van de Leemput J, Glatt SJ, Tsuang MT. The potential of genetic and gene expression analysis in the diagnosis of neuropsychiatric disorders. Expert Rev Mol Diagn 2016; 16:677-95. [DOI: 10.1586/14737159.2016.1171714] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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21
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Haenisch F, Cooper JD, Reif A, Kittel-Schneider S, Steiner J, Leweke FM, Rothermundt M, van Beveren NJM, Crespo-Facorro B, Niebuhr DW, Cowan DN, Weber NS, Yolken RH, Penninx BWJH, Bahn S. Towards a blood-based diagnostic panel for bipolar disorder. Brain Behav Immun 2016; 52:49-57. [PMID: 26441135 DOI: 10.1016/j.bbi.2015.10.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2015] [Revised: 09/08/2015] [Accepted: 10/02/2015] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Bipolar disorder (BD) is a costly, devastating and life shortening mental disorder that is often misdiagnosed, especially on initial presentation. Misdiagnosis frequently results in ineffective treatment. We investigated the utility of a biomarker panel as a diagnostic test for BD. METHODS AND FINDINGS We performed a meta-analysis of eight case-control studies to define a diagnostic biomarker panel for BD. After validating the panel on established BD patients, we applied it to undiagnosed BD patients. We analysed 249 BD, 122 pre-diagnostic BD, 75 pre-diagnostic schizophrenia and 90 first onset major depression disorder (MDD) patients and 371 controls. The biomarker panel was identified using ten-fold cross-validation with lasso regression applied to the 87 analytes available across the meta-analysis studies. We identified 20 protein analytes with excellent predictive performance [area under the curve (AUC)⩾0.90]. Importantly, the panel had a good predictive performance (AUC 0.84) to differentiate 12 misdiagnosed BD patients from 90 first onset MDD patients, and a fair to good predictive performance (AUC 0.79) to differentiate between 110 pre-diagnostic BD patients and 184 controls. We also demonstrated the disease specificity of the panel. CONCLUSIONS An early and accurate diagnosis has the potential to delay or even prevent the onset of BD. This study demonstrates the potential utility of a biomarker panel as a diagnostic test for BD.
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Affiliation(s)
- Frieder Haenisch
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Jason D Cooper
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Hospital Frankfurt, Germany
| | - Sarah Kittel-Schneider
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University Hospital Frankfurt, Germany
| | - Johann Steiner
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Magdeburg, Magdeburg, Germany
| | - F Markus Leweke
- Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | | | - Nico J M van Beveren
- Department of Psychiatry, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Benedicto Crespo-Facorro
- CIBERSAM, University Hospital Marqués de Valdecilla, University of Cantabria - IDIVAL, Department of Psychiatry, Santander, Spain
| | - David W Niebuhr
- Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - David N Cowan
- Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Natalya S Weber
- Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Robert H Yolken
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Brenda W J H Penninx
- Department of Psychiatry, EMGO Institute, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Sabine Bahn
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.
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22
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Logotheti M, Pilalis E, Venizelos N, Kolisis F, Chatziioannou A. Development and validation of a skin fibroblast biomarker profile for schizophrenic patients. AIMS BIOENGINEERING 2016. [DOI: 10.3934/bioeng.2016.4.552] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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