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Gene Expression Analysis of Fibroblasts from Patients with Bipolar Disorder
Örebro University, School of Health and Medical Sciences, Örebro University, Sweden. Neuropsychiatric Research Laboratory, Faculty of Medicine and Health; Metabolic Engineering and Bioinformatics Group, National Hellenic Research Foundation, Athens, Greece; Laboratory of Biotechnology, School of Chemical Engineering, National Technical University of Athens, Athens, Greece. (Experimentell neuropsykiatri)
Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
Örebro University, School of Health and Medical Sciences, Örebro University, Sweden. Neuropsychiatric Research Laboratory, Faculty of Medicine and Health. (Experimentell neuropsykiatri)ORCID iD: 0000-0001-8102-1804
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2015 (English)In: Journal of Neuropsychopharmacology & Mental Health, ISSN 2472-095X, Vol. 1, no 1, 1-9 p., 1000103Article in journal (Refereed) Published
Abstract [en]

Bipolar disorder is a severe, lifelong psychiatric disease. The main underlying pathophysiology of the disease is still incomprehensible. Various studies have suggested that many genes of small impact in combination with environmental factors contribute to the expression of the disease. In this study comparative transcriptomic profiling to characterize skin fibroblasts’ gene expression of bipolar disorder patients compared to healthy controls has been performed. Skin fibroblast cells from bipolar disorder patients (n=10) and marched healthy controls (n=5) have been cultured. RNA was extracted and then hybridized onto Illumina Human HT-12 v4 Expression BeadChips. Differentially expressed genes between bipolar disorder samples and healthy controls were identified by performing unequal t-test on log 2 transformed expression values. The resulting gene list was obtained by setting the p-value threshold to 0.05 and by removing genes that presented a fold change ≥ |0.5| (in log 2 scale). We concluded to 457 differentially expressed genes. Among them 127 showed an upregulation and 330 were downregulated. Τhe expression alterations of selected genes were validated by quantitative real-time polymerase chain reaction. In order to derive better insight into the biological mechanisms related to the differentially expressed genes, the lists of significant genes were subjected to pathway analysis and target prioritization indicating various processes such as calcium ion homeostasis, positive regulation of apoptotic process and cellular response to retinoic acid.

Place, publisher, year, edition, pages
OMICS International , 2015. Vol. 1, no 1, 1-9 p., 1000103
Keyword [en]
Skin fibroblasts, Bbipolar disorder, transcriptome, psychiatric diseases, pathway analysis, microarrays
National Category
Medical and Health Sciences Psychiatry
Research subject
Psychiatry; Molecular Medicine (Genetics and Pathology); Biomedicine
Identifiers
URN: urn:nbn:se:oru:diva-47705DOI: 10.4172/jnpmh.1000103OAI: oai:DiVA.org:oru-47705DiVA: diva2:896197
Available from: 2016-01-20 Created: 2016-01-20 Last updated: 2017-10-18Bibliographically approved
In thesis
1. Integration of functional genomics and data mining methodologies in the study of bipolar disorder and schizophrenia
Open this publication in new window or tab >>Integration of functional genomics and data mining methodologies in the study of bipolar disorder and schizophrenia
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Bipolar disorder and schizophrenia are two severe psychiatric disorders characterized by a complex genetic basis, coupled to the influence of environmental factors. In this thesis, functional genomic analysis tools were used for the study of the underlying pathophysiology of these disorders, focusing on gene expression and function on a global scale with the application of high-throughput methods. Datasets from public databases regarding transcriptomic data of postmortem brain and skin fibroblast cells of patients with either schizophrenia or bipolar disorder were analyzed in order to identify differentially expressed genes. In addition, fibroblast cells of bipolar disorder patients obtained from the Biobank of the Neuropsychiatric Research Laboratory of Örebro University were cultured, RNA was extracted and used for microarray analysis. In order to gain deeper insight into the biological mechanisms related to the studied psychiatric disorders, the differentially expressed gene lists were subjected to pathway and target prioritization analysis, using proprietary tools developed by the group of Metabolic Engineering and Bioinformatics, of the National Hellenic Research Foundation, thus indicating various cellular processes as significantly altered. Many of the molecular processes derived from the analysis of the postmortem brain data of schizophrenia and bipolar disorder were also identified in the skin fibroblast cells. Additionally, through the use of machine learning methods, gene expression data from patients with schizophrenia were exploited for the identification of a subset of genes with discriminative ability between schizophrenia and healthy control subjects. Interestingly, a set of genes with high separating efficiency was derived from fibroblast gene expression profiling. This thesis suggests the suitability of skin fibroblasts as a reliable model for the diagnostic evaluation of psychiatric disorders and schizophrenia in particular, through the construction of promising machine-learning based classification models, exploiting gene expression data from peripheral tissues.

Place, publisher, year, edition, pages
Örebro: Örebro university, 2016. 98 p.
Series
Örebro Studies in Medicine, ISSN 1652-4063 ; 153
Keyword
Bipolar Disorder, Schizophrenia, Fibroblasts, DNA Microarrays, Machine Learning, Functional Analysis, Gene Expression, Transcriptomics
National Category
Other Basic Medicine
Identifiers
urn:nbn:se:oru:diva-52644 (URN)978-91-7529-168-0 (ISBN)
Public defence
2016-12-09, Campus USÖ, hörsal C3, Södra Grev Rosengatan 32, Örebro, 09:00 (English)
Opponent
Supervisors
Available from: 2016-09-28 Created: 2016-09-28 Last updated: 2017-10-17Bibliographically approved

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Logotheti, MarianthiVenizelos, Nikolaos
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