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  • 1.
    Logotheti, Marianthi
    Örebro University, School of Medical Sciences.
    Integration of functional genomics and data mining methodologies in the study of bipolar disorder and schizophrenia2016Doctoral 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.

    List of papers
    1. A Comparative Genomic Study in Schizophrenic and in Bipolar Disorder Patients, Based on Microarray Expression Profiling Meta-Analysis
    Open this publication in new window or tab >>A Comparative Genomic Study in Schizophrenic and in Bipolar Disorder Patients, Based on Microarray Expression Profiling Meta-Analysis
    Show others...
    2013 (English)In: Scientific World Journal, ISSN 1537-744X, E-ISSN 1537-744X, Vol. 2013, no 685917, p. 1-14, article id 685917Article in journal (Refereed) Published
    Abstract [en]

    Schizophrenia affecting almost 1% and bipolar disorder affecting almost 3%-5% of the global population constitute two severe mental disorders. The catecholaminergic and the serotonergic pathways have been proved to play an important role in the development of schizophrenia, bipolar disorder, and other related psychiatric disorders. The aim of the study was to perform and interpret the results of a comparative genomic profiling study in schizophrenic patients as well as in healthy controls and in patients with bipolar disorder and try to relate and integrate our results with an aberrant amino acid transport through cell membranes. In particular we have focused on genes and mechanisms involved in amino acid transport through cell membranes from whole genome expression profiling data. We performed bioinformatic analysis on raw data derived from four different published studies. In two studies postmortem samples from prefrontal cortices, derived from patients with bipolar disorder, schizophrenia, and control subjects, have been used. In another study we used samples from postmortem orbitofrontal cortex of bipolar subjects while the final study was performed based on raw data from a gene expression profiling dataset in the postmortem superior temporal cortex of schizophrenics. The data were downloaded from NCBI's GEO datasets

    Place, publisher, year, edition, pages
    New York, USA: Hindawi Publishing Corporation, 2013
    National Category
    Medical and Health Sciences
    Research subject
    Biomedicine; Psychiatry
    Identifiers
    urn:nbn:se:oru:diva-42590 (URN)10.1155/2013/685917 (DOI)000316470600001 ()23554570 (PubMedID)2-s2.0-84876539074 (Scopus ID)
    Available from: 2015-02-11 Created: 2015-02-11 Last updated: 2018-05-26Bibliographically approved
    2. Gene Expression Analysis of Fibroblasts from Patients with Bipolar Disorder
    Open this publication in new window or tab >>Gene Expression Analysis of Fibroblasts from Patients with Bipolar Disorder
    Show others...
    2015 (English)In: Journal of Neuropsychopharmacology & Mental Health, ISSN 2472-095X, Vol. 1, no 1, p. 1-9, article id 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
    Keywords
    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:nbn:se:oru:diva-47705 (URN)10.4172/jnpmh.1000103 (DOI)
    Available from: 2016-01-20 Created: 2016-01-20 Last updated: 2018-07-02Bibliographically approved
    3. Studying Microarray Gene Expression Data of Schizophrenic Patients for Derivation of a Diagnostic Signature through the Aid of Machine Learning
    Open this publication in new window or tab >>Studying Microarray Gene Expression Data of Schizophrenic Patients for Derivation of a Diagnostic Signature through the Aid of Machine Learning
    Show others...
    2016 (English)In: Biometrics & Biostatistics International Journal, ISSN 2378-315X, Vol. 4, no 5, article id 00106Article in journal (Refereed) Published
    Abstract [en]

    Schizophrenia is a complex psychiatric disease that is affected by multiple genes, some of which could be used as biomarkers for specific diagnosis of the disease. In this work, we explore the power of machine learning methodologies for predicting schizophrenia, through the derivation of a biomarker gene signature for robust diagnostic classification purposes. Postmortem brain gene expression data from the anterior prefrontal cortex of schizophrenia patients were used as training data for the construction of the classifiers. Several machine learning algorithms, such as support vector machines, random forests, and extremely randomized trees classifiers were developed and their performance was tested. After applying the feature selection method of support vector machines recursive feature elimination a 21-gene model was derived. Using these genes for developing classification models, the random forests algorithm outperformed all examined algorithms achieving an area under the curve of 0.98 and sensitivity of 0.89, discriminating schizophrenia from healthy control samples with high efficiency. The 21-gene model that was derived from the feature selection is suggested for classifying schizophrenic patients, as it was successfully applied on an independent dataset of postmortem brain samples from the superior temporal cortex, and resulted in a classification model that achieved an area under the curve score of 0.91. Additionally, the functional analysis of the statistically significant genes indicated many mechanisms related to the immune system.

    Place, publisher, year, edition, pages
    MedCrave, 2016
    Keywords
    Classification, Schizophrenia, Machine learning, Gene expression, Microarray studies, Support vector machines, Adaboost
    National Category
    Other Basic Medicine
    Research subject
    Biomedicine
    Identifiers
    urn:nbn:se:oru:diva-53536 (URN)
    Note

    DOI 10.15406/bbij.2016.04.00106

    Available from: 2016-11-17 Created: 2016-11-17 Last updated: 2018-04-27Bibliographically approved
    4. Development and validation of a skin fibroblast biomarker profile for schizophrenic patients
    Open this publication in new window or tab >>Development and validation of a skin fibroblast biomarker profile for schizophrenic patients
    Show others...
    (English)Manuscript (preprint) (Other academic)
    National Category
    Other Basic Medicine
    Research subject
    Biomedicine
    Identifiers
    urn:nbn:se:oru:diva-53538 (URN)
    Available from: 2016-11-17 Created: 2016-11-17 Last updated: 2018-01-13Bibliographically approved
  • 2.
    Logotheti, Marianthi
    et al.
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden. Neuropsychiatric Research Laboratory, Faculty of Medicine and Health, Örebro University, Örebro, Sweden; 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.
    Papadodima, Olga
    Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
    Chatziioannou, Aristotelis
    Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
    Venizelos, Nikolaos
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden. Neuropsychiatric Research Laboratory, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
    Kolisis, Fragiskos
    Laboratory of Biotechnology, School of Chemical Engineering, National Technical University of Athens, Athens, Greece.
    Gene Expression Analysis of Fibroblasts from Patients with Bipolar Disorder2015In: Journal of Neuropsychopharmacology & Mental Health, ISSN 2472-095X, Vol. 1, no 1, p. 1-9, article id 1000103Article in journal (Refereed)
    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.

  • 3.
    Logotheti, Marianthi
    et al.
    Neuropsychiatric Research Laboratory, Department of Clinical Medicine, Örebro University, Örebro, Sweden; Metabolic Engineering and Bioinformatics Program, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece; Laboratory of Biotechnology, School of Chemical Engineering, National Technical University of Athens, Athens, Greece.
    Papadodima, Olga
    Metabolic Engineering and Bioinformatics Program, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
    Venizelos, Nikolaos
    Neuropsychiatric Research Laboratory, Department of Clinical Medicine, Örebro University, Örebro, Sweden.
    Chatziioannou, Aristitelis
    Metabolic Engineering and Bioinformatics Program, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
    Kolisis, Fragiskos
    Laboratory of Biotechnology, School of Chemical Engineering, National Technical University of Athens, Athens, Greece.
    A Comparative Genomic Study in Schizophrenic and in Bipolar Disorder Patients, Based on Microarray Expression Profiling Meta-Analysis2013In: Scientific World Journal, ISSN 1537-744X, E-ISSN 1537-744X, Vol. 2013, no 685917, p. 1-14, article id 685917Article in journal (Refereed)
    Abstract [en]

    Schizophrenia affecting almost 1% and bipolar disorder affecting almost 3%-5% of the global population constitute two severe mental disorders. The catecholaminergic and the serotonergic pathways have been proved to play an important role in the development of schizophrenia, bipolar disorder, and other related psychiatric disorders. The aim of the study was to perform and interpret the results of a comparative genomic profiling study in schizophrenic patients as well as in healthy controls and in patients with bipolar disorder and try to relate and integrate our results with an aberrant amino acid transport through cell membranes. In particular we have focused on genes and mechanisms involved in amino acid transport through cell membranes from whole genome expression profiling data. We performed bioinformatic analysis on raw data derived from four different published studies. In two studies postmortem samples from prefrontal cortices, derived from patients with bipolar disorder, schizophrenia, and control subjects, have been used. In another study we used samples from postmortem orbitofrontal cortex of bipolar subjects while the final study was performed based on raw data from a gene expression profiling dataset in the postmortem superior temporal cortex of schizophrenics. The data were downloaded from NCBI's GEO datasets

  • 4.
    Logotheti, Marianthi
    et al.
    Örebro University, School of Medical Sciences. Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
    Pilalis, Eleftherios
    Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece; e-NIOS Applications PC, Athens, Greece .
    Venizelos, Nikolaos
    Örebro University, School of Health Sciences.
    Kolisis, Fragiskos
    Laboratory of Biotechnology, School of Chemical Engineering, National Technical University of Athens, Athens, Greece.
    Chatziioannou, Aristotelis
    Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece; e-NIOS Applications PC, Athens, Greece .
    Development and validation of a skin fibroblast biomarker profile for schizophrenic patientsManuscript (preprint) (Other academic)
  • 5.
    Logotheti, Marianthi
    et al.
    Örebro University, School of Medical Sciences. Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece.
    Pilalis, Eleftherios
    Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece; e-NIOS Applications PC, Athens, Greece .
    Venizelos, Nikolaos
    Neuropsychiatric Research Laboratory, Faculty of Medicine and Health, School of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Kolisis, Fragiskos
    Laboratory of Biotechnology, School of Chemical Engineering, National Technical University of Athens, Athens, Greece.
    Chatziioannou, Aristotelis
    Metabolic Engineering and Bioinformatics Group, Institute of Biology, Medicinal Chemistry and Biotechnology, National Hellenic Research Foundation, Athens, Greece; e-NIOS Applications PC, Athens, Greece .
    Studying Microarray Gene Expression Data of Schizophrenic Patients for Derivation of a Diagnostic Signature through the Aid of Machine Learning2016In: Biometrics & Biostatistics International Journal, ISSN 2378-315X, Vol. 4, no 5, article id 00106Article in journal (Refereed)
    Abstract [en]

    Schizophrenia is a complex psychiatric disease that is affected by multiple genes, some of which could be used as biomarkers for specific diagnosis of the disease. In this work, we explore the power of machine learning methodologies for predicting schizophrenia, through the derivation of a biomarker gene signature for robust diagnostic classification purposes. Postmortem brain gene expression data from the anterior prefrontal cortex of schizophrenia patients were used as training data for the construction of the classifiers. Several machine learning algorithms, such as support vector machines, random forests, and extremely randomized trees classifiers were developed and their performance was tested. After applying the feature selection method of support vector machines recursive feature elimination a 21-gene model was derived. Using these genes for developing classification models, the random forests algorithm outperformed all examined algorithms achieving an area under the curve of 0.98 and sensitivity of 0.89, discriminating schizophrenia from healthy control samples with high efficiency. The 21-gene model that was derived from the feature selection is suggested for classifying schizophrenic patients, as it was successfully applied on an independent dataset of postmortem brain samples from the superior temporal cortex, and resulted in a classification model that achieved an area under the curve score of 0.91. Additionally, the functional analysis of the statistically significant genes indicated many mechanisms related to the immune system.

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