Örebro Studies in Medicine 153 I
ÖREBRO 2016 2016M
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marianthi logotheti has received her diploma in che-mical engineering from the National Technical University of Athens (NTUA) in 2010. In 2012 she was enrolled as a PhD student in Medical Science at the Neuropsychiatric Research Laboratory of School of Health and Medical Sciences, in Örebro University of Sweden. Her PhD studies were perfor-med in collaboration with the Metabolic Engineering and Bioinformatics Group of the Institute of Biology, Medicinal Chemistry and Biotechnology in National Hellenic Research Foundation of Greece and with the Laboratory of Biotechnology of the School of Chemical Engineering of NTUA. She has also participated in research programs related to gene expression profiling in cancer and in the field of in silico aided meta-bolic engineering of microorganisms for improved biotechnological products. Additionally, she has participated in many educational activities in combination with her PhD studies.
Bipolar disorder and schizophrenia are two severe psychiatric disorders charac-terized by a complex genetic basis, coupled to the influence of environmental factors. The present thesis aims to dissect this complexity through the exploita-tion of transcriptomic profiling data in order to derive molecular circuits linked to schizophrenia and bipolar disorder. Systematic bioinformatic analysis on gene expression data from postmortem brain samples or skin fibroblast cells, of subjects with either schizophrenia or bipolar disorder, implicates specific molecular pathways and genes in the pathophysiology of the studied psychiatric disorders. Noteworthy, a part of the altered molecular mechanisms occurring in the postmortem brain studies are also captured in the skin fibroblast cells. Additionally, through the use of machine learning methods, gene expression data from patients with schizophrenia are exploited for the identification of a subset of genes with discriminative ability between schizophrenia and healthy control subjects. Interestingly, a set of genes is derived from fibroblast gene expression profiling with high separating efficiency, thus supporting further development of effective classification models.
This thesis suggests the suitability of skin fibroblasts as a reliable means for diagnostic evaluation of psychiatric disorders and schizophrenia in particular, through the construction of machine-learning based classification models, exploiting gene expression data from peripheral tissues.
issn 1652-4063 isbn 978-91-7529-168-0
