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- Logotheti, Marianthi, 1986-, et al.
(författare)
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A Comparative Genomic Study in Schizophrenic and in Bipolar Disorder Patients, Based on Microarray Expression Profiling Meta-Analysis
- 2013
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Ingår i: Scientific World Journal. - New York, USA : Hindawi Publishing Corporation. - 1537-744X. ; 2013:685917, s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- 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
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| 2. |
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| 3. |
- Logotheti, Marianthi, 1986-, et al.
(författare)
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Development and validation of a skin fibroblast biomarker profile for schizophrenic patients
- 2016
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Ingår i: AIMS Bioengineering. - Springfield, USA : AIMS press. - 2375-1487 .- 2375-1495. ; 3:4, s. 552-565
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Tidskriftsartikel (refereegranskat)abstract
- Gene expression profiles of non-neural tissues through microarray technology could be used in schizophrenia studies, adding more information to the results from similar studies on postmortem brain tissue. The ultimate goal of such studies is to develop accessible biomarkers. Supervised machine learning methodologies were used, in order to examine if the gene expression from skin fibroblast cells could be exploited for the classification of schizophrenic subjects. A dataset of skin fibroblasts gene expression of schizophrenia patients was obtained from Gene Expression Omnibus database. After applying statistical criteria, we concluded to genes that present a differential expression between the schizophrenic patients and the healthy controls. Based on those genes, functional profiling was performed with the BioInfoMiner web tool. After the statistical analysis, 63 genes were identified as differentially expressed. The functional profiling revealed interesting terms and pathways, such as mitogen activated protein kinase and cyclic adenosine monophosphate signaling pathways, as well as immune-related mechanisms. A subset of 16 differentially expressed genes from fibroblast gene expression profiling that occurred after Support Vector Machines Recursive Feature Elimination could efficiently separate schizophrenic from healthy controls subjects. These findings suggest that through the analysis of fibroblast based gene 553 expression signature and with the application of machine learning methodologies we might conclude to a diagnostic classification model in schizophrenia.
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| 4. |
- Logotheti, Marianthi, et al.
(författare)
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Gene Expression Analysis of Fibroblasts from Patients with Bipolar Disorder
- 2015
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Ingår i: Journal of Neuropsychopharmacology & Mental Health. - : OMICS International. - 2472-095X. ; 1:1, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Logotheti, Marianthi, 1986-
(författare)
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Integration of functional genomics and data mining methodologies in the study of bipolar disorder and schizophrenia
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 6. |
- Logotheti, Marianthi, et al.
(författare)
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Recent Advancements in Bipolar Disorder studies through Genomic, Epigenomic and Metagenomic Approaches
- 2019
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Ingår i: Journal of Psychiatry and Psychology Research. - : SciTech Central Inc.. - 2640-6136. ; 2:1, s. 56-66
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Forskningsöversikt (refereegranskat)abstract
- Bipolar disorder is a complex and highly heritable psychiatric disorder characterized by severe mood alterations. The precise geneticunderpinnings of the disease have not been identified so far, despite numerous genome-wide association findings. This review describes thecurrent state of genetic studies based on next generation sequencing technologies including whole exome and whole genome sequencing, aswell as RNA-sequencing and highlights the fact that the integration of these studies can reveal novel knowledge such as the functional roleof gene variants. However, due to the complexity of bipolar disorder, it is a compelling candidate for studies beyond DNA and RNAsequencing. Epigenetic alterations, defined as heritable but reversible modifications including DNA methylation, DNAhydroxymethylation, histone modifications and non-coding RNAs may be the link between genome and environment interactions.Additionally, a possible source of the reported immune activation in bipolar disorder is the micro biome of gastrointestinal tract, due torecent studies that indicate its pivotal role in brain function through the ‘gut-brain’ axis. The identification of methods able to modulate themicro biome emerges as a promising path for novel diagnostic and treatment options in bipolar disorder, thus the number of metagenomicstudies in bipolar disorder has substantially increased the last years. Overall, the paper aims to review the most recent literature ongenomic, epigenomic and metagenomic studies that have contributed to our understanding of the pathophysiology of bipolar disorder sofar. The paper also focuses on the exploitation of recent advancements in high-throughput technologies for the elucidation of bipolardisorder through different approaches that may provide complementary knowledge and concludes to the need for merging the gap betweenall the gathered knowledge from the analysis of high-throughput data.
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| 7. |
- Logotheti, Marianthi, 1986-, et al.
(författare)
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Studying Microarray Gene Expression Data of Schizophrenic Patients for Derivation of a Diagnostic Signature through the Aid of Machine Learning
- 2016
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Ingår i: Biometrics & Biostatistics International Journal. - : MedCrave. - 2378-315X. ; 4:5
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Tidskriftsartikel (refereegranskat)abstract
- 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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