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101. Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB

Author

Kolisis Fragiskos N, Moulos Panagiotis, and Chatziioannou Aristotelis

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods. Results We describe here Gene ARMADA (Automated Robust MicroArray Data Analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form (Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime. Conclusion Gene ARMADA provides a highly adaptable, integrative, yet flexible tool which can be used for automated quality control, analysis, annotation and visualization of microarray data, constituting a starting point for further data interpretation and integration with numerous other tools.

Published
2009
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102. KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database

Author

Kolisis Fragiskos N, Maglogiannis Ilias, Chatziioannou Aristotelis, Kanaris Ioannis, and Moutselos Konstantinos

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling. In this project, KEGGconverter is described, implemented also as a web-based application, which uses as source KGML files, in order to construct integrated pathway SBML models fully functional for simulation purposes. Results A case study of the integration of six human metabolic pathways from KEGG depicts the ability of KEGGconverter to automatically produce merged and converted to SBML fully functional pathway models, enhanced with default kinetics. The suitability of the developed tool is demonstrated through a comparison with other state-of-the art relevant software tools for the same data fusion and conversion tasks, thus illustrating the problems and the relevant workflows. Moreover, KEGGconverter permits the inclusion of additional reactions in the resulting model which represent flux cross-talk with neighbouring pathways, providing in this way improved simulative accuracy. These additional reactions are introduced by exploiting relevant semantic information for the elements of the KEGG Pathways database. The architecture and functionalities of the web-based application are presented. Conclusion KEGGconverter is capable of producing integrated analogues of metabolic pathways appropriate for simulation tasks, by inputting only KGML files. The web application acts as a user friendly shell which transparently enables the automated biochemically correct pathway merging, conversion to SBML format, proper renaming of the species, and insertion of default kinetic properties for the pertaining reactions. The tool is available at: http://www.grissom.gr/keggconverter

Published
2009
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103. Novel IRE1-dependent proinflammatory signaling controls tumor infiltration by myeloid cells

Author

Obacz, Joanna, primary, Archambeau, Jérôme, additional, Sicari, Daria, additional, Le Reste, Pierre Jean, additional, Pineau, Raphael, additional, Martin, Sophie, additional, Barroso, Kim, additional, Vlachavas, Efstathios, additional, Voutetakis, Konstantinos, additional, Fainsod-Levi, Tanya, additional, Obiedat, Akram, additional, Granot, Zvi, additional, Tirosh, Boaz, additional, Samal, Juhi, additional, Pandit, Abhay, additional, Patterson, John, additional, Zheng, Qinping, additional, Négroni, Luc, additional, Chatziioannou, Aristotelis, additional, Quillien, Véronique, additional, Avril, Tony, additional, and Chevet, Eric, additional

Published
2019
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105. Transcription of the NKG2D ligand MICA is suppressed by the IRE1/XBP1 pathway of the unfolded protein response through the regulation of E2F1

Author

Obiedat, Akram, primary, Seidel, Einat, additional, Mahameed, Mohamed, additional, Bernani, Orit, additional, Tsukerman, Pinchas, additional, Voutetakis, Konstantinos, additional, Chatziioannou, Aristotelis, additional, Mcmahon, Mari, additional, Avril, Tony, additional, Chevet, Eric, additional, Mandelboim, Ofer, additional, and Tirosh, Boaz, additional

Published
2018
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110. Pre-diagnostic blood immune markers, incidence and progression of B-cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses

Author

One Health Chemisch, dIRAS RA-2, Sub Physical Oceanography, Vermeulen, Roel, Saberi Hosnijeh, Fatemeh, Bodinier, Barbara, Portengen, Lützen, Liquet, Benoît, Garrido-Manriquez, Javiera, Lokhorst, Henk, Bergdahl, Ingvar A., Kyrtopoulos, Soterios A., Johansson, Ann Sofie, Georgiadis, Panagiotis, Melin, Beatrice, Palli, Domenico, Krogh, Vittorio, Panico, Salvatore, Sacerdote, Carlotta, Tumino, Rosario, Vineis, Paolo, Castagné, Raphaële, Chadeau-Hyam, Marc, Botsivali, Maria, Chatziioannou, Aristotelis, Valavanis, Ioannis, Kleinjans, Jos C.S., de Kok, Theo M.C.M., Keun, Hector C., Athersuch, Toby J., Kelly, Rachel, Lenner, Per, Hallmans, Goran, Stephanou, Euripides G., Myridakis, Antonis, Kogevinas, Manolis, Fazzo, Lucia, De Santis, Marco, Comba, Pietro, Bendinelli, Benedetta, Kiviranta, Hannu, Rantakokko, Panu, Airaksinen, Riikka, Ruokojarvi, Paivi, Gilthorpe, Mark, Fleming, Sarah, Fleming, Thomas, Tu, Yu Kang, Lundh, Thomas, Chien, Kuo Liong, Chen, Wei J., Lee, Wen Chung, Kate Hsiao, Chuhsing, Kuo, Po Hsiu, Hung, Hung, Liao, Shu Fen, One Health Chemisch, dIRAS RA-2, Sub Physical Oceanography, Vermeulen, Roel, Saberi Hosnijeh, Fatemeh, Bodinier, Barbara, Portengen, Lützen, Liquet, Benoît, Garrido-Manriquez, Javiera, Lokhorst, Henk, Bergdahl, Ingvar A., Kyrtopoulos, Soterios A., Johansson, Ann Sofie, Georgiadis, Panagiotis, Melin, Beatrice, Palli, Domenico, Krogh, Vittorio, Panico, Salvatore, Sacerdote, Carlotta, Tumino, Rosario, Vineis, Paolo, Castagné, Raphaële, Chadeau-Hyam, Marc, Botsivali, Maria, Chatziioannou, Aristotelis, Valavanis, Ioannis, Kleinjans, Jos C.S., de Kok, Theo M.C.M., Keun, Hector C., Athersuch, Toby J., Kelly, Rachel, Lenner, Per, Hallmans, Goran, Stephanou, Euripides G., Myridakis, Antonis, Kogevinas, Manolis, Fazzo, Lucia, De Santis, Marco, Comba, Pietro, Bendinelli, Benedetta, Kiviranta, Hannu, Rantakokko, Panu, Airaksinen, Riikka, Ruokojarvi, Paivi, Gilthorpe, Mark, Fleming, Sarah, Fleming, Thomas, Tu, Yu Kang, Lundh, Thomas, Chien, Kuo Liong, Chen, Wei J., Lee, Wen Chung, Kate Hsiao, Chuhsing, Kuo, Po Hsiu, Hung, Hung, and Liao, Shu Fen

Published
2018

111. Blood-based omic profiling supports female susceptibility to tobacco smoke-induced cardiovascular diseases

Author

Chatziioannou, Aristotelis, Georgiadis, Panagiotis, Hebels, Dennie G, Liampa, Irene, Valavanis, Ioannis, Bergdahl, Ingvar A, Johansson, Anders, Palli, Domenico, Chadeau-Hyam, Marc, Siskos, Alexandros P., Keun, Hector, Botsivali, Maria, de Kok, Theo M C M, Pérez, Almudena Espín, Kleinjans, Jos C S, Vineis, Paolo, Kyrtopoulos, Soterios A, Gottschalk, Ralph, Van Leeuwen, Danitsja, Timmermans, Leen, Bendinelli, Benedetta, Kelly, Rachel S., Vermeulen, Roel, Portengen, Lutzen, Saberi-Hosnijeh, Fatemeh, Melin, Beatrice, Hallmans, Goran, Lenner, Per, Athersuch, Toby J., Kogevinas, Manolis, Stephanou, Euripides G., Myridakis, Antonis, Fazzo, Lucia, De Santis, Marco, Comba, Pietro, Kiviranta, Hannu, Rantakokko, Panu, Airaksinen, Riikka, Ruokojarvi, Paivi, Gilthorpe, Mark, Fleming, Sarah, Fleming, Thomas D, Tu, Yu Kang, Jonsson, Bo A. G., Lundh, Thomas, Chen, Wei J., Lee, Wen Chung, Hsiao, Chuhsing Kate, Chien, Kuo Liong, Kuo, Po Hsiu, Hung, Hung, Liao, Shu Fen, LS IRAS EEPI GRA (Gezh.risico-analyse), Sub IRAS EEPI Algemeen, LS Knijn, dIRAS RA-2, CBITE, RS: MERLN - Cell Biology - Inspired Tissue Engineering (CBITE), Toxicogenomics, RS: GROW - R1 - Prevention, RS: FHML MaCSBio, RS: FPN MaCSBio, RS: FSE MaCSBio, and Promovendi ODB

Subjects
0301 basic medicine, Adult, Epigenomics, Male, SEX-DIFFERENCES, PLATELET ACTIVATION, Disease, Biology, OBSTRUCTIVE PULMONARY-DISEASE, Article, Epigenesis, Genetic, Transcriptome, 03 medical and health sciences, LUNG-CANCER, Sex Factors, RISK-FACTOR, Humans, Cardiac and Cardiovascular Systems, CORONARY-HEART-DISEASE, Gene Regulatory Networks, Platelet activation, Epigenetics, GENE-EXPRESSION, Aged, Regulation of gene expression, Kardiologi, Science & Technology, Multidisciplinary, Gene Expression Profiling, Public Health, Global Health, Social Medicine and Epidemiology, DNA Methylation, Middle Aged, Multidisciplinary Sciences, Gene expression profiling, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, 030104 developmental biology, MYOCARDIAL-INFARCTION, Gene Expression Regulation, Cardiovascular Diseases, DNA methylation, Immunology, Science & Technology - Other Topics, CpG Islands, Female, Tobacco Smoke Pollution, CIGARETTE-SMOKING, HEME OXYGENASE-1
Abstract

We recently reported that differential gene expression and DNA methylation profiles in blood leukocytes of apparently healthy smokers predicts with remarkable efficiency diseases and conditions known to be causally associated with smoking, suggesting that blood-based omic profiling of human populations may be useful for linking environmental exposures to potential health effects. Here we report on the sex-specific effects of tobacco smoking on transcriptomic and epigenetic features derived from genome-wide profiling in white blood cells, identifying 26 expression probes and 92 CpG sites, almost all of which are affected only in female smokers. Strikingly, these features relate to numerous genes with a key role in the pathogenesis of cardiovascular disease, especially thrombin signaling, including the thrombin receptors on platelets F2R (coagulation factor II (thrombin) receptor; PAR1) and GP5 (glycoprotein 5), as well as HMOX1 (haem oxygenase 1) and BCL2L1 (BCL2-like 1) which are involved in protection against oxidative stress and apoptosis, respectively. These results are in concordance with epidemiological evidence of higher female susceptibility to tobacco-induced cardiovascular disease and underline the potential of blood-based omic profiling in hazard and risk assessment.

Published
2017
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112. Additional file 3: Figure S1. of Evolving DNA methylation and gene expression markers of B-cell chronic lymphocytic leukemia are present in pre-diagnostic blood samples more than 10Â years prior to diagnosis

Author

Georgiadis, Panagiotis, Liampa, Irene, Hebels, Dennie, Krauskopf, Julian, Chatziioannou, Aristotelis, Valavanis, Ioannis, Kok, Theo De, Kleinjans, Jos, Bergdahl, Ingvar, Melin, Beatrice, Spaeth, Florentin, Palli, Domenico, R.C.H. Vermeulen, J. Vlaanderen, Chadeau-Hyam, Marc, Vineis, Paolo, and Soterios Kyrtopoulos

Abstract

The ontological tree based on DM genes. Figure S2. Methylation of HOX and IRX gene clusters in the short- and long-TtD subgroups. Figure S3. B-cell distribution in controls and CLL cases. (PDF 1281 kb)

Published
2017
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113. Dual IRE 1 RN ase functions dictate glioblastoma development

Author

Lhomond, Stéphanie, primary, Avril, Tony, additional, Dejeans, Nicolas, additional, Voutetakis, Konstantinos, additional, Doultsinos, Dimitrios, additional, McMahon, Mari, additional, Pineau, Raphaël, additional, Obacz, Joanna, additional, Papadodima, Olga, additional, Jouan, Florence, additional, Bourien, Heloise, additional, Logotheti, Marianthi, additional, Jégou, Gwénaële, additional, Pallares‐Lupon, Néstor, additional, Schmit, Kathleen, additional, Le Reste, Pierre‐Jean, additional, Etcheverry, Amandine, additional, Mosser, Jean, additional, Barroso, Kim, additional, Vauléon, Elodie, additional, Maurel, Marion, additional, Samali, Afshin, additional, Patterson, John B, additional, Pluquet, Olivier, additional, Hetz, Claudio, additional, Quillien, Véronique, additional, Chatziioannou, Aristotelis, additional, and Chevet, Eric, additional

Published
2018
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118. Evolving DNA methylation and gene expression markers of B-cell chronic lymphocytic leukemia are present in pre-diagnostic blood samples more than 10 years prior to diagnosis

Author

Georgiadis, Panagiotis, Liampa, Irene, Hebels, Dennie G, Krauskopf, Julian, Chatziioannou, Aristotelis, Valavanis, Ioannis, de Kok, Theo M C M, Kleinjans, Jos C S, Bergdahl, Ingvar A, Melin, Beatrice, Spaeth, Florentin, Palli, Domenico, Vermeulen, R C H, Vlaanderen, J, Chadeau-Hyam, Marc, Vineis, Paolo, Kyrtopoulos, Soterios A, EnviroGenomarkers consortium, Georgiadis, Panagiotis, Liampa, Irene, Hebels, Dennie G, Krauskopf, Julian, Chatziioannou, Aristotelis, Valavanis, Ioannis, de Kok, Theo M C M, Kleinjans, Jos C S, Bergdahl, Ingvar A, Melin, Beatrice, Spaeth, Florentin, Palli, Domenico, Vermeulen, R C H, Vlaanderen, J, Chadeau-Hyam, Marc, Vineis, Paolo, Kyrtopoulos, Soterios A, and EnviroGenomarkers consortium

Abstract

BACKGROUND: B-cell chronic lymphocytic leukemia (CLL) is a common type of adult leukemia. It often follows an indolent course and is preceded by monoclonal B-cell lymphocytosis, an asymptomatic condition, however it is not known what causes subjects with this condition to progress to CLL. Hence the discovery of prediagnostic markers has the potential to improve the identification of subjects likely to develop CLL and may also provide insights into the pathogenesis of the disease of potential clinical relevance.RESULTS: We employed peripheral blood buffy coats of 347 apparently healthy subjects, of whom 28 were diagnosed with CLL 2.0-15.7 years after enrollment, to derive for the first time genome-wide DNA methylation, as well as gene and miRNA expression, profiles associated with the risk of future disease. After adjustment for white blood cell composition, we identified 722 differentially methylated CpG sites and 15 differentially expressed genes (Bonferroni-corrected p < 0.05) as well as 2 miRNAs (FDR < 0.05) which were associated with the risk of future CLL. The majority of these signals have also been observed in clinical CLL, suggesting the presence in prediagnostic blood of CLL-like cells. Future CLL cases who, at enrollment, had a relatively low B-cell fraction (<10%), and were therefore less likely to have been suffering from undiagnosed CLL or a precursor condition, showed profiles involving smaller numbers of the same differential signals with intensities, after adjusting for B-cell content, generally smaller than those observed in the full set of cases. A similar picture was obtained when the differential profiles of cases with time-to-diagnosis above the overall median period of 7.4 years were compared with those with shorted time-to-disease. Differentially methylated genes of major functional significance include numerous genes that encode for transcription factors, especially members of the homeobox family, while differentially expre

Published
2017

119. Blood-based omic profiling supports female susceptibility to tobacco smoke-induced cardiovascular diseases

Author

LS IRAS EEPI GRA (Gezh.risico-analyse), Sub IRAS EEPI Algemeen, LS Knijn, dIRAS RA-2, Chatziioannou, Aristotelis, Georgiadis, Panagiotis, Hebels, Dennie G, Liampa, Irene, Valavanis, Ioannis, Bergdahl, Ingvar A, Johansson, Anders, Palli, Domenico, Chadeau-Hyam, Marc, Siskos, Alexandros P., Keun, Hector, Botsivali, Maria, de Kok, Theo M C M, Pérez, Almudena Espín, Kleinjans, Jos C S, Vineis, Paolo, Kyrtopoulos, Soterios A, Gottschalk, Ralph, Van Leeuwen, Danitsja, Timmermans, Leen, Bendinelli, Benedetta, Kelly, Rachel S., Vermeulen, Roel, Portengen, Lutzen, Saberi-Hosnijeh, Fatemeh, Melin, Beatrice, Hallmans, Goran, Lenner, Per, Athersuch, Toby J., Kogevinas, Manolis, Stephanou, Euripides G., Myridakis, Antonis, Fazzo, Lucia, De Santis, Marco, Comba, Pietro, Kiviranta, Hannu, Rantakokko, Panu, Airaksinen, Riikka, Ruokojarvi, Paivi, Gilthorpe, Mark, Fleming, Sarah, Fleming, Thomas D, Tu, Yu Kang, Jonsson, Bo A. G., Lundh, Thomas, Chen, Wei J., Lee, Wen Chung, Hsiao, Chuhsing Kate, Chien, Kuo Liong, Kuo, Po Hsiu, Hung, Hung, Liao, Shu Fen, LS IRAS EEPI GRA (Gezh.risico-analyse), Sub IRAS EEPI Algemeen, LS Knijn, dIRAS RA-2, Chatziioannou, Aristotelis, Georgiadis, Panagiotis, Hebels, Dennie G, Liampa, Irene, Valavanis, Ioannis, Bergdahl, Ingvar A, Johansson, Anders, Palli, Domenico, Chadeau-Hyam, Marc, Siskos, Alexandros P., Keun, Hector, Botsivali, Maria, de Kok, Theo M C M, Pérez, Almudena Espín, Kleinjans, Jos C S, Vineis, Paolo, Kyrtopoulos, Soterios A, Gottschalk, Ralph, Van Leeuwen, Danitsja, Timmermans, Leen, Bendinelli, Benedetta, Kelly, Rachel S., Vermeulen, Roel, Portengen, Lutzen, Saberi-Hosnijeh, Fatemeh, Melin, Beatrice, Hallmans, Goran, Lenner, Per, Athersuch, Toby J., Kogevinas, Manolis, Stephanou, Euripides G., Myridakis, Antonis, Fazzo, Lucia, De Santis, Marco, Comba, Pietro, Kiviranta, Hannu, Rantakokko, Panu, Airaksinen, Riikka, Ruokojarvi, Paivi, Gilthorpe, Mark, Fleming, Sarah, Fleming, Thomas D, Tu, Yu Kang, Jonsson, Bo A. G., Lundh, Thomas, Chen, Wei J., Lee, Wen Chung, Hsiao, Chuhsing Kate, Chien, Kuo Liong, Kuo, Po Hsiu, Hung, Hung, and Liao, Shu Fen

Published
2017

120. Evolving DNA methylation and gene expression markers of B-cell chronic lymphocytic leukemia are present in pre-diagnostic blood samples more than 10 years prior to diagnosis

Author

LS IRAS EEPI GRA (Gezh.risico-analyse), dIRAS RA-2, Georgiadis, Panagiotis, Liampa, Irene, Hebels, Dennie G, Krauskopf, Julian, Chatziioannou, Aristotelis, Valavanis, Ioannis, de Kok, Theo M C M, Kleinjans, Jos C S, Bergdahl, Ingvar A, Melin, Beatrice, Spaeth, Florentin, Palli, Domenico, Vermeulen, R C H, Vlaanderen, J, Chadeau-Hyam, Marc, Vineis, Paolo, Kyrtopoulos, Soterios A, EnviroGenomarkers consortium, LS IRAS EEPI GRA (Gezh.risico-analyse), dIRAS RA-2, Georgiadis, Panagiotis, Liampa, Irene, Hebels, Dennie G, Krauskopf, Julian, Chatziioannou, Aristotelis, Valavanis, Ioannis, de Kok, Theo M C M, Kleinjans, Jos C S, Bergdahl, Ingvar A, Melin, Beatrice, Spaeth, Florentin, Palli, Domenico, Vermeulen, R C H, Vlaanderen, J, Chadeau-Hyam, Marc, Vineis, Paolo, Kyrtopoulos, Soterios A, and EnviroGenomarkers consortium

Published
2017

121. Omics for prediction of environmental health effects: Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking

Author

Georgiadis, Panagiotis, Hebels, Dennie G, Valavanis, Ioannis, Liampa, Irene, Bergdahl, Ingvar A, Johansson, Anders, Palli, Domenico, Chadeau-Hyam, Marc, Chatziioannou, Aristotelis, Jennen, Danyel G J, Krauskopf, Julian, Jetten, Marlon J, Kleinjans, Jos C S, Vineis, Paolo, Kyrtopoulos, Soterios A, EnviroGenomarkers consortium, Georgiadis, Panagiotis, Hebels, Dennie G, Valavanis, Ioannis, Liampa, Irene, Bergdahl, Ingvar A, Johansson, Anders, Palli, Domenico, Chadeau-Hyam, Marc, Chatziioannou, Aristotelis, Jennen, Danyel G J, Krauskopf, Julian, Jetten, Marlon J, Kleinjans, Jos C S, Vineis, Paolo, Kyrtopoulos, Soterios A, and EnviroGenomarkers consortium

Abstract

The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment.

Published
2016

122. Development and validation of a skin fibroblast biomarker profile for schizophrenic patients

Author

Logotheti, Marianthi, Pilalis, Eleftherios, Venizelos, Nikolaos, Kolisis, Fragiskos, Chatziioannou, Aristotelis, Logotheti, Marianthi, Pilalis, Eleftherios, Venizelos, Nikolaos, Kolisis, Fragiskos, and Chatziioannou, Aristotelis

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.

Published
2016
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123. Omics for prediction of environmental health effects: Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking

Author

dIRAS RA-I&I RA, dIRAS RA-2, Georgiadis, Panagiotis, Hebels, Dennie G, Valavanis, Ioannis, Liampa, Irene, Bergdahl, Ingvar A, Johansson, Anders, Palli, Domenico, Chadeau-Hyam, Marc, Chatziioannou, Aristotelis, Jennen, Danyel G J, Krauskopf, Julian, Jetten, Marlon J, Kleinjans, Jos C S, Vineis, Paolo, Kyrtopoulos, Soterios A, EnviroGenomarkers consortium, dIRAS RA-I&I RA, dIRAS RA-2, Georgiadis, Panagiotis, Hebels, Dennie G, Valavanis, Ioannis, Liampa, Irene, Bergdahl, Ingvar A, Johansson, Anders, Palli, Domenico, Chadeau-Hyam, Marc, Chatziioannou, Aristotelis, Jennen, Danyel G J, Krauskopf, Julian, Jetten, Marlon J, Kleinjans, Jos C S, Vineis, Paolo, Kyrtopoulos, Soterios A, and EnviroGenomarkers consortium

Published
2016

124. Studying Microarray Gene Expression Data of Schizophrenic Patients for Derivation of a Diagnostic Signature through the Aid of Machine Learning

Author

Logotheti, Marianthi, Pilalis, Eleftherios, Venizelos, Nikolaos, Kolisis, Fragiskos, Chatziioannou, Aristotelis, Logotheti, Marianthi, Pilalis, Eleftherios, Venizelos, Nikolaos, Kolisis, Fragiskos, and Chatziioannou, Aristotelis

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., DOI 10.15406/bbij.2016.04.00106

Published
2016

125. Discovery and characterization of a thermostable and highly halotolerant GH5 cellulase from an Icelandic hot spring isolate

Author

Zarafeta, Dimitra, Kissas, Dimitrios, Sayer, Christopher, Islin, Sóley Ruth, Ladoukakis, Efthymios, Isupov, Michail N., Chatziioannou, Aristotelis, Peng, Xu, Littlechild, Jennifer A., Skretas, Georgios, Kolisis, Fragiskos N., Zarafeta, Dimitra, Kissas, Dimitrios, Sayer, Christopher, Islin, Sóley Ruth, Ladoukakis, Efthymios, Isupov, Michail N., Chatziioannou, Aristotelis, Peng, Xu, Littlechild, Jennifer A., Skretas, Georgios, and Kolisis, Fragiskos N.

Published
2016

128. The Development of an Angiogenic Protein “Signature” in Ovarian Cancer Ascites as a Tool for Biologic and Prognostic Profiling

Author

Trachana, Sofia-Paraskevi, primary, Pilalis, Eleftherios, additional, Gavalas, Nikos G., additional, Tzannis, Kimon, additional, Papadodima, Olga, additional, Liontos, Michalis, additional, Rodolakis, Alexandros, additional, Vlachos, Georgios, additional, Thomakos, Nikolaos, additional, Haidopoulos, Dimitrios, additional, Lykka, Maria, additional, Koutsoukos, Konstantinos, additional, Kostouros, Efthimios, additional, Terpos, Evagelos, additional, Chatziioannou, Aristotelis, additional, Dimopoulos, Meletios-Athanasios, additional, and Bamias, Aristotelis, additional

Published
2016
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132. Discovery and Characterization of a Thermostable and Highly Halotolerant GH5 Cellulase from an Icelandic Hot Spring Isolate

Author

Zarafeta, Dimitra, primary, Kissas, Dimitrios, additional, Sayer, Christopher, additional, Gudbergsdottir, Sóley R., additional, Ladoukakis, Efthymios, additional, Isupov, Michail N., additional, Chatziioannou, Aristotelis, additional, Peng, Xu, additional, Littlechild, Jennifer A., additional, Skretas, Georgios, additional, and Kolisis, Fragiskos N., additional

Published
2016
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133. Gene Expression Analysis of Fibroblasts from Patients with Bipolar Disorder

Author

Logotheti, Marianthi, Papadodima, Olga, Chatziioannou, Aristotelis, Venizelos, Nikolaos, Kolisis, Fragiskos, Logotheti, Marianthi, Papadodima, Olga, Chatziioannou, Aristotelis, Venizelos, Nikolaos, and Kolisis, Fragiskos

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.

Published
2015
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137. E. coli genome-wide promoter analysis in search for potential AtoC target elements

Author

Pilalis, E, Grigoroudis, A, Chatziioannou, Aristotelis, Panagiotidis, A.C., Kolisis, Fragiskos N., and Kyriakidis, D.A.

Subjects
Χημεία, Chemistry, Βιολογία, Biology
Abstract

Journal URL: http://www3.interscience.wiley.com/journal/119878145 Abstract Poster Presentation The AtoSC two-component system in E. coli activates atoDAEB operon genes to catabolize short-chain fatty acids, acetoacetate being the inducer. The AtoC response regulator was first described as the Antizyme of the polyamine biosynthetic ornithine decarboxylase. The AtoC binding site within the ato promoter was experimentally verified as an inverted 20 bp palindromic repeat. Transcriptome and phenotypic analyses however, indicated the involvement of atoSC to various other cellular activities, such as flagellar synthesis. Here we report a bioinformatic analysis for the localization of the AtoC binding motif within the E. coli genome that could indicate a putative implication, direct or indirect, of the AtoC in other operons regulation. A genome-wide promoter analysis was performed using a program for the de novo detection of overrepresented motifs within promoters. The motif sampling was initiated using the intergenic region where the AtoC binding site is located. By applying an iterative algorithm, including TCS regulated promoters, the program resulted to promoter sets comprising high-scoring putative cis-regulatory elements. The AtoC binding ability to these elements was tested by chromatin immunoprecipitation, using anti-AtoC polyclonal antibody. MotifScanner identified occurrences of different motifs connected to the ato palindromic sequence, and those related to established AtoC functions were singled out and used as templates for the design of primers, in order to analyse the immunoprecipitated chromatin in strains lacking or comprising atoSC, with or without acetoacetate induction. Experimental verification of predicted correlations could provide an insight to gene regulatory networks in bacteria, enabling us to foresee potential targets for response regulators.

Published
2010

138. Identification of additional Escherichia coli AtoC binding target elements gives new insight to bacterial regulatory networks: molecular modeling of AtoC-DNA binding domain amino acids

Author

Grigoroudis, Asterios I., Panagiotidis, Christos A., Pilalis, Eleftherios, Chatziioannou, Aristotelis, Kolisis, Fragiskos N., Papadopoulos, Georgios, and Kyriakidis, D.A.

Subjects
Χημεία, Chemistry, Βιολογία, Biology
Abstract

Journal URL: http://www.springerlink.com/content/104405/ Abstract Bacterial environmental adaptation is mediated by networks of crosstalk between two component systems, while signal transduction takes place via phosphor-transfer from sensor histidine kinases to aspartic residues of response regulators, the signal output carriers. Transcriptome and phenotypic analyses indicated the involvement of the AtoSC two component system of E. coli in various cellular activities, apart from the activation of the atoDAEB operon expression, upon acetoacetate induction. The activated response regulator AtoC is reported to act as transcriptional activator and its cis-regulatory binding site was identified as a palindromic repeat within the atoDAEB promoter. The nature of the bonds formed between theAtoCamino acids and the specific DNA residues was investigated with molecular modeling. Moreover, in an attempt to define novel AtoC binding elements which would indicate an AtoC-related cross-regulatory function, we employed an ab initio motif detection approach, coupled with gene ontology analysis. The genome-wide computational screening indicated several putative matching sites within the E. coli genome. Chromatin Immunoprecipitation assayswere carried out to assess the in vivo binding ofAtoC to the predicted sites. This process verified twenty-one additional AtoC binding sites, albeit their sequence divergence, located not only within intergenic, but also within gene-encoding regions. The elucidation of the molecular conditions governing the AtoC binding to these sites in vitro and in vivo, its dimerization and putative participation of other factor(s) gives an insight to a possible AtoC-related regulatory network which may contribute to the pleiotropic effects of AtoC.

Published
2010

139. SETTING A RATIONAL FRAMEWORK FOR EXPERIMENTAL DESIGN AND ANALYSIS OF HIGH-THROUGHPUT DNA MICROARRAY EXPERIMENTS AND DATA

Author

Lambrou, George I. Chatziioannou, Aristotelis Sifakis, Emmanouil and Prentza, Andriana Koutsouris, Dimitris Koultouki, Eleftheria and Tzortzatou-Stathopoulou, Fotini

Abstract

Microarray experimentation has been widely used for screening and high throughput discovery of mechanisms underlying biological systems. However, there are many factors that need to be taken into account for an effective microarray experimental design. Such factors are the abundance of starting material (RNA), number of replicates or experimental cost. The present work proposes rational strategies for microarray experimental design, with the scope to minimize signal variance and improve precision, with the minimum amount of technical replicates needed, thus alleviating the burden of biological material collection and the cost of the experiment. In this sense, alternative microarray design strategies are tested (direct comparisons, all paired comparisons, indirect comparisons through the implementation of various loop strategies), targeting to minimize the wet lab experimental effort needed, for the more precise interpretation of the biological question investigated. In this work we propose a methodological approach for the computational implementation of comparisons between different microarray experimental samples which are not co-hybridized together. Moreover, this approach is tested in-silico, on a showcase of a simple “loop” experimental design, including 3 biological triplicates. The proposed methodological approach introduces a robust method for calculating and comparing gene expression profiles between sets of microarray experiments by using the minimum number of experiments on one hand and getting robust results on the other. Further extension of this work could form a rational operational computational framework for the design of microarray or more general high-throughput biological experiments.

Published
2010

140. A transcriptomic computational analysis of mastic oil-treated Lewis lung carcinomas reveals molecular mechanisms targeting tumor cell growth and survival

Author

Moulos, Panagiotis Papadodima, Olga Chatziioannou, Aristotelis and Loutrari, Heleni Roussos, Charis Kolisis, Fragiskos N.

Abstract

Background: Mastic oil from Pistacia lentiscus variation chia, a blend of bioactive terpenes with recognized medicinal properties, has been recently shown to exert anti-tumor growth activity through inhibition of cancer cell proliferation, survival, angiogenesis and inflammatory response. However, no studies have addressed its mechanisms of action at genome-wide gene expression level. Methods: To investigate molecular mechanisms triggered by mastic oil, Lewis Lung Carcinoma cells were treated with mastic oil or DMSO and RNA was collected at five distinct time points (3-48 h). Microarray expression profiling was performed using Illumina mouse-6 v1 beadchips, followed by computational analysis. For a number of selected genes, RT-PCR validation was performed in LLC cells as well as in three human cancer cell lines of different origin (A549, HCT116, K562). PTEN specific inhibition by a bisperovanadium compound was applied to validate its contribution to mastic oil-mediated anti-tumor growth effects. Results: In this work we demonstrated that exposure of Lewis lung carcinomas to mastic oil caused a time-dependent alteration in the expression of 925 genes. GO analysis associated expression profiles with several biological processes and functions. Among them, modifications on cell cycle/proliferation, survival and NF-kappa B cascade in conjunction with concomitant regulation of genes encoding for PTEN, E2F7, HMOX1 (up-regulation) and NOD1 (down-regulation) indicated some important mechanistic links underlying the anti-proliferative, proapoptotic and anti-inflammatory effects of mastic oil. The expression profiles of Hmox1, Pten and E2f7 genes were similarly altered by mastic oil in the majority of test cancer cell lines. Inhibition of PTEN partially reversed mastic oil effects on tumor cell growth, indicating a multi-target mechanism of action. Finally, k-means clustering, organized the significant gene list in eight clusters demonstrating a similar expression profile. Promoter analysis in a representative cluster revealed shared putative cis-elements suggesting a common regulatory transcription mechanism. Conclusions: Present results provide novel evidence on the molecular basis of tumor growth inhibition mediated by mastic oil and set a rational basis for application of genomics and bioinformatic methodologies in the screening of natural compounds with potential cancer chemopreventive activities.

Published
2009

147. Controlling complexity: the clinical relevance of mouse complex genetics.

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group) [research center], Schughart, Klaus, Libert, Claude, Kas, Martien J., Andreux, Penelope, Aransay, Ana Maria, Auwerx, Johan, Balling, Rudi, Banerjee, Sreeparna, Bezerianos, Anastasios, Bragonzi, Alessandra Desj, Brockmann, Gudrun A., Brown, Steve, Campbell-Tofte, Joan, Cendelin, Jan, Chatziioannou, Aristotelis, Chen, Duan, Crusio, Wim, Dimitrakopoulou, Konstantina, Falcon, Juan M., Forejt, Jiri, Franken, Paul, Frohlich, Leopold F., Herault, Yann, Hovatta, Iiris, Iraqi, Fuad A., Jansen, Ritsert C., Kaczmarek, Leszek, Kas, Martien, Knapska, Ewelina, Koks, Sulev, Kolisis, Fragiskos, Korostynski, Michal, Lammert, Frank, Lehrach, Hans, Lengeling, Andreas, Montagutelli, Xavier, Morahan, Grant, Mott, Richard, Panthier, Jean-Jacques, Przewlocki, Ryszard, Ranki, Annamari, Santos, Javier, Severcan, Feride, Schalkwyk, Leonard, Smit, August B., Terasmaa, Anton, Vasar, Eero, Zatloukal, Kurt, Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group) [research center], Schughart, Klaus, Libert, Claude, Kas, Martien J., Andreux, Penelope, Aransay, Ana Maria, Auwerx, Johan, Balling, Rudi, Banerjee, Sreeparna, Bezerianos, Anastasios, Bragonzi, Alessandra Desj, Brockmann, Gudrun A., Brown, Steve, Campbell-Tofte, Joan, Cendelin, Jan, Chatziioannou, Aristotelis, Chen, Duan, Crusio, Wim, Dimitrakopoulou, Konstantina, Falcon, Juan M., Forejt, Jiri, Franken, Paul, Frohlich, Leopold F., Herault, Yann, Hovatta, Iiris, Iraqi, Fuad A., Jansen, Ritsert C., Kaczmarek, Leszek, Kas, Martien, Knapska, Ewelina, Koks, Sulev, Kolisis, Fragiskos, Korostynski, Michal, Lammert, Frank, Lehrach, Hans, Lengeling, Andreas, Montagutelli, Xavier, Morahan, Grant, Mott, Richard, Panthier, Jean-Jacques, Przewlocki, Ryszard, Ranki, Annamari, Santos, Javier, Severcan, Feride, Schalkwyk, Leonard, Smit, August B., Terasmaa, Anton, Vasar, Eero, and Zatloukal, Kurt

Abstract

Experimental animal models are essential to obtain basic knowledge of the underlying biological mechanisms in human diseases. Here, we review major contributions to biomedical research and discoveries that were obtained in the mouse model by using forward genetics approaches and that provided key insights into the biology of human diseases and paved the way for the development of novel therapeutic approaches.

Published
2013

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