Ioannis Michalopoulos

Publications (17) View all

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  Available from: Ioannis Michalopoulos

  Article: Human gene correlation analysis (HGCA): A tool for the identification of transcriptionally co-expressed genes.

  Ioannis Michalopoulos, Georgios A Pavlopoulos, Apostolos Malatras, Alexandros Karelas, Myrto-Areti Kostadima, Reinhard Schneider, Sophia Kossida
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  ABSTRACT: Bioinformatics and high-throughput technologies such as microarray studies allow the measure of the expression levels of large numbers of genes simultaneously, thus helping us to understand the molecular mechanisms of various biological processes in a cell. We calculate the Pearson Correlation Coefficient (r-value) between probe set signal values from Affymetrix Human Genome Microarray samples and cluster the human genes according to the r-value correlation matrix using the Neighbour Joining (NJ) clustering method. A hyper-geometric distribution is applied on the text annotations of the probe sets to quantify the term overrepresentations. The aim of the tool is the identification of closely correlated genes for a given gene of interest and/or the prediction of its biological function, which is based on the annotations of the respective gene cluster. Human Gene Correlation Analysis (HGCA) is a tool to classify human genes according to their coexpression levels and to identify overrepresented annotation terms in correlated gene groups. It is available at: http://biobank-informatics.bioacademy.gr/coexpression/.
  BMC Research Notes 06/2012; 5:265.
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  Article: Progression of mouse skin carcinogenesis is associated with increased ERα levels and is repressed by a dominant negative form of ERα.

  Stella Logotheti, Dimitra Papaevangeliou, Ioannis Michalopoulos, Maria Sideridou, Katerina Tsimaratou, Ioannis Christodoulou, Katerina Pyrillou, Vassilis Gorgoulis, Spiros Vlahopoulos, Vassilis Zoumpourlis
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  ABSTRACT: Estrogen receptors (ER), namely ERα and ERβ, are hormone-activated transcription factors with an important role in carcinogenesis. In the present study, we aimed at elucidating the implication of ERα in skin cancer, using chemically-induced mouse skin tumours, as well as cell lines representing distinct stages of mouse skin oncogenesis. First, using immunohistochemical staining we showed that ERα is markedly increased in aggressive mouse skin tumours in vivo as compared to the papilloma tumours, whereas ERβ levels are low and become even lower in the aggressive spindle tumours of carcinogen-treated mice. Then, using the multistage mouse skin carcinogenesis model, we showed that ERα gradually increases during promotion and progression stages of mouse skin carcinogenesis, peaking at the most aggressive stage, whereas ERβ levels only slightly change throughout skin carcinogenesis. Stable transfection of the aggressive, spindle CarB cells with a dominant negative form of ERα (dnERα) resulted in reduced ERα levels and reduced binding to estrogen responsive elements (ERE)-containing sequences. We characterized two highly conserved EREs on the mouse ERα promoter through which dnERα decreased endogenous ERα levels. The dnERα-transfected CarB cells presented altered protein levels of cytoskeletal and cell adhesion molecules, slower growth rate and impaired anchorage-independent growth in vitro, whereas they gave smaller tumours with extended latency period of tumour onset in vivo. Our findings suggest an implication of ERα in the aggressiveness of spindle mouse skin cancer cells, possibly through regulation of genes affecting cell shape and adhesion, and they also provide hints for the effective targeting of spindle cancer cells by dnERα.
  PLoS ONE 01/2012; 7(8):e41957. · 4.09 Impact Factor
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  Article: State-of-the-art bioinformatics protein structure prediction tools (Review).

  Athanasia Pavlopoulou, Ioannis Michalopoulos
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  ABSTRACT: Knowledge of the native structure of a protein could provide an understanding of the molecular basis of its function. However, in the postgenomics era, there is a growing gap between proteins with experimentally determined structures and proteins without known structures. To deal with the overwhelming data, a collection of automated methods as bioinformatics tools which determine the structure of a protein from its amino acid sequence have emerged. The aim of this paper is to provide the experimental biologists with a set of cutting-edge, carefully evaluated, user-friendly computational tools for protein structure prediction that would be helpful for the interpretation of their results and the rational design of new experiments.
  International Journal of Molecular Medicine 05/2011; 28(3):295-310. · 1.98 Impact Factor
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  Article: Sp1 binds to the external promoter of the p73 gene and induces the expression of TAp73gamma in lung cancer.

  Stella Logotheti, Ioannis Michalopoulos, Maria Sideridou, Alexandros Daskalos, Sophia Kossida, Demetrios A Spandidos, John K Field, Borek Vojtesek, Triantafyllos Liloglou, Vassilis Gorgoulis, Vassilis Zoumpourlis
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  ABSTRACT: The p73 gene possesses an extrinsic P1 promoter and an intrinsic P2 promoter, resulting in TAp73 and DeltaNup73 isoforms, respectively. The ultimate effect of p73 in oncogenesis is thought to depend on the apoptotic TA to antiapoptotic DeltaN isoforms' ratio. This study was aimed at identifying novel transcription factors that affect TA isoform synthesis. With the use of bioinformatics tools, in vitro binding assays, and chromatin immunoprecipitation analysis, a region extending -233 to -204 bp upstream of the transcription start site of the human p73 P1 promoter, containing conserved Sp1-binding sites, was characterized. Treatment of cells with Sp1 RNAi and Sp1 inhibitor functionally suppress TAp73 expression, indicating positive regulation of P1 by the Sp1 protein. Notably Sp1 inhibition or knockdown also reduces DeltaNup73 protein levels. Therefore, Sp1 directly regulates TAp73 transcription and affects DeltaNup73 levels in lung cancer. TAp73gamma was shown to be the only TA isoform overexpressed in several lung cancer cell lines and in 26 non-small cell lung cancers, consistent with Sp1 overexpression, thereby questioning the apoptotic role of this specific p73 isoform in lung cancer.
  FEBS Journal 07/2010; 277(14):3014-27. · 3.79 Impact Factor
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  Available from: PubMed Central

  Article: Evolutionary history of tissue kallikreins.

  Athanasia Pavlopoulou, Georgios Pampalakis, Ioannis Michalopoulos, Georgia Sotiropoulou
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  ABSTRACT: The gene family of human kallikrein-related peptidases (KLKs) encodes proteins with diverse and pleiotropic functions in normal physiology as well as in disease states. Currently, the most widely known KLK is KLK3 or prostate-specific antigen (PSA) that has applications in clinical diagnosis and monitoring of prostate cancer. The KLK gene family encompasses the largest contiguous cluster of serine proteases in humans which is not interrupted by non-KLK genes. This exceptional and unique characteristic of KLKs makes them ideal for evolutionary studies aiming to infer the direction and timing of gene duplication events. Previous studies on the evolution of KLKs were restricted to mammals and the emergence of KLKs was suggested about 150 million years ago (mya). In order to elucidate the evolutionary history of KLKs, we performed comprehensive phylogenetic analyses of KLK homologous proteins in multiple genomes including those that have been completed recently. Interestingly, we were able to identify novel reptilian, avian and amphibian KLK members which allowed us to trace the emergence of KLKs 330 mya. We suggest that a series of duplication and mutation events gave rise to the KLK gene family. The prominent feature of the KLK family is that it consists of tandemly and uninterruptedly arrayed genes in all species under investigation. The chromosomal co-localization in a single cluster distinguishes KLKs from trypsin and other trypsin-like proteases which are spread in different genetic loci. All the defining features of the KLKs were further found to be conserved in the novel KLK protein sequences. The study of this unique family will further assist in selecting new model organisms for functional studies of proteolytic pathways involving KLKs.
  PLoS ONE 01/2010; 5(11):e13781. · 4.09 Impact Factor