• Regulation of TGF-β1 expression by androgen deprivation therapy of prostate cancer.

    Fuzio P, Ditonno P, Rutigliano M, Battaglia M, Bettocchi C, Loverre A, Grandaliano G, Perlino E.
    Cancer Lett. 2012 May 28;318(2):135-44. doi: 10.1016/j.canlet.2011.08.034. Epub 2011 Oct 1. PubMed PMID: 22269108.
    (PMID: 22269108)


    In this paper we studied the in vivo neoadjuvant Androgen Deprivation Therapy (ADT) effect on the expression of TGF-β1 and its receptor Tβ-RII. Mechanisms of androgen dependence are critical to understanding prostate cancer progression to androgen independence associated with disease mortality, and TGF-β is thought to support prostatic apoptosis as its expression coincides with androgen ablation in benign and cancer tissues.
    Increase of both mRNA and protein level were shown for the first time only in the patients who underwent neoadjuvant ADT for 1-month. This transient increase of TGF-β expression after androgen ablation suggested cooperation of the pathways in prostate regression. Since no alteration was observed in the gene transcriptional activity, the molecular mechanism of this cooperation, probably act at the post-transcriptional level.
    TGF-β1 and Tβ-RII specific signals were co-localized within the neoplastic prostate epithelium. Our results suggests that the androgens deprivation by means of ADT for 1-month, involves a shift of the TGF-β1 mechanism in prostate cancer, suggesting that the TGF-β1 promotes prostate epithelial cell proliferation and inhibits apoptosis in a autocrine way.

  • TRIM8 modulates p53 activity to dictate cell cycle arrest.

    Caratozzolo MF, Micale L, Turturo MG, Cornacchia S, Fusco C, Marzano F, Augello B, D’Erchia AM, Guerrini L, Pesole G, Sbisà E, Merla G, Tullo A.
    Cell Cycle. 2012 Feb 1;11(3):511-23. Epub 2012 Feb 1.
    (PMID: 22262183)


    p53 is a central hub in controlling cell proliferation. To maintain genome integrity in response to cellular stress, p53 directly regulates the transcription of genes involved in cell cycle arrest, DNA repair, apoptosis and/or senescence. An array of post-translational modifications and protein-protein interactions modulates its stability and activities in order to avoid malignant transformation. However, to date it is still not clear how cells decide their own fate in response to different types of stress. We described here that the human TRIM8 protein, a member of the TRIM family, is a new modulator of the p53-mediated tumor suppression mechanism. We showed that under stress conditions, such as UV exposure, p53 induced the expression of TRIM8, which in turn stabilized p53 leading to cell cycle arrest and reduction of cell proliferation through enhancement of CDKN1A (p21) and GADD45 expression. TRIM8 silencing reduced the capacity of p53 to activate genes involved in cell cycle arrest and DNA repair, in response to cellular stress. Concurrently, TRIM8 overexpression induced the degradation of the MDM2 protein, the principal regulator of p53 stability. Co-immunoprecipitation experiments showed that TRIM8 physically interacted with p53, impairing its interaction with MDM2. Altogether, our results reveal a previously unknown regulatory pathway controlling p53 activity and suggest TRIM8 as a novel therapeutic target to enhance p53 tumor suppressor activity.

  • Viroids: how to infect a host and cause disease without encoding proteins.

    Navarro B, Gisel A, Rodio ME, Delgado S, Flores R, Di Serio F.
    Biochimie. 2012 Jul;94(7):1474-80.
    (PMID: 22738729)

  • Small RNAs containing the pathogenic determinant of a chloroplast-replicating viroid guide degradation of a host mRNA as predicted by RNA silencing.

    Navarro, B Gisel, A Rodio, ME Degado, S Flores, R Di Serio, F
    Plant J Feb 14 2012
    (PMID: 22332758)

  • Hierarchical and Overlapping Co-Clustering of mRNA:miRNA Interactions

    Pio G, Ceci M, Loglisci C, D’Elia D, Malerba D.
    In Frontiers in Artificial Intelligence and Applications. 20th European Conference on Artificial Intelligence. IOS Press Books Online, ECAI 2012: 654-659. doi:10.3233/978-1-61499-098-7-654.

    microRNAs (miRNAs) are an important class of regulatory factors controlling gene expression at post-transcriptional level. Studies on interactions between different miRNAs and their target genes are of utmost importance to understand the role of miRNAs in the control of biological processes. This paper contributes to these studies by proposing a method for the extraction of co-clusters of miRNAs and messenger RNAs (mRNAs). Different from several already available co-clustering algorithms, our approach efficiently extracts a set of possibly overlapping, exhaustive and hierarchically organized co-clusters. The algorithm is well-suited for the task at hand since: i) mRNAs and miRNAs can be involved in different regulatory networks that may or may not be co-active under some conditions, ii) exhaustive co-clusters guarantee that possible co-regulations are not lost, iii) hierarchical browsing of co-clusters facilitates biologists in the interpretation of results. Results on synthetic and on real human miRNA:mRNA data show the effectiveness of the approach.

  • A novel biclustering algorithm for the discovery of meaningful biological correlations between miRNAs and mRNAs.

    G Pio, M Ceci, D D’Elia, C Loglisci, D Malerba
    EMBnet.journal 2012, 18 (A):43-44. DOI: http://dx.doi.org/10.14806/ej.18.A.375

    microRNAs (miRNAs) are post-transcriptional regulators which represent one of the major regulatory gene families in animals, plants and viruses and that plays a key role in almost all main cellular processes. The computational prediction of miRNA target genes is important for the functional annotation of genomes and, on the other side, functional annotation of target genes can be of great help in suggesting specific biological functions of miRNAs [1]. This work aims to contribute to the elucidation of miRNAs role in the regulation of gene expression, by proposing a method for the hierarchical and overlapping biclustering of miRNAs and target messenger RNAs (mRNAs). The method allows to discover possible miRNA:mRNA functional relationships, at different granularity levels, in large datasets produced by miRNA target site prediction algorithms, thus reducing the impact of noise on the significance of the resulting biclusters. (2012) A novel biclustering algorithm for the discovery of meaningful biological correlations between miRNAs and mRNAs. Abstract in Conference Proceedings. G. Pio, M. Ceci, D. D’Elia, C. Loglisci, D. Malerba (2012) The integration of microRNA target data by biclustering techniques opens new roads for signaling networks analysis.

  • The integration of microRNA target data by biclustering techniques opens new roads for signaling networks analysis.

    Gianvito Pio, Michelangelo Ceci, Corrado Loglisci, Donato Malerba, Domenica D’Elia EMBnet.journal 2012, 18 (B):142-144. DOI: http://dx.doi.org/10.14806/ej.18.B.582

    MicroRNAs (miRNAs) are key modulators of gene expression. In addition to their recognised role in embryonic and adult cell proliferation and differentiation (Ren et al., 2009), many recent studies on diverse types of human cancer have demonstrated that miRNAs are functionally integrated into those oncogenic pathways that are central to tumorogenesis (Olive et al., 2010). Although microarray profiling and next generation sequencing technologies have allowed researchers to discover much of their structural and functional features as well as many new miRNAs, the current challenge is to understand their specific biological functions and mechanisms through which they are able to ensure cell homeostasis and to control developmental timing and cancer progression. This is not a trivial task because the post-transcriptional regulation of gene expression mediated by miRNAs is rarely resolved by a simple one-to-one interaction between a miRNA and a target gene. It is much more complex, often involving multiple binding of the same miRNA and/or of different miRNAs in a cooperative manner. The combinatorial effects of different miRNAs on the same gene, or on different genes of the same pathway, is an essential part of the mechanism through which they are able to fine-tune signaling pathways (Inui et al., 2010). Indeed, the effect of a miRNA may change depending on which other miRNAs are co-expressed or silenced, which in turn depends on the specific context in which the cell, the tissue or the organism is considered. This makes the interpretation of miRNAs expression profile really difficult and a mere analysis of the list of differentially expressed genes cannot provide enough information to elucidate the multiplicity of potential miRNA:mRNA interactions. In this context, the exploitation of data mining techniques, and in particular of biclustering algorithms, is considered as a useful approach to search the correlations among miRNAs and mRNAs. However, as each miRNA may target hundreds of genes, the selection of the most significant results for further experimental validations still remains a challenging task for many biologists.
    The proposed method, which is implemented in the system HOCCLUS2, has been designed to analyse data of miRNA:mRNA interactions (derived from expression arrays or from large sets of predictions) in order to detect significant co-regulatory partnerships. In particular, the aim is to provide the biologists with a tool which can support them in two challenging tasks, that is, the detection of actual miRNAs target genes and the identification of the context-specific co-associations of different miRNAs. A further contribution to the considered research consists in the ranking of the extracted biclusters on the basis of the semantic similarity between the target genes, which allows the biologists to easily select the most significant results, from a biological view point.

    Availability: http://www.di.uniba.it/~ceci/micFiles/systems/HOCCLUS2/index.html

  • MBLabDB: a social database for molecular biodiversity data.

    Flavio Licciulli, Domenico Catalano, Domenica D’Elia, Giorgio De Caro, Giorgio Grillo, Pietro Leo, Giuseppina Mulè, Paolo Pannarale, Graziano Pappadà, Francesco Rubino, Antonella Susca, Saverio Vicario, Gaetano Scioscia
    EMBnet.journal 2012, 18 (B):121-123. DOI: http://dx.doi.org/10.14806/ej.18.B.574

    The biodiversity is nowadays one of the main scientific area of interest because of its importance for a sustainable development in many technological domains such as biotechnologies as well as for agriculture and human health. For instance, plant genetic resources are the basis of food security and consist of diversity of seeds and planting material of traditional varieties or modern cultivars and crop wild relatives. These resources are used as food, feed for domesticated animals and in recent years for the identification of new chemical compounds to be used in clinical therapeutic protocols.
    Biodiversity research communities have to deal with data coming from many different domains (e.g., biology, geography, evolutionary studies, genomics, taxonomy, environmental sciences, etc.). Collecting and integrating data from so many disparate resources is not a trivial task, data are extremely scattered, heterogeneous in format and purpose, often protected in repositories of diverse research institutes.
    With the advent of next generation technologies, molecular biodiversity research is producing large amounts of data that researchers use for complex comparative analyses exploiting information present both in public databases (like GenBank) and in their personal repositories. Improving the management of molecular data and their integration with related information present in the genetic resources databases such as morphologic, geographic and ecologic data will lead to new valuable biodiversity knowledge.
    Driven by the widely diffused trend of the web of sharing information through aggregation of people with the same interests (social networks), and by the new type of database architecture defined as dynamic distributed federated database, here we present MBlabDB, a tool representing a new paradigm of data integration in the biodiversity domain.