MicroRNA expression patterns in osteosarcomas and their potential role in tumour progression

There is an increasing evidence that microRNAs are involved in control of developmental timing, cell proliferation, apoptosis, morphogenesis, fat metabolism. In many studies, performed to investigate the genes and gene products that drive the metastatic process, it has become evident that, in addition to alterations in protein-encoding genes, abnormalities in non-coding genes can also contribute to cancer pathogenesis. Changes in miRNA levels may be related to dysregulated growth in some cancer cells and in this field the differential expression of miRNA may have substantial diagnostic and prognostic value. In the context of microRNAs in tumours, our aim was to evaluate whether pro-metastatic and non-metastatic sarcoma cells, may differ in their microRNA expression, in the effort to identify single pro-metastatic microRNAs in bone and soft-tissue sarcomas. microRNAs were separated from total RNA of MG-63 and 143B osteosarcoma cells with different intravasation behaviour and malignancy degree. Specific libraries were constructed using TopoTA cloning system supported by 5’- and 3’ adaptors. Differentially expressed microRNA were identified by sequencing followed by bioinformatic analysis. A number of microRNAs reported in the data base miRNA registry were found to be differential expressed in the two osteosarcoma model cell lines. Of the over 19 microRNAs identified, the majority correspond to “oncomiR”, i.e. microRNAs involved in neoplastic transformation and progression. One of the analysed sequences, localized on chromosome 7 of the human genome, showed miRNA configuration and studies are in progress to define its precise characteristics. Two of the differential expressed microRNA, miR-93 and miR-210, for which no functional data were available, were analyzed in different cell lines and tissues and investigated for their potential involvement in motility phenomena by their mis-expression through transduction in to osteosarcoma cells. To identify their molecular targets, different approaches were performed using bioinformatic softwares, through PCR-based strategies and DNA microarray analyses. The expression of another set of four miRNAs (miR-9, miR-183, miR-196a, miR-484) differentially expressed, was identified through a global expression analysis, and analyzed in surgical specimens of low- and high-grade osteosarcoma patients. To better define the significance of the expression pattern of these microRNAs, studies on a wider patient cohort are in progress

MicroRNA global profiling in cystic fibrosis cell lines reveals dysregulated pathways related with inflammation, cancer, growth, glucose and lipid metabolism, and fertility: an exploratory study

Cystic fibrosis (CF), is due to CF transmembrane conductance regulator (CFTR) loss of function, and is associated with comorbidities. The increasing longevity of CF patients has been associated with increased cancer risk besides the other known comorbidities. The significant heterogeneity among patients, suggests potential epigenetic regulation. Little attention has been given to how CFTR influences microRNA (miRNA) expression and how this may impact on biological processes and pathways

Parma consensus statement on metabolic disruptors

A multidisciplinary group of experts gathered in Parma Italy for a workshop hosted by the University of Parma, May 16–18, 2014 to address concerns about the potential relationship between environmental metabolic disrupting chemicals, obesity and related metabolic disorders. The objectives of the workshop were to: 1. Review findings related to the role of environmental chemicals, referred to as “metabolic disruptors”, in obesity and metabolic syndrome with special attention to recent discoveries from animal model and epidemiology studies; 2. Identify conclusions that could be drawn with confidence from existing animal and human data; 3. Develop predictions based on current data; and 4. Identify critical knowledge gaps and areas of uncertainty. The consensus statements are intended to aid in expanding understanding of the role of metabolic disruptors in the obesity and metabolic disease epidemics, to move the field forward by assessing the current state of the science and to identify research needs on the role of environmental chemical exposures in these diseases. We propose broadening the definition of obesogens to that of metabolic disruptors, to encompass chemicals that play a role in altered susceptibility to obesity, diabetes and related metabolic disorders including metabolic syndrome

Data Mining of Determinants of Intrauterine Growth Retardation Revisited Using Novel Algorithms Generating Semantic Maps and Prototypical Discriminating Variable Profiles

<div><p>Objectives</p><p>Intra-uterine growth retardation is often of unknown origin, and is of great interest as a “Fetal Origin of Adult Disease” has been now well recognized. We built a benchmark based upon a previously analysed data set related to Intrauterine Growth Retardation with 46 subjects described by 14 variables, related with the insulin-like growth factor system and pro-inflammatory cytokines, namely interleukin -6 and tumor necrosis factor -α.</p><p>Design and Methods</p><p>We used new algorithms for optimal information sorting based on the combination of two neural network algorithms: Auto-contractive Map and Activation and Competition System. Auto-Contractive Map spatializes the relationships among variables or records by constructing a suitable embedding space where ‘closeness’ among variables or records reflects accurately their associations. The Activation and Competition System algorithm instead works as a dynamic non linear associative memory on the weight matrices of other algorithms, and is able to produce a prototypical variable profile of a given target.</p><p>Results</p><p>Classical statistical analysis, proved to be unable to distinguish intrauterine growth retardation from appropriate-for-gestational age (AGA) subjects due to the high non-linearity of underlying functions. Auto-contractive map succeeded in clustering and differentiating completely the conditions under study, while Activation and Competition System allowed to develop the profile of variables which discriminated the two conditions under study better than any other previous form of attempt. In particular, Activation and Competition System showed that ppropriateness for gestational age was explained by IGF-2 relative gene expression, and by IGFBP-2 and TNF-α placental contents. IUGR instead was explained by IGF-I, IGFBP-1, IGFBP-2 and IL-6 gene expression in placenta.</p><p>Conclusion</p><p>This further analysis provided further insight into the placental key-players of fetal growth within the insulin-like growth factor and cytokine systems. Our previous published analysis could identify only which variables were predictive of fetal growth in general, and identified only some relationships.</p></div