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Granulosa cell tumor (GCT) is a rare and severe form of sex-cord stromal ovarian tumor that is characterized by its long natural history and tendency to recur years after surgical ablation. Because there is no efficient curative treatment beyond surgery, ~20% of patients die of the consequences of their tumor. However, very little is known of the molecular etiology of this pathology. About 70% of GCT patients present with elevated circulating estradiol (E2). Because this hormone is known to increase tumor growth and progression in a number of cancers, we investigated the possible role of E2 in GCTs. Cell-based studies with human GCT metastases and primary tumor-derived cells, ie KGN and COV434 cells, respectively, aimed at evaluating E2 effect on cell growth, migration and invasion. Importantly, we found that E2 did not affect GCT cell growth, but that it significantly decreased the migration and matrix invasion of metastatic GCT cells. Noteworthy, our molecular studies revealed that this effect was accompanied by the inhibition through non-genomic mechanisms of extracellular signal-regulated kinase 1/2 (ERK1/2), which is constitutively activated in GCTs. By using pharmacological and RNA silencing approaches, we found that E2 action was mediated by G protein-coupled estrogen receptor 1 (GPER1) signaling pathway. Analyses of GPER1 expression on tissue microarrays from human GCTs confirmed its expression in ~90% of GCTs. Overall, our study reveals that E2 would act via non-classical pathways to prevent metastasis spreading in GCTs and also reveals GPER1 as a possible target in this disease.

Design of Experiments (DoE) statistical methodology permits the simultaneous evaluation of the effects of different factors on experimental performance and the analysis of their interactions in order to identify their optimal combinations. Compared to classical approaches based on changing only one factor at a time (OFAT), DoE facilitates the exploration of a broader range of parameters combinations, as well as providing the possibility to select a limited number of combinations covering the whole frame. The advantage of DoE is to maximise the amount of information provided and to save both time and money. DoE has been primarily used in industry to maximise process robustness, but recently it has also been applied in biomedical research to different types of multivariable analyses, from determination of the best cell media composition to the optimisation of entire multi-step laboratory protocols such as cell transfection.

The Design of Experiment (DoE) is a statistical methodology, very useful in the case of multivariable assays, that permits to evaluate simultaneously the influence of different factors on a specific output and to analyse the interactions among them in order to identify their optimal combinations. Moreover, DoE allows to select from the huge numbers of combinations only a limited number, in order to cover the whole frame, allowing to save both time and money. DoE was mostly used in industrial field in order to maximize robust processes, but recently it has been also applied in biomedical research field. Different studies have demonstrated the advantages of using a DoE approach compared to the classical one (a single parameter is tested in each assay) in the context of automated experiments, determination of cell media [1] compositions or HPLC tuning [2]. In the present study, we utilize for the first time the DoE methodology to optimize the transfection protocol of neural cells, as an example of DoE application to a laboratory procedure. Neural precursor cells are hard to transfect and refractory to lipidic reagents [3], for this reason we choose as transfectant reagent the cationic not-lipidic Poliethylenimmine (PEI). The DoE approach allowed us to identify the main variables (factors) affecting the transfection efficiency and to discover their optimal combinations, developing a protocol that let us to triplicate the transfection efficiency respect to the initial conditions. Moreover, the covariance analysis unmasked significant variable interactions impossible to calculate in one factor-variation tests used in normal laboratory practices. Our results indicate that DoE might be very helpful also in research for the identification of the better experimental conditions and the analysis of interactions between different variables.

The G protein-coupled receptor 30 (GPR30) has been claimed as an estrogen receptor. However, the literature reports controversial findings and the physiological function of GPR30 is not fully understood yet. Consistent with studies assigning a role of GPR30 in the cardiovascular and metabolic systems, GPR30 expression has been reported in small arterial vessels, pancreas and chief gastric cells of the stomach. Therefore, we hypothesized a role of GPR30 in the onset and progression of cardiovascular and metabolic diseases. In order to test our hypothesis, we investigated the effects of a high-fat diet on the metabolic and cardiovascular profiles of Gpr30-deficient mice (GPR30-lacZ mice). We found that GPR30-lacZ female, rather than male, mice had significant lower levels of HDL along with an increase in fat liver accumulation as compared to control mice. However, two indicators of cardiac performance assessed by echocardiography, ejection fraction and fractional shortening were both decreased in an age-dependent manner only in Gpr30-lacZ male mice. Collectively our results point to a potential role of Gpr30 in preserving lipid metabolism and cardiac function in a sex- and age-dependent fashion. (C) 2014 Elsevier B.V. All rights reserved.

The IT Future of Medicine (ITFoM, http://www.itfom.eu/ ) initiative will produce computational models of individuals to enable the prediction of their future health risks, progression of diseases and selection and efficacy of treatments while minimising side effects. To be able to move our health care system to treat patients as individuals rather than as members of larger, divergent groups, the ITFoM initiative, proposes to integrate molecular, physiological and anatomical data of every person in ‘virtual patient’ models. The establishment of such ‘virtual patient’ models is now possible due to the enormous progress in analytical techniques, particularly in the ‘-omics’ technology areas and in imaging, as well as in sensor technologies, but also due to the immense developments in the ICT field. As one of six Future and Emerging Technologies (FET) Flagship Pilot Projects funded by the European Commission, ITFoM with more than 150 academic and industrial partners from 34 countries, will foster the development in functional genomics and computer technologies to generate ‘virtual patient’ models to make them available for clinical application. The increase in the capacity of next generation sequencing systems will enable the high-throughput analysis of a large number of individuals generating huge amounts of genome, epigenome and transcriptome data, but making it feasible to apply deep sequencing in the clinic to characterise not only the patient's genome, but also individual samples, for example, from tumours. The genome profile will be integrated with proteome and metabolome information generated via new powerful chromatography, mass spectrometry and nuclear magnetic resonance techniques. The individualised model will not only enable the analysis of the current situation, but will allow the prediction of the response of the patient to different therapy options or intolerance for certain drugs.

Molecular medicine is undergoing fundamental changes driving the whole area towards a revolution in modern medicine. The breakthrough was generated the fast-developing technologies in molecular biology since the first draft sequence of the human genome was published. The technological advances enabled the analysis of biological samples from cells and organs to whole organisms in a depth that was not possible before. These technologies are increasingly implemented in the medical and health care system to study diseases and refine diagnostics. As a consequence, the understanding of diseases and the health status of an individual patient is now based on an enormous amount of data that can only be interpreted in the context of the body as a whole. Systems biology as a new field in the life sciences develops new approaches for data integration and interpretation. Systems medicine as a specialized aspect of systems biology combines in an interdisciplinary approach all expertise necessary to decipher the human body in all its complexity. This created new challenges in the area of information and communication technologies to provide the infrastructure and technology needed to cope with the data flood that will accompany the next generation of medicine. The new initiative 'IT Future of Medicine' aims at driving this development even further and integrates not only molecular data (especially genomic information), but also anatomical, physiological, environmental, and lifestyle data in a predictive model approach-the 'virtual patient'-that will allow the clinician or the general practitioner to predict and anticipate the optimal treatment for the individual patient. The application of the virtual patient model will allow truly personalized medicine.

Molecular medicine is undergoing fundamental changes driving the whole area towards a revolution in modern medicine. The breakthrough was generated the fast-developing technologies in molecular biology since the first draft sequence of the human genome was published. The technological advances enabled the analysis of biological samples from cells and organs to whole organisms in a depth that was not possible before. These technologies are increasingly implemented in the medical and health care system to study diseases and refine diagnostics. As a consequence, the understanding of diseases and the health status of an individual patient is now based on an enormous amount of data that can only be interpreted in the context of the body as a whole. Systems biology as a new field in the life sciences develops new approaches for data integration and interpretation. Systems medicine as a specialized aspect of systems biology combines in an interdisciplinary approach all expertise necessary to decipher the human body in all its complexity. This created new challenges in the area of information and communication technologies to provide the infrastructure and technology needed to cope with the data flood that will accompany the next generation of medicine. The new initiative 'IT Future of Medicine' aims at driving this development even further and integrates not only molecular data (especially genomic information), but also anatomical, physiological, environmental, and lifestyle data in a predictive model approach-the 'virtual patient'-that will allow the clinician or the general practitioner to predict and anticipate the optimal treatment for the individual patient. The application of the virtual patient model will allow truly personalized medicine.

Multiple reports implicated the function of G protein-coupled receptor (GPR)-30 with nongenomic effects of estrogen, suggesting that GPR30 might be a G-protein coupled estrogen receptor. However, the findings are controversial and the expression pattern of GPR30 on a cell type level as well as its function in vivo remains unclear. Therefore, the objective of this study was to identify cell types that express Gpr30 in vivo by analyzing a mutant mouse model that harbors a lacZ reporter (Gpr30-lacZ) in the Gpr30 locus leading to a partial deletion of the Gpr30 coding sequence. Using this strategy, we identified the following cell types expressing Gpr30: 1) an endothelial cell subpopulation in small arterial vessels of multiple tissues, 2) smooth muscle cells and pericytes in the brain, 3) gastric chief cells in the stomach, 4) neuronal subpopulations in the cortex as well as the polymorph layer of the dentate gyrus, 5) cell populations in the intermediate and anterior lobe of the pituitary gland, and 6) in the medulla of the adrenal gland. In further experiments, we aimed to decipher the function of Gpr30 by analyzing the phenotype of Gpr30-lacZ mice. The body weight as well as fat mass was unchanged in Gpr30-lacZ mice, even if fed with a high-fat diet. Flow cytometric analysis revealed lower frequencies of T cells in both sexes of Gpr30-lacZ mice. Within the T-cell cluster, the amount of CD62L-expressing cells was clearly reduced, suggesting an impaired production of T cells in the thymus of Gpr30-lacZ mice.

Increased expression of Bcl-xL in cancer has been shown to confer resistance to a broad range of apoptotic stimuli and to modulate a number of other aspects of cellular physiology, including energy metabolism, cell cycle, autophagy, mitochondrial fission/fusion and cellular adhesion. However, only few of these activities have a mechanistic explanation. Here we used Tandem Affinity purification to identify novel Bcl-xL interacting proteins that could explain the pleiotropic effects of Bcl-xL overexpression. Among the several proteins co-purifying with Bcl-xL, we focused on Praf2, a protein with a predicted role in trafficking. The interaction of Praf2 with Bcl-xL was found to be dependent on the transmembrane domain of Bcl-xL. We found that Bcl-2 also interacts with Praf2 and that Bcl-xL and Bcl-2 can interact also with Arl6IP5, an homologue of Praf2. Interestingly, overexpression of Praf2 results in the translocation of Bax to mitochondria and the induction of apoptotic cell death. Praf2 dependent cell death is prevented by the co-transfection of Bcl-xL but not by its transmembrane domain deleted mutant. Accordingly, knock-down of Praf2 increases clonogenicity of U2OS cells following etoposide treatment by reducing cell death. In conclusion a screen for Bcl-xL-interacting membrane proteins let us identify a novel proapoptotic protein whose activity is strongly counteracted exclusively by membrane targeted Bcl-xL.