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Uva P.,CRS4 Bioinformatics Laboratory | Lahm A.,IRBM | Sbardellati A.,CRS4 Bioinformatics Laboratory | Grigoriadis A.,King's College London | And 2 more authors.
PLoS ONE | Year: 2010

Despite the wide use of cell lines in cancer research, the extent to which their surface properties correspond to those of primary tumors is poorly characterized. The present study addresses this problem from a transcriptional standpoint, analyzing the expression of membrane protein genes - the Membranome - in primary tumors and immortalized in-vitro cultured tumor cells. 409 human samples, deriving from ten independent studies, were analyzed. These comprise normal tissues, primary tumors and tumor derived cell lines deriving from eight different tissues: brain, breast, colon, kidney, leukemia, lung, melanoma, and ovary. We demonstrated that the Membranome has greater power than the remainder of the transcriptome when used as input for the automatic classification of tumor samples. This feature is maintained in tumor derived cell lines. In most cases primary tumors show maximal similarity in Membranome expression with cell lines of same tissue origin. Differences in Membranome expression between tumors and cell lines were analyzed also at the pathway level and biological themes were identified that were differentially regulated in the two settings. Moreover, by including normal samples in the analysis, we quantified the degree to which cell lines retain the Membranome up- and down- regulations observed in primary tumors with respect to their normal counterparts. We showed that most of the Membranome up-regulations observed in primary tumors are lost in the in-vitro cultured cells. Conversely, the majority of Membranome genes down-regulated upon tumor transformation maintain lower expression levels also in the cell lines. This study points towards a central role of Membranome genes in the definition of the tumor phenotype. The comparative analysis of primary tumors and cell lines identifies the limits of cell lines as a model for the study of cancer-related processes mediated by the cell surface. Results presented allow for a more rational use of the cell lines as a model of cancer. © 2010 Uva et al.

Odorisio T.,Instituto Dermopatico dellImmacolata IDI IRCCS | di Salvio M.,Instituto Dermopatico dellImmacolata IDI IRCCS | Orecchia A.,Instituto Dermopatico dellImmacolata IDI IRCCS | di Zenzo G.,Instituto Dermopatico dellImmacolata IDI IRCCS | And 8 more authors.
Human Molecular Genetics | Year: 2014

Recessive dystrophic epidermolysis bullosa (RDEB) is a genodermatosis characterized by fragile skin forming blisters that heal invariably with scars. It is due to mutations in the COL7A1 gene encoding type VII collagen, the major component of anchoring fibrils connecting the cutaneous basement membrane to the dermis. Identical COL7A1 mutations often result in inter-and intra-familial disease variability, suggesting that additional modifiers contribute to RDEB course. Here, we studied amonozygotic twin pair with RDEB presenting markedly different phenotypic manifestations, while expressing similar amounts of collagen VII. Genome-wide expression analysis in twins' fibroblasts showed differential expression of genesassociated with TGF-βpathway inhibition. In particular, decorin, a skin matrix component with anti-fibrotic properties, was found to be more expressed in the less affected twin. Accordingly, fibroblasts from the more affected sibling manifested a profibrotic and contractile phenotype characterized by enhanced α-smooth muscle actin and plasminogen activator inhibitor 1 expression, collagen I release and collagen lattice contraction. These cells also produced increased amounts of proinflammatory cytokines interleukin 6 and monocyte chemoattractant protein-1. Both TGF-β canonical (Smads) and non-canonical (MAPKs) pathways were basally more activated in the fibroblasts of the more affected twin. The profibrotic behaviour of these fibroblasts was suppressed by decorin delivery to cells. Our data show that the amount of type VII collagen is not the only determinant of RDEB clinical severity, and indicate aninvolvementofTGF-βpathwaysin modulating disease variability. Moreover, our findings identify decorin asa possible anti-fibrotic/inflammatory agent for RDEB therapeutic intervention. © The Author 2014. Published by Oxford University Press. All rights reserved.

Masotti A.,Bambino Gesu Childrens Hospital | Uva P.,CRS4 Bioinformatics Laboratory | Davis-Keppen L.,University of South Dakota | Basel-Vanagaite L.,Pediatric Genetics Unit | And 12 more authors.
American Journal of Human Genetics | Year: 2015

Keppen-Lubinsky syndrome (KPLBS) is a rare disease mainly characterized by severe developmental delay and intellectual disability, microcephaly, large prominent eyes, a narrow nasal bridge, a tented upper lip, a high palate, an open mouth, tightly adherent skin, an aged appearance, and severe generalized lipodystrophy. We sequenced the exomes of three unrelated individuals affected by KPLBS and found de novo heterozygous mutations in KCNJ6 (GIRK2), which encodes an inwardly rectifying potassium channel and maps to the Down syndrome critical region between DIRK1A and DSCR4. In particular, two individuals shared an in-frame heterozygous deletion of three nucleotides (c.455-457del) leading to the loss of one amino acid (p.Thr152del). The third individual was heterozygous for a missense mutation (c.460G>A) which introduces an amino acid change from glycine to serine (p.Gly154Ser). In agreement with animal models, the present data suggest that these mutations severely impair the correct functioning of this potassium channel. Overall, these results establish KPLBS as a channelopathy and suggest that KCNJ6 (GIRK2) could also be a candidate gene for other lipodystrophies. We hope that these results will prompt investigations in this unexplored class of inwardly rectifying K+ channels. © 2015 The American Society of Human Genetics.

Floris M.,CRS4 Bioinformatics Laboratory | Masciocchi J.,University of Bordeaux Segalen | Fanton M.,University of Padua | Moro S.,University of Padua
Nucleic Acids Research | Year: 2011

pepMMsMIMIC is a novel web-oriented peptidomimetic compound virtual screening tool based on a multi-conformers three-dimensional (3D)-similarity search strategy. Key to the development of pepMMsMIMIC has been the creation of a library of 17 million conformers calculated from 3.9 million commercially available chemicals collected in the MMsINC® database. Using as input the 3D structure of a peptide bound to a protein, pepMMsMIMIC suggests which chemical structures are able to mimic the protein-protein recognition of this natural peptide using both pharmacophore and shape similarity techniques. We hope that the accessibility of pepMMsMIMIC (freely available at http://mms.dsfarm.unipd. it/pepMMsMIMIC) will encourage medicinal chemists to de-peptidize protein-protein recognition processes of biological interest, thus increasing the potential of in silico peptidomimetic compound screening of known small molecules to expedite drug development. © 2011 The Author(s).

Uva P.,CRS4 Bioinformatics Laboratory | Da Sacco L.,Bambino Gesu Childrens Hospital | Corno M.D.,Instituto Superiore Of Sanita | Baldassarre A.,Bambino Gesu Childrens Hospital | And 5 more authors.
RNA | Year: 2013

MicroRNAs (miRNAs) are a class of small noncoding RNAs acting as post-transcriptional gene expression regulators in many physiological and pathological conditions. During the last few years, many novel mammalian miRNAs have been predicted experimentally with bioinformatics approaches and validated by next-generation sequencing. Although these strategies have prompted the discovery of several miRNAs, the total number of these genes still seems larger. Here, by exploiting the species conservation of human, mouse, and rat hairpin miRNAs, we discovered a novel rat microRNA, mir-155. We found that mature miR-155 is overexpressed in rat spleen myeloid cells treated with LPS, similarly to humans and mice. Rat mir-155 is annotated only on the alternate genome, suggesting the presence of other 'hidden' miRNAs on this assembly. Therefore, we comprehensively extended the homology search also to mice and humans, finally validating 34 novel mammalian miRNAs (two in humans, five in mice, and up to 27 in rats). Surprisingly, 15 of these novel miRNAs (one for mice and 14 for rats) were found only on the alternate and not on the reference genomic assembly. To date, our findings indicate that the choice of genomic assembly, when mapping small RNA reads, is an important option that should be carefully considered, at least for these animal models. Finally, the discovery of these novel mammalian miRNA genes may contribute to a better understanding of already acquired experimental data, thereby paving the way to still unexplored investigations and to unraveling the function of miRNAs in disease models. Copyright © 2013 RNA Society.

Capobianco E.,CRS4 Bioinformatics Laboratory
Journal of Computational Science | Year: 2011

Networks represent a main methodological instrument in systems biology applications. In particular, modularity is widely investigated in the attempt to elucidate the regulative and correlative nature of gene and protein associations, respectively. However, modular maps are only approximate representations due to two main factors. First, the resolution spectrum that has to be covered is wide and method-dependent. Second, the randomness underlying network dynamics and influencing them through fluctuations and system perturbations, is difficult to measure. We investigate both aspects by an application to the yeast protein interactome network, and suggest that a non-extensive characterization of entropy may play a role for elucidating both random and biological variation. © 2011 Elsevier B.V.

Capobianco E.,CRS4 Bioinformatics Laboratory
Mathematical Biosciences | Year: 2010

High-throughput microarray technologies measure the abundance of thousands of mRNA targets simultaneously. Due to the usual disparity between a few available samples (from limited conditions or time course points) and many gene expression values (entire genomes), a complex high-dimensional genomic system has to be analyzed, for instance by reverse engineering methods. The latter aim to reconstruct gene networks from experimentally observed expression changes caused by various kinds of perturbations. In particular, elucidating regulatory paths and assessing their reliability across replicates are central topics in this article. The reconstruction problem requires efficiency and accuracy from numerical optimization algorithms and statistical inference techniques. To this end, we focus on methods but also on the available experimental information produced in technical replicates. We propose a model-based approach based on a few steps. First, feature selection is performed by a projective method aimed to combine the gene measurements observed across replicates. Second, a quite heuristic sieving strategy is pursued to bypass the usual recourse to averaging. Third, the impact of dimensionality reduction on the biological system under study is evaluated. Evidence is obtained from the application of our approach to microarray time course experimental replicated data, and suggests that gene features, once identified, can be used for stabilization purposes relatively to the replicate variability. Both quantitative representation and qualitative assessment of the observed gene feature interference are reported in order to decipher specific gene regulatory map and the pathway-associated dynamics. © 2010 Elsevier Inc.

Marras E.,CRS4 Bioinformatics Laboratory | Travaglione A.,CRS4 Bioinformatics Laboratory | Capobianco E.,CRS4 Bioinformatics Laboratory
Statistical Applications in Genetics and Molecular Biology | Year: 2010

Inferring the structure of networks usually involves the attempt of retrieving their modular organization and knowing its possible interpretation, while quantifying the involved computational complexity through the methods and algorithms to be used. In protein interactomics, it is assumed that even the most recently created interactomes are known only up to a certain degree of coverage and accuracy, due to both experimental and computational limitations. Therefore, we need to infer from the measured interactomes about real interactomes as much as we infer from samples relative to a reference population. In order to exploit additional information sources, it is common to integrate multiple omic data and pursue method fusion. Particularly after the advent of high-throughput technologies, the increased complexity of data-intensive applications has determined an important role for network inference. Consequently, advances in spectral clustering, community detection algorithms and modularity optimization methods have been proposed, according to both deterministic and probabilistic solutions. We have considered the two kinds of approaches, and applied some of the available methods to two human interactomes obtained from high-throughput small-scale experiments and mass spectrometry measurements. The main motivation of this study is refining the resolution spectrum at which protein modularity maps can be studied. First, we started by a coarse-grained interactome decomposition through core and community structures, and by applying sub-sampling to the interactome adjacency matrix. Then, we switched to stochastic methods to uncover fine-grained interactome components, and applied both variational and mixture statistical models. Lastly, we integrated our analysis with the biological validation of the retrieved modules. Overall, the proposed approach shows potential for calibrating modularity detection in protein interactomes at different resolutions. © 2010 The Berkeley Electronic Press. All rights reserved.

Floris M.,CRS4 Bioinformatics Laboratory | Raimondo D.,University of Rome La Sapienza | Leoni G.,University of Rome La Sapienza | Orsini M.,CRS4 Bioinformatics Laboratory | And 2 more authors.
Bioinformatics | Year: 2011

Motivation: Analysis of the human genome revealed that the amount of transcribed sequence is an order of magnitude greater than the number of predicted and well-characterized genes. A sizeable fraction of these transcripts is related to alternatively spliced forms of known protein coding genes. Inspection of the alternatively spliced transcripts identified in the pilot phase of the ENCODE project has clearly shown that often their structure might substantially differ from that of other isoforms of the same gene, and therefore that they might perform unrelated functions, or that they might even not correspond to a functional protein. Identifying these cases is obviously relevant for the functional assignment of gene products and for the interpretation of the effect of variations in the corresponding proteins. Results: Here we describe a publicly available tool that, given a gene or a protein, retrieves and analyses all its annotated isoforms, provides users with three-dimensional models of the isoform(s) of his/her interest whenever possible and automatically assesses whether homology derived structural models correspond to plausible structures. This information is clearly relevant. When the homology model of some isoforms of a gene does not seem structurally plausible, the implications are that either they assume a structure unrelated to that of the other isoforms of the same gene with presumably significant functional differences, or do not correspond to functional products. We provide indications that the second hypothesis is likely to be true for a substantial fraction of the cases. © The Author(s) 2011. Published by Oxford University Press.

Marras E.,CRS4 Bioinformatics Laboratory | Travaglione A.,CRS4 Bioinformatics Laboratory | Capobianco E.,CRS4 Bioinformatics Laboratory
Journal of Computational Biology | Year: 2011

Many studies and applications in the post-genomic era have been devoted to analyze complex biological systems by computational inference methods. We propose to apply manifold learning methods to protein-protein interaction networks (PPIN). Despite their popularity in data-intensive applications, these methods have received limited attention in the context of biological networks. We show that there is both utility and unexplored potential in adopting manifold learning for network inference purposes. In particular, the following advantages are highlighted: (a) fusion with diagnostic statistical tools designed to assign significance to protein interactions based on pre-selected topological features; (b) dissection into components of the interactome in order to elucidate global and local connectivity organization; (c) relevance of embedding the interactome in reduced dimensions for biological validation purposes. We have compared the performances of three well-known techniques - kernel-PCA, RADICAL ICA, and ISOMAP - relatively to their power of mapping the interactome onto new coordinate dimensions where important associations among proteins can be detected, and then back projected such that the corresponding sub-interactomes are reconstructed. This recovery has been done selectively, by using significant information according to a robust statistical procedure, and then standard biological annotation has been provided to validate the results. We expect that a byproduct of using subspace analysis by the proposed techniques is a possible calibration of interactome modularity studies. © Copyright 2011, Mary Ann Liebert, Inc.

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