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Author Occhi, G.; Regazzo, D.; Trivellin, G.; Boaretto, F.; Ciato, D.; Bobisse, S.; Ferasin, S.; Cetani, F.; Pardi, E.; Korbonits, M.; Pellegata, N.S.; Sidarovich, V.; Quattrone, A.; Opocher, G.; Mantero, F.; Scaroni, C.
Title A novel mutation in the upstream open reading frame of the CDKN1B gene causes a MEN4 phenotype Type Journal Article
Year 2013 Publication PLoS Genetics Abbreviated Journal PLoS Genet
Volume 9 Issue 3 Pages (down) e1003350
Keywords
Abstract The CDKN1B gene encodes the cyclin-dependent kinase inhibitor p27(KIP1), an atypical tumor suppressor playing a key role in cell cycle regulation, cell proliferation, and differentiation. Impaired p27(KIP1) expression and/or localization are often observed in tumor cells, further confirming its central role in regulating the cell cycle. Recently, germline mutations in CDKN1B have been associated with the inherited multiple endocrine neoplasia syndrome type 4, an autosomal dominant syndrome characterized by varying combinations of tumors affecting at least two endocrine organs. In this study we identified a 4-bp deletion in a highly conserved regulatory upstream ORF (uORF) in the 5'UTR of the CDKN1B gene in a patient with a pituitary adenoma and a well-differentiated pancreatic neoplasm. This deletion causes the shift of the uORF termination codon with the consequent lengthening of the uORF-encoded peptide and the drastic shortening of the intercistronic space. Our data on the immunohistochemical analysis of the patient's pancreatic lesion, functional studies based on dual-luciferase assays, site-directed mutagenesis, and on polysome profiling show a negative influence of this deletion on the translation reinitiation at the CDKN1B starting site, with a consequent reduction in p27(KIP1) expression. Our findings demonstrate that, in addition to the previously described mechanisms leading to reduced p27(KIP1) activity, such as degradation via the ubiquitin/proteasome pathway or non-covalent sequestration, p27(KIP1) activity can also be modulated by an uORF and mutations affecting uORF could change p27(KIP1) expression. This study adds the CDKN1B gene to the short list of genes for which mutations that either create, delete, or severely modify their regulatory uORFs have been associated with human diseases.
Address Department of Medicine, Endocrinology Unit, University of Padova, Padova, Italy. gianluca.occhi@unipd.it
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ISSN 1553-7390 ISBN Medium
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Notes PMID:23555276 Approved no
Call Number refbase @ admin @ Serial 17143
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Author Hemani, G.; Knott, S.; Haley, C.
Title An Evolutionary Perspective on Epistasis and the Missing Heritability Type Journal Article
Year 2013 Publication PLoS Genet Abbreviated Journal
Volume 9 Issue 2 Pages (down) e1003295
Keywords
Abstract Author SummaryIn this study we have shown that two independent problems may have a common cause. Why do traits under selection exhibit additive genetic variance, and why is the proportion of the heritability explained by additive effects much smaller than the total heritability estimated to exist? Our results indicate that epistatic interactions can allow deleterious mutations to persist under selection and that these interactions can abate the depletion of additive genetic variation. Furthermore, a much larger element of non-additive genetic variance is maintained, which supports the notion that the heritability estimated from family studies could be a mixture of both additive and non-additive components. We show that searching directly for epistatic effects greatly improves the discovery of variants under selection, despite the multiple testing penalty being much larger. Finally, we demonstrate that common practices in genome-wide association studies could lead to both an ascertainment bias in detecting additive effects and a confirmation bias in perceiving that most of the genetic variance is additive.
Address
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Notes Approved no
Call Number ref @ user @ Hemani2013a Serial 72099
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Author Hemani, G.; Knott, S.; Haley, C.
Title An Evolutionary Perspective on Epistasis and the Missing Heritability Type Journal Article
Year 2013 Publication PLoS Genet Abbreviated Journal
Volume 9 Issue 2 Pages (down) e1003295
Keywords
Abstract Author SummaryIn this study we have shown that two independent problems may have a common cause. Why do traits under selection exhibit additive genetic variance, and why is the proportion of the heritability explained by additive effects much smaller than the total heritability estimated to exist? Our results indicate that epistatic interactions can allow deleterious mutations to persist under selection and that these interactions can abate the depletion of additive genetic variation. Furthermore, a much larger element of non-additive genetic variance is maintained, which supports the notion that the heritability estimated from family studies could be a mixture of both additive and non-additive components. We show that searching directly for epistatic effects greatly improves the discovery of variants under selection, despite the multiple testing penalty being much larger. Finally, we demonstrate that common practices in genome-wide association studies could lead to both an ascertainment bias in detecting additive effects and a confirmation bias in perceiving that most of the genetic variance is additive.
Address
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Language Summary Language Original Title
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Notes Approved no
Call Number ref @ user @ Hemani2013b Serial 72136
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Author Melas, I.N.; Samaga, R.; Alexopoulos, L.G.; Klamt, S.
Title Detecting and removing inconsistencies between experimental data and signaling network topologies using integer linear programming on interaction graphs Type Journal Article
Year 2013 Publication PLoS Computational Biology Abbreviated Journal PLoS Comput Biol
Volume 9 Issue 9 Pages (down) e1003204
Keywords
Abstract Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes. These constraints are posed by the network topology and their formulation as ILP allows us to predict the possible qualitative changes (up, down, no effect) of the activation levels of the nodes for a given stimulus. We provide four basic operations to detect and remove inconsistencies between measurements and predicted behavior: (i) find a topology-consistent explanation for responses of signaling nodes measured in a stimulus-response experiment (if none exists, find the closest explanation); (ii) determine a minimal set of nodes that need to be corrected to make an inconsistent scenario consistent; (iii) determine the optimal subgraph of the given network topology which can best reflect measurements from a set of experimental scenarios; (iv) find possibly missing edges that would improve the consistency of the graph with respect to a set of experimental scenarios the most. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGFR/ErbB signaling against a library of high-throughput phosphoproteomic data measured in primary hepatocytes. Our methods detect interactions that are likely to be inactive in hepatocytes and provide suggestions for new interactions that, if included, would significantly improve the goodness of fit. Our framework is highly flexible and the underlying model requires only easily accessible biological knowledge. All related algorithms were implemented in a freely available toolbox SigNetTrainer making it an appealing approach for various applications.
Address National Technical University of Athens, Athens, Greece
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ISSN 1553-734X ISBN Medium
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Notes PMID:24039561 Approved no
Call Number refbase @ admin @ Serial 34843
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Author Shankaran, H.; Zhang, Y.; Tan, Y.; Resat, H.
Title Model-based analysis of HER activation in cells co-expressing EGFR, HER2 and HER3 Type Journal Article
Year 2013 Publication PLoS Computational Biology Abbreviated Journal PLoS Comput Biol
Volume 9 Issue 8 Pages (down) e1003201
Keywords Antibodies, Monoclonal; Cell Line; Computer Simulation; Epithelial Cells/cytology/enzymology/metabolism; Humans; MAP Kinase Signaling System; Mammary Glands, Human/cytology; Mitogen-Activated Protein Kinases/metabolism; *Models, Biological; Phosphorylation; Proto-Oncogene Proteins c-akt/metabolism; Receptor, Epidermal Growth Factor/*metabolism; Receptor, erbB-2/chemistry/*metabolism; Receptor, erbB-3/chemistry/*metabolism; Reproducibility of Results
Abstract The HER/ErbB family of receptor tyrosine kinases drives critical responses in normal physiology and cancer, and the expression levels of the various HER receptors are critical determinants of clinical outcomes. HER activation is driven by the formation of various dimer complexes between members of this receptor family. The HER dimer types can have differential effects on downstream signaling and phenotypic outcomes. We constructed an integrated mathematical model of HER activation, and trafficking to quantitatively link receptor expression levels to dimerization and activation. We parameterized the model with a comprehensive set of HER phosphorylation and abundance data collected in a panel of human mammary epithelial cells expressing varying levels of EGFR/HER1, HER2 and HER3. Although parameter estimation yielded multiple solutions, predictions for dimer phosphorylation were in agreement with each other. We validated the model using experiments where pertuzumab was used to block HER2 dimerization. We used the model to predict HER dimerization and activation patterns in a panel of human mammary epithelial cells lines with known HER expression levels in response to stimulations with ligands EGF and HRG. Simulations over the range of expression levels seen in various cell lines indicate that: i) EGFR phosphorylation is driven by HER1-HER1 and HER1-HER2 dimers, and not HER1-HER3 dimers, ii) HER1-HER2 and HER2-HER3 dimers both contribute significantly to HER2 activation with the EGFR expression level determining the relative importance of these species, and iii) the HER2-HER3 dimer is largely responsible for HER3 activation. The model can be used to predict phosphorylated dimer levels for any given HER expression profile. This information in turn can be used to quantify the potencies of the various HER dimers, and can potentially inform personalized therapeutic approaches.
Address Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, Washington, United States of America
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Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1553-734X ISBN Medium
Area Expedition Conference
Notes PMID:23990774 Approved no
Call Number refbase @ admin @ Serial 34863
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