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- Abenius, Tobias, 1979, et al.
(författare)
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System-scale network modeling of cancer using EPoC
- 2012
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Ingår i: Advances in Experimental Medicine and Biology. - New York, NY : Springer New York. - 0065-2598. - 9781441972095 ; 736:5, s. 617-643
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Tidskriftsartikel (refereegranskat)abstract
- One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks.
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| 2. |
- Gerlee, Philip, 1980, et al.
(författare)
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Searching for Synergies: Matrix Algebraic Approaches for Efficient Pair Screening
- 2013
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:7
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Tidskriftsartikel (refereegranskat)abstract
- Functionally interacting perturbations, such as synergistic drugs pairs or synthetic lethal gene pairs, are of key interest in both pharmacology and functional genomics. However, to find such pairs by traditional screening methods is both time consuming and costly. We present a novel computational-experimental framework for efficient identification of synergistic target pairs, applicable for screening of systems with sizes on the order of current drug, small RNA or SGA (Synthetic Genetic Array) libraries (>1000 targets). This framework exploits the fact that the response of a drug pair in a given system, or a pair of genes' propensity to interact functionally, can be partly predicted by computational means from (i) a small set of experimentally determined target pairs, and (ii) pre-existing data (e.g. gene ontology, PPI) on the similarities between targets. Predictions are obtained by a novel matrix algebraic technique, based on cyclical projections onto convex sets. We demonstrate the efficiency of the proposed method using drug-drug interaction data from seven cancer cell lines and gene-gene interaction data from yeast SGA screens. Our protocol increases the rate of synergism discovery significantly over traditional screening, by up to 7-fold. Our method is easy to implement and could be applied to accelerate pair screening for both animal and microbial systems.
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| 3. |
- Jörnsten, Rebecka, 1971, et al.
(författare)
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Network modeling of the transcriptional effects of copy number aberrations in glioblastoma
- 2011
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Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.
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| 4. |
- Hansson, Caroline, 1981, et al.
(författare)
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Influence of ghrelin on the central serotonergic signaling system in mice
- 2014
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Ingår i: Neuropharmacology. - : Elsevier BV. - 0028-3908. ; 79, s. 498-505
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Tidskriftsartikel (refereegranskat)abstract
- The central ghrelin signaling system engages key pathways of importance for feeding control, recently shown to include those engaged in anxiety-like behavior in rodents. Here we sought to determine whether ghrelin impacts on the central serotonin system, which has an important role in anxiety. We focused on two brain areas, the amygdala (of importance for the mediation of fear and anxiety) and the dorsal raphe (i.e. the site of origin of major afferent serotonin pathways, including those that project to the amygdala). In these brain areas, we measured serotonergic turnover (using HPLC) and the mRNA expression of a number of serotonin-related genes (using real-time PCR). We found that acute central administration of ghrelin to mice increased the serotonergic turnover in the amygdala. It also increased the mRNA expression of a number of serotonin receptors, both in the amygdala and in the dorsal raphe. Studies in ghrelin receptor (GHS-R1A) knock-out mice showed a decreased mRNA expression of serotonergic receptors in both the amygdala and the dorsal raphe, relative to their wild-type littermates. We conclude that the central serotonin system is a target for ghrelin, providing a candidate neurochemical substrate of importance for ghrelin's effects on mood. © 2013 The Authors. Published by Elsevier Ltd. All rights reserved.
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| 5. |
- Karlsson-Lindahl, Linda, 1972, et al.
(författare)
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Heparanase affects food intake and regulates energy balance in mice.
- 2012
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Ingår i: PloS one. - San Francisco : Public Library of Science (PLoS). - 1932-6203. ; 7:3
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Tidskriftsartikel (refereegranskat)abstract
- Mutation of the melanocortin-receptor 4 (MC4R) is the most frequent cause of severe obesity in humans. Binding of agouti-related peptide (AgRP) to MC4R involves the co-receptor syndecan-3, a heparan sulfate proteoglycan. The proteoglycan can be structurally modified by the enzyme heparanase. Here we tested the hypothesis that heparanase plays a role in food intake behaviour and energy balance regulation by analysing body weight, body composition and food intake in genetically modified mice that either lack or overexpress heparanase. We also assessed food intake and body weight following acute central intracerebroventricular administration of heparanase; such treatment reduced food intake in wildtype mice, an effect that was abolished in mice lacking MC4R. By contrast, heparanase knockout mice on a high-fat diet showed increased food intake and maturity-onset obesity, with up to a 40% increase in body fat. Mice overexpressing heparanase displayed essentially the opposite phenotypes, with a reduced fat mass. These results implicate heparanase in energy balance control via the central melanocortin system. Our data indicate that heparanase acts as a negative modulator of AgRP signaling at MC4R, through cleavage of heparan sulfate chains presumably linked to syndecan-3.
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