@article {1198, title = {Chronic subordination stress selectively downregulates the insulin signaling pathway in liver and skeletal muscle but not in adipose tissue of male mice.}, journal = {Stress (Amsterdam, Netherlands)}, year = {2016}, month = {2016 Mar 7}, pages = {1-11}, abstract = {Chronic stress has been associated with obesity, glucose intolerance, and insulin resistance. We developed a model of chronic psychosocial stress (CPS) in which subordinate mice are vulnerable to obesity and the metabolic-like syndrome while dominant mice exhibit a healthy metabolic phenotype. Here we tested the hypothesis that the metabolic difference between subordinate and dominant mice is associated with changes in functional pathways relevant for insulin sensitivity, glucose and lipid homeostasis. Male mice were exposed to CPS for four weeks and fed either a standard diet or a high-fat diet (HFD). We first measured, by real-time PCR candidate genes, in the liver, skeletal muscle, and the perigonadal white adipose tissue (pWAT). Subsequently, we used a probabilistic analysis approach to analyze different ways in which signals can be transmitted across the pathways in each tissue. Results showed that subordinate mice displayed a drastic downregulation of the insulin pathway in liver and muscle, indicative of insulin resistance, already on standard diet. Conversely, pWAT showed molecular changes suggestive of facilitated fat deposition in an otherwise insulin-sensitive tissue. The molecular changes in subordinate mice fed a standard diet were greater compared to HFD-fed controls. Finally, dominant mice maintained a substantially normal metabolic and molecular phenotype even when fed a HFD. Overall, our data demonstrate that subordination stress is a potent stimulus for the downregulation of the insulin signaling pathway in liver and muscle and a major risk factor for the development of obesity, insulin resistance, and type 2 diabetes mellitus.}, keywords = {Adipose tissue, insulin, IRS1, IRS2, metabolic syndrome, obesity, pathway analysis}, issn = {1607-8888}, doi = {10.3109/10253890.2016.1151491}, url = {http://www.tandfonline.com/doi/abs/10.3109/10253890.2016.1151491?journalCode=ists20}, author = {Sanghez, Valentina and Cubuk, Cankut and Sebasti{\'a}n-Leon, Patricia and Carobbio, Stefania and Dopazo, Joaquin and Vidal-Puig, Antonio and Bartolomucci, Alessandro} } @article {1128, title = {Assessing the impact of mutations found in next generation sequencing data over human signaling pathways.}, journal = {Nucleic acids research}, volume = {43}, number = {W1}, year = {2015}, month = {2015 Apr 16}, pages = {W270-W275}, abstract = {Modern sequencing technologies produce increasingly detailed data on genomic variation. However, conventional methods for relating either individual variants or mutated genes to phenotypes present known limitations given the complex, multigenic nature of many diseases or traits. Here we present PATHiVar, a web-based tool that integrates genomic variation data with gene expression tissue information. PATHiVar constitutes a new generation of genomic data analysis methods that allow studying variants found in next generation sequencing experiment in the context of signaling pathways. Simple Boolean models of pathways provide detailed descriptions of the impact of mutations in cell functionality so as, recurrences in functionality failures can easily be related to diseases, even if they are produced by mutations in different genes. Patterns of changes in signal transmission circuits, often unpredictable from individual genes mutated, correspond to patterns of affected functionalities that can be related to complex traits such as disease progression, drug response, etc. PATHiVar is available at: http://pathivar.babelomics.org.}, keywords = {NGS, pathways, signalling, Systems biology}, issn = {1362-4962}, doi = {10.1093/nar/gkv349}, url = {http://nar.oxfordjournals.org/content/43/W1/W270}, author = {Hernansaiz-Ballesteros, Rosa D and Salavert, Francisco and Sebasti{\'a}n-Leon, Patricia and Alem{\'a}n, Alejandro and Medina, Ignacio and Joaqu{\'\i}n Dopazo} } @article {474, title = {Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity.}, journal = {Sci Rep}, volume = {5}, year = {2015}, month = {2015 Dec 18}, pages = {18494}, abstract = {

Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA).

}, keywords = {Algorithms, Antineoplastic Agents, biomarkers, Cell Line, Tumor, Cell Survival, gene expression, Humans, Lethal Dose 50, Neoplasms, Phosphorylation, Proteins, Signal Transduction}, issn = {2045-2322}, doi = {10.1038/srep18494}, author = {Amadoz, Alicia and Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Salavert, Francisco and Dopazo, Joaquin} } @article {565, title = {Understanding disease mechanisms with models of signaling pathway activities}, journal = {BMC systems biology}, volume = {8}, year = {2014}, month = {10}, pages = {121}, doi = {10.1186/s12918-014-0121-3}, author = {Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Minguez, Pablo and Conesa, Ana and Tarazona, Sonia and Amadoz, Alicia and Armero, Carmen and Salavert Torres, Francisco and Vidal-Puig, Antonio and Montaner, David and Dopazo, Joaquin} } @article {1093, title = {Understanding disease mechanisms with models of signaling pathway activities.}, journal = {BMC systems biology}, volume = {8}, year = {2014}, month = {2014 Oct 25}, pages = {121}, abstract = {BackgroundUnderstanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine.ResultsHere we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets.ConclusionsThe proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system.}, keywords = {Disease mechanism, pathway, signalling, Systems biology}, issn = {1752-0509}, doi = {10.1186/s12918-014-0121-3}, url = {http://www.biomedcentral.com/1752-0509/8/121/abstract}, author = {Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Minguez, Pablo and Ana Conesa and Sonia Tarazona and Amadoz, Alicia and Armero, Carmen and Salavert, Francisco and Vidal-Puig, Antonio and Montaner, David and Joaqu{\'\i}n Dopazo} } @article {500, title = {Inferring the functional effect of gene expression changes in signaling pathways.}, journal = {Nucleic Acids Res}, volume = {41}, year = {2013}, month = {2013 Jul}, pages = {W213-7}, abstract = {

Signaling pathways constitute a valuable source of information that allows interpreting the way in which alterations in gene activities affect to particular cell functionalities. There are web tools available that allow viewing and editing pathways, as well as representing experimental data on them. However, few methods aimed to identify the signaling circuits, within a pathway, associated to the biological problem studied exist and none of them provide a convenient graphical web interface. We present PATHiWAYS, a web-based signaling pathway visualization system that infers changes in signaling that affect cell functionality from the measurements of gene expression values in typical expression microarray case-control experiments. A simple probabilistic model of the pathway is used to estimate the probabilities for signal transmission from any receptor to any final effector molecule (taking into account the pathway topology) using for this the individual probabilities of gene product presence/absence inferred from gene expression values. Significant changes in these probabilities allow linking different cell functionalities triggered by the pathway to the biological problem studied. PATHiWAYS is available at: http://pathiways.babelomics.org/.

}, keywords = {Animals, Humans, Internet, Mice, Models, Statistical, Receptors, Cell Surface, Signal Transduction, Software, Transcriptome}, issn = {1362-4962}, doi = {10.1093/nar/gkt451}, author = {Sebasti{\'a}n-Leon, Patricia and Carbonell, Jos{\'e} and Salavert, Francisco and S{\'a}nchez, Rub{\'e}n and Medina, Ignacio and Dopazo, Joaquin} } @article {21335611, title = {B2G-FAR, a species centered GO annotation repository.}, journal = {Bioinformatics (Oxford, England)}, volume = {27}, number = {7}, year = {2011}, month = {2011 Feb 18}, pages = {919-924}, abstract = {

MOTIVATION: Functional genomics research has expanded enormously in the last decade thanks to the cost-reduction in high-throughput technologies and the development of computational tools that generate, standardize and share information on gene and protein function such as the Gene Ontology (GO). Nevertheless many biologists, especially working with non-model organisms, still suffer from non-existing or low coverage functional annotation, or simply struggle retrieving, summarizing and querying these data. RESULTS: The Blast2GO Functional Annotation Repository (B2G-FAR) is a bioinformatics resource envisaged to provide functional information for otherwise uncharacterized sequence-data and offers data-mining tools to analyze a larger repertoire of species than currently available. This new annotation resource has been created by applying the Blast2GO functional annotation engine in a strongly high-throughput manner to the entire space of public available sequences. The resulting repository contains GO term predictions for over 13.2 million non-redundant protein sequences based on BLAST search alignments from the SIMAP database. We generated GO annotation for approximately 150.000 different taxa making available the 2000 species with the highest coverage through B2G-FAR. A second section within B2G-FAR holds functional annotations for 17 non-model organism Affymetrix GeneChips. Conclusions: B2G-FAR provides easy access to exhaustive functional annotation for 2000 species offering a good balance between quality and quantity, thereby supporting functional genomics research especially in the case of non-model organisms. AVAILABILITY: The annotation resource is available at http://b2gfar.bioinfo.cipf.es. CONTACT: aconesa@cipf.es, sgoetz@cipf.es.

}, author = {G{\"o}tz, Stefan and Arnold, Roland and Sebasti{\'a}n-Leon, Patricia and Mart{\'\i}n-Rodr{\'\i}guez, Samuel and Tischler, Patrick and Jehl, Marc-Andr{\'e} and Joaqu{\'\i}n Dopazo and Rattei, Thomas and Ana Conesa} }