02634nas a2200289   4500008004100000022001400041245015800055210006900213260001500282300000900297520167800306653001901984653001202003653000902015653000902024653002302033653001202056653002102068100002302089700001802112700003002130700002302160700002002183700002402203700002902227856008802256       2016                        eng d  a1607-888800aChronic subordination stress selectively downregulates the insulin signaling pathway in liver and skeletal muscle but not in adipose tissue of male mice.0 aChronic subordination stress selectively downregulates the insul  c2016 Mar 7  a1-113 aChronic 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.10aAdipose tissue10ainsulin10aIRS110aIRS210ametabolic syndrome10aobesity10apathway analysis1 aSanghez, Valentina1 aCubuk, Cankut1 aSebastián-Leon, Patricia1 aCarobbio, Stefania1 aDopazo, Joaquin1 aVidal-Puig, Antonio1 aBartolomucci, Alessandro  uhttp://www.tandfonline.com/doi/abs/10.3109/10253890.2016.1151491?journalCode=ists2001932nas a2200253   4500008004100000022001400041245011000055210006900165260001600234300001400250490000700264520114400271653000801415653001301423653001501436653002001451100003601471700002401507700003001531700002301561700002001584700002101604856005301625       2015                        eng d  a1362-496200aAssessing the impact of mutations found in next generation sequencing data over human signaling pathways.0 aAssessing the impact of mutations found in next generation seque  c2015 Apr 16  aW270-W2750 v433 aModern 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.10aNGS10apathways10asignalling10aSystems biology1 aHernansaiz-Ballesteros, Rosa, D1 aSalavert, Francisco1 aSebastián-Leon, Patricia1 aAlemán, Alejandro1 aMedina, Ignacio1 aDopazo, Joaquín  uhttp://nar.oxfordjournals.org/content/43/W1/W27001866nas a2200337   4500008004100000022001400041245010900055210006900164260001600233300001000249490000600259520081400265653001501079653002601094653001501120653002101135653001801156653002001174653001101194653001901205653001401224653002001238653001301258653002401271100001901295700003001314700001901344700002401363700002001387856012101407       2015                        eng d  a2045-232200aUsing activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity.0 aUsing activation status of signaling pathways as mechanismbased   c2015 Dec 18  a184940 v53 a<p>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). </p>10aAlgorithms10aAntineoplastic Agents10abiomarkers10aCell Line, Tumor10aCell Survival10agene expression10aHumans10aLethal Dose 5010aNeoplasms10aPhosphorylation10aProteins10aSignal Transduction1 aAmadoz, Alicia1 aSebastián-Leon, Patricia1 aVidal, Enrique1 aSalavert, Francisco1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/using-activation-status-signaling-pathways-mechanism-based-biomarkers-predict-drug02249nas a2200313   4500008004100000022001400041245008200055210006900137260001600206300000800222490000600230520134100236653002201577653001201599653001501611653002001626100003001646700001901676700001901695700001601714700002001730700001901750700001901769700002401788700002401812700002001836700002101856856005801877       2014                        eng d  a1752-050900aUnderstanding disease mechanisms with models of signaling pathway activities.0 aUnderstanding disease mechanisms with models of signaling pathwa  c2014 Oct 25  a1210 v83 aBackgroundUnderstanding 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.10aDisease mechanism10apathway10asignalling10aSystems biology1 aSebastián-Leon, Patricia1 aVidal, Enrique1 aMinguez, Pablo1 aConesa, Ana1 aTarazona, Sonia1 aAmadoz, Alicia1 aArmero, Carmen1 aSalavert, Francisco1 aVidal-Puig, Antonio1 aMontaner, David1 aDopazo, Joaquín  uhttp://www.biomedcentral.com/1752-0509/8/121/abstract00801nas a2200241   4500008004100000245008100041210006900122260000700191300000800198490000600206100003000212700001900242700001900261700001600280700002000296700001900316700001900335700003200354700002400386700002000410700002000430856010900450       2014                        eng d00aUnderstanding disease mechanisms with models of signaling pathway activities0 aUnderstanding disease mechanisms with models of signaling pathwa  c10  a1210 v81 aSebastián-Leon, Patricia1 aVidal, Enrique1 aMinguez, Pablo1 aConesa, Ana1 aTarazona, Sonia1 aAmadoz, Alicia1 aArmero, Carmen1 aTorres, Francisco, Salavert1 aVidal-Puig, Antonio1 aMontaner, David1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/understanding-disease-mechanisms-models-signaling-pathway-activities-102226nas a2200313   4500008004100000022001400041245008600055210006900141260001300210300001100223490000700234520127400241653001201515653001101527653001301538653000901551653002401560653002801584653002401612653001301636653001801649100003001667700002101697700002401718700002101742700002001763700002001783856010901803       2013                        eng d  a1362-496200aInferring the functional effect of gene expression changes in signaling pathways.0 aInferring the functional effect of gene expression changes in si  c2013 Jul  aW213-70 v413 a<p>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/. </p>10aAnimals10aHumans10aInternet10aMice10aModels, Statistical10aReceptors, Cell Surface10aSignal Transduction10aSoftware10aTranscriptome1 aSebastián-Leon, Patricia1 aCarbonell, José1 aSalavert, Francisco1 aSánchez, Rubén1 aMedina, Ignacio1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/inferring-functional-effect-gene-expression-changes-signaling-pathways02485nas a2200229   4500008004100000245005800041210005500099260001600154300001200170490000700182520178000189100001801969700001901987700003002006700003102036700002202067700002202089700002102111700001902132700001602151856008802167       2011                        eng d00aB2G-FAR, a species centered GO annotation repository.0 aB2GFAR a species centered GO annotation repository  c2011 Feb 18  a919-9240 v273 a<p>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.</p>1 aGötz, Stefan1 aArnold, Roland1 aSebastián-Leon, Patricia1 aMartín-Rodríguez, Samuel1 aTischler, Patrick1 aJehl, Marc-André1 aDopazo, Joaquín1 aRattei, Thomas1 aConesa, Ana  uhttp://clinbioinfosspa.es/content/b2g-far-species-centered-go-annotation-repository