01707nas a2200169   4500008004100000022001400041245008500055210006900140260001600209490000700225520110800232100001801340700002001358700002401378700002001402856011501422       2023                        eng d  a1422-006700aCrosstalk between Metabolite Production and Signaling Activity in Breast Cancer.0 aCrosstalk between Metabolite Production and Signaling Activity i  c2023 Apr 180 v243 a<p>The reprogramming of metabolism is a recognized cancer hallmark. It is well known that different signaling pathways regulate and orchestrate this reprogramming that contributes to cancer initiation and development. However, recent evidence is accumulating, suggesting that several metabolites could play a relevant role in regulating signaling pathways. To assess the potential role of metabolites in the regulation of signaling pathways, both metabolic and signaling pathway activities of Breast invasive Carcinoma (BRCA) have been modeled using mechanistic models. Gaussian Processes, powerful machine learning methods, were used in combination with SHapley Additive exPlanations (SHAP), a recent methodology that conveys causality, to obtain potential causal relationships between the production of metabolites and the regulation of signaling pathways. A total of 317 metabolites were found to have a strong impact on signaling circuits. The results presented here point to the existence of a complex crosstalk between signaling and metabolic pathways more complex than previously was thought.</p>1 aCubuk, Cankut1 aLoucera, Carlos1 aPeña-Chilet, Maria1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/crosstalk-between-metabolite-production-and-signaling-activity-breast-cancer02229nas a2200325   4500008004100000022001400041245014800055210006900203260001600272300001100288490000700299520106600306100002701372700003201399700002401431700001701455700002601472700001901498700002101517700001801538700002201556700001701578700001901595700002901614700002001643700002301663700002301686700002401709856017001733       2023                        eng d  a2211-124700aDefective extracellular matrix remodeling in brown adipose tissue is associated with fibro-inflammation and reduced diet-induced thermogenesis.0 aDefective extracellular matrix remodeling in brown adipose tissu  c2023 Jun 13  a1126400 v423 a<p>The relevance of extracellular matrix (ECM) remodeling is reported in white adipose tissue (AT) and obesity-related dysfunctions, but little is known about the importance of ECM remodeling in brown AT (BAT) function. Here, we show that a time course of high-fat diet (HFD) feeding progressively impairs diet-induced thermogenesis concomitantly with the development of fibro-inflammation in BAT. Higher markers of fibro-inflammation are associated with lower cold-induced BAT activity in humans. Similarly, when mice are housed at thermoneutrality, inactivated BAT features fibro-inflammation. We validate the pathophysiological relevance of BAT ECM remodeling in response to temperature challenges and HFD using a model of a primary defect in the collagen turnover mediated by partial ablation of the Pepd prolidase. Pepd-heterozygous mice display exacerbated dysfunction and BAT fibro-inflammation at thermoneutrality and in HFD. Our findings show the relevance of ECM remodeling in BAT activation and provide a mechanism for BAT dysfunction in obesity.</p>1 aPellegrinelli, Vanessa1 aFigueroa-Juárez, Elizabeth1 aSamuelson, Isabella1 aU-Din, Mueez1 aRodriguez-Fdez, Sonia1 aVirtue, Samuel1 aLeggat, Jennifer1 aCubuk, Cankut1 aPeirce, Vivian, J1 aNiemi, Tarja1 aCampbell, Mark1 aRodriguez-Cuenca, Sergio1 aDopazo, Joaquin1 aCarobbio, Stefania1 aVirtanen, Kirsi, A1 aVidal-Puig, Antonio  uhttp://clinbioinfosspa.es/content/defective-extracellular-matrix-remodeling-brown-adipose-tissue-associated-fibro-inflammation-and-reduced-diet-induced-thermogenesis00806nas a2200229   4500008004100000022001300041245014700054210006900201260001600270300001600286490000700302100001600309700002300325700001800348700002200366700002000388700002700408700003000435700002400465700002000489856006700509       2021                        eng d  a2001037000aGenome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data0 aGenomescale mechanistic modeling of signaling pathways made easy  cJan-01-2021  a2968 - 29780 v191 aRian, Kinza1 aHidalgo, Marta, R.1 aCubuk, Cankut1 aFalco, Matias, M.1 aLoucera, Carlos1 aEsteban-Medina, Marina1 aAlamo-Alvarez, Inmaculada1 aPeña-Chilet, Maria1 aDopazo, Joaquin  uhttps://linkinghub.elsevier.com/retrieve/pii/S200103702100203801572nas a2200253   4500008004100000022001400041245005600055210005300111260001600164300000600180490000700186520083300193100001601026700002701042700002201069700001801091700002101109700002001130700001901150700002301169700002401192700002001216856008201236       2021                        eng d  a1756-038100aMechanistic modeling of the SARS-CoV-2 disease map.0 aMechanistic modeling of the SARSCoV2 disease map  c2021 Jan 21  a50 v143 a<p>Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map. In this framework, the detailed activity of the human signaling circuits related to the viral infection, covering from the entry and replication mechanisms to the downstream consequences as inflammation and antigenic response, can be inferred from gene expression experiments. Moreover, the effect of potential interventions, such as knock-downs, or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied. This freely available tool not only provides an unprecedentedly detailed view of the mechanisms of viral invasion and the consequences in the cell but has also the potential of becoming an invaluable asset in the search for efficient antiviral treatments.</p>1 aRian, Kinza1 aEsteban-Medina, Marina1 aHidalgo, Marta, R1 aCubuk, Cankut1 aFalco, Matias, M1 aLoucera, Carlos1 aGunyel, Devrim1 aOstaszewski, Marek1 aPeña-Chilet, Maria1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/mechanistic-modeling-sars-cov-2-disease-map03596nas a2200505   4500008004100000022001400041245013900055210006900194260001500263300000700278490000700285520188200292653001702174653002602191653001402217653003202231653000902263653001102272653001502283653001702298653003202315653002902347653001402376100003502390700003102425700003302456700002002489700001802509700002802527700003202555700002902587700003002616700002602646700001902672700003602691700002202727700002902749700002802778700003102806700002602837700002602863700003302889700004002922856012802962       2021                        eng d  a1528-365800aTaxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.0 aTaxonomic variations in the gut microbiome of gout patients with  c2021 05 24  a500 v273 a<p><b>OBJECTIVE: </b>To evaluate the taxonomic composition of the gut microbiome in gout patients with and without tophi formation, and predict bacterial functions that might have an impact on urate metabolism.</p><p><b>METHODS: </b>Hypervariable V3-V4 regions of the bacterial 16S rRNA gene from fecal samples of gout patients with and without tophi (n = 33 and n = 25, respectively) were sequenced and compared to fecal samples from 53 healthy controls. We explored predictive functional profiles using bioinformatics in order to identify differences in taxonomy and metabolic pathways.</p><p><b>RESULTS: </b>We identified a microbiome characterized by the lowest richness and a higher abundance of Phascolarctobacterium, Bacteroides, Akkermansia, and Ruminococcus_gnavus_group genera in patients with gout without tophi when compared to controls. The Proteobacteria phylum and the Escherichia-Shigella genus were more abundant in patients with tophaceous gout than in controls. Fold change analysis detected nine genera enriched in healthy controls compared to gout groups (Bifidobacterium, Butyricicoccus, Oscillobacter, Ruminococcaceae_UCG_010, Lachnospiraceae_ND2007_group, Haemophilus, Ruminococcus_1, Clostridium_sensu_stricto_1, and Ruminococcaceae_UGC_013). We found that the core microbiota of both gout groups shared Bacteroides caccae, Bacteroides stercoris ATCC 43183, and Bacteroides coprocola DSM 17136. These bacteria might perform functions linked to one-carbon metabolism, nucleotide binding, amino acid biosynthesis, and purine biosynthesis. Finally, we observed differences in key bacterial enzymes involved in urate synthesis, degradation, and elimination.</p><p><b>CONCLUSION: </b>Our findings revealed that taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism.</p>10aBiodiversity10aComputational Biology10aDysbiosis10aGastrointestinal Microbiome10aGout10aHumans10aMetagenome10ametagenomics10aProtein Interaction Mapping10aProtein Interaction Maps10aUric Acid1 aMéndez-Salazar, Eder, Orlando1 aVázquez-Mellado, Janitzia1 aCasimiro-Soriguer, Carlos, S1 aDopazo, Joaquin1 aCubuk, Cankut1 aZamudio-Cuevas, Yessica1 aFrancisco-Balderas, Adriana1 aMartínez-Flores, Karina1 aFernández-Torres, Javier1 aLozada-Pérez, Carlos1 aPineda, Carlos1 aSánchez-González, Austreberto1 aSilveira, Luis, H1 aBurguete-García, Ana, I1 aOrbe-Orihuela, Citlalli1 aLagunas-Martínez, Alfredo1 aVazquez-Gomez, Alonso1 aLópez-Reyes, Alberto1 aPalacios-González, Berenice1 aMartínez-Nava, Gabriela, Angélica  uhttp://clinbioinfosspa.es/content/taxonomic-variations-gut-microbiome-gout-patients-and-without-tophi-might-have-functional02316nas a2200241   4500008004100000022001400041245014100055210006900196260001500265490000600280520146700286653001101753653004301764653001101807653000901818653001401827653002401841100001801865700001801883700002401901700002001925856012901945       2020                        eng d  a2073-440900aMechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments.0 aMechanistic Models of Signaling Pathways Reveal the Drug Action   c2020 06 290 v93 a<p>Despite the existence of differences in gene expression across numerous genes between males and females having been known for a long time, these have been mostly ignored in many studies, including drug development and its therapeutic use. In fact, the consequences of such differences over the disease mechanisms or the drug action mechanisms are completely unknown. Here we applied mechanistic mathematical models of signaling activity to reveal the ultimate functional consequences that gender-specific gene expression activities have over cell functionality and fate. Moreover, we also used the mechanistic modeling framework to simulate the drug interventions and unravel how drug action mechanisms are affected by gender-specific differential gene expression. Interestingly, some cancers have many biological processes significantly affected by these gender-specific differences (e.g., bladder or head and neck carcinomas), while others (e.g., glioblastoma or rectum cancer) are almost insensitive to them. We found that many of these gender-specific differences affect cancer-specific pathways or in physiological signaling pathways, also involved in cancer origin and development. Finally, mechanistic models have the potential to be used for finding alternative therapeutic interventions on the pathways targeted by the drug, which lead to similar results compensating the downstream consequences of gender-specific differences in gene expression.</p>10aFemale10aGene Expression Regulation, Neoplastic10aHumans10aMale10aNeoplasms10aSignal Transduction1 aCubuk, Cankut1 aCan, Fatma, E1 aPeña-Chilet, Maria1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/mechanistic-models-signaling-pathways-reveal-drug-action-mechanisms-behind-gender-specific01814nas a2200265   4500008004100000022001400041245007700055210006900132260001600201300001400217490000700231520098400238653001501222653001101237653002301248653002401271653002001295653001801315100001901333700002201352700001801374700003101392700002001423856010501443       2019                        eng d  a1477-405400aA comparison of mechanistic signaling pathway activity analysis methods.0 acomparison of mechanistic signaling pathway activity analysis me  c2019 Sep 27  a1655-16680 v203 a<p>Understanding the aspects of cell functionality that account for disease mechanisms or drug modes of action is a main challenge for precision medicine. Classical gene-based approaches ignore the modular nature of most human traits, whereas conventional pathway enrichment approaches produce only illustrative results of limited practical utility. Recently, a family of new methods has emerged that change the focus from the whole pathways to the definition of elementary subpathways within them that have any mechanistic significance and to the study of their activities. Thus, mechanistic pathway activity (MPA) methods constitute a new paradigm that allows recoding poorly informative genomic measurements into cell activity quantitative values and relate them to phenotypes. Here we provide a review on the MPA methods available and explain their contribution to systems medicine approaches for addressing challenges in the diagnostic and treatment of complex diseases.</p>10aAlgorithms10aHumans10aPostmortem Changes10aSignal Transduction10aSystems biology10aTranscriptome1 aAmadoz, Alicia1 aHidalgo, Marta, R1 aCubuk, Cankut1 aCarbonell-Caballero, José1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/comparison-mechanistic-signaling-pathway-activity-analysis-methods02834nas a2200397   4500008004100000022001400041245011600055210006900171260000900240300000600249490000600255520157600261653002601837653002401863653001901887653002901906653001101935653001301946653003601959653002301995653001402018653001402032653001302046653001802059100001802077700002202095700001902117700001602136700002402152700002202176700002102198700002002219700003102239700002002270856014602290       2019                        eng d  a2056-718900aDifferential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.0 aDifferential metabolic activity and discovery of therapeutic tar  c2019  a70 v53 a<p>In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.</p>10aComputational Biology10aComputer Simulation10aDrug discovery10aGene Regulatory Networks10aHumans10aInternet10aMetabolic Networks and Pathways10aModels, Biological10aNeoplasms10aPhenotype10aSoftware10aTranscriptome1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aRian, Kinza1 aSalavert, Francisco1 aPujana, Miguel, A1 aMateo, Francesca1 aHerranz, Carmen1 aCarbonell-Caballero, José1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/differential-metabolic-activity-and-discovery-therapeutic-targets-using-summarized-metabolic-pathway-models02573nas a2200529   4500008004100000022001400041245008700055210006900142260001600211300000800227490000600235520103000241653001001271653001801281653001001299653001101309653002001320653001101340653002301351653002301374653001901397653002501416653001801441100002301459700002701482700002101509700002501530700001601555700001901571700001701590700002201607700002401629700003101653700002101684700002401705700001901729700002401748700001801772700001801790700002101808700002001829700002001849700002101869700002301890700002001913856011001933       2018                        eng d  a2041-172300aThe effects of death and post-mortem cold ischemia on human tissue transcriptomes.0 aeffects of death and postmortem cold ischemia on human tissue tr  c2018 Feb 13  a4900 v93 a<p>Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.</p>10aBlood10aCold Ischemia10aDeath10aFemale10agene expression10aHumans10aModels, Biological10aPostmortem Changes10aRNA, Messenger10aStochastic Processes10aTranscriptome1 aFerreira, Pedro, G1 aMuñoz-Aguirre, Manuel1 aReverter, Ferran1 aGodinho, Caio, P Sá1 aSousa, Abel1 aAmadoz, Alicia1 aSodaei, Reza1 aHidalgo, Marta, R1 aPervouchine, Dmitri1 aCarbonell-Caballero, José1 aNurtdinov, Ramil1 aBreschi, Alessandra1 aAmador, Raziel1 aOliveira, Patrícia1 aCubuk, Cankut1 aCurado, João1 aAguet, François1 aOliveira, Carla1 aDopazo, Joaquin1 aSammeth, Michael1 aArdlie, Kristin, G1 aGuigó, Roderic  uhttp://clinbioinfosspa.es/content/effects-death-and-post-mortem-cold-ischemia-human-tissue-transcriptomes02946nas a2200445   4500008004100000022001400041245009500055210006900150260001600219300001400235490000700249520156700256653002101823653002101844653002401865653003001889653004301919653002901962653001101991653002602002653001502028653001302043653001402056653001402070653001402084653001402098653002702112653002702139653001802166653002202184100001802206700002202224700001902246700002202265700002102287700002002308700003102328700002002359856012102379       2018                        eng d  a1538-744500aGene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.0 aGene Expression Integration into Pathway Modules Reveals a PanCa  c2018 Nov 01  a6059-60720 v783 a<p>Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies. Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. .</p>10aCell Line, Tumor10aCluster Analysis10aDisease Progression10aGene Expression Profiling10aGene Expression Regulation, Neoplastic10aGene Regulatory Networks10aHumans10aKaplan-Meier Estimate10aMetabolome10amutation10aNeoplasms10aOncogenes10aPhenotype10aPrognosis10aRNA, Small Interfering10aSequence Analysis, RNA10aTranscriptome10aTreatment Outcome1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aPujana, Miguel, A1 aMateo, Francesca1 aHerranz, Carmen1 aCarbonell-Caballero, José1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/gene-expression-integration-pathway-modules-reveals-pan-cancer-metabolic-landscape02251nas a2200277   4500008004100000022001400041245011400055210006900169260001600238300000700254490000700261520126600268653002601534653004301560653001101603653004201614653002401656653001801680653002401698100002201722700001901744700001801763700003101781700002001812856014101832       2018                        eng d  a1745-615000aModels of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome.0 aModels of cell signaling uncover molecular mechanisms of highris  c2018 Aug 22  a160 v133 a<p><b>BACKGROUND: </b>Despite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40-50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.</p><p><b>RESULTS: </b>Since signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.</p><p><b>CONCLUSION: </b>We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.</p><p><b>REVIEWERS: </b>This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.</p>10aComputational Biology10aGene Expression Regulation, Neoplastic10aHumans10aJNK Mitogen-Activated Protein Kinases10aModels, Theoretical10aNeuroblastoma10aSignal Transduction1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aCubuk, Cankut1 aCarbonell-Caballero, José1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/models-cell-signaling-uncover-molecular-mechanisms-high-risk-neuroblastoma-and-predict-disease-outcome02079nas a2200313   4500008004100000022001400041245012900055210006900184260001600253300001400269490000600283520099600289653002601285653002001311653002901331653001101360653001301371653001401384653002301398653002701421653002401448100002201472700001801494700001901512700002401531700003101555700002001586856015901606       2017                        eng d  a1949-255300aHigh throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.0 aHigh throughput estimation of functional cell activities reveals  c2017 Jan 17  a5160-51780 v83 a<p>Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.</p>10aComputational Biology10agene expression10aGene Regulatory Networks10aHumans10amutation10aNeoplasms10aPrecision Medicine10aSequence Analysis, RNA10aSignal Transduction1 aHidalgo, Marta, R1 aCubuk, Cankut1 aAmadoz, Alicia1 aSalavert, Francisco1 aCarbonell-Caballero, José1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/high-throughput-estimation-functional-cell-activities-reveals-disease-mechanisms-and-predicts-relevant-clinical-outcomes02685nas a2200349   4500008004100000022001400041245011500055210006900170260001600239300001400255490000700269520162000276653001201896653002201908653001101930653001301941653001301954653001101967653002401978653002002002653003102022653001302053100003102066700001902097700002002116700002202136700001802158700001802176700002802194700002002222856009302242       2017                        eng d  a1367-481100aReference genome assessment from a population scale perspective: an accurate profile of variability and noise.0 aReference genome assessment from a population scale perspective   c2017 Nov 15  a3511-35170 v333 a<p><b>MOTIVATION: </b>Current plant and animal genomic studies are often based on newly assembled genomes that have not been properly consolidated. In this scenario, misassembled regions can easily lead to false-positive findings. Despite quality control scores are included within genotyping protocols, they are usually employed to evaluate individual sample quality rather than reference sequence reliability. We propose a statistical model that combines quality control scores across samples in order to detect incongruent patterns at every genomic region. Our model is inherently robust since common artifact signals are expected to be shared between independent samples over misassembled regions of the genome.</p><p><b>RESULTS: </b>The reliability of our protocol has been extensively tested through different experiments and organisms with accurate results, improving state-of-the-art methods. Our analysis demonstrates synergistic relations between quality control scores and allelic variability estimators, that improve the detection of misassembled regions, and is able to find strong artifact signals even within the human reference assembly. Furthermore, we demonstrated how our model can be trained to properly rank the confidence of a set of candidate variants obtained from new independent samples.</p><p><b>AVAILABILITY AND IMPLEMENTATION: </b>This tool is freely available at http://gitlab.com/carbonell/ces.</p><p><b>CONTACT: </b>jcarbonell.cipf@gmail.com or joaquin.dopazo@juntadeandalucia.es.</p><p><b>SUPPLEMENTARY INFORMATION: </b>Supplementary data are available at Bioinformatics online.</p>10aAnimals10aGenetic Variation10aGenome10aGenomics10aGenotype10aHumans10aModels, Statistical10aQuality Control10aReproducibility of Results10aSoftware1 aCarbonell-Caballero, José1 aAmadoz, Alicia1 aAlonso, Roberto1 aHidalgo, Marta, R1 aCubuk, Cankut1 aConesa, David1 aLópez-Quílez, Antonio1 aDopazo, Joaquin  uhttps://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx48201971nas a2200301   4500008004100000022001400041245010200055210006900157260001500226520100600241653002101247653002201268653002601290653001901316653002101335653002601356653001501382653002401397100002401421700002101445700001901466700001801485700002001503700001901523700003101542700002101573856007501594       2016                        eng d  a1362-496200aActionable pathways: interactive discovery of therapeutic targets using signaling pathway models.0 aActionable pathways interactive discovery of therapeutic targets  c2016 May 23 aThe discovery of actionable targets is crucial for targeted therapies and is also a constituent part of the drug discovery process. The success of an intervention over a target depends critically on its contribution, within the complex network of gene interactions, to the cellular processes responsible for disease progression or therapeutic response. Here we present PathAct, a web server that predicts the effect that interventions over genes (inhibitions or activations that simulate knock-outs, drug treatments or over-expressions) can have over signal transmission within signaling pathways and, ultimately, over the cell functionalities triggered by them. PathAct implements an advanced graphical interface that provides a unique interactive working environment in which the suitability of potentially actionable genes, that could eventually become drug targets for personalized or individualized therapies, can be easily tested. The PathAct tool can be found at: http://pathact.babelomics.org.10aactionable genes10aDisease mechanism10adrug action mechanism10aDrug discovery10apathway analysis10apersonalized medicine10asignalling10atherapeutic targets1 aSalavert, Francisco1 aHidago, Marta, R1 aAmadoz, Alicia1 aCubuk, Cankut1 aMedina, Ignacio1 aCrespo, Daniel1 aCarbonell-Caballero, José1 aDopazo, Joaquín  uhttp://nar.oxfordjournals.org/content/early/2016/05/02/nar.gkw369.full02634nas 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=ists2003441nas a2200277   4500008004100000022001400041245011600055210006900171260001300240300001000253490000600263520251800269100001902787700002102806700002002827700002202847700001802869700001902887700001702906700002202923700002002945700002402965700001902989700002903008856012603037       2016                        eng d  a2212-877800aStress-induced activation of brown adipose tissue prevents obesity in conditions of low adaptive thermogenesis.0 aStressinduced activation of brown adipose tissue prevents obesit  c2016 Jan  a19-330 v53 a<p><b>BACKGROUND: </b>Stress-associated conditions such as psychoemotional reactivity and depression have been paradoxically linked to either weight gain or weight loss. This bi-directional effect of stress is not understood at the functional level. Here we tested the hypothesis that pre-stress level of adaptive thermogenesis and brown adipose tissue (BAT) functions explain the vulnerability or resilience to stress-induced obesity.</p><p><b>METHODS: </b>We used wt and triple β1,β2,β3-Adrenergic Receptors knockout (β-less) mice exposed to a model of chronic subordination stress (CSS) at either room temperature (22 °C) or murine thermoneutrality (30 °C). A combined behavioral, physiological, molecular, and immunohistochemical analysis was conducted to determine stress-induced modulation of energy balance and BAT structure and function. Immortalized brown adipocytes were used for in vitro assays.</p><p><b>RESULTS: </b>Departing from our initial observation that βARs are dispensable for cold-induced BAT browning, we demonstrated that under physiological conditions promoting low adaptive thermogenesis and BAT activity (e.g. thermoneutrality or genetic deletion of the βARs), exposure to CSS acted as a stimulus for BAT activation and thermogenesis, resulting in resistance to diet-induced obesity despite the presence of hyperphagia. Conversely, in wt mice acclimatized to room temperature, and therefore characterized by sustained BAT function, exposure to CSS increased vulnerability to obesity. Exposure to CSS enhanced the sympathetic innervation of BAT in wt acclimatized to thermoneutrality and in β-less mice. Despite increased sympathetic innervation suggesting adrenergic-mediated browning, norepinephrine did not promote browning in βARs knockout brown adipocytes, which led us to identify an alternative sympathetic/brown adipocytes purinergic pathway in the BAT. This pathway is downregulated under conditions of low adaptive thermogenesis requirements, is induced by stress, and elicits activation of UCP1 in wt and β-less brown adipocytes. Importantly, this purinergic pathway is conserved in human BAT.</p><p><b>CONCLUSION: </b>Our findings demonstrate that thermogenesis and BAT function are determinant of the resilience or vulnerability to stress-induced obesity. Our data support a model in which adrenergic and purinergic pathways exert complementary/synergistic functions in BAT, thus suggesting an alternative to βARs agonists for the activation of human BAT.</p>1 aRazzoli, Maria1 aFrontini, Andrea1 aGurney, Allison1 aMondini, Eleonora1 aCubuk, Cankut1 aKatz, Liora, S1 aCero, Cheryl1 aBolan, Patrick, J1 aDopazo, Joaquin1 aVidal-Puig, Antonio1 aCinti, Saverio1 aBartolomucci, Alessandro  uhttp://clinbioinfosspa.es/content/stress-induced-activation-brown-adipose-tissue-prevents-obesity-conditions-low-adaptive02445nas a2200469   4500008004100000022001400041245008300055210006900138260001600207300001400223490000700237520108700244653001501331653002101346653002201367653001601389653002101405653000801426653001201434653002001446653002001466100002001486700002401506700002901530700003101559700001701590700002401607700002301631700002201654700002401676700002101700700001801721700002201739700001901761700003601780700002301816700002301839700002001862700002001882700002001902856005301922       2015                        eng d  a1362-496200aBabelomics 5.0: functional interpretation for new generations of genomic data.0 aBabelomics 50 functional interpretation for new generations of g  c2015 Apr 20  aW117-W1210 v433 aBabelomics has been running for more than one decade offering a user-friendly interface for the functional analysis of gene expression and genomic data. Here we present its fifth release, which includes support for Next Generation Sequencing data including gene expression (RNA-seq), exome or genome resequencing. Babelomics has simplified its interface, being now more intuitive. Improved visualization options, such as a genome viewer as well as an interactive network viewer, have been implemented. New technical enhancements at both, client and server sides, makes the user experience faster and more dynamic. Babelomics offers user-friendly access to a full range of methods that cover: (i) primary data analysis, (ii) a variety of tests for different experimental designs and (iii) different enrichment and network analysis algorithms for the interpretation of the results of such tests in the proper functional context. In addition to the public server, local copies of Babelomics can be downloaded and installed. Babelomics is freely available at: http://www.babelomics.org.10ababelomics10adata integration10agene set analysis10ainteractome10anetwork analysis10aNGS10aRNA-seq10aSystems biology10atranscriptomics1 aAlonso, Roberto1 aSalavert, Francisco1 aGarcia-Garcia, Francisco1 aCarbonell-Caballero, José1 aBleda, Marta1 aGarcía-Alonso, Luz1 aSanchis-Juan, Alba1 aPerez-Gil, Daniel1 aMarin-Garcia, Pablo1 aSánchez, Rubén1 aCubuk, Cankut1 aHidalgo, Marta, R1 aAmadoz, Alicia1 aHernansaiz-Ballesteros, Rosa, D1 aAlemán, Alejandro1 aTárraga, Joaquín1 aMontaner, David1 aMedina, Ignacio1 aDopazo, Joaquin  uhttp://nar.oxfordjournals.org/content/43/W1/W117