01814nas 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-outcome02943nas a2200541   4500008004100000022001400041245008900055210006900144260000900213300001300222490000700235520138200242653001001624653000901634653001801643653002101661653001101682653002901693653003201722653002601754653001101780653001401791653002901805653000901834653001601843653001301859653002201872653001801894653001401912100002101926700003101947700002001978700002401998700002402022700002602046700001602072700001902088700001902107700002502126700002002151700001902171700002002190700002002210700002402230700002002254700001902274856010802293       2018                        eng d  a1932-620300aThe modular network structure of the mutational landscape of Acute Myeloid Leukemia.0 amodular network structure of the mutational landscape of Acute M  c2018  ae02029260 v133 a<p>Acute myeloid leukemia (AML) is associated with the sequential accumulation of acquired genetic alterations. Although at diagnosis cytogenetic alterations are frequent in AML, roughly 50% of patients present an apparently normal karyotype (NK), leading to a highly heterogeneous prognosis. Due to this significant heterogeneity, it has been suggested that different molecular mechanisms may trigger the disease with diverse prognostic implications. We performed whole-exome sequencing (WES) of tumor-normal matched samples of de novo AML-NK patients lacking mutations in NPM1, CEBPA or FLT3-ITD to identify new gene mutations with potential prognostic and therapeutic relevance to patients with AML. Novel candidate-genes, together with others previously described, were targeted resequenced in an independent cohort of 100 de novo AML patients classified in the cytogenetic intermediate-risk (IR) category. A mean of 4.89 mutations per sample were detected in 73 genes, 35 of which were mutated in more than one patient. After a network enrichment analysis, we defined a single in silico model and established a set of seed-genes that may trigger leukemogenesis in patients with normal karyotype. The high heterogeneity of gene mutations observed in AML patients suggested that a specific alteration could not be as essential as the interaction of deregulated pathways.</p>10aAdult10aAged10aCytodiagnosis10aExome Sequencing10aFemale10aGene Regulatory Networks10aGenetic Association Studies10aGenetic Heterogeneity10aHumans10aKaryotype10aLeukemia, Myeloid, Acute10aMale10aMiddle Aged10amutation10aNeoplasm Proteins10aNucleophosmin10aPrognosis1 aIbáñez, Mariam1 aCarbonell-Caballero, José1 aSuch, Esperanza1 aGarcía-Alonso, Luz1 aLiquori, Alessandro1 aLópez-Pavía, María1 aLLop, Marta1 aAlonso, Carmen1 aBarragán, Eva1 aGómez-Seguí, Inés1 aNeef, Alexander1 aHervás, David1 aMontesinos, Pau1 aSanz, Guillermo1 aSanz, Miguel, Angel1 aDopazo, Joaquin1 aCervera, José  uhttp://clinbioinfosspa.es/content/modular-network-structure-mutational-landscape-acute-myeloid-leukemia02079nas 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/btx48202859nas a2200433   4500008004100000022001400041245013500055210006900190260000900259300001300268490000700281520145800288653001001746653002901756653001801785653001101803653001901814653003501833653001301868653001801881653003601899653003101935100002101966700003101987700002402018700002002042700002902062700001902091700001902110700002602129700001602155700001902171700002502190700002002215700002002235700002002255700001902275856013102294       2016                        eng d  a1932-620300aThe Mutational Landscape of Acute Promyelocytic Leukemia Reveals an Interacting Network of Co-Occurrences and Recurrent Mutations.0 aMutational Landscape of Acute Promyelocytic Leukemia Reveals an   c2016  ae01483460 v113 a<p>Preliminary Acute Promyelocytic Leukemia (APL) whole exome sequencing (WES) studies have identified a huge number of somatic mutations affecting more than a hundred different genes mainly in a non-recurrent manner, suggesting that APL is a heterogeneous disease with secondary relevant changes not yet defined. To extend our knowledge of subtle genetic alterations involved in APL that might cooperate with PML/RARA in the leukemogenic process, we performed a comprehensive analysis of somatic mutations in APL combining WES with sequencing of a custom panel of targeted genes by next-generation sequencing. To select a reduced subset of high confidence candidate driver genes, further in silico analysis were carried out. After prioritization and network analysis we found recurrent deleterious mutations in 8 individual genes (STAG2, U2AF1, SMC1A, USP9X, IKZF1, LYN, MYCBP2 and PTPN11) with a strong potential of being involved in APL pathogenesis. Our network analysis of multiple mutations provides a reliable approach to prioritize genes for additional analysis, improving our knowledge of the leukemogenesis interactome. Additionally, we have defined a functional module in the interactome of APL. The hypothesis is that the number, or the specific combinations, of mutations harbored in each patient might not be as important as the disturbance caused in biological key functions, triggered by several not necessarily recurrent mutations. </p>10aExome10aGene Regulatory Networks10aGenome, Human10aHumans10aINDEL Mutation10aLeukemia, Promyelocytic, Acute10amutation10aMutation Rate10aPolymorphism, Single Nucleotide10aReproducibility of Results1 aIbáñez, Mariam1 aCarbonell-Caballero, José1 aGarcía-Alonso, Luz1 aSuch, Esperanza1 aJiménez-Almazán, Jorge1 aVidal, Enrique1 aBarragán, Eva1 aLópez-Pavía, María1 aLLop, Marta1 aMartín, Iván1 aGómez-Seguí, Inés1 aMontesinos, Pau1 aSanz, Miguel, A1 aDopazo, Joaquin1 aCervera, José  uhttp://clinbioinfosspa.es/content/mutational-landscape-acute-promyelocytic-leukemia-reveals-interacting-network-co-occurrences02932nas a2200397   4500008004100000022001400041245010000055210006900155260001600224300000800240490000700248520172500255653001201980653001001992653001702002653002202019653002502041653001802066653001302084653001102097653002002108653001302128653001402141653002502155653002902180653002702209653001102236100002402247700002902271700003102300700002202331700002702353700002502380700002002405856010902425       2014                        eng d  a1744-429200aThe role of the interactome in the maintenance of deleterious variability in human populations.0 arole of the interactome in the maintenance of deleterious variab  c2014 Sep 26  a7520 v103 a<p>Recent genomic projects have revealed the existence of an unexpectedly large amount of deleterious variability in the human genome. Several hypotheses have been proposed to explain such an apparently high mutational load. However, the mechanisms by which deleterious mutations in some genes cause a pathological effect but are apparently innocuous in other genes remain largely unknown. This study searched for deleterious variants in the 1,000 genomes populations, as well as in a newly sequenced population of 252 healthy Spanish individuals. In addition, variants causative of monogenic diseases and somatic variants from 41 chronic lymphocytic leukaemia patients were analysed. The deleterious variants found were analysed in the context of the interactome to understand the role of network topology in the maintenance of the observed mutational load. Our results suggest that one of the mechanisms whereby the effect of these deleterious variants on the phenotype is suppressed could be related to the configuration of the protein interaction network. Most of the deleterious variants observed in healthy individuals are concentrated in peripheral regions of the interactome, in combinations that preserve their connectivity, and have a marginal effect on interactome integrity. On the contrary, likely pathogenic cancer somatic deleterious variants tend to occur in internal regions of the interactome, often with associated structural consequences. Finally, variants causative of monogenic diseases seem to occupy an intermediate position. Our observations suggest that the real pathological potential of a variant might be more a systems property rather than an intrinsic property of individual proteins. </p>10aAlleles10aExome10aGene Library10aGenetic Variation10aGenetics, Population10aGenome, Human10aGenomics10aHumans10aModels, Genetic10amutation10aPhenotype10aProtein Conformation10aProtein Interaction Maps10aSequence Analysis, DNA10aWhites1 aGarcía-Alonso, Luz1 aJiménez-Almazán, Jorge1 aCarbonell-Caballero, José1 aVela-Boza, Alicia1 aSantoyo-López, Javier1 aAntiňolo, Guillermo1 aDopazo, Joaquin  uhttp://clinbioinfosspa.es/content/role-interactome-maintenance-deleterious-variability-human-populations