TY - JOUR T1 - Mechanistic modeling of the SARS-CoV-2 disease map. JF - BioData Min Y1 - 2021 A1 - Rian, Kinza A1 - Esteban-Medina, Marina A1 - Hidalgo, Marta R A1 - Cubuk, Cankut A1 - Falco, Matias M A1 - Loucera, Carlos A1 - Gunyel, Devrim A1 - Ostaszewski, Marek A1 - Peña-Chilet, Maria A1 - Dopazo, Joaquin AB -

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.

VL - 14 IS - 1 U1 - https://www.ncbi.nlm.nih.gov/pubmed/33478554?dopt=Abstract ER - TY - JOUR T1 - Mechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscapeAbstract JF - NAR Cancer Y1 - 2020 A1 - Falco, Matias M A1 - Peña-Chilet, Maria A1 - Loucera, Carlos A1 - Hidalgo, Marta R A1 - Dopazo, Joaquin VL - 2 UR - https://academic.oup.com/narcancer/article/doi/10.1093/narcan/zcaa011/5862620http://academic.oup.com/narcancer/article-pdf/2/2/zcaa011/33428092/zcaa011.pdfhttp://academic.oup.com/narcancer/article-pdf/2/2/zcaa011/33428092/zcaa011.pdf IS - 2 ER - TY - JOUR T1 - A comparison of mechanistic signaling pathway activity analysis methods. JF - Brief Bioinform Y1 - 2019 A1 - Amadoz, Alicia A1 - Hidalgo, Marta R A1 - Cubuk, Cankut A1 - Carbonell-Caballero, José A1 - Dopazo, Joaquin KW - Algorithms KW - Humans KW - Postmortem Changes KW - Signal Transduction KW - Systems biology KW - Transcriptome AB -

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.

VL - 20 IS - 5 U1 - https://www.ncbi.nlm.nih.gov/pubmed/29868818?dopt=Abstract ER - TY - JOUR T1 - Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models. JF - NPJ Syst Biol Appl Y1 - 2019 A1 - Cubuk, Cankut A1 - Hidalgo, Marta R A1 - Amadoz, Alicia A1 - Rian, Kinza A1 - Salavert, Francisco A1 - Pujana, Miguel A A1 - Mateo, Francesca A1 - Herranz, Carmen A1 - Carbonell-Caballero, José A1 - Dopazo, Joaquin KW - Computational Biology KW - Computer Simulation KW - Drug discovery KW - Gene Regulatory Networks KW - Humans KW - Internet KW - Metabolic Networks and Pathways KW - Models, Biological KW - Neoplasms KW - Phenotype KW - Software KW - Transcriptome AB -

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.

VL - 5 U1 - https://www.ncbi.nlm.nih.gov/pubmed/30854222?dopt=Abstract ER - TY - JOUR T1 - The effects of death and post-mortem cold ischemia on human tissue transcriptomes. JF - Nat Commun Y1 - 2018 A1 - Ferreira, Pedro G A1 - Muñoz-Aguirre, Manuel A1 - Reverter, Ferran A1 - Sá Godinho, Caio P A1 - Sousa, Abel A1 - Amadoz, Alicia A1 - Sodaei, Reza A1 - Hidalgo, Marta R A1 - Pervouchine, Dmitri A1 - Carbonell-Caballero, José A1 - Nurtdinov, Ramil A1 - Breschi, Alessandra A1 - Amador, Raziel A1 - Oliveira, Patrícia A1 - Cubuk, Cankut A1 - Curado, João A1 - Aguet, François A1 - Oliveira, Carla A1 - Dopazo, Joaquin A1 - Sammeth, Michael A1 - Ardlie, Kristin G A1 - Guigó, Roderic KW - Blood KW - Cold Ischemia KW - Death KW - Female KW - gene expression KW - Humans KW - Models, Biological KW - Postmortem Changes KW - RNA, Messenger KW - Stochastic Processes KW - Transcriptome AB -

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.

VL - 9 IS - 1 U1 - https://www.ncbi.nlm.nih.gov/pubmed/29440659?dopt=Abstract ER - TY - JOUR T1 - Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape. JF - Cancer Res Y1 - 2018 A1 - Cubuk, Cankut A1 - Hidalgo, Marta R A1 - Amadoz, Alicia A1 - Pujana, Miguel A A1 - Mateo, Francesca A1 - Herranz, Carmen A1 - Carbonell-Caballero, José A1 - Dopazo, Joaquin KW - Cell Line, Tumor KW - Cluster Analysis KW - Disease Progression KW - Gene Expression Profiling KW - Gene Expression Regulation, Neoplastic KW - Gene Regulatory Networks KW - Humans KW - Kaplan-Meier Estimate KW - Metabolome KW - mutation KW - Neoplasms KW - Oncogenes KW - Phenotype KW - Prognosis KW - RNA, Small Interfering KW - Sequence Analysis, RNA KW - Transcriptome KW - Treatment Outcome AB -

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. .

VL - 78 IS - 21 U1 - https://www.ncbi.nlm.nih.gov/pubmed/30135189?dopt=Abstract ER - TY - JOUR T1 - Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome. JF - Biol Direct Y1 - 2018 A1 - Hidalgo, Marta R A1 - Amadoz, Alicia A1 - Cubuk, Cankut A1 - Carbonell-Caballero, José A1 - Dopazo, Joaquin KW - Computational Biology KW - Gene Expression Regulation, Neoplastic KW - Humans KW - JNK Mitogen-Activated Protein Kinases KW - Models, Theoretical KW - Neuroblastoma KW - Signal Transduction AB -

BACKGROUND: 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.

RESULTS: 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.

CONCLUSION: We have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.

REVIEWERS: This article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers' comments section.

VL - 13 IS - 1 U1 - https://www.ncbi.nlm.nih.gov/pubmed/30134948?dopt=Abstract ER - TY - JOUR T1 - High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes. JF - Oncotarget Y1 - 2017 A1 - Hidalgo, Marta R A1 - Cubuk, Cankut A1 - Amadoz, Alicia A1 - Salavert, Francisco A1 - Carbonell-Caballero, José A1 - Dopazo, Joaquin KW - Computational Biology KW - gene expression KW - Gene Regulatory Networks KW - Humans KW - mutation KW - Neoplasms KW - Precision Medicine KW - Sequence Analysis, RNA KW - Signal Transduction AB -

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.

VL - 8 IS - 3 U1 - https://www.ncbi.nlm.nih.gov/pubmed/28042959?dopt=Abstract ER - TY - JOUR T1 - Reference genome assessment from a population scale perspective: an accurate profile of variability and noise. JF - Bioinformatics Y1 - 2017 A1 - Carbonell-Caballero, José A1 - Amadoz, Alicia A1 - Alonso, Roberto A1 - Hidalgo, Marta R A1 - Cubuk, Cankut A1 - Conesa, David A1 - López-Quílez, Antonio A1 - Dopazo, Joaquin KW - Animals KW - Genetic Variation KW - Genome KW - Genomics KW - Genotype KW - Humans KW - Models, Statistical KW - Quality Control KW - Reproducibility of Results KW - Software AB -

Motivation: 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.

Results: 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.

Availability and implementation: This tool is freely available at http://gitlab.com/carbonell/ces.

Contact: jcarbonell.cipf@gmail.com or joaquin.dopazo@juntadeandalucia.es.

Supplementary information: Supplementary data are available at Bioinformatics online.

VL - 33 UR - https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx482 IS - 22 U1 - https://www.ncbi.nlm.nih.gov/pubmed/28961772?dopt=Abstract ER - TY - JOUR T1 - Babelomics 5.0: functional interpretation for new generations of genomic data. JF - Nucleic acids research Y1 - 2015 A1 - Alonso, Roberto A1 - Salavert, Francisco A1 - Garcia-Garcia, Francisco A1 - Carbonell-Caballero, José A1 - Bleda, Marta A1 - García-Alonso, Luz A1 - Sanchis-Juan, Alba A1 - Perez-Gil, Daniel A1 - Marin-Garcia, Pablo A1 - Sánchez, Rubén A1 - Cubuk, Cankut A1 - Hidalgo, Marta R A1 - Amadoz, Alicia A1 - Hernansaiz-Ballesteros, Rosa D A1 - Alemán, Alejandro A1 - Tárraga, Joaquín A1 - Montaner, David A1 - Medina, Ignacio A1 - Dopazo, Joaquin KW - babelomics KW - data integration KW - gene set analysis KW - interactome KW - network analysis KW - NGS KW - RNA-seq KW - Systems biology KW - transcriptomics AB - Babelomics 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. VL - 43 UR - http://nar.oxfordjournals.org/content/43/W1/W117 ER -