A comparison of mechanistic signaling pathway activity analysis methods. - Amadoz, Alicia; Hidalgo, Marta R; Cubuk, Cankut; Carbonell-Caballero, José; Dopazo, Joaquin (Brief Bioinform, 2019)
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.
Keywords: Algorithms; Humans; Postmortem Changes; Signal Transduction; Systems biology; Transcriptome

Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models. - Cubuk, Cankut; Hidalgo, Marta R; Amadoz, Alicia; Rian, Kinza; Salavert, Francisco; Pujana, Miguel A; Mateo, Francesca; Herranz, Carmen; Carbonell-Caballero, José; Dopazo, Joaquin (NPJ Syst Biol Appl, 2019)
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.
Keywords: Computational Biology; Computer Simulation; Drug discovery; Gene Regulatory Networks; Humans; Internet; Metabolic Networks and Pathways; Models, Biological; Neoplasms; Phenotype; Software; Transcriptome

The effects of death and post-mortem cold ischemia on human tissue transcriptomes. - Ferreira, Pedro G; Muñoz-Aguirre, Manuel; Reverter, Ferran; Sá Godinho, Caio P; Sousa, Abel; Amadoz, Alicia; Sodaei, Reza; Hidalgo, Marta R; Pervouchine, Dmitri; Carbonell-Caballero, José; Nurtdinov, Ramil; Breschi, Alessandra; et al. (Nat Commun, 2018)
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.
Keywords: Blood; Cold Ischemia; Death; Female; gene expression; Humans; Models, Biological; Postmortem Changes; RNA, Messenger; Stochastic Processes; Transcriptome

Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape. - Cubuk, Cankut; Hidalgo, Marta R; Amadoz, Alicia; Pujana, Miguel A; Mateo, Francesca; Herranz, Carmen; Carbonell-Caballero, José; Dopazo, Joaquin (Cancer Res, 2018)
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. .
Keywords: Cell Line, Tumor; Cluster Analysis; Disease Progression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Humans; Kaplan-Meier Estimate; Metabolome; mutation; Neoplasms; Oncogenes; Phenotype; Prognosis; RNA, Small Interfering; Sequence Analysis, RNA; Transcriptome; Treatment Outcome

Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome. - Hidalgo, Marta R; Amadoz, Alicia; Cubuk, Cankut; Carbonell-Caballero, José; Dopazo, Joaquin (Biol Direct, 2018)
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.
Keywords: Computational Biology; Gene Expression Regulation, Neoplastic; Humans; JNK Mitogen-Activated Protein Kinases; Models, Theoretical; Neuroblastoma; Signal Transduction

High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes. - Hidalgo, Marta R; Cubuk, Cankut; Amadoz, Alicia; Salavert, Francisco; Carbonell-Caballero, José; Dopazo, Joaquin (Oncotarget, 2017)
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.
Keywords: Computational Biology; gene expression; Gene Regulatory Networks; Humans; mutation; Neoplasms; Precision Medicine; Sequence Analysis, RNA; Signal Transduction

Reference genome assessment from a population scale perspective: an accurate profile of variability and noise. - Carbonell-Caballero, José; Amadoz, Alicia; Alonso, Roberto; Hidalgo, Marta R; Cubuk, Cankut; Conesa, David; López-Quílez, Antonio; Dopazo, Joaquin (Bioinformatics, 2017)
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.
Keywords: Animals; Genetic Variation; Genome; Genomics; Genotype; Humans; Models, Statistical; Quality Control; Reproducibility of Results; Software

Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity. - Amadoz, Alicia; Sebastián-Leon, Patricia; Vidal, Enrique; Salavert, Francisco; Dopazo, Joaquin (Sci Rep, 2015)
Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA).
Keywords: Algorithms; Antineoplastic Agents; biomarkers; Cell Line, Tumor; Cell Survival; gene expression; Humans; Lethal Dose 50; Neoplasms; Phosphorylation; Proteins; Signal Transduction

Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments. - García-Alonso, Luz; Alonso, Roberto; Vidal, Enrique; Amadoz, Alicia; De Maria, Alejandro; Minguez, Pablo; Medina, Ignacio; Dopazo, Joaquin (Nucleic Acids Res, 2012)
Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.
Keywords: Bipolar Disorder; Fanconi Anemia; Gene Regulatory Networks; Genes, Neoplasm; Genome-Wide Association Study; Genomics; Humans; Protein Interaction Mapping