@article {422, title = {Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.}, journal = {NPJ Syst Biol Appl}, volume = {5}, year = {2019}, month = {2019}, pages = {7}, abstract = {

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}, issn = {2056-7189}, doi = {10.1038/s41540-019-0087-2}, author = {Cubuk, Cankut and Hidalgo, Marta R and Amadoz, Alicia and Rian, Kinza and Salavert, Francisco and Pujana, Miguel A and Mateo, Francesca and Herranz, Carmen and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin} } @article {405, title = {Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.}, journal = {Cancer Res}, volume = {78}, year = {2018}, month = {2018 Nov 01}, pages = {6059-6072}, abstract = {

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}, issn = {1538-7445}, doi = {10.1158/0008-5472.CAN-17-2705}, author = {Cubuk, Cankut and Hidalgo, Marta R and Amadoz, Alicia and Pujana, Miguel A and Mateo, Francesca and Herranz, Carmen and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin} } @article {494, title = {The role of the interactome in the maintenance of deleterious variability in human populations.}, journal = {Mol Syst Biol}, volume = {10}, year = {2014}, month = {2014 Sep 26}, pages = {752}, abstract = {

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.

}, keywords = {Alleles, Exome, Gene Library, Genetic Variation, Genetics, Population, Genome, Human, Genomics, Humans, Models, Genetic, mutation, Phenotype, Protein Conformation, Protein Interaction Maps, Sequence Analysis, DNA, Whites}, issn = {1744-4292}, doi = {10.15252/msb.20145222}, author = {Garc{\'\i}a-Alonso, Luz and Jim{\'e}nez-Almaz{\'a}n, Jorge and Carbonell-Caballero, Jos{\'e} and Vela-Boza, Alicia and Santoyo-L{\'o}pez, Javier and Anti{\v n}olo, Guillermo and Dopazo, Joaquin} }