Nuestros investigadores

Publicaciones científicas más recientes (desde 2010)

Autores: Ponce-de-Leon, M. ; Apaolaza Emparanza, Iñigo; Valencia, A. , (Autor de correspondencia); et al.
ISSN 1367-4803  Vol. 36  Nº 6  2020  págs. 1986 - 1988
Autores: Fuertes, Alvaro; Perez-Burillo, Sergio; Apaolaza Emparanza, Iñigo; et al.
ISSN 1664-302X  Vol. 10  2019 
Predicting the metabolic behavior of the human gut microbiota in different contexts is one of the most promising areas of constraint-based modeling. Recently, we presented a supra-organismal approach to build context-specific metabolic networks of bacterial communities using functional and taxonomic assignments of meta-omics data. In this work, this algorithm is applied to elucidate the metabolic changes induced over the first year after birth in the gut microbiota of a cohort of Spanish infants. We used metagenomics data of fecal samples and nutritional data of 13 infants at five time points. The resulting networks for each time point were analyzed, finding significant alterations once solid food is introduced in the diet. Our work shows that solid food leads to a different pattern of output metabolites that can be potentially released from the gut microbiota to the host. Experimental validation is presented for ferulate, a neuroprotective metabolite involved in the gut-brain axis
Autores: Apaolaza Emparanza, Iñigo; Valcarcel, L. V.; Planes Pedreño, Francisco Javier (Autor de correspondencia)
ISSN 1367-4803  Vol. 35  Nº 3  2019  págs. 535 - 537
Motivation: The identification of minimal gene knockout strategies to engineer metabolic systems constitutes one of the most relevant applications of the COnstraint-Based Reconstruction and Analysis (COBRA) framework. In the last years, the minimal cut sets (MCSs) approach has emerged as a promising tool to carry out this task. However, MCSs define reaction knockout strategies, which are not necessarily transformed into feasible strategies at the gene level. Results: We present a more general, easy-to-use and efficient computational implementation of a previously published algorithm to calculate MCSs to the gene level (gMCSs). Our tool was compared with existing methods in order to calculate essential genes and synthetic lethals in metabolic networks of different complexity, showing a significant reduction in model size and computation time.
Autores: Heirendt, L.; Arreckx, S.; Pfau, T.; et al.
ISSN 1754-2189  Vol. 14  Nº 3  2019  págs. 639 - 702
Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.
Autores: Cortazar, A.R.; Torrano, V.; Martín-Martín, N.; et al.
ISSN 0008-5472  Vol. 78  Nº 21  2018  págs. 6320 - 6328
With the advent of OMICs technologies, both individual research groups and consortia have spear-headed the characterization of human samples of multiple pathophysiologic origins, resulting in thousands of archived genomes and transcriptomes. Although a variety of web tools are now available to extract information from OMICs data, their utility has been limited by the capacity of nonbioinformatician researchers to exploit the information. To address this problem, we have developed CANCERTOOL, a web-based interface that aims to overcome the major limitations of public transcriptomics dataset analysis for highly prevalent types of cancer (breast, prostate, lung, and colorectal). CANCERTOOL provides rapid and comprehensive visualization of gene expression data for the gene(s) of interest in well-annotated cancer datasets. This visualization is accompanied by generation of reports customized to the interest of the researcher (e.g., editable figures, detailed statistical analyses, and access to raw data for reanalysis). It also carries out gene-to-gene correlations in multiple datasets at the same time or using preset patient groups. Finally, this new tool solves the time-consuming task of performing functional enrichment analysis with gene sets of interest using up to 11 different databases at the same time. Collectively, CANCERTOOL represents a simple and freely accessible interface to interrogate well-annotated datasets and obtain publishable representations that can contribute to refinement and guidance of cancer-related investigations at all levels of hypotheses and design.Significance: In order to facilitate access of research groups without bioinformatics support to public transcriptomics data, we have developed a free online tool with an easy-to-use interface that allows researchers to obtain quality information in a readily publishable format.
Autores: Apaolaza Emparanza, Iñigo; San José Enériz, Edurne; Aguirre Ena, Xabier; et al.
ISSN 2372-3556  Vol. 30  Nº 5  2018  págs. e1389672.
The identification of therapeutic strategies exploiting the metabolic alterations of malignant cells is a relevant area in cancer research. Here, we discuss a novel computational method, based on the COBRA (COnstraint-Based Reconstruction and Analysis) framework for metabolic networks, to perform this task. Current and future steps are presented.
Autores: Pey Pérez, Jon; San José Enériz, Edurne; Ochoa Nieto, Maria del Carmen; et al.
ISSN 2045-2322  Vol. 7  2017 
Constraint-based modeling for genome-scale metabolic networks has emerged in the last years as a promising approach to elucidate drug targets in cancer. Beyond the canonical biosynthetic routes to produce biomass, it is of key importance to focus on metabolic routes that sustain the proliferative capacity through the regulation of other biological means in order to improve in-silico gene essentiality analyses. Polyamines are polycations with central roles in cancer cell proliferation, through the regulation of transcription and translation among other things, but are typically neglected in in silico cancer metabolic models. In this study, we analysed essential genes for the biosynthesis of polyamines. Our analysis corroborates the importance of previously known regulators of the pathway, such as Adenosylmethionine Decarboxylase 1 (AMD1) and uncovers novel enzymes predicted to be relevant for polyamine homeostasis. We focused on Adenine Phosphoribosyltransferase (APRT) and demonstrated the detrimental consequence of APRT gene silencing on different leukaemia cell lines. Our results highlight the importance of revisiting the metabolic models used for in-silico gene essentiality analyses in order to maximize the potential for drug target identification in cancer.
Autores: Apaolaza Emparanza, Iñigo; San José Enériz, Edurne; Tobalina Oraa, Eva; et al.
ISSN 2041-1723  Vol. 8  Nº 1  2017  págs. 459
Synthetic lethality is a promising concept in cancer research, potentially opening new possibilities for the development of more effective and selective treatments. Here, we present a computational method to predict and exploit synthetic lethality in cancer metabolism. Our approach relies on the concept of genetic minimal cut sets and gene expression data, demonstrating a superior performance to previous approaches predicting metabolic vulnerabilities in cancer. Our genetic minimal cut set computational framework is applied to evaluate the lethality of ribonucleotide reductase catalytic subunit M1 (RRM1) inhibition in multiple myeloma. We present a computational and experimental study of the effect of RRM1 inhibition in four multiple myeloma cell lines. In addition, using publicly available genome-scale loss-of-function screens, a possible mechanism by which the inhibition of RRM1 is effective in cancer is established. Overall, our approach shows promising results and lays the foundation to build a novel family of algorithms to target metabolism in cancer.