Apaolaza-Emparanza, I.(Iñigo)
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- Adaptation of the human gut microbiota metabolic network during the first year after birth(Frontiers Media SA, 2019) Vallès, Y. (Yvonne); Fuertes, A. (Alvaro); Rufián-Henares, J.Á. (Ángel José); Francino, M.P. (M. Pilar); Planes-Pedreño, F.J. (Francisco Javier); Apaolaza-Emparanza, I.(Iñigo); Pérez-Burillo, S. (Sergio)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.
- A network-based approach to integrate nutrient microenvironment in the prediction of synthetic lethality in cancer metabolism(2022) San-Jose-Eneriz, E. (Edurne); Valcárcel-García, L.V. (Luis Vitores); Aguirre-Ena, X. (Xabier); Planes-Pedreño, F.J. (Francisco Javier); Apaolaza-Emparanza, I.(Iñigo); Prosper-Cardoso, F. (Felipe)Synthetic Lethality (SL) is currently defined as a type of genetic interaction in which the loss of function of either of two genes individually has limited effect in cell viability but inactivation of both genes simultaneously leads to cell death. Given the profound genomic aberrations acquired by tumor cells, which can be systematically identified with -omics data, SL is a promising concept in cancer research. In particular, SL has received much attention in the area of cancer metabolism, due to the fact that relevant functional alterations concentrate on key metabolic pathways that promote cellular proliferation. With the extensive prior knowledge about human metabolic networks, a number of computational methods have been developed to predict SL in cancer metabolism, including the genetic Minimal Cut Sets (gMCSs) approach. A major challenge in the application of SL approaches to cancer metabolism is to systematically integrate tumor microenvironment, given that genetic interactions and nutritional availability are interconnected to support proliferation. Here, we propose a more general definition of SL for cancer metabolism that combines genetic and environmental interactions, namely loss of gene functions and absence of nutrients in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase (DHFR) inhibition in different environments. Finally, our approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.
- COBRA methods and metabolic drug targets in cancer(Taylor & Francis, 2018) San-Jose-Eneriz, E. (Edurne); Planes-Pedreño, F.J. (Francisco Javier); Apaolaza-Emparanza, I.(Iñigo); Prosper-Cardoso, F. (Felipe); Aguirre-Ena, X. (Xabier)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.
- Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells(2023) Valcárcel-García, L.V. (Luis Vitores); Planes-Pedreño, F.J. (Francisco Javier); Barrena, N. (Naroa); Apaolaza-Emparanza, I.(Iñigo); Olaverri, D. (Danel)Synthetic lethality (SL) is a promising concept in cancer research. A wide array of computational tools has been developed to predict and exploit synthetic lethality for the identification of tumour-specific vulnerabilities. Previously, we introduced the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to SL developed for genome-scale metabolic networks. The major challenge in our gMCS framework is to go beyond metabolic networks and extend existing algorithms to more complex protein-protein interactions. In this article, we take a step further and incorporate linear regulatory pathways into our gMCS approach. Extensive algorithmic modifications to compute gMCSs in integrated metabolic and regulatory models are presented in detail. Our extended approach is applied to calculate gMCSs in integrated models of human cells. In particular, we integrate the most recent genome-scale metabolic network, Human1, with 3 different regulatory network databases: Omnipath, Dorothea and TRRUST. Based on the computed gMCSs and transcriptomic data, we discovered new essential genes and their associated synthetic lethal for different cancer cell lines. The performance of the different integrated models is assessed with available large-scale in-vitro gene silencing data. Finally, we discuss the most relevant gene essentiality predictions based on published literature in cancer research.
- Novel Constrain-Based Modeling approaches for the identification of metabolic drug targets in cancer.(Sevicio de Publicaciones. Universidad de Navarra, 2020-01-13) Apaolaza-Emparanza, I.(Iñigo); Planes-Pedreño, F.J. (Francisco Javier)Metabolic reprogramming has been defined to be a hallmark of cancer. One major question in cancer research is how to exploit these metabolic alterations for the identification of novel therapeutic strategies. With the outbreak of high throughput –omics data and the advances in genomics, novel holistic and integrative approaches are required to address this question. Systems Biology aims at responding to these needs and has provided the scientific community with a large variety of algorithms and approaches. Among different computational approaches in Systems Biology, Constraint-Based Modeling, based on genome-scale metabolic networks, has received much attention in the last years. They have provided different promising tools to predict metabolic targets in cancer, but, so far, with limited predictive power when compared to experimental data. In this doctoral thesis, we present a novel methodology to more accurately predict metabolic targets in cancer. Our approach is radically different to previous approaches in the literature and relies on a novel concept termed genetic Minimal Cut Sets. The relevance of our approach is shown in two different case studies. First, we applied it to explain the role that RRM1 plays in Multiple Myeloma. Second, we aimed at identifying selective therapeutic strategies in tamoxifen-resistant breast cancer.
- Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.(2019) Heirendt, L. (Laurent); Arreckx, S. (Sylvain); Pfau, Thomas; Mendoza, S.N. (Sebastián N.); Richelle, A. (Anne); Heinken, Almut; Haraldsdóttir, H.S. (Hulda S.); Wachowiak, J. (Jacek); Keating, S.M. (Sarah M.); Vlasov, V. (Vanja); Magnusdóttir, S. (Stefania); Ng, C. Y. (Chiam Yu); Preciat, G. (German); Zagare, A. (Alise); Chan, S.H.J. (Siu H.J.); Aurich, M.K. (Maike K.); Clancy, C.M. (Catherine M.); Modamio, J. (Jennifer); Sauls, J.T. (John T.); Noronha, A. (Alberto); Bordbar, A. (Aarash); Cousins, B. (Benjamin); El Assal, D.C. (Diana C.); Valcárcel-García, L.V. (Luis Vitores); Apaolaza-Emparanza, I.(Iñigo); Ghaderi, S. (Susan); Ahookhosh, M. (Masoud); Ben Guebila, M. (Marouen); Kostromins, A. (Andrejs); Sompairac, N. (Nicolas); Le, H.M. (Hoai M.); Ma, D. (Ding); Sun, Y. (Yuekai); Wang, L. (Lin); Yurkovich, J.T. (James T.); Oliveira, M.A.P. (Miguel A.P.); Vuong, P.T. (Phan T.); El Assal, L.P. (Lemmer P.); Kuperstein, I. (Inna); Zinovyev, A. (Andrei); Hinton, H.S. (H.Scott); Bryant, W.A. (William A.); Aragón-Artacho, F.J. (Francisco J.); Planes-Pedreño, F.J. (Francisco Javier); Stalidzans, E. (Egils); Maass, A. (Alejandro); Vempala, S. (Santosh); Hucka, M. (Michael); Saunders, M.A. (Michael A.); Maranas, C.D. (Costas D.); Lewis, N.E. (Nathan E.); Sauter, T. (Thomas); Palsson, B. O. (Bernhard O.); Thiele, I. (Inés); Fleming, R.M.T. (Ronan M.T.)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.