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de Atauri P, Foguet C, Cascante M. Control analysis in the identification of key enzymes driving metabolic adaptations: Towards drug target discovery. Biosystems 2023; 231:104984. [PMID: 37506820 DOI: 10.1016/j.biosystems.2023.104984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/18/2023] [Accepted: 07/25/2023] [Indexed: 07/30/2023]
Abstract
Metabolic Control Analysis (MCA) marked a turning point in understanding the design principles of metabolic network control by establishing control coefficients as a means to quantify the degree of control that an enzyme exerts on flux or metabolite concentrations. MCA has demonstrated that control of metabolic pathways is distributed among many enzymes rather than depending on a single rate-limiting step. MCA also proved that this distribution depends not only on the stoichiometric structure of the network but also on other kinetic determinants, such as the degree of saturation of the enzyme active site, the distance to thermodynamic equilibrium, and metabolite feedback regulatory loops. Consequently, predicting the alterations that occur during metabolic adaptation in response to strong changes involving a redistribution in such control distribution can be challenging. Here, using the framework provided by MCA, we illustrate how control distribution in a metabolic pathway/network depends on enzyme kinetic determinants and to what extent the redistribution of control affects our predictions on candidate enzymes suitable as targets for small molecule inhibition in the drug discovery process. Our results uncover that kinetic determinants can lead to unexpected control distribution and outcomes that cannot be predicted solely from stoichiometric determinants. We also unveil that the inference of key enzyme-drivers of an observed metabolic adaptation can be dramatically improved using mean control coefficients and ruling out those enzyme activities that are associated with low control coefficients. As the use of constraint-based stoichiometric genome-scale metabolic models (GSMMs) becomes increasingly prevalent for identifying genes/enzymes that could be potential drug targets, we anticipate that incorporating kinetic determinants and ruling out enzymes with low control coefficients into GSMM workflows will facilitate more accurate predictions and reveal novel therapeutic targets.
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Affiliation(s)
- Pedro de Atauri
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of Universitat de Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Madrid, 28020, Spain.
| | - Carles Foguet
- British Heart Foundation Cardiovascular Epidemiology Unit and Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, CB2 0BD, United Kingdom
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of Universitat de Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, 08028, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Madrid, 28020, Spain.
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2
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Yang J, Virostko J, Liu J, Jarrett AM, Hormuth DA, Yankeelov TE. Comparing mechanism-based and machine learning models for predicting the effects of glucose accessibility on tumor cell proliferation. Sci Rep 2023; 13:10387. [PMID: 37369672 DOI: 10.1038/s41598-023-37238-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 06/19/2023] [Indexed: 06/29/2023] Open
Abstract
Glucose plays a central role in tumor metabolism and development and is a target for novel therapeutics. To characterize the response of cancer cells to blockade of glucose uptake, we collected time-resolved microscopy data to track the growth of MDA-MB-231 breast cancer cells. We then developed a mechanism-based, mathematical model to predict how a glucose transporter (GLUT1) inhibitor (Cytochalasin B) influences the growth of the MDA-MB-231 cells by limiting access to glucose. The model includes a parameter describing dose dependent inhibition to quantify both the total glucose level in the system and the glucose level accessible to the tumor cells. Four common machine learning models were also used to predict tumor cell growth. Both the mechanism-based and machine learning models were trained and validated, and the prediction error was evaluated by the coefficient of determination (R2). The random forest model provided the highest accuracy predicting cell dynamics (R2 = 0.92), followed by the decision tree (R2 = 0.89), k-nearest-neighbor regression (R2 = 0.84), mechanism-based (R2 = 0.77), and linear regression model (R2 = 0.69). Thus, the mechanism-based model has a predictive capability comparable to machine learning models with the added benefit of elucidating biological mechanisms.
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Affiliation(s)
- Jianchen Yang
- Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keaton, BME Building, 1 University Station, C0800, Austin, TX, 78712, USA
| | - Jack Virostko
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX, 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Junyan Liu
- Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keaton, BME Building, 1 University Station, C0800, Austin, TX, 78712, USA
| | - Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, 78712, USA
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, 107 W. Dean Keaton, BME Building, 1 University Station, C0800, Austin, TX, 78712, USA.
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, 78712, USA.
- Department of Oncology, The University of Texas at Austin, Austin, TX, 78712, USA.
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, 78712, USA.
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, 78712, USA.
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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Vieira V, Ferreira J, Rocha M. A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale. PLoS Comput Biol 2022; 18:e1009294. [PMID: 35749559 PMCID: PMC9278738 DOI: 10.1371/journal.pcbi.1009294] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 07/13/2022] [Accepted: 04/15/2022] [Indexed: 11/18/2022] Open
Abstract
Constraint-based (CB) metabolic models provide a mathematical framework and scaffold for in silico cell metabolism analysis and manipulation. In the past decade, significant efforts have been done to model human metabolism, enabled by the increased availability of multi-omics datasets and curated genome-scale reconstructions, as well as the development of several algorithms for context-specific model (CSM) reconstruction. Although CSM reconstruction has revealed insights on the deregulated metabolism of several pathologies, the process of reconstructing representative models of human tissues still lacks benchmarks and appropriate integrated software frameworks, since many tools required for this process are still disperse across various software platforms, some of which are proprietary. In this work, we address this challenge by assembling a scalable CSM reconstruction pipeline capable of integrating transcriptomics data in CB models. We combined omics preprocessing methods inspired by previous efforts with in-house implementations of existing CSM algorithms and new model refinement and validation routines, all implemented in the Troppo Python-based open-source framework. The pipeline was validated with multi-omics datasets from the Cancer Cell Line Encyclopedia (CCLE), also including reference fluxomics measurements for the MCF7 cell line. We reconstructed over 6000 models based on the Human-GEM template model for 733 cell lines featured in the CCLE, using MCF7 models as reference to find the best parameter combinations. These reference models outperform earlier studies using the same template by comparing gene essentiality and fluxomics experiments. We also analysed the heterogeneity of breast cancer cell lines, identifying key changes in metabolism related to cancer aggressiveness. Despite the many challenges in CB modelling, we demonstrate using our pipeline that combining transcriptomics data in metabolic models can be used to investigate key metabolic shifts. Significant limitations were found on these models ability for reliable quantitative flux prediction, thus motivating further work in genome-wide phenotype prediction. Genome-scale models of human metabolism are promising tools capable of contextualising large omics datasets within a framework that enables analysis and manipulation of metabolic phenotypes. Despite various successes in applying these methods to provide mechanistic hypotheses for deregulated metabolism in disease, there is no standardized workflow to extract these models using existing methods and the tools required to do so are mostly implemented using proprietary software. We have assembled a generic pipeline to extract and validate context-specific metabolic models using multi-omics datasets and implemented it using the troppo framework. We first validate our pipeline using MCF7 cell line models and assess their ability to predict lethal gene knockouts as well as flux activity using multi-omics data. We also demonstrate how this approach can be generalized for large-scale transcriptomics datasets and used to generate insights on the metabolic heterogeneity of cancer and relevant features for other data mining approaches. The pipeline is available as part of an open-source framework that is generic for a variety of applications.
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Affiliation(s)
- Vítor Vieira
- Centre of Biological Engineering (CEB), Universidade do Minho, Braga, Portugal
| | - Jorge Ferreira
- Centre of Biological Engineering (CEB), Universidade do Minho, Braga, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering (CEB), Universidade do Minho, Braga, Portugal
- LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
- * E-mail:
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Islam MM, Goertzen A, Singh PK, Saha R. Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling. iScience 2022; 25:104483. [PMID: 35712079 PMCID: PMC9194136 DOI: 10.1016/j.isci.2022.104483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/08/2022] [Accepted: 05/23/2022] [Indexed: 11/18/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a major research focus because of its poor therapy response and dismal prognosis. PDAC cells adapt their metabolism to the surrounding environment, often relying on diverse nutrient sources. Because traditional experimental techniques appear exhaustive to find a viable therapeutic strategy, a highly curated and omics-informed PDAC genome-scale metabolic model was reconstructed using patient-specific transcriptomics data. From the model-predictions, several new metabolic functions were explored as potential therapeutic targets in addition to the known metabolic hallmarks of PDAC. Significant downregulation in the peroxisomal beta oxidation pathway, flux modulation in the carnitine shuttle system, and upregulation in the reactive oxygen species detoxification pathway reactions were observed. These unique metabolic traits of PDAC were correlated with potential drug combinations targeting genes with poor prognosis in PDAC. Overall, this study provides a better understanding of the metabolic vulnerabilities in PDAC and will lead to novel effective therapeutic strategies.
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Affiliation(s)
- Mohammad Mazharul Islam
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Andrea Goertzen
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
| | - Pankaj K. Singh
- Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
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5
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Cardio-onco-metabolism: metabolic remodelling in cardiovascular disease and cancer. Nat Rev Cardiol 2022; 19:414-425. [PMID: 35440740 PMCID: PMC10112835 DOI: 10.1038/s41569-022-00698-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/17/2022] [Indexed: 02/07/2023]
Abstract
Cardiovascular disease and cancer are the two leading causes of morbidity and mortality in the world. The emerging field of cardio-oncology has revealed that these seemingly disparate disease processes are intertwined, owing to the cardiovascular sequelae of anticancer therapies, shared risk factors that predispose individuals to both cardiovascular disease and cancer, as well the possible potentiation of cancer growth by cardiac dysfunction. As a result, interest has increased in understanding the fundamental biological mechanisms that are central to the relationship between cardiovascular disease and cancer. Metabolism, appropriate regulation of energy, energy substrate utilization, and macromolecular synthesis and breakdown are fundamental processes for cellular and organismal survival. In this Review, we explore the emerging data identifying metabolic dysregulation as an important theme in cardio-oncology. We discuss the growing recognition of metabolic reprogramming in cardiovascular disease and cancer and view the novel area of cardio-oncology through the lens of metabolism.
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Di Filippo M, Pescini D, Galuzzi BG, Bonanomi M, Gaglio D, Mangano E, Consolandi C, Alberghina L, Vanoni M, Damiani C. INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLoS Comput Biol 2022; 18:e1009337. [PMID: 35130273 PMCID: PMC8853556 DOI: 10.1371/journal.pcbi.1009337] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 02/17/2022] [Accepted: 01/13/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.
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Affiliation(s)
- Marzia Di Filippo
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
| | - Dario Pescini
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
| | - Bruno Giovanni Galuzzi
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Marcella Bonanomi
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Daniela Gaglio
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Segrate, Italy
| | - Eleonora Mangano
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate, Italy
| | - Clarissa Consolandi
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate, Italy
| | - Lilia Alberghina
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Marco Vanoni
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Chiara Damiani
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
- * E-mail:
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7
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Argininosuccinate lyase is a metabolic vulnerability in breast development and cancer. NPJ Syst Biol Appl 2021; 7:36. [PMID: 34535676 PMCID: PMC8448827 DOI: 10.1038/s41540-021-00195-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 08/09/2021] [Indexed: 02/07/2023] Open
Abstract
Epithelial-to-mesenchymal transition (EMT) is fundamental to both normal tissue development and cancer progression. We hypothesized that EMT plasticity defines a range of metabolic phenotypes and that individual breast epithelial metabolic phenotypes are likely to fall within this phenotypic landscape. To determine EMT metabolic phenotypes, the metabolism of EMT was described within genome-scale metabolic models (GSMMs) using either transcriptomic or proteomic data from the breast epithelial EMT cell culture model D492. The ability of the different data types to describe breast epithelial metabolism was assessed using constraint-based modeling which was subsequently verified using 13C isotope tracer analysis. The application of proteomic data to GSMMs provided relatively higher accuracy in flux predictions compared to the transcriptomic data. Furthermore, the proteomic GSMMs predicted altered cholesterol metabolism and increased dependency on argininosuccinate lyase (ASL) following EMT which were confirmed in vitro using drug assays and siRNA knockdown experiments. The successful verification of the proteomic GSMMs afforded iBreast2886, a breast GSMM that encompasses the metabolic plasticity of EMT as defined by the D492 EMT cell culture model. Analysis of breast tumor proteomic data using iBreast2886 identified vulnerabilities within arginine metabolism that allowed prognostic discrimination of breast cancer patients on a subtype-specific level. Taken together, we demonstrate that the metabolic reconstruction iBreast2886 formalizes the metabolism of breast epithelial cell development and can be utilized as a tool for the functional interpretation of high throughput clinical data.
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Polat IH, Tarrado-Castellarnau M, Benito A, Hernandez-Carro C, Centelles J, Marin S, Cascante M. Glutamine Modulates Expression and Function of Glucose 6-Phosphate Dehydrogenase via NRF2 in Colon Cancer Cells. Antioxidants (Basel) 2021; 10:antiox10091349. [PMID: 34572981 PMCID: PMC8472416 DOI: 10.3390/antiox10091349] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 08/21/2021] [Accepted: 08/23/2021] [Indexed: 12/02/2022] Open
Abstract
Nucleotide pools need to be constantly replenished in cancer cells to support cell proliferation. The synthesis of nucleotides requires glutamine and 5-phosphoribosyl-1-pyrophosphate produced from ribose-5-phosphate via the oxidative branch of the pentose phosphate pathway (ox-PPP). Both PPP and glutamine also play a key role in maintaining the redox status of cancer cells. Enhanced glutamine metabolism and increased glucose 6-phosphate dehydrogenase (G6PD) expression have been related to a malignant phenotype in tumors. However, the association between G6PD overexpression and glutamine consumption in cancer cell proliferation is still incompletely understood. In this study, we demonstrated that both inhibition of G6PD and glutamine deprivation decrease the proliferation of colon cancer cells and induce cell cycle arrest and apoptosis. Moreover, we unveiled that glutamine deprivation induce an increase of G6PD expression that is mediated through the activation of the nuclear factor (erythroid-derived 2)-like 2 (NRF2). This crosstalk between G6PD and glutamine points out the potential of combined therapies targeting oxidative PPP enzymes and glutamine catabolism to combat colon cancer.
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Affiliation(s)
- Ibrahim H. Polat
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028 Barcelona, Spain; (I.H.P.); (M.T.-C.); (A.B.); (C.H.-C.); (J.C.)
- Institute of Biomedicine, Universitat de Barcelona (IBUB), 08028 Barcelona, Spain
- Equipe Environnement et Prédiction de la Santé des Populations, Laboratoire TIMC (UMR 5525), CHU de Grenoble, Université Grenoble Alpes, CEDEX, 38700 La Tronche, France
| | - Míriam Tarrado-Castellarnau
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028 Barcelona, Spain; (I.H.P.); (M.T.-C.); (A.B.); (C.H.-C.); (J.C.)
- Institute of Biomedicine, Universitat de Barcelona (IBUB), 08028 Barcelona, Spain
- CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
| | - Adrian Benito
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028 Barcelona, Spain; (I.H.P.); (M.T.-C.); (A.B.); (C.H.-C.); (J.C.)
- Institute of Biomedicine, Universitat de Barcelona (IBUB), 08028 Barcelona, Spain
| | - Claudia Hernandez-Carro
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028 Barcelona, Spain; (I.H.P.); (M.T.-C.); (A.B.); (C.H.-C.); (J.C.)
- Institute of Biomedicine, Universitat de Barcelona (IBUB), 08028 Barcelona, Spain
| | - Josep Centelles
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028 Barcelona, Spain; (I.H.P.); (M.T.-C.); (A.B.); (C.H.-C.); (J.C.)
- Institute of Biomedicine, Universitat de Barcelona (IBUB), 08028 Barcelona, Spain
- CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
| | - Silvia Marin
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028 Barcelona, Spain; (I.H.P.); (M.T.-C.); (A.B.); (C.H.-C.); (J.C.)
- Institute of Biomedicine, Universitat de Barcelona (IBUB), 08028 Barcelona, Spain
- CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
- Correspondence: (S.M.); (M.C.)
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028 Barcelona, Spain; (I.H.P.); (M.T.-C.); (A.B.); (C.H.-C.); (J.C.)
- Institute of Biomedicine, Universitat de Barcelona (IBUB), 08028 Barcelona, Spain
- CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
- Correspondence: (S.M.); (M.C.)
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Karakitsou E, Foguet C, Contreras Mostazo MG, Kurrle N, Schnütgen F, Michaelis M, Cinatl J, Marin S, Cascante M. Genome-scale integration of transcriptome and metabolome unveils squalene synthase and dihydrofolate reductase as targets against AML cells resistant to chemotherapy. Comput Struct Biotechnol J 2021; 19:4059-4066. [PMID: 34377370 PMCID: PMC8326745 DOI: 10.1016/j.csbj.2021.06.049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 06/29/2021] [Accepted: 06/29/2021] [Indexed: 01/06/2023] Open
Abstract
The development of resistance to chemotherapeutic agents, such as Doxorubicin (DOX) and cytarabine (AraC), is one of the greatest challenges to the successful treatment of Acute Myeloid Leukemia (AML). Such acquisition is often underlined by a metabolic reprogramming that can provide a therapeutic opportunity, as it can lead to the emergence of vulnerabilities and dependencies to be exploited as targets against the resistant cells. In this regard, genome-scale metabolic models (GSMMs) have emerged as powerful tools to integrate multiple layers of data to build cancer-specific models and identify putative metabolic vulnerabilities. Here, we use genome-scale metabolic modelling to reconstruct a GSMM of the THP1 AML cell line and two derivative cell lines, one with acquired resistance to AraC and the second with acquired resistance to DOX. We also explore how, adding to the transcriptomic layer, the metabolomic layer enhances the selectivity of the resulting condition specific reconstructions. The resulting models enabled us to identify and experimentally validate that drug-resistant THP1 cells are sensitive to the FDA-approved antifolate methotrexate. Moreover, we discovered and validated that the resistant cell lines could be selectively targeted by inhibiting squalene synthase, providing a new and promising strategy to directly inhibit cholesterol synthesis in AML drug resistant cells.
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Affiliation(s)
- Effrosyni Karakitsou
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, 08028 Barcelona, Spain
- Institute of Biomedicine of University of Barcelona, 08028 Barcelona, Spain
- CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, 08028 Barcelona, Spain
- Institute of Biomedicine of University of Barcelona, 08028 Barcelona, Spain
- CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
- Metabolomics Node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
| | - Miriam G. Contreras Mostazo
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, 08028 Barcelona, Spain
- Institute of Biomedicine of University of Barcelona, 08028 Barcelona, Spain
- CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
| | - Nina Kurrle
- Department of Medicine, Hematology/Oncology, University Hospital Frankfurt, Goethe-University, 60590 Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Frankfurt Cancer Institute (FCI), Goethe University, 60590 Frankfurt am Main, Germany
| | - Frank Schnütgen
- Department of Medicine, Hematology/Oncology, University Hospital Frankfurt, Goethe-University, 60590 Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Frankfurt Cancer Institute (FCI), Goethe University, 60590 Frankfurt am Main, Germany
| | - Martin Michaelis
- School of Biosciences, University of Kent, Canterbury, United Kingdom
| | - Jindrich Cinatl
- Institut für Medizinische Virologie, Klinikum der Goethe-Universität, Frankfurt am Main, Germany
| | - Silvia Marin
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, 08028 Barcelona, Spain
- Institute of Biomedicine of University of Barcelona, 08028 Barcelona, Spain
- CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
- Metabolomics Node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, 08028 Barcelona, Spain
- Institute of Biomedicine of University of Barcelona, 08028 Barcelona, Spain
- CIBER of Hepatic and Digestive Diseases (CIBEREHD), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
- Metabolomics Node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain
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10
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Auslander N, Gussow AB, Koonin EV. Incorporating Machine Learning into Established Bioinformatics Frameworks. Int J Mol Sci 2021; 22:2903. [PMID: 33809353 PMCID: PMC8000113 DOI: 10.3390/ijms22062903] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/08/2021] [Accepted: 03/10/2021] [Indexed: 12/23/2022] Open
Abstract
The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.
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Affiliation(s)
| | | | - Eugene V. Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA;
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Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches. Processes (Basel) 2021. [DOI: 10.3390/pr9020322] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.
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Dugourd A, Kuppe C, Sciacovelli M, Gjerga E, Gabor A, Emdal KB, Vieira V, Bekker‐Jensen DB, Kranz J, Bindels E, Costa AS, Sousa A, Beltrao P, Rocha M, Olsen JV, Frezza C, Kramann R, Saez‐Rodriguez J. Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses. Mol Syst Biol 2021; 17:e9730. [PMID: 33502086 PMCID: PMC7838823 DOI: 10.15252/msb.20209730] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 12/18/2020] [Accepted: 12/21/2020] [Indexed: 01/07/2023] Open
Abstract
Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.
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Affiliation(s)
- Aurelien Dugourd
- Faculty of Medicine, and Heidelberg University HospitalInstitute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Faculty of MedicineJoint Research Centre for Computational Biomedicine (JRC‐COMBINE)RWTH Aachen UniversityAachenGermany
- Faculty of MedicineInstitute of Experimental Medicine and Systems BiologyRWTH Aachen UniversityAachenGermany
- Division of Nephrology and Clinical ImmunologyFaculty of MedicineRWTH Aachen UniversityAachenGermany
| | - Christoph Kuppe
- Faculty of MedicineInstitute of Experimental Medicine and Systems BiologyRWTH Aachen UniversityAachenGermany
- Division of Nephrology and Clinical ImmunologyFaculty of MedicineRWTH Aachen UniversityAachenGermany
- Department of Internal Medicine, Nephrology and TransplantationErasmus Medical CenterRotterdamThe Netherlands
| | - Marco Sciacovelli
- MRC Cancer UnitHutchison/MRC Research CentreUniversity of CambridgeCambridgeUK
| | - Enio Gjerga
- Faculty of Medicine, and Heidelberg University HospitalInstitute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Faculty of MedicineJoint Research Centre for Computational Biomedicine (JRC‐COMBINE)RWTH Aachen UniversityAachenGermany
| | - Attila Gabor
- Faculty of Medicine, and Heidelberg University HospitalInstitute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Kristina B. Emdal
- Faculty of Health and Medical SciencesProteomics ProgramNovo Nordisk Foundation Center for Protein ResearchUniversity of CopenhagenCopenhagenDenmark
| | - Vitor Vieira
- Centre of Biological EngineeringUniversity of Minho ‐ Campus de GualtarBragaPortugal
| | - Dorte B. Bekker‐Jensen
- Faculty of Health and Medical SciencesProteomics ProgramNovo Nordisk Foundation Center for Protein ResearchUniversity of CopenhagenCopenhagenDenmark
| | - Jennifer Kranz
- Faculty of MedicineInstitute of Experimental Medicine and Systems BiologyRWTH Aachen UniversityAachenGermany
- Department of Urology and Pediatric UrologySt. Antonius Hospital EschweilerAcademic Teaching Hospital of RWTH AachenEschweilerGermany
- Department of Urology and Kidney TransplantationMartin Luther UniversityHalle (Saale)Germany
| | | | - Ana S.H. Costa
- MRC Cancer UnitHutchison/MRC Research CentreUniversity of CambridgeCambridgeUK
- Present address:
Cold Spring Harbor LaboratoryCold Spring HarborNYUSA
| | - Abel Sousa
- Institute for Research and Innovation in Health (i3s)PortoPortugal
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)HinxtonUK
| | - Pedro Beltrao
- European Molecular Biology LaboratoryEuropean Bioinformatics Institute (EMBL‐EBI)HinxtonUK
| | - Miguel Rocha
- Centre of Biological EngineeringUniversity of Minho ‐ Campus de GualtarBragaPortugal
| | - Jesper V. Olsen
- Faculty of Health and Medical SciencesProteomics ProgramNovo Nordisk Foundation Center for Protein ResearchUniversity of CopenhagenCopenhagenDenmark
| | - Christian Frezza
- MRC Cancer UnitHutchison/MRC Research CentreUniversity of CambridgeCambridgeUK
| | - Rafael Kramann
- Faculty of MedicineInstitute of Experimental Medicine and Systems BiologyRWTH Aachen UniversityAachenGermany
- Division of Nephrology and Clinical ImmunologyFaculty of MedicineRWTH Aachen UniversityAachenGermany
- Department of Internal Medicine, Nephrology and TransplantationErasmus Medical CenterRotterdamThe Netherlands
| | - Julio Saez‐Rodriguez
- Faculty of Medicine, and Heidelberg University HospitalInstitute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Faculty of MedicineJoint Research Centre for Computational Biomedicine (JRC‐COMBINE)RWTH Aachen UniversityAachenGermany
- Molecular Medicine Partnership Unit, European Molecular Biology LaboratoryHeidelberg UniversityHeidelbergGermany
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Damiani C, Gaglio D, Sacco E, Alberghina L, Vanoni M. Systems metabolomics: from metabolomic snapshots to design principles. Curr Opin Biotechnol 2020; 63:190-199. [PMID: 32278263 DOI: 10.1016/j.copbio.2020.02.013] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 02/11/2020] [Accepted: 02/18/2020] [Indexed: 02/07/2023]
Abstract
Metabolomics is a rapidly expanding technology that finds increasing application in a variety of fields, form metabolic disorders to cancer, from nutrition and wellness to design and optimization of cell factories. The integration of metabolic snapshots with metabolic fluxes, physiological readouts, metabolic models, and knowledge-informed Artificial Intelligence tools, is required to obtain a system-level understanding of metabolism. The emerging power of multi-omic approaches and the development of integrated experimental and computational tools, able to dissect metabolic features at cellular and subcellular resolution, provide unprecedented opportunities for understanding design principles of metabolic (dis)regulation and for the development of precision therapies in multifactorial diseases, such as cancer and neurodegenerative diseases.
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Affiliation(s)
- Chiara Damiani
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy; ISBE.IT, SYSBIO Centre of Systems Biology, Piazza della Scienza 2, Milan 20126, Italy
| | - Daniela Gaglio
- ISBE.IT, SYSBIO Centre of Systems Biology, Piazza della Scienza 2, Milan 20126, Italy; Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Segrate, Milan, Italy
| | - Elena Sacco
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy; ISBE.IT, SYSBIO Centre of Systems Biology, Piazza della Scienza 2, Milan 20126, Italy
| | - Lilia Alberghina
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy; ISBE.IT, SYSBIO Centre of Systems Biology, Piazza della Scienza 2, Milan 20126, Italy
| | - Marco Vanoni
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy; ISBE.IT, SYSBIO Centre of Systems Biology, Piazza della Scienza 2, Milan 20126, Italy.
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