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Wertheim KY, Puniya BL, La Fleur A, Shah AR, Barberis M, Helikar T. A multi-approach and multi-scale platform to model CD4+ T cells responding to infections. PLoS Comput Biol 2021; 17:e1009209. [PMID: 34343169 PMCID: PMC8376204 DOI: 10.1371/journal.pcbi.1009209] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 08/19/2021] [Accepted: 06/23/2021] [Indexed: 12/24/2022] Open
Abstract
Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility and facilitates data integration. We validated our technology by reproducing known experimental results, including differentiation patterns of CD4+ T cells triggered by different combinations of cytokines, metabolic regulation by IL2 in these cells, and their response to influenza infection. In doing so, we added multi-scale insights to single-scale studies and demonstrated its predictive power by discovering switch-like and oscillatory behaviors of CD4+ T cells that arise from nonlinear dynamics interwoven across three scales. We identified the inflamed lymph node’s ability to retain naive CD4+ T cells as a key mechanism in generating these emergent behaviors. We envision our model and the generic framework encompassing it to serve as a tool for understanding cellular and molecular immunological problems through the lens of systems immunology. CD4+ T cells are a key part of the adaptive immune system. They differentiate into different phenotypes to carry out different functions. They do so by secreting molecules called cytokines to regulate other immune cells. Multi-scale modeling can potentially explain their emergent behaviors by integrating biological phenomena occurring at different spatial (intracellular, cellular, and systemic), temporal, and organizational scales (signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport). We built a computational platform by combining disparate modeling frameworks (compartmental ordinary differential equations, agent-based modeling, Boolean network modeling, and constraint-based modeling). We validated the platform’s ability to predict CD4+ T cells’ emergent behaviors by reproducing their differentiation patterns, metabolic regulation, and population dynamics in response to influenza infection. We then used it to predict and explain novel switch-like and oscillatory behaviors for CD4+ T cells. On the basis of these results, we believe that our multi-approach and multi-scale platform will be a valuable addition to the systems immunology toolkit. In addition to its immediate relevance to CD4+ T cells, it also has the potential to become the foundation of a virtual immune system.
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Affiliation(s)
- Kenneth Y. Wertheim
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
- Department of Computer Science and Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Bhanwar Lal Puniya
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
| | - Alyssa La Fleur
- Department of Biochemistry, Department of Mathematics and Computer Science, Whitworth University, Spokane, Washington, United States of America
| | - Ab Rauf Shah
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
| | - Matteo Barberis
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Guildford, United Kingdom
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- * E-mail: , (MB); (TH)
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
- * E-mail: , (MB); (TH)
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102
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Gawthrop PJ, Pan M, Crampin EJ. Modular dynamic biomolecular modelling with bond graphs: the unification of stoichiometry, thermodynamics, kinetics and data. J R Soc Interface 2021; 18:20210478. [PMID: 34428949 PMCID: PMC8385351 DOI: 10.1098/rsif.2021.0478] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 08/02/2021] [Indexed: 12/14/2022] Open
Abstract
Renewed interest in dynamic simulation models of biomolecular systems has arisen from advances in genome-wide measurement and applications of such models in biotechnology and synthetic biology. In particular, genome-scale models of cellular metabolism beyond the steady state are required in order to represent transient and dynamic regulatory properties of the system. Development of such whole-cell models requires new modelling approaches. Here, we propose the energy-based bond graph methodology, which integrates stoichiometric models with thermodynamic principles and kinetic modelling. We demonstrate how the bond graph approach intrinsically enforces thermodynamic constraints, provides a modular approach to modelling, and gives a basis for estimation of model parameters leading to dynamic models of biomolecular systems. The approach is illustrated using a well-established stoichiometric model of Escherichia coli and published experimental data.
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Affiliation(s)
- Peter J. Gawthrop
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
| | - Michael Pan
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, School of Chemical and Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
| | - Edmund J. Crampin
- Systems Biology Laboratory, School of Mathematics and Statistics, and Department of Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, School of Chemical and Biomedical Engineering, University of Melbourne, Victoria 3010, Australia
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103
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Echeverri-Peña OY, Salazar-Barreto DA, Rodríguez-Lopez A, González J, Alméciga-Díaz CJ, Verano-Guevara CH, Barrera LA. Use of a neuron-glia genome-scale metabolic reconstruction to model the metabolic consequences of the Arylsulphatase a deficiency through a systems biology approach. Heliyon 2021; 7:e07671. [PMID: 34381909 PMCID: PMC8340118 DOI: 10.1016/j.heliyon.2021.e07671] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/10/2021] [Accepted: 07/23/2021] [Indexed: 12/26/2022] Open
Abstract
Metachromatic leukodystrophy (MLD) is a human neurodegenerative disorder characterized by progressive damage on the myelin band in the nervous system. MLD is caused by the impaired function of the lysosomal enzyme Arylsulphatase A (ARSA). The physiopathology mechanisms and the biochemical consequences in the brain of ARSA deficiency are not entirely understood. In recent years, the use of genome-scale metabolic (GEM) models has been explored as a tool for the study of the biochemical alterations in MLD. Previously, we modeled the metabolic consequences of different lysosomal storage diseases using single GEMs. In the case of MLD, using a glia GEM, we previously predicted that the metabolism of glycosphingolipids and neurotransmitters was altered. The results also suggested that mitochondrial metabolism and amino acid transport were the main reactions affected. In this study, we extended the modeling of the metabolic consequences of ARSA deficiency through the integration of neuron and glial cell metabolic models. Cell-specific models were generated from Recon2, and these were used to create a neuron-glial bi-cellular model. We propose a workflow for the integration of this type of model and its subsequent study. The results predicted the impairment pathways involved in the transport of amino acids, lipids metabolism, and catabolism of purines and pyrimidines. The use of this neuron-glial GEM metabolic reconstruction allowed to improve the prediction capacity of the metabolic consequences of ARSA deficiency, which might pave the way for the modeling of the biochemical alterations of other inborn errors of metabolism with central nervous system involvement.
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Affiliation(s)
- Olga Y Echeverri-Peña
- Institute for the Study of Inborn Errors of Metabolism, Faculty of Science, Pontificia Universidad Javeriana, Bogotá D.C., Colombia
| | - Diego A Salazar-Barreto
- Centro para la Optimización y Probabilidad Aplicada (COPA), Department of Industrial Engineering, Faculty of Engineering, Universidad de los Andes, Bogotá D.C., Colombia.,Grupo de Bioquímica Computacional, Estructural y Bioinformática, Department of Nutrition and Biochemistry, Faculty of Science, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Alexander Rodríguez-Lopez
- Institute for the Study of Inborn Errors of Metabolism, Faculty of Science, Pontificia Universidad Javeriana, Bogotá D.C., Colombia.,Licenciatura en Química, Universidad Distrital Francisco Jose de Caldas, Bogota D.C., Colombia.,Molecular Biology and Immunology Department, Fundación Instituto de Inmunología de Colombia (FIDIC), Bogotá D.C., Colombia
| | - Janneth González
- Grupo de Bioquímica Computacional, Estructural y Bioinformática, Department of Nutrition and Biochemistry, Faculty of Science, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Carlos J Alméciga-Díaz
- Institute for the Study of Inborn Errors of Metabolism, Faculty of Science, Pontificia Universidad Javeriana, Bogotá D.C., Colombia
| | | | - Luis A Barrera
- Institute for the Study of Inborn Errors of Metabolism, Faculty of Science, Pontificia Universidad Javeriana, Bogotá D.C., Colombia.,Clínica de Errores Innatos del Metabolismo, Hospital Universitario San Ignacio, Bogotá D.C., Colombia
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104
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Richelle A, Kellman BP, Wenzel AT, Chiang AW, Reagan T, Gutierrez JM, Joshi C, Li S, Liu JK, Masson H, Lee J, Li Z, Heirendt L, Trefois C, Juarez EF, Bath T, Borland D, Mesirov JP, Robasky K, Lewis NE. Model-based assessment of mammalian cell metabolic functionalities using omics data. CELL REPORTS METHODS 2021; 1:100040. [PMID: 34761247 PMCID: PMC8577426 DOI: 10.1016/j.crmeth.2021.100040] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/24/2021] [Accepted: 05/24/2021] [Indexed: 12/30/2022]
Abstract
Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie).
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Affiliation(s)
- Anne Richelle
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
| | - Benjamin P. Kellman
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Alexander T. Wenzel
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Medicine, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Austin W.T. Chiang
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
| | - Tyler Reagan
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
| | - Jahir M. Gutierrez
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Chintan Joshi
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
| | - Shangzhong Li
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Joanne K. Liu
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Helen Masson
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jooyong Lee
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
| | - Zerong Li
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Christophe Trefois
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Edwin F. Juarez
- Department of Medicine, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Tyler Bath
- Department of Biomedical Informatics, UC San Diego Health, University of California, San Diego, La Jolla, CA 92093, USA
| | - David Borland
- Renaissance Computing Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
| | - Jill P. Mesirov
- Department of Medicine, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
- Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Kimberly Robasky
- Renaissance Computing Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Carolina Health and Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Nathan E. Lewis
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
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105
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Kyle JE, Aimo L, Bridge AJ, Clair G, Fedorova M, Helms JB, Molenaar MR, Ni Z, Orešič M, Slenter D, Willighagen E, Webb-Robertson BJM. Interpreting the lipidome: bioinformatic approaches to embrace the complexity. Metabolomics 2021; 17:55. [PMID: 34091802 DOI: 10.1007/s11306-021-01802-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/18/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites. AIM OF REVIEW To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome. KEY SCIENTIFIC CONCEPTS OF REVIEW Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.
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Affiliation(s)
- Jennifer E Kyle
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Lucila Aimo
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, 1 rue Michel-Servet, 1211, Geneva 4, Switzerland
| | - Alan J Bridge
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, 1 rue Michel-Servet, 1211, Geneva 4, Switzerland
| | - Geremy Clair
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Maria Fedorova
- Institute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, Center for Biotechnology and Biomedicine, Universität Leipzig, Deutscher Platz 5, Leipzig, Germany
| | - J Bernd Helms
- Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Martijn R Molenaar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Zhixu Ni
- Institute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, Center for Biotechnology and Biomedicine, Universität Leipzig, Deutscher Platz 5, Leipzig, Germany
| | - Matej Orešič
- School of Medical Sciences, Örebro University, 702 81, Örebro, Sweden
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland
| | - Denise Slenter
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, 6229 ER, Maastricht, The Netherlands
| | - Egon Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, 6229 ER, Maastricht, The Netherlands
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106
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Timón-Reina S, Rincón M, Martínez-Tomás R. An overview of graph databases and their applications in the biomedical domain. Database (Oxford) 2021; 2021:baab026. [PMID: 34003247 PMCID: PMC8130509 DOI: 10.1093/database/baab026] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 03/24/2021] [Accepted: 04/30/2021] [Indexed: 01/18/2023]
Abstract
Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because of the interconnected nature of its data, the biomedical domain has been one of the early adopters of graph databases, enabling more natural representation models and better data integration workflows, exploration and analysis facilities. In this work, we survey the literature to explore the evolution, performance and how the most recent graph database solutions are applied in the biomedical domain, compiling a great variety of use cases. With this evidence, we conclude that the available graph database management systems are fit to support data-intensive, integrative applications, targeted at both basic research and exploratory tasks closer to the clinic.
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Affiliation(s)
- Santiago Timón-Reina
- Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16 Ciudad Universitaria, Madrid 28040, Spain
| | - Mariano Rincón
- Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16 Ciudad Universitaria, Madrid 28040, Spain
| | - Rafael Martínez-Tomás
- Departamento de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED), C/Juan del Rosal, 16 Ciudad Universitaria, Madrid 28040, Spain
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107
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Genome-wide bioinformatic analyses predict key host and viral factors in SARS-CoV-2 pathogenesis. Commun Biol 2021; 4:590. [PMID: 34002013 PMCID: PMC8128904 DOI: 10.1038/s42003-021-02095-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 04/05/2021] [Indexed: 02/03/2023] Open
Abstract
The novel betacoronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a worldwide pandemic (COVID-19) after emerging in Wuhan, China. Here we analyzed public host and viral RNA sequencing data to better understand how SARS-CoV-2 interacts with human respiratory cells. We identified genes, isoforms and transposable element families that are specifically altered in SARS-CoV-2-infected respiratory cells. Well-known immunoregulatory genes including CSF2, IL32, IL-6 and SERPINA3 were differentially expressed, while immunoregulatory transposable element families were upregulated. We predicted conserved interactions between the SARS-CoV-2 genome and human RNA-binding proteins such as the heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) and eukaryotic initiation factor 4 (eIF4b). We also identified a viral sequence variant with a statistically significant skew associated with age of infection, that may contribute to intracellular host-pathogen interactions. These findings can help identify host mechanisms that can be targeted by prophylactics and/or therapeutics to reduce the severity of COVID-19.
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108
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Boccard J, Schvartz D, Codesido S, Hanafi M, Gagnebin Y, Ponte B, Jourdan F, Rudaz S. Gaining Insights Into Metabolic Networks Using Chemometrics and Bioinformatics: Chronic Kidney Disease as a Clinical Model. Front Mol Biosci 2021; 8:682559. [PMID: 34055893 PMCID: PMC8163225 DOI: 10.3389/fmolb.2021.682559] [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: 03/18/2021] [Accepted: 04/19/2021] [Indexed: 01/21/2023] Open
Abstract
Because of its ability to generate biological hypotheses, metabolomics offers an innovative and promising approach in many fields, including clinical research. However, collecting specimens in this setting can be difficult to standardize, especially when groups of patients with different degrees of disease severity are considered. In addition, despite major technological advances, it remains challenging to measure all the compounds defining the metabolic network of a biological system. In this context, the characterization of samples based on several analytical setups is now recognized as an efficient strategy to improve the coverage of metabolic complexity. For this purpose, chemometrics proposes efficient methods to reduce the dimensionality of these complex datasets spread over several matrices, allowing the integration of different sources or structures of metabolic information. Bioinformatics databases and query tools designed to describe and explore metabolic network models offer extremely useful solutions for the contextualization of potential biomarker subsets, enabling mechanistic hypotheses to be considered rather than simple associations. In this study, network principal component analysis was used to investigate samples collected from three cohorts of patients including multiple stages of chronic kidney disease. Metabolic profiles were measured using a combination of four analytical setups involving different separation modes in liquid chromatography coupled to high resolution mass spectrometry. Based on the chemometric model, specific patterns of metabolites, such as N-acetyl amino acids, could be associated with the different subgroups of patients. Further investigation of the metabolic signatures carried out using genome-scale network modeling confirmed both tryptophan metabolism and nucleotide interconversion as relevant pathways potentially associated with disease severity. Metabolic modules composed of chemically adjacent or close compounds of biological relevance were further investigated using carbon transfer reaction paths. Overall, the proposed integrative data analysis strategy allowed deeper insights into the metabolic routes associated with different groups of patients to be gained. Because of their complementary role in the knowledge discovery process, the association of chemometrics and bioinformatics in a common workflow is therefore shown as an efficient methodology to gain meaningful insights in a clinical context.
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Affiliation(s)
- Julien Boccard
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
| | - Domitille Schvartz
- Translational Biomarker Group, Department of Internal Medicine Specialties, University of Geneva, Geneva, Switzerland
| | - Santiago Codesido
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
| | - Mohamed Hanafi
- Unité Statistique, Sensométrie et Chimiométrie, Nantes, France
| | - Yoric Gagnebin
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
| | - Belén Ponte
- Service of Nephrology and Hypertension, Department of Medicine, Geneva University Hospitals (HUG), Geneva, Switzerland
| | - Fabien Jourdan
- Toxalim, Research Centre in Food Toxicology, Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Serge Rudaz
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
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109
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Sudhakar P, Machiels K, Verstockt B, Korcsmaros T, Vermeire S. Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions. Front Microbiol 2021; 12:618856. [PMID: 34046017 PMCID: PMC8148342 DOI: 10.3389/fmicb.2021.618856] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 03/19/2021] [Indexed: 12/11/2022] Open
Abstract
The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.
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Affiliation(s)
- Padhmanand Sudhakar
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Kathleen Machiels
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
| | - Bram Verstockt
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Tamas Korcsmaros
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Séverine Vermeire
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders (TARGID), KU Leuven, Leuven, Belgium
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, KU Leuven, Leuven, Belgium
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110
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Stuani L, Sabatier M, Saland E, Cognet G, Poupin N, Bosc C, Castelli FA, Gales L, Turtoi E, Montersino C, Farge T, Boet E, Broin N, Larrue C, Baran N, Cissé MY, Conti M, Loric S, Kaoma T, Hucteau A, Zavoriti A, Sahal A, Mouchel PL, Gotanègre M, Cassan C, Fernando L, Wang F, Hosseini M, Chu-Van E, Le Cam L, Carroll M, Selak MA, Vey N, Castellano R, Fenaille F, Turtoi A, Cazals G, Bories P, Gibon Y, Nicolay B, Ronseaux S, Marszalek JR, Takahashi K, DiNardo CD, Konopleva M, Pancaldi V, Collette Y, Bellvert F, Jourdan F, Linares LK, Récher C, Portais JC, Sarry JE. Mitochondrial metabolism supports resistance to IDH mutant inhibitors in acute myeloid leukemia. J Exp Med 2021; 218:e20200924. [PMID: 33760042 PMCID: PMC7995203 DOI: 10.1084/jem.20200924] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 11/25/2020] [Accepted: 01/11/2021] [Indexed: 12/17/2022] Open
Abstract
Mutations in IDH induce epigenetic and transcriptional reprogramming, differentiation bias, and susceptibility to mitochondrial inhibitors in cancer cells. Here, we first show that cell lines, PDXs, and patients with acute myeloid leukemia (AML) harboring an IDH mutation displayed an enhanced mitochondrial oxidative metabolism. Along with an increase in TCA cycle intermediates, this AML-specific metabolic behavior mechanistically occurred through the increase in electron transport chain complex I activity, mitochondrial respiration, and methylation-driven CEBPα-induced fatty acid β-oxidation of IDH1 mutant cells. While IDH1 mutant inhibitor reduced 2-HG oncometabolite and CEBPα methylation, it failed to reverse FAO and OxPHOS. These mitochondrial activities were maintained through the inhibition of Akt and enhanced activation of peroxisome proliferator-activated receptor-γ coactivator-1 PGC1α upon IDH1 mutant inhibitor. Accordingly, OxPHOS inhibitors improved anti-AML efficacy of IDH mutant inhibitors in vivo. This work provides a scientific rationale for combinatory mitochondrial-targeted therapies to treat IDH mutant AML patients, especially those unresponsive to or relapsing from IDH mutant inhibitors.
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MESH Headings
- Acute Disease
- Aminopyridines/pharmacology
- Animals
- Cell Line, Tumor
- Doxycycline/pharmacology
- Drug Resistance, Neoplasm/drug effects
- Drug Resistance, Neoplasm/genetics
- Enzyme Inhibitors/pharmacology
- Epigenesis, Genetic/drug effects
- Glycine/analogs & derivatives
- Glycine/pharmacology
- HL-60 Cells
- Humans
- Isocitrate Dehydrogenase/antagonists & inhibitors
- Isocitrate Dehydrogenase/genetics
- Isocitrate Dehydrogenase/metabolism
- Isoenzymes/antagonists & inhibitors
- Isoenzymes/genetics
- Isoenzymes/metabolism
- Leukemia, Myeloid/drug therapy
- Leukemia, Myeloid/genetics
- Leukemia, Myeloid/metabolism
- Mice, Inbred NOD
- Mice, Knockout
- Mice, SCID
- Mitochondria/drug effects
- Mitochondria/genetics
- Mitochondria/metabolism
- Mutation
- Oxadiazoles/pharmacology
- Oxidative Phosphorylation/drug effects
- Piperidines/pharmacology
- Pyridines/pharmacology
- Triazines/pharmacology
- Xenograft Model Antitumor Assays/methods
- Mice
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Affiliation(s)
- Lucille Stuani
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Marie Sabatier
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Estelle Saland
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Guillaume Cognet
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Nathalie Poupin
- UMR1331 Toxalim, Université de Toulouse, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, Ecole Nationale Vétérinaire de Toulouse, INP-Purpan, Université Paul Sabatier, Toulouse, France
| | - Claudie Bosc
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Florence A. Castelli
- CEA/DSV/iBiTec-S/SPI, Laboratoire d’Etude du Métabolisme des Médicaments, MetaboHUB-Paris, Gif-sur-Yvette, France
| | - Lara Gales
- Toulouse Biotechnology Institute, Université de Toulouse, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Institut National des sciences appliquées, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Evgenia Turtoi
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
- Montpellier Alliance for Metabolomics and Metabolism Analysis, Platform for Translational Oncometabolomics, Biocampus, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Montpellier, France
| | - Camille Montersino
- Aix-Marseille University, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Institut Paoli-Calmettes, Centre de Recherches en Cancérologie de Marseille, Marseille, France
| | - Thomas Farge
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Emeline Boet
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Nicolas Broin
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Clément Larrue
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Natalia Baran
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Madi Y. Cissé
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
| | - Marc Conti
- Institut National de la Santé et de la Recherché Médicale U938, Hôpital St Antoine, Paris, France
- Integracell, Longjumeau, France
| | - Sylvain Loric
- Institut National de la Santé et de la Recherché Médicale U938, Hôpital St Antoine, Paris, France
| | - Tony Kaoma
- Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Alexis Hucteau
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Aliki Zavoriti
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Ambrine Sahal
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Pierre-Luc Mouchel
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
- Service d'Hématologie, Institut Universitaire du Cancer de Toulouse-Oncopole, CHU de Toulouse, Toulouse, France
| | - Mathilde Gotanègre
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Cédric Cassan
- UMR1332 Biologie du Fruit et Pathologie, Plateforme Métabolome Bordeaux, Institut National de la Recherche Agronomique, Université de Bordeaux, Villenave d'Ornon, France
| | - Laurent Fernando
- UMR1331 Toxalim, Université de Toulouse, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, Ecole Nationale Vétérinaire de Toulouse, INP-Purpan, Université Paul Sabatier, Toulouse, France
| | - Feng Wang
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Mohsen Hosseini
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Emeline Chu-Van
- CEA/DSV/iBiTec-S/SPI, Laboratoire d’Etude du Métabolisme des Médicaments, MetaboHUB-Paris, Gif-sur-Yvette, France
| | - Laurent Le Cam
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
| | - Martin Carroll
- Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Mary A. Selak
- Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Norbert Vey
- Aix-Marseille University, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Institut Paoli-Calmettes, Centre de Recherches en Cancérologie de Marseille, Marseille, France
| | - Rémy Castellano
- Aix-Marseille University, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Institut Paoli-Calmettes, Centre de Recherches en Cancérologie de Marseille, Marseille, France
| | - François Fenaille
- CEA/DSV/iBiTec-S/SPI, Laboratoire d’Etude du Métabolisme des Médicaments, MetaboHUB-Paris, Gif-sur-Yvette, France
| | - Andrei Turtoi
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
| | - Guillaume Cazals
- Laboratoire de Mesures Physiques, Université de Montpellier, Montpellier, France
| | - Pierre Bories
- Réseau Régional de Cancérologie Onco-Occitanie, Toulouse, France
| | - Yves Gibon
- UMR1332 Biologie du Fruit et Pathologie, Plateforme Métabolome Bordeaux, Institut National de la Recherche Agronomique, Université de Bordeaux, Villenave d'Ornon, France
| | | | | | - Joseph R. Marszalek
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Koichi Takahashi
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Courtney D. DiNardo
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Marina Konopleva
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Véra Pancaldi
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Yves Collette
- Aix-Marseille University, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Institut Paoli-Calmettes, Centre de Recherches en Cancérologie de Marseille, Marseille, France
| | - Floriant Bellvert
- Toulouse Biotechnology Institute, Université de Toulouse, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Institut National des sciences appliquées, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Fabien Jourdan
- UMR1331 Toxalim, Université de Toulouse, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, Ecole Nationale Vétérinaire de Toulouse, INP-Purpan, Université Paul Sabatier, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Laetitia K. Linares
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
| | - Christian Récher
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
- Service d'Hématologie, Institut Universitaire du Cancer de Toulouse-Oncopole, CHU de Toulouse, Toulouse, France
| | - Jean-Charles Portais
- Toulouse Biotechnology Institute, Université de Toulouse, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Institut National des sciences appliquées, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
- STROMALab, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale U1031, EFS, INP-ENVT, UPS, Toulouse, France
| | - Jean-Emmanuel Sarry
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
- Centre Hospitalier Universitaire de Toulouse, Toulouse, France
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111
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Zhao J, Xu H, Yang L. A dynamic metabolic map for diabetes. NATURE COMPUTATIONAL SCIENCE 2021; 1:309-310. [PMID: 38217215 DOI: 10.1038/s43588-021-00075-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Affiliation(s)
- Jiao Zhao
- Department of Chemical Engineering, Queen's University, Kingston, Ontario, Canada
| | - Hao Xu
- Department of Chemical Engineering, Queen's University, Kingston, Ontario, Canada
| | - Laurence Yang
- Department of Chemical Engineering, Queen's University, Kingston, Ontario, Canada.
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112
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Cakmak A, Celik MH. Personalized Metabolic Analysis of Diseases. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1014-1025. [PMID: 32750887 DOI: 10.1109/tcbb.2020.3008196] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The metabolic wiring of patient cells is altered drastically in many diseases, including cancer. Understanding the nature of such changes may pave the way for new therapeutic opportunities as well as the development of personalized treatment strategies for patients. In this paper, we propose an algorithm called Metabolitics, which allows systems-level analysis of changes in the biochemical network of cells in disease states. It enables the study of a disease at both reaction- and pathway-level granularities for a detailed and summarized view of disease etiology. Metabolitics employs flux variability analysis with a dynamically built objective function based on biofluid metabolomics measurements in a personalized manner. Moreover, Metabolitics builds supervised classification models to discriminate between patients and healthy subjects based on the computed metabolic network changes. The use of Metabolitics is demonstrated for three distinct diseases, namely, breast cancer, Crohn's disease, and colorectal cancer. Our results show that the constructed supervised learning models successfully differentiate patients from healthy individuals by an average f1-score of 88 percent. Besides, in addition to the confirmation of previously reported breast cancer-associated pathways, we discovered that Biotin Metabolism along with Arginine and Proline Metabolism is subject to a significant increase in flux capacity, which have not been reported before.
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113
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Weglarz-Tomczak E, Mondeel TDGA, Piebes DGE, Westerhoff HV. Simultaneous Integration of Gene Expression and Nutrient Availability for Studying the Metabolism of Hepatocellular Carcinoma Cell Lines. Biomolecules 2021; 11:biom11040490. [PMID: 33805227 PMCID: PMC8064315 DOI: 10.3390/biom11040490] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 03/07/2021] [Accepted: 03/19/2021] [Indexed: 01/08/2023] Open
Abstract
How cancer cells utilize nutrients to support their growth and proliferation in complex nutritional systems is still an open question. However, it is certainly determined by both genetics and an environmental-specific context. The interactions between them lead to profound metabolic specialization, such as consuming glucose and glutamine and producing lactate at prodigious rates. To investigate whether and how glucose and glutamine availability impact metabolic specialization, we integrated computational modeling on the genome-scale metabolic reconstruction with an experimental study on cell lines. We used the most comprehensive human metabolic network model to date, Recon3D, to build cell line-specific models. RNA-Seq data was used to specify the activity of genes in each cell line and the uptake rates were quantitatively constrained according to nutrient availability. To integrated both constraints we applied a novel method, named Gene Expression and Nutrients Simultaneous Integration (GENSI), that translates the relative importance of gene expression and nutrient availability data into the metabolic fluxes based on an observed experimental feature(s). We applied GENSI to study hepatocellular carcinoma addiction to glucose/glutamine. We were able to identify that proliferation, and lactate production is associated with the presence of glucose but does not necessarily increase with its concentration when the latter exceeds the physiological concentration. There was no such association with glutamine. We show that the integration of gene expression and nutrient availability data into genome-wide models improves the prediction of metabolic phenotypes.
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Affiliation(s)
- Ewelina Weglarz-Tomczak
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, 1098 XH Amsterdam, The Netherlands; (T.D.G.A.M.); (D.G.E.P.); (H.V.W.)
- Correspondence:
| | - Thierry D. G. A. Mondeel
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, 1098 XH Amsterdam, The Netherlands; (T.D.G.A.M.); (D.G.E.P.); (H.V.W.)
| | - Diewertje G. E. Piebes
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, 1098 XH Amsterdam, The Netherlands; (T.D.G.A.M.); (D.G.E.P.); (H.V.W.)
| | - Hans V. Westerhoff
- Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, 1098 XH Amsterdam, The Netherlands; (T.D.G.A.M.); (D.G.E.P.); (H.V.W.)
- Molecular Cell Physiology, Amsterdam Institute for Molecules, Medicines and Systems, Faculty of Science, Vrije Universiteit Amsterdam, 1081 HZ Amsterdam, The Netherlands
- Manchester Centre for Integrative Systems Biology, School for Chemical Engineering and Analytical Sciences, University of Manchester, Manchester M1 7DN, UK
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114
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Dougherty BV, Rawls KD, Kolling GL, Vinnakota KC, Wallqvist A, Papin JA. Identifying functional metabolic shifts in heart failure with the integration of omics data and a heart-specific, genome-scale model. Cell Rep 2021; 34:108836. [PMID: 33691118 DOI: 10.1016/j.celrep.2021.108836] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 01/07/2021] [Accepted: 02/17/2021] [Indexed: 11/28/2022] Open
Abstract
In diseased states, the heart can shift to use different carbon substrates, measured through changes in uptake of metabolites by imaging methods or blood metabolomics. However, it is not known whether these measured changes are a result of transcriptional changes or external factors. Here, we explore transcriptional changes in late-stage heart failure using publicly available data integrated with a model of heart metabolism. First, we present a heart-specific genome-scale metabolic network reconstruction (GENRE), iCardio. Next, we demonstrate the utility of iCardio in interpreting heart failure gene expression data by identifying tasks inferred from differential expression (TIDEs), which represent metabolic functions associated with changes in gene expression. We identify decreased gene expression for nitric oxide (NO) and N-acetylneuraminic acid (Neu5Ac) synthesis as common metabolic markers of heart failure. The methods presented here for constructing a tissue-specific model and identifying TIDEs can be extended to multiple tissues and diseases of interest.
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Affiliation(s)
- Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA
| | - Kalyan C Vinnakota
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.
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115
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Towards the routine use of in silico screenings for drug discovery using metabolic modelling. Biochem Soc Trans 2021; 48:955-969. [PMID: 32369553 PMCID: PMC7329353 DOI: 10.1042/bst20190867] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 04/01/2020] [Accepted: 04/06/2020] [Indexed: 12/12/2022]
Abstract
Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.
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116
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Jean-Pierre F, Henson MA, O’Toole GA. Metabolic Modeling to Interrogate Microbial Disease: A Tale for Experimentalists. Front Mol Biosci 2021; 8:634479. [PMID: 33681294 PMCID: PMC7930556 DOI: 10.3389/fmolb.2021.634479] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 01/19/2021] [Indexed: 12/14/2022] Open
Abstract
The explosion of microbiome analyses has helped identify individual microorganisms and microbial communities driving human health and disease, but how these communities function is still an open question. For example, the role for the incredibly complex metabolic interactions among microbial species cannot easily be resolved by current experimental approaches such as 16S rRNA gene sequencing, metagenomics and/or metabolomics. Resolving such metabolic interactions is particularly challenging in the context of polymicrobial communities where metabolite exchange has been reported to impact key bacterial traits such as virulence and antibiotic treatment efficacy. As novel approaches are needed to pinpoint microbial determinants responsible for impacting community function in the context of human health and to facilitate the development of novel anti-infective and antimicrobial drugs, here we review, from the viewpoint of experimentalists, the latest advances in metabolic modeling, a computational method capable of predicting metabolic capabilities and interactions from individual microorganisms to complex ecological systems. We use selected examples from the literature to illustrate how metabolic modeling has been utilized, in combination with experiments, to better understand microbial community function. Finally, we propose how such combined, cross-disciplinary efforts can be utilized to drive laboratory work and drug discovery moving forward.
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Affiliation(s)
- Fabrice Jean-Pierre
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
| | - Michael A. Henson
- Department of Chemical Engineering and Institute for Applied Life Sciences, University of Massachusetts, Amherst, MA, United States
| | - George A. O’Toole
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
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Perrin-Cocon L, Vidalain PO, Jacquemin C, Aublin-Gex A, Olmstead K, Panthu B, Rautureau GJP, André P, Nyczka P, Hütt MT, Amoedo N, Rossignol R, Filipp FV, Lotteau V, Diaz O. A hexokinase isoenzyme switch in human liver cancer cells promotes lipogenesis and enhances innate immunity. Commun Biol 2021; 4:217. [PMID: 33594203 PMCID: PMC7886870 DOI: 10.1038/s42003-021-01749-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 12/11/2020] [Indexed: 12/15/2022] Open
Abstract
During the cancerous transformation of normal hepatocytes into hepatocellular carcinoma (HCC), the enzyme catalyzing the first rate-limiting step of glycolysis, namely the glucokinase (GCK), is replaced by the higher affinity isoenzyme, hexokinase 2 (HK2). Here, we show that in HCC tumors the highest expression level of HK2 is inversely correlated to GCK expression, and is associated to poor prognosis for patient survival. To further explore functional consequences of the GCK-to-HK2 isoenzyme switch occurring during carcinogenesis, HK2 was knocked-out in the HCC cell line Huh7 and replaced by GCK, to generate the Huh7-GCK+/HK2− cell line. HK2 knockdown and GCK expression rewired central carbon metabolism, stimulated mitochondrial respiration and restored essential metabolic functions of normal hepatocytes such as lipogenesis, VLDL secretion, glycogen storage. It also reactivated innate immune responses and sensitivity to natural killer cells, showing that consequences of the HK switch extend beyond metabolic reprogramming. Many cancers fuel their rapid growth by replacing glucokinase with its higher affinity isoenzyme, hexokinase 2 (HK2), making HK2 an attractive drug target. In this study, Perrin-Cocon and Vidalain et al. use CRISPR/Cas-9 gene editing to reverse this enzymatic switch in human liver cancer cells, and find this restores innate immune function as well as reversing cancer-associated metabolic reprogramming.
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Affiliation(s)
- Laure Perrin-Cocon
- CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 21 Avenue Tony Garnier, Lyon, F-69007, France
| | - Pierre-Olivier Vidalain
- CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 21 Avenue Tony Garnier, Lyon, F-69007, France
| | - Clémence Jacquemin
- CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 21 Avenue Tony Garnier, Lyon, F-69007, France
| | - Anne Aublin-Gex
- CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 21 Avenue Tony Garnier, Lyon, F-69007, France
| | - Keedrian Olmstead
- Cancer Systems Biology, Institute for Diabetes and Cancer, Helmholtz Zentrum München, Ingolstädter Landstraße 1, München, D-85764, Germany
| | - Baptiste Panthu
- CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 21 Avenue Tony Garnier, Lyon, F-69007, France.,Univ Lyon, CarMeN Laboratory, Inserm, INRA, INSA Lyon, Université Claude Bernard Lyon 1, Hôpital Lyon Sud, Bâtiment CENS ELI-2D, 165 Chemin du grand Revoyet, Pierre-Bénite, F-69310, France
| | - Gilles Jeans Philippe Rautureau
- Université de Lyon, CNRS, Université Claude Bernard Lyon 1, ENS de Lyon, Centre de RMN à Très Hauts Champs (CRMN), FRE 2034, 5 rue de la Doua, Villeurbanne, F-69100, France
| | - Patrice André
- CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 21 Avenue Tony Garnier, Lyon, F-69007, France
| | - Piotr Nyczka
- Department of Life Sciences and Chemistry, Jacobs University, Campus Ring 1, Bremen, D-28759, Germany
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University, Campus Ring 1, Bremen, D-28759, Germany
| | - Nivea Amoedo
- CELLOMET, Centre de Génomique Fonctionnelle de Bordeaux, 146 Rue Léo Saignat, Bordeaux, F-33000, France
| | - Rodrigue Rossignol
- CELLOMET, Centre de Génomique Fonctionnelle de Bordeaux, 146 Rue Léo Saignat, Bordeaux, F-33000, France.,Univ. Bordeaux, Inserm U1211, MRGM, Centre hospitalier universitaire Pellegrin, place Amélie Raba Léon, Bordeaux, F-33076, France
| | - Fabian Volker Filipp
- Cancer Systems Biology, Institute for Diabetes and Cancer, Helmholtz Zentrum München, Ingolstädter Landstraße 1, München, D-85764, Germany.,School of Life Sciences Weihenstephan, Technical University München, Maximus-von-Imhof-Forum 3, Freising, D-85354, Germany
| | - Vincent Lotteau
- CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 21 Avenue Tony Garnier, Lyon, F-69007, France.
| | - Olivier Diaz
- CIRI, Centre International de Recherche en Infectiologie, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, 21 Avenue Tony Garnier, Lyon, F-69007, France.
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Cheng Y, Schlosser P, Hertel J, Sekula P, Oefner PJ, Spiekerkoetter U, Mielke J, Freitag DF, Schmidts M, Kronenberg F, Eckardt KU, Thiele I, Li Y, Köttgen A. Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism. Nat Commun 2021; 12:964. [PMID: 33574263 PMCID: PMC7878905 DOI: 10.1038/s41467-020-20877-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 12/21/2020] [Indexed: 02/07/2023] Open
Abstract
Metabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e-7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.
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Affiliation(s)
- Yurong Cheng
- grid.5963.9Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany ,grid.5963.9Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Pascal Schlosser
- grid.5963.9Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Johannes Hertel
- grid.6142.10000 0004 0488 0789School of Medicine, National University of Ireland, Galway, University Road, Galway, Ireland ,grid.5603.0University of Greifswald, University Medicine Greifswald, Department of Psychiatry and Psychotherapy, Greifswald, Germany
| | - Peggy Sekula
- grid.5963.9Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Peter J. Oefner
- grid.7727.50000 0001 2190 5763Institute of Functional Genomics, University of Regensburg, Regensburg, Germany
| | - Ute Spiekerkoetter
- grid.5963.9Department of General Pediatrics and Adolescent Medicine, Medical Center and Faculty of Medicine - University of Freiburg, Freiburg, Germany
| | - Johanna Mielke
- grid.420044.60000 0004 0374 4101Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Daniel F. Freitag
- grid.420044.60000 0004 0374 4101Bayer AG, Division Pharmaceuticals, Open Innovation & Digital Technologies, Wuppertal, Germany
| | - Miriam Schmidts
- grid.5963.9Department of General Pediatrics and Adolescent Medicine, Medical Center and Faculty of Medicine - University of Freiburg, Freiburg, Germany
| | | | - Florian Kronenberg
- grid.5361.10000 0000 8853 2677Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Kai-Uwe Eckardt
- grid.5330.50000 0001 2107 3311Department of Nephrology and Hypertension, University of Erlangen-Nürnberg, Erlangen, Germany ,grid.6363.00000 0001 2218 4662Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ines Thiele
- grid.6142.10000 0004 0488 0789School of Medicine, National University of Ireland, Galway, University Road, Galway, Ireland ,grid.6142.10000 0004 0488 0789Division of Microbiology, National University of Ireland, Galway, University Road, Galway, Ireland ,APC Microbiome Ireland, Galway, Ireland
| | - Yong Li
- grid.5963.9Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Anna Köttgen
- grid.5963.9Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany ,grid.5963.9CIBSS – Centre for Integrative Biological Signalling Studies, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany
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119
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Karta J, Bossicard Y, Kotzamanis K, Dolznig H, Letellier E. Mapping the Metabolic Networks of Tumor Cells and Cancer-Associated Fibroblasts. Cells 2021; 10:304. [PMID: 33540679 PMCID: PMC7912987 DOI: 10.3390/cells10020304] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/20/2021] [Accepted: 01/26/2021] [Indexed: 12/12/2022] Open
Abstract
Metabolism is considered to be the core of all cellular activity. Thus, extensive studies of metabolic processes are ongoing in various fields of biology, including cancer research. Cancer cells are known to adapt their metabolism to sustain high proliferation rates and survive in unfavorable environments with low oxygen and nutrient concentrations. Hence, targeting cancer cell metabolism is a promising therapeutic strategy in cancer research. However, cancers consist not only of genetically altered tumor cells but are interwoven with endothelial cells, immune cells and fibroblasts, which together with the extracellular matrix (ECM) constitute the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), which are linked to poor prognosis in different cancer types, are one important component of the TME. CAFs play a significant role in reprogramming the metabolic landscape of tumor cells, but how, and in what manner, this interaction takes place remains rather unclear. This review aims to highlight the metabolic landscape of tumor cells and CAFs, including their recently identified subtypes, in different tumor types. In addition, we discuss various in vitro and in vivo metabolic techniques as well as different in silico computational tools that can be used to identify and characterize CAF-tumor cell interactions. Finally, we provide our view on how mapping the complex metabolic networks of stromal-tumor metabolism will help in finding novel metabolic targets for cancer treatment.
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Affiliation(s)
- Jessica Karta
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belval, Luxembourg; (J.K.); (Y.B.); (K.K.)
| | - Ysaline Bossicard
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belval, Luxembourg; (J.K.); (Y.B.); (K.K.)
| | - Konstantinos Kotzamanis
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belval, Luxembourg; (J.K.); (Y.B.); (K.K.)
| | - Helmut Dolznig
- Tumor Stroma Interaction Group, Institute of Medical Genetics, Medical University of Vienna, Währinger Strasse 10, 1090 Vienna, Austria;
| | - Elisabeth Letellier
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belval, Luxembourg; (J.K.); (Y.B.); (K.K.)
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120
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Rawls KD, Dougherty BV, Vinnakota KC, Pannala VR, Wallqvist A, Kolling GL, Papin JA. Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model. Toxicol Appl Pharmacol 2021; 412:115390. [PMID: 33387578 PMCID: PMC7859602 DOI: 10.1016/j.taap.2020.115390] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 11/02/2020] [Accepted: 12/26/2020] [Indexed: 12/12/2022]
Abstract
The kidneys are metabolically active organs with importance in several physiological tasks such as the secretion of soluble wastes into the urine and synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to four compounds with varying levels of nephrotoxicity (acetaminophen, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six and twenty-four hours, and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. For the transcriptomics data, we observed changes in fatty acid metabolism and amino acid metabolism, as well as changes in existing markers of kidney function such as Clu (clusterin). The iRno metabolic network reconstruction was used to predict alterations in these same pathways after integrating transcriptomics data and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism as a step towards identifying novel metabolic biomarkers of kidney function and dysfunction.
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Affiliation(s)
- Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Kalyan C Vinnakota
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, MD 20817, USA
| | - Venkat R Pannala
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, MD 20817, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.
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Machicao J, Craighero F, Maspero D, Angaroni F, Damiani C, Graudenzi A, Antoniotti M, Bruno OM. On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples. Curr Genomics 2021; 22:88-97. [PMID: 34220296 PMCID: PMC8188584 DOI: 10.2174/1389202922666210301084151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 12/16/2020] [Accepted: 12/18/2020] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND The increasing availability of omics data collected from patients affected by severe pathologies, such as cancer, is fostering the development of data science methods for their analysis. INTRODUCTION The combination of data integration and machine learning approaches can provide new powerful instruments to tackle the complexity of cancer development and deliver effective diagnostic and prognostic strategies. METHODS We explore the possibility of exploiting the topological properties of sample-specific metabolic networks as features in a supervised classification task. Such networks are obtained by projecting transcriptomic data from RNA-seq experiments on genome-wide metabolic models to define weighted networks modeling the overall metabolic activity of a given sample. RESULTS We show the classification results on a labeled breast cancer dataset from the TCGA database, including 210 samples (cancer vs. normal). In particular, we investigate how the performance is affected by a threshold-based pruning of the networks by comparing Artificial Neural Networks, Support Vector Machines and Random Forests. Interestingly, the best classification performance is achieved within a small threshold range for all methods, suggesting that it might represent an effective choice to recover useful information while filtering out noise from data. Overall, the best accuracy is achieved with SVMs, which exhibit performances similar to those obtained when gene expression profiles are used as features. CONCLUSION These findings demonstrate that the topological properties of sample-specific metabolic networks are effective in classifying cancer and normal samples, suggesting that useful information can be extracted from a relatively limited number of features.
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Affiliation(s)
- Jeaneth Machicao
- Address correspondence to these authors at the São Carlos Institute of Physics, University of São Paulo, São Carlos, Brazil; Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche (IBFM-CNR), Segrate, Milan, Italy E-mails: , ,
| | | | | | | | | | - Alex Graudenzi
- Address correspondence to these authors at the São Carlos Institute of Physics, University of São Paulo, São Carlos, Brazil; Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche (IBFM-CNR), Segrate, Milan, Italy E-mails: , ,
| | | | - Odemir M. Bruno
- Address correspondence to these authors at the São Carlos Institute of Physics, University of São Paulo, São Carlos, Brazil; Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche (IBFM-CNR), Segrate, Milan, Italy E-mails: , ,
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Virtual metabolic human dynamic model for pathological analysis and therapy design for diabetes. iScience 2021; 24:102101. [PMID: 33615200 PMCID: PMC7878987 DOI: 10.1016/j.isci.2021.102101] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 12/21/2020] [Accepted: 01/20/2021] [Indexed: 12/11/2022] Open
Abstract
A virtual metabolic human model is a valuable complement to experimental biology and clinical studies, because in vivo research involves serious ethical and technical problems. I have proposed a multi-organ and multi-scale kinetic model that formulates the reactions of enzymes and transporters with the regulation of hormonal actions at postprandial and postabsorptive states. The computational model consists of 202 ordinary differential equations for metabolites with 217 reaction rates and 1,140 kinetic parameter constants. It is the most comprehensive, largest, and highly predictive model of the whole-body metabolism. Use of the model revealed the mechanisms by which individual disorders, such as steatosis, β cell dysfunction, and insulin resistance, were combined to cause diabetes. The model predicted a glycerol kinase inhibitor to be an effective medicine for type 2 diabetes, which not only decreased hepatic triglyceride but also reduced plasma glucose. The model also enabled us to rationally design combination therapy. A standard of virtual metabolic human dynamic models is proposed It integrates the three scales of molecules, organs, and whole body It gets insight into pathological mechanisms of type 1 and type 2 diabetes It enables the computer-aided design of medication treatment for diabetes
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Martins Conde P, Pfau T, Pires Pacheco M, Sauter T. A dynamic multi-tissue model to study human metabolism. NPJ Syst Biol Appl 2021; 7:5. [PMID: 33483512 PMCID: PMC7822846 DOI: 10.1038/s41540-020-00159-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 10/19/2020] [Indexed: 11/08/2022] Open
Abstract
Metabolic modeling enables the study of human metabolism in healthy and in diseased conditions, e.g., the prediction of new drug targets and biomarkers for metabolic diseases. To accurately describe blood and urine metabolite dynamics, the integration of multiple metabolically active tissues is necessary. We developed a dynamic multi-tissue model, which recapitulates key properties of human metabolism at the molecular and physiological level based on the integration of transcriptomics data. It enables the simulation of the dynamics of intra-cellular and extra-cellular metabolites at the genome scale. The predictive capacity of the model is shown through the accurate simulation of different healthy conditions (i.e., during fasting, while consuming meals or during exercise), and the prediction of biomarkers for a set of Inborn Errors of Metabolism with a precision of 83%. This novel approach is useful to prioritize new biomarkers for many metabolic diseases, as well as for the integration of various types of personal omics data, towards the personalized analysis of blood and urine metabolites.
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Affiliation(s)
- Patricia Martins Conde
- Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Megeno S.A., Esch-sur-Alzette, Luxembourg
| | - Thomas Pfau
- Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
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Integrative computational approach identifies drug targets in CD4 + T-cell-mediated immune disorders. NPJ Syst Biol Appl 2021; 7:4. [PMID: 33483502 PMCID: PMC7822845 DOI: 10.1038/s41540-020-00165-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 12/08/2020] [Indexed: 12/12/2022] Open
Abstract
CD4+ T cells provide adaptive immunity against pathogens and abnormal cells, and they are also associated with various immune-related diseases. CD4+ T cells’ metabolism is dysregulated in these pathologies and represents an opportunity for drug discovery and development. Genome-scale metabolic modeling offers an opportunity to accelerate drug discovery by providing high-quality information about possible target space in the context of a modeled disease. Here, we develop genome-scale models of naïve, Th1, Th2, and Th17 CD4+ T-cell subtypes to map metabolic perturbations in rheumatoid arthritis, multiple sclerosis, and primary biliary cholangitis. We subjected these models to in silico simulations for drug response analysis of existing FDA-approved drugs and compounds. Integration of disease-specific differentially expressed genes with altered reactions in response to metabolic perturbations identified 68 drug targets for the three autoimmune diseases. In vitro experimental validation, together with literature-based evidence, showed that modulation of fifty percent of identified drug targets suppressed CD4+ T cells, further increasing their potential impact as therapeutic interventions. Our approach can be generalized in the context of other diseases, and the metabolic models can be further used to dissect CD4+ T-cell metabolism.
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125
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Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models. Sci Rep 2021; 11:213. [PMID: 33420254 PMCID: PMC7794450 DOI: 10.1038/s41598-020-80561-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 12/11/2020] [Indexed: 01/29/2023] Open
Abstract
Research on new cancer drugs is performed either through gene knockout studies or phenotypic screening of drugs in cancer cell-lines. Both of these approaches are costly and time-consuming. Computational framework, e.g., genome-scale metabolic models (GSMMs), could be a good alternative to find potential drug targets. The present study aims to investigate the applicability of gene knockout strategies to be used as the finding of drug targets using GSMMs. We performed single-gene knockout studies on existing GSMMs of the NCI-60 cell-lines obtained from 9 tissue types. The metabolic genes responsible for the growth of cancerous cells were identified and then ranked based on their cellular growth reduction. The possible growth reduction mechanisms, which matches with the gene knockout results, were described. Gene ranking was used to identify potential drug targets, which reduce the growth rate of cancer cells but not of the normal cells. The gene ranking results were also compared with existing shRNA screening data. The rank-correlation results for most of the cell-lines were not satisfactory for a single-gene knockout, but it played a significant role in deciding the activity of drug against cell proliferation, whereas multiple gene knockout analysis gave better correlation results. We validated our theoretical results experimentally and showed that the drugs mitotane and myxothiazol can inhibit the growth of at least four cell-lines of NCI-60 database.
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Wörheide MA, Krumsiek J, Kastenmüller G, Arnold M. Multi-omics integration in biomedical research - A metabolomics-centric review. Anal Chim Acta 2021; 1141:144-162. [PMID: 33248648 PMCID: PMC7701361 DOI: 10.1016/j.aca.2020.10.038] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/09/2020] [Accepted: 10/19/2020] [Indexed: 02/07/2023]
Abstract
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
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Affiliation(s)
- Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
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127
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Delattre H, Sasidharan K, Soyer OS. Inhibiting the reproduction of SARS-CoV-2 through perturbations in human lung cell metabolic network. Life Sci Alliance 2021; 4:e202000869. [PMID: 33234678 PMCID: PMC7723300 DOI: 10.26508/lsa.202000869] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 11/02/2020] [Accepted: 11/11/2020] [Indexed: 01/04/2023] Open
Abstract
Viruses rely on their host for reproduction. Here, we made use of genomic and structural information to create a biomass function capturing the amino and nucleic acid requirements of SARS-CoV-2. Incorporating this biomass function into a stoichiometric metabolic model of the human lung cell and applying metabolic flux balance analysis, we identified host-based metabolic perturbations inhibiting SARS-CoV-2 reproduction. Our results highlight reactions in the central metabolism, as well as amino acid and nucleotide biosynthesis pathways. By incorporating host cellular maintenance into the model based on available protein expression data from human lung cells, we find that only few of these metabolic perturbations are able to selectively inhibit virus reproduction. Some of the catalysing enzymes of such reactions have demonstrated interactions with existing drugs, which can be used for experimental testing of the presented predictions using gene knockouts and RNA interference techniques. In summary, the developed computational approach offers a platform for rapid, experimentally testable generation of drug predictions against existing and emerging viruses based on their biomass requirements.
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Affiliation(s)
| | - Kalesh Sasidharan
- School of Life Sciences, University of Warwick, UK
- Bio-Electrical Engineering Innovation Hub, University of Warwick, UK
| | - Orkun S Soyer
- School of Life Sciences, University of Warwick, UK
- Bio-Electrical Engineering Innovation Hub, University of Warwick, UK
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128
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Monraz Gomez LC, Kondratova M, Sompairac N, Lonjou C, Ravel JM, Barillot E, Zinovyev A, Kuperstein I. Atlas of Cancer Signaling Network: A Resource of Multi-Scale Biological Maps to Study Disease Mechanisms. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11683-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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129
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MEN LH, PI ZF, HU MX, LIU S, LIU ZQ, SONG FR, CHEN X, LIU ZY. Serum Metabolomics Coupled with Network Pharmacology Strategy to Explore Therapeutic Effects of Scutellaria Baicalensis Georgi on Diabetic Nephropathy. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2021. [DOI: 10.1016/s1872-2040(20)60075-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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130
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131
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Messa GM, Napolitano F, Elsea SH, di Bernardo D, Gao X. A Siamese neural network model for the prioritization of metabolic disorders by integrating real and simulated data. Bioinformatics 2020; 36:i787-i794. [PMID: 33381827 DOI: 10.1093/bioinformatics/btaa841] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Untargeted metabolomic approaches hold a great promise as a diagnostic tool for inborn errors of metabolisms (IEMs) in the near future. However, the complexity of the involved data makes its application difficult and time consuming. Computational approaches, such as metabolic network simulations and machine learning, could significantly help to exploit metabolomic data to aid the diagnostic process. While the former suffers from limited predictive accuracy, the latter is normally able to generalize only to IEMs for which sufficient data are available. Here, we propose a hybrid approach that exploits the best of both worlds by building a mapping between simulated and real metabolic data through a novel method based on Siamese neural networks (SNN). RESULTS The proposed SNN model is able to perform disease prioritization for the metabolic profiles of IEM patients even for diseases that it was not trained to identify. To the best of our knowledge, this has not been attempted before. The developed model is able to significantly outperform a baseline model that relies on metabolic simulations only. The prioritization performances demonstrate the feasibility of the method, suggesting that the integration of metabolic models and data could significantly aid the IEM diagnosis process in the near future. AVAILABILITY AND IMPLEMENTATION Metabolic datasets used in this study are publicly available from the cited sources. The original data produced in this study, including the trained models and the simulated metabolic profiles, are also publicly available (Messa et al., 2020).
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Affiliation(s)
- Gian Marco Messa
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Francesco Napolitano
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Sarah H Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Diego di Bernardo
- Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli 80078, Italy.,Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, 80125 Naples, Italy
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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Alves MA, Lamichhane S, Dickens A, McGlinchey A, Ribeiro HC, Sen P, Wei F, Hyötyläinen T, Orešič M. Systems biology approaches to study lipidomes in health and disease. Biochim Biophys Acta Mol Cell Biol Lipids 2020; 1866:158857. [PMID: 33278596 DOI: 10.1016/j.bbalip.2020.158857] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/15/2022]
Abstract
Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.
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Affiliation(s)
- Marina Amaral Alves
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Santosh Lamichhane
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Alex Dickens
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
| | - Aidan McGlinchey
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | | | - Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland; School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | - Fang Wei
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, PR China
| | | | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland; School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden.
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133
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Borodina I, Kenny LC, McCarthy CM, Paramasivan K, Pretorius E, Roberts TJ, van der Hoek SA, Kell DB. The biology of ergothioneine, an antioxidant nutraceutical. Nutr Res Rev 2020; 33:190-217. [PMID: 32051057 PMCID: PMC7653990 DOI: 10.1017/s0954422419000301] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 11/20/2019] [Accepted: 11/25/2019] [Indexed: 02/07/2023]
Abstract
Ergothioneine (ERG) is an unusual thio-histidine betaine amino acid that has potent antioxidant activities. It is synthesised by a variety of microbes, especially fungi (including in mushroom fruiting bodies) and actinobacteria, but is not synthesised by plants and animals who acquire it via the soil and their diet, respectively. Animals have evolved a highly selective transporter for it, known as solute carrier family 22, member 4 (SLC22A4) in humans, signifying its importance, and ERG may even have the status of a vitamin. ERG accumulates differentially in various tissues, according to their expression of SLC22A4, favouring those such as erythrocytes that may be subject to oxidative stress. Mushroom or ERG consumption seems to provide significant prevention against oxidative stress in a large variety of systems. ERG seems to have strong cytoprotective status, and its concentration is lowered in a number of chronic inflammatory diseases. It has been passed as safe by regulatory agencies, and may have value as a nutraceutical and antioxidant more generally.
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Affiliation(s)
- Irina Borodina
- The Novo Nordisk Foundation Center for Biosustainability, Building 220, Chemitorvet 200, Technical University of Denmark, 2800Kongens Lyngby, Denmark
| | - Louise C. Kenny
- Department of Women’s and Children’s Health, Institute of Translational Medicine, University of Liverpool, Crown Street, LiverpoolL8 7SS, UK
| | - Cathal M. McCarthy
- Irish Centre for Fetal and Neonatal Translational Research (INFANT), Cork University Maternity Hospital, Cork, Republic of Ireland
- Department of Pharmacology and Therapeutics, Western Gateway Building, University College Cork, Cork, Republic of Ireland
| | - Kalaivani Paramasivan
- The Novo Nordisk Foundation Center for Biosustainability, Building 220, Chemitorvet 200, Technical University of Denmark, 2800Kongens Lyngby, Denmark
| | - Etheresia Pretorius
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, Private Bag X1 Matieland, 7602, South Africa
| | - Timothy J. Roberts
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, Private Bag X1 Matieland, 7602, South Africa
- Department of Biochemistry, Institute of Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown Street, LiverpoolL69 7ZB, UK
| | - Steven A. van der Hoek
- The Novo Nordisk Foundation Center for Biosustainability, Building 220, Chemitorvet 200, Technical University of Denmark, 2800Kongens Lyngby, Denmark
| | - Douglas B. Kell
- The Novo Nordisk Foundation Center for Biosustainability, Building 220, Chemitorvet 200, Technical University of Denmark, 2800Kongens Lyngby, Denmark
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, Private Bag X1 Matieland, 7602, South Africa
- Department of Biochemistry, Institute of Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown Street, LiverpoolL69 7ZB, UK
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134
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Fang X, Lloyd CJ, Palsson BO. Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat Rev Microbiol 2020; 18:731-743. [PMID: 32958892 PMCID: PMC7981288 DOI: 10.1038/s41579-020-00440-4] [Citation(s) in RCA: 111] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2020] [Indexed: 02/06/2023]
Abstract
Escherichia coli is considered to be the best-known microorganism given the large number of published studies detailing its genes, its genome and the biochemical functions of its molecular components. This vast literature has been systematically assembled into a reconstruction of the biochemical reaction networks that underlie E. coli's functions, a process which is now being applied to an increasing number of microorganisms. Genome-scale reconstructed networks are organized and systematized knowledge bases that have multiple uses, including conversion into computational models that interpret and predict phenotypic states and the consequences of environmental and genetic perturbations. These genome-scale models (GEMs) now enable us to develop pan-genome analyses that provide mechanistic insights, detail the selection pressures on proteome allocation and address stress phenotypes. In this Review, we first discuss the overall development of GEMs and their applications. Next, we review the evolution of the most complete GEM that has been developed to date: the E. coli GEM. Finally, we explore three emerging areas in genome-scale modelling of microbial phenotypes: collections of strain-specific models, metabolic and macromolecular expression models, and simulation of stress responses.
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Affiliation(s)
- Xin Fang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Colton J Lloyd
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
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135
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O’Hagan S, Kell DB. Structural Similarities between Some Common Fluorophores Used in Biology, Marketed Drugs, Endogenous Metabolites, and Natural Products. Mar Drugs 2020; 18:E582. [PMID: 33238416 PMCID: PMC7700180 DOI: 10.3390/md18110582] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 11/16/2020] [Accepted: 11/20/2020] [Indexed: 12/12/2022] Open
Abstract
It is known that at least some fluorophores can act as 'surrogate' substrates for solute carriers (SLCs) involved in pharmaceutical drug uptake, and this promiscuity is taken to reflect at least a certain structural similarity. As part of a comprehensive study seeking the 'natural' substrates of 'orphan' transporters that also serve to take up pharmaceutical drugs into cells, we have noted that many drugs bear structural similarities to natural products. A cursory inspection of common fluorophores indicates that they too are surprisingly 'drug-like', and they also enter at least some cells. Some are also known to be substrates of efflux transporters. Consequently, we sought to assess the structural similarity of common fluorophores to marketed drugs, endogenous mammalian metabolites, and natural products. We used a set of some 150 fluorophores along with standard fingerprinting methods and the Tanimoto similarity metric. Results: The great majority of fluorophores tested exhibited significant similarity (Tanimoto similarity > 0.75) to at least one drug, as judged via descriptor properties (especially their aromaticity, for identifiable reasons that we explain), by molecular fingerprints, by visual inspection, and via the "quantitative estimate of drug likeness" technique. It is concluded that this set of fluorophores does overlap with a significant part of both the drug space and natural products space. Consequently, fluorophores do indeed offer a much wider opportunity than had possibly been realised to be used as surrogate uptake molecules in the competitive or trans-stimulation assay of membrane transporter activities.
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Affiliation(s)
- Steve O’Hagan
- Department of Chemistry, The University of Manchester, Manchester M13 9PT, UK;
- Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester M1 7DN, UK
| | - Douglas B. Kell
- Department of Biochemistry and Systems Biology, Institute of Molecular, Integrative and Systems Biology, Biosciences Building, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kongens Lyngby, Denmark
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136
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Chowdhury S, Fong SS. Leveraging genome-scale metabolic models for human health applications. Curr Opin Biotechnol 2020; 66:267-276. [PMID: 33120253 DOI: 10.1016/j.copbio.2020.08.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 08/27/2020] [Accepted: 08/31/2020] [Indexed: 02/07/2023]
Abstract
Genome-scale metabolic modeling is a scalable and extensible computational method for analyzing and predicting biological function. With the ongoing improvements in computational methods and experimental capabilities, genome-scale metabolic models (GEMs) are demonstrating utility in addressing human health applications. The initial areas of highest impact are likely to be health applications where disease states involve metabolic changes. In this review, we focus on recent application of GEMs to studying cancer and the human microbiome by describing the enabling methodologies and outcomes of these studies. We conclude with proposing some areas of research that are likely to arise as a result of recent methodological advances.
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Affiliation(s)
- Shomeek Chowdhury
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Main Street, Richmond, 23284, VA, USA
| | - Stephen S Fong
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Main Street, Richmond, 23284, VA, USA; Chemical and Life Science Engineering, Virginia Commonwealth University, 601 West Main Street, Richmond, 23284, VA, USA.
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137
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N Kolodkin A, Sharma RP, Colangelo AM, Ignatenko A, Martorana F, Jennen D, Briedé JJ, Brady N, Barberis M, Mondeel TDGA, Papa M, Kumar V, Peters B, Skupin A, Alberghina L, Balling R, Westerhoff HV. ROS networks: designs, aging, Parkinson's disease and precision therapies. NPJ Syst Biol Appl 2020; 6:34. [PMID: 33106503 PMCID: PMC7589522 DOI: 10.1038/s41540-020-00150-w] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 08/28/2020] [Indexed: 12/11/2022] Open
Abstract
How the network around ROS protects against oxidative stress and Parkinson's disease (PD), and how processes at the minutes timescale cause disease and aging after decades, remains enigmatic. Challenging whether the ROS network is as complex as it seems, we built a fairly comprehensive version thereof which we disentangled into a hierarchy of only five simpler subnetworks each delivering one type of robustness. The comprehensive dynamic model described in vitro data sets from two independent laboratories. Notwithstanding its five-fold robustness, it exhibited a relatively sudden breakdown, after some 80 years of virtually steady performance: it predicted aging. PD-related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the collapse, while antioxidants or caffeine retarded it. Introducing a new concept (aging-time-control coefficient), we found that as many as 25 out of 57 molecular processes controlled aging. We identified new targets for "life-extending interventions": mitochondrial synthesis, KEAP1 degradation, and p62 metabolism.
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Affiliation(s)
- Alexey N Kolodkin
- Infrastructure for Systems Biology Europe (ISBE.NL), Amsterdam, The Netherlands.
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands.
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
| | - Raju Prasad Sharma
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands
- Environmental Engineering Laboratory, Departament d'Enginyeria Quimica, Universitat Rovira i Virgili, Tarragona, Spain
| | - Anna Maria Colangelo
- Infrastructure for Systems Biology Europe (ISBE.IT), Milan, Italy
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
- Laboratory of Neuroscience "R Levi-Montalcini" Dept of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Andrew Ignatenko
- Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg
| | - Francesca Martorana
- Infrastructure for Systems Biology Europe (ISBE.IT), Milan, Italy
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
- Laboratory of Neuroscience "R Levi-Montalcini" Dept of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Danyel Jennen
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Jacco J Briedé
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Nathan Brady
- Department of Molecular Microbiology & Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Matteo Barberis
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Surrey, UK
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Surrey, UK
| | - Thierry D G A Mondeel
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Surrey, UK
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Surrey, UK
| | - Michele Papa
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
- Infrastructure for Systems Biology Europe (ISBE.IT), Naples, Italy
- Department of Mental and Physical Health, University of Campania-L. Vanvitelli, Napoli, Italia
| | - Vikas Kumar
- Environmental Engineering Laboratory, Departament d'Enginyeria Quimica, Universitat Rovira i Virgili, Tarragona, Spain
- IISPV, Hospital Universitari Sant Joan de Reus, Universitat Rovira I Virgili, Reus, Spain
| | - Bernhard Peters
- Faculty of Science, Technology and Communication, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Lilia Alberghina
- Infrastructure for Systems Biology Europe (ISBE.IT), Milan, Italy
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Hans V Westerhoff
- Infrastructure for Systems Biology Europe (ISBE.NL), Amsterdam, The Netherlands.
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands.
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
- Manchester Centre for Integrative Systems Biology, School for Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK.
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138
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Guebila MB. VFFVA: dynamic load balancing enables large-scale flux variability analysis. BMC Bioinformatics 2020; 21:424. [PMID: 32993482 PMCID: PMC7523073 DOI: 10.1186/s12859-020-03711-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 08/10/2020] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Genome-scale metabolic models are increasingly employed to predict the phenotype of various biological systems pertaining to healthcare and bioengineering. To characterize the full metabolic spectrum of such systems, Fast Flux Variability Analysis (FFVA) is commonly used in parallel with static load balancing. This approach assigns to each core an equal number of biochemical reactions without consideration of their solution complexity. RESULTS Here, we present Very Fast Flux Variability Analysis (VFFVA) as a parallel implementation that dynamically balances the computation load between the cores in runtime which guarantees equal convergence time between them. VFFVA allowed to gain a threefold speedup factor with coupled models and up to 100 with ill-conditioned models along with a 14-fold decrease in memory usage. CONCLUSIONS VFFVA exploits the parallel capabilities of modern machines to enable biological insights through optimizing systems biology modeling. VFFVA is available in C, MATLAB, and Python at https://github.com/marouenbg/VFFVA .
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Affiliation(s)
- Marouen Ben Guebila
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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139
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Moolamalla STR, Vinod PK. Genome-scale metabolic modelling predicts biomarkers and therapeutic targets for neuropsychiatric disorders. Comput Biol Med 2020; 125:103994. [PMID: 32980779 DOI: 10.1016/j.compbiomed.2020.103994] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 09/06/2020] [Accepted: 09/07/2020] [Indexed: 01/06/2023]
Abstract
Distinguishing neuropsychiatric disorders is challenging due to the overlap in symptoms and genetic risk factors. People suffering from these disorders face personal and professional challenges. Understanding the dysregulation of brain metabolism under disease condition can aid in effective diagnosis and in developing treatment strategies based on the metabolism. In this study, we reconstructed the metabolic network of three major neuropsychiatric disorders, schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) using transcriptomic data and constrained based modelling approach. We integrated brain transcriptomic data from six independent studies with a recent comprehensive genome-scale metabolic model Recon3D. The analysis of the reconstructed network revealed the flux-level alterations in the peroxisome-mitochondria-golgi axis in neuropsychiatric disorders. We also extracted reporter metabolites and pathways that distinguish these three neuropsychiatric disorders. We found differences with respect to fatty acid oxidation, aromatic and branched chain amino acid metabolism, bile acid synthesis, glycosaminoglycans synthesis and modifications, and phospholipid metabolism. Further, we predicted network perturbations that transform the disease metabolic state to a healthy metabolic state for each disorder. These analyses provide local and global views of the metabolic changes in SCZ, BD and MDD, which may have clinical implications.
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Affiliation(s)
- S T R Moolamalla
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - P K Vinod
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
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140
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Sulheim S, Kumelj T, van Dissel D, Salehzadeh-Yazdi A, Du C, van Wezel GP, Nieselt K, Almaas E, Wentzel A, Kerkhoven EJ. Enzyme-Constrained Models and Omics Analysis of Streptomyces coelicolor Reveal Metabolic Changes that Enhance Heterologous Production. iScience 2020; 23:101525. [PMID: 32942174 PMCID: PMC7501462 DOI: 10.1016/j.isci.2020.101525] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/19/2020] [Accepted: 08/31/2020] [Indexed: 02/06/2023] Open
Abstract
Many biosynthetic gene clusters (BGCs) require heterologous expression to realize their genetic potential, including silent and metagenomic BGCs. Although the engineered Streptomyces coelicolor M1152 is a widely used host for heterologous expression of BGCs, a systemic understanding of how its genetic modifications affect the metabolism is lacking and limiting further development. We performed a comparative analysis of M1152 and its ancestor M145, connecting information from proteomics, transcriptomics, and cultivation data into a comprehensive picture of the metabolic differences between these strains. Instrumental to this comparison was the application of an improved consensus genome-scale metabolic model (GEM) of S. coelicolor. Although many metabolic patterns are retained in M1152, we find that this strain suffers from oxidative stress, possibly caused by increased oxidative metabolism. Furthermore, precursor availability is likely not limiting polyketide production, implying that other strategies could be beneficial for further development of S. coelicolor for heterologous production of novel compounds.
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Affiliation(s)
- Snorre Sulheim
- Department of Biotechnology and Nanomedicine, SINTEF Industry, 7034 Trondheim, Norway
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Tjaša Kumelj
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Dino van Dissel
- Department of Biotechnology and Nanomedicine, SINTEF Industry, 7034 Trondheim, Norway
| | - Ali Salehzadeh-Yazdi
- Department of Systems Biology and Bioinformatics, Faculty of Computer Science and Electrical Engineering, University of Rostock, 18057 Rostock, Germany
| | - Chao Du
- Microbial Biotechnology, Institute of Biology, Leiden University, 2300 Leiden, the Netherlands
| | - Gilles P. van Wezel
- Microbial Biotechnology, Institute of Biology, Leiden University, 2300 Leiden, the Netherlands
| | - Kay Nieselt
- Integrative Transcriptomics, Center for Bioinformatics, University of Tübingen, 72070 Tübingen, Germany
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Alexander Wentzel
- Department of Biotechnology and Nanomedicine, SINTEF Industry, 7034 Trondheim, Norway
| | - Eduard J. Kerkhoven
- Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, 412 96 Gothenburg, Sweden
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141
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Sen P, Lamichhane S, Mathema VB, McGlinchey A, Dickens AM, Khoomrung S, Orešič M. Deep learning meets metabolomics: a methodological perspective. Brief Bioinform 2020; 22:1531-1542. [PMID: 32940335 DOI: 10.1093/bib/bbaa204] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 08/08/2020] [Accepted: 08/10/2020] [Indexed: 12/15/2022] Open
Abstract
Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.,School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | - Santosh Lamichhane
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Vivek B Mathema
- Metabolomics and Systems Biology, Department of Biochemistry, and Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Aidan McGlinchey
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | - Alex M Dickens
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Sakda Khoomrung
- Metabolomics and Systems Biology, Department of Biochemistry, and Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.,Center for Innovation in Chemistry (PERCH), Faculty of Science, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland.,School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
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142
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Martyushenko N, Almaas E. ErrorTracer: an algorithm for identifying the origins of inconsistencies in genome-scale metabolic models. Bioinformatics 2020; 36:1644-1646. [PMID: 31598631 PMCID: PMC7703767 DOI: 10.1093/bioinformatics/btz761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 09/25/2019] [Accepted: 10/03/2019] [Indexed: 01/24/2023] Open
Abstract
MOTIVATION The number and complexity of genome-scale metabolic models is steadily increasing, empowered by automated model-generation algorithms. The quality control of the models, however, has always remained a significant challenge, the most fundamental being reactions incapable of carrying flux. Numerous automated gap-filling algorithms try to address this problem, but can rarely resolve all of a model's inconsistencies. The need for fast inconsistency checking algorithms has also been emphasized with the recent community push for automated model-validation before model publication. Previously, we wrote a graphical software to allow the modeller to solve the remaining errors manually. Nevertheless, model size and complexity remained a hindrance to efficiently tracking origins of inconsistency. RESULTS We developed the ErrorTracer algorithm in order to address the shortcomings of existing approaches: ErrorTracer searches for inconsistencies, classifies them and identifies their origins. The algorithm is ∼2 orders of magnitude faster than current community standard methods, using only seconds even for large-scale models. This allows for interactive exploration in direct combination with model visualization, markedly simplifying the whole error-identification and correction work flow. AVAILABILITY AND IMPLEMENTATION Windows and Linux executables and source code are available under the EPL 2.0 Licence at https://github.com/TheAngryFox/ModelExplorer and https://www.ntnu.edu/almaaslab/downloads. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Eivind Almaas
- Department of Biotechnology.,Department of Public Health and General Practice, K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim N-7491, Norway
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143
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Integrated Analyses of Microbiome and Longitudinal Metabolome Data Reveal Microbial-Host Interactions on Sulfur Metabolism in Parkinson's Disease. Cell Rep 2020; 29:1767-1777.e8. [PMID: 31722195 PMCID: PMC6856723 DOI: 10.1016/j.celrep.2019.10.035] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 07/17/2019] [Accepted: 10/09/2019] [Indexed: 02/07/2023] Open
Abstract
Parkinson’s disease (PD) exhibits systemic effects on the human metabolism, with emerging roles for the gut microbiome. Here, we integrate longitudinal metabolome data from 30 drug-naive, de novo PD patients and 30 matched controls with constraint-based modeling of gut microbial communities derived from an independent, drug-naive PD cohort, and prospective data from the general population. Our key results are (1) longitudinal trajectory of metabolites associated with the interconversion of methionine and cysteine via cystathionine differed between PD patients and controls; (2) dopaminergic medication showed strong lipidomic signatures; (3) taurine-conjugated bile acids correlated with the severity of motor symptoms, while low levels of sulfated taurolithocholate were associated with PD incidence in the general population; and (4) computational modeling predicted changes in sulfur metabolism, driven by A. muciniphila and B. wadsworthia, which is consistent with the changed metabolome. The multi-omics integration reveals PD-specific patterns in microbial-host sulfur co-metabolism that may contribute to PD severity. Longitudinal metabolomics reveal disturbed transsulfuration in Parkinson’s disease Metabolic modeling of gut microbiomes show altered microbial sulfur metabolism Changed microbial sulfur metabolism is linked to B. wadsworthia and A. muciniphila Taurine-conjugated bile acids are associated with incident Parkinson’s disease
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144
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Verma A, Sharda S, Rathi B, Somvanshi P, Pandey BD. Elucidating potential molecular signatures through host-microbe interactions for reactive arthritis and inflammatory bowel disease using combinatorial approach. Sci Rep 2020; 10:15131. [PMID: 32934294 PMCID: PMC7492238 DOI: 10.1038/s41598-020-71674-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Accepted: 07/06/2020] [Indexed: 02/08/2023] Open
Abstract
Reactive Arthritis (ReA), a rare seronegative inflammatory arthritis, lacks exquisite classification under rheumatic autoimmunity. ReA is solely established using differential clinical diagnosis of the patient cohorts, where pathogenic triggers linked to enteric and urogenital microorganisms e.g. Salmonella, Shigella, Yersinia, Campylobacter, Chlamydia have been reported. Inflammatory Bowel Disease (IBD), an idiopathic enteric disorder co-evolved and attuned to present gut microbiome dysbiosis, can be correlated to the genesis of enteropathic arthropathies like ReA. Gut microbes symbolically modulate immune system homeostasis and are elementary for varied disease patterns in autoimmune disorders. The gut-microbiota axis structured on the core host-microbe interactions execute an imperative role in discerning the etiopathogenesis of ReA and IBD. This study predicts the molecular signatures for ReA with co-evolved IBD through the enveloped host-microbe interactions and microbe-microbe 'interspecies communication', using synonymous gene expression data for selective microbes. We have utilized a combinatorial approach that have concomitant in-silico work-pipeline and experimental validation to corroborate the findings. In-silico analysis involving text mining, metabolic network reconstruction, simulation, filtering, host-microbe interaction, docking and molecular mimicry studies results in robust drug target/s and biomarker/s for co-evolved IBD and ReA. Cross validation of the target/s or biomarker/s was done by targeted gene expression analysis following a non-probabilistic convenience sampling. Studies were performed to substantiate the host-microbe disease network consisting of protein-marker-symptom/disease-pathway-drug associations resulting in possible identification of vital drug targets, biomarkers, pathways and inhibitors for IBD and ReA.Our study identified Na(+)/H(+) anti-porter (NHAA) and Kynureninase (KYNU) to be robust early and essential host-microbe interacting targets for IBD co-evolved ReA. Other vital host-microbe interacting genes, proteins, pathways and drugs include Adenosine Deaminase (ADA), Superoxide Dismutase 2 (SOD2), Catalase (CAT), Angiotensin I Converting Enzyme (ACE), carbon metabolism (folate biosynthesis) and methotrexate. These can serve as potential prognostic/theranostic biomarkers and signatures that can be extrapolated to stratify ReA and related autoimmunity patient cohorts for further pilot studies.
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Affiliation(s)
- Anukriti Verma
- Amity Institute of Biotechnology, J-3 Block, Amity University Campus, Sector-125, Noida, UP, 201313, India
| | - Shivani Sharda
- Amity Institute of Biotechnology, J-3 Block, Amity University Campus, Sector-125, Noida, UP, 201313, India.
| | - Bhawna Rathi
- Amity Institute of Biotechnology, J-3 Block, Amity University Campus, Sector-125, Noida, UP, 201313, India
| | - Pallavi Somvanshi
- Department of Biotechnology, TERI School of Advanced Studies, 10, Institutional Area, Vasant Kunj, New Delhi, 110070, India
| | - Bimlesh Dhar Pandey
- Fortis Hospital, B-22, Sector 62, Gautam Buddh Nagar, Noida, Uttar Pradesh, 201301, India
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145
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Sharma A, Rejeeth C, Vivek R, Babu VN, Ding X. Novel Green Silver Nanoparticles as Matrix in the Detection of Small Molecules Using Matrix-Assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS). J Pharm Innov 2020. [DOI: 10.1007/s12247-020-09486-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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146
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González J, Pinzón A, Angarita-Rodríguez A, Aristizabal AF, Barreto GE, Martín-Jiménez C. Advances in Astrocyte Computational Models: From Metabolic Reconstructions to Multi-omic Approaches. Front Neuroinform 2020; 14:35. [PMID: 32848690 PMCID: PMC7426703 DOI: 10.3389/fninf.2020.00035] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 07/14/2020] [Indexed: 12/12/2022] Open
Abstract
The growing importance of astrocytes in the field of neuroscience has led to a greater number of computational models devoted to the study of astrocytic functions and their metabolic interactions with neurons. The modeling of these interactions demands a combined understanding of brain physiology and the development of computational frameworks based on genomic-scale reconstructions, system biology, and dynamic models. These computational approaches have helped to highlight the neuroprotective mechanisms triggered by astrocytes and other glial cells, both under normal conditions and during neurodegenerative processes. In the present review, we evaluate some of the most relevant models of astrocyte metabolism, including genome-scale reconstructions and astrocyte-neuron interactions developed in the last few years. Additionally, we discuss novel strategies from the multi-omics perspective and computational models of other glial cell types that will increase our knowledge in brain metabolism and its association with neurodegenerative diseases.
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Affiliation(s)
- Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Andrés Pinzón
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia Bogotá, Bogotá, Colombia
| | - Andrea Angarita-Rodríguez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia.,Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia Bogotá, Bogotá, Colombia
| | - Andrés Felipe Aristizabal
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - George E Barreto
- Department of Biological Sciences, University of Limerick, Limerick, Ireland.,Health Research Institute, University of Limerick, Limerick, Ireland
| | - Cynthia Martín-Jiménez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
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147
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Seyed Tabib NS, Madgwick M, Sudhakar P, Verstockt B, Korcsmaros T, Vermeire S. Big data in IBD: big progress for clinical practice. Gut 2020; 69:1520-1532. [PMID: 32111636 PMCID: PMC7398484 DOI: 10.1136/gutjnl-2019-320065] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 02/05/2020] [Accepted: 02/06/2020] [Indexed: 12/12/2022]
Abstract
IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.
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Affiliation(s)
| | - Matthew Madgwick
- Organisms and Ecosystems, Earlham Institute, Norwich, UK
- Gut microbes in health and disease, Quadram Institute Bioscience, Norwich, UK
| | - Padhmanand Sudhakar
- Department of Chronic Diseases, Metabolism and Ageing, TARGID, KU Leuven, Leuven, Belgium
- Organisms and Ecosystems, Earlham Institute, Norwich, UK
- Gut microbes in health and disease, Quadram Institute Bioscience, Norwich, UK
| | - Bram Verstockt
- Translational Research in GastroIntestinal Disorders, KU Leuven, Leuven, Belgium
- Department of Gastroenterology and Hepatology, KU Leuven University Hospitals Leuven, Leuven, Belgium
| | - Tamas Korcsmaros
- Organisms and Ecosystems, Earlham Institute, Norwich, UK
- Gut microbes in health and disease, Quadram Institute Bioscience, Norwich, UK
| | - Séverine Vermeire
- Department of Chronic Diseases, Metabolism and Ageing, TARGID, KU Leuven, Leuven, Belgium
- Department of Gastroenterology and Hepatology, KU Leuven University Hospitals Leuven, Leuven, Belgium
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148
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Liu X, Feng S, Zhang XD, Li J, Zhang K, Wu M, Thorne RF. Non-coding RNAs, metabolic stress and adaptive mechanisms in cancer. Cancer Lett 2020; 491:60-69. [PMID: 32726612 DOI: 10.1016/j.canlet.2020.06.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/12/2020] [Accepted: 06/28/2020] [Indexed: 12/18/2022]
Abstract
Metabolic reprogramming in cancer describes the multifaceted alterations in metabolism that contribute to tumorigenesis. Major determinants of metabolic phenotypes are the changes in signalling pathways associated with oncogenic activation together with cues from the tumor microenvironment. Therein, depleted oxygen and nutrient levels elicit metabolic stress, requiring cancer cells to engage adaptive mechanisms. Non-coding RNAs (ncRNAs) act as regulatory elements within metabolic pathways and their widespread dysregulation in cancer contributes to altered metabolic phenotypes. Indeed, ncRNAs are the regulatory accomplices of many prominent effectors of metabolic reprogramming including c-MYC and HIFs that are activated by metabolic stress. By example, this review illustrates the range of ncRNAs mechanisms impacting these effectors throughout their DNA-RNA-protein lifecycle along with presenting the mechanistic roles of ncRNAs in adaptive responses to glucose, glutamine and lipid deprivation. We also discuss the facultative activation of metabolic enzymes by ncRNAs, a phenomenon which may reflect a broad but currently invisible level of metabolic regulation. Finally, the translational challenges associated with ncRNA discoveries are discussed, emphasizing the gaps in knowledge together with importance of understanding the molecular basis of ncRNA regulatory mechanisms.
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Affiliation(s)
- Xiaoying Liu
- Translational Research Institute of Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Molecular Pathology Centre, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, 450053, China; School of Life Sciences, Anhui Medical University, Hefei, 230032, China
| | - Shanshan Feng
- Key Laboratory of Regenerative Medicine, Ministry of Education, Department of Developmental & Regenerative Biology, School of Life Science and Technology, Jinan University, Guangzhou, China
| | - Xu Dong Zhang
- Translational Research Institute of Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Molecular Pathology Centre, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, 450053, China; School of Biomedical Sciences & Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Jinming Li
- Translational Research Institute of Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Molecular Pathology Centre, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, 450053, China
| | - Kaiguang Zhang
- The First Affiliated Hospital of University of Science and Technology of China, Hefei, 230027, China.
| | - Mian Wu
- Translational Research Institute of Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Molecular Pathology Centre, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, 450053, China; The First Affiliated Hospital of University of Science and Technology of China, Hefei, 230027, China; Key Laboratory of Stem Cell Differentiation & Modification, School of Clinical Medicine, Henan University, Zhengzhou, China.
| | - Rick F Thorne
- Translational Research Institute of Henan Provincial People's Hospital and People's Hospital of Zhengzhou University, Molecular Pathology Centre, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, 450053, China; School of Environmental & Life Sciences, University of Newcastle, NSW, Australia.
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149
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Kedaigle AJ, Fraenkel E, Atwal RS, Wu M, Gusella JF, MacDonald ME, Kaye JA, Finkbeiner S, Mattis VB, Tom CM, Svendsen C, King AR, Chen Y, Stocksdale JT, Lim RG, Casale M, Wang PH, Thompson LM, Akimov SS, Ratovitski T, Arbez N, Ross CA. Bioenergetic deficits in Huntington's disease iPSC-derived neural cells and rescue with glycolytic metabolites. Hum Mol Genet 2020; 29:1757-1771. [PMID: 30768179 PMCID: PMC7372552 DOI: 10.1093/hmg/ddy430] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 12/09/2018] [Accepted: 12/11/2018] [Indexed: 12/14/2022] Open
Abstract
Altered cellular metabolism is believed to be an important contributor to pathogenesis of the neurodegenerative disorder Huntington's disease (HD). Research has primarily focused on mitochondrial toxicity, which can cause death of the vulnerable striatal neurons, but other aspects of metabolism have also been implicated. Most previous studies have been carried out using postmortem human brain or non-human cells. Here, we studied bioenergetics in an induced pluripotent stem cell-based model of the disease. We found decreased adenosine triphosphate (ATP) levels in HD cells compared to controls across differentiation stages and protocols. Proteomics data and multiomics network analysis revealed normal or increased levels of mitochondrial messages and proteins, but lowered expression of glycolytic enzymes. Metabolic experiments showed decreased spare glycolytic capacity in HD neurons, while maximal and spare respiratory capacities driven by oxidative phosphorylation were largely unchanged. ATP levels in HD neurons could be rescued with addition of pyruvate or late glycolytic metabolites, but not earlier glycolytic metabolites, suggesting a role for glycolytic deficits as part of the metabolic disturbance in HD neurons. Pyruvate or other related metabolic supplements could have therapeutic benefit in HD.
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Affiliation(s)
| | - Amanda J Kedaigle
- Computational and Systems Biology Graduate Program and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ernest Fraenkel
- Computational and Systems Biology Graduate Program and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ranjit S Atwal
- Center for Genomic Medicine, Massachusetts General Hospital, Simches Research Building, Cambridge Street, Boston, MA, USA
| | - Min Wu
- Center for Genomic Medicine, Massachusetts General Hospital, Simches Research Building, Cambridge Street, Boston, MA, USA
| | - James F Gusella
- Center for Genomic Medicine, Massachusetts General Hospital, Simches Research Building, Cambridge Street, Boston, MA, USA
| | - Marcy E MacDonald
- Center for Genomic Medicine, Massachusetts General Hospital, Simches Research Building, Cambridge Street, Boston, MA, USA
| | - Julia A Kaye
- Gladstone Institutes and Taube/Koret Center of Neurodegenerative Disease Research, Roddenberry Stem Cell Research Program, Departments of Neurology and Physiology, University of California, San Francisco, CA, USA
| | - Steven Finkbeiner
- Gladstone Institutes and Taube/Koret Center of Neurodegenerative Disease Research, Roddenberry Stem Cell Research Program, Departments of Neurology and Physiology, University of California, San Francisco, CA, USA
| | - Virginia B Mattis
- Board of Governors Regenerative Medicine Institute and Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Colton M Tom
- Board of Governors Regenerative Medicine Institute and Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Clive Svendsen
- Board of Governors Regenerative Medicine Institute and Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Alvin R King
- Department of Psychiatry and Human Behavior, Department of Neurobiology and Behavior, Department of Medicine, Sue and Bill Gross Stem Cell Center and UCI MIND, University of California, Irvine, CA, USA
| | - Yumay Chen
- Department of Psychiatry and Human Behavior, Department of Neurobiology and Behavior, Department of Medicine, Sue and Bill Gross Stem Cell Center and UCI MIND, University of California, Irvine, CA, USA
| | - Jennifer T Stocksdale
- Department of Psychiatry and Human Behavior, Department of Neurobiology and Behavior, Department of Medicine, Sue and Bill Gross Stem Cell Center and UCI MIND, University of California, Irvine, CA, USA
| | - Ryan G Lim
- Department of Psychiatry and Human Behavior, Department of Neurobiology and Behavior, Department of Medicine, Sue and Bill Gross Stem Cell Center and UCI MIND, University of California, Irvine, CA, USA
| | - Malcolm Casale
- Department of Psychiatry and Human Behavior, Department of Neurobiology and Behavior, Department of Medicine, Sue and Bill Gross Stem Cell Center and UCI MIND, University of California, Irvine, CA, USA
| | - Ping H Wang
- Department of Psychiatry and Human Behavior, Department of Neurobiology and Behavior, Department of Medicine, Sue and Bill Gross Stem Cell Center and UCI MIND, University of California, Irvine, CA, USA
| | - Leslie M Thompson
- Department of Psychiatry and Human Behavior, Department of Neurobiology and Behavior, Department of Medicine, Sue and Bill Gross Stem Cell Center and UCI MIND, University of California, Irvine, CA, USA
| | - Sergey S Akimov
- Division of Neurobiology, Departments of Psychiatry, Neurology, Pharmacology, and Neuroscience, Johns Hopkins University School of Medicine, North Wolfe Street, Baltimore, MA, USA
| | - Tamara Ratovitski
- Division of Neurobiology, Departments of Psychiatry, Neurology, Pharmacology, and Neuroscience, Johns Hopkins University School of Medicine, North Wolfe Street, Baltimore, MA, USA
| | - Nicolas Arbez
- Division of Neurobiology, Departments of Psychiatry, Neurology, Pharmacology, and Neuroscience, Johns Hopkins University School of Medicine, North Wolfe Street, Baltimore, MA, USA
| | - Christopher A Ross
- Division of Neurobiology, Departments of Psychiatry, Neurology, Pharmacology, and Neuroscience, Johns Hopkins University School of Medicine, North Wolfe Street, Baltimore, MA, USA
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150
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Rosario D, Boren J, Uhlen M, Proctor G, Aarsland D, Mardinoglu A, Shoaie S. Systems Biology Approaches to Understand the Host-Microbiome Interactions in Neurodegenerative Diseases. Front Neurosci 2020; 14:716. [PMID: 32733199 PMCID: PMC7360858 DOI: 10.3389/fnins.2020.00716] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 06/12/2020] [Indexed: 12/12/2022] Open
Abstract
Neurodegenerative diseases (NDDs) comprise a broad range of progressive neurological disorders with multifactorial etiology contributing to disease pathophysiology. Evidence of the microbiome involvement in the gut-brain axis urges the interest in understanding metabolic interactions between the microbiota and host physiology in NDDs. Systems Biology offers a holistic integrative approach to study the interplay between the different biologic systems as part of a whole, and may elucidate the host–microbiome interactions in NDDs. We reviewed direct and indirect pathways through which the microbiota can modulate the bidirectional communication of the gut-brain axis, and explored the evidence of microbial dysbiosis in Alzheimer’s and Parkinson’s diseases. As the gut microbiota being strongly affected by diet, the potential approaches to targeting the human microbiota through diet for the stimulation of neuroprotective microbial-metabolites secretion were described. We explored the potential of Genome-scale metabolic models (GEMs) to infer microbe-microbe and host-microbe interactions and to identify the microbiome contribution to disease development or prevention. Finally, a systemic approach based on GEMs and ‘omics integration, that would allow the design of sustainable personalized anti-inflammatory diets in NDDs prevention, through the modulation of gut microbiota was described.
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Affiliation(s)
- Dorines Rosario
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom
| | - Jan Boren
- Department of Molecular and Clinical Medicine, Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Gordon Proctor
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom
| | - Dag Aarsland
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom.,Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom.,Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
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