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Schmidt S, Stautner C, Vu DT, Heinz A, Regensburger M, Karayel O, Trümbach D, Artati A, Kaltenhäuser S, Nassef MZ, Hembach S, Steinert L, Winner B, Jürgen W, Jastroch M, Luecken MD, Theis FJ, Westmeyer GG, Adamski J, Mann M, Hiller K, Giesert F, Vogt Weisenhorn DM, Wurst W. A reversible state of hypometabolism in a human cellular model of sporadic Parkinson's disease. Nat Commun 2023; 14:7674. [PMID: 37996418 PMCID: PMC10667251 DOI: 10.1038/s41467-023-42862-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 10/23/2023] [Indexed: 11/25/2023] Open
Abstract
Sporadic Parkinson's Disease (sPD) is a progressive neurodegenerative disorder caused by multiple genetic and environmental factors. Mitochondrial dysfunction is one contributing factor, but its role at different stages of disease progression is not fully understood. Here, we showed that neural precursor cells and dopaminergic neurons derived from induced pluripotent stem cells (hiPSCs) from sPD patients exhibited a hypometabolism. Further analysis based on transcriptomics, proteomics, and metabolomics identified the citric acid cycle, specifically the α-ketoglutarate dehydrogenase complex (OGDHC), as bottleneck in sPD metabolism. A follow-up study of the patients approximately 10 years after initial biopsy demonstrated a correlation between OGDHC activity in our cellular model and the disease progression. In addition, the alterations in cellular metabolism observed in our cellular model were restored by interfering with the enhanced SHH signal transduction in sPD. Thus, inhibiting overactive SHH signaling may have potential as neuroprotective therapy during early stages of sPD.
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Affiliation(s)
- Sebastian Schmidt
- Institute of Developmental Genetics, Helmholtz Zentrum München, Neuherberg, Germany.
- Munich Institute of Biomedical Engineering, Department of Chemistry, Technical University of Munich, Munich, Germany.
| | - Constantin Stautner
- Institute of Developmental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Duc Tung Vu
- Department for Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Alexander Heinz
- Department of Bioinformatics and Biochemistry and Braunschweig Integrated Center of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
| | - Martin Regensburger
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Ozge Karayel
- Department for Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Dietrich Trümbach
- Institute of Developmental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Metabolism and Cell Death, Helmholtz Zentrum München, Neuherberg, Germany
| | - Anna Artati
- Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Neuherberg, Germany
| | - Sabine Kaltenhäuser
- Department of Bioinformatics and Biochemistry and Braunschweig Integrated Center of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
| | - Mohamed Zakaria Nassef
- Department of Bioinformatics and Biochemistry and Braunschweig Integrated Center of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
| | - Sina Hembach
- Institute of Developmental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Letyfee Steinert
- Institute of Developmental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Beate Winner
- Department of Stem Cell Biology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Winkler Jürgen
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Martin Jastroch
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Malte D Luecken
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Mathematics, Technische Universität München, Garching bei München, Germany
| | - Gil Gregor Westmeyer
- Munich Institute of Biomedical Engineering, Department of Chemistry, Technical University of Munich, Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Zentrum München, Neuherberg, Germany
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Matthias Mann
- Department for Proteomics and Signal Transduction, Max-Planck Institute of Biochemistry, Martinsried, Germany
- NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Karsten Hiller
- Department of Bioinformatics and Biochemistry and Braunschweig Integrated Center of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
| | - Florian Giesert
- Institute of Developmental Genetics, Helmholtz Zentrum München, Neuherberg, Germany
| | | | - Wolfgang Wurst
- Institute of Developmental Genetics, Helmholtz Zentrum München, Neuherberg, Germany.
- Chair of Developmental Genetics, Munich School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany.
- German Center for Neurodegenerative Diseases (DZNE) site Munich, Munich, Germany.
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2
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Moiz B, Li A, Padmanabhan S, Sriram G, Clyne AM. Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease. Metabolites 2022; 12:1066. [PMID: 36355149 PMCID: PMC9694183 DOI: 10.3390/metabo12111066] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 10/25/2022] [Accepted: 10/27/2022] [Indexed: 04/28/2024] Open
Abstract
Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.
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Affiliation(s)
- Bilal Moiz
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Andrew Li
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Surya Padmanabhan
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Ganesh Sriram
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA
| | - Alisa Morss Clyne
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
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Zhao S, Zhang L, Ji W, Shi Y, Lai G, Chi H, Huang W, Cheng C. Machine learning-based characterization of cuprotosis-related biomarkers and immune infiltration in Parkinson's disease. Front Genet 2022; 13:1010361. [PMID: 36338988 PMCID: PMC9629507 DOI: 10.3389/fgene.2022.1010361] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 10/04/2022] [Indexed: 07/22/2023] Open
Abstract
Background: Parkinson's disease (PD) is a neurodegenerative disease commonly seen in the elderly. On the other hand, cuprotosis is a new copper-dependent type of cell death that can be observed in various diseases. Methods: This study aimed to identify potential novel biomarkers of Parkinson's disease by biomarker analysis and to explore immune cell infiltration during the onset of cuprotosis. Gene expression profiles were retrieved from the GEO database for the GSE8397, GSE7621, GSE20163, and GSE20186 datasets. Three machine learning algorithms: the least absolute shrinkage and selection operator (LASSO), random forest, and support vector machine-recursive feature elimination (SVM-RFE) were used to screen for signature genes for Parkinson's disease onset and cuprotosis-related genes (CRG). Immune cell infiltration was estimated by ssGSEA, and cuprotosis-related genes associated with immune cells and immune function were examined using spearman correlation analysis. Nomogram was created to validate the accuracy of these cuprotosis-related genes in predicting PD disease progression. Classification of Parkinson's specimens using consensus clustering methods. Result: Three PD datasets from the Gene Expression Omnibus (GEO) database were combined after eliminating batch effects. By ssGSEA, we identified three cuprotosis-related genes ATP7A, SLC31A1, and DBT associated with immune cells or immune function in PD and more accurate for the diagnosis of Parkinson's disease course. Patients could benefit clinically from a characteristic line graph based on these genes. Consistent clustering analysis identified two subtypes, with the C2 subtype exhibiting higher immune cell infiltration and immune function. Conclusion: In conclusion, our study reveals that several newly identified cuprotosis-related genes intervene in the progression of Parkinson's disease through immune cell infiltration.
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Affiliation(s)
- Songyun Zhao
- Department of Neurosurgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, Jiangsu, China
| | - Li Zhang
- Department of Neurology, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, Jiangsu, China
| | - Wei Ji
- Department of Neurosurgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, Jiangsu, China
| | - Yachen Shi
- Department of Neurology, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, Jiangsu, China
| | - Guichuan Lai
- Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University, Chongqing, China
| | - Hao Chi
- Clinical Medicine College, Southwest Medical University, Luzhou, China
| | - Weiyi Huang
- Department of Neurosurgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, Jiangsu, China
| | - Chao Cheng
- Department of Neurosurgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, Jiangsu, China
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Shichkova P, Coggan JS, Markram H, Keller D. A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation. Front Mol Neurosci 2021; 14:604559. [PMID: 34858137 PMCID: PMC8631404 DOI: 10.3389/fnmol.2021.604559] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 10/05/2021] [Indexed: 12/12/2022] Open
Abstract
Accurate molecular concentrations are essential for reliable analyses of biochemical networks and the creation of predictive models for molecular and systems biology, yet protein and metabolite concentrations used in such models are often poorly constrained or irreproducible. Challenges of using data from different sources include conflicts in nomenclature and units, as well as discrepancies in experimental procedures, data processing and implementation of the model. To obtain a consistent estimate of protein and metabolite levels, we integrated and normalized data from a large variety of sources to calculate Adjusted Molecular Concentrations. We found a high degree of reproducibility and consistency of many molecular species across brain regions and cell types, consistent with tight homeostatic regulation. We demonstrated the value of this normalization with differential protein expression analyses related to neurodegenerative diseases, brain regions and cell types. We also used the results in proof-of-concept simulations of brain energy metabolism. The standardized Brain Molecular Atlas overcomes the obstacles of missing or inconsistent data to support systems biology research and is provided as a resource for biomolecular modeling.
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Affiliation(s)
- Polina Shichkova
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Jay S Coggan
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland.,Laboratory of Neural Microcircuitry, Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Daniel Keller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
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Lewis JE, Forshaw TE, Boothman DA, Furdui CM, Kemp ML. Personalized Genome-Scale Metabolic Models Identify Targets of Redox Metabolism in Radiation-Resistant Tumors. Cell Syst 2021; 12:68-81.e11. [PMID: 33476554 PMCID: PMC7905848 DOI: 10.1016/j.cels.2020.12.001] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 08/04/2020] [Accepted: 12/16/2020] [Indexed: 12/14/2022]
Abstract
Redox cofactor production is integral toward antioxidant generation, clearance of reactive oxygen species, and overall tumor response to ionizing radiation treatment. To identify systems-level alterations in redox metabolism that confer resistance to radiation therapy, we developed a bioinformatics pipeline for integrating multi-omics data into personalized genome-scale flux balance analysis models of 716 radiation-sensitive and 199 radiation-resistant tumors. These models collectively predicted that radiation-resistant tumors reroute metabolic flux to increase mitochondrial NADPH stores and reactive oxygen species (ROS) scavenging. Simulated genome-wide knockout screens agreed with experimental siRNA gene knockdowns in matched radiation-sensitive and radiation-resistant cancer cell lines, revealing gene targets involved in mitochondrial NADPH production, central carbon metabolism, and folate metabolism that allow for selective inhibition of glutathione production and H2O2 clearance in radiation-resistant cancers. This systems approach represents a significant advancement in developing quantitative genome-scale models of redox metabolism and identifying personalized metabolic targets for improving radiation sensitivity in individual cancer patients.
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Affiliation(s)
- Joshua E. Lewis
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Tom E. Forshaw
- Department of Internal Medicine, Section on Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - David A. Boothman
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Cristina M. Furdui
- Department of Internal Medicine, Section on Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Melissa L. Kemp
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA,Corresponding Author: Correspondence:
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Bell SM, Burgess T, Lee J, Blackburn DJ, Allen SP, Mortiboys H. Peripheral Glycolysis in Neurodegenerative Diseases. Int J Mol Sci 2020; 21:E8924. [PMID: 33255513 PMCID: PMC7727792 DOI: 10.3390/ijms21238924] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 11/20/2020] [Accepted: 11/21/2020] [Indexed: 12/11/2022] Open
Abstract
Neurodegenerative diseases are a group of nervous system conditions characterised pathologically by the abnormal deposition of protein throughout the brain and spinal cord. One common pathophysiological change seen in all neurodegenerative disease is a change to the metabolic function of nervous system and peripheral cells. Glycolysis is the conversion of glucose to pyruvate or lactate which results in the generation of ATP and has been shown to be abnormal in peripheral cells in Alzheimer's disease, Parkinson's disease, and Amyotrophic Lateral Sclerosis. Changes to the glycolytic pathway are seen early in neurodegenerative disease and highlight how in multiple neurodegenerative conditions pathology is not always confined to the nervous system. In this paper, we review the abnormalities described in glycolysis in the three most common neurodegenerative diseases. We show that in all three diseases glycolytic changes are seen in fibroblasts, and red blood cells, and that liver, kidney, muscle and white blood cells have abnormal glycolysis in certain diseases. We highlight there is potential for peripheral glycolysis to be developed into multiple types of disease biomarker, but large-scale bio sampling and deciphering how glycolysis is inherently altered in neurodegenerative disease in multiple patients' needs to be accomplished first to meet this aim.
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Affiliation(s)
- Simon M. Bell
- Sheffield Institute for Translational Neurosciences, University of Sheffield, Sheffield S10 2HQ, UK; (T.B.); (J.L.); (D.J.B.); (S.P.A.); (H.M.)
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7
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Curran DM, Grote A, Nursimulu N, Geber A, Voronin D, Jones DR, Ghedin E, Parkinson J. Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets. eLife 2020; 9:e51850. [PMID: 32779567 PMCID: PMC7419141 DOI: 10.7554/elife.51850] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 07/14/2020] [Indexed: 12/17/2022] Open
Abstract
The filarial nematode Brugia malayi represents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteria Wolbachia-present in many filariae-which is vital to the worm. Genome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but have only been applied to multicellular eukaryotic organisms more recently. Here, we present iDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 102 reactions essential to the survival of B. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.
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Affiliation(s)
- David M Curran
- Program in Molecular Medicine, Hospital for Sick ChildrenTorontoCanada
| | - Alexandra Grote
- Department of Biology, Center for Genomics and Systems Biology, New York UniversityNew YorkUnited States
| | - Nirvana Nursimulu
- Program in Molecular Medicine, Hospital for Sick ChildrenTorontoCanada
- Department of Computer Science, University of TorontoTorontoCanada
| | - Adam Geber
- Department of Biology, Center for Genomics and Systems Biology, New York UniversityNew YorkUnited States
| | | | - Drew R Jones
- Department of Biochemistry and Molecular Pharmacology, New York University School of MedicineNew YorkUnited States
| | - Elodie Ghedin
- Department of Biology, Center for Genomics and Systems Biology, New York UniversityNew YorkUnited States
- Department of Epidemiology, School of Global Public Health, New York UniversityNew YorkUnited States
| | - John Parkinson
- Program in Molecular Medicine, Hospital for Sick ChildrenTorontoCanada
- Department of Computer Science, University of TorontoTorontoCanada
- Department of Biochemistry, University of TorontoTorontoCanada
- Department of Molecular Genetics, University of TorontoTorontoCanada
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8
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Lam S, Bayraktar A, Zhang C, Turkez H, Nielsen J, Boren J, Shoaie S, Uhlen M, Mardinoglu A. A systems biology approach for studying neurodegenerative diseases. Drug Discov Today 2020; 25:1146-1159. [PMID: 32442631 DOI: 10.1016/j.drudis.2020.05.010] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 04/13/2020] [Accepted: 05/13/2020] [Indexed: 01/06/2023]
Abstract
Neurodegenerative diseases (NDDs), such as Alzheimer's (AD) and Parkinson's (PD), are among the leading causes of lost years of healthy life and exert a great strain on public healthcare systems. Despite being first described more than a century ago, no effective cure exists for AD or PD. Although extensively characterised at the molecular level, traditional neurodegeneration research remains marred by narrow-sense approaches surrounding amyloid β (Aβ), tau, and α-synuclein (α-syn). A systems biology approach enables the integration of multi-omics data and informs discovery of biomarkers, drug targets, and treatment strategies. Here, we present a comprehensive timeline of high-throughput data collection, and associated biotechnological advancements and computational analysis related to AD and PD. We hereby propose that a philosophical change in the definitions of AD and PD is now needed.
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Affiliation(s)
- Simon Lam
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Abdulahad Bayraktar
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, 25240, Turkey
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-41296, Gothenburg, Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, The Wallenberg Laboratory, Sahlgrenska University Hospital, Gothenburg, SE-413 45, Sweden
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, UK; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden.
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Germeys C, Vandoorne T, Bercier V, Van Den Bosch L. Existing and Emerging Metabolomic Tools for ALS Research. Genes (Basel) 2019; 10:genes10121011. [PMID: 31817338 PMCID: PMC6947647 DOI: 10.3390/genes10121011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 11/23/2019] [Accepted: 12/03/2019] [Indexed: 12/12/2022] Open
Abstract
Growing evidence suggests that aberrant energy metabolism could play an important role in the pathogenesis of amyotrophic lateral sclerosis (ALS). Despite this, studies applying advanced technologies to investigate energy metabolism in ALS remain scarce. The rapidly growing field of metabolomics offers exciting new possibilities for ALS research. Here, we review existing and emerging metabolomic tools that could be used to further investigate the role of metabolism in ALS. A better understanding of the metabolic state of motor neurons and their surrounding cells could hopefully result in novel therapeutic strategies.
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Affiliation(s)
- Christine Germeys
- Department of Neurosciences, Experimental Neurology, and Leuven Brain Institute (LBI), KU Leuven—University of Leuven, 3000 Leuven, Belgium; (C.G.); (T.V.); (V.B.)
- VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, 3000 Leuven, Belgium
| | - Tijs Vandoorne
- Department of Neurosciences, Experimental Neurology, and Leuven Brain Institute (LBI), KU Leuven—University of Leuven, 3000 Leuven, Belgium; (C.G.); (T.V.); (V.B.)
- VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, 3000 Leuven, Belgium
| | - Valérie Bercier
- Department of Neurosciences, Experimental Neurology, and Leuven Brain Institute (LBI), KU Leuven—University of Leuven, 3000 Leuven, Belgium; (C.G.); (T.V.); (V.B.)
- VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, 3000 Leuven, Belgium
| | - Ludo Van Den Bosch
- Department of Neurosciences, Experimental Neurology, and Leuven Brain Institute (LBI), KU Leuven—University of Leuven, 3000 Leuven, Belgium; (C.G.); (T.V.); (V.B.)
- VIB, Center for Brain & Disease Research, Laboratory of Neurobiology, 3000 Leuven, Belgium
- Correspondence: ; Tel.: +32-16-33-06-81
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