151
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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: 119] [Impact Index Per Article: 29.8] [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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152
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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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153
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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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154
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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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155
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Campit SE, Meliki A, Youngson NA, Chandrasekaran S. Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling. Bioessays 2020; 42:e2000083. [PMID: 32638413 DOI: 10.1002/bies.202000083] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/23/2020] [Indexed: 12/19/2022]
Abstract
Several metabolites serve as substrates for histone modifications and communicate changes in the metabolic environment to the epigenome. Technologies such as metabolomics and proteomics have allowed us to reconstruct the interactions between metabolic pathways and histones. These technologies have shed light on how nutrient availability can have a dramatic effect on various histone modifications. This metabolism-epigenome cross talk plays a fundamental role in development, immune function, and diseases like cancer. Yet, major challenges remain in understanding the interactions between cellular metabolism and the epigenome. How the levels and fluxes of various metabolites impact epigenetic marks is still unclear. Discussed herein are recent applications and the potential of systems biology methods such as flux tracing and metabolic modeling to address these challenges and to uncover new metabolic-epigenetic interactions. These systems approaches can ultimately help elucidate how nutrients shape the epigenome of microbes and mammalian cells.
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Affiliation(s)
- Scott E Campit
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Alia Meliki
- Center for Bioinformatics and Computational Medicine, Ann Arbor, MI, 48109, USA
| | - Neil A Youngson
- Institute of Hepatology, Foundation for Liver Research, London, SE5 9NT, UK.,Faculty of Life Sciences and Medicine, King's College London, London, WC2R 2LS, UK.,School of Medical Sciences, UNSW Sydney, Sydney, 2052, Australia
| | - Sriram Chandrasekaran
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, 48109, USA.,Center for Bioinformatics and Computational Medicine, Ann Arbor, MI, 48109, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.,Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
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156
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Frioux C, Singh D, Korcsmaros T, Hildebrand F. From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes. Comput Struct Biotechnol J 2020; 18:1722-1734. [PMID: 32670511 PMCID: PMC7347713 DOI: 10.1016/j.csbj.2020.06.028] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 06/16/2020] [Accepted: 06/17/2020] [Indexed: 12/12/2022] Open
Abstract
Metagenomic sequencing of complete microbial communities has greatly enhanced our understanding of the taxonomic composition of microbiotas. This has led to breakthrough developments in bioinformatic disciplines such as assembly, gene clustering, metagenomic binning of species genomes and the discovery of an incredible, so far undiscovered, taxonomic diversity. However, functional annotations and estimating metabolic processes from single species - or communities - is still challenging. Earlier approaches relied mostly on inferring the presence of key enzymes for metabolic pathways in the whole metagenome, ignoring the genomic context of such enzymes, resulting in the 'bag-of-genes' approach to estimate functional capacities of microbiotas. Here, we review recent developments in metagenomic bioinformatics, with a special focus on emerging technologies to simulate and estimate metabolic information, that can be derived from metagenomic assembled genomes. Genome-scale metabolic models can be used to model the emergent properties of microbial consortia and whole communities, and the progress in this area is reviewed. While this subfield of metagenomics is still in its infancy, it is becoming evident that there is a dire need for further bioinformatic tools to address the complex combinatorial problems in modelling the metabolism of large communities as a 'bag-of-genomes'.
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Affiliation(s)
- Clémence Frioux
- Inria, CNRS, INRAE Bordeaux, France
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
| | - Dipali Singh
- Microbes in the Food Chain, Quadram Institute Bioscience, Norwich, Norfolk, UK
| | - Tamas Korcsmaros
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
- Digital Biology, Earlham Institute, Norwich, Norfolk, UK
| | - Falk Hildebrand
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
- Digital Biology, Earlham Institute, Norwich, Norfolk, UK
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157
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KARAKURT HU, PİR P. Integration of transcriptomic profile of SARS-CoV-2 infected normal human bronchial epithelial cells with metabolic and protein-protein interaction networks. Turk J Biol 2020; 44:168-177. [PMID: 32595353 PMCID: PMC7314513 DOI: 10.3906/biy-2005-115] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
A novel coronavirus (SARS-CoV-2, formerly known as nCoV-2019) that causes an acute respiratory disease has emerged in Wuhan, China and spread globally in early 2020. On January the 30th, the World Health Organization (WHO) declared spread of this virus as an epidemic and a public health emergency. With its highly contagious characteristic and long incubation time, confinement of SARS-CoV-2 requires drastic lock-down measures to be taken and therefore early diagnosis is crucial. We analysed transcriptome of SARS-CoV-2 infected human lung epithelial cells, compared it with mock-infected cells, used network-based reporter metabolite approach and integrated the transcriptome data with protein-protein interaction network to elucidate the early cellular response. Significantly affected metabolites have the potential to be used in diagnostics while pathways of protein clusters have the potential to be used as targets for supportive or novel therapeutic approaches. Our results are in accordance with the literature on response of IL6 family of cytokines and their importance, in addition, we find that matrix metalloproteinase 2 (MMP2) and matrix metalloproteinase 9 (MMP9) with keratan sulfate synthesis pathway may play a key role in the infection. We hypothesize that MMP9 inhibitors have potential to prevent "cytokine storm" in severely affected patients.
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Affiliation(s)
- Hamza Umut KARAKURT
- Department of Bioengineering, Faculty of Engineering, Gebze Technical University, KocaeliTurkey
- Idea Technology Solutions, İstanbulTurkey
| | - Pınar PİR
- Department of Bioengineering, Faculty of Engineering, Gebze Technical University, KocaeliTurkey
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158
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San-Millán I, Stefanoni D, Martinez JL, Hansen KC, D’Alessandro A, Nemkov T. Metabolomics of Endurance Capacity in World Tour Professional Cyclists. Front Physiol 2020; 11:578. [PMID: 32581847 PMCID: PMC7291837 DOI: 10.3389/fphys.2020.00578] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 05/08/2020] [Indexed: 12/23/2022] Open
Abstract
The study of elite athletes provides a unique opportunity to define the upper limits of human physiology and performance. Across a variety of sports, these individuals have trained to optimize the physiological parameters of their bodies in order to compete on the world stage. To characterize endurance capacity, techniques such as heart rate monitoring, indirect calorimetry, and whole blood lactate measurement have provided insight into oxygen utilization, and substrate utilization and preference, as well as total metabolic capacity. However, while these techniques enable the measurement of individual, representative variables critical for sports performance, they lack the molecular resolution that is needed to understand which metabolic adaptations are necessary to influence these metrics. Recent advancements in mass spectrometry-based analytical approaches have enabled the measurement of hundreds to thousands of metabolites in a single analysis. Here we employed targeted and untargeted metabolomics approaches to investigate whole blood responses to exercise in elite World Tour (including Tour de France) professional cyclists before and after a graded maximal physiological test. As cyclists within this group demonstrated varying blood lactate accumulation as a function of power output, which is an indicator of performance, we compared metabolic profiles with respect to lactate production to identify adaptations associated with physiological performance. We report that numerous metabolic adaptations occur within this physically elite population (n = 21 males, 28.2 ± 4.7 years old) in association with the rate of lactate accumulation during cycling. Correlation of metabolite values with lactate accumulation has revealed metabolic adaptations that occur in conjunction with improved endurance capacity. In this population, cycling induced increases in tricarboxylic acid (TCA) cycle metabolites and Coenzyme A precursors. These responses occurred proportionally to lactate accumulation, suggesting a link between enhanced mitochondrial networks and the ability to sustain higher workloads. In association with lactate accumulation, altered levels of amino acids before and after exercise point to adaptations that confer unique substrate preference for energy production or to promote more rapid recovery. Cyclists with slower lactate accumulation also have higher levels of basal oxidative stress markers, suggesting long term physiological adaptations in these individuals that support their premier competitive status in worldwide competitions.
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Affiliation(s)
- Iñigo San-Millán
- Department of Human Physiology and Nutrition, University of Colorado Colorado Springs, Colorado Springs, CO, United States
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Department of Research and Development, UAE Team Emirates, Abu Dhabi, United Arab Emirates
| | - Davide Stefanoni
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Janel L. Martinez
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Kirk C. Hansen
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Angelo D’Alessandro
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Travis Nemkov
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
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159
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Masid M, Ataman M, Hatzimanikatis V. Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN. Nat Commun 2020; 11:2821. [PMID: 32499584 PMCID: PMC7272419 DOI: 10.1038/s41467-020-16549-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 05/07/2020] [Indexed: 01/31/2023] Open
Abstract
Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells.
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Affiliation(s)
- Maria Masid
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Meric Ataman
- Computational and Systems Biology, Biozentrum, University of Basel, Basel, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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160
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Liang XL, Liang ZM, Wang S, Chen XH, Ruan Y, Zhang QY, Zhang HY. An analysis of the mechanism underlying photocatalytic disinfection based on integrated metabolic networks and transcriptional data. J Environ Sci (China) 2020; 92:28-37. [PMID: 32430131 DOI: 10.1016/j.jes.2020.02.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 02/08/2020] [Accepted: 02/08/2020] [Indexed: 06/11/2023]
Abstract
Photocatalytic disinfection has long been used to combat pathogenic bacteria. However, the specific mechanism underlying photocatalytic disinfection and its corresponding targets remain unclear. In this study, an analysis of the potential mechanism underlying photocatalytic disinfection was performed based on integrated metabolic networks and transcriptional data. Two sets of RNA-seq data (wild type and a photocatalysis-resistant mutant mediated by titanium dioxide (TiO2)) were processed to constrain the genome scale metabolic models (GSMM) of E. coli. By analyzing the metabolic network, the differential metabolic flux of every reaction was computed in constrained GSMM, and several significantly differential metabolic fluxes in reactions were extracted and analyzed. Most of these reactions were involved in the transmembrane transport of substances and occurred on the inner membrane or were an important component of the cell membrane. These results, which are consistent with the reported information, validated our analysis process. In addition, our work also identified other new and valuable metabolic pathways, such as the reaction ALCD2x, which has a great effect on the energy production process under bacterial anaerobic conditions. The DHAK reaction is also related to the metabolic process of ATP. These reactions with large differential metabolic fluxes merit further research. Additionally, to provide a strategy to address photocatalysis-resistant mutant bacteria, a metabolic compensation analysis was also performed. The metabolic compensation analysis results provided suggestions for a combined method that can effectively combat resistant bacteria. This method could also be used to explore the mechanisms of drug resistance in other microorganisms.
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Affiliation(s)
- Xiao-Long Liang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, PR China.
| | - Zhan-Min Liang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, PR China
| | - Shuo Wang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, PR China
| | - Xiao-Hui Chen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, PR China
| | - Yao Ruan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, PR China
| | - Qing-Ye Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, PR China.
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, PR China
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161
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Toroghi MK, Cluett WR, Mahadevan R. A Personalized Multiscale Modeling Framework for Dose Selection in Precision Medicine. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Masood Khaksar Toroghi
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
| | - William R. Cluett
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada, M5S 3E5
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162
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Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker. Nat Biotechnol 2020; 38:1087-1096. [PMID: 32440005 DOI: 10.1038/s41587-020-0502-7] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 03/27/2020] [Indexed: 02/07/2023]
Abstract
Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.
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163
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Noronha A, Modamio J, Jarosz Y, Guerard E, Sompairac N, Preciat G, Daníelsdóttir AD, Krecke M, Merten D, Haraldsdóttir HS, Heinken A, Heirendt L, Magnúsdóttir S, Ravcheev DA, Sahoo S, Gawron P, Friscioni L, Garcia B, Prendergast M, Puente A, Rodrigues M, Roy A, Rouquaya M, Wiltgen L, Žagare A, John E, Krueger M, Kuperstein I, Zinovyev A, Schneider R, Fleming RMT, Thiele I. The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease. Nucleic Acids Res 2020; 47:D614-D624. [PMID: 30371894 PMCID: PMC6323901 DOI: 10.1093/nar/gky992] [Citation(s) in RCA: 192] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 10/09/2018] [Indexed: 12/31/2022] Open
Abstract
A multitude of factors contribute to complex diseases and can be measured with ‘omics’ methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, www.vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources ‘Human metabolism’, ‘Gut microbiome’, ‘Disease’, ‘Nutrition’, and ‘ReconMaps’. The VMH captures 5180 unique metabolites, 17 730 unique reactions, 3695 human genes, 255 Mendelian diseases, 818 microbes, 632 685 microbial genes and 8790 food items. The VMH’s unique features are (i) the hosting of the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; (ii) seven human metabolic maps for data visualization; (iii) a nutrition designer; (iv) a user-friendly webpage and application-programming interface to access its content; (v) user feedback option for community engagement and (vi) the connection of its entities to 57 other web resources. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community.
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Affiliation(s)
- Alberto Noronha
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Jennifer Modamio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Yohan Jarosz
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Elisabeth Guerard
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - German Preciat
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Anna Dröfn Daníelsdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Max Krecke
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Diane Merten
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Hulda S Haraldsdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Almut Heinken
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Stefanía Magnúsdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Dmitry A Ravcheev
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Swagatika Sahoo
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Lucia Friscioni
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Beatriz Garcia
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Mabel Prendergast
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Alberto Puente
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Mariana Rodrigues
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Akansha Roy
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Mouss Rouquaya
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Luca Wiltgen
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Alise Žagare
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Elisabeth John
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Maren Krueger
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Inna Kuperstein
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, INSERM U900, F-75005 Paris, France and CBIO-Centre for Computational Biology, MINES ParisTech, PSL Research University, F-75006 Paris, France
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
| | - Ronan M T Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg.,Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, University of Leiden, Leiden 2333, The Netherlands
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-sur-Alzette L-4367, Luxembourg
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164
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Stalidzans E, Zanin M, Tieri P, Castiglione F, Polster A, Scheiner S, Pahle J, Stres B, List M, Baumbach J, Lautizi M, Van Steen K, Schmidt HH. Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice. NETWORK AND SYSTEMS MEDICINE 2020. [DOI: 10.1089/nsm.2020.0002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Egils Stalidzans
- Computational Systems Biology Group, University of Latvia, Riga, Latvia
- Latvian Biomedical Reasearch and Study Centre, Riga, Latvia
| | - Massimiliano Zanin
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, Spain
| | - Paolo Tieri
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | - Filippo Castiglione
- CNR National Research Council, IAC Institute for Applied Computing, Rome, Italy
| | | | - Stefan Scheiner
- Institute for Mechanics of Materials and Structures, Vienna University of Technology, Vienna, Austria
| | - Jürgen Pahle
- BioQuant, Heidelberg University, Heidelberg, Germany
| | - Blaž Stres
- Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
- Department of Automation, Biocybernetics and Robotics, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Markus List
- Big Data in BioMedicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Manuela Lautizi
- Computational Systems Medicine Research Group, Chair of Experimental Bioinformatics, TUM School of Weihenstephan, Technical University of Munich, Freising, Germany
| | - Kristel Van Steen
- BIO-Systems Genetics, GIGA-R, University of Liège, Liège, Belgium
- BIO3—Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Harald H.H.W. Schmidt
- Department of Pharmacology and Personalised Medicine, Faculty of Health, Medicine and Life Science, Maastricht University, Maastricht, The Netherlands
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Yeo HC, Hong J, Lakshmanan M, Lee DY. Enzyme capacity-based genome scale modelling of CHO cells. Metab Eng 2020; 60:138-147. [PMID: 32330653 DOI: 10.1016/j.ymben.2020.04.005] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/21/2020] [Accepted: 04/14/2020] [Indexed: 10/24/2022]
Abstract
Chinese hamster ovary (CHO) cells are most prevalently used for producing recombinant therapeutics in biomanufacturing. Recently, more rational and systems approaches have been increasingly exploited to identify key metabolic bottlenecks and engineering targets for cell line engineering and process development based on the CHO genome-scale metabolic model which mechanistically characterizes cell culture behaviours. However, it is still challenging to quantify plausible intracellular fluxes and discern metabolic pathway usages considering various clonal traits and bioprocessing conditions. Thus, we newly incorporated enzyme kinetic information into the updated CHO genome-scale model (iCHO2291) and added enzyme capacity constraints within the flux balance analysis framework (ecFBA) to significantly reduce the flux variability in biologically meaningful manner, as such improving the accuracy of intracellular flux prediction. Interestingly, ecFBA could capture the overflow metabolism under the glucose excess condition where the usage of oxidative phosphorylation is limited by the enzyme capacity. In addition, its applicability was successfully demonstrated via a case study where the clone- and media-specific lactate metabolism was deciphered, suggesting that the lactate-pyruvate cycling could be beneficial for CHO cells to efficiently utilize the mitochondrial redox capacity. In summary, iCHO2296 with ecFBA can be used to confidently elucidate cell cultures and effectively identify key engineering targets, thus guiding bioprocess optimization and cell engineering efforts as a part of digital twin model for advanced biomanufacturing in future.
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Affiliation(s)
- Hock Chuan Yeo
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, 138668, Singapore; Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, 117585, Singapore
| | - Jongkwang Hong
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, 138668, Singapore
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, 138668, Singapore.
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01, 138668, Singapore; School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea.
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166
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Trilla-Fuertes L, Gámez-Pozo A, López-Camacho E, Prado-Vázquez G, Zapater-Moros A, López-Vacas R, Arevalillo JM, Díaz-Almirón M, Navarro H, Maín P, Espinosa E, Zamora P, Fresno Vara JÁ. Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer. BMC Cancer 2020; 20:307. [PMID: 32293335 PMCID: PMC7265650 DOI: 10.1186/s12885-020-06764-x] [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: 08/26/2019] [Accepted: 03/19/2020] [Indexed: 01/25/2023] Open
Abstract
Background Metabolomics has a great potential in the development of new biomarkers in cancer and it has experiment recent technical advances. Methods In this study, metabolomics and gene expression data from 67 localized (stage I to IIIB) breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without other a priori information; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways. Results On the one hand, both analyses highlighted the importance of glutamine in breast cancer. Moreover, cell experiments showed that treating breast cancer cells with drugs targeting glutamine metabolism significantly affects cell viability. On the other hand, these computational methods suggested some hypotheses and have demonstrated their utility in the analysis of metabolomics data and in associating metabolomics with patient’s clinical outcome. Conclusions Computational analyses applied to metabolomics data suggested that glutamine metabolism is a relevant process in breast cancer. Cell experiments confirmed this hypothesis. In addition, these computational analyses allow associating metabolomics data with patient prognosis.
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Affiliation(s)
| | - Angelo Gámez-Pozo
- Biomedica Molecular Medicine SL, C/ Faraday, 7, 28049, Madrid, Spain.,Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Paseo de la Castellana, 261, 28046, Madrid, Spain
| | - Elena López-Camacho
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Paseo de la Castellana, 261, 28046, Madrid, Spain
| | | | - Andrea Zapater-Moros
- Biomedica Molecular Medicine SL, C/ Faraday, 7, 28049, Madrid, Spain.,Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Paseo de la Castellana, 261, 28046, Madrid, Spain
| | - Rocío López-Vacas
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Paseo de la Castellana, 261, 28046, Madrid, Spain
| | - Jorge M Arevalillo
- Department of Statistics, Operational Research and Numerical Analysis, National University of Distance Education (UNED), Paseo Senda del Rey, 9, 28040, Madrid, Spain
| | - Mariana Díaz-Almirón
- Biostatistics Unit, La Paz University Hospital-IdiPAZ, Paseo de la Castellana, 261, 28046, Madrid, Spain
| | - Hilario Navarro
- Department of Statistics, Operational Research and Numerical Analysis, National University of Distance Education (UNED), Paseo Senda del Rey, 9, 28040, Madrid, Spain
| | - Paloma Maín
- Department of Statistics and Operations Research, Faculty of Mathematics, Complutense University of Madrid, Plaza de las Ciencias, 3, 28040, Madrid, Spain
| | - Enrique Espinosa
- Medical Oncology Service, La Paz University Hospital-IdiPAZ, Paseo de la Castellana, 261, 28046, Madrid, Spain.,Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, C/Melchor Fernández Almagro, 3, 28029, Madrid, Spain
| | - Pilar Zamora
- Medical Oncology Service, La Paz University Hospital-IdiPAZ, Paseo de la Castellana, 261, 28046, Madrid, Spain.,Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, C/Melchor Fernández Almagro, 3, 28029, Madrid, Spain
| | - Juan Ángel Fresno Vara
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, La Paz University Hospital-IdiPAZ, Paseo de la Castellana, 261, 28046, Madrid, Spain. .,Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, C/Melchor Fernández Almagro, 3, 28029, Madrid, Spain.
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167
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Li Y, He CL, Li WX, Zhang RX, Duan Y. Transcriptome analysis reveals gender-specific differences in overall metabolic response of male and female patients in lung adenocarcinoma. PLoS One 2020; 15:e0230796. [PMID: 32236130 PMCID: PMC7112214 DOI: 10.1371/journal.pone.0230796] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Accepted: 03/08/2020] [Indexed: 12/23/2022] Open
Abstract
Background Evidence from multiple studies suggests metabolic abnormalities play an important role in lung cancer. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. The present study aimed to explore differences in the global metabolic response between male and female patients in LUAD and to identify the metabolic genes associated with lung cancer susceptibility. Methods Transcriptome and clinical LUAD data were acquired from The Cancer Genome Atlas (TCGA) database. Information on metabolic genes and metabolic subsystems were collected from the Recon3D human metabolic model. Two validation datasets (GSE68465 and GSE72094) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis, gene set enrichment analysis and protein-protein interaction networks were used to identified key metabolic pathways and genes. Functional experiments were used to verify the effects of genes on proliferation, migration, and invasion in lung cancer cells in vitro. Results Samples of tumors and adjacent non-tumor tissue from both male and female patients exhibited distinct global patterns of gene expression. In addition, we found large differences in methionine and cysteine metabolism, pyruvate metabolism, cholesterol metabolism, nicotinamide adenine dinucleotide (NAD) metabolism, and nuclear transport between male and female LUAD patients. We identified 34 metabolic genes associated with lung cancer susceptibility in males and 15 in females. Most of the metabolic cancer-susceptibility genes had high prediction accuracy for lung cancer (AUC > 0.9). Furthermore, both bioinformatics analysis and experimental results showed that TAOK2 was down-regulated and ASAH1 was up-regulated in male tumor tissue and female tumor tissue in LUAD. Functional experiments showed that inhibiting ASAH1 suppressed the proliferation, migration, and invasion of lung cancer cells. Conclusions Metabolic cancer-susceptibility genes may be used alone or in combination as diagnostic markers for LUAD. Further studies are required to elucidate the functions of these genes in LUAD.
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Affiliation(s)
- Ya Li
- Yunnan Key Laboratory of Laboratory Medicine, Kunming, Yunnan, China
- Yunnan Institute of Laboratory Diagnosis, Kunming, Yunnan, China
- Department of Clinical Laboratory, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Cheng-Lu He
- Yunnan Key Laboratory of Laboratory Medicine, Kunming, Yunnan, China
- Yunnan Institute of Laboratory Diagnosis, Kunming, Yunnan, China
- Department of Clinical Laboratory, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Wen-Xing Li
- Key Laboratory of Animal Models and Human Disease Mechanisms, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Rui-Xian Zhang
- Yunnan Center for Disease Control and Prevention, Kunming, Yunnan, China
| | - Yong Duan
- Yunnan Key Laboratory of Laboratory Medicine, Kunming, Yunnan, China
- Yunnan Institute of Laboratory Diagnosis, Kunming, Yunnan, China
- Department of Clinical Laboratory, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
- * E-mail:
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168
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Trilla-Fuertes L, Ghanem I, Gámez-Pozo A, Maurel J, G-Pastrián L, Mendiola M, Peña C, López-Vacas R, Prado-Vázquez G, López-Camacho E, Zapater-Moros A, Heredia V, Cuatrecasas M, García-Alfonso P, Capdevila J, Conill C, García-Carbonero R, Ramos-Ruiz R, Fortes C, Llorens C, Nanni P, Fresno Vara JÁ, Feliu J. Genetic Profile and Functional Proteomics of Anal Squamous Cell Carcinoma: Proposal for a Molecular Classification. Mol Cell Proteomics 2020; 19:690-700. [PMID: 32107283 PMCID: PMC7124473 DOI: 10.1074/mcp.ra120.001954] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Indexed: 12/21/2022] Open
Abstract
Anal squamous cell carcinoma is a rare tumor. Chemo-radiotherapy yields a 50% 3-year relapse-free survival rate in advanced anal cancer, so improved predictive markers and therapeutic options are needed. High-throughput proteomics and whole-exome sequencing were performed in 46 paraffin samples from anal squamous cell carcinoma patients. Hierarchical clustering was used to establish groups de novo Then, probabilistic graphical models were used to study the differences between groups of patients at the biological process level. A molecular classification into two groups of patients was established, one group with increased expression of proteins related to adhesion, T lymphocytes and glycolysis; and the other group with increased expression of proteins related to translation and ribosomes. The functional analysis by the probabilistic graphical model showed that these two groups presented differences in metabolism, mitochondria, translation, splicing and adhesion processes. Additionally, these groups showed different frequencies of genetic variants in some genes, such as ATM, SLFN11 and DST Finally, genetic and proteomic characteristics of these groups suggested the use of some possible targeted therapies, such as PARP inhibitors or immunotherapy.
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Affiliation(s)
| | - Ismael Ghanem
- Medical Oncology Department, Hospital Universitario La Paz, Paseo de la Castellana 261, 28046, Madrid, Spain
| | - Angelo Gámez-Pozo
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz -IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
| | - Joan Maurel
- Medical Oncology Department, Hospital Clinic of Barcelona, Translational Genomics and Targeted Therapeutics in Solid Tumors Group, IDIBAPS, University of Barcelona, Carrer de Villarroel 170, 08036, Barcelona, Spain
| | - Laura G-Pastrián
- Pathology Department, Hospital Universitario La Paz, Paseo de la Castellana 261, 28046, Madrid, Spain; Molecular Pathology and Therapeutic Targets Group, Hospital Universitario La Paz-IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
| | - Marta Mendiola
- Molecular Pathology and Therapeutic Targets Group, Hospital Universitario La Paz-IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain; Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, Av. Monforte de Lemos 5, 28029, Madrid, Spain
| | - Cristina Peña
- Pathology Department, Hospital Universitario La Paz, Paseo de la Castellana 261, 28046, Madrid, Spain
| | - Rocío López-Vacas
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz -IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
| | | | - Elena López-Camacho
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz -IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
| | - Andrea Zapater-Moros
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz -IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
| | - Victoria Heredia
- Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, Av. Monforte de Lemos 5, 28029, Madrid, Spain; Translational Oncology Lab, Hospital Universitario La Paz -IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain
| | - Miriam Cuatrecasas
- Pathology Department, Hospital Clínic Universitari de Barcelona, Carrer de Villarroel 170, 08036, Barcelona, Spain
| | - Pilar García-Alfonso
- Medical Oncology Department, Hospital General Universitario Gregorio Marañón, /Dr. Esquerdo 46, 28007, Madrid, Spain
| | - Jaume Capdevila
- Medical Oncology Service, Vall Hebron University Hospital. Vall Hebron Institute of Oncology (VHIO), Paseigg de la Vall d'Hebron 119, 08035, Barcelona, Spain
| | - Carles Conill
- Radiotherapy Oncology Department, Hospital Clínic Universitari de Barcelona, Carrer de Villarroel 170, 08036, Barcelona, Spain
| | - Rocío García-Carbonero
- Medical Oncology Service, Hospital Universitario 12 de Ocubre, Av. de Córdoba s/n, 28041, Madrid, Spain
| | - Ricardo Ramos-Ruiz
- Genomics Unit Cantoblanco, Parque Científico de Madrid, C/ Faraday 7, 28049, Madrid, Spain
| | - Claudia Fortes
- Functional Genomics Center Zurich, University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland
| | - Carlos Llorens
- Biotechvana SL, Parque Científico de Madrid, C/ Faraday 7, 28049, Madrid, Spain
| | - Paolo Nanni
- Functional Genomics Center Zurich, University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland
| | - Juan Ángel Fresno Vara
- Molecular Oncology & Pathology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz -IdiPAZ, Paseo de la Castellana 261, 28046, Madrid, Spain; Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, Av. Monforte de Lemos 5, 28029, Madrid, Spain
| | - Jaime Feliu
- Medical Oncology Department, Hospital Universitario La Paz, Paseo de la Castellana 261, 28046, Madrid, Spain; Biomedical Research Networking Center on Oncology-CIBERONC, ISCIII, Av. Monforte de Lemos 5, 28029, Madrid, Spain; Cátedra UAM-Amgen, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, 28049, Madrid, Spain.
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169
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Abstract
A key goal of cancer systems biology is to use big data to elucidate the molecular networks by which cancer develops. However, to date there has been no systematic evaluation of how far these efforts have progressed. In this Analysis, we survey six major systems biology approaches for mapping and modelling cancer pathways with attention to how well their resulting network maps cover and enhance current knowledge. Our sample of 2,070 systems biology maps captures all literature-curated cancer pathways with significant enrichment, although the strong tendency is for these maps to recover isolated mechanisms rather than entire integrated processes. Systems biology maps also identify previously underappreciated functions, such as a potential role for human papillomavirus-induced chromosomal alterations in ovarian tumorigenesis, and they add new genes to known cancer pathways, such as those related to metabolism, Hippo signalling and immunity. Notably, we find that many cancer networks have been provided only in journal figures and not for programmatic access, underscoring the need to deposit network maps in community databases to ensure they can be readily accessed. Finally, few of these findings have yet been clinically translated, leaving ample opportunity for future translational studies. Periodic surveys of cancer pathway maps, such as the one reported here, are critical to assess progress in the field and identify underserved areas of methodology and cancer biology.
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Affiliation(s)
- Brent M Kuenzi
- Division of Genetics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Trey Ideker
- Division of Genetics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.
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170
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Sigmarsdóttir Þ, McGarrity S, Rolfsson Ó, Yurkovich JT, Sigurjónsson ÓE. Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine. Front Bioeng Biotechnol 2020; 8:239. [PMID: 32296688 PMCID: PMC7136564 DOI: 10.3389/fbioe.2020.00239] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 03/09/2020] [Indexed: 12/15/2022] Open
Abstract
Mesenchymal stem cells are a promising source for externally grown tissue replacements and patient-specific immunomodulatory treatments. This promise has not yet been fulfilled in part due to production scaling issues and the need to maintain the correct phenotype after re-implantation. One aspect of extracorporeal growth that may be manipulated to optimize cell growth and differentiation is metabolism. The metabolism of MSCs changes during and in response to differentiation and immunomodulatory changes. MSC metabolism may be linked to functional differences but how this occurs and influences MSC function remains unclear. Understanding how MSC metabolism relates to cell function is however important as metabolite availability and environmental circumstances in the body may affect the success of implantation. Genome-scale constraint based metabolic modeling can be used as a tool to fill gaps in knowledge of MSC metabolism, acting as a framework to integrate and understand various data types (e.g., genomic, transcriptomic and metabolomic). These approaches have long been used to optimize the growth and productivity of bacterial production systems and are being increasingly used to provide insights into human health research. Production of tissue for implantation using MSCs requires both optimized production of cell mass and the understanding of the patient and phenotype specific metabolic situation. This review considers the current knowledge of MSC metabolism and how it may be optimized along with the current and future uses of genome scale constraint based metabolic modeling to further this aim.
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Affiliation(s)
- Þóra Sigmarsdóttir
- The Blood Bank, Landspitali – The National University Hospital of Iceland, Reykjavik, Iceland
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
| | - Sarah McGarrity
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Óttar Rolfsson
- Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Ólafur E. Sigurjónsson
- The Blood Bank, Landspitali – The National University Hospital of Iceland, Reykjavik, Iceland
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
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171
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Wegrzyn AB, Herzog K, Gerding A, Kwiatkowski M, Wolters JC, Dolga AM, van Lint AEM, Wanders RJA, Waterham HR, Bakker BM. Fibroblast-specific genome-scale modelling predicts an imbalance in amino acid metabolism in Refsum disease. FEBS J 2020; 287:5096-5113. [PMID: 32160399 PMCID: PMC7754141 DOI: 10.1111/febs.15292] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 02/25/2020] [Accepted: 03/10/2020] [Indexed: 12/14/2022]
Abstract
Refsum disease (RD) is an inborn error of metabolism that is characterised by a defect in peroxisomal α‐oxidation of the branched‐chain fatty acid phytanic acid. The disorder presents with late‐onset progressive retinitis pigmentosa and polyneuropathy and can be diagnosed biochemically by elevated levels of phytanate in plasma and tissues of patients. To date, no cure exists for RD, but phytanate levels in patients can be reduced by plasmapheresis and a strict diet. In this study, we reconstructed a fibroblast‐specific genome‐scale model based on the recently published, FAD‐curated model, based on Recon3D reconstruction. We used transcriptomics (available via GEO database with identifier GSE138379), metabolomics and proteomics (available via ProteomeXchange with identifier PXD015518) data, which we obtained from healthy controls and RD patient fibroblasts incubated with phytol, a precursor of phytanic acid. Our model correctly represents the metabolism of phytanate and displays fibroblast‐specific metabolic functions. Using this model, we investigated the metabolic phenotype of RD at the genome scale, and we studied the effect of phytanate on cell metabolism. We identified 53 metabolites that were predicted to discriminate between healthy and RD patients, several of which with a link to amino acid metabolism. Ultimately, these insights in metabolic changes may provide leads for pathophysiology and therapy. Databases Transcriptomics data are available via GEO database with identifier GSE138379, and proteomics data are available via ProteomeXchange with identifier PXD015518.
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Affiliation(s)
- Agnieszka B Wegrzyn
- Systems Medicine of Metabolism and Signalling, Laboratory of Paediatrics, University of Groningen, University Medical Centre Groningen, The Netherlands.,Analytical Biosciences and Metabolomics, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands
| | - Katharina Herzog
- Laboratory Genetic Metabolic Diseases, Department of Clinical Chemistry, Amsterdam UMC, Location AMC, University of Amsterdam, The Netherlands.,Centre for Analysis and Synthesis, Department of Chemistry, Lund University, Sweden
| | - Albert Gerding
- Systems Medicine of Metabolism and Signalling, Laboratory of Paediatrics, University of Groningen, University Medical Centre Groningen, The Netherlands.,Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Marcel Kwiatkowski
- Pharmacokinetics, Toxicology and Targeting, Groningen Research Institute of Pharmacy (GRIP), University of Groningen, The Netherlands.,Mass Spectrometric Proteomics and Metabolomics, Institute of Biochemistry, University of Innsbruck, Austria
| | - Justina C Wolters
- Laboratory of Paediatrics, University Medical Centre Groningen, University of Groningen, The Netherlands
| | - Amalia M Dolga
- Department of Molecular Pharmacology, Groningen Research Institute of Pharmacy, University of Groningen, The Netherlands
| | - Alida E M van Lint
- Laboratory Genetic Metabolic Diseases, Department of Clinical Chemistry, Amsterdam UMC, Location AMC, University of Amsterdam, The Netherlands
| | - Ronald J A Wanders
- Laboratory Genetic Metabolic Diseases, Department of Clinical Chemistry, Amsterdam UMC, Location AMC, University of Amsterdam, The Netherlands
| | - Hans R Waterham
- Laboratory Genetic Metabolic Diseases, Department of Clinical Chemistry, Amsterdam UMC, Location AMC, University of Amsterdam, The Netherlands
| | - Barbara M Bakker
- Systems Medicine of Metabolism and Signalling, Laboratory of Paediatrics, University of Groningen, University Medical Centre Groningen, The Netherlands
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172
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Poupin N, Vinson F, Moreau A, Batut A, Chazalviel M, Colsch B, Fouillen L, Guez S, Khoury S, Dalloux-Chioccioli J, Tournadre A, Le Faouder P, Pouyet C, Van Delft P, Viars F, Bertrand-Michel J, Jourdan F. Improving lipid mapping in Genome Scale Metabolic Networks using ontologies. Metabolomics 2020; 16:44. [PMID: 32215752 PMCID: PMC7096385 DOI: 10.1007/s11306-020-01663-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 03/10/2020] [Indexed: 10/28/2022]
Abstract
INTRODUCTION To interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructions. However, mapping experimentally measured molecules onto metabolic networks is challenging due to differences in identifiers and level of annotation between data and metabolic networks, especially for lipids. OBJECTIVES To help linking lipids from lipidomics datasets with lipids in metabolic networks, we developed a new matching method based on the ChEBI ontology. The implementation is freely available as a python library and in MetExplore webserver. METHODS Our matching method is more flexible than an exact identifier-based correspondence since it allows establishing a link between molecules even if a different level of precision is provided in the dataset and in the metabolic network. For instance, it can associate a generic class of lipids present in the network with the molecular species detailed in the lipidomics dataset. This mapping is based on the computation of a distance between molecules in ChEBI ontology. RESULTS We applied our method to a chemical library (968 lipids) and an experimental dataset (32 modulated lipids) and showed that using ontology-based mapping improves and facilitates the link with genome scale metabolic networks. Beyond network mapping, the results provide ways for improvements in terms of network curation and lipidomics data annotation. CONCLUSION This new method being generic, it can be applied to any metabolomics data and therefore improve our comprehension of metabolic modulations.
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Affiliation(s)
- Nathalie Poupin
- UMR1331, Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300, Toulouse, France
| | - Florence Vinson
- UMR1331, Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300, Toulouse, France
| | - Arthur Moreau
- UMR1331, Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300, Toulouse, France
| | - Aurélie Batut
- MetaToul-Lipidomic Core Facility, MetaboHUB, Inserm I2MC, 31000, Toulouse, France
| | | | - Benoit Colsch
- Université Paris Saclay, CEA, INRAE, Médicaments Et Technologies Pour La santé (MTS), 91191, Gif-sur-Yvette, France
| | - Laetitia Fouillen
- Université de Bordeaux, CNRS, Laboratoire de Biogenèse Membranaire, UMR 5200, 33140, Villenave d'Ornon, France
| | - Sarah Guez
- MetaToul-Lipidomic Core Facility, MetaboHUB, Inserm I2MC, 31000, Toulouse, France
| | - Spiro Khoury
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000, Clermont-Ferrand, France
| | | | - Anthony Tournadre
- MetaToul-Lipidomic Core Facility, MetaboHUB, Inserm I2MC, 31000, Toulouse, France
| | - Pauline Le Faouder
- MetaToul-Lipidomic Core Facility, MetaboHUB, Inserm I2MC, 31000, Toulouse, France
| | - Corinne Pouyet
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, 63000, Clermont-Ferrand, France
| | - Pierre Van Delft
- Université de Bordeaux, CNRS, Laboratoire de Biogenèse Membranaire, UMR 5200, 33140, Villenave d'Ornon, France
| | - Fanny Viars
- MetaToul-Lipidomic Core Facility, MetaboHUB, Inserm I2MC, 31000, Toulouse, France
| | | | - Fabien Jourdan
- UMR1331, Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300, Toulouse, France.
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173
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Robinson JL, Kocabaş P, Wang H, Cholley PE, Cook D, Nilsson A, Anton M, Ferreira R, Domenzain I, Billa V, Limeta A, Hedin A, Gustafsson J, Kerkhoven EJ, Svensson LT, Palsson BO, Mardinoglu A, Hansson L, Uhlén M, Nielsen J. An atlas of human metabolism. Sci Signal 2020; 13:13/624/eaaz1482. [PMID: 32209698 DOI: 10.1126/scisignal.aaz1482] [Citation(s) in RCA: 175] [Impact Index Per Article: 43.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.
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Affiliation(s)
- Jonathan L Robinson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Pınar Kocabaş
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Hao Wang
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Molecular and Translational Medicine, University of Gothenburg, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Pierre-Etienne Cholley
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Daniel Cook
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Avlant Nilsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Raphael Ferreira
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Iván Domenzain
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Virinchi Billa
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Angelo Limeta
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Alex Hedin
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Johan Gustafsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - L Thomas Svensson
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Bernhard O Palsson
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.,Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.,Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Adil Mardinoglu
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, SE-10044 Stockholm, Sweden.,Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London WC2R 2LS, UK
| | - Lena Hansson
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Novo Nordisk Research Centre Oxford, Oxford OX3 7FZ, UK
| | - Mathias Uhlén
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.,Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, SE-10044 Stockholm, Sweden.,Wallenberg Center for Protein Research, KTH-Royal Institute of Technology, SE-10044 Stockholm, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden. .,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.,BioInnovation Institute, Ole Maaløes Vej 3, DK-2200 Copenhagen, Denmark
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174
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Li J, Lu Y, Li N, Li P, Su J, Wang Z, Wang T, Yang Z, Yang Y, Chen H, Xiao L, Duan H, Wu W, Liu X. Muscle metabolomics analysis reveals potential biomarkers of exercise‑dependent improvement of the diaphragm function in chronic obstructive pulmonary disease. Int J Mol Med 2020; 45:1644-1660. [PMID: 32186768 PMCID: PMC7169662 DOI: 10.3892/ijmm.2020.4537] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 02/17/2020] [Indexed: 12/25/2022] Open
Abstract
Decreased diaphragm function is a crucial factor leading to reduced ventilatory efficiency and worsening of quality of life in chronic obstructive pulmonary disease (COPD). Exercise training has been demonstrated to effectively improve the function of the diaphragm. However, the mechanism of this process has not been identified. The emergence of metabolomics has allowed the exploration of new ideas. The present study aimed to analyze the potential biomarkers of exercise-dependent enhancement of diaphragm function in COPD using metabolomics. Sprague Dawley rats were divided into three groups: COPD + exercise group (CEG); COPD model group (CMG); and control group (CG). The first two groups were exposed to cigarette smoke for 16 weeks to establish a COPD model. Then, the rats in the CEG underwent aerobic exercise training for 9 weeks. Following confirmation that exercise effectively improved the diaphragm function, a gas chromatography tandem time-of-flight mass spectrometry analysis system was used to detect the differential metabolites and associated pathways in the diaphragm muscles of the different groups. Following exercise intervention, the pulmonary function and diaphragm contractility of the CEG rats were significantly improved compared with those of the CMG rats. A total of 36 different metabolites were identified in the comparison between the CMG and the CG. Pathway enrichment analysis indicated that these different metabolites were involved in 17 pathways. A total of 29 different metabolites were identified in the comparison between the CMG and the CEG, which are involved in 14 pathways. Candidate biomarkers were selected, and the pathways analysis of these metabolites demonstrated that 2 types of metabolic pathways, the nicotinic acid and nicotinamide metabolism and arginine and proline metabolism pathways, were associated with exercise-induced pulmonary rehabilitation.
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Affiliation(s)
- Jian Li
- Department of Sports Medicine, Shanghai University of Sport, Shanghai 200438, P.R. China
| | - Yufan Lu
- Department of Sports Medicine, Shanghai University of Sport, Shanghai 200438, P.R. China
| | - Ning Li
- Department of Sports Medicine, Shanghai University of Sport, Shanghai 200438, P.R. China
| | - Peijun Li
- Department of Sports Medicine, Shanghai University of Sport, Shanghai 200438, P.R. China
| | - Jianqing Su
- Department of Sports Medicine, Shanghai University of Sport, Shanghai 200438, P.R. China
| | - Zhengrong Wang
- Department of Sports Medicine, Shanghai University of Sport, Shanghai 200438, P.R. China
| | - Ting Wang
- Department of Sports Medicine, Shanghai University of Sport, Shanghai 200438, P.R. China
| | - Zhaoyu Yang
- Department of Sports Medicine, Shanghai University of Sport, Shanghai 200438, P.R. China
| | - Yahui Yang
- Department of Sports Medicine, Shanghai University of Sport, Shanghai 200438, P.R. China
| | - Haixia Chen
- School of Physical Education and Sport Training, Shanghai University of Sport, Shanghai 200438, P.R. China
| | - Lu Xiao
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, P.R. China
| | - Hongxia Duan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, P.R. China
| | - Weibing Wu
- Department of Sports Medicine, Shanghai University of Sport, Shanghai 200438, P.R. China
| | - Xiaodan Liu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, P.R. China
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175
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Wu WH, Li FY, Shu YC, Lai JM, Chang PMH, Huang CYF, Wang FS. Oncogene inference optimization using constraint-based modelling incorporated with protein expression in normal and tumour tissues. ROYAL SOCIETY OPEN SCIENCE 2020; 7:191241. [PMID: 32269785 PMCID: PMC7137941 DOI: 10.1098/rsos.191241] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 02/26/2020] [Indexed: 05/02/2023]
Abstract
Cancer cells are known to exhibit unusual metabolic activity, and yet few metabolic cancer driver genes are known. Genetic alterations and epigenetic modifications of cancer cells result in the abnormal regulation of cellular metabolic pathways that are different when compared with normal cells. Such a metabolic reprogramming can be simulated using constraint-based modelling approaches towards predicting oncogenes. We introduced the tri-level optimization problem to use the metabolic reprogramming towards inferring oncogenes. The algorithm incorporated Recon 2.2 network with the Human Protein Atlas to reconstruct genome-scale metabolic network models of the tissue-specific cells at normal and cancer states, respectively. Such reconstructed models were applied to build the templates of the metabolic reprogramming between normal and cancer cell metabolism. The inference optimization problem was formulated to use the templates as a measure towards predicting oncogenes. The nested hybrid differential evolution algorithm was applied to solve the problem to overcome solving difficulty for transferring the inner optimization problem into the single one. Head and neck squamous cells were applied as a case study to evaluate the algorithm. We detected 13 of the top-ranked one-hit dysregulations and 17 of the top-ranked two-hit oncogenes with high similarity ratios to the templates. According to the literature survey, most inferred oncogenes are consistent with the observation in various tissues. Furthermore, the inferred oncogenes were highly connected with the TP53/AKT/IGF/MTOR signalling pathway through PTEN, which is one of the most frequently detected tumour suppressor genes in human cancer.
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Affiliation(s)
- Wu-Hsiung Wu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Fan-Yu Li
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Yi-Chen Shu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Jin-Mei Lai
- Department of Life Science, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Peter Mu-Hsin Chang
- Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Chi-Ying F. Huang
- Institute of Biopharmaceutical Sciences, National Yang-Ming University, Taipei, Taiwan
- Department of Biotechnology and Laboratory Science in Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
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176
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Wang Y, Ma S, Ruzzo WL. Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities. Sci Rep 2020; 10:3490. [PMID: 32103057 PMCID: PMC7044328 DOI: 10.1038/s41598-020-60384-w] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 02/11/2020] [Indexed: 01/24/2023] Open
Abstract
Spatial heterogeneity is a fundamental feature of the tumor microenvironment (TME), and tackling spatial heterogeneity in neoplastic metabolic aberrations is critical for tumor treatment. Genome-scale metabolic network models have been used successfully to simulate cancer metabolic networks. However, most models use bulk gene expression data of entire tumor biopsies, ignoring spatial heterogeneity in the TME. To account for spatial heterogeneity, we performed spatially-resolved metabolic network modeling of the prostate cancer microenvironment. We discovered novel malignant-cell-specific metabolic vulnerabilities targetable by small molecule compounds. We predicted that inhibiting the fatty acid desaturase SCD1 may selectively kill cancer cells based on our discovery of spatial separation of fatty acid synthesis and desaturation. We also uncovered higher prostaglandin metabolic gene expression in the tumor, relative to the surrounding tissue. Therefore, we predicted that inhibiting the prostaglandin transporter SLCO2A1 may selectively kill cancer cells. Importantly, SCD1 and SLCO2A1 have been previously shown to be potently and selectively inhibited by compounds such as CAY10566 and suramin, respectively. We also uncovered cancer-selective metabolic liabilities in central carbon, amino acid, and lipid metabolism. Our novel cancer-specific predictions provide new opportunities to develop selective drug targets for prostate cancer and other cancers where spatial transcriptomics datasets are available.
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Affiliation(s)
- Yuliang Wang
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, 98109, USA.
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, 98195, USA.
| | - Shuyi Ma
- Department of Microbiology, University of Washington, Seattle, WA, 98195, USA
| | - Walter L Ruzzo
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, 98195, USA
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA
- Fred Hutchinson Cancer Research Center, Seattle, WA, 98102, USA
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177
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Bjerkelund Røkke G, Hohmann-Marriott MF, Almaas E. An adjustable algal chloroplast plug-and-play model for genome-scale metabolic models. PLoS One 2020; 15:e0229408. [PMID: 32092117 PMCID: PMC7039451 DOI: 10.1371/journal.pone.0229408] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 02/05/2020] [Indexed: 01/25/2023] Open
Abstract
The chloroplast is a central part of plant cells, as this is the organelle where the photosynthesis, fixation of inorganic carbon, and other key functions related to fatty acid synthesis and amino acid synthesis occur. Since this organelle should be an integral part of any genome-scale metabolic model for a microalgae or a higher plant, it is of great interest to generate a detailed and standardized chloroplast model. Additionally, we see the need for a novel type of sub-model template, or organelle model, which could be incorporated into a larger, less specific genome-scale metabolic model, while allowing for minor differences between chloroplast-containing organisms. The result of this work is the very first standardized chloroplast model, iGR774, consisting of 788 reactions, 764 metabolites, and 774 genes. The model is currently able to run in three different modes, mimicking the chloroplast metabolism of three photosynthetic microalgae-Nannochloropsis gaditana, Chlamydomonas reinhardtii and Phaeodactylum tricornutum. In addition to developing the chloroplast metabolic network reconstruction, we have developed multiple software tools for working with this novel type of sub-model in the COBRA Toolbox for MATLAB, including tools for connecting the chloroplast model to a genome-scale metabolic reconstruction in need of a chloroplast, for switching the model between running in different organism modes, and for expanding it by introducing more reactions either related to one of the current organisms included in the model, or to a new organism.
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Affiliation(s)
- Gunvor Bjerkelund Røkke
- Department of Biotechnology and Food Science, The Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Eivind Almaas
- Department of Biotechnology and Food Science, The Norwegian University of Science and Technology, Trondheim, Norway
- K. G. Jebsen Center for Genetic Epidemiology, The Norwegian University of Science and Technology, Trondheim, Norway
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178
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Li GH, Dai S, Han F, Li W, Huang J, Xiao W. FastMM: an efficient toolbox for personalized constraint-based metabolic modeling. BMC Bioinformatics 2020; 21:67. [PMID: 32085724 PMCID: PMC7035665 DOI: 10.1186/s12859-020-3410-4] [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: 03/12/2019] [Accepted: 02/12/2020] [Indexed: 11/24/2022] Open
Abstract
Background Constraint-based metabolic modeling has been applied to understand metabolism related disease mechanisms, to predict potential new drug targets and anti-metabolites, and to identify biomarkers of complex diseases. Although the state-of-art modeling toolbox, COBRA 3.0, is powerful, it requires substantial computing time conducting flux balance analysis, knockout analysis, and Markov Chain Monte Carlo (MCMC) sampling, which may limit its application in large scale genome-wide analysis. Results Here, we rewrote the underlying code of COBRA 3.0 using C/C++, and developed a toolbox, termed FastMM, to effectively conduct constraint-based metabolic modeling. The results showed that FastMM is 2~400 times faster than COBRA 3.0 in performing flux balance analysis and knockout analysis and returns consistent outputs. When applied to MCMC sampling, FastMM is 8 times faster than COBRA 3.0. FastMM is also faster than some efficient metabolic modeling applications, such as Cobrapy and Fast-SL. In addition, we developed a Matlab/Octave interface for fast metabolic modeling. This interface was fully compatible with COBRA 3.0, enabling users to easily perform complex applications for metabolic modeling. For example, users who do not have deep constraint-based metabolic model knowledge can just type one command in Matlab/Octave to perform personalized metabolic modeling. Users can also use the advance and multiple threading parameters for complex metabolic modeling. Thus, we provided an efficient and user-friendly solution to perform large scale genome-wide metabolic modeling. For example, FastMM can be applied to the modeling of individual cancer metabolic profiles of hundreds to thousands of samples in the Cancer Genome Atlas (TCGA). Conclusion FastMM is an efficient and user-friendly toolbox for large-scale personalized constraint-based metabolic modeling. It can serve as a complementary and invaluable improvement to the existing functionalities in COBRA 3.0. FastMM is under GPL license and can be freely available at GitHub site: https://github.com/GonghuaLi/FastMM.
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Affiliation(s)
- Gong-Hua Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Shaoxing Dai
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Feifei Han
- Immue and Metabolic Computational Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Wenxing Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China
| | - Jingfei Huang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, Yunnan, China. .,Collaborative Innovation Center for Natural Products and Biological Drugs of Yunnan, Kunming, 650223, Yunnan, China.
| | - Wenzhong Xiao
- Immue and Metabolic Computational Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA. .,Stanford Genome Technology Center, Stanford University, Palo Alto, CA, 94304, USA.
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179
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Struck A, Walsh B, Buchanan A, Lee JA, Spangler R, Stuart JM, Ellrott K. Exploring Integrative Analysis Using the BioMedical Evidence Graph. JCO Clin Cancer Inform 2020; 4:147-159. [PMID: 32097025 PMCID: PMC7049249 DOI: 10.1200/cci.19.00110] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/16/2020] [Indexed: 12/22/2022] Open
Abstract
PURPOSE The analysis of cancer biology data involves extremely heterogeneous data sets, including information from RNA sequencing, genome-wide copy number, DNA methylation data reporting on epigenetic regulation, somatic mutations from whole-exome or whole-genome analyses, pathology estimates from imaging sections or subtyping, drug response or other treatment outcomes, and various other clinical and phenotypic measurements. Bringing these different resources into a common framework, with a data model that allows for complex relationships as well as dense vectors of features, will unlock integrated data set analysis. METHODS We introduce the BioMedical Evidence Graph (BMEG), a graph database and query engine for discovery and analysis of cancer biology. The BMEG is unique from other biologic data graphs in that sample-level molecular and clinical information is connected to reference knowledge bases. It combines gene expression and mutation data with drug-response experiments, pathway information databases, and literature-derived associations. RESULTS The construction of the BMEG has resulted in a graph containing > 41 million vertices and 57 million edges. The BMEG system provides a graph query-based application programming interface to enable analysis, with client code available for Python, Javascript, and R, and a server online at bmeg.io. Using this system, we have demonstrated several forms of cross-data set analysis to show the utility of the system. CONCLUSION The BMEG is an evolving resource dedicated to enabling integrative analysis. We have demonstrated queries on the system that illustrate mutation significance analysis, drug-response machine learning, patient-level knowledge-base queries, and pathway level analysis. We have compared the resulting graph to other available integrated graph systems and demonstrated the former is unique in the scale of the graph and the type of data it makes available.
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Affiliation(s)
- Adam Struck
- Biomedical Engineering, Oregon Health and Science University, Portland OR
| | - Brian Walsh
- Biomedical Engineering, Oregon Health and Science University, Portland OR
| | - Alexander Buchanan
- Biomedical Engineering, Oregon Health and Science University, Portland OR
| | - Jordan A. Lee
- Biomedical Engineering, Oregon Health and Science University, Portland OR
| | - Ryan Spangler
- Biomedical Engineering, Oregon Health and Science University, Portland OR
| | - Joshua M. Stuart
- Biomolecular Engineering Department, University of California, Santa Cruz, Santa Cruz, CA
- University of California Santa Cruz Genomics Institute, University of California, Santa Cruz Santa Cruz, CA
| | - Kyle Ellrott
- Biomedical Engineering, Oregon Health and Science University, Portland OR
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180
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Chowdhury S, Fong SS. Computational Modeling of the Human Microbiome. Microorganisms 2020; 8:microorganisms8020197. [PMID: 32023941 PMCID: PMC7074762 DOI: 10.3390/microorganisms8020197] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 01/23/2020] [Accepted: 01/27/2020] [Indexed: 12/20/2022] Open
Abstract
The impact of microorganisms on human health has long been acknowledged and studied, but recent advances in research methodologies have enabled a new systems-level perspective on the collections of microorganisms associated with humans, the human microbiome. Large-scale collaborative efforts such as the NIH Human Microbiome Project have sought to kick-start research on the human microbiome by providing foundational information on microbial composition based upon specific sites across the human body. Here, we focus on the four main anatomical sites of the human microbiome: gut, oral, skin, and vaginal, and provide information on site-specific background, experimental data, and computational modeling. Each of the site-specific microbiomes has unique organisms and phenomena associated with them; there are also high-level commonalities. By providing an overview of different human microbiome sites, we hope to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research.
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Affiliation(s)
- Shomeek Chowdhury
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Cary Street, Richmond, VA 23284 USA;
| | - Stephen S. Fong
- Chemical and Life Science Engineering, Virginia Commonwealth University, 601 West Main Street, Richmond, VA 23284, USA
- Correspondence:
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181
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Osorio D, Pinzón A, Martín-Jiménez C, Barreto GE, González J. Multiple Pathways Involved in Palmitic Acid-Induced Toxicity: A System Biology Approach. Front Neurosci 2020; 13:1410. [PMID: 32076395 PMCID: PMC7006434 DOI: 10.3389/fnins.2019.01410] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 12/12/2019] [Indexed: 01/26/2023] Open
Abstract
Inflammation is a complex biological response to injuries, metabolic disorders or infections. In the brain, astrocytes play an important role in the inflammatory processes during neurodegenerative diseases. Recent studies have shown that the increase of free saturated fatty acids such as palmitic acid produces a metabolic inflammatory response in astrocytes generally associated with damaging mechanisms such as oxidative stress, endoplasmic reticulum stress, and autophagic defects. In this aspect, the synthetic neurosteroid tibolone has shown to exert protective functions against inflammation in neuronal experimental models without the tumorigenic effects exerted by sexual hormones such as estradiol and progesterone. However, there is little information regarding the specific mechanisms of tibolone in astrocytes during inflammatory insults. In the present study, we performed a genome-scale metabolic reconstruction of astrocytes that was used to study astrocytic response during an inflammatory insult by palmitate through Flux Balance Analysis methods and data mining. In this aspect, we assessed the metabolic fluxes of human astrocytes under three different scenarios: healthy (normal conditions), induced inflammation by palmitate, and tibolone treatment under palmitate inflammation. Our results suggest that tibolone reduces the L-glutamate-mediated neurotoxicity in astrocytes through the modulation of several metabolic pathways involved in glutamate uptake. We also identified a set of reactions associated with the protective effects of tibolone, including the upregulation of taurine metabolism, gluconeogenesis, cPPAR and the modulation of calcium signaling pathways. In conclusion, the different scenarios studied in our model allowed us to identify several metabolic fluxes perturbed under an inflammatory response and the protective mechanisms exerted by tibolone.
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Affiliation(s)
- Daniel Osorio
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, United States
| | - Andrés Pinzón
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Cynthia Martín-Jiménez
- 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
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
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182
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Duan C, Kuang L, Xiang X, Zhang J, Zhu Y, Wu Y, Yan Q, Liu L, Li T. Activated Drp1-mediated mitochondrial ROS influence the gut microbiome and intestinal barrier after hemorrhagic shock. Aging (Albany NY) 2020; 12:1397-1416. [PMID: 31954373 PMCID: PMC7053642 DOI: 10.18632/aging.102690] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 12/24/2019] [Indexed: 12/12/2022]
Abstract
A role of the mitochondrial dynamin-related protein (Drp1) on gut microbiome composition and intestinal barrier function after hemorrhagic shock has not been identified previously and thus addressed in this study. Here, we used a combination of 16S rRNA gene sequencing and mass spectrometry-based metabolomics profiling in WT and Drp1 KO mouse models to examine the functional impact of activated Drp1 on the gut microbiome as well as mitochondrial metabolic regulation after hemorrhagic shock. Our data showed that changes in mitochondrial Drp1 activity participated in the regulation of intestinal barrier function after hemorrhagic shock. Activated Drp1 significantly perturbed gut microbiome composition in the Bacteroidetes phylum. The abundance of short-chain fatty acid (SCFA) producing microbes, such as Bacteroides, Butyricimonas and Odoribacter, was markedly decreased in mice after shock, and was inversely correlated with both the distribution of the tight junction protein ZO1 and intestinal permeability. Together, these data suggest that Drp1 activation perturbs the gut microbiome community and SCFA production in a ROS-specific manner and thereby substantially disturbs tight junctions and intestinal barrier function after hemorrhagic shock. Our findings provide novel insights for targeting Drp1-mediated mitochondrial function as well as the microbiome in the treatment of intestinal barrier dysfunction after shock.
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Affiliation(s)
- Chenyang Duan
- State Key Laboratory of Trauma, Burns and Combined Injury, Second Department of Research Institute of Surgery, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Lei Kuang
- State Key Laboratory of Trauma, Burns and Combined Injury, Second Department of Research Institute of Surgery, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Xinming Xiang
- State Key Laboratory of Trauma, Burns and Combined Injury, Second Department of Research Institute of Surgery, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Jie Zhang
- State Key Laboratory of Trauma, Burns and Combined Injury, Second Department of Research Institute of Surgery, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Yu Zhu
- State Key Laboratory of Trauma, Burns and Combined Injury, Second Department of Research Institute of Surgery, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Yue Wu
- State Key Laboratory of Trauma, Burns and Combined Injury, Second Department of Research Institute of Surgery, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Qingguang Yan
- State Key Laboratory of Trauma, Burns and Combined Injury, Second Department of Research Institute of Surgery, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Liangming Liu
- State Key Laboratory of Trauma, Burns and Combined Injury, Second Department of Research Institute of Surgery, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Tao Li
- State Key Laboratory of Trauma, Burns and Combined Injury, Second Department of Research Institute of Surgery, Daping Hospital, Army Medical University, Chongqing 400042, China
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183
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Cesur MF, Siraj B, Uddin R, Durmuş S, Çakır T. Network-Based Metabolism-Centered Screening of Potential Drug Targets in Klebsiella pneumoniae at Genome Scale. Front Cell Infect Microbiol 2020; 9:447. [PMID: 31993376 PMCID: PMC6970976 DOI: 10.3389/fcimb.2019.00447] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 12/12/2019] [Indexed: 01/28/2023] Open
Abstract
Klebsiella pneumoniae is an opportunistic bacterial pathogen leading to life-threatening nosocomial infections. Emergence of highly resistant strains poses a major challenge in the management of the infections by healthcare-associated K. pneumoniae isolates. Thus, despite intensive efforts, the current treatment strategies remain insufficient to eradicate such infections. Failure of the conventional infection-prevention and treatment efforts explicitly indicates the requirement of new therapeutic approaches. This prompted us to systematically analyze the K. pneumoniae metabolism to investigate drug targets. Genome-scale metabolic networks (GMNs) facilitating the systematic analysis of the metabolism are promising platforms. Thus, we used a GMN of K. pneumoniae MGH 78578 to determine putative targets through gene- and metabolite-centric approaches. To develop more realistic infection models, we performed the bacterial growth simulations within different host-mimicking media, using an improved biomass formation reaction. We selected more suitable targets based on several property-based prioritization procedures. KdsA was identified as the high-ranked putative target satisfying most of the target prioritization criteria specified under the gene-centric approach. Through a structure-based virtual screening protocol, we identified potential KdsA inhibitors. In addition, the metabolite-centric approach extended the drug target list based on synthetic lethality. This revealed the importance of combined metabolic analyses for a better understanding of the metabolism. To our knowledge, this is the first comprehensive effort on the investigation of the K. pneumoniae metabolism for drug target prediction through the constraint-based analysis of its GMN in conjunction with several bioinformatic approaches. This study can guide the researchers for the future drug designs by providing initial findings regarding crucial components of the Klebsiella metabolism.
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Affiliation(s)
- Müberra Fatma Cesur
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Turkey
| | - Bushra Siraj
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Reaz Uddin
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Saliha Durmuş
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Turkey
| | - Tunahan Çakır
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, Gebze, Turkey
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184
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Romers J, Thieme S, Münzner U, Krantz M. A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models. NPJ Syst Biol Appl 2020; 6:2. [PMID: 31934349 PMCID: PMC6954118 DOI: 10.1038/s41540-019-0120-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2018] [Accepted: 11/20/2019] [Indexed: 11/09/2022] Open
Abstract
The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. Similar methods have not been established for signal transduction networks, where the representation of complexes and internal states leads to scalability issues in both model formulation and execution. While rule- and agent-based methods allow efficient model definition and execution, respectively, model parametrisation introduces an additional layer of uncertainty due to the sparsity of reliably measured parameters. Here, we present a scalable method for parameter-free simulation of mechanistic signal transduction networks. It is based on rxncon and uses a bipartite Boolean logic with separate update rules for reactions and states. Using two generic update rules, we enable translation of any rxncon model into a unique Boolean model, which can be used for network validation and simulation-allowing the prediction of system-level function directly from molecular mechanistic data. Through scalable model definition and simulation, and the independence of quantitative parameters, it opens up for simulation and validation of mechanistic genome-scale models of signal transduction networks.
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Affiliation(s)
- Jesper Romers
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Thieme
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ulrike Münzner
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan
| | - Marcus Krantz
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
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185
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Rodchenkov I, Babur O, Luna A, Aksoy BA, Wong JV, Fong D, Franz M, Siper MC, Cheung M, Wrana M, Mistry H, Mosier L, Dlin J, Wen Q, O’Callaghan C, Li W, Elder G, Smith PT, Dallago C, Cerami E, Gross B, Dogrusoz U, Demir E, Bader GD, Sander C. Pathway Commons 2019 Update: integration, analysis and exploration of pathway data. Nucleic Acids Res 2020; 48:D489-D497. [PMID: 31647099 PMCID: PMC7145667 DOI: 10.1093/nar/gkz946] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/07/2019] [Accepted: 10/10/2019] [Indexed: 12/14/2022] Open
Abstract
Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g. metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (i) tools to search this comprehensive resource, (ii) a download site offering integrated bulk sets of pathway data (e.g. tables of interactions and gene sets), (iii) reusable software libraries for working with pathway information in several programming languages (Java, R, Python and Javascript) and (iv) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4794 detailed human biochemical processes (i.e. pathways) and ∼2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.
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Affiliation(s)
- Igor Rodchenkov
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Ozgun Babur
- Department of Molecular and Medical Genetics, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Augustin Luna
- cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
| | - Bulent Arman Aksoy
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA
| | - Jeffrey V Wong
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Dylan Fong
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Max Franz
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Metin Can Siper
- Department of Molecular and Medical Genetics, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Manfred Cheung
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Michael Wrana
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Harsh Mistry
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Logan Mosier
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Jonah Dlin
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Qizhi Wen
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Caitlin O’Callaghan
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Wanxin Li
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Geoffrey Elder
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Peter T Smith
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Christian Dallago
- Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA 02215, USA
- Department of Informatics, Technische Universität München, 85748 Garching, Germany
| | - Ethan Cerami
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Benjamin Gross
- Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Ugur Dogrusoz
- Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey
| | - Emek Demir
- Department of Molecular and Medical Genetics, School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Chris Sander
- cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
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186
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From computational genomics to systems metabolomics for precision cancer medicine and drug discovery. Pharmacol Res 2020; 151:104479. [DOI: 10.1016/j.phrs.2019.104479] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 10/03/2019] [Indexed: 11/24/2022]
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187
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McGarrity S, Karvelsson ST, Sigurjónsson ÓE, Rolfsson Ó. Comparative Metabolic Network Flux Analysis to Identify Differences in Cellular Metabolism. Methods Mol Biol 2020; 2088:223-269. [PMID: 31893377 DOI: 10.1007/978-1-0716-0159-4_11] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Metabolic network flux analysis uses genome-scale metabolic reconstructions to integrate transcriptomics, proteomics, and/or metabolomics data to allow for comprehensive interpretation of genotype to metabolic phenotype relationships. The compilation of many Constraint-based model analysis methods into one MATLAB package, the COBRAtoolbox, has opened the possibility of using these methods to the many biologists with some knowledge of the commonly used statistical program, MATLAB. Here we outline the steps required to take a published genome-scale metabolic reconstruction and interrogate its consistency and biological feasibility. Subsequently, we demonstrate how mRNA expression data and metabolomics data, relating to one or more cell types or biological contexts, can be applied to constrain and generate metabolic models descriptive of metabolic flux phenotypes. Finally, we describe the comparison of the resulting models and model outputs with the aim of identifying metabolic biomarkers and changes in cellular metabolism.
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Affiliation(s)
- Sarah McGarrity
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
- Center for Systems Biology, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Sigurður T Karvelsson
- Center for Systems Biology, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Ólafur E Sigurjónsson
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
- Center for Systems Biology, School of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Óttar Rolfsson
- Center for Systems Biology, School of Health Sciences, University of Iceland, Reykjavik, Iceland.
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188
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Abstract
Recent advances in analytical techniques, particularly LC-MS, generate increasingly large and complex metabolomics datasets. Pathway analysis tools help place the experimental observations into relevant biological or disease context. This chapter provides an overview of the general concepts and common tools for pathway analysis, including Mummichog for untargeted metabolomics. Examples of pathway mapping, MetScape, and Mummichog are explained. This serves as both a practical tutorial and a timely survey of pathway analysis for label-free metabolomics data.
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189
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Rawls K, Dougherty BV, Papin J. Metabolic Network Reconstructions to Predict Drug Targets and Off-Target Effects. Methods Mol Biol 2020; 2088:315-330. [PMID: 31893380 DOI: 10.1007/978-1-0716-0159-4_14] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The drug development pipeline has stalled because of the difficulty in identifying new drug targets while minimizing off-target effects. Computational methods, such as the use of metabolic network reconstructions, may provide a cost-effective platform to test new hypotheses for drug targets and prevent off-target effects. Here, we summarize available methods to identify drug targets and off-target effects using either reaction-centric, gene-centric, or metabolite-centric approaches with genome-scale metabolic network reconstructions.
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Affiliation(s)
- Kristopher Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Jason Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
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190
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Waller TC, Berg JA, Lex A, Chapman BE, Rutter J. Compartment and hub definitions tune metabolic networks for metabolomic interpretations. Gigascience 2020; 9:giz137. [PMID: 31972021 PMCID: PMC6977586 DOI: 10.1093/gigascience/giz137] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 08/31/2019] [Accepted: 10/27/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism's cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments. RESULTS We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications. CONCLUSIONS Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.
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Affiliation(s)
- T Cameron Waller
- Division of Medical Genetics, Department of Medicine, School of Medicine, University of California San Diego, Room 1318A, 9500 Gilman Drive #0606, La Jolla, California 92093-0606, United States of America
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
| | - Jordan A Berg
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
| | - Alexander Lex
- School of Computing, University of Utah, Room 3190, 50 South Central Campus Drive, Salt Lake City, Utah 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Room 3750, 72 South Central Campus Drive, Salt Lake City, Utah 84112, USA
| | - Brian E Chapman
- Department of Radiology and Imaging Sciences, School of Medicine, University of Utah, Room 1A071, 30 North 1900 East, Salt Lake City, Utah 84132, USA
- Department of Biomedical Informatics, School of Medicine, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, Utah 84108, USA
| | - Jared Rutter
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
- Howard Hughes Medical Institute, School of Medicine, University of Utah, Room AC101, 30 North 1900 East, Salt Lake City, Utah 84132, USA
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191
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Mugahid DA, Sengul TG, You X, Wang Y, Steil L, Bergmann N, Radke MH, Ofenbauer A, Gesell-Salazar M, Balogh A, Kempa S, Tursun B, Robbins CT, Völker U, Chen W, Nelson L, Gotthardt M. Proteomic and Transcriptomic Changes in Hibernating Grizzly Bears Reveal Metabolic and Signaling Pathways that Protect against Muscle Atrophy. Sci Rep 2019; 9:19976. [PMID: 31882638 PMCID: PMC6934745 DOI: 10.1038/s41598-019-56007-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 12/05/2019] [Indexed: 12/31/2022] Open
Abstract
Muscle atrophy is a physiological response to disuse and malnutrition, but hibernating bears are largely resistant to this phenomenon. Unlike other mammals, they efficiently reabsorb amino acids from urine, periodically activate muscle contraction, and their adipocytes differentially responds to insulin. The contribution of myocytes to the reduced atrophy remains largely unknown. Here we show how metabolism and atrophy signaling are regulated in skeletal muscle of hibernating grizzly bear. Metabolic modeling of proteomic changes suggests an autonomous increase of non-essential amino acids (NEAA) in muscle and treatment of differentiated myoblasts with NEAA is sufficient to induce hypertrophy. Our comparison of gene expression in hibernation versus muscle atrophy identified several genes differentially regulated during hibernation, including Pdk4 and Serpinf1. Their trophic effects extend to myoblasts from non-hibernating species (including C. elegans), as documented by a knockdown approach. Together, these changes reflect evolutionary favored adaptations that, once translated to the clinics, could help improve atrophy treatment.
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Affiliation(s)
- D A Mugahid
- Neuromuscular and Cardiovascular Cell Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - T G Sengul
- Neuromuscular and Cardiovascular Cell Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - X You
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - Y Wang
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - L Steil
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - N Bergmann
- Neuromuscular and Cardiovascular Cell Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - M H Radke
- Neuromuscular and Cardiovascular Cell Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - A Ofenbauer
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - M Gesell-Salazar
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - A Balogh
- Experimental and Clinical Research Center, Charité & Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - S Kempa
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - B Tursun
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - C T Robbins
- School of the Environment and School of Biological Sciences, Washington State University, Pullman, Washington, USA
| | - U Völker
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - W Chen
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany
| | - L Nelson
- College of Veterinary Medicine and Department of Veterinary Clinical Science, Washington State University, Pullman, Washington, USA
| | - M Gotthardt
- Neuromuscular and Cardiovascular Cell Biology, Max Delbrück Center for Molecular Medicine, Berlin, Germany. .,Charité Universitätsmedizin Berlin, Berlin, Germany. .,DZHK (German Center for Cardiovascular Research), partner site Berlin, Berlin, Germany.
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192
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Wang FS, Wu WH, Hsiu WS, Liu YJ, Chuang KW. Genome-Scale Metabolic Modeling with Protein Expressions of Normal and Cancerous Colorectal Tissues for Oncogene Inference. Metabolites 2019; 10:metabo10010016. [PMID: 31881674 PMCID: PMC7022839 DOI: 10.3390/metabo10010016] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 12/10/2019] [Accepted: 12/21/2019] [Indexed: 12/23/2022] Open
Abstract
Although cancer has historically been regarded as a cell proliferation disorder, it has recently been considered a metabolic disease. The first discovery of metabolic alterations in cancer cells refers to Otto Warburg’s observations. Cancer metabolism results in alterations in metabolic fluxes that are evident in cancer cells compared with most normal tissue cells. This study applied protein expressions of normal and cancer cells to reconstruct two tissue-specific genome-scale metabolic models. Both models were employed in a tri-level optimization framework to infer oncogenes. Moreover, this study also introduced enzyme pseudo-coding numbers in the gene association expression to avoid performing posterior decision-making that is necessary for the reaction-based method. Colorectal cancer (CRC) was the topic of this case study, and 20 top-ranked oncogenes were determined. Notably, these dysregulated genes were involved in various metabolic subsystems and compartments. We found that the average similarity ratio for each dysregulation is higher than 98%, and the extent of similarity for flux changes is higher than 93%. On the basis of surveys of PubMed and GeneCards, these oncogenes were also investigated in various carcinomas and diseases. Most dysregulated genes connect to catalase that acts as a hub and connects protein signaling pathways, such as those involving TP53, mTOR, AKT1, MAPK1, EGFR, MYC, CDK8, and RAS family.
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193
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Hill BG, Shiva S, Ballinger S, Zhang J, Darley-Usmar VM. Bioenergetics and translational metabolism: implications for genetics, physiology and precision medicine. Biol Chem 2019; 401:3-29. [PMID: 31815377 PMCID: PMC6944318 DOI: 10.1515/hsz-2019-0268] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 06/24/2019] [Indexed: 12/25/2022]
Abstract
It is now becoming clear that human metabolism is extremely plastic and varies substantially between healthy individuals. Understanding the biochemistry that underlies this physiology will enable personalized clinical interventions related to metabolism. Mitochondrial quality control and the detailed mechanisms of mitochondrial energy generation are central to understanding susceptibility to pathologies associated with aging including cancer, cardiac and neurodegenerative diseases. A precision medicine approach is also needed to evaluate the impact of exercise or caloric restriction on health. In this review, we discuss how technical advances in assessing mitochondrial genetics, cellular bioenergetics and metabolomics offer new insights into developing metabolism-based clinical tests and metabolotherapies. We discuss informatics approaches, which can define the bioenergetic-metabolite interactome and how this can help define healthy energetics. We propose that a personalized medicine approach that integrates metabolism and bioenergetics with physiologic parameters is central for understanding the pathophysiology of diseases with a metabolic etiology. New approaches that measure energetics and metabolomics from cells isolated from human blood or tissues can be of diagnostic and prognostic value to precision medicine. This is particularly significant with the development of new metabolotherapies, such as mitochondrial transplantation, which could help treat complex metabolic diseases.
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Affiliation(s)
- Bradford G. Hill
- Envirome Institute, Diabetes and Obesity Center, Department of Medicine, University of Louisville, Louisville, KY 40202
| | - Sruti Shiva
- Department of Pharmacology & Chemical Biology, Vascular Medicine Institute, Center for Metabolism & Mitochondrial Medicine, University of Pittsburgh, Pittsburgh, PA 15143
| | - Scott Ballinger
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294
- Mitochondrial Medicine Laboratory, University of Alabama at Birmingham, Birmingham, AL 35294
- Center for Free Radical Biology, University of Alabama at Birmingham, Birmingham, AL 35294
| | - Jianhua Zhang
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294
- Mitochondrial Medicine Laboratory, University of Alabama at Birmingham, Birmingham, AL 35294
- Center for Free Radical Biology, University of Alabama at Birmingham, Birmingham, AL 35294
- Department of Veteran Affairs Medical Center, Birmingham, AL 35294
| | - Victor M. Darley-Usmar
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294
- Mitochondrial Medicine Laboratory, University of Alabama at Birmingham, Birmingham, AL 35294
- Center for Free Radical Biology, University of Alabama at Birmingham, Birmingham, AL 35294
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194
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n-Butylamine for Improving the Efficiency of Untargeted Mass Spectrometry Analysis of Plasma Metabolite Composition. Int J Mol Sci 2019; 20:ijms20235957. [PMID: 31783473 PMCID: PMC6929023 DOI: 10.3390/ijms20235957] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 11/22/2019] [Accepted: 11/25/2019] [Indexed: 12/21/2022] Open
Abstract
A comparative study of the impact of n-butylamine and traditionally used additives (ammonium hydroxide and formic acid) on the efficiency of the electrospray ionization (ESI) process for the enhancement of metabolite coverage was performed by direct injection mass spectrometry (MS) analysis in negative mode. Evaluation of obtained MS data showed that n-butylamine is one of the most effective additives for the analysis of metabolite composition in ESI in negative ion mode (ESI(-)) The limitations of the use of n-butylamine and other alkylamines in the analysis of metabolic composition and a decontamination procedure that can reduce MS device contamination after their application are discussed. The proposed procedure allows the performance of high-sensitivity analysis of low-molecular-weight compounds on the same MS device in both polarities.
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195
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Blencowe M, Karunanayake T, Wier J, Hsu N, Yang X. Network Modeling Approaches and Applications to Unravelling Non-Alcoholic Fatty Liver Disease. Genes (Basel) 2019; 10:E966. [PMID: 31771247 PMCID: PMC6947017 DOI: 10.3390/genes10120966] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 11/18/2019] [Accepted: 11/22/2019] [Indexed: 12/12/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a progressive condition of the liver encompassing a range of pathologies including steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma. Research into this disease is imperative due to its rapid growth in prevalence, economic burden, and current lack of FDA approved therapies. NAFLD involves a highly complex etiology that calls for multi-tissue multi-omics network approaches to uncover the pathogenic genes and processes, diagnostic biomarkers, and potential therapeutic strategies. In this review, we first present a basic overview of disease pathogenesis, risk factors, and remaining knowledge gaps, followed by discussions of the need and concepts of multi-tissue multi-omics approaches, various network methodologies and application examples in NAFLD research. We highlight the findings that have been uncovered thus far including novel biomarkers, genes, and biological pathways involved in different stages of NAFLD, molecular connections between NAFLD and its comorbidities, mechanisms underpinning sex differences, and druggable targets. Lastly, we outline the future directions of implementing network approaches to further improve our understanding of NAFLD in order to guide diagnosis and therapeutics.
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Affiliation(s)
- Montgomery Blencowe
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA; (M.B.); (T.K.); (J.W.); (N.H.)
- Molecular, Cellular, and Integrative Physiology Interdepartmental Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
| | - Tilan Karunanayake
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA; (M.B.); (T.K.); (J.W.); (N.H.)
| | - Julian Wier
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA; (M.B.); (T.K.); (J.W.); (N.H.)
| | - Neil Hsu
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA; (M.B.); (T.K.); (J.W.); (N.H.)
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA; (M.B.); (T.K.); (J.W.); (N.H.)
- Molecular, Cellular, and Integrative Physiology Interdepartmental Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
- Interdepartmental Program of Bioinformatics, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
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196
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Chowdhury R, Maranas CD. From directed evolution to computational enzyme engineering—A review. AIChE J 2019. [DOI: 10.1002/aic.16847] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Ratul Chowdhury
- Department of Chemical Engineering The Pennsylvania State University University Park Pennsylvania
| | - Costas D. Maranas
- Department of Chemical Engineering The Pennsylvania State University University Park Pennsylvania
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197
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Toroghi MK, Cluett WR, Mahadevan R. A multi-scale model for low-density lipoprotein cholesterol (LDL-C) regulation in the human body: Application to quantitative systems pharmacology. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.06.032] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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198
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Frainay C, Aros S, Chazalviel M, Garcia T, Vinson F, Weiss N, Colsch B, Sedel F, Thabut D, Junot C, Jourdan F. MetaboRank: network-based recommendation system to interpret and enrich metabolomics results. Bioinformatics 2019; 35:274-283. [PMID: 29982278 PMCID: PMC6330003 DOI: 10.1093/bioinformatics/bty577] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 07/04/2018] [Indexed: 12/19/2022] Open
Abstract
Motivation Metabolomics has shown great potential to improve the understanding of complex diseases, potentially leading to therapeutic target identification. However, no single analytical method allows monitoring all metabolites in a sample, resulting in incomplete metabolic fingerprints. This incompleteness constitutes a stumbling block to interpretation, raising the need for methods that can enrich those fingerprints. We propose MetaboRank, a new solution inspired by social network recommendation systems for the identification of metabolites potentially related to a metabolic fingerprint. Results MetaboRank method had been used to enrich metabolomics data obtained on cerebrospinal fluid samples from patients suffering from hepatic encephalopathy (HE). MetaboRank successfully recommended metabolites not present in the original fingerprint. The quality of recommendations was evaluated by using literature automatic search, in order to check that recommended metabolites could be related to the disease. Complementary mass spectrometry experiments and raw data analysis were performed to confirm these suggestions. In particular, MetaboRank recommended the overlooked α-ketoglutaramate as a metabolite which should be added to the metabolic fingerprint of HE, thus suggesting that metabolic fingerprints enhancement can provide new insight on complex diseases. Availability and implementation Method is implemented in the MetExplore server and is available at www.metexplore.fr. A tutorial is available at https://metexplore.toulouse.inra.fr/com/tutorials/MetaboRank/2017-MetaboRank.pdf. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Clément Frainay
- Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France
| | | | | | - Thomas Garcia
- Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France
| | - Florence Vinson
- Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France
| | - Nicolas Weiss
- Unité de Réanimation Neurologique, Département de Neurologie, Pôle des Maladies du Système Nerveux Central, Groupement Hospitalier Pitié-Salpêtrière Charles Foix, Assistance Publique - Hôpitaux de Paris, Paris, France.,Brain Liver Pitié-Salpêtrière (BLIPS) Study Group, Groupement Hospitalier Pitié-Salpêtrière-Charles Foix, Assistance Publique - Hôpitaux de Paris & INSERM UMR_S 938, CDR Saint-Antoine Maladies Métaboliques, Biliaires et Fibro-inflammatoire du Foie & Institut de Cardiométabolisme et Nutrition, ICAN, Paris, France
| | - Benoit Colsch
- Service de Pharmacologie et Immunoanalyse (SPI), CEA, INRA, Université Paris-Saclay, MetaboHUB, Gif-sur-Yvette, France and
| | | | - Dominique Thabut
- Brain Liver Pitié-Salpêtrière (BLIPS) Study Group, Groupement Hospitalier Pitié-Salpêtrière-Charles Foix, Assistance Publique - Hôpitaux de Paris & INSERM UMR_S 938, CDR Saint-Antoine Maladies Métaboliques, Biliaires et Fibro-inflammatoire du Foie & Institut de Cardiométabolisme et Nutrition, ICAN, Paris, France.,Unité de Soins Intensifs d'Hépato-gastroentérologie, Groupement Hospitalier Pitié-Salpêtrière-Charles Foix, Assistance Publique - Hôpitaux de Paris et Université Pierre et Marie Curie Paris 6, Paris, France
| | - Christophe Junot
- Service de Pharmacologie et Immunoanalyse (SPI), CEA, INRA, Université Paris-Saclay, MetaboHUB, Gif-sur-Yvette, France and
| | - Fabien Jourdan
- Toxalim, Université de Toulouse, INRA, Université de Toulouse 3 Paul Sabatier, Toulouse, France
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199
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Yang M, Rajeeve K, Rudel T, Dandekar T. Comprehensive Flux Modeling of Chlamydia trachomatis Proteome and qRT-PCR Data Indicate Biphasic Metabolic Differences Between Elementary Bodies and Reticulate Bodies During Infection. Front Microbiol 2019; 10:2350. [PMID: 31681215 PMCID: PMC6803457 DOI: 10.3389/fmicb.2019.02350] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Accepted: 09/26/2019] [Indexed: 11/13/2022] Open
Abstract
Metabolic adaptation to the host cell is important for obligate intracellular pathogens such as Chlamydia trachomatis (Ct). Here we infer the flux differences for Ct from proteome and qRT-PCR data by comprehensive pathway modeling. We compare the comparatively inert infectious elementary body (EB) and the active replicative reticulate body (RB) systematically using a genome-scale metabolic model with 321 metabolites and 277 reactions. This did yield 84 extreme pathways based on a published proteomics dataset at three different time points of infection. Validation of predictions was done by quantitative RT-PCR of enzyme mRNA expression at three time points. Ct’s major active pathways are glycolysis, gluconeogenesis, glycerol-phospholipid (GPL) biosynthesis (support from host acetyl-CoA) and pentose phosphate pathway (PPP), while its incomplete TCA and fatty acid biosynthesis are less active. The modeled metabolic pathways are much more active in RB than in EB. Our in silico model suggests that EB and RB utilize folate to generate NAD(P)H using independent pathways. The only low metabolic flux inferred for EB involves mainly carbohydrate metabolism. RB utilizes energy -rich compounds to generate ATP in nucleic acid metabolism. Validation data for the modeling include proteomics experiments (model basis) as well as qRT-PCR confirmation of selected metabolic enzyme mRNA expression differences. The metabolic modeling is made fully available here. Its detailed insights and models on Ct metabolic adaptations during infection are a useful modeling basis for future studies.
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Affiliation(s)
- Manli Yang
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Karthika Rajeeve
- Department of Microbiology, Biocenter, University of Würzburg, Würzburg, Germany.,Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Thomas Rudel
- Department of Microbiology, Biocenter, University of Würzburg, Würzburg, Germany
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany.,European Molecular Biology Laboratory, Computational Biology and Structures Program, Heidelberg, Germany
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200
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Dai Z, Yang S, Xu L, Hu H, Liao K, Wang J, Wang Q, Gao S, Li B, Lai L. Identification of Cancer-associated metabolic vulnerabilities by modeling multi-objective optimality in metabolism. Cell Commun Signal 2019; 17:124. [PMID: 31601242 PMCID: PMC6785927 DOI: 10.1186/s12964-019-0439-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 09/10/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Cancer cells undergo global reprogramming of cellular metabolism to satisfy demands of energy and biomass during proliferation and metastasis. Computational modeling of genome-scale metabolic models is an effective approach for designing new therapeutics targeting dysregulated cancer metabolism by identifying metabolic enzymes crucial for satisfying metabolic goals of cancer cells, but nearly all previous studies neglect the existence of metabolic demands other than biomass synthesis and trade-offs between these contradicting metabolic demands. It is thus necessary to develop computational models covering multiple metabolic objectives to study cancer metabolism and identify novel metabolic targets. METHODS We developed a multi-objective optimization model for cancer cell metabolism at genome-scale and an integrated, data-driven workflow for analyzing the Pareto optimality of this model in achieving multiple metabolic goals and identifying metabolic enzymes crucial for maintaining cancer-associated metabolic phenotypes. Using this workflow, we constructed cell line-specific models for a panel of cancer cell lines and identified lists of metabolic targets promoting or suppressing cancer cell proliferation or the Warburg Effect. The targets were then validated using knockdown and over-expression experiments in cultured cancer cell lines. RESULTS We found that the multi-objective optimization model correctly predicted phenotypes including cell growth rates, essentiality of metabolic genes and cell line specific sensitivities to metabolic perturbations. To our surprise, metabolic enzymes promoting proliferation substantially overlapped with those suppressing the Warburg Effect, suggesting that simply targeting the overlapping enzymes may lead to complicated outcomes. We also identified lists of metabolic enzymes important for maintaining rapid proliferation or high Warburg Effect while having little effect on the other. The importance of these enzymes in cancer metabolism predicted by the model was validated by their association with cancer patient survival and knockdown and overexpression experiments in a variety of cancer cell lines. CONCLUSIONS These results confirm this multi-objective optimization model as a novel and effective approach for studying trade-off between metabolic demands of cancer cells and identifying cancer-associated metabolic vulnerabilities, and suggest novel metabolic targets for cancer treatment.
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Affiliation(s)
- Ziwei Dai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Shiyu Yang
- Program of Cancer Research, Affiliated Guangzhou Women and Children's Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Liyan Xu
- Program of Cancer Research, Affiliated Guangzhou Women and Children's Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Hongrong Hu
- Program of Cancer Research, Affiliated Guangzhou Women and Children's Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Kun Liao
- Program of Cancer Research, Affiliated Guangzhou Women and Children's Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Jianghuang Wang
- Program of Cancer Research, Affiliated Guangzhou Women and Children's Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Qian Wang
- Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Shuaishi Gao
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Bo Li
- Program of Cancer Research, Affiliated Guangzhou Women and Children's Hospital, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China. .,Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China. .,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
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