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Fresnais L, Perin O, Riu A, Grall R, Ott A, Fromenty B, Gallardo JC, Stingl M, Frainay C, Jourdan F, Poupin N. A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques. BMC Bioinformatics 2024; 25:234. [PMID: 38992584 PMCID: PMC11238488 DOI: 10.1186/s12859-024-05845-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 06/14/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. RESULTS To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. CONCLUSION The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module ( https://pypi.org/project/manamodeller/ ) and jupyter notebooks ( https://github.com/LouisonF/MANA ).
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Affiliation(s)
- Louison Fresnais
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France.
| | - Olivier Perin
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Anne Riu
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Romain Grall
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Alban Ott
- L'Oréal Research and Innovation, Aulnay-sous-Bois, France
| | - Bernard Fromenty
- Institut NUMECAN (Nutrition Metabolisms and Cancer) UMR_A 1317, UMR_S 1241, INSERM, Univ Rennes, INRAE, 35000, Rennes, France
| | - Jean-Clément Gallardo
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Maximilian Stingl
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Clément Frainay
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Fabien Jourdan
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Nathalie Poupin
- UMR1331 Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
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2
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Noecker C, Turnbaugh PJ. Emerging tools and best practices for studying gut microbial community metabolism. Nat Metab 2024; 6:1225-1236. [PMID: 38961185 DOI: 10.1038/s42255-024-01074-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 05/30/2024] [Indexed: 07/05/2024]
Abstract
The human gut microbiome vastly extends the set of metabolic reactions catalysed by our own cells, with far-reaching consequences for host health and disease. However, our knowledge of gut microbial metabolism relies on a handful of model organisms, limiting our ability to interpret and predict the metabolism of complex microbial communities. In this Perspective, we discuss emerging tools for analysing and modelling the metabolism of gut microorganisms and for linking microorganisms, pathways and metabolites at the ecosystem level, highlighting promising best practices for researchers. Continued progress in this area will also require infrastructure development to facilitate cross-disciplinary synthesis of scientific findings. Collectively, these efforts can enable a broader and deeper understanding of the workings of the gut ecosystem and open new possibilities for microbiome manipulation and therapy.
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Affiliation(s)
- Cecilia Noecker
- Department of Biological Sciences, Minnesota State University, Mankato, Mankato, MN, USA
- Department of Microbiology & Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Peter J Turnbaugh
- Department of Microbiology & Immunology, University of California, San Francisco, San Francisco, CA, USA.
- Chan Zuckerberg Biohub-San Francisco, San Francisco, CA, USA.
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3
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Gong Z, Chen J, Jiao X, Gong H, Pan D, Liu L, Zhang Y, Tan T. Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects. Biotechnol Adv 2024; 72:108319. [PMID: 38280495 DOI: 10.1016/j.biotechadv.2024.108319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/04/2024] [Accepted: 01/18/2024] [Indexed: 01/29/2024]
Abstract
The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.
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Affiliation(s)
- Zhijin Gong
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jiayao Chen
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Xinyu Jiao
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Hao Gong
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Danzi Pan
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Lingli Liu
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yang Zhang
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Tianwei Tan
- National Energy R&D Center for Biorefinery, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Beijing Key Laboratory of Bioprocess, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
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4
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Mandwal A, Bishop SL, Castellanos M, Westlund A, Chaconas G, Davidsen J, Lewis IA. MINNO: An Open Source Software for Refining Metabolic Networks and Investigating Complex Network Activity Using Empirical Metabolomics Data. Anal Chem 2024; 96:3382-3388. [PMID: 38359900 PMCID: PMC10902815 DOI: 10.1021/acs.analchem.3c04501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/18/2023] [Accepted: 01/19/2024] [Indexed: 02/17/2024]
Abstract
Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms. Metabolic network modeling is commonly used to frame metabolomics data in the context of a broader biological system. However, network modeling of poorly characterized nonmodel organisms remains challenging due to gene homology mismatches which lead to network architecture errors. To address this, we developed the Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that uses empirical metabolomics data to refine metabolic networks. MINNO allows users to create, modify, and interact with metabolic pathway visualizations for thousands of organisms, in both individual and multispecies contexts. Herein, we illustrate the use of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme and relapsing fever diseases. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for threeBorrelia species. Using these empirically refined networks, we were able to metabolically differentiate these species via their nucleotide metabolism, which cannot be predicted from genomic networks. Additionally, using MINNO, we identified 18 missing reactions from the KEGG database, of which nine were supported by the primary literature. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study nonmodel organisms.
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Affiliation(s)
- Ayush Mandwal
- Department
of Physics and Astronomy, University of
Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
| | - Stephanie L. Bishop
- Alberta
Centre for Advanced Diagnostics, Department of Biological Sciences, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
| | - Mildred Castellanos
- Department
of Biochemistry and Molecular Biology, Cumming School of Medicine,
Snyder Institute for Chronic Diseases, University
of Calgary, 2500 University
Dr NW, Calgary T2N 1N4, Alberta, Canada
| | - Anika Westlund
- Alberta
Centre for Advanced Diagnostics, Department of Biological Sciences, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
| | - George Chaconas
- Department
of Biochemistry and Molecular Biology, Cumming School of Medicine,
Snyder Institute for Chronic Diseases, University
of Calgary, 2500 University
Dr NW, Calgary T2N 1N4, Alberta, Canada
- Department
of Microbiology, Immunology and Infectious Diseases, Cumming School
of Medicine, Snyder Institute for Chronic Diseases, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
| | - Jörn Davidsen
- Department
of Physics and Astronomy, University of
Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
- Hotchkiss
Brain Institute, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
| | - Ian A. Lewis
- Alberta
Centre for Advanced Diagnostics, Department of Biological Sciences, University of Calgary, 2500 University Dr NW, Calgary T2N 1N4, Alberta, Canada
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5
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Mandwal A, Bishop SL, Castellanos M, Westlund A, Chaconas G, Lewis I, Davidsen J. Metabolic Interactive Nodular Network for Omics (MINNO): Refining and investigating metabolic networks based on empirical metabolomics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.14.548964. [PMID: 37503268 PMCID: PMC10370097 DOI: 10.1101/2023.07.14.548964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms, and metabolic network modeling is commonly used to frame results in the context of a broader homeostatic system. However, network modeling of poorly characterized, non-model organisms remains challenging due to gene homology mismatches. To address this challenge, we developed Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that takes in empirical metabolomics data to refine metabolic networks for both model and unusual organisms. MINNO allows users to create and modify interactive metabolic pathway visualizations for thousands of organisms, in both individual and multi-species contexts. Herein, we demonstrate an important application of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme disease and relapsing fever. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for three Borrelia species. Using these empirically refined networks, we were able to metabolically differentiate these genetically similar species via their nucleotide and nicotinate metabolic pathways that cannot be predicted from genomic networks. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study non-model organisms.
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Affiliation(s)
- Ayush Mandwal
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada
| | - Stephanie L. Bishop
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - Mildred Castellanos
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada
| | - Anika Westlund
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - George Chaconas
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada
| | - Ian Lewis
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - Jörn Davidsen
- Department of Physics and Astronomy, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
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6
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Garcia-Segura ME, Durainayagam BR, Liggi S, Graça G, Jimenez B, Dehghan A, Tzoulaki I, Karaman I, Elliott P, Griffin JL. Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers. J Neurochem 2023; 164:57-76. [PMID: 36326588 PMCID: PMC10107183 DOI: 10.1111/jnc.15719] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 09/14/2022] [Accepted: 09/16/2022] [Indexed: 11/06/2022]
Abstract
Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of the importance of metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late-onset AD. This study analyzed genome-wide association studies (GWAS), transcriptomics, and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi-omics elements in mouse models of AD. We characterized the metabolic modulation in these data sets using gene ontology, transcription factor, pathway, and cell-type enrichment analyses. A predicted lipid signature was extracted from genome-scale metabolic networks (GSMN) and subsequently validated in a lipidomic data set derived from cortical tissue of ABCA-7 null mice, a mouse model of one of the genes associated with late-onset AD. Moreover, a metabolome-wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins, and 58 DE GWAS-derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetic metabolic pathways were significantly over-represented across the AD multi-omics data sets. Microglia and astrocytes were significantly enriched in the lipid-predominant AD-metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modeled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298 AD single nucleotide polymorphisms-metabolite associations, of which 70% corresponded to lipid classes. These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi-omics data into GSMNs to identify metabolic alterations.
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Affiliation(s)
- Monica Emili Garcia-Segura
- Department of Brain Sciences, Imperial College London, London, UK.,Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Brenan R Durainayagam
- Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.,UK-Dementia Research Institute (UK-DRI) at Imperial College London, London, UK
| | - Sonia Liggi
- Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Gonçalo Graça
- Section of Bioinformatics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Beatriz Jimenez
- Section of Bioanalytical Chemistry and the National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Abbas Dehghan
- UK-Dementia Research Institute (UK-DRI) at Imperial College London, London, UK.,Department of Epidemiology and Biostatistics, Imperial College London, London, UK.,MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Ioanna Tzoulaki
- UK-Dementia Research Institute (UK-DRI) at Imperial College London, London, UK.,Department of Epidemiology and Biostatistics, Imperial College London, London, UK.,National Institute for Health Research Imperial Biomedical Research Centre, Imperial College London, UK.,Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Ibrahim Karaman
- Section of Bioinformatics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.,Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Paul Elliott
- UK-Dementia Research Institute (UK-DRI) at Imperial College London, London, UK.,Department of Epidemiology and Biostatistics, Imperial College London, London, UK.,MRC Centre for Environment and Health, Imperial College London, London, UK.,National Institute for Health Research Imperial Biomedical Research Centre, Imperial College London, UK
| | - Julian L Griffin
- Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.,UK-Dementia Research Institute (UK-DRI) at Imperial College London, London, UK.,Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.,The Rowett Institute, University of Aberdeen, Aberdeen, Scotland
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7
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Garcia-Segura ME, Durainayagam BR, Liggi S, Graça G, Jimenez B, Dehghan A, Tzoulaki I, Karaman I, Elliott P, Griffin JL. Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer's disease mouse model and risk loci carriers. J Neurochem 2023. [PMID: 36326588 DOI: 10.1101/2021.05.10.21255052v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder. Despite increasing evidence of the importance of metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late-onset AD. This study analyzed genome-wide association studies (GWAS), transcriptomics, and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi-omics elements in mouse models of AD. We characterized the metabolic modulation in these data sets using gene ontology, transcription factor, pathway, and cell-type enrichment analyses. A predicted lipid signature was extracted from genome-scale metabolic networks (GSMN) and subsequently validated in a lipidomic data set derived from cortical tissue of ABCA-7 null mice, a mouse model of one of the genes associated with late-onset AD. Moreover, a metabolome-wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins, and 58 DE GWAS-derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetic metabolic pathways were significantly over-represented across the AD multi-omics data sets. Microglia and astrocytes were significantly enriched in the lipid-predominant AD-metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modeled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298 AD single nucleotide polymorphisms-metabolite associations, of which 70% corresponded to lipid classes. These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi-omics data into GSMNs to identify metabolic alterations.
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Affiliation(s)
- Monica Emili Garcia-Segura
- Department of Brain Sciences, Imperial College London, London, UK
- Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Brenan R Durainayagam
- Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- UK-Dementia Research Institute (UK-DRI) at Imperial College London, London, UK
| | - Sonia Liggi
- Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Gonçalo Graça
- Section of Bioinformatics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Beatriz Jimenez
- Section of Bioanalytical Chemistry and the National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Abbas Dehghan
- UK-Dementia Research Institute (UK-DRI) at Imperial College London, London, UK
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Ioanna Tzoulaki
- UK-Dementia Research Institute (UK-DRI) at Imperial College London, London, UK
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, Imperial College London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Ibrahim Karaman
- Section of Bioinformatics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Paul Elliott
- UK-Dementia Research Institute (UK-DRI) at Imperial College London, London, UK
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- MRC Centre for Environment and Health, Imperial College London, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, Imperial College London, UK
| | - Julian L Griffin
- Section of Biomolecular Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- UK-Dementia Research Institute (UK-DRI) at Imperial College London, London, UK
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
- The Rowett Institute, University of Aberdeen, Aberdeen, Scotland
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8
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Banimfreg BH, Shamayleh A, Alshraideh H. Survey for Computer-Aided Tools and Databases in Metabolomics. Metabolites 2022; 12:metabo12101002. [PMID: 36295904 PMCID: PMC9610953 DOI: 10.3390/metabo12101002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/08/2022] [Accepted: 10/12/2022] [Indexed: 11/14/2022] Open
Abstract
Metabolomics has advanced from innovation and functional genomics tools and is currently a basis in the big data-led precision medicine era. Metabolomics is promising in the pharmaceutical field and clinical research. However, due to the complexity and high throughput data generated from such experiments, data mining and analysis are significant challenges for researchers in the field. Therefore, several efforts were made to develop a complete workflow that helps researchers analyze data. This paper introduces a review of the state-of-the-art computer-aided tools and databases in metabolomics established in recent years. The paper provides computational tools and resources based on functionality and accessibility and provides hyperlinks to web pages to download or use. This review aims to present the latest computer-aided tools, databases, and resources to the metabolomics community in one place.
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9
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Guignard D, Canlet C, Tremblay-Franco M, Chaillou E, Gautier R, Gayrard V, Picard-Hagen N, Schroeder H, Jourdan F, Zalko D, Viguié C, Cabaton NJ. Gestational exposure to bisphenol A induces region-specific changes in brain metabolomic fingerprints in sheep. ENVIRONMENT INTERNATIONAL 2022; 165:107336. [PMID: 35700571 DOI: 10.1016/j.envint.2022.107336] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 06/02/2022] [Accepted: 06/02/2022] [Indexed: 06/15/2023]
Abstract
Fetal brain development depends on maternofetal thyroid function. In rodents and sheep, perinatal BPA exposure is associated with maternal and/or fetal thyroid disruption and alterations in central nervous system development as demonstrated by metabolic modulations in the encephala of mice. We hypothesized that a gestational exposure to a low dose of BPA affects maternofetal thyroid function and fetal brain development in a region-specific manner. Pregnant ewes, a relevant model for human thyroid and brain development, were exposed to BPA (5 µg/kg bw/d, sc). The thyroid status of ewes during gestation and term fetuses at delivery was monitored. Fetal brain development was assessed by metabolic fingerprints at birth in 10 areas followed by metabolic network-based analysis. BPA treatment was associated with a significant time-dependent decrease in maternal TT4 serum concentrations. For 8 fetal brain regions, statistical models allowed discriminating BPA-treated from control lambs. Metabolic network computational analysis revealed that prenatal exposure to BPA modulated several metabolic pathways, in particular excitatory and inhibitory amino-acid, cholinergic, energy and lipid homeostasis pathways. These pathways might contribute to BPA-related neurobehavioral and cognitive disorders. Discrimination was particularly clear for the dorsal hippocampus, the cerebellar vermis, the dorsal hypothalamus, the caudate nucleus and the lateral part of the frontal cortex. Compared with previous results in rodents, the use of a larger animal model allowed to examine specific brain areas, and generate evidence of the distinct region-specific effects of fetal BPA exposure on the brain metabolome. These modifications occur concomitantly to subtle maternal thyroid function alteration. The functional link between such moderate thyroid changes and fetal brain metabolomic fingerprints remains to be determined as well as the potential implication of other modes of action triggered by BPA such as estrogenic ones. Our results pave the ways for new scientific strategies aiming at linking environmental endocrine disruption and altered neurodevelopment.
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Affiliation(s)
- Davy Guignard
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Cécile Canlet
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France; Metatoul-AXIOM Platform, National Infrastructure for Metabolomics and Fluxomics: MetaboHUB, Toxalim, INRAE, Toulouse, France
| | - Marie Tremblay-Franco
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France; Metatoul-AXIOM Platform, National Infrastructure for Metabolomics and Fluxomics: MetaboHUB, Toxalim, INRAE, Toulouse, France
| | - Elodie Chaillou
- CNRS, IFCE, INRAE, Université de Tours, PRC, Nouzilly, France
| | - Roselyne Gautier
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France; Metatoul-AXIOM Platform, National Infrastructure for Metabolomics and Fluxomics: MetaboHUB, Toxalim, INRAE, Toulouse, France
| | - Véronique Gayrard
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nicole Picard-Hagen
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Henri Schroeder
- Université de Lorraine, INSERM U1256, NGERE, Nutrition Génétique et Exposition aux Risques Environnementaux, 54000 Nancy, France
| | - Fabien Jourdan
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Daniel Zalko
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Catherine Viguié
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
| | - Nicolas J Cabaton
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
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10
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Capela J, Lagoa D, Rodrigues R, Cunha E, Cruz F, Barbosa A, Bastos J, Lima D, Ferreira EC, Rocha M, Dias O. merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models. Nucleic Acids Res 2022; 50:6052-6066. [PMID: 35694833 PMCID: PMC9226533 DOI: 10.1093/nar/gkac459] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/10/2022] [Indexed: 01/18/2023] Open
Abstract
Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms' metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models' reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.
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Affiliation(s)
- João Capela
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Davide Lagoa
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Ruben Rodrigues
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Emanuel Cunha
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Fernando Cruz
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Ana Barbosa
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - José Bastos
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Diogo Lima
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Eugénio C Ferreira
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Oscar Dias
- Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal.,LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
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11
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Yanibada B, Hohenester U, Pétéra M, Canlet C, Durand S, Jourdan F, Ferlay A, Morgavi DP, Boudra H. Milk metabolome reveals variations on enteric methane emissions from dairy cows fed a specific inhibitor of the methanogenesis pathway. J Dairy Sci 2021; 104:12553-12566. [PMID: 34531049 DOI: 10.3168/jds.2021-20477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 07/26/2021] [Indexed: 11/19/2022]
Abstract
Metabolome profiling in biological fluids is an interesting approach for exploring markers of methane emissions in ruminants. In this study, a multiplatform metabolomics approach was used for investigating changes in milk metabolic profiles related to methanogenesis in dairy cows. For this purpose, 25 primiparous Holstein cows at similar lactation stage were fed the same diet supplemented with (treated, n = 12) or without (control, n = 13) a specific antimethanogenic additive that reduced enteric methane production by 23% with no changes in intake, milk production, and health status. The study lasted 6 wk, with sampling and measures performed in wk 5 and 6. Milk samples were analyzed using 4 complementary analytical methods, including 2 untargeted (nuclear magnetic resonance and liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer) and 2 targeted (liquid chromatography-tandem mass spectrometry and gas chromatography coupled to a flame ionization detector) approaches. After filtration, variable selection and normalization data from each analytical platform were then analyzed using multivariate orthogonal partial least square discriminant analysis. All 4 analytical methods were able to differentiate cows from treated and control groups. Overall, 38 discriminant metabolites were identified, which affected 10 metabolic pathways including methane metabolism. Some of these metabolites such as dimethylsulfoxide, dimethylsulfone, and citramalic acid, detected by nuclear magnetic resonance or liquid chromatography-mass spectrometry methods, originated from the rumen microbiota or had a microbial-host animal co-metabolism that could be associated with methanogenesis. Also, discriminant milk fatty acids detected by targeted gas chromatography were mostly of ruminal microbial origin. Other metabolites and metabolic pathways significantly affected were associated with AA metabolism. These findings provide new insight on the potential role of milk metabolites as indicators of enteric methane modifications in dairy cows.
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Affiliation(s)
- Bénédict Yanibada
- INRAE, Université Clermont Auvergne, Vetagro Sup, UMRH, 63122, Saint-Genès-Champanelle, France
| | - Ulli Hohenester
- INRAE, Université Clermont Auvergne, Vetagro Sup, UMRH, 63122, Saint-Genès-Champanelle, France
| | - Mélanie Pétéra
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, F-63000 Clermont-Ferrand, France
| | - Cécile Canlet
- Toxalim, Research Centre in Food Toxicology, Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, F-31027, Toulouse, France; Axiom Platform, MetaToul-MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, F-31027, Toulouse, France
| | - Stéphanie Durand
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, F-63000 Clermont-Ferrand, France
| | - Fabien Jourdan
- Toxalim, Research Centre in Food Toxicology, Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, F-31027, Toulouse, France
| | - Anne Ferlay
- INRAE, Université Clermont Auvergne, Vetagro Sup, UMRH, 63122, Saint-Genès-Champanelle, France
| | - Diego P Morgavi
- INRAE, Université Clermont Auvergne, Vetagro Sup, UMRH, 63122, Saint-Genès-Champanelle, France.
| | - Hamid Boudra
- INRAE, Université Clermont Auvergne, Vetagro Sup, UMRH, 63122, Saint-Genès-Champanelle, France.
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12
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Chiappino-Pepe A, Pandey V, Billker O. Genome reconstructions of metabolism of Plasmodium RBC and liver stages. Curr Opin Microbiol 2021; 63:259-266. [PMID: 34461385 DOI: 10.1016/j.mib.2021.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 08/09/2021] [Accepted: 08/15/2021] [Indexed: 11/18/2022]
Abstract
Genome scale metabolic models (GEMs) offer a powerful means of integrating genome and biochemical information on an organism to make testable predictions of metabolic functions at different conditions and to systematically predict essential genes that may be targeted by drugs. This review describes how Plasmodium GEMs have become increasingly more accurate through the integration of omics and experimental genetic data. We also discuss how GEMs contribute to our increasing understanding of how Plasmodium metabolism is reprogrammed between life cycle stages.
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Affiliation(s)
- Anush Chiappino-Pepe
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Wyss Institute for Biologically Inspired Engineering, Boston, MA 02115, USA; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Vikash Pandey
- Department of Molecular Biology, Umeå University, Umeå, 90187, Sweden; The Laboratory for Molecular Infection Medicine Sweden, Umeå, 90187, Sweden
| | - Oliver Billker
- Department of Molecular Biology, Umeå University, Umeå, 90187, Sweden; The Laboratory for Molecular Infection Medicine Sweden, Umeå, 90187, Sweden
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13
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Petrovs R, Stalidzans E, Pentjuss A. IMFLer: A Web Application for Interactive Metabolic Flux Analysis and Visualization. J Comput Biol 2021; 28:1021-1032. [PMID: 34424732 DOI: 10.1089/cmb.2021.0056] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Increasing genome-wide data in biological sciences and medicine has contributed to the development of a variety of visualization tools. Several automatic, semiautomatic, and manual visualization tools have already been developed. Some even have integrated flux balance analysis (FBA), but in most cases, it depends on separately installed third party software that is proprietary and does not allow customization of its functionality and has many restrictions for easy data distribution and analysis. In this study, we present an interactive metabolic flux analyzer and visualizer (IMFLer)-a static single-page web application that enables the reading and management of metabolic model layout maps, as well as immediate visualization of results from both FBA and flux variability analysis (FVA). IMFLer uses the Escher Builder tool to load, show, edit, and save metabolic pathway maps. This makes IMFLer an attractive and easily applicable tool with a user-friendly interface. Moreover, it allows to faster interpret results from FBA and FVA and improves data interoperability by using a standardized file format for the genome-scale metabolic model. IMFLer is a fully open-source tool that enables the rapid visualization and interpretation of the results of FBA and FVA with no time setup and no programming skills required, available at https://lv-csbg.github.io/IMFLer/.
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Affiliation(s)
- Rudolfs Petrovs
- Computational Systems Biology Group, Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Egils Stalidzans
- Computational Systems Biology Group, Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia.,Biosystems Group, Department of Computer Systems, Latvia University of Life Sciences and Technologies, Jelgava, Latvia
| | - Agris Pentjuss
- Computational Systems Biology Group, Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
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14
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Abstract
Oxidative stress and reactive oxygen species (ROS) are central to many physiological and pathophysiological processes. However, due to multiple technical challenges, it is hard to capture a comprehensive readout of the cell, involving both biochemical and functional status. We addressed this problem by developing a fully parallelized workflow for metabolomics (providing absolute quantities for > 100 metabolites including TCA cycle, pentose phosphate pathway, purine metabolism, glutathione metabolism, cysteine and methionine metabolism, glycolysis and gluconeogenesis) and live cell imaging microscopy. The correlative imaging strategy was applied to study morphological and metabolic adaptation of cancer cells upon short-term hydrogen peroxide (H2O2) exposure in vitro. The combination provided rich metabolic information at the endpoint of exposure together with imaging of mitochondrial effects. As a response, superoxide concentrations were elevated with a strong mitochondrial localization, and multi-parametric image analysis revealed a shift towards fragmentation. In line with this, metabolism reflected both the impaired mitochondrial function and shifts to support the first-line cellular defense and compensate for energy loss. The presented workflow combining high-end technologies demonstrates the applicability for the study of short-term oxidative stress, but it can be suitable for the in-depth study of various short-term oxidative and other cellular stress-related phenomena.
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15
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Comte B, Monnerie S, Brandolini-Bunlon M, Canlet C, Castelli F, Chu-Van E, Colsch B, Fenaille F, Joly C, Jourdan F, Lenuzza N, Lyan B, Martin JF, Migné C, Morais JA, Pétéra M, Poupin N, Vinson F, Thevenot E, Junot C, Gaudreau P, Pujos-Guillot E. Multiplatform metabolomics for an integrative exploration of metabolic syndrome in older men. EBioMedicine 2021; 69:103440. [PMID: 34161887 PMCID: PMC8237302 DOI: 10.1016/j.ebiom.2021.103440] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 05/20/2021] [Accepted: 06/01/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Metabolic syndrome (MetS), a cluster of factors associated with risks of developing cardiovascular diseases, is a public health concern because of its growing prevalence. Considering the combination of concomitant components, their development and severity, MetS phenotypes are largely heterogeneous, inducing disparity in diagnosis. METHODS A case/control study was designed within the NuAge longitudinal cohort on aging. From a 3-year follow-up of 123 stable individuals, we present a deep phenotyping approach based on a multiplatform metabolomics and lipidomics untargeted strategy to better characterize metabolic perturbations in MetS and define a comprehensive MetS signature stable over time in older men. FINDINGS We characterize significant changes associated with MetS, involving modulations of 476 metabolites and lipids, and representing 16% of the detected serum metabolome/lipidome. These results revealed a systemic alteration of metabolism, involving various metabolic pathways (urea cycle, amino-acid, sphingo- and glycerophospholipid, and sugar metabolisms…) not only intrinsically interrelated, but also reflecting environmental factors (nutrition, microbiota, physical activity…). INTERPRETATION These findings allowed identifying a comprehensive MetS signature, reduced to 26 metabolites for future translation into clinical applications for better diagnosing MetS. FUNDING The NuAge Study was supported by a research grant from the Canadian Institutes of Health Research (CIHR; MOP-62842). The actual NuAge Database and Biobank, containing data and biologic samples of 1,753 NuAge participants (from the initial 1,793 participants), are supported by the Fonds de recherche du Québec (FRQ; 2020-VICO-279753), the Quebec Network for Research on Aging, a thematic network funded by the Fonds de Recherche du Québec - Santé (FRQS) and by the Merck-Frost Chair funded by La Fondation de l'Université de Sherbrooke. All metabolomics and lipidomics analyses were funded and performed within the metaboHUB French infrastructure (ANR-INBS-0010). All authors had full access to the full data in the study and accept responsibility to submit for publication.
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Affiliation(s)
- Blandine Comte
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Stéphanie Monnerie
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Marion Brandolini-Bunlon
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Cécile Canlet
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, Toulouse 31300, France
| | - Florence Castelli
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - Emeline Chu-Van
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - Benoit Colsch
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - François Fenaille
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - Charlotte Joly
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Fabien Jourdan
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, Toulouse 31300, France
| | - Natacha Lenuzza
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - Bernard Lyan
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Jean-François Martin
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, Toulouse 31300, France
| | - Carole Migné
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - José A Morais
- Division de Gériatrie, McGill University, Center de recherche du Center universitaire de santé McGill, Montreal, Canada
| | - Mélanie Pétéra
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Nathalie Poupin
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, Toulouse 31300, France
| | - Florence Vinson
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, Toulouse 31300, France
| | - Etienne Thevenot
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - Christophe Junot
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (DMTS), MetaboHUB, F-91191 Gif sur Yvette, France
| | - Pierrette Gaudreau
- Center de Recherche du Center hospitalier de l'Université de Montréal, Montreal, Canada; Département de médecine, Université de Montréal, Montreal, Canada
| | - Estelle Pujos-Guillot
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France.
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16
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Boccard J, Schvartz D, Codesido S, Hanafi M, Gagnebin Y, Ponte B, Jourdan F, Rudaz S. Gaining Insights Into Metabolic Networks Using Chemometrics and Bioinformatics: Chronic Kidney Disease as a Clinical Model. Front Mol Biosci 2021; 8:682559. [PMID: 34055893 PMCID: PMC8163225 DOI: 10.3389/fmolb.2021.682559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 04/19/2021] [Indexed: 01/21/2023] Open
Abstract
Because of its ability to generate biological hypotheses, metabolomics offers an innovative and promising approach in many fields, including clinical research. However, collecting specimens in this setting can be difficult to standardize, especially when groups of patients with different degrees of disease severity are considered. In addition, despite major technological advances, it remains challenging to measure all the compounds defining the metabolic network of a biological system. In this context, the characterization of samples based on several analytical setups is now recognized as an efficient strategy to improve the coverage of metabolic complexity. For this purpose, chemometrics proposes efficient methods to reduce the dimensionality of these complex datasets spread over several matrices, allowing the integration of different sources or structures of metabolic information. Bioinformatics databases and query tools designed to describe and explore metabolic network models offer extremely useful solutions for the contextualization of potential biomarker subsets, enabling mechanistic hypotheses to be considered rather than simple associations. In this study, network principal component analysis was used to investigate samples collected from three cohorts of patients including multiple stages of chronic kidney disease. Metabolic profiles were measured using a combination of four analytical setups involving different separation modes in liquid chromatography coupled to high resolution mass spectrometry. Based on the chemometric model, specific patterns of metabolites, such as N-acetyl amino acids, could be associated with the different subgroups of patients. Further investigation of the metabolic signatures carried out using genome-scale network modeling confirmed both tryptophan metabolism and nucleotide interconversion as relevant pathways potentially associated with disease severity. Metabolic modules composed of chemically adjacent or close compounds of biological relevance were further investigated using carbon transfer reaction paths. Overall, the proposed integrative data analysis strategy allowed deeper insights into the metabolic routes associated with different groups of patients to be gained. Because of their complementary role in the knowledge discovery process, the association of chemometrics and bioinformatics in a common workflow is therefore shown as an efficient methodology to gain meaningful insights in a clinical context.
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Affiliation(s)
- Julien Boccard
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
| | - Domitille Schvartz
- Translational Biomarker Group, Department of Internal Medicine Specialties, University of Geneva, Geneva, Switzerland
| | - Santiago Codesido
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
| | - Mohamed Hanafi
- Unité Statistique, Sensométrie et Chimiométrie, Nantes, France
| | - Yoric Gagnebin
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
| | - Belén Ponte
- Service of Nephrology and Hypertension, Department of Medicine, Geneva University Hospitals (HUG), Geneva, Switzerland
| | - Fabien Jourdan
- Toxalim, Research Centre in Food Toxicology, Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Serge Rudaz
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland
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17
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Stuani L, Sabatier M, Saland E, Cognet G, Poupin N, Bosc C, Castelli FA, Gales L, Turtoi E, Montersino C, Farge T, Boet E, Broin N, Larrue C, Baran N, Cissé MY, Conti M, Loric S, Kaoma T, Hucteau A, Zavoriti A, Sahal A, Mouchel PL, Gotanègre M, Cassan C, Fernando L, Wang F, Hosseini M, Chu-Van E, Le Cam L, Carroll M, Selak MA, Vey N, Castellano R, Fenaille F, Turtoi A, Cazals G, Bories P, Gibon Y, Nicolay B, Ronseaux S, Marszalek JR, Takahashi K, DiNardo CD, Konopleva M, Pancaldi V, Collette Y, Bellvert F, Jourdan F, Linares LK, Récher C, Portais JC, Sarry JE. Mitochondrial metabolism supports resistance to IDH mutant inhibitors in acute myeloid leukemia. J Exp Med 2021; 218:e20200924. [PMID: 33760042 PMCID: PMC7995203 DOI: 10.1084/jem.20200924] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 11/25/2020] [Accepted: 01/11/2021] [Indexed: 12/17/2022] Open
Abstract
Mutations in IDH induce epigenetic and transcriptional reprogramming, differentiation bias, and susceptibility to mitochondrial inhibitors in cancer cells. Here, we first show that cell lines, PDXs, and patients with acute myeloid leukemia (AML) harboring an IDH mutation displayed an enhanced mitochondrial oxidative metabolism. Along with an increase in TCA cycle intermediates, this AML-specific metabolic behavior mechanistically occurred through the increase in electron transport chain complex I activity, mitochondrial respiration, and methylation-driven CEBPα-induced fatty acid β-oxidation of IDH1 mutant cells. While IDH1 mutant inhibitor reduced 2-HG oncometabolite and CEBPα methylation, it failed to reverse FAO and OxPHOS. These mitochondrial activities were maintained through the inhibition of Akt and enhanced activation of peroxisome proliferator-activated receptor-γ coactivator-1 PGC1α upon IDH1 mutant inhibitor. Accordingly, OxPHOS inhibitors improved anti-AML efficacy of IDH mutant inhibitors in vivo. This work provides a scientific rationale for combinatory mitochondrial-targeted therapies to treat IDH mutant AML patients, especially those unresponsive to or relapsing from IDH mutant inhibitors.
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MESH Headings
- Acute Disease
- Aminopyridines/pharmacology
- Animals
- Cell Line, Tumor
- Doxycycline/pharmacology
- Drug Resistance, Neoplasm/drug effects
- Drug Resistance, Neoplasm/genetics
- Enzyme Inhibitors/pharmacology
- Epigenesis, Genetic/drug effects
- Glycine/analogs & derivatives
- Glycine/pharmacology
- HL-60 Cells
- Humans
- Isocitrate Dehydrogenase/antagonists & inhibitors
- Isocitrate Dehydrogenase/genetics
- Isocitrate Dehydrogenase/metabolism
- Isoenzymes/antagonists & inhibitors
- Isoenzymes/genetics
- Isoenzymes/metabolism
- Leukemia, Myeloid/drug therapy
- Leukemia, Myeloid/genetics
- Leukemia, Myeloid/metabolism
- Mice, Inbred NOD
- Mice, Knockout
- Mice, SCID
- Mitochondria/drug effects
- Mitochondria/genetics
- Mitochondria/metabolism
- Mutation
- Oxadiazoles/pharmacology
- Oxidative Phosphorylation/drug effects
- Piperidines/pharmacology
- Pyridines/pharmacology
- Triazines/pharmacology
- Xenograft Model Antitumor Assays/methods
- Mice
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Affiliation(s)
- Lucille Stuani
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Marie Sabatier
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Estelle Saland
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Guillaume Cognet
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Nathalie Poupin
- UMR1331 Toxalim, Université de Toulouse, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, Ecole Nationale Vétérinaire de Toulouse, INP-Purpan, Université Paul Sabatier, Toulouse, France
| | - Claudie Bosc
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Florence A. Castelli
- CEA/DSV/iBiTec-S/SPI, Laboratoire d’Etude du Métabolisme des Médicaments, MetaboHUB-Paris, Gif-sur-Yvette, France
| | - Lara Gales
- Toulouse Biotechnology Institute, Université de Toulouse, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Institut National des sciences appliquées, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Evgenia Turtoi
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
- Montpellier Alliance for Metabolomics and Metabolism Analysis, Platform for Translational Oncometabolomics, Biocampus, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Montpellier, France
| | - Camille Montersino
- Aix-Marseille University, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Institut Paoli-Calmettes, Centre de Recherches en Cancérologie de Marseille, Marseille, France
| | - Thomas Farge
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Emeline Boet
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Nicolas Broin
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Clément Larrue
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Natalia Baran
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Madi Y. Cissé
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
| | - Marc Conti
- Institut National de la Santé et de la Recherché Médicale U938, Hôpital St Antoine, Paris, France
- Integracell, Longjumeau, France
| | - Sylvain Loric
- Institut National de la Santé et de la Recherché Médicale U938, Hôpital St Antoine, Paris, France
| | - Tony Kaoma
- Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Alexis Hucteau
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Aliki Zavoriti
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Ambrine Sahal
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Pierre-Luc Mouchel
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
- Service d'Hématologie, Institut Universitaire du Cancer de Toulouse-Oncopole, CHU de Toulouse, Toulouse, France
| | - Mathilde Gotanègre
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Cédric Cassan
- UMR1332 Biologie du Fruit et Pathologie, Plateforme Métabolome Bordeaux, Institut National de la Recherche Agronomique, Université de Bordeaux, Villenave d'Ornon, France
| | - Laurent Fernando
- UMR1331 Toxalim, Université de Toulouse, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, Ecole Nationale Vétérinaire de Toulouse, INP-Purpan, Université Paul Sabatier, Toulouse, France
| | - Feng Wang
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Mohsen Hosseini
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Emeline Chu-Van
- CEA/DSV/iBiTec-S/SPI, Laboratoire d’Etude du Métabolisme des Médicaments, MetaboHUB-Paris, Gif-sur-Yvette, France
| | - Laurent Le Cam
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
| | - Martin Carroll
- Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Mary A. Selak
- Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Norbert Vey
- Aix-Marseille University, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Institut Paoli-Calmettes, Centre de Recherches en Cancérologie de Marseille, Marseille, France
| | - Rémy Castellano
- Aix-Marseille University, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Institut Paoli-Calmettes, Centre de Recherches en Cancérologie de Marseille, Marseille, France
| | - François Fenaille
- CEA/DSV/iBiTec-S/SPI, Laboratoire d’Etude du Métabolisme des Médicaments, MetaboHUB-Paris, Gif-sur-Yvette, France
| | - Andrei Turtoi
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
| | - Guillaume Cazals
- Laboratoire de Mesures Physiques, Université de Montpellier, Montpellier, France
| | - Pierre Bories
- Réseau Régional de Cancérologie Onco-Occitanie, Toulouse, France
| | - Yves Gibon
- UMR1332 Biologie du Fruit et Pathologie, Plateforme Métabolome Bordeaux, Institut National de la Recherche Agronomique, Université de Bordeaux, Villenave d'Ornon, France
| | | | | | - Joseph R. Marszalek
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Koichi Takahashi
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Courtney D. DiNardo
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Marina Konopleva
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Véra Pancaldi
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Yves Collette
- Aix-Marseille University, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Institut Paoli-Calmettes, Centre de Recherches en Cancérologie de Marseille, Marseille, France
| | - Floriant Bellvert
- Toulouse Biotechnology Institute, Université de Toulouse, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Institut National des sciences appliquées, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Fabien Jourdan
- UMR1331 Toxalim, Université de Toulouse, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, Ecole Nationale Vétérinaire de Toulouse, INP-Purpan, Université Paul Sabatier, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Laetitia K. Linares
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
| | - Christian Récher
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
- Service d'Hématologie, Institut Universitaire du Cancer de Toulouse-Oncopole, CHU de Toulouse, Toulouse, France
| | - Jean-Charles Portais
- Toulouse Biotechnology Institute, Université de Toulouse, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Institut National des sciences appliquées, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
- STROMALab, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale U1031, EFS, INP-ENVT, UPS, Toulouse, France
| | - Jean-Emmanuel Sarry
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
- Centre Hospitalier Universitaire de Toulouse, Toulouse, France
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18
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Bergès C, Cahoreau E, Millard P, Enjalbert B, Dinclaux M, Heuillet M, Kulyk H, Gales L, Butin N, Chazalviel M, Palama T, Guionnet M, Sokol S, Peyriga L, Bellvert F, Heux S, Portais JC. Exploring the Glucose Fluxotype of the E. coli y-ome Using High-Resolution Fluxomics. Metabolites 2021; 11:metabo11050271. [PMID: 33926117 PMCID: PMC8145925 DOI: 10.3390/metabo11050271] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/16/2021] [Accepted: 04/23/2021] [Indexed: 01/26/2023] Open
Abstract
We have developed a robust workflow to measure high-resolution fluxotypes (metabolic flux phenotypes) for large strain libraries under fully controlled growth conditions. This was achieved by optimizing and automating the whole high-throughput fluxomics process and integrating all relevant software tools. This workflow allowed us to obtain highly detailed maps of carbon fluxes in the central carbon metabolism in a fully automated manner. It was applied to investigate the glucose fluxotypes of 180 Escherichia coli strains deleted for y-genes. Since the products of these y-genes potentially play a role in a variety of metabolic processes, the experiments were designed to be agnostic as to their potential metabolic impact. The obtained data highlight the robustness of E. coli’s central metabolism to y-gene deletion. For two y-genes, deletion resulted in significant changes in carbon and energy fluxes, demonstrating the involvement of the corresponding y-gene products in metabolic function or regulation. This work also introduces novel metrics to measure the actual scope and quality of high-throughput fluxomics investigations.
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Affiliation(s)
- Cécilia Bergès
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Edern Cahoreau
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Pierre Millard
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
| | - Brice Enjalbert
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
| | - Mickael Dinclaux
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
| | - Maud Heuillet
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Hanna Kulyk
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Lara Gales
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Noémie Butin
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
- RESTORE, Université de Toulouse, Inserm U1031, CNRS 5070, UPS, EFS, 31100 Toulouse, France
| | - Maxime Chazalviel
- Toxalim (Research Centre in Food Toxicology), UMR1331, Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, 31300 Toulouse, France;
| | - Tony Palama
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Matthieu Guionnet
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Sergueï Sokol
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
| | - Lindsay Peyriga
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Floriant Bellvert
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
| | - Stéphanie Heux
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
| | - Jean-Charles Portais
- Toulouse Biotechnology Institute (TBI), Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France; (C.B.); (E.C.); (P.M.); (B.E.); (M.D.); (M.H.); (H.K.); (L.G.); (N.B.); (T.P.); (M.G.); (S.S.); (L.P.); (F.B.); (S.H.)
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077 Toulouse, France
- RESTORE, Université de Toulouse, Inserm U1031, CNRS 5070, UPS, EFS, 31100 Toulouse, France
- Correspondence:
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19
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Paley S, Karp PD. The BioCyc Metabolic Network Explorer. BMC Bioinformatics 2021; 22:208. [PMID: 33882841 PMCID: PMC8060992 DOI: 10.1186/s12859-021-04132-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/14/2021] [Indexed: 11/30/2022] Open
Abstract
Background The Metabolic Network Explorer is a new addition to the BioCyc.org website and the Pathway Tools software suite that supports the interactive exploration of metabolic networks. Any metabolic network visualization tool must by necessity show only a subset of all possible metabolite connections, or the results will be visually overwhelming. Existing tools, even those that purport to show an organism’s full metabolic network, limit the set of displayed connections based on predefined pathways or other preselected criteria. We sought instead to provide a tool that would give the user dynamic control over which connections to follow. Results The Metabolic Network Explorer is an easy-to-use, web-based software tool that allows the user to specify a starting metabolite of interest and interactively explore its immediate metabolic neighborhood in either or both directions to any desired depth, letting the user select from the full set of connected reactions. Although, as for other tools, only a small portion of the metabolic network is visible at a time, that portion is selected by the user, based on the full reaction complement, and it is easy to switch among alternate paths of interest. The display is intuitive, customizable, and provides copious links to more detailed information pages. Conclusions The Metabolic Network Explorer fills a gap in the set of metabolic network visualization tools and complements other modes of exploration. Its primary strengths are its ease of use, diagrams that are intuitive to biologists, and its integration with the broader corpus of data provided by a BioCyc Pathway/Genome Database.
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Affiliation(s)
- Suzanne Paley
- Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA.
| | - Peter D Karp
- Bioinformatics Research Group, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA
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20
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Kokova D, Verhoeven A, Perina EA, Ivanov VV, Heijink M, Yazdanbakhsh M, Mayboroda OA. Metabolic Homeostasis in Chronic Helminth Infection Is Sustained by Organ-Specific Metabolic Rewiring. ACS Infect Dis 2021; 7:906-916. [PMID: 33764039 PMCID: PMC8154418 DOI: 10.1021/acsinfecdis.1c00026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Indexed: 11/28/2022]
Abstract
Opisthorchiasis, is a hepatobiliary disease caused by flukes of the trematode family Opisthorchiidae. A chronic form of the disease implies a prolonged coexistence of a host and the parasite. The pathological changes inflicted by the worm to the host's hepatobiliary system are well documented. Yet, the response to the infection also triggers a deep remodeling of the host systemic metabolism reaching a new homeostasis and affecting the organs beyond the worm location. Understanding the metabolic alternation in chronic opisthorchiasis, could help us to pinpoint pathways that underlie infection opening possibilities for the development of more selective treatment strategies. Here, with this report we apply an integrative, multicompartment metabolomics analysis, using multiple biofluids, stool samples and tissue extracts to describe metabolic changes in Opisthorchis felineus infected animals at the chronic stage. We show that the shift in lipid metabolism in the serum, a depletion of the amino acids pool, an alteration of the ketogenic pathways in the jejunum and a suppressed metabolic activity of the spleen are the key features of the metabolic host adaptation at the chronic stage of O. felineus infection. We describe this combination of the metabolic changes as a "metabolically mediated immunosuppressive status of organism" which develops during a chronic infection. This status in combination with other factors (e.g., parasite-derived immunomodulators) might increase risk of infection-related malignancy.
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Affiliation(s)
- Daria Kokova
- Department
of Parasitology, Leiden University Medical
Center, Leiden, 2333ZA, The Netherlands
- Laboratory
of Clinical Metabolomics, Tomsk State University, Tomsk 634050, Russian Federation
| | - Aswin Verhoeven
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Leiden, 2333ZA, The Netherlands
| | - Ekaterina A. Perina
- Central
Research Laboratory Siberian State Medical University, Tomsk 634050, Russian Federation
| | - Vladimir V. Ivanov
- Central
Research Laboratory Siberian State Medical University, Tomsk 634050, Russian Federation
| | - Marieke Heijink
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Leiden, 2333ZA, The Netherlands
| | - Maria Yazdanbakhsh
- Department
of Parasitology, Leiden University Medical
Center, Leiden, 2333ZA, The Netherlands
| | - Oleg A. Mayboroda
- Center
for Proteomics and Metabolomics, Leiden
University Medical Center, Leiden, 2333ZA, The Netherlands
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21
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Liu Y, Benitez MG, Chen J, Harrison E, Khusnutdinova AN, Mahadevan R. Opportunities and Challenges for Microbial Synthesis of Fatty Acid-Derived Chemicals (FACs). Front Bioeng Biotechnol 2021; 9:613322. [PMID: 33575251 PMCID: PMC7870715 DOI: 10.3389/fbioe.2021.613322] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 01/04/2021] [Indexed: 11/13/2022] Open
Abstract
Global warming and uneven distribution of fossil fuels worldwide concerns have spurred the development of alternative, renewable, sustainable, and environmentally friendly resources. From an engineering perspective, biosynthesis of fatty acid-derived chemicals (FACs) is an attractive and promising solution to produce chemicals from abundant renewable feedstocks and carbon dioxide in microbial chassis. However, several factors limit the viability of this process. This review first summarizes the types of FACs and their widely applications. Next, we take a deep look into the microbial platform to produce FACs, give an outlook for the platform development. Then we discuss the bottlenecks in metabolic pathways and supply possible solutions correspondingly. Finally, we highlight the most recent advances in the fast-growing model-based strain design for FACs biosynthesis.
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Affiliation(s)
- Yilan Liu
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Mauricio Garcia Benitez
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Jinjin Chen
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Emma Harrison
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Anna N. Khusnutdinova
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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22
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Rampler E, Abiead YE, Schoeny H, Rusz M, Hildebrand F, Fitz V, Koellensperger G. Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput. Anal Chem 2021; 93:519-545. [PMID: 33249827 PMCID: PMC7807424 DOI: 10.1021/acs.analchem.0c04698] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Evelyn Rampler
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
| | - Yasin El Abiead
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Harald Schoeny
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Mate Rusz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Institute of Inorganic
Chemistry, University of Vienna, Währinger Straße 42, 1090 Vienna, Austria
| | - Felina Hildebrand
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Veronika Fitz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
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23
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Hari A, Lobo D. Fluxer: a web application to compute, analyze and visualize genome-scale metabolic flux networks. Nucleic Acids Res 2020; 48:W427-W435. [PMID: 32442279 PMCID: PMC7319574 DOI: 10.1093/nar/gkaa409] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/20/2020] [Accepted: 05/06/2020] [Indexed: 12/19/2022] Open
Abstract
Next-generation sequencing has paved the way for the reconstruction of genome-scale metabolic networks as a powerful tool for understanding metabolic circuits in any organism. However, the visualization and extraction of knowledge from these large networks comprising thousands of reactions and metabolites is a current challenge in need of user-friendly tools. Here we present Fluxer (https://fluxer.umbc.edu), a free and open-access novel web application for the computation and visualization of genome-scale metabolic flux networks. Any genome-scale model based on the Systems Biology Markup Language can be uploaded to the tool, which automatically performs Flux Balance Analysis and computes different flux graphs for visualization and analysis. The major metabolic pathways for biomass growth or for biosynthesis of any metabolite can be interactively knocked-out, analyzed and visualized as a spanning tree, dendrogram or complete graph using different layouts. In addition, Fluxer can compute and visualize the k-shortest metabolic paths between any two metabolites or reactions to identify the main metabolic routes between two compounds of interest. The web application includes >80 whole-genome metabolic reconstructions of diverse organisms from bacteria to human, readily available for exploration. Fluxer enables the efficient analysis and visualization of genome-scale metabolic models toward the discovery of key metabolic pathways.
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Affiliation(s)
- Archana Hari
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, Maryland 21250, USA
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24
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Schultz A, Akbani R. SAMMI: a semi-automated tool for the visualization of metabolic networks. Bioinformatics 2020; 36:2616-2617. [PMID: 31851289 DOI: 10.1093/bioinformatics/btz927] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 11/14/2019] [Accepted: 12/16/2019] [Indexed: 01/26/2023] Open
Abstract
SUMMARY Here we present a browser-based Semi-Automated Metabolic Map Illustrator (SAMMI) for the visualization of metabolic networks. While automated features allow for easy network partitioning, navigation, and node positioning, SAMMI also offers a wide array of manual map editing features. This combination allows for fast, context-specific visualization of metabolic networks as well as the development of standardized, large-scale, visually appealing maps. The implementation of SAMMI with popular constraint-based modeling toolboxes also allows for effortless visualization of simulation results of genome-scale metabolic models. AVAILABILITY AND IMPLEMENTATION SAMMI has been implemented as a standalone web-based tool and as plug-ins for the COBRA and COBRApy toolboxes. SAMMI and its COBRA plugins are available under the GPL 3.0 license and are available along with documentation, tutorials, and source code at www.SammiTool.com. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andre Schultz
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rehan Akbani
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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25
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Inhibition of enteric methanogenesis in dairy cows induces changes in plasma metabolome highlighting metabolic shifts and potential markers of emission. Sci Rep 2020; 10:15591. [PMID: 32973203 PMCID: PMC7515923 DOI: 10.1038/s41598-020-72145-w] [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: 11/25/2019] [Accepted: 08/12/2020] [Indexed: 12/21/2022] Open
Abstract
There is scarce information on whether inhibition of rumen methanogenesis induces metabolic changes on the host ruminant. Understanding these possible changes is important for the acceptance of methane-reducing practices by producers. In this study we explored the changes in plasma profiles associated with the reduction of methane emissions. Plasma samples were collected from lactating primiparous Holstein cows fed the same diet with (Treated, n = 12) or without (Control, n = 13) an anti-methanogenic feed additive for six weeks. Daily methane emissions (CH4, g/d) were reduced by 23% in the Treated group with no changes in milk production, feed intake, body weight, and biochemical indicators of health status. Plasma metabolome analyses were performed using untargeted [nuclear magnetic resonance (NMR) and liquid chromatography-mass spectrometry (LC–MS)] and targeted (LC–MS/MS) approaches. We identified 48 discriminant metabolites. Some metabolites mainly of microbial origin such as dimethylsulfone, formic acid and metabolites containing methylated groups like stachydrine, can be related to rumen methanogenesis and can potentially be used as markers. The other discriminant metabolites are produced by the host or have a mixed microbial-host origin. These metabolites, which increased in treated cows, belong to general pathways of amino acids and energy metabolism suggesting a systemic non-negative effect on the animal.
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26
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Sarathy C, Kutmon M, Lenz M, Adriaens ME, Evelo CT, Arts IC. EFMviz: A COBRA Toolbox extension to visualize Elementary Flux Modes in Genome-Scale Metabolic Models. Metabolites 2020; 10:metabo10020066. [PMID: 32059585 PMCID: PMC7074156 DOI: 10.3390/metabo10020066] [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: 12/31/2019] [Revised: 02/06/2020] [Accepted: 02/07/2020] [Indexed: 12/22/2022] Open
Abstract
Elementary Flux Modes (EFMs) are a tool for constraint-based modeling and metabolic network analysis. However, systematic and automated visualization of EFMs, capable of integrating various data types is still a challenge. In this study, we developed an extension for the widely adopted COBRA Toolbox, EFMviz, for analysis and graphical visualization of EFMs as networks of reactions, metabolites and genes. The analysis workflow offers a platform for EFM visualization to improve EFM interpretability by connecting COBRA toolbox with the network analysis and visualization software Cytoscape. The biological applicability of EFMviz is demonstrated in two use cases on medium (Escherichia coli, iAF1260) and large (human, Recon 2.2) genome-scale metabolic models. EFMviz is open-source and integrated into COBRA Toolbox. The analysis workflows used for the two use cases are detailed in the two tutorials provided with EFMviz along with the data used in this study.
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Affiliation(s)
- Chaitra Sarathy
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
- Correspondence:
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
- Department of Bioinformatics—BiGCaT, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Michael Lenz
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, 55128 Mainz, Germany
- Preventive Cardiology and Preventive Medicine—Center for Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
| | - Michiel E. Adriaens
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Chris T. Evelo
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
- Department of Bioinformatics—BiGCaT, School of Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Ilja C.W. Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands
- Department of Epidemiology, CARIM School for Cardiovascular Diseases, Maastricht University, 6229 ER Maastricht, The Netherlands
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27
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Cottret L, Frainay C, Chazalviel M, Cabanettes F, Gloaguen Y, Camenen E, Merlet B, Heux S, Portais JC, Poupin N, Vinson F, Jourdan F. MetExplore: collaborative edition and exploration of metabolic networks. Nucleic Acids Res 2019; 46:W495-W502. [PMID: 29718355 PMCID: PMC6030842 DOI: 10.1093/nar/gky301] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 04/11/2018] [Indexed: 12/16/2022] Open
Abstract
Metabolism of an organism is composed of hundreds to thousands of interconnected biochemical reactions responding to environmental or genetic constraints. This metabolic network provides a rich knowledge to contextualize omics data and to elaborate hypotheses on metabolic modulations. Nevertheless, performing this kind of integrative analysis is challenging for end users with not sufficiently advanced computer skills since it requires the use of various tools and web servers. MetExplore offers an all-in-one online solution composed of interactive tools for metabolic network curation, network exploration and omics data analysis. In particular, it is possible to curate and annotate metabolic networks in a collaborative environment. The network exploration is also facilitated in MetExplore by a system of interactive tables connected to a powerful network visualization module. Finally, the contextualization of metabolic elements in the network and the calculation of over-representation statistics make it possible to interpret any kind of omics data. MetExplore is a sustainable project maintained since 2010 freely available at https://metexplore.toulouse.inra.fr/metexplore2/.
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Affiliation(s)
- Ludovic Cottret
- LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France
| | | | - Maxime Chazalviel
- INRA, UMR1331, Toxalim, F-31000 Toulouse, France.,MedDay Pharmaceuticals, Paris, France
| | | | - Yoann Gloaguen
- Berlin Institute of Health Metabolomics Platform, 10178 Berlin, Germany.,Core Unit Bioinformatics, Berlin Institute of Health, 10178 Berlin, Germany.,Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany
| | | | | | - Stéphanie Heux
- Université de Toulouse; INSA, UPS, INP; LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France.,INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France.,CNRS, UMR5504, F-31400 Toulouse, France
| | - Jean-Charles Portais
- Université de Toulouse; INSA, UPS, INP; LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, France.,INRA, UMR792, Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, France.,CNRS, UMR5504, F-31400 Toulouse, France
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28
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Ponnaiah M, Gilard F, Gakière B, El-Maarouf-Bouteau H, Bailly C. Regulatory actors and alternative routes for Arabidopsis seed germination are revealed using a pathway-based analysis of transcriptomic datasets. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 99:163-175. [PMID: 30868664 DOI: 10.1111/tpj.14311] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 02/07/2019] [Accepted: 03/05/2019] [Indexed: 06/09/2023]
Abstract
Regulation of seed germination by dormancy relies on a complex network of transcriptional and post-transcriptional modifications during seed imbibition that controls seed adaptive responses to environmental cues. High-throughput technologies have brought significant progress in the understanding of this phenomenon and have led to identify major regulators of seed germination, mostly by studying the behaviour of highly differentially expressed genes. However, the actual models of transcriptome analysis cannot catch additive effects of small variations of gene expression in individual signalling or metabolic pathways, which are also likely to control germination. Therefore, the comprehension of the molecular mechanism regulating germination is still incomplete and to gain knowledge about this process we have developed a pathway-based analysis of transcriptomic Arabidopsis datasets, to identify regulatory actors of seed germination. The method allowed quantifying the level of deregulation of a wide range of pathways in dormant versus non-dormant seeds. Clustering pathway deregulation scores of germinating and dormant seed samples permitted the identification of mechanisms involved in seed germination such as RNA transport or vitamin B6 metabolism, for example. Using this method, which was validated by metabolomics analysis, we also demonstrated that Col and Cvi seeds follow different metabolic routes for completing germination, demonstrating the genetic plasticity of this process. We finally provided an extensive basis of analysed transcriptomic datasets that will allow further identification of mechanisms controlling seed germination.
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Affiliation(s)
- Maharajah Ponnaiah
- Laboratoire de Biologie du Développement, Sorbonne Université, CNRS, F-75005, Paris, France
| | - Françoise Gilard
- Institute of Plant Sciences Paris-Saclay (IPS2), UMR 9213/UMR1403, CNRS, INRA, Université d'Evry, Université Paris-Diderot, Université Paris-Sud, Sorbonne Paris-Cité, Saclay Plant Sciences, Orsay, France
| | - Bertrand Gakière
- Institute of Plant Sciences Paris-Saclay (IPS2), UMR 9213/UMR1403, CNRS, INRA, Université d'Evry, Université Paris-Diderot, Université Paris-Sud, Sorbonne Paris-Cité, Saclay Plant Sciences, Orsay, France
| | | | - Christophe Bailly
- Laboratoire de Biologie du Développement, Sorbonne Université, CNRS, F-75005, Paris, France
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29
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Pseudomonas aeruginosa Alters Its Transcriptome Related to Carbon Metabolism and Virulence as a Possible Survival Strategy in Blood from Trauma Patients. mSystems 2019; 4:mSystems00312-18. [PMID: 31086830 PMCID: PMC6506614 DOI: 10.1128/msystems.00312-18] [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: 12/05/2018] [Accepted: 04/07/2019] [Indexed: 01/09/2023] Open
Abstract
While a considerable body of knowledge regarding sepsis in trauma patients is available, the potential influence of trauma-induced changes in the blood of these patients on the pathogenesis of Pseudomonas aeruginosa is basically an unexplored area. Rather than using standard laboratory media, we grew P. aeruginosa in whole blood from either healthy volunteers or trauma patients. The specific changes in the P. aeruginosa transcriptome in response to growth in blood from trauma patients reflect the adaptation of this organism to the bloodstream environment. This knowledge is vital for understanding the strategies this pathogen uses to adapt and survive within the host during systemic infection. Such information will help researchers and clinicians to develop new approaches for treatment of sepsis caused by P. aeruginosa in trauma patients, especially in terms of recognizing the effects of specific therapies (e.g., iron, zinc, or mannitol) on the organism. Further, this information can most likely be extrapolated to all patients with P. aeruginosa septicemia. Trauma patients (TPs) are highly susceptible to infections, which often lead to sepsis. Among the numerous causative agents, Pseudomonas aeruginosa is especially important, as P. aeruginosa sepsis is often fatal. Understanding the mechanism of its pathogenesis in bloodstream infections is imperative; however, this mechanism has not been previously described. To examine the effect of trauma-induced changes in blood on the expression of P. aeruginosa genes, we grew strain UCBPP-PA14 (PA14) in blood samples from eight TPs and seven healthy volunteers (HVs). Compared with its growth in blood from HVs, the growth of PA14 in blood from TPs significantly altered the expression of 285 genes. Genes whose expression was significantly increased were related to carbon metabolism, especially malonate utilization and mannitol uptake, and efflux of heavy metals. Genes whose expression was significantly reduced included genes of the type VI secretion system, genes related to uptake and metabolism of amino acids, and genes related to biosynthesis and transport of the siderophores pyoverdine and pyochelin. These results suggest that during systemic infection in trauma patients, and to adapt to the trauma-induced changes in blood, P. aeruginosa adjusts positively and negatively the expression of numerous genes related to carbon metabolism and virulence, respectively. IMPORTANCE While a considerable body of knowledge regarding sepsis in trauma patients is available, the potential influence of trauma-induced changes in the blood of these patients on the pathogenesis of Pseudomonas aeruginosa is basically an unexplored area. Rather than using standard laboratory media, we grew P. aeruginosa in whole blood from either healthy volunteers or trauma patients. The specific changes in the P. aeruginosa transcriptome in response to growth in blood from trauma patients reflect the adaptation of this organism to the bloodstream environment. This knowledge is vital for understanding the strategies this pathogen uses to adapt and survive within the host during systemic infection. Such information will help researchers and clinicians to develop new approaches for treatment of sepsis caused by P. aeruginosa in trauma patients, especially in terms of recognizing the effects of specific therapies (e.g., iron, zinc, or mannitol) on the organism. Further, this information can most likely be extrapolated to all patients with P. aeruginosa septicemia. Author Video: An author video summary of this article is available.
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30
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Martyushenko N, Almaas E. ModelExplorer - software for visual inspection and inconsistency correction of genome-scale metabolic reconstructions. BMC Bioinformatics 2019; 20:56. [PMID: 30691403 PMCID: PMC6348647 DOI: 10.1186/s12859-019-2615-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 01/07/2019] [Indexed: 12/25/2022] Open
Abstract
Background Genome-scale metabolic network reconstructions are low level chemical representations of biological organisms. These models allow the system-level investigation of metabolic phenotypes using a variety of computational approaches. The link between a metabolic network model and an organisms’ higher-level behaviour is usually found using a constraint-based analysis approach, such as FBA (Flux Balance Analysis). However, the process of model reconstruction rarely proceeds without error. Often, considerable parts of a model cannot carry flux under any condition. This is termed model inconsistency and is caused by faulty topology and/or stoichiometry of the underlying reconstructed network. While there exist several automated gap-filling tools that may solve some of the inconsistencies, much of the work still needs to be carried out manually. The common “linear list” format of writing biochemical reactions makes it difficult to intuit what is at the root of the inconsistent behaviour. Unfortunately, we have frequently observed that model builders do not correct their models past the abilities of automated tools, leaving many widely used models significantly inconsistent. Results We have developed the software ModelExplorer, which main purpose is to fill this gap by providing an intuitive and visual framework that allows the user to explore and correct inconsistencies in genome-scale metabolic models. The software will automatically visualize metabolic networks as graphs with distinct separation and delineation of cellular compartments. ModelExplorer highlights reactions and species that are unable to carry flux (blocked), with several different consistency checking modes available. Our software also allows the automatic identification of neighbours and production pathways of any species or reaction. Additionally, the user may focus on any chosen inconsistent part of the model on its own. This facilitates a rapid and visual identification of reactions and species responsible for model inconsistencies. Finally, ModelExplorer lets the user freely edit, add or delete model elements, allowing straight-forward correction of discovered issues. Conclusion Overall, ModelExplorer is currently the fastest real-time metabolic network visualization program available. It implements several consistency checking algorithms, which in combination with its set of tracking tools, gives an efficient and systematic model-correction process. Electronic supplementary material The online version of this article (10.1186/s12859-019-2615-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nikolay Martyushenko
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7491, Norway.
| | - Eivind Almaas
- Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7491, Norway. .,K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, N-7491, Norway.
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31
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Poupin N, Corlu A, Cabaton NJ, Dubois-Pot-Schneider H, Canlet C, Person E, Bruel S, Frainay C, Vinson F, Maurier F, Morel F, Robin MA, Fromenty B, Zalko D, Jourdan F. Large-Scale Modeling Approach Reveals Functional Metabolic Shifts during Hepatic Differentiation. J Proteome Res 2018; 18:204-216. [PMID: 30394098 DOI: 10.1021/acs.jproteome.8b00524] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Being able to explore the metabolism of broad metabolizing cells is of critical importance in many research fields. This article presents an original modeling solution combining metabolic network and omics data to identify modulated metabolic pathways and changes in metabolic functions occurring during differentiation of a human hepatic cell line (HepaRG). Our results confirm the activation of hepato-specific functionalities and newly evidence modulation of other metabolic pathways, which could not be evidenced from transcriptomic data alone. Our method takes advantage of the network structure to detect changes in metabolic pathways that do not have gene annotations and exploits flux analyses techniques to identify activated metabolic functions. Compared to the usual cell-specific metabolic network reconstruction approaches, it limits false predictions by considering several possible network configurations to represent one phenotype rather than one arbitrarily selected network. Our approach significantly enhances the comprehensive and functional assessment of cell metabolism, opening further perspectives to investigate metabolic shifts occurring within various biological contexts.
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Affiliation(s)
- Nathalie Poupin
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Anne Corlu
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Nicolas J Cabaton
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Hélène Dubois-Pot-Schneider
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Cécile Canlet
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Elodie Person
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Sandrine Bruel
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Clément Frainay
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Florence Vinson
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Florence Maurier
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Fabrice Morel
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Marie-Anne Robin
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Bernard Fromenty
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Daniel Zalko
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Fabien Jourdan
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
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Cabaton NJ, Poupin N, Canlet C, Tremblay-Franco M, Audebert M, Cravedi JP, Riu A, Jourdan F, Zalko D. An Untargeted Metabolomics Approach to Investigate the Metabolic Modulations of HepG2 Cells Exposed to Low Doses of Bisphenol A and 17β-Estradiol. Front Endocrinol (Lausanne) 2018; 9:571. [PMID: 30319551 PMCID: PMC6167423 DOI: 10.3389/fendo.2018.00571] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 09/06/2018] [Indexed: 12/11/2022] Open
Abstract
The model xeno-estrogen bisphenol A (BPA) has been extensively studied over the past two decades, contributing to major advances in the field of endocrine disrupting chemicals research. Besides its well documented adverse effects on reproduction and development observed in rodents, latest studies strongly suggest that BPA disrupts several endogenous metabolic pathways, with suspected steatogenic and obesogenic effects. BPA's adverse effects on reproduction are attributed to its ability to activate estrogen receptors (ERs), but its effects on metabolism and its mechanism(s) of action at low doses are so far only marginally understood. Metabolomics based approaches are increasingly used in toxicology to investigate the biological changes induced by model toxicants and chemical mixtures, to identify markers of toxicity and biological effects. In this study, we used proton nuclear magnetic resonance (1H-NMR) based untargeted metabolite profiling, followed by multivariate statistics and computational analysis of metabolic networks to examine the metabolic modulation induced in human hepatic cells (HepG2) by an exposure to low and very low doses of BPA (10-6M, 10-9M, and 10-12M), vs. the female reference hormone 17β-estradiol (E2, 10-9M, 10-12M, and 10-15M). Metabolomic analysis combined to metabolic network reconstruction highlighted different mechanisms at lower doses of exposure. At the highest dose, our results evidence that BPA shares with E2 the capability to modulate several major metabolic routes that ensure cellular functions and detoxification processes, although the effects of the model xeno-estrogen and of the natural hormone can still be distinguished.
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Affiliation(s)
- Nicolas J. Cabaton
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Cécile Canlet
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
- Axiom Platform, MetaToul-MetaboHub, National Infrastructure for Metabolomics and Fluxomics, Toulouse, France
| | - Marie Tremblay-Franco
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
- Axiom Platform, MetaToul-MetaboHub, National Infrastructure for Metabolomics and Fluxomics, Toulouse, France
| | - Marc Audebert
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Jean-Pierre Cravedi
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Anne Riu
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Fabien Jourdan
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Daniel Zalko
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France
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33
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FluxVisualizer, a Software to Visualize Fluxes through Metabolic Networks. Processes (Basel) 2018. [DOI: 10.3390/pr6050039] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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34
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Current and future perspectives of functional metabolomics in disease studies-A review. Anal Chim Acta 2018; 1037:41-54. [PMID: 30292314 DOI: 10.1016/j.aca.2018.04.006] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 03/20/2018] [Accepted: 04/13/2018] [Indexed: 12/16/2022]
Abstract
Functional metabolomics is a new concept, which studies the functions of metabolites and related enzymes focused on metabolomics. It overcomes the shortcomings of traditional discovery metabolomics of mainly relying on literatures for biological interpretation. Functional metabolomics has many advantages. Firstly, the functional roles of metabolites and related metabolic enzymes are focused. Secondly, the in vivo and in vitro experiments are conducted to validate the metabolomics findings, therefore, increasing the reliability of metabolomics study and producing the new knowledge. Thirdly, functional metabolomics can be used by biologists to investigate functions of metabolites, and related genes and proteins. In this review, we summarize the analytical, biological and clinical platforms used in functional metabolomics studies. Recent progresses of functional metabolomics in cancer, metabolic diseases and biological phenotyping are reviewed, and future development is also predicted. Because of the tremendous advantages of functional metabolomics, it will have a bright future.
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