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Liu Y, Kwok W, Yoon H, Ryu JC, Stevens P, Hawkinson TR, Shedlock CJ, Ribas RA, Medina T, Keohane SB, Scharre D, Bruschweiler-Li L, Bruschweiler R, Gaultier A, Obrietan K, Sun RC, Yoon SO. Imbalance in Glucose Metabolism Regulates the Transition of Microglia from Homeostasis to Disease-Associated Microglia Stage 1. J Neurosci 2024; 44:e1563232024. [PMID: 38565291 PMCID: PMC11097271 DOI: 10.1523/jneurosci.1563-23.2024] [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: 08/16/2023] [Revised: 03/21/2024] [Accepted: 03/23/2024] [Indexed: 04/04/2024] Open
Abstract
Microglia undergo two-stage activation in neurodegenerative diseases, known as disease-associated microglia (DAM). TREM2 mediates the DAM2 stage transition, but what regulates the first DAM1 stage transition is unknown. We report that glucose dyshomeostasis inhibits DAM1 activation and PKM2 plays a role. As in tumors, PKM2 was aberrantly elevated in both male and female human AD brains, but unlike in tumors, it is expressed as active tetramers, as well as among TREM2+ microglia surrounding plaques in 5XFAD male and female mice. snRNAseq analyses of microglia without Pkm2 in 5XFAD mice revealed significant increases in DAM1 markers in a distinct metabolic cluster, which is enriched in genes for glucose metabolism, DAM1, and AD risk. 5XFAD mice incidentally exhibited a significant reduction in amyloid pathology without microglial Pkm2 Surprisingly, microglia in 5XFAD without Pkm2 exhibited increases in glycolysis and spare respiratory capacity, which correlated with restoration of mitochondrial cristae alterations. In addition, in situ spatial metabolomics of plaque-bearing microglia revealed an increase in respiratory activity. These results together suggest that it is not only glycolytic but also respiratory inputs that are critical to the development of DAM signatures in 5XFAD mice.
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Affiliation(s)
- Yuxi Liu
- Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210
| | - Witty Kwok
- Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210
| | - Hyojung Yoon
- Department of Neuroscience, The Ohio State University, Columbus, Ohio 43210
| | - Jae Cheon Ryu
- Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210
| | - Patrick Stevens
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210
| | - Tara R Hawkinson
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, Florida 32610
- Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, Florida, 32610
| | - Cameron J Shedlock
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, Florida 32610
- Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, Florida, 32610
| | - Roberto A Ribas
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, Florida 32610
- Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, Florida, 32610
| | - Terrymar Medina
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, Florida 32610
- Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, Florida, 32610
| | - Shannon B Keohane
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, Florida 32610
- Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, Florida, 32610
| | - Douglas Scharre
- Department of Neurology, The Ohio State University, Columbus, Ohio 43210
| | - Lei Bruschweiler-Li
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210
| | - Rafael Bruschweiler
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210
| | - Alban Gaultier
- Center for Brain Immunology and Glia, University of Virginia, Charlottesville, Virginia, 22908
| | - Karl Obrietan
- Department of Neuroscience, The Ohio State University, Columbus, Ohio 43210
| | - Ramon C Sun
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, Florida 32610
- Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, Florida, 32610
| | - Sung Ok Yoon
- Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210
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2
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Theorell A, Jadebeck JF, Wiechert W, McFadden J, Nöh K. Rethinking 13C-metabolic flux analysis - The Bayesian way of flux inference. Metab Eng 2024; 83:137-149. [PMID: 38582144 DOI: 10.1016/j.ymben.2024.03.005] [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: 09/20/2023] [Revised: 03/22/2024] [Accepted: 03/23/2024] [Indexed: 04/08/2024]
Abstract
Metabolic reaction rates (fluxes) play a crucial role in comprehending cellular phenotypes and are essential in areas such as metabolic engineering, biotechnology, and biomedical research. The state-of-the-art technique for estimating fluxes is metabolic flux analysis using isotopic labelling (13C-MFA), which uses a dataset-model combination to determine the fluxes. Bayesian statistical methods are gaining popularity in the field of life sciences, but the use of 13C-MFA is still dominated by conventional best-fit approaches. The slow take-up of Bayesian approaches is, at least partly, due to the unfamiliarity of Bayesian methods to metabolic engineering researchers. To address this unfamiliarity, we here outline similarities and differences between the two approaches and highlight particular advantages of the Bayesian way of flux analysis. With a real-life example, re-analysing a moderately informative labelling dataset of E. coli, we identify situations in which Bayesian methods are advantageous and more informative, pointing to potential pitfalls of current 13C-MFA evaluation approaches. We propose the use of Bayesian model averaging (BMA) for flux inference as a means of overcoming the problem of model uncertainty through its tendency to assign low probabilities to both, models that are unsupported by data, and models that are overly complex. In this capacity, BMA resembles a tempered Ockham's razor. With the tempered razor as a guide, BMA-based 13C-MFA alleviates the problem of model selection uncertainty and is thereby capable of becoming a game changer for metabolic engineering by uncovering new insights and inspiring novel approaches.
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Affiliation(s)
- Axel Theorell
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Johann F Jadebeck
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52062 Aachen, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, 52062 Aachen, Germany
| | - Johnjoe McFadden
- Department of Microbial and Cellular Sciences, University of Surrey, GU2 7XH Guildford, United Kingdom
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
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3
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Geffen O, Achaintre D, Treves H. 13CO 2-labelling and Sampling in Algae for Flux Analysis of Photosynthetic and Central Carbon Metabolism. Bio Protoc 2023; 13:e4808. [PMID: 37719071 PMCID: PMC10501915 DOI: 10.21769/bioprotoc.4808] [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: 12/20/2022] [Revised: 06/09/2023] [Accepted: 06/14/2023] [Indexed: 09/19/2023] Open
Abstract
The flux in photosynthesis can be studied by performing 13CO2 pulse labelling and analysing the temporal labelling kinetics of metabolic intermediates using gas or liquid chromatography linked to mass spectrometry. Metabolic flux analysis (MFA) is the primary approach for analysing metabolic network function and quantifying intracellular metabolic fluxes. Different MFA approaches differ based on the metabolic state (steady vs. non-steady state) and the use of stable isotope tracers. The main methodology used to investigate metabolic systems is metabolite steady state associated with stable isotope labelling experiments. Specifically, in biological systems like photoautotrophic organisms, isotopic non-stationary 113C metabolic flux analysis at metabolic steady state with transient isotopic labelling (13C-INST-MFA) is required. The common requirement for metabolic steady state, alongside its very short half-timed reactions, complicates robust MFA of photosynthetic metabolism. While custom gas chambers design has addressed these challenges in various model plants, no similar tools were developed for liquid photosynthetic cultures (e.g., algae, cyanobacteria), where diffusion and equilibration of inorganic carbon species in the medium entails a new dimension of complexity. Recently, a novel tailor-made microfluidics labelling system has been introduced, supplying short 13CO2 pulses at steady state, and resolving fluxes across most photosynthetic metabolic pathways in algae. The system involves injecting algal cultures and medium containing pre-equilibrated inorganic 13C into a microfluidic mixer, followed by rapid metabolic quenching, enabling precise seconds-level label pulses. This was complemented by a 13CO2-bubbling-based open labelling system (photobioreactor), allowing long pulses (minutes-hours) required for investigating fluxes into central C metabolism and major products. This combined labelling procedure provides a comprehensive fluxome cover for most algal photosynthetic and central C metabolism pathways, thus allowing comparative flux analyses across algae and plants.
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Affiliation(s)
- Or Geffen
- School of Plant Sciences and Food Security, Faculty of Biology, Tel-Aviv University, Tel Aviv-Yafo, Israel
| | - David Achaintre
- School of Plant Sciences and Food Security, Faculty of Biology, Tel-Aviv University, Tel Aviv-Yafo, Israel
| | - Haim Treves
- School of Plant Sciences and Food Security, Faculty of Biology, Tel-Aviv University, Tel Aviv-Yafo, Israel
- Plant Metabolism Group, Faculty of Biology, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, Germany
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4
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Volke DC, Gurdo N, Milanesi R, Nikel PI. Time-resolved, deuterium-based fluxomics uncovers the hierarchy and dynamics of sugar processing by Pseudomonas putida. Metab Eng 2023; 79:159-172. [PMID: 37454792 DOI: 10.1016/j.ymben.2023.07.004] [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: 05/16/2023] [Revised: 06/30/2023] [Accepted: 07/13/2023] [Indexed: 07/18/2023]
Abstract
Pseudomonas putida, a microbial host widely adopted for metabolic engineering, processes glucose through convergent peripheral pathways that ultimately yield 6-phosphogluconate. The periplasmic gluconate shunt (PGS), composed by glucose and gluconate dehydrogenases, sequentially transforms glucose into gluconate and 2-ketogluconate. Although the secretion of these organic acids by P. putida has been extensively recognized, the mechanism and spatiotemporal regulation of the PGS remained elusive thus far. To address this challenge, we adopted a dynamic 13C- and 2H-metabolic flux analysis strategy, termed D-fluxomics. D-fluxomics demonstrated that the PGS underscores a highly dynamic metabolic architecture in glucose-dependent batch cultures of P. putida, characterized by hierarchical carbon uptake by the PGS throughout the cultivation. Additionally, we show that gluconate and 2-ketogluconate accumulation and consumption can be solely explained as a result of the interplay between growth rate-coupled and decoupled metabolic fluxes. As a consequence, the formation of these acids in the PGS is inversely correlated to the bacterial growth rate-unlike the widely studied overflow metabolism of Escherichia coli and yeast. Our findings, which underline survival strategies of soil bacteria thriving in their natural environments, open new avenues for engineering P. putida towards efficient, sugar-based bioprocesses.
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Affiliation(s)
- Daniel C Volke
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.
| | - Nicolas Gurdo
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
| | - Riccardo Milanesi
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, 20126, Milano, Italy
| | - Pablo I Nikel
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.
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5
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Faure L, Mollet B, Liebermeister W, Faulon JL. A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models. Nat Commun 2023; 14:4669. [PMID: 37537192 PMCID: PMC10400647 DOI: 10.1038/s41467-023-40380-0] [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: 12/01/2022] [Accepted: 07/19/2023] [Indexed: 08/05/2023] Open
Abstract
Constraint-based metabolic models have been used for decades to predict the phenotype of microorganisms in different environments. However, quantitative predictions are limited unless labor-intensive measurements of media uptake fluxes are performed. We show how hybrid neural-mechanistic models can serve as an architecture for machine learning providing a way to improve phenotype predictions. We illustrate our hybrid models with growth rate predictions of Escherichia coli and Pseudomonas putida grown in different media and with phenotype predictions of gene knocked-out Escherichia coli mutants. Our neural-mechanistic models systematically outperform constraint-based models and require training set sizes orders of magnitude smaller than classical machine learning methods. Our hybrid approach opens a doorway to enhancing constraint-based modeling: instead of constraining mechanistic models with additional experimental measurements, our hybrid models grasp the power of machine learning while fulfilling mechanistic constrains, thus saving time and resources in typical systems biology or biological engineering projects.
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Affiliation(s)
- Léon Faure
- MICALIS Institute, INRAE, AgroParisTech, University of Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Bastien Mollet
- Ecole Normale Supérieure of Lyon, 69342, Lyon, France
- UMR MIA, INRAE, AgroParisTech, University of Paris-Saclay, 91120, Palaiseau, France
| | | | - Jean-Loup Faulon
- MICALIS Institute, INRAE, AgroParisTech, University of Paris-Saclay, 78350, Jouy-en-Josas, France.
- Manchester Institute of Biotechnology, University of Manchester, Manchester, M1 7DN, UK.
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6
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Mitosch K, Beyß M, Phapale P, Drotleff B, Nöh K, Alexandrov T, Patil KR, Typas A. A pathogen-specific isotope tracing approach reveals metabolic activities and fluxes of intracellular Salmonella. PLoS Biol 2023; 21:e3002198. [PMID: 37594988 PMCID: PMC10468081 DOI: 10.1371/journal.pbio.3002198] [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: 05/10/2023] [Revised: 08/30/2023] [Accepted: 06/16/2023] [Indexed: 08/20/2023] Open
Abstract
Pathogenic bacteria proliferating inside mammalian host cells need to rapidly adapt to the intracellular environment. How they achieve this and scavenge essential nutrients from the host has been an open question due to the difficulties in distinguishing between bacterial and host metabolites in situ. Here, we capitalized on the inability of mammalian cells to metabolize mannitol to develop a stable isotopic labeling approach to track Salmonella enterica metabolites during intracellular proliferation in host macrophage and epithelial cells. By measuring label incorporation into Salmonella metabolites with liquid chromatography-mass spectrometry (LC-MS), and combining it with metabolic modeling, we identify relevant carbon sources used by Salmonella, uncover routes of their metabolization, and quantify relative reaction rates in central carbon metabolism. Our results underline the importance of the Entner-Doudoroff pathway (EDP) and the phosphoenolpyruvate carboxylase for intracellularly proliferating Salmonella. More broadly, our metabolic labeling strategy opens novel avenues for understanding the metabolism of pathogens inside host cells.
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Affiliation(s)
- Karin Mitosch
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Martin Beyß
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
- RWTH Aachen University, Computational Systems Biotechnology, Aachen, Germany
| | - Prasad Phapale
- Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Bernhard Drotleff
- Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Theodore Alexandrov
- Metabolomics Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- BioInnovation Institute, Copenhagen, Denmark
| | - Kiran R. Patil
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Athanasios Typas
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
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7
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Yuan Y, Xu F, Ke X, Lu J, Huang M, Chu J. Ammonium sulfate supplementation enhances erythromycin biosynthesis by augmenting intracellular metabolism and precursor supply in Saccharopolyspora erythraea. Bioprocess Biosyst Eng 2023:10.1007/s00449-023-02898-x. [PMID: 37392219 DOI: 10.1007/s00449-023-02898-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 06/21/2023] [Indexed: 07/03/2023]
Abstract
In this study, the cellular metabolic mechanisms regarding ammonium sulfate supplementation on erythromycin production were investigated by employing targeted metabolomics and metabolic flux analysis. The results suggested that the addition of ammonium sulfate stimulates erythromycin biosynthesis. Targeted metabolomics analysis uncovered that the addition of ammonium sulfate during the late stage of fermentation resulted in an augmented intracellular amino acid metabolism pool, guaranteeing an ample supply of precursors for organic acids and coenzyme A-related compounds. Therefore, adequate precursors facilitated cellular maintenance and erythromycin biosynthesis. Subsequently, an optimal supplementation rate of 0.02 g/L/h was determined. The results exhibited that erythromycin titer (1311.1 μg/mL) and specific production rate (0.008 mmol/gDCW/h) were 101.3% and 41.0% higher than those of the process without ammonium sulfate supplementation, respectively. Moreover, the erythromycin A component proportion increased from 83.2% to 99.5%. Metabolic flux analysis revealed increased metabolic fluxes with the supplementation of three ammonium sulfate rates.
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Affiliation(s)
- Yujie Yuan
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China
| | - Feng Xu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China
| | - Xiang Ke
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China
| | - Ju Lu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China
| | - Mingzhi Huang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China.
| | - Ju Chu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China.
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8
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Ortega AD. Real-Time Assessment of Intracellular Metabolites in Single Cells through RNA-Based Sensors. Biomolecules 2023; 13:biom13050765. [PMID: 37238635 DOI: 10.3390/biom13050765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/28/2023] Open
Abstract
Quantification of the concentration of particular cellular metabolites reports on the actual utilization of metabolic pathways in physiological and pathological conditions. Metabolite concentration also constitutes the readout for screening cell factories in metabolic engineering. However, there are no direct approaches that allow for real-time assessment of the levels of intracellular metabolites in single cells. In recent years, the modular architecture of natural bacterial RNA riboswitches has inspired the design of genetically encoded synthetic RNA devices that convert the intracellular concentration of a metabolite into a quantitative fluorescent signal. These so-called RNA-based sensors are composed of a metabolite-binding RNA aptamer as the sensor domain, connected through an actuator segment to a signal-generating reporter domain. However, at present, the variety of available RNA-based sensors for intracellular metabolites is still very limited. Here, we go through natural mechanisms for metabolite sensing and regulation in cells across all kingdoms, focusing on those mediated by riboswitches. We review the design principles underlying currently developed RNA-based sensors and discuss the challenges that hindered the development of novel sensors and recent strategies to address them. We finish by introducing the current and potential applicability of synthetic RNA-based sensors for intracellular metabolites.
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Affiliation(s)
- Alvaro Darío Ortega
- Department of Cell Biology, Faculty of Biological Sciences, Complutense University of Madrid, 28040 Madrid, Spain
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9
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González-Arrué N, Inostroza I, Conejeros R, Rivas-Astroza M. Phenotype-specific estimation of metabolic fluxes using gene expression data. iScience 2023; 26:106201. [PMID: 36915687 PMCID: PMC10006673 DOI: 10.1016/j.isci.2023.106201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/30/2022] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
A cell's genome influences its metabolism via the expression of enzyme-related genes, but transcriptome and fluxome are not perfectly correlated as post-transcriptional mechanisms also regulate reaction's kinetics. Here, we addressed the question: given a transcriptome, how unobserved mechanisms of reaction kinetics should be systematically accounted for when inferring the fluxome? To infer the most likely and least biased fluxome, we present Pheflux, a constraint-based model maximizing Shannon's entropy of fluxes per mRNA. Benchmarked against 13C fluxes of yeast and bacteria, Pheflux accurately estimates the carbon core metabolism. We applied Pheflux to thousands of normal and tumor cell transcriptomes obtained from The Cancer Genome Atlas. Pheflux showed statistically significantly higher glucose yields on lactate in breast, kidney, and bronchus-lung tumoral cells than their normal counterparts. Results are consistent with the Warburg effect, a hallmark of cancer metabolism, suggesting that Pheflux can be efficiently used to study the metabolism of eukaryotic cells.
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Affiliation(s)
- Nicolás González-Arrué
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
| | - Isidora Inostroza
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
| | - Raúl Conejeros
- Pontificia Universidad Católica de Valparaíso, Escuela de Ingeniería Bioquímica, Valparaíso, 2362803, Chile
| | - Marcelo Rivas-Astroza
- Universidad Tecnológica Metropolitana, Departamento de Biotecnología, Ñuñoa, Santiago 7800003, Chile
- Corresponding author
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Borah Slater K, Beyß M, Xu Y, Barber J, Costa C, Newcombe J, Theorell A, Bailey MJ, Beste DJV, McFadden J, Nöh K. One-shot 13 C 15 N-metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux. Mol Syst Biol 2023; 19:e11099. [PMID: 36705093 PMCID: PMC9996240 DOI: 10.15252/msb.202211099] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 01/06/2023] [Accepted: 01/13/2023] [Indexed: 01/28/2023] Open
Abstract
Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the tuberculosis pathogen, this is particularly important in informing the development of effective drugs targeting the pathogen's metabolism. Here we performed 13 C15 N dual isotopic labeling of Mycobacterium bovis BCG steady state cultures, quantified intracellular carbon and nitrogen fluxes and inferred reaction bidirectionalities. This was achieved by model scope extension and refinement, implemented in a multi-atom transition model, within the statistical framework of Bayesian model averaging (BMA). Using BMA-based 13 C15 N-metabolic flux analysis, we jointly resolve carbon and nitrogen fluxes quantitatively. We provide the first nitrogen flux distributions for amino acid and nucleotide biosynthesis in mycobacteria and establish glutamate as the central node for nitrogen metabolism. We improved resolution of the notoriously elusive anaplerotic node in central carbon metabolism and revealed possible operation modes. Our study provides a powerful and statistically rigorous platform to simultaneously infer carbon and nitrogen metabolism in any biological system.
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Affiliation(s)
| | - Martin Beyß
- Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences, IBG-1: Biotechnology, Jülich, Germany.,Computational Systems Biotechnology, RWTH Aachen University, Aachen, Germany
| | - Ye Xu
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Jim Barber
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Catia Costa
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK
| | - Jane Newcombe
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Axel Theorell
- Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences, IBG-1: Biotechnology, Jülich, Germany
| | - Melanie J Bailey
- Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK
| | - Dany J V Beste
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Johnjoe McFadden
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Katharina Nöh
- Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences, IBG-1: Biotechnology, Jülich, Germany
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11
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Thommen Q, Hurbain J, Pfeuty B. Stochastic simulation algorithm for isotope-based dynamic flux analysis. Metab Eng 2023; 75:100-109. [PMID: 36402409 DOI: 10.1016/j.ymben.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 10/05/2022] [Accepted: 11/03/2022] [Indexed: 11/19/2022]
Abstract
Carbon isotope labeling method is a standard metabolic engineering tool for flux quantification in living cells. To cope with the high dimensionality of isotope labeling systems, diverse algorithms have been developed to reduce the number of variables or operations in metabolic flux analysis (MFA), but lacks generalizability to non-stationary metabolic conditions. In this study, we present a stochastic simulation algorithm (SSA) derived from the chemical master equation of the isotope labeling system. This algorithm allows to compute the time evolution of isotopomer concentrations in non-stationary conditions, with the valuable property that computational time does not scale with the number of isotopomers. The efficiency and limitations of the algorithm is benchmarked for the forward and inverse problems of 13C-DMFA in the pentose phosphate pathways, and is compared with EMU-based methods for NMFA and MFA including the central carbon metabolism. Overall, SSA constitutes an alternative class to deterministic approaches for metabolic flux analysis that is well adapted to comprehensive dataset including parallel labeling experiments, and whose limitations associated to the sampling size can be overcome by using Monte Carlo sampling approaches.
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Affiliation(s)
- Quentin Thommen
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, UMR9020-U1277 - CANTHER - Cancer Heterogeneity Plasticity and Resistance to Therapies, F-59000, Lille, France.
| | - Julien Hurbain
- Univ. Lille, CNRS, UMR 8523 - PhLAM - Physique des Lasers Atomes et Molécules, F-59000, Lille, France
| | - Benjamin Pfeuty
- Univ. Lille, CNRS, UMR 8523 - PhLAM - Physique des Lasers Atomes et Molécules, F-59000, Lille, France
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12
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Zhang R, Chen B, Zhang H, Tu L, Luan T. Stable isotope-based metabolic flux analysis: A robust tool for revealing toxicity pathways of emerging contaminants. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2022.116909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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13
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Volk MJ, Tran VG, Tan SI, Mishra S, Fatma Z, Boob A, Li H, Xue P, Martin TA, Zhao H. Metabolic Engineering: Methodologies and Applications. Chem Rev 2022; 123:5521-5570. [PMID: 36584306 DOI: 10.1021/acs.chemrev.2c00403] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.
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Affiliation(s)
- Michael J Volk
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Vinh G Tran
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Shih-I Tan
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Shekhar Mishra
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Zia Fatma
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Aashutosh Boob
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Hongxiang Li
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Pu Xue
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Teresa A Martin
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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14
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Magazzù G, Zampieri G, Angione C. Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods. Comput Biol Med 2022; 151:106244. [PMID: 36343407 DOI: 10.1016/j.compbiomed.2022.106244] [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: 06/15/2022] [Revised: 10/07/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Recently, multi-omic machine learning architectures have been proposed for the early detection of cancer. However, for rare cancers and their associated small datasets, it is still unclear how to use the available multi-omics data to achieve a mechanistic prediction of cancer onset and progression, due to the limited data available. Hepatoblastoma is the most frequent liver cancer in infancy and childhood, and whose incidence has been lately increasing in several developed countries. Even though some studies have been conducted to understand the causes of its onset and discover potential biomarkers, the role of metabolic rewiring has not been investigated in depth so far. METHODS Here, we propose and implement an interpretable multi-omics pipeline that combines mechanistic knowledge from genome-scale metabolic models with machine learning algorithms, and we use it to characterise the underlying mechanisms controlling hepatoblastoma. RESULTS AND CONCLUSIONS While the obtained machine learning models generally present a high diagnostic classification accuracy, our results show that the type of omics combinations used as input to the machine learning models strongly affects the detection of important genes, reactions and metabolic pathways linked to hepatoblastoma. Our method also suggests that, in the context of computer-aided diagnosis of cancer, optimal diagnostic accuracy can be achieved by adopting a combination of omics that depends on the patient's clinical characteristics.
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Affiliation(s)
- Giuseppe Magazzù
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, United Kingdom
| | - Guido Zampieri
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, United Kingdom; Department of Biology, University of Padova, Padova, Italy
| | - Claudio Angione
- School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, England, United Kingdom; Centre for Digital Innovation, Teesside University, Middlesbrough, England, United Kingdom; National Horizons Centre, Teesside University, Darlington, England, United Kingdom.
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15
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Mohd Kamal K, Mahamad Maifiah MH, Zhu Y, Abdul Rahim N, Hashim YZHY, Abdullah Sani MS. Isotopic Tracer for Absolute Quantification of Metabolites of the Pentose Phosphate Pathway in Bacteria. Metabolites 2022; 12:1085. [PMID: 36355168 PMCID: PMC9697766 DOI: 10.3390/metabo12111085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 11/02/2022] [Accepted: 11/07/2022] [Indexed: 10/18/2023] Open
Abstract
The pentose phosphate pathway (PPP) plays a key role in many metabolic functions, including the generation of NADPH, biosynthesis of nucleotides, and carbon homeostasis. In particular, the intermediates of PPP have been found to be significantly perturbed in bacterial metabolomic studies. Nonetheless, detailed analysis to gain mechanistic information of PPP metabolism remains limited as most studies are unable to report on the absolute levels of the metabolites. Absolute quantification of metabolites is a prerequisite to study the details of fluxes and its regulations. Isotope tracer or labeling studies are conducted in vivo and in vitro and have significantly improved the analysis and understanding of PPP. Due to the laborious procedure and limitations in the in vivo method, an in vitro approach known as Group Specific Internal Standard Technology (GSIST) has been successfully developed to measure the absolute levels of central carbon metabolism, including PPP. The technique adopts derivatization of an experimental sample and a corresponding internal standard with isotope-coded reagents to provide better precision for accurate identification and absolute quantification. In this review, we highlight bacterial studies that employed isotopic tracers as the tagging agents used for the absolute quantification analysis of PPP metabolites.
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Affiliation(s)
- Khairunnisa Mohd Kamal
- International Institute for Halal Research and Training (INHART), International Islamic University Malaysia (IIUM), Jalan Gombak 53100, Selangor, Malaysia
| | - Mohd Hafidz Mahamad Maifiah
- International Institute for Halal Research and Training (INHART), International Islamic University Malaysia (IIUM), Jalan Gombak 53100, Selangor, Malaysia
| | - Yan Zhu
- Infection and Immunity Program, Department of Microbiology, Biomedicine Discovery Institute, Monash University, Victoria 3800, Australia
| | - Nusaibah Abdul Rahim
- Faculty of Pharmacy, University of Malaya, Kuala Lumpur 50603, Selangor, Malaysia
| | - Yumi Zuhanis Has-Yun Hashim
- International Institute for Halal Research and Training (INHART), International Islamic University Malaysia (IIUM), Jalan Gombak 53100, Selangor, Malaysia
| | - Muhamad Shirwan Abdullah Sani
- International Institute for Halal Research and Training (INHART), International Islamic University Malaysia (IIUM), Jalan Gombak 53100, Selangor, Malaysia
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16
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Moiz B, Li A, Padmanabhan S, Sriram G, Clyne AM. Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease. Metabolites 2022; 12:1066. [PMID: 36355149 PMCID: PMC9694183 DOI: 10.3390/metabo12111066] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 10/25/2022] [Accepted: 10/27/2022] [Indexed: 04/28/2024] Open
Abstract
Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.
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Affiliation(s)
- Bilal Moiz
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Andrew Li
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Surya Padmanabhan
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Ganesh Sriram
- Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, USA
| | - Alisa Morss Clyne
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
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17
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Ali A, Davidson S, Fraenkel E, Gilmore I, Hankemeier T, Kirwan JA, Lane AN, Lanekoff I, Larion M, McCall LI, Murphy M, Sweedler JV, Zhu C. Single cell metabolism: current and future trends. Metabolomics 2022; 18:77. [PMID: 36181583 PMCID: PMC10063251 DOI: 10.1007/s11306-022-01934-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 09/05/2022] [Indexed: 11/29/2022]
Abstract
Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.
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Affiliation(s)
- Ahmed Ali
- Leiden Academic Centre for Drug Research, University of Leiden, Gorlaeus Building Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Shawn Davidson
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Ernest Fraenkel
- Department of Biological Engineering and the Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ian Gilmore
- National Physical Laboratory, Teddington, TW11 0LW, Middlesex, UK
| | - Thomas Hankemeier
- Leiden Academic Centre for Drug Research, University of Leiden, Room number GW4.07, Gorlaeus Building, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Jennifer A Kirwan
- Berlin Institute of Health, Metabolomics Platform, Translational Research Unit of the Charite-Universitätsmedizin Berlin, Anna-Louisa-Karsch-Str 2, 10178, Berlin, Germany
| | - Andrew N Lane
- Department of Toxicology and Cancer Biology, and Center for Environmental and Systems Biochemistry, University of Kentucky, 789 S. Limestone St, Lexington, KY, 40536, USA.
| | - Ingela Lanekoff
- Department of Chemistry-BMC, Uppsala University, Husargatan 3 (576), 751 23, Uppsala, Sweden
| | - Mioara Larion
- Center for Cancer Research, National Cancer Institute, Building 37, Room 1136A, Bethesda, MD, 20892, USA
| | - Laura-Isobel McCall
- Department of Chemistry & Biochemistry, Department of Microbiology and Plant Biology, Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, 101 Stephenson Parkway, room 3750, Norman, OK, 73019-5251, USA
| | - Michael Murphy
- Departments of Biological Engineering, Department of Electrical Engineering, and Computer Science and the Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, USA
| | - Jonathan V Sweedler
- Department of Chemistry, and the Beckman Institute, University of Illinois Urbana-Champaign, 505 South Mathews Avenue, Urbana, IL, 61801, USA
| | - Caigang Zhu
- Department of Biomedical Engineering, University of Kentucky, Lexington, KY, 40536, USA
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18
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Theillet FX, Luchinat E. In-cell NMR: Why and how? PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2022; 132-133:1-112. [PMID: 36496255 DOI: 10.1016/j.pnmrs.2022.04.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 04/19/2022] [Accepted: 04/27/2022] [Indexed: 06/17/2023]
Abstract
NMR spectroscopy has been applied to cells and tissues analysis since its beginnings, as early as 1950. We have attempted to gather here in a didactic fashion the broad diversity of data and ideas that emerged from NMR investigations on living cells. Covering a large proportion of the periodic table, NMR spectroscopy permits scrutiny of a great variety of atomic nuclei in all living organisms non-invasively. It has thus provided quantitative information on cellular atoms and their chemical environment, dynamics, or interactions. We will show that NMR studies have generated valuable knowledge on a vast array of cellular molecules and events, from water, salts, metabolites, cell walls, proteins, nucleic acids, drugs and drug targets, to pH, redox equilibria and chemical reactions. The characterization of such a multitude of objects at the atomic scale has thus shaped our mental representation of cellular life at multiple levels, together with major techniques like mass-spectrometry or microscopies. NMR studies on cells has accompanied the developments of MRI and metabolomics, and various subfields have flourished, coined with appealing names: fluxomics, foodomics, MRI and MRS (i.e. imaging and localized spectroscopy of living tissues, respectively), whole-cell NMR, on-cell ligand-based NMR, systems NMR, cellular structural biology, in-cell NMR… All these have not grown separately, but rather by reinforcing each other like a braided trunk. Hence, we try here to provide an analytical account of a large ensemble of intricately linked approaches, whose integration has been and will be key to their success. We present extensive overviews, firstly on the various types of information provided by NMR in a cellular environment (the "why", oriented towards a broad readership), and secondly on the employed NMR techniques and setups (the "how", where we discuss the past, current and future methods). Each subsection is constructed as a historical anthology, showing how the intrinsic properties of NMR spectroscopy and its developments structured the accessible knowledge on cellular phenomena. Using this systematic approach, we sought i) to make this review accessible to the broadest audience and ii) to highlight some early techniques that may find renewed interest. Finally, we present a brief discussion on what may be potential and desirable developments in the context of integrative studies in biology.
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Affiliation(s)
- Francois-Xavier Theillet
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France.
| | - Enrico Luchinat
- Dipartimento di Scienze e Tecnologie Agro-Alimentari, Alma Mater Studiorum - Università di Bologna, Piazza Goidanich 60, 47521 Cesena, Italy; CERM - Magnetic Resonance Center, and Neurofarba Department, Università degli Studi di Firenze, 50019 Sesto Fiorentino, Italy
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19
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Tian B, Chen M, Liu L, Rui B, Deng Z, Zhang Z, Shen T. 13C metabolic flux analysis: Classification and characterization from the perspective of mathematical modeling and application in physiological research of neural cell. Front Mol Neurosci 2022; 15:883466. [PMID: 36157075 PMCID: PMC9493264 DOI: 10.3389/fnmol.2022.883466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 08/15/2022] [Indexed: 11/17/2022] Open
Abstract
13C metabolic flux analysis (13C-MFA) has emerged as a forceful tool for quantifying in vivo metabolic pathway activity of different biological systems. This technology plays an important role in understanding intracellular metabolism and revealing patho-physiology mechanism. Recently, it has evolved into a method family with great diversity in experiments, analytics, and mathematics. In this review, we classify and characterize the various branch of 13C-MFA from a unified perspective of mathematical modeling. By linking different parts in the model to each step of its workflow, the specific technologies of 13C-MFA are put into discussion, including the isotope labeling model (ILM), isotope pattern measuring technique, optimization algorithm and statistical method. Its application in physiological research in neural cell has also been reviewed.
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Affiliation(s)
- Birui Tian
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, China
| | - Meifeng Chen
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountainous Areas of Southwestern China, Key Laboratory of Plant Physiology and Development Regulation, School of Life Science, Guizhou Normal University, Guiyang, China
| | - Lunxian Liu
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountainous Areas of Southwestern China, Key Laboratory of Plant Physiology and Development Regulation, School of Life Science, Guizhou Normal University, Guiyang, China
| | - Bin Rui
- Eurofins Lancaster Laboratories Professional Scientific Services, Lancaster, PA, United States
| | - Zhouhui Deng
- China Guizhou Science Data Center Gui’an Supercomputing Center, Guiyang, China
| | - Zhengdong Zhang
- College of Mathematics and Information Science, Guiyang University, Guiyang, China
- *Correspondence: Zhengdong Zhang,
| | - Tie Shen
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, China
- Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Karst Mountainous Areas of Southwestern China, Key Laboratory of Plant Physiology and Development Regulation, School of Life Science, Guizhou Normal University, Guiyang, China
- Tie Shen,
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20
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Emwas AH, Szczepski K, Al-Younis I, Lachowicz JI, Jaremko M. Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways. Front Pharmacol 2022; 13:805782. [PMID: 35387341 PMCID: PMC8977530 DOI: 10.3389/fphar.2022.805782] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 01/24/2022] [Indexed: 12/18/2022] Open
Abstract
Fluxomics is an innovative -omics research field that measures the rates of all intracellular fluxes in the central metabolism of biological systems. Fluxomics gathers data from multiple different -omics fields, portraying the whole picture of molecular interactions. Recently, fluxomics has become one of the most relevant approaches to investigate metabolic phenotypes. Metabolic flux using 13C-labeled molecules is increasingly used to monitor metabolic pathways, to probe the corresponding gene-RNA and protein-metabolite interaction networks in actual time. Thus, fluxomics reveals the functioning of multi-molecular metabolic pathways and is increasingly applied in biotechnology and pharmacology. Here, we describe the main fluxomics approaches and experimental platforms. Moreover, we summarize recent fluxomic results in different biological systems.
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Affiliation(s)
- Abdul-Hamid Emwas
- King Abdullah University of Science and Technology, Core Labs, Thuwal, Saudi Arabia
| | - Kacper Szczepski
- Smart-Health Initiative (SHI) and Red Sea Research Center (RSRC), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Inas Al-Younis
- King Abdullah University of Science and Technology (KAUST), Biological and Environmental Sciences & Engineering Division (BESE), Thuwal, Saudi Arabia
| | - Joanna Izabela Lachowicz
- Department of Medical Sciences and Public Health, University of Cagliari, Cittadella Universitaria, Monserrato, Italy
| | - Mariusz Jaremko
- Smart-Health Initiative (SHI) and Red Sea Research Center (RSRC), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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21
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Quantitative metabolic fluxes regulated by trans-omic networks. Biochem J 2022; 479:787-804. [PMID: 35356967 PMCID: PMC9022981 DOI: 10.1042/bcj20210596] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 12/21/2022]
Abstract
Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.
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22
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Liu Z, Jeffrey W, Rui B. Metabolomics as a promising tool for improving understanding of Multiple Sclerosis: a review of recent advances. Biomed J 2022; 45:594-606. [PMID: 35042018 PMCID: PMC9486246 DOI: 10.1016/j.bj.2022.01.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 12/29/2021] [Accepted: 01/10/2022] [Indexed: 12/23/2022] Open
Abstract
Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system that usually affects young adults. The development of MS is closely related to the changes in the metabolome. Metabolomics studies have been performed using biofluids or tissue samples from rodent models and human patients to reveal metabolic alterations associated with MS progression. This review aims to provide an overview of the applications of metabolomics that for the investigations of the perturbed metabolic pathways in MS and to reveal the potential of metabolomics in personalizing treatments. In conclusion, informative variations of metabolites can be potential biomarkers in advancing our understanding of MS pathogenesis for MS diagnosis, predicting the progression of the disease, and estimating drug effects. Metabolomics will be a promising technique for improving clinical care in MS.
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Affiliation(s)
- Zhicheng Liu
- Anhui Provincial laboratory of inflammatory and immunity disease, Anhui Institute of Innovative Drugs School of Pharmacy, Anhui Medical University, 81 Meishan Road, Hefei 230032, China.
| | - Waters Jeffrey
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA
| | - Bin Rui
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA.
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23
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Yonamine Y, Asai T, Suzuki Y, Ito T, Ozeki Y, Hoshino Y. Probing the Biogenesis of Polysaccharide Granules in Algal Cells at Sub-Organellar Resolution via Raman Microscopy with Stable Isotope Labeling. Anal Chem 2021; 93:16796-16803. [PMID: 34870976 DOI: 10.1021/acs.analchem.1c03216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Phototrophs assimilate CO2 into organic compounds that accumulate in storage organelles. Elucidation of the carbon dynamics of storage organelles could enhance the production efficiency of valuable compounds and facilitate the screening of strains with high photosynthetic activity. To comprehensively elucidate the carbon dynamics of these organelles, the intraorganellar distribution of the carbon atoms that accumulate at specific time periods should be probed. In this study, the biosynthesis of polysaccharides in storage organelles was spatiotemporally probed via stimulated Raman scattering (SRS) microscopy using a stable isotope (13C) as the tracking probe. Paramylon granules (a storage organelle of β-1,3-glucan) accumulated in a unicellular photosynthetic alga, Euglena gracilis, were investigated as a model organelle. The carbon source of the culture medium was switched from NaH12CO3 to NaH13CO3 during the production of the paramylon granules; this resulted in the distribution of the 12C and 13C constituents in the granules, so that the biosynthetic process could be tracked. Taking advantage of high-resolution SRS imaging and label switching, the localization of the 12C and 13C constituents inside a single paramylon granule could be visualized in three dimensions, thus revealing the growth process of paramylon granules. We propose that this method can be used for comprehensive elucidation of the dynamic activities of storage organelles.
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Affiliation(s)
- Yusuke Yonamine
- Research Institute for Electronic Science, Hokkaido University, Kita21, Nishi10, Kita-ku, Sapporo 001-0021, Japan
| | - Takuya Asai
- Department of Electrical Engineering and Information Systems, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Yuta Suzuki
- Department of Electrical Engineering and Information Systems, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Takuro Ito
- Department of Chemistry, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.,Japan Science and Technology Agency, 4-1-8, Honcho, Kawaguchi, Saitama 332-0012, Japan.,Department of Creative Engineering, National Institute of Technology (KOSEN), Tsuruoka College, 104 Sawada, Inooka, Tsuruoka, Yamagata 997-8511, Japan
| | - Yasuyuki Ozeki
- Department of Electrical Engineering and Information Systems, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Yu Hoshino
- Department of Chemical Engineering, Kyushu University, 744 Motooka, Fukuoka 819-0395, Japan
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Letertre MPM, Giraudeau P, de Tullio P. Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives. Front Mol Biosci 2021; 8:698337. [PMID: 34616770 PMCID: PMC8488110 DOI: 10.3389/fmolb.2021.698337] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022] Open
Abstract
Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.
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Affiliation(s)
| | | | - Pascal de Tullio
- Metabolomics Group, Center for Interdisciplinary Research of Medicine (CIRM), Department of Pharmacy, Université de Liège, Liège, Belgique
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Voss K, Hong HS, Bader JE, Sugiura A, Lyssiotis CA, Rathmell JC. A guide to interrogating immunometabolism. Nat Rev Immunol 2021; 21:637-652. [PMID: 33859379 PMCID: PMC8478710 DOI: 10.1038/s41577-021-00529-8] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/01/2021] [Indexed: 02/07/2023]
Abstract
The metabolic charts memorized in early biochemistry courses, and then later forgotten, have come back to haunt many immunologists with new recognition of the importance of these pathways. Metabolites and the activity of metabolic pathways drive energy production, macromolecule synthesis, intracellular signalling, post-translational modifications and cell survival. Immunologists who identify a metabolic phenotype in their system are often left wondering where to begin and what does it mean? Here, we provide a framework for navigating and selecting the appropriate biochemical techniques to explore immunometabolism. We offer recommendations for initial approaches to develop and test metabolic hypotheses and how to avoid common mistakes. We then discuss how to take things to the next level with metabolomic approaches, such as isotope tracing and genetic approaches. By proposing strategies and evaluating the strengths and weaknesses of different methodologies, we aim to provide insight, note important considerations and discuss ways to avoid common misconceptions. Furthermore, we highlight recent studies demonstrating the power of these metabolic approaches to uncover the role of metabolism in immunology. By following the framework in this Review, neophytes and seasoned investigators alike can venture into the emerging realm of cellular metabolism and immunity with confidence and rigour.
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Affiliation(s)
- Kelsey Voss
- Vanderbilt Center for Immunobiology, Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hanna S Hong
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - Jackie E Bader
- Vanderbilt Center for Immunobiology, Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ayaka Sugiura
- Vanderbilt Center for Immunobiology, Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Costas A Lyssiotis
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, MI, USA
| | - Jeffrey C Rathmell
- Vanderbilt Center for Immunobiology, Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA.
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Integrating systemic and molecular levels to infer key drivers sustaining metabolic adaptations. PLoS Comput Biol 2021; 17:e1009234. [PMID: 34297714 PMCID: PMC8336858 DOI: 10.1371/journal.pcbi.1009234] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 08/04/2021] [Accepted: 07/01/2021] [Indexed: 12/02/2022] Open
Abstract
Metabolic adaptations to complex perturbations, like the response to pharmacological treatments in multifactorial diseases such as cancer, can be described through measurements of part of the fluxes and concentrations at the systemic level and individual transporter and enzyme activities at the molecular level. In the framework of Metabolic Control Analysis (MCA), ensembles of linear constraints can be built integrating these measurements at both systemic and molecular levels, which are expressed as relative differences or changes produced in the metabolic adaptation. Here, combining MCA with Linear Programming, an efficient computational strategy is developed to infer additional non-measured changes at the molecular level that are required to satisfy these constraints. An application of this strategy is illustrated by using a set of fluxes, concentrations, and differentially expressed genes that characterize the response to cyclin-dependent kinases 4 and 6 inhibition in colon cancer cells. Decreases and increases in transporter and enzyme individual activities required to reprogram the measured changes in fluxes and concentrations are compared with down-regulated and up-regulated metabolic genes to unveil those that are key molecular drivers of the metabolic response. Deciphering the essential events in the reprogramming of metabolic networks subjected to complex perturbations, including the response to pharmacological treatments in multifactorial diseases like cancer, is crucial for the design of efficient therapies. Yet, tools to infer the molecular drivers sustaining such metabolic responses remain elusive for large metabolic networks. Here we develop an efficient computational strategy that integrates measured changes at systemic and molecular levels and combines metabolic control analysis with linear programming tools to infer key molecular drivers sustaining the metabolic adaptations to complex perturbations, such as an antitumoral drug therapy. The collective behavior is approximated using linear expressions where the adaptation of systemic concentrations and fluxes to a perturbation is described as a function of the molecular reprogramming of transport and enzyme activities. Starting from measured changes in fluxes and concentrations, we identify changes in the reprogramming of transporter and enzyme activities that are required to orchestrate the metabolic adaptation of colon cancer cells to a cell cycle inhibitor.
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Maturation Kinetics of a Multiprotein Complex Revealed by Metabolic Labeling. Cell 2020; 183:1785-1800.e26. [DOI: 10.1016/j.cell.2020.11.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 09/21/2020] [Accepted: 10/30/2020] [Indexed: 12/12/2022]
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Abstract
Methanol is inexpensive, is easy to transport, and can be produced both from renewable and from fossil resources without mobilizing arable lands. As such, it is regarded as a potential carbon source to transition toward a greener industrial chemistry. Metabolic engineering of bacteria and yeast able to efficiently consume methanol is expected to provide cell factories that will transform methanol into higher-value chemicals in the so-called methanol economy. Toward that goal, the study of natural methylotrophs such as Bacillus methanolicus is critical to understand the origin of their efficient methylotrophy. This knowledge will then be leveraged to transform such natural strains into new cell factories or to design methylotrophic capability in other strains already used by the industry. Bacillus methanolicus MGA3 is a thermotolerant and relatively fast-growing methylotroph able to secrete large quantities of glutamate and lysine. These natural characteristics make B. methanolicus a good candidate to become a new industrial chassis organism, especially in a methanol-based economy. Intriguingly, the only substrates known to support B. methanolicus growth as sole sources of carbon and energy are methanol, mannitol, and, to a lesser extent, glucose and arabitol. Because fluxomics provides the most direct readout of the cellular phenotype, we hypothesized that comparing methylotrophic and nonmethylotrophic metabolic states at the flux level would yield new insights into MGA3 metabolism. In this study, we designed and performed a 13C metabolic flux analysis (13C-MFA) of the facultative methylotroph B. methanolicus MGA3 growing on methanol, mannitol, and arabitol to compare the associated metabolic states. On methanol, results showed a greater flux in the ribulose monophosphate (RuMP) pathway than in the tricarboxylic acid (TCA) cycle, thus validating previous findings on the methylotrophy of B. methanolicus. New insights related to the utilization of cyclic RuMP versus linear dissimilation pathways and between the RuMP variants were generated. Importantly, we demonstrated that the linear detoxification pathways and the malic enzyme shared with the pentose phosphate pathway have an important role in cofactor regeneration. Finally, we identified, for the first time, the metabolic pathway used to assimilate arabitol. Overall, those data provide a better understanding of this strain under various environmental conditions. IMPORTANCE Methanol is inexpensive, is easy to transport, and can be produced both from renewable and from fossil resources without mobilizing arable lands. As such, it is regarded as a potential carbon source to transition toward a greener industrial chemistry. Metabolic engineering of bacteria and yeast able to efficiently consume methanol is expected to provide cell factories that will transform methanol into higher-value chemicals in the so-called methanol economy. Toward that goal, the study of natural methylotrophs such as Bacillus methanolicus is critical to understand the origin of their efficient methylotrophy. This knowledge will then be leveraged to transform such natural strains into new cell factories or to design methylotrophic capability in other strains already used by the industry.
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Balcerczyk A, Damblon C, Elena-Herrmann B, Panthu B, Rautureau GJP. Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures. Int J Mol Sci 2020; 21:E6843. [PMID: 32961865 PMCID: PMC7554780 DOI: 10.3390/ijms21186843] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/11/2020] [Accepted: 09/14/2020] [Indexed: 12/12/2022] Open
Abstract
Biological organisms are constantly exposed to an immense repertoire of molecules that cover environmental or food-derived molecules and drugs, triggering a continuous flow of stimuli-dependent adaptations. The diversity of these chemicals as well as their concentrations contribute to the multiplicity of induced effects, including activation, stimulation, or inhibition of physiological processes and toxicity. Metabolism, as the foremost phenotype and manifestation of life, has proven to be immensely sensitive and highly adaptive to chemical stimuli. Therefore, studying the effect of endo- or xenobiotics over cellular metabolism delivers valuable knowledge to apprehend potential cellular activity of individual molecules and evaluate their acute or chronic benefits and toxicity. The development of modern metabolomics technologies such as mass spectrometry or nuclear magnetic resonance spectroscopy now offers unprecedented solutions for the rapid and efficient determination of metabolic profiles of cells and more complex biological systems. Combined with the availability of well-established cell culture techniques, these analytical methods appear perfectly suited to determine the biological activity and estimate the positive and negative effects of chemicals in a variety of cell types and models, even at hardly detectable concentrations. Metabolic phenotypes can be estimated from studying intracellular metabolites at homeostasis in vivo, while in vitro cell cultures provide additional access to metabolites exchanged with growth media. This article discusses analytical solutions available for metabolic phenotyping of cell culture metabolism as well as the general metabolomics workflow suitable for testing the biological activity of molecular compounds. We emphasize how metabolic profiling of cell supernatants and intracellular extracts can deliver valuable and complementary insights for evaluating the effects of xenobiotics on cellular metabolism. We note that the concepts and methods discussed primarily for xenobiotics exposure are widely applicable to drug testing in general, including endobiotics that cover active metabolites, nutrients, peptides and proteins, cytokines, hormones, vitamins, etc.
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Affiliation(s)
- Aneta Balcerczyk
- Department of Molecular Biophysics, Faculty of Biology and Environmental Protection, University of Lodz, Pomorska 141/143, 90-236 Lodz, Poland;
| | - Christian Damblon
- Unité de Recherche MolSys, Faculté des sciences, Université de Liège, 4000 Liège, Belgium;
| | | | - Baptiste Panthu
- CarMeN Laboratory, INSERM, INRA, INSA Lyon, Univ Lyon, Université Claude Bernard Lyon 1, 69921 Oullins CEDEX, France;
- Hospices Civils de Lyon, Faculté de Médecine, Hôpital Lyon Sud, 69921 Oullins CEDEX, France
| | - Gilles J. P. Rautureau
- Centre de Résonance Magnétique Nucléaire à Très Hauts Champs (CRMN FRE 2034 CNRS, UCBL, ENS Lyon), Université Claude Bernard Lyon 1, 69100 Villeurbanne, France
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30
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A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth. Proc Natl Acad Sci U S A 2020; 117:18869-18879. [PMID: 32675233 DOI: 10.1073/pnas.2002959117] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype-phenotype-environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning-based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.
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Correa SM, Fernie AR, Nikoloski Z, Brotman Y. Towards model-driven characterization and manipulation of plant lipid metabolism. Prog Lipid Res 2020; 80:101051. [PMID: 32640289 DOI: 10.1016/j.plipres.2020.101051] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 06/20/2020] [Accepted: 06/21/2020] [Indexed: 01/09/2023]
Abstract
Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.
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Affiliation(s)
- Sandra M Correa
- Genetics of Metabolic Traits Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Department of Life Sciences, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel; Departamento de Ciencias Exactas y Naturales, Universidad de Antioquia, Medellín 050010, Colombia.
| | - Alisdair R Fernie
- Central Metabolism Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Zoran Nikoloski
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria; Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modelling Group, Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm 14476, Germany.
| | - Yariv Brotman
- Genetics of Metabolic Traits Group, Max Planck Institute for Molecular Plant Physiology, Potsdam 14476, Germany; Department of Life Sciences, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
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Resolving Metabolic Heterogeneity in Experimental Models of the Tumor Microenvironment from a Stable Isotope Resolved Metabolomics Perspective. Metabolites 2020; 10:metabo10060249. [PMID: 32549391 PMCID: PMC7345423 DOI: 10.3390/metabo10060249] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 06/02/2020] [Accepted: 06/04/2020] [Indexed: 12/11/2022] Open
Abstract
The tumor microenvironment (TME) comprises complex interactions of multiple cell types that determines cell behavior and metabolism such as nutrient competition and immune suppression. We discuss the various types of heterogeneity that exist in solid tumors, and the complications this invokes for studies of TME. As human subjects and in vivo model systems are complex and difficult to manipulate, simpler 3D model systems that are compatible with flexible experimental control are necessary for studying metabolic regulation in TME. Stable Isotope Resolved Metabolomics (SIRM) is a valuable tool for tracing metabolic networks in complex systems, but at present does not directly address heterogeneous metabolism at the individual cell level. We compare the advantages and disadvantages of different model systems for SIRM experiments, with a focus on lung cancer cells, their interactions with macrophages and T cells, and their response to modulators in the immune microenvironment. We describe the experimental set up, illustrate results from 3D cultures and co-cultures of lung cancer cells with human macrophages, and outline strategies to address the heterogeneous TME.
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33
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Zhang Z, Liu Z, Meng Y, Chen Z, Han J, Wei Y, Shen T, Yi Y, Xie X. Parallel isotope differential modeling for instationary 13C fluxomics at the genome scale. BIOTECHNOLOGY FOR BIOFUELS 2020; 13:103. [PMID: 32523616 PMCID: PMC7278083 DOI: 10.1186/s13068-020-01737-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 05/22/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND A precise map of the metabolic fluxome, the closest surrogate to the physiological phenotype, is becoming progressively more important in the metabolic engineering of photosynthetic organisms for biofuel and biomass production. For photosynthetic organisms, the state-of-the-art method for this purpose is instationary 13C fluxomics, which has arisen as a sibling of transcriptomics or proteomics. Instationary 13C data processing requires solving high-dimensional nonlinear differential equations and leads to large computational and time costs when its scope is expanded to a genome-scale metabolic network. RESULT Here, we present a parallelized method to model instationary 13C labeling data. The elementary metabolite unit (EMU) framework is reorganized to allow treating individual mass isotopomers and breaking up of their networks into strongly connected components (SCCs). A variable domain parallel algorithm is introduced to process ordinary differential equations in a parallel way. 15-fold acceleration is achieved for constant-step-size modeling and ~ fivefold acceleration for adaptive-step-size modeling. CONCLUSION This algorithm is universally applicable to isotope granules such as EMUs and cumomers and can substantially accelerate instationary 13C fluxomics modeling. It thus has great potential to be widely adopted in any instationary 13C fluxomics modeling.
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Affiliation(s)
- Zhengdong Zhang
- College of Mathematics and Information Science, Guiyang University, Guiyang, Guizhou China
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou China
| | - Zhentao Liu
- College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou China
| | - Yafei Meng
- College of Mathematics and Information Science, Guiyang University, Guiyang, Guizhou China
| | - Zhen Chen
- School of Mathematics and Sciences, Guizhou Normal University, Guiyang, Guizhou China
| | - Jiayu Han
- School of Mathematics and Sciences, Guizhou Normal University, Guiyang, Guizhou China
| | - Yimin Wei
- School of Mathematics Sciences and Key Laboratory of Mathematics for Nonlinear Sciences, Fudan University, Shanghai, China
| | - Tie Shen
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou China
| | - Yin Yi
- College of Life Science, Guizhou Normal University, Guiyang, Guizhou China
| | - Xiaoyao Xie
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guiyang, Guizhou China
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Clark TJ, Guo L, Morgan J, Schwender J. Modeling Plant Metabolism: From Network Reconstruction to Mechanistic Models. ANNUAL REVIEW OF PLANT BIOLOGY 2020; 71:303-326. [PMID: 32017600 DOI: 10.1146/annurev-arplant-050718-100221] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For 13C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.
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Affiliation(s)
- Teresa J Clark
- Biology Department, Brookhaven National Laboratory, Upton, New York 11973, USA; ,
| | - Longyun Guo
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, USA; ,
| | - John Morgan
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, USA; ,
| | - Jorg Schwender
- Biology Department, Brookhaven National Laboratory, Upton, New York 11973, USA; ,
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35
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Fernández-García J, Altea-Manzano P, Pranzini E, Fendt SM. Stable Isotopes for Tracing Mammalian-Cell Metabolism In Vivo. Trends Biochem Sci 2020; 45:185-201. [PMID: 31955965 DOI: 10.1016/j.tibs.2019.12.002] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 12/11/2019] [Accepted: 12/12/2019] [Indexed: 02/07/2023]
Abstract
Metabolism is at the cornerstone of all cellular functions and mounting evidence of its deregulation in different diseases emphasizes the importance of a comprehensive understanding of metabolic regulation at the whole-organism level. Stable-isotope measurements are a powerful tool for probing cellular metabolism and, as a result, are increasingly used to study metabolism in in vivo settings. The additional complexity of in vivo metabolic measurements requires paying special attention to experimental design and data interpretation. Here, we review recent work where in vivo stable-isotope measurements have been used to address relevant biological questions within an in vivo context, summarize different experimental and data interpretation approaches and their limitations, and discuss future opportunities in the field.
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Affiliation(s)
- Juan Fernández-García
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, VIB, Herestraat 49, 3000 Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Herestraat 49, 3000 Leuven, Belgium.
| | - Patricia Altea-Manzano
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, VIB, Herestraat 49, 3000 Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Herestraat 49, 3000 Leuven, Belgium
| | - Erica Pranzini
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, VIB, Herestraat 49, 3000 Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Herestraat 49, 3000 Leuven, Belgium; Department of Experimental and Clinical Biomedical Sciences, University of Florence, Viale Morgagni 50, 50134 Florence, Italy
| | - Sarah-Maria Fendt
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, VIB, Herestraat 49, 3000 Leuven, Belgium; Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Herestraat 49, 3000 Leuven, Belgium.
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36
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Giraudeau P. NMR-based metabolomics and fluxomics: developments and future prospects. Analyst 2020; 145:2457-2472. [DOI: 10.1039/d0an00142b] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Recent NMR developments are acting as game changers for metabolomics and fluxomics – a critical and perspective review.
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37
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Metabolic flux ratio analysis by parallel 13C labeling of isoprenoid biosynthesis in Rhodobacter sphaeroides. Metab Eng 2019; 57:228-238. [PMID: 31843486 DOI: 10.1016/j.ymben.2019.12.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 12/02/2019] [Accepted: 12/12/2019] [Indexed: 11/21/2022]
Abstract
Metabolic engineering for increased isoprenoid production often benefits from the simultaneous expression of the two naturally available isoprenoid metabolic routes, namely the 2-methyl-D-erythritol 4-phosphate (MEP) pathway and the mevalonate (MVA) pathway. Quantification of the contribution of these pathways to the overall isoprenoid production can help to obtain a better understanding of the metabolism within a microbial cell factory. Such type of investigation can benefit from 13C metabolic flux ratio studies. Here, we designed a method based on parallel labeling experiments (PLEs), using [1-13C]- and [4-13C]glucose as tracers to quantify the metabolic flux ratios in the glycolytic and isoprenoid pathways. By just analyzing a reporter isoprenoid molecule and employing only four equations, we could describe the metabolism involved from substrate catabolism to product formation. These equations infer 13C atom incorporation into the universal isoprenoid building blocks, isopentenyl-pyrophosphate (IPP) and dimethylallyl-pyrophosphate (DMAPP). Therefore, this renders the method applicable to the study of any of isoprenoid of interest. As proof of principle, we applied it to study amorpha-4,11-diene biosynthesis in the bacterium Rhodobacter sphaeroides. We confirmed that in this species the Entner-Doudoroff pathway is the major pathway for glucose catabolism, while the Embden-Meyerhof-Parnas pathway contributes to a lesser extent. Additionally, we demonstrated that co-expression of the MEP and MVA pathways caused a mutual enhancement of their metabolic flux capacity. Surprisingly, we also observed that the isoprenoid flux ratio remains constant under exponential growth conditions, independently from the expression level of the MVA pathway. Apart from proposing and applying a tool for studying isoprenoid biosynthesis within a microbial cell factory, our work reveals important insights from the co-expression of MEP and MVA pathways, including the existence of a yet unclear interaction between them.
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Kappelmann J, Beyß M, Nöh K, Noack S. Separation of 13C- and 15N-Isotopologues of Amino Acids with a Primary Amine without Mass Resolution by Means of O-Phthalaldehyde Derivatization and Collision Induced Dissociation. Anal Chem 2019; 91:13407-13417. [PMID: 31577133 DOI: 10.1021/acs.analchem.9b01788] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Computational and experimental advances of recent years have culminated in establishing 13C-Metabolic Flux Analysis (13C-MFA) as a routine methodology to unravel the fluxome. As the acronym suggests, 13C-MFA has relied on the relative abundance of 13C-isotopes in metabolites for flux inference, most commonly measured by mass spectrometry. In this manuscript we expand the scope of labeling measurements to the case of simultaneous 13C- and 15N-labeling of amino acids. Analytically, the separation of isotopologues of this metabolite class can only be achieved at resolving power beyond 65,000. In this manuscript we harvest an overlooked property of the collision induced dissociation of amino acid adducts to discern 13C- and 15N- isotopologues of amino acids with a primary amine without separating them in the m/z domain.
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Affiliation(s)
- Jannick Kappelmann
- Institute of Bio- and Geosciences I, IBG-1: Biotechnology , Forschungszentrum Jülich GmbH , 52425 Jülich , Germany
| | - Martin Beyß
- Institute of Bio- and Geosciences I, IBG-1: Biotechnology , Forschungszentrum Jülich GmbH , 52425 Jülich , Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences I, IBG-1: Biotechnology , Forschungszentrum Jülich GmbH , 52425 Jülich , Germany
| | - Stephan Noack
- Institute of Bio- and Geosciences I, IBG-1: Biotechnology , Forschungszentrum Jülich GmbH , 52425 Jülich , Germany.,Bioeconomy Science Center (BioSC) , Forschungszentrum Jülich GmbH , 52425 Jülich , Germany
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Balcells C, Foguet C, Tarragó-Celada J, de Atauri P, Marin S, Cascante M. Tracing metabolic fluxes using mass spectrometry: Stable isotope-resolved metabolomics in health and disease. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2018.12.025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Yakubovskaya E, Zaliznyak T, Martínez Martínez J, Taylor GT. Tear Down the Fluorescent Curtain: A New Fluorescence Suppression Method for Raman Microspectroscopic Analyses. Sci Rep 2019; 9:15785. [PMID: 31673106 PMCID: PMC6823364 DOI: 10.1038/s41598-019-52321-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 10/16/2019] [Indexed: 01/30/2023] Open
Abstract
The near exponential proliferation of published Raman microspectroscopic applications over the last decade bears witness to the strengths and versatility of this technology. However, laser-induced fluorescence often severely impedes its application to biological samples. Here we report a new approach for near complete elimination of laser-induced background fluorescence in highly pigmented biological specimens (e.g., microalgae) enabling interrogation by Raman microspectroscopy. Our simple chemiphotobleaching method combines mild hydrogen peroxide oxidation with broad spectrum visible light irradiation of the entire specimen. This treatment permits observing intracellular distributions of macromolecular pools, isotopic tracers, and even viral propagation within cells previously not amenable to Raman microspectroscopic examination. Our approach demonstrates the potential for confocal Raman microspectroscopy becoming an indispensable tool to obtain spatially-resolved data on the chemical composition of highly fluorescent biological samples from individual cells to environmental samples.
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Affiliation(s)
- Elena Yakubovskaya
- School Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Tatiana Zaliznyak
- School Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, 11794, USA
| | | | - Gordon T Taylor
- School Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, 11794, USA.
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Emami Khoonsari P, Moreno P, Bergmann S, Burman J, Capuccini M, Carone M, Cascante M, de Atauri P, Foguet C, Gonzalez-Beltran AN, Hankemeier T, Haug K, He S, Herman S, Johnson D, Kale N, Larsson A, Neumann S, Peters K, Pireddu L, Rocca-Serra P, Roger P, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone SA, Schober D, Selivanov V, Thévenot EA, van Vliet M, Zanetti G, Steinbeck C, Kultima K, Spjuth O. Interoperable and scalable data analysis with microservices: applications in metabolomics. Bioinformatics 2019; 35:3752-3760. [PMID: 30851093 PMCID: PMC6761976 DOI: 10.1093/bioinformatics/btz160] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 02/25/2019] [Accepted: 03/08/2019] [Indexed: 12/20/2022] Open
Abstract
MOTIVATION Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. RESULTS We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science. AVAILABILITY AND IMPLEMENTATION The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Payam Emami Khoonsari
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Joachim Burman
- Department of Neuroscience, Uppsala University, Uppsala, Sweden
| | - Marco Capuccini
- Department of Information Technology, Uppsala University, Uppsala, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Matteo Carone
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, and Institute of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics Node at INB-Bioinfarmatics Platform, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Pedro de Atauri
- Department of Biochemistry and Molecular Biomedicine, and Institute of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics Node at INB-Bioinfarmatics Platform, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, and Institute of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics Node at INB-Bioinfarmatics Platform, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | | | - Thomas Hankemeier
- Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Kenneth Haug
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Sijin He
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Stephanie Herman
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - David Johnson
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Namrata Kale
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Anders Larsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Steffen Neumann
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany
| | - Kristian Peters
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany
| | - Luca Pireddu
- CRS4: Center for Advanced Studies, Research and Development in Sardinia, Distributed Computing Group, Pula, Italy
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Pierrick Roger
- CEA, LIST, Laboratory for Data Analysis and Systems' Intelligence, MetaboHUB, Gif-sur-Yvette, France
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Christoph Ruttkies
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany
| | - Noureddin Sadawi
- Faculty of Medicine, Department of Surgery & Cancer, Imperial College London, London, UK
| | - Reza M Salek
- International Agency for Research on Cancer, 69372 Lyon CEDEX 08, France
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Daniel Schober
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany
| | - Vitaly Selivanov
- Department of Biochemistry and Molecular Biomedicine, and Institute of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics Node at INB-Bioinfarmatics Platform, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Analysis and Systems' Intelligence, MetaboHUB, Gif-sur-Yvette, France
| | - Michael van Vliet
- Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Gianluigi Zanetti
- CRS4: Center for Advanced Studies, Research and Development in Sardinia, Distributed Computing Group, Pula, Italy
| | - Christoph Steinbeck
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University, Jena, Germany
| | - Kim Kultima
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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Foguet C, Jayaraman A, Marin S, Selivanov VA, Moreno P, Messeguer R, de Atauri P, Cascante M. p13CMFA: Parsimonious 13C metabolic flux analysis. PLoS Comput Biol 2019; 15:e1007310. [PMID: 31490922 PMCID: PMC6750616 DOI: 10.1371/journal.pcbi.1007310] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 09/18/2019] [Accepted: 08/06/2019] [Indexed: 12/05/2022] Open
Abstract
Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are 13C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions. However, 13C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious 13C MFA (p13CMFA), an approach that runs a secondary optimization in the 13C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with 13C data. As proof of concept, we demonstrate how p13CMFA can be used to estimate intracellular flux distributions from 13C measurements and transcriptomics data. We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2). 13C Metabolic Flux Analysis (13C MFA) is a well-established technique that has proven to be a valuable tool in quantifying the metabolic flux profile of central carbon metabolism. When a biological system is incubated with a 13C-labeled substrate, 13C propagates to metabolites throughout the metabolic network in a flux and pathway-dependent manner. 13C MFA integrates measurements of 13C enrichment in metabolites to identify the flux distributions consistent with the measured 13C propagation. However, there is often a range of flux values that can lead to the observed 13C distribution. Indeed, either when the metabolic network is large or a small set of measurements are integrated, the range of valid solutions can be too wide to accurately estimate part of the underlying flux distribution. Here we propose to use flux minimization to select the best flux solution in the13C MFA solution space. Furthermore, this approach can integrate gene expression data to give greater weight to the minimization of fluxes through enzymes with low gene expression evidence in order to ensure that the selected solution is biologically relevant. The concept of using flux minimization to select the best solution is widely used in flux balance analysis, but it had never been applied in the framework of 13C MFA. We have termed this new approach parsimonious 13C MFA (p13CMFA).
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Affiliation(s)
- Carles Foguet
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Anusha Jayaraman
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | - Silvia Marin
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Vitaly A. Selivanov
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
| | - Ramon Messeguer
- LEITAT Technological Center, Health & Biomedicine Unit, Barcelona, Spain
| | - Pedro de Atauri
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- * E-mail: (PdA); (MC)
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine & Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- * E-mail: (PdA); (MC)
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Zampieri G, Vijayakumar S, Yaneske E, Angione C. Machine and deep learning meet genome-scale metabolic modeling. PLoS Comput Biol 2019; 15:e1007084. [PMID: 31295267 PMCID: PMC6622478 DOI: 10.1371/journal.pcbi.1007084] [Citation(s) in RCA: 150] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.
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Affiliation(s)
- Guido Zampieri
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Supreeta Vijayakumar
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Elisabeth Yaneske
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
| | - Claudio Angione
- Department of Computer Science and Information Systems, Teesside University, Middlesbrough, United Kingdom
- Healthcare Innovation Centre, Teesside University, Middlesbrough, United Kingdom
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Karakitsou E, Foguet C, de Atauri P, Kultima K, Khoonsari PE, Martins dos Santos VA, Saccenti E, Rosato A, Cascante M. Metabolomics in systems medicine: an overview of methods and applications. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.coisb.2019.03.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Beyß M, Azzouzi S, Weitzel M, Wiechert W, Nöh K. The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis. Front Microbiol 2019; 10:1022. [PMID: 31178829 PMCID: PMC6543931 DOI: 10.3389/fmicb.2019.01022] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 04/24/2019] [Indexed: 12/16/2022] Open
Abstract
13C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13C MFA, however, stands out from other "-omics sciences," in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an "all-around carefree package" for 13C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13C MFA.
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Affiliation(s)
- Martin Beyß
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Salah Azzouzi
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Michael Weitzel
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Wolfgang Wiechert
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany.,Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany
| | - Katharina Nöh
- Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich GmbH, Jülich, Germany
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Presnell KV, Alper HS. Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering. Biotechnol J 2019; 14:e1800416. [PMID: 30927499 DOI: 10.1002/biot.201800416] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 02/20/2019] [Indexed: 12/30/2022]
Abstract
The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of 'omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data-driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system-scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.
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Affiliation(s)
- Kristin V Presnell
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St. Stop C0400, Austin, TX, 78712, USA
| | - Hal S Alper
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St. Stop C0400, Austin, TX, 78712, USA.,Institute for Cellular and Molecular Biology, The University of Texas at Austin, 100 E 24 St., Austin, TX, 78712, USA
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Peters K, Bradbury J, Bergmann S, Capuccini M, Cascante M, de Atauri P, Ebbels TMD, Foguet C, Glen R, Gonzalez-Beltran A, Günther UL, Handakas E, Hankemeier T, Haug K, Herman S, Holub P, Izzo M, Jacob D, Johnson D, Jourdan F, Kale N, Karaman I, Khalili B, Emami Khonsari P, Kultima K, Lampa S, Larsson A, Ludwig C, Moreno P, Neumann S, Novella JA, O'Donovan C, Pearce JTM, Peluso A, Piras ME, Pireddu L, Reed MAC, Rocca-Serra P, Roger P, Rosato A, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone SA, Selivanov V, Spjuth O, Schober D, Thévenot EA, Tomasoni M, van Rijswijk M, van Vliet M, Viant MR, Weber RJM, Zanetti G, Steinbeck C. PhenoMeNal: processing and analysis of metabolomics data in the cloud. Gigascience 2019; 8:giy149. [PMID: 30535405 PMCID: PMC6377398 DOI: 10.1093/gigascience/giy149] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 10/19/2018] [Accepted: 11/20/2018] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. FINDINGS PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. CONCLUSIONS PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains.
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Affiliation(s)
- Kristian Peters
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - James Bradbury
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marco Capuccini
- Division of Scientific Computing, Department of Information Technology, Uppsala University, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Pedro de Atauri
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Timothy M D Ebbels
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Robert Glen
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB21EW, United Kingdom
| | - Alejandra Gonzalez-Beltran
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Ulrich L Günther
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Evangelos Handakas
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Thomas Hankemeier
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, 2333 CC, The Netherlands
| | - Kenneth Haug
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Stephanie Herman
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | | | - Massimiliano Izzo
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Daniel Jacob
- INRA, University of Bordeaux, Plateforme Métabolome Bordeaux-MetaboHUB, 33140 Villenave d'Ornon, France
| | - David Johnson
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
- Department of Informatics and Media, Uppsala University, Box 513, 751 20 Uppsala, Sweden
| | - Fabien Jourdan
- INRA - French National Institute for Agricultural Research, UMR1331, Toxalim, Research Centre in Food Toxicology, Toulouse, France
| | - Namrata Kale
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Ibrahim Karaman
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St. Mary's Campus, Norfolk Place, W2 1PG, London, United Kingdom
| | - Bita Khalili
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Payam Emami Khonsari
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | - Kim Kultima
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | - Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Anders Larsson
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Christian Ludwig
- Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany
| | - Jon Ander Novella
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Jake T M Pearce
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Alina Peluso
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | | | | | - Michelle A C Reed
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Pierrick Roger
- CEA, LIST, Laboratory for Data Analysis and Systems’ Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France
| | - Antonio Rosato
- Magnetic Resonance Center (CERM) and Department of Chemistry, University of Florence and CIRMMP, 50019 Sesto Fiorentino, Florence, Italy
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Christoph Ruttkies
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - Noureddin Sadawi
- Department of Computer Science, College of Engineering, Design and Physical Sciences, Brunel University, London, UK
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Reza M Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Vitaly Selivanov
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Daniel Schober
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Analysis and Systems’ Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France
| | - Mattia Tomasoni
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Merlijn van Rijswijk
- Netherlands Metabolomics Center, Leiden, 2333 CC, Netherlands
- ELIXIR-NL, Dutch Techcentre for Life Sciences, Utrecht, 3503 RM, Netherlands
| | - Michael van Vliet
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, 2333 CC, The Netherlands
| | - Mark R Viant
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Ralf J M Weber
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | | | - Christoph Steinbeck
- Cheminformatics and Computational Metabolomics, Institute for Analytical Chemistry, Lessingstr. 8, 07743 Jena, Germany
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Küken A, Eloundou-Mbebi JMO, Basler G, Nikoloski Z. Cellular determinants of metabolite concentration ranges. PLoS Comput Biol 2019; 15:e1006687. [PMID: 30677015 PMCID: PMC6345444 DOI: 10.1371/journal.pcbi.1006687] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 11/29/2018] [Indexed: 11/19/2022] Open
Abstract
Cellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component’s concentration resides in a given range remain elusive. We present network properties which suffice to identify components whose concentration ranges can be efficiently computed in mass-action metabolic networks. We show that the derived ranges are in excellent agreement with simulations from a detailed kinetic metabolic model of Escherichia coli. We demonstrate that the approach can be used with genome-scale metabolic models to arrive at predictions concordant with measurements from Escherichia coli under different growth scenarios. By application to 14 genome-scale metabolic models from diverse species, our approach specifies the cellular determinants of concentration ranges that can be effectively employed to make predictions for a variety of biotechnological and medical applications. We present a computational approach for inferring concentration ranges from genome-scale metabolic models. The approach specifies a determinant and molecular mechanism underling facile control of concentration ranges for components in large-scale cellular networks. Most importantly, the predictions about concentration ranges do not require knowledge of kinetic parameters (which are difficult to specify at a genome scale), provided measurements of concentrations in a reference state. The approach assumes that reaction rates follow the mass action law used in the derivations of other types of kinetics. We apply the approach with large-scale kinetic and stoichiometric metabolic models of organisms from different kingdoms of life to show that we can identify a proportion of metabolites to which our approach is applicable. By challenging the predictions of concentration ranges in the genome-scale metabolic network of E. coli with real-world data sets, we further demonstrate the prediction power and limitations of the approach.
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Affiliation(s)
- Anika Küken
- System Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany
| | - Jeanne M. O. Eloundou-Mbebi
- System Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany
| | - Georg Basler
- System Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
| | - Zoran Nikoloski
- System Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany
- * E-mail:
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Fernández-García J, Fendt SM. Assessing the Impact of the Nutrient Microenvironment on the Metabolism of Effector CD8 + T Cells. Methods Mol Biol 2019; 1862:187-216. [PMID: 30315469 DOI: 10.1007/978-1-4939-8769-6_14] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Immune cell function is tightly regulated by cellular metabolism, which in turn is strongly linked to the nutrient availability in the microenvironment surrounding the cells. This link is critical for effector CD8+ T cells which, after activation, must migrate from nutrient-rich environments into nutrient-scarce regions such as the tumor microenvironment. Assessing how nutrient availability modulates the metabolism of effector CD8+ T cells is thus key for understanding how harsh environments may impair their proliferation and effector function. Here, we describe an approach to systematically study the impact of the nutrient microenvironment on the metabolism of effector CD8+ T cells, based on performing stable 13C isotope labeling measurements on in vitro-differentiated murine effector CD8+ T cells.
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Affiliation(s)
- Juan Fernández-García
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB Center for Cancer Biology, VIB, Leuven, Belgium.,Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, Leuven Cancer Institute (LKI), KU Leuven, Leuven, Belgium
| | - Sarah-Maria Fendt
- Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium.
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50
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Jönsson TJ, Schäfer HL, Herling AW, Brönstrup M. A metabolome-wide characterization of the diabetic phenotype in ZDF rats and its reversal by pioglitazone. PLoS One 2018; 13:e0207210. [PMID: 30481177 PMCID: PMC6258476 DOI: 10.1371/journal.pone.0207210] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 10/26/2018] [Indexed: 12/19/2022] Open
Abstract
Type 2 diabetes (T2D) is a complex metabolic disease associated with alterations in glucose, lipid and protein metabolism. In order to characterize the biochemical phenotype of the Zucker diabetic fatty (ZDF) rat, the most common animal model for the study of T2D, and the impact of the insulin sensitizer pioglitazone, a global, mass spectrometry-based analysis of the metabolome was conducted. Overall, 420 metabolites in serum, 443 in the liver and 603 in the intestine were identified at study end. In comparison to two control groups, obese diabetic ZDF rats showed characteristic metabolic signatures that included hyperglycemia, elevated β-oxidation, dyslipidemia—featured by an increase in saturated and monounsaturated fatty acids and a decrease of medium chain and of polyunsaturated fatty acids in serum–and decreased amino acid levels, consistent with their utilization in hepatic gluconeogenesis. A 13-week treatment with the PPARγ agonist pioglitazone reversed most of these signatures: Pioglitazone improved glycemic control and the fatty acid profile, elevated amino acid levels in the liver, but decreased branched chain amino acids in serum. The hitherto most comprehensive metabolic profiling study identified a biochemical blueprint for the ZDF diabetic model and captured the impact of genetic, nutritional and pharmacological perturbations. The in-depth characterization on the molecular level deepens the understanding and further validates the ZDF rat as a suitable preclinical model of diabetes in humans.
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Affiliation(s)
| | | | | | - Mark Brönstrup
- Helmholtz Centre for Infection Research and German Center for Infection Research (DZIF), Braunschweig, Germany
- * E-mail:
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