1
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Patil N, Mirveis Z, Byrne HJ. Kinetic modelling of the cellular metabolic responses underpinning in vitro glycolysis assays. FEBS Open Bio 2024; 14:466-486. [PMID: 38217078 PMCID: PMC10909989 DOI: 10.1002/2211-5463.13765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/21/2023] [Accepted: 01/02/2024] [Indexed: 01/14/2024] Open
Abstract
This study aims to demonstrate the benefits of augmenting commercially available, real-time, in vitro glycolysis assays with phenomenological rate equation-based kinetic models, describing the contributions of the underpinning metabolic pathways. To this end, a commercially available glycolysis assay, sensitive to changes in extracellular acidification (extracellular pH), was used to derive the glycolysis pathway kinetics. The pathway was numerically modelled using a series of ordinary differential rate equations, to simulate the obtained experimental results. The sensitivity of the model to the key equation parameters was also explored. The cellular glycolysis pathway kinetics were determined for three different cell-lines, under nonmodulated and modulated conditions. Over the timescale studied, the assay demonstrated a two-phase metabolic response, representing the differential kinetics of glycolysis pathway rate as a function of time, and this behaviour was faithfully reproduced by the model simulations. The model enabled quantitative comparison of the pathway kinetics of three cell lines, and also the modulating effect of two known drugs. Moreover, the modelling tool allows the subtle differences between different cell lines to be better elucidated and also allows augmentation of the assay sensitivity. A simplistic numerical model can faithfully reproduce the differential pathway kinetics for three different cell lines, with and without pathway-modulating drugs, and furthermore provides insights into the cellular metabolism by elucidating the underlying mechanisms leading to the pathway end-product. This study demonstrates that augmenting a relatively simple, real-time, in vitro assay with a model of the underpinning metabolic pathway provides considerable insights into the observed differences in cellular systems.
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Affiliation(s)
- Nitin Patil
- FOCAS Research InstituteTU DublinIreland
- School of Physics, Optometric and Clinical SciencesTU DublinIreland
| | - Zohreh Mirveis
- FOCAS Research InstituteTU DublinIreland
- School of Physics, Optometric and Clinical SciencesTU DublinIreland
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2
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Orlowska MK, Krycer JR, Reid JD, Mills RJ, Doran MR, Hudson JE. A miniaturized culture platform for control of the metabolic environment. BIOMICROFLUIDICS 2024; 18:024101. [PMID: 38434908 PMCID: PMC10908563 DOI: 10.1063/5.0169143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 02/05/2024] [Indexed: 03/05/2024]
Abstract
The heart is a metabolic "omnivore" and adjusts its energy source depending on the circulating metabolites. Human cardiac organoids, a three-dimensional in vitro model of the heart wall, are a useful tool to study cardiac physiology and pathology. However, cardiac tissue naturally experiences shear stress and nutrient fluctuations via blood flow in vivo, whilst in vitro models are conventionally cultivated in a static medium. This necessitates the regular refreshing of culture media, which creates acute cellular disturbances and large metabolic fluxes. To culture human cardiac organoids in a more physiological manner, we have developed a perfused bioreactor for cultures in a 96-well plate format. The designed bioreactor is easy to fabricate using a common culture plate and a 3D printer. Its open system allows for the use of traditional molecular biology techniques, prevents flow blockage issues, and provides easy access for sampling and cell assays. We hypothesized that a perfused culture would create more stable environment improving cardiac function and maturation. We found that lactate is rapidly produced by human cardiac organoids, resulting in large fluctuations in this metabolite under static culture. Despite this, neither medium perfusion in bioreactor culture nor lactate supplementation improved cardiac function or maturation. In fact, RNA sequencing revealed little change across the transcriptome. This demonstrates that cardiac organoids are robust in response to fluctuating environmental conditions under normal physiological conditions. Together, we provide a framework for establishing an easily accessible perfusion system that can be adapted to a range of miniaturized cell culture systems.
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3
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Medina A, Bruno J, Alemán JO. Metabolic flux analysis in adipose tissue reprogramming. IMMUNOMETABOLISM (COBHAM, SURREY) 2024; 6:e00039. [PMID: 38455681 PMCID: PMC10916752 DOI: 10.1097/in9.0000000000000039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 01/29/2024] [Indexed: 03/09/2024]
Abstract
Obesity is a growing epidemic in the United States and worldwide and is associated with insulin resistance and cardiovascular disease, among other comorbidities. Understanding of the pathology that links overnutrition to these disease processes is ongoing. Adipose tissue is a heterogeneous organ comprised of multiple different cell types and it is likely that dysregulated metabolism within these cell populations disrupts both inter- and intracellular interactions and is a key driver of human disease. In recent years, metabolic flux analysis, which offers a precise quantification of metabolic pathway fluxes in biological systems, has emerged as a candidate strategy for uncovering the metabolic changes that stoke these disease processes. In this mini review, we discuss metabolic flux analysis as an experimental tool, with a specific emphasis on mass spectrometry with isotope tracing as this is the technique most frequently used for metabolic flux analysis in adipocytes. Furthermore, we examine existing literature that uses metabolic flux analysis to further our understanding of adipose tissue biology. Our group has a specific interest in understanding the role of white adipose tissue inflammation in the progression of cardiometabolic disease, as we know that in obesity the accumulation of pro-inflammatory adipose tissue macrophages is associated with significant morbidity, so we use this as a paradigm throughout our review for framing the application of these experimental techniques. However, there are many other biological applications to which they can be applied to further understanding of not only adipose tissue biology but also systemic homeostasis.
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Affiliation(s)
- Ashley Medina
- Laboratory of Translational Obesity Research, New York University Grossman School of Medicine, New York, NY, USA
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Joanne Bruno
- Laboratory of Translational Obesity Research, New York University Grossman School of Medicine, New York, NY, USA
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - José O. Alemán
- Laboratory of Translational Obesity Research, New York University Grossman School of Medicine, New York, NY, USA
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
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4
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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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5
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Luo H, Shen T, Xie X. Stochastic simulation of enzymatic kinetics for 13C isotope labeling at the single-cell scale. REACTION KINETICS MECHANISMS AND CATALYSIS 2022. [DOI: 10.1007/s11144-022-02262-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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6
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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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7
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Schlicker L, Zhao G, Dudek CA, Boers HM, Meyer-Hermann M, Jacobs DM, Hiller K. Systemic Lactate Acts as a Metabolic Buffer in Humans and Prevents Nutrient Overflow in the Postprandial Phase. Front Nutr 2022; 9:785999. [PMID: 35360693 PMCID: PMC8961325 DOI: 10.3389/fnut.2022.785999] [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: 09/29/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
On an organismal level, metabolism needs to react in a well-orchestrated manner to metabolic challenges such as nutrient uptake. Key metabolic hubs in human blood are pyruvate and lactate, both of which are constantly interconverted by very fast exchange fluxes. The quantitative contribution of different food sources to these metabolite pools remains unclear. Here, we applied in vivo stable isotope labeling to determine postprandial metabolic fluxes in response to two carbohydrate sources of different complexity. Depending on the ingested carbohydrate source, glucose or wheat flour, the net direction of the lactate dehydrogenase, and the alanine amino transferase fluxes were adjusted in a way to ensure sufficient availability, while, at the same time, preventing an overflow in the respective metabolite pools. The systemic lactate pool acts as a metabolic buffer which is fueled in the early- and depleted in the late-postprandial phase and thus plays a key role for systemic metabolic homeostasis.
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Affiliation(s)
- Lisa Schlicker
- Department for Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Brunswick, Germany
- Deutsches Krebsforschungszentrum, Heidelberg, Germany
| | - Gang Zhao
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Brunswick, Germany
- Centre for Individualised Infection Medicine, Hanover, Germany
| | - Christian-Alexander Dudek
- Department for Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Brunswick, Germany
- BRENDA Enzyme Database, BRICS, Technische Universität Braunschweig, Brunswick, Germany
| | - Hanny M. Boers
- Unilever Foods Innovation Centre, Wageningen, Netherlands
| | - Michael Meyer-Hermann
- Department of Systems Immunology and Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Brunswick, Germany
- Centre for Individualised Infection Medicine, Hanover, Germany
- Institute of Biochemistry, Biotechnology and Bioinformatics, BRICS, Technische Universität Braunschweig, Brunswick, Germany
| | | | - Karsten Hiller
- Department for Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Brunswick, Germany
- *Correspondence: Karsten Hiller
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8
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Insulin resistance rewires the metabolic gene program and glucose utilization in human white adipocytes. Int J Obes (Lond) 2022; 46:535-543. [PMID: 34799672 DOI: 10.1038/s41366-021-01021-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 10/22/2021] [Accepted: 11/02/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND In obesity, adipose tissue dysfunction resulting from excessive fat accumulation leads to systemic insulin resistance (IR), the underlying alteration of Type 2 Diabetes. The specific pathways dysregulated in dysfunctional adipocytes and the extent to which it affects adipose metabolic functions remain incompletely characterized. METHODS We interrogated the transcriptional adaptation to increased adiposity in association with insulin resistance in visceral white adipose tissue from lean men, or men presenting overweight/obesity (BMI from 19 to 33) and discordant for insulin sensitivity. In human adipocytes in vitro, we investigated the direct contribution of IR in altering metabolic gene programming and glucose utilization using 13C-isotopic glucose tracing. RESULTS We found that gene expression associated with impaired glucose and lipid metabolism and inflammation represented the strongest association with systemic insulin resistance, independently of BMI. In addition, we showed that inducing IR in mature human white adipocytes was sufficient to reprogram the transcriptional profile of genes involved in important metabolic functions such as glycolysis, the pentose phosphate pathway and de novo lipogenesis. Finally, we found that IR induced a rewiring of glucose metabolism, with higher incorporation of glucose into citrate, but not into downstream metabolites within the TCA cycle. CONCLUSIONS Collectively, our data highlight the importance of obesity-derived insulin resistance in impacting the expression of key metabolic genes and impairing the metabolic processes of glucose utilization, and reveal a role for metabolic adaptation in adipose dysfunction in humans.
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9
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Yadav M, Joshi C, Paritosh K, Thakur J, Pareek N, Masakapalli SK, Vivekanand V. Reprint of:Organic waste conversion through anaerobic digestion: A critical insight into the metabolic pathways and microbial interactions. Metab Eng 2022; 71:62-76. [DOI: 10.1016/j.ymben.2022.02.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/17/2021] [Accepted: 11/30/2021] [Indexed: 12/25/2022]
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10
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Krycer JR, Lor M, Fitzsimmons RL, Hudson JE. A cell culture platform for quantifying metabolic substrate oxidation in bicarbonate-buffered medium. J Biol Chem 2021; 298:101547. [PMID: 34971704 PMCID: PMC8819040 DOI: 10.1016/j.jbc.2021.101547] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/21/2021] [Accepted: 12/23/2021] [Indexed: 12/21/2022] Open
Abstract
Complex diseases such as cancer and diabetes are underpinned by changes in metabolism, specifically by which and how nutrients are catabolized. Substrate utilization can be directly examined by measuring a metabolic endpoint rather than an intermediate (such as tricarboxylic cycle metabolite). For instance, oxidation of specific substrates can be measured in vitro by incubation of live cultures with substrates containing radiolabeled carbon and measuring radiolabeled carbon dioxide. To increase throughput, we previously developed a miniaturized platform to measure substrate oxidation of both adherent and suspension cells using multiwell plates rather than flasks. This enabled multiple conditions to be examined simultaneously, ideal for drug screens and mechanistic studies. However, like many metabolic assays, this was not compatible with bicarbonate-buffered media, which is susceptible to alkalinization upon exposure to gas containing little carbon dioxide such as air. While other buffers such as HEPES can overcome this problem, bicarbonate has additional biological roles as a metabolic substrate and in modulating hormone signaling. Here, we create a bicarbonate-buffered well-plate platform to measure substrate oxidation. This was achieved by introducing a sealed environment within each well that was equilibrated with carbon dioxide, enabling bicarbonate buffering. As proof of principle, we assessed metabolic flux in cultured adipocytes, demonstrating that bicarbonate-buffered medium increased lipogenesis, glucose oxidation, and sensitivity to insulin in comparison to HEPES-buffered medium. This convenient and high-throughput method facilitates the study and screening of metabolic activity under more physiological conditions to aid biomedical research.
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Affiliation(s)
- James R Krycer
- QIMR Berghofer Medical Research Institute; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology
| | - Mary Lor
- QIMR Berghofer Medical Research Institute
| | | | - James E Hudson
- QIMR Berghofer Medical Research Institute; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology; School of Biomedical Sciences, Faculty of Medicine, The University of Queensland.
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11
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Oates EH, Antoniewicz MR. Coordinated reprogramming of metabolism and cell function in adipocytes from proliferation to differentiation. Metab Eng 2021; 69:221-230. [PMID: 34929419 DOI: 10.1016/j.ymben.2021.12.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 09/25/2021] [Accepted: 12/14/2021] [Indexed: 12/12/2022]
Abstract
Adipose tissue plays a major role in regulating lipid and energy homeostasis by storing excess nutrients, releasing energetic substrates through lipolysis, and regulating metabolism of other tissues and organs through endocrine and paracrine signaling. Adipocytes within fat tissues store excess nutrients through increased cell number (hyperplasia), increased cell size (hypertrophy), or both. The differentiation of pre-adipocytes into mature lipid-accumulating adipocytes requires a complex interaction of metabolic pathways that is still incompletely understood. Here, we applied parallel labeling experiments and 13C-metabolic flux analysis to quantify precise metabolic fluxes in proliferating and differentiated 3T3-L1 cells, a widely used model to study adipogenesis. We found that morphological and biomass composition changes in adipocytes were accompanied by significant shifts in metabolic fluxes, encompassing all major metabolic pathways. In contrast to proliferating cells, differentiated adipocytes 1) increased glucose uptake and redirected glucose utilization from lactate production to lipogenesis and energy generation; 2) increased pathway fluxes through glycolysis, oxidative pentose phosphate pathway and citric acid cycle; 3) reduced lactate secretion, resulting in increased ATP generation via oxidative phosphorylation; 4) rewired glutamine metabolism, from glutaminolysis to de novo glutamine synthesis; 5) increased cytosolic NADPH production, driven mostly by increased cytosolic malic enzyme flux; 6) increased production of monounsaturated C16:1; and 7) activated a mitochondrial pyruvate cycle through simultaneous activity of pyruvate carboxylase, malate dehydrogenase and malic enzyme. Taken together, these results quantitatively highlight the complex interplay between pathway fluxes and cell function in adipocytes, and suggest a functional role for metabolic reprogramming in adipose differentiation and lipogenesis.
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Affiliation(s)
- Eleanor H Oates
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE, 19716, USA
| | - Maciek R Antoniewicz
- Department of Chemical and Biomolecular Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, Newark, DE, 19716, USA.
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12
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Organic waste conversion through anaerobic digestion: A critical insight into the metabolic pathways and microbial interactions. Metab Eng 2021; 69:323-337. [PMID: 34864213 DOI: 10.1016/j.ymben.2021.11.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/17/2021] [Accepted: 11/30/2021] [Indexed: 11/23/2022]
Abstract
Anaerobic digestion is a promising method for energy recovery through conversion of organic waste to biogas and other industrial valuables. However, to tap the full potential of anaerobic digestion, deciphering the microbial metabolic pathway activities and their underlying bioenergetics is required. In addition, the behavior of organisms in consortia along with the analytical abilities to kinetically measure their metabolic interactions will allow rational optimization of the process. This review aims to explore the metabolic bottlenecks of the microbial communities adopting latest advances of profiling and 13C tracer-based analysis using state of the art analytical platforms (GC, GC-MS, LC-MS, NMR). The review summarizes the phases of anaerobic digestion, the role of microbial communities, key process parameters of significance, syntrophic microbial interactions and the bottlenecks that are critical for optimal bioenergetics and enhanced production of valuables. Considerations into the designing of efficient synthetic microbial communities as well as the latest advances in capturing their metabolic cross talk will be highlighted. The review further explores how the presence of additives and inhibiting factors affect the metabolic pathways. The critical insight into the reaction mechanism covered in this review may be helpful to optimize and upgrade the anaerobic digestion system.
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13
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Liu P, Hua Y, Zhang W, Xie T, Zhuang Y, Xia J, Noorman H. A new strategy for dynamic metabolic flux estimation by integrating transient metabolome data into genome-scale metabolic models. Bioprocess Biosyst Eng 2021; 44:2553-2565. [PMID: 34459987 DOI: 10.1007/s00449-021-02626-3] [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: 05/31/2021] [Accepted: 08/17/2021] [Indexed: 10/20/2022]
Abstract
Metabolic flux analysis (MFA) is a powerful tool for studying microbial cell physiology. Isotope-based MFA is the accepted standard for studying metabolic fluxes under steady-state conditions. However, its application under dynamic extracellular conditions is limited due to lack of proper techniques, such as rapid sampling and quenching, high cost and laborious execution. Here, we propose a new strategy to tackle this through incorporating dynamic metabolite abundance data into genome-scale metabolic models (GEM). First, a dummy extracellular pool concept is proposed for each dynamically changing metabolite, which represents a "sink" or "source", with corresponding dummy reactions coded into the GEM model. The dynamic model (expressed as differential equations) is then transformed into a quasi-steady-state model (expressed as linear equations), which can be easily solved by constraining the GEM model with the dynamic metabolite quantification data. For this, common linear-programming optimization algorithms were utilized to estimate the dynamic fluxes. Dynamic high-accuracy metabolite abundance data were obtained through the Isotope Dilution Mass Spectrometry (IDMS) method and high-speed sampling-quenching, and it was demonstrated that the newly proposed strategy could be successfully applied to obtain intracellular dynamic fluxes of Aspergillus niger under regimes of single and periodic extracellular glucose pulses. The applicability of the new method was also tested on dynamic fluxes estimation in a glucose pulse-response study of Saccharomyces cerevisiae. The proposed method provides a powerful tool to investigate cell physiology under dynamic conditions, especially relevant for bioprocess scale-up to industrial-scale bioreactors.
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Affiliation(s)
- Peng Liu
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Ye Hua
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Wei Zhang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Tingting Xie
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China.
| | - Jianye Xia
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China.
| | - Henk Noorman
- DSM Biotechnology Center, A. Fleminglaan 1, 2613 AX, Delft, The Netherlands
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14
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Egami R, Kokaji T, Hatano A, Yugi K, Eto M, Morita K, Ohno S, Fujii M, Hironaka KI, Uematsu S, Terakawa A, Bai Y, Pan Y, Tsuchiya T, Ozaki H, Inoue H, Uda S, Kubota H, Suzuki Y, Matsumoto M, Nakayama KI, Hirayama A, Soga T, Kuroda S. Trans-omic analysis reveals obesity-associated dysregulation of inter-organ metabolic cycles between the liver and skeletal muscle. iScience 2021; 24:102217. [PMID: 33748705 PMCID: PMC7961104 DOI: 10.1016/j.isci.2021.102217] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 02/01/2021] [Accepted: 02/18/2021] [Indexed: 12/12/2022] Open
Abstract
Systemic metabolic homeostasis is regulated by inter-organ metabolic cycles involving multiple organs. Obesity impairs inter-organ metabolic cycles, resulting in metabolic diseases. The systemic landscape of dysregulated inter-organ metabolic cycles in obesity has yet to be explored. Here, we measured the transcriptome, proteome, and metabolome in the liver and skeletal muscle and the metabolome in blood of fasted wild-type and leptin-deficient obese (ob/ob) mice, identifying components with differential abundance and differential regulation in ob/ob mice. By constructing and evaluating the trans-omic network controlling the differences in metabolic reactions between fasted wild-type and ob/ob mice, we provided potential mechanisms of the obesity-associated dysfunctions of metabolic cycles between liver and skeletal muscle involving glucose-alanine, glucose-lactate, and ketone bodies. Our study revealed obesity-associated systemic pathological mechanisms of dysfunction of inter-organ metabolic cycles. Multi-omic data in liver and skeletal muscle of WT and ob/ob mice were measured We developed the trans-omic network of differentially regulated metabolic reactions Dysregulation of inter-organ metabolic cycles associated with obesity was revealed
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Affiliation(s)
- Riku Egami
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Toshiya Kokaji
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Atsushi Hatano
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.,Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Department of Omics and Systems Biology, Graduate School of Medical and Dental Sciences, Niigata University, 757 Ichibancho, Asahimachi-dori, Chuo-ku, Niigata City, Niigata 951-8510, Japan
| | - Katsuyuki Yugi
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.,Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Institute for Advanced Biosciences, Keio University, Fujisawa, 252-8520, Japan.,PRESTO, Japan Science and Technology Agency, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Miki Eto
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Keigo Morita
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Satoshi Ohno
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.,Molecular Genetics Research Laboratory, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Masashi Fujii
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.,Molecular Genetics Research Laboratory, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.,Department of Mathematical and Life Sciences, Graduate School of Integrated Sciences for Life, Hiroshima University, 1-3-1 Kagamiyama, Higashi-hiroshima City, Hiroshima, 739-8526, Japan
| | - Ken-Ichi Hironaka
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Saori Uematsu
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Akira Terakawa
- Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Yunfan Bai
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Yifei Pan
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Takaho Tsuchiya
- Bioinformatics Laboratory, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.,Center for Artificial Intelligence Research, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
| | - Haruka Ozaki
- Bioinformatics Laboratory, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.,Center for Artificial Intelligence Research, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan
| | - Hiroshi Inoue
- Metabolism and Nutrition Research Unit, Institute for Frontier Science Initiative, Kanazawa University, 13-1 Takaramachi, Kanazawa, Ishikawa, 920-8641, Japan
| | - Shinsuke Uda
- Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Hiroyuki Kubota
- Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Yutaka Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
| | - Masaki Matsumoto
- Department of Omics and Systems Biology, Graduate School of Medical and Dental Sciences, Niigata University, 757 Ichibancho, Asahimachi-dori, Chuo-ku, Niigata City, Niigata 951-8510, Japan
| | - Keiichi I Nakayama
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Akiyoshi Hirayama
- Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan
| | - Shinya Kuroda
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan.,Department of Biological Sciences, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.,Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency, Bunkyo-ku, Tokyo 113-0033, Japan
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15
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Audano M, Pedretti S, Ligorio S, Giavarini F, Caruso D, Mitro N. Investigating metabolism by mass spectrometry: From steady state to dynamic view. JOURNAL OF MASS SPECTROMETRY : JMS 2021; 56:e4658. [PMID: 33084147 DOI: 10.1002/jms.4658] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/10/2020] [Accepted: 09/14/2020] [Indexed: 06/11/2023]
Abstract
Metabolism is the set of life-sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so-called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady-state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.
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Affiliation(s)
- Matteo Audano
- DiSFeB, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milan, 20133, Italy
| | - Silvia Pedretti
- DiSFeB, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milan, 20133, Italy
| | - Simona Ligorio
- DiSFeB, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milan, 20133, Italy
| | - Flavio Giavarini
- DiSFeB, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milan, 20133, Italy
| | - Donatella Caruso
- DiSFeB, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milan, 20133, Italy
| | - Nico Mitro
- DiSFeB, Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Milan, 20133, Italy
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16
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Ohno S, Quek LE, Krycer JR, Yugi K, Hirayama A, Ikeda S, Shoji F, Suzuki K, Soga T, James DE, Kuroda S. Kinetic Trans-omic Analysis Reveals Key Regulatory Mechanisms for Insulin-Regulated Glucose Metabolism in Adipocytes. iScience 2020; 23:101479. [PMID: 32891058 PMCID: PMC7479629 DOI: 10.1016/j.isci.2020.101479] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 07/17/2020] [Accepted: 08/17/2020] [Indexed: 12/24/2022] Open
Abstract
Insulin regulates glucose metabolism through thousands of regulatory mechanisms; however, which regulatory mechanisms are keys to control glucose metabolism remains unknown. Here, we performed kinetic trans-omic analysis by integrating isotope-tracing glucose flux and phosphoproteomic data from insulin-stimulated adipocytes and built a kinetic mathematical model to identify key allosteric regulatory and phosphorylation events for enzymes. We identified nine reactions regulated by allosteric effectors and one by enzyme phosphorylation and determined the regulatory mechanisms for three of these reactions. Insulin stimulated glycolysis by promoting Glut4 activity by enhancing phosphorylation of AS160 at S595, stimulated fatty acid synthesis by promoting Acly activity through allosteric activation by glucose 6-phosphate or fructose 6-phosphate, and stimulated glutamate synthesis by alleviating allosteric inhibition of Gls by glutamate. Most of glycolytic reactions were regulated by amounts of substrates and products. Thus, phosphorylation or allosteric modulator-based regulation of only a few key enzymes was sufficient to change insulin-induced metabolism.
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Affiliation(s)
- Satoshi Ohno
- Molecular Genetics Research Laboratory, Graduate School of Science, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, Japan
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Lake-Ee Quek
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia
| | - James R. Krycer
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Katsuyuki Yugi
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
- Laboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- PRESTO, Japan Science and Technology Agency, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Akiyoshi Hirayama
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
- AMED-CREST, AMED, 1-7-1 Otemachi, Chiyoda-Ku, Tokyo 100-0004, Japan
| | - Satsuki Ikeda
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Futaba Shoji
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Kumi Suzuki
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
- AMED-CREST, AMED, 1-7-1 Otemachi, Chiyoda-Ku, Tokyo 100-0004, Japan
| | - David E. James
- Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
- Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia
| | - Shinya Kuroda
- Molecular Genetics Research Laboratory, Graduate School of Science, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo, Japan
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
- Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency, Bunkyo-ku, Tokyo 113-0033, Japan
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17
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Krycer JR, Quek LE, Francis D, Zadoorian A, Weiss FC, Cooke KC, Nelson ME, Diaz-Vegas A, Humphrey SJ, Scalzo R, Hirayama A, Ikeda S, Shoji F, Suzuki K, Huynh K, Giles C, Varney B, Nagarajan SR, Hoy AJ, Soga T, Meikle PJ, Cooney GJ, Fazakerley DJ, James DE. Insulin signaling requires glucose to promote lipid anabolism in adipocytes. J Biol Chem 2020; 295:13250-13266. [PMID: 32723868 DOI: 10.1074/jbc.ra120.014907] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 07/14/2020] [Indexed: 12/12/2022] Open
Abstract
Adipose tissue is essential for metabolic homeostasis, balancing lipid storage and mobilization based on nutritional status. This is coordinated by insulin, which triggers kinase signaling cascades to modulate numerous metabolic proteins, leading to increased glucose uptake and anabolic processes like lipogenesis. Given recent evidence that glucose is dispensable for adipocyte respiration, we sought to test whether glucose is necessary for insulin-stimulated anabolism. Examining lipogenesis in cultured adipocytes, glucose was essential for insulin to stimulate the synthesis of fatty acids and glyceride-glycerol. Importantly, glucose was dispensable for lipogenesis in the absence of insulin, suggesting that distinct carbon sources are used with or without insulin. Metabolic tracing studies revealed that glucose was required for insulin to stimulate pathways providing carbon substrate, NADPH, and glycerol 3-phosphate for lipid synthesis and storage. Glucose also displaced leucine as a lipogenic substrate and was necessary to suppress fatty acid oxidation. Together, glucose provided substrates and metabolic control for insulin to promote lipogenesis in adipocytes. This contrasted with the suppression of lipolysis by insulin signaling, which occurred independently of glucose. Given previous observations that signal transduction acts primarily before glucose uptake in adipocytes, these data are consistent with a model whereby insulin initially utilizes protein phosphorylation to stimulate lipid anabolism, which is sustained by subsequent glucose metabolism. Consequently, lipid abundance was sensitive to glucose availability, both during adipogenesis and in Drosophila flies in vivo Together, these data highlight the importance of glucose metabolism to support insulin action, providing a complementary regulatory mechanism to signal transduction to stimulate adipose anabolism.
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Affiliation(s)
- James R Krycer
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Lake-Ee Quek
- Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia; School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia
| | - Deanne Francis
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Armella Zadoorian
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Fiona C Weiss
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Kristen C Cooke
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Marin E Nelson
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Alexis Diaz-Vegas
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Sean J Humphrey
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Richard Scalzo
- Faculty of Engineering and Information Technologies, University of Sydney, Sydney, New South Wales, Australia
| | - Akiyoshi Hirayama
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan; AMED-CREST, Japan Agency for Medical Research and Development (AMED), Otemachi, Chiyoda-Ku, Tokyo, Japan
| | - Satsuki Ikeda
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Futaba Shoji
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Kumi Suzuki
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Bianca Varney
- Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia; Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Shilpa R Nagarajan
- Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia; Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Andrew J Hoy
- Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia; Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan; AMED-CREST, Japan Agency for Medical Research and Development (AMED), Otemachi, Chiyoda-Ku, Tokyo, Japan
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Gregory J Cooney
- Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia; Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - Daniel J Fazakerley
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia
| | - David E James
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia; Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia; Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia.
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18
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Wang G. Body Mass Dynamics Is Determined by the Metabolic Ohm's Law and Adipocyte-Autonomous Fat Mass Homeostasis. iScience 2020; 23:101176. [PMID: 32480131 PMCID: PMC7262567 DOI: 10.1016/j.isci.2020.101176] [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: 01/19/2020] [Revised: 04/05/2020] [Accepted: 05/14/2020] [Indexed: 11/06/2022] Open
Abstract
An ODE model integrating metabolic mechanisms with clinical data reveals an Ohm's law governing lifetime body mass dynamics, where fat and lean tissues are analogous to a parallel nonlinear capacitor and resistor, respectively. The law unexpectedly decouples weight stability (a cell-autonomous property of adipocytes) and weight change (a parabolic trajectory governed by Ohm's law). In middle age, insulin resistance causes fat accumulation to avoid excessive body shrinkage in old age. Moderate middle-age spread is thus natural, not an anomaly caused by hypothalamic defects, as proposed by lipostatic theory. These discoveries provide valuable insights into health care practices such as weight control and health assessment, explain certain observed phenomena, make testable predictions, and may help to resolve major conundrums in the field. The ODE model, which is more comprehensive than Ohm's law, is useful to study metabolism at the detailed microscopic levels.
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Affiliation(s)
- Guanyu Wang
- Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China; Guangdong Provincial Key Laboratory of Computational Science and Material Design, Shenzhen, Guangdong 518055, China; Guangdong Provincial Key Laboratory of Cell Microenviroment and Disease Research, Shenzhen, Guangdong 518055, China; Shenzhen Key Laboratory of Cell Microenviroment, Shenzhen, Guangdong 518055, China.
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