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Onesto V, Forciniti S, Alemanno F, Narayanankutty K, Chandra A, Prasad S, Azzariti A, Gigli G, Barra A, De Martino A, De Martino D, del Mercato LL. Probing Single-Cell Fermentation Fluxes and Exchange Networks via pH-Sensing Hybrid Nanofibers. ACS NANO 2023; 17:3313-3323. [PMID: 36573897 PMCID: PMC9979640 DOI: 10.1021/acsnano.2c06114] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 12/19/2022] [Indexed: 05/31/2023]
Abstract
The homeostatic control of their environment is an essential task of living cells. It has been hypothesized that, when microenvironmental pH inhomogeneities are induced by high cellular metabolic activity, diffusing protons act as signaling molecules, driving the establishment of exchange networks sustained by the cell-to-cell shuttling of overflow products such as lactate. Despite their fundamental role, the extent and dynamics of such networks is largely unknown due to the lack of methods in single-cell flux analysis. In this study, we provide direct experimental characterization of such exchange networks. We devise a method to quantify single-cell fermentation fluxes over time by integrating high-resolution pH microenvironment sensing via ratiometric nanofibers with constraint-based inverse modeling. We apply our method to cell cultures with mixed populations of cancer cells and fibroblasts. We find that the proton trafficking underlying bulk acidification is strongly heterogeneous, with maximal single-cell fluxes exceeding typical values by up to 3 orders of magnitude. In addition, a crossover in time from a networked phase sustained by densely connected "hubs" (corresponding to cells with high activity) to a sparse phase dominated by isolated dipolar motifs (i.e., by pairwise cell-to-cell exchanges) is uncovered, which parallels the time course of bulk acidification. Our method addresses issues ranging from the homeostatic function of proton exchange to the metabolic coupling of cells with different energetic demands, allowing for real-time noninvasive single-cell metabolic flux analysis.
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Affiliation(s)
- Valentina Onesto
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
| | - Stefania Forciniti
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
| | - Francesco Alemanno
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
- Dipartimento
di Matematica e Fisica E. De Giorgi, University
of Salento, 73100Lecce, Italy
- Istituto
Nazionale di Fisica Nucleare (INFN), Sezione di Lecce, 73100Lecce, Italy
| | | | - Anil Chandra
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
| | - Saumya Prasad
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
| | - Amalia Azzariti
- IRCCS
Istituto Tumori Giovanni Paolo II, V.le O. Flacco, 65, 70124Bari, Italy
| | - Giuseppe Gigli
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
- Dipartimento
di Matematica e Fisica E. De Giorgi, University
of Salento, 73100Lecce, Italy
| | - Adriano Barra
- Dipartimento
di Matematica e Fisica E. De Giorgi, University
of Salento, 73100Lecce, Italy
- Istituto
Nazionale di Fisica Nucleare (INFN), Sezione di Lecce, 73100Lecce, Italy
| | - Andrea De Martino
- Politecnico
di Torino, Corso Duca degli Abruzzi, 24, I-10129Torino, Italy
- Italian Institute
for Genomic Medicine, IRCCS Candiolo, SP-142, I-10060Candiolo, Italy
| | - Daniele De Martino
- Biofisika
Institutua (UPV/EHU, CSIC) and Fundación Biofísica Bizkaia, LeioaE-48940, Spain
- Ikerbasque
Foundation, Bilbao48013, Spain
| | - Loretta L. del Mercato
- Institute
of Nanotechnology, National Research Council
(CNR-NANOTEC), c/o Campus Ecotekne, via Monteroni, 73100Lecce, Italy
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Niu D, Wu Y, Lei Z, Zhang M, Xie Z, Tang S. Lactic acid, a driver of tumor-stroma interactions. Int Immunopharmacol 2022; 106:108597. [DOI: 10.1016/j.intimp.2022.108597] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/23/2022] [Accepted: 01/27/2022] [Indexed: 12/11/2022]
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Frades I, Foguet C, Cascante M, Araúzo-Bravo MJ. Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment. Cancers (Basel) 2021; 13:4609. [PMID: 34572839 PMCID: PMC8470216 DOI: 10.3390/cancers13184609] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/06/2021] [Accepted: 09/09/2021] [Indexed: 12/31/2022] Open
Abstract
The tumor's physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. Tumor cells can reprogram their metabolism to meet the high demand of building blocks and ATP for proliferation, and to gain an advantage over the action of immune cells. The study of the metabolic reprogramming mechanisms underlying cancer requires the quantification of metabolic fluxes which can be estimated at the genome-scale with constraint-based or kinetic modeling. Constraint-based models use a set of linear constraints to simulate steady-state metabolic fluxes, whereas kinetic models can simulate both the transient behavior and steady-state values of cellular fluxes and concentrations. The integration of cell- or tissue-specific data enables the construction of context-specific models that reflect cell-type- or tissue-specific metabolic properties. While the available modeling frameworks enable limited modeling of the metabolic crosstalk between tumor and immune cells in the tumor stroma, future developments will likely involve new hybrid kinetic/stoichiometric formulations.
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Affiliation(s)
- Itziar Frades
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, 20009 San Sebastian, Spain;
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain; (C.F.); (M.C.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) (CB17/04/00023) and Metabolomics Node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), 28020 Madrid, Spain
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain; (C.F.); (M.C.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) (CB17/04/00023) and Metabolomics Node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), 28020 Madrid, Spain
| | - Marcos J. Araúzo-Bravo
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, 20009 San Sebastian, Spain;
- Max Planck Institute of Molecular Biomedicine, 48167 Münster, Germany
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERfes), 28015 Madrid, Spain
- Translational Bioinformatics Network (TransBioNet), 8001 Barcelona, Spain
- Ikerbasque, Basque Foundation for Science, 48012 Bilbao, Spain
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Karta J, Bossicard Y, Kotzamanis K, Dolznig H, Letellier E. Mapping the Metabolic Networks of Tumor Cells and Cancer-Associated Fibroblasts. Cells 2021; 10:304. [PMID: 33540679 PMCID: PMC7912987 DOI: 10.3390/cells10020304] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/20/2021] [Accepted: 01/26/2021] [Indexed: 12/12/2022] Open
Abstract
Metabolism is considered to be the core of all cellular activity. Thus, extensive studies of metabolic processes are ongoing in various fields of biology, including cancer research. Cancer cells are known to adapt their metabolism to sustain high proliferation rates and survive in unfavorable environments with low oxygen and nutrient concentrations. Hence, targeting cancer cell metabolism is a promising therapeutic strategy in cancer research. However, cancers consist not only of genetically altered tumor cells but are interwoven with endothelial cells, immune cells and fibroblasts, which together with the extracellular matrix (ECM) constitute the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), which are linked to poor prognosis in different cancer types, are one important component of the TME. CAFs play a significant role in reprogramming the metabolic landscape of tumor cells, but how, and in what manner, this interaction takes place remains rather unclear. This review aims to highlight the metabolic landscape of tumor cells and CAFs, including their recently identified subtypes, in different tumor types. In addition, we discuss various in vitro and in vivo metabolic techniques as well as different in silico computational tools that can be used to identify and characterize CAF-tumor cell interactions. Finally, we provide our view on how mapping the complex metabolic networks of stromal-tumor metabolism will help in finding novel metabolic targets for cancer treatment.
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Affiliation(s)
- Jessica Karta
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belval, Luxembourg; (J.K.); (Y.B.); (K.K.)
| | - Ysaline Bossicard
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belval, Luxembourg; (J.K.); (Y.B.); (K.K.)
| | - Konstantinos Kotzamanis
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belval, Luxembourg; (J.K.); (Y.B.); (K.K.)
| | - Helmut Dolznig
- Tumor Stroma Interaction Group, Institute of Medical Genetics, Medical University of Vienna, Währinger Strasse 10, 1090 Vienna, Austria;
| | - Elisabeth Letellier
- Molecular Disease Mechanisms Group, Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 6 avenue du Swing, L-4367 Belval, Luxembourg; (J.K.); (Y.B.); (K.K.)
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A control theoretic three timescale model for analyzing energy management in mammalian cancer cells. Comput Struct Biotechnol J 2020; 19:477-508. [PMID: 33510857 PMCID: PMC7809419 DOI: 10.1016/j.csbj.2020.12.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 11/26/2020] [Accepted: 12/13/2020] [Indexed: 02/06/2023] Open
Abstract
Developed a three timescale model of integrated biochemical pathway. Simulated “Warburg Effect” using support vector regression and genetic algorithm. Identified rational drug targets using nonlinear controller. Explored energy and cell proliferation management of cancer cells. Validated the model by previous in vivo/in vitro/in silico experiments.
Interaction among different pathways, such as metabolic, signaling and gene regulatory networks, of cellular system is responsible to maintain homeostasis in a mammalian cell. Malfunctioning of this cooperation may lead to many complex diseases, such as cancer and type 2 diabetes. Timescale differences among these pathways make their integration a daunting task. Metabolic, signaling and gene regulatory networks have three different timescales, such as, ultrafast, fast and slow respectively. The article deals with this problem by developing a support vector regression (SVR) based three timescale model with the application of genetic algorithm based nonlinear controller. The proposed model can successfully capture the nonlinear transient dynamics and regulations of such integrated biochemical pathway under consideration. Besides, the model is quite capable of predicting the effects of certain drug targets for many types of complex diseases. Here, energy and cell proliferation management of mammalian cancer cells have been explored and analyzed with the help of the proposed novel approach. Previous investigations including in silico/in vivo/in vitro experiments have validated the results (the regulations of glucose transporter 1 (glut1), hexokinase (HK), and hypoxia-inducible factor-1α (HIF-1α) among others, and the switching of pyruvate kinase (M2 isoform) between dimer and tetramer) generated by this model proving its effectiveness. Subsequently, the model predicts the effects of six selected drug targets, such as, the deactivation of transketolase and glucose-6-phosphate isomerase among others, in the case of mammalian malignant cells in terms of growth, proliferation, fermentation, and energy supply in the form of adenosine triphosphate (ATP).
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Damiani C, Gaglio D, Sacco E, Alberghina L, Vanoni M. Systems metabolomics: from metabolomic snapshots to design principles. Curr Opin Biotechnol 2020; 63:190-199. [PMID: 32278263 DOI: 10.1016/j.copbio.2020.02.013] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 02/11/2020] [Accepted: 02/18/2020] [Indexed: 02/07/2023]
Abstract
Metabolomics is a rapidly expanding technology that finds increasing application in a variety of fields, form metabolic disorders to cancer, from nutrition and wellness to design and optimization of cell factories. The integration of metabolic snapshots with metabolic fluxes, physiological readouts, metabolic models, and knowledge-informed Artificial Intelligence tools, is required to obtain a system-level understanding of metabolism. The emerging power of multi-omic approaches and the development of integrated experimental and computational tools, able to dissect metabolic features at cellular and subcellular resolution, provide unprecedented opportunities for understanding design principles of metabolic (dis)regulation and for the development of precision therapies in multifactorial diseases, such as cancer and neurodegenerative diseases.
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Affiliation(s)
- Chiara Damiani
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy; ISBE.IT, SYSBIO Centre of Systems Biology, Piazza della Scienza 2, Milan 20126, Italy
| | - Daniela Gaglio
- ISBE.IT, SYSBIO Centre of Systems Biology, Piazza della Scienza 2, Milan 20126, Italy; Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Segrate, Milan, Italy
| | - Elena Sacco
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy; ISBE.IT, SYSBIO Centre of Systems Biology, Piazza della Scienza 2, Milan 20126, Italy
| | - Lilia Alberghina
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy; ISBE.IT, SYSBIO Centre of Systems Biology, Piazza della Scienza 2, Milan 20126, Italy
| | - Marco Vanoni
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy; ISBE.IT, SYSBIO Centre of Systems Biology, Piazza della Scienza 2, Milan 20126, Italy.
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De Martino D, Capuani F, De Martino A. Quantifying the entropic cost of cellular growth control. Phys Rev E 2018; 96:010401. [PMID: 29347168 DOI: 10.1103/physreve.96.010401] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Indexed: 11/07/2022]
Abstract
Viewing the ways a living cell can organize its metabolism as the phase space of a physical system, regulation can be seen as the ability to reduce the entropy of that space by selecting specific cellular configurations that are, in some sense, optimal. Here we quantify the amount of regulation required to control a cell's growth rate by a maximum-entropy approach to the space of underlying metabolic phenotypes, where a configuration corresponds to a metabolic flux pattern as described by genome-scale models. We link the mean growth rate achieved by a population of cells to the minimal amount of metabolic regulation needed to achieve it through a phase diagram that highlights how growth suppression can be as costly (in regulatory terms) as growth enhancement. Moreover, we provide an interpretation of the inverse temperature β controlling maximum-entropy distributions based on the underlying growth dynamics. Specifically, we show that the asymptotic value of β for a cell population can be expected to depend on (i) the carrying capacity of the environment, (ii) the initial size of the colony, and (iii) the probability distribution from which the inoculum was sampled. Results obtained for E. coli and human cells are found to be remarkably consistent with empirical evidence.
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Affiliation(s)
- Daniele De Martino
- Institute of Science and Technology Austria, 3400 Klosterneuburg, Austria
| | - Fabrizio Capuani
- Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy
| | - Andrea De Martino
- Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, 00185 Rome, Italy.,Italian Institute for Genomic Medicine, 10126 Turin, Italy
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Fernandez-de-Cossio-Diaz J, Leon K, Mulet R. Characterizing steady states of genome-scale metabolic networks in continuous cell cultures. PLoS Comput Biol 2017; 13:e1005835. [PMID: 29131817 PMCID: PMC5703580 DOI: 10.1371/journal.pcbi.1005835] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 11/27/2017] [Accepted: 10/20/2017] [Indexed: 12/15/2022] Open
Abstract
In the continuous mode of cell culture, a constant flow carrying fresh media replaces culture fluid, cells, nutrients and secreted metabolites. Here we present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity. We demonstrate our approach in a toy model first, and then in a genome-scale metabolic network of the Chinese hamster ovary cell line, obtaining results that are in qualitative agreement with experimental observations. We derive a number of consequences from the model that are independent of parameter values. The ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties. This conclusion is robust even in the presence of multi-stability, which is explained in our model by a negative feedback loop due to toxic byproduct accumulation. A complex landscape of steady states emerges from our simulations, including multiple metabolic switches, which also explain why cell-line and media benchmarks carried out in batch culture cannot be extrapolated to perfusion. On the other hand, we predict invariance laws between continuous cell cultures with different parameters. A practical consequence is that the chemostat is an ideal experimental model for large-scale high-density perfusion cultures, where the complex landscape of metabolic transitions is faithfully reproduced.
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Affiliation(s)
- Jorge Fernandez-de-Cossio-Diaz
- Systems Biology Department, Center of Molecular Immunlogy, Havana, Cuba
- Group of Complex Systems and Statistical Physics. Department of Theoretical Physics, Physics Faculty, University of Havana, Cuba
| | - Kalet Leon
- Systems Biology Department, Center of Molecular Immunlogy, Havana, Cuba
| | - Roberto Mulet
- Group of Complex Systems and Statistical Physics. Department of Theoretical Physics, Physics Faculty, University of Havana, Cuba
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Dasgupta A, Paul D, De RK. A fuzzy logic controller based approach to model the switching mechanism of the mammalian central carbon metabolic pathway in normal and cancer cells. MOLECULAR BIOSYSTEMS 2017; 12:2490-505. [PMID: 27225801 DOI: 10.1039/c6mb00131a] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Dynamics of large nonlinear complex systems, like metabolic networks, depend on several parameters. A metabolic pathway may switch to another pathway in accordance with the current state of parameters in both normal and cancer cells. Here, most of the parameter values are unknown to us. A fuzzy logic controller (FLC) has been developed here for the purpose of modeling metabolic networks by approximating the reasons for the behaviour of a system and applying expert knowledge to track switching between metabolic pathways. The simulation results can track the switching between glycolysis and gluconeogenesis, as well as glycolysis and pentose phosphate pathways (PPP) in normal cells. Unlike normal cells, pyruvate kinase (M2 isoform) (PKM2) switches alternatively between its two oligomeric forms, i.e. an active tetramer and a relatively low activity dimer, in cancer cells. Besides, there is a coordination among PKM2 switching and enzymes catalyzing PPP. These phenomena help cancer cells to maintain their high energy demand and macromolecular synthesis. However, the reduction of initial adenosine triphosphate (ATP) to a very low concentration, decreasing initial glucose uptake, destroying coordination between glycolysis and PPP, and replacement of PKM2 by its relatively inactive oligomeric form (dimer) or inhibition of the translation of PKM2 may destabilize the mutated control mechanism of the mammalian central carbon metabolic (CCM) pathway in cancer cells. The performance of the model is compared appropriately with some existing ones.
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Affiliation(s)
- Abhijit Dasgupta
- Machine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, West Bengal, India.
| | - Debjyoti Paul
- Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, West Bengal, India.
| | - Rajat K De
- Machine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700108, West Bengal, India.
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Fernandez-de-Cossio-Diaz J, De Martino A, Mulet R. Microenvironmental cooperation promotes early spread and bistability of a Warburg-like phenotype. Sci Rep 2017; 7:3103. [PMID: 28596605 PMCID: PMC5465218 DOI: 10.1038/s41598-017-03342-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 04/27/2017] [Indexed: 12/31/2022] Open
Abstract
We introduce an in silico model for the initial spread of an aberrant phenotype with Warburg-like overflow metabolism within a healthy homeostatic tissue in contact with a nutrient reservoir (the blood), aimed at characterizing the role of the microenvironment for aberrant growth. Accounting for cellular metabolic activity, competition for nutrients, spatial diffusion and their feedbacks on aberrant replication and death rates, we obtain a phase portrait where distinct asymptotic whole-tissue states are found upon varying the tissue-blood turnover rate and the level of blood-borne primary nutrient. Over a broad range of parameters, the spreading dynamics is bistable as random fluctuations can impact the final state of the tissue. Such a behaviour turns out to be linked to the re-cycling of overflow products by non-aberrant cells. Quantitative insight on the overall emerging picture is provided by a spatially homogeneous version of the model.
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Affiliation(s)
| | - Andrea De Martino
- Soft and Living Matter Lab, Istituto di Nanotecnologia (CNR-NANOTEC), Rome, Italy.
- Human Genetics Foundation, Turin, Italy.
| | - Roberto Mulet
- Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, Physics Faculty, University of Havana, La Habana, Cuba
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Mason S. Lactate Shuttles in Neuroenergetics-Homeostasis, Allostasis and Beyond. Front Neurosci 2017; 11:43. [PMID: 28210209 PMCID: PMC5288365 DOI: 10.3389/fnins.2017.00043] [Citation(s) in RCA: 132] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Accepted: 01/20/2017] [Indexed: 12/19/2022] Open
Abstract
Understanding brain energy metabolism—neuroenergetics—is becoming increasingly important as it can be identified repeatedly as the source of neurological perturbations. Within the scientific community we are seeing a shift in paradigms from the traditional neurocentric view to that of a more dynamic, integrated one where astrocytes are no longer considered as being just supportive, and activated microglia have a profound influence. Lactate is emerging as the “good guy,” contrasting its classical “bad guy” position in the now superseded medical literature. This review begins with the evolution of the concept of “lactate shuttles”; goes on to the recent shift in ideas regarding normal neuroenergetics (homeostasis)—specifically, the astrocyte–neuron lactate shuttle; and progresses to covering the metabolic implications whereby homeostasis is lost—a state of allostasis, and the function of microglia. The role of lactate, as a substrate and shuttle, is reviewed in light of allostatic stress, and beyond—in an acute state of allostatic stress in terms of physical brain trauma, and reflected upon with respect to persistent stress as allostatic overload—neurodegenerative diseases. Finally, the recently proposed astrocyte–microglia lactate shuttle is discussed in terms of chronic neuroinflammatory infectious diseases, using tuberculous meningitis as an example. The novelty extended by this review is that the directionality of lactate, as shuttles in the brain, in neuropathophysiological states is emerging as crucial in neuroenergetics.
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Affiliation(s)
- Shayne Mason
- Centre for Human Metabolomics, North-West University Potchefstroom, South Africa
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Martino DD, Capuani F, Martino AD. Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli. Phys Biol 2016; 13:036005. [PMID: 27232645 DOI: 10.1088/1478-3975/13/3/036005] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The solution space of genome-scale models of cellular metabolism provides a map between physically viable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the corresponding growth rates. By sampling the solution space of E. coli's metabolic network, we show that empirical growth rate distributions recently obtained in experiments at single-cell resolution can be explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the higher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of a large bacterial population that captures this trade-off. The scaling relationships observed in experiments encode, in such frameworks, for the same distance from the maximum achievable growth rate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being grounded on genome-scale metabolic network reconstructions, these results allow for multiple implications and extensions in spite of the underlying conceptual simplicity.
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Affiliation(s)
- Daniele De Martino
- Institute of Science and Technology Austria (IST Austria), Am Campus 1, Klosterneuburg A-3400, Austria
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