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      Integration of single-cell RNA-seq data into population models to characterize cancer metabolism

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          Abstract

          Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites. The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.

          Author summary

          Cytotoxicity of chemotherapeutic agents and resistance to targeted treatments are the main reasons why cancer is still one of the top causes of death. As tumor cells are intrinsically resistant to therapies that target signaling pathways, targeting the metabolic hallmarks of cancer holds promise for more incisive treatments. Regrettably, the heterogeneity of cancer metabolism hinders the identification of effective treatments. To fully uncover the metabolic heterogeneity within tumors, characterization of metabolic programs (metabolic flux distributions) at the single-cell level is required. To fill the gap between current technologies for genomics and future technologies for fluxomics, both at the single-cell and the genome-wide scale, we propose to integrate cancer data from: 1) single-cell transcriptomics and 2) bulk metabolomics, into a multi-scale stoichiometric model, to deliver for the first time metabolic fluxomes at the single-cell level. To this end, we introduce a new paradigm for flux balance analysis and data integration in cancer metabolism to: 1) characterize metabolic heterogeneity, not only at the inter-, but also at the intra-tumor level 2) identify the metabolic interactions between cancer populations, whose role in resistance to metabolic treatments has been recently recognized 3) predict the collective response to drug targeting of metabolism.

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          Most cited references53

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          Drug resistance and the solid tumor microenvironment.

          Resistance of human tumors to anticancer drugs is most often ascribed to gene mutations, gene amplification, or epigenetic changes that influence the uptake, metabolism, or export of drugs from single cells. Another important yet little-appreciated cause of anticancer drug resistance is the limited ability of drugs to penetrate tumor tissue and to reach all of the tumor cells in a potentially lethal concentration. To reach all viable cells in the tumor, anticancer drugs must be delivered efficiently through the tumor vasculature, cross the vessel wall, and traverse the tumor tissue. In addition, heterogeneity within the tumor microenvironment leads to marked gradients in the rate of cell proliferation and to regions of hypoxia and acidity, all of which can influence the sensitivity of the tumor cells to drug treatment. In this review, we describe how the tumor microenvironment may be involved in the resistance of solid tumors to chemotherapy and discuss potential strategies to improve the effectiveness of drug treatment by modifying factors relating to the tumor microenvironment.
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            Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

            Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.
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              The effects of alternate optimal solutions in constraint-based genome-scale metabolic models.

              Genome-scale constraint-based models of several organisms have now been constructed and are being used for model driven research. A key issue that may arise in the use of such models is the existence of alternate optimal solutions wherein the same maximal objective (e.g., growth rate) can be achieved through different flux distributions. Herein, we investigate the effects that alternate optimal solutions may have on the predicted range of flux values calculated using currently practiced linear (LP) and quadratic programming (QP) methods. An efficient LP-based strategy is described to calculate the range of flux variability that can be present in order to achieve optimal as well as suboptimal objective states. Sample results are provided for growth predictions of E. coli using glucose, acetate, and lactate as carbon substrates. These results demonstrate the extent of flux variability to be highly dependent on environmental conditions and network composition. In addition we examined the impact of alternate optima for growth under gene knockout conditions as calculated using QP-based methods. It was observed that calculations using QP-based methods can show significant variation in growth rate if the flux variability among alternate optima is high. The underlying biological significance and general source of such flux variability is further investigated through the identification of redundancies in the network (equivalent reaction sets) that lead to alternate solutions. Collectively, these results illustrate the variability inherent in metabolic flux distributions and the possible implications of this heterogeneity for constraint-based modeling approaches. These methods also provide an efficient and robust method to calculate the range of flux distributions that can be derived from quantitative fermentation data.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: SoftwareRole: Visualization
                Role: InvestigationRole: MethodologyRole: Visualization
                Role: InvestigationRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Project administrationRole: Writing – review & editing
                Role: Project administrationRole: Writing – review & editing
                Role: Funding acquisitionRole: Project administrationRole: Resources
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                February 2019
                28 February 2019
                : 15
                : 2
                : e1006733
                Affiliations
                [1 ] Dept. of Informatics, Systems and Communication, University of Milan-Bicocca, 20126, Milan, Italy
                [2 ] SYSBIO Centre of Systems Biology, 20126, Milan, Italy
                [3 ] Dept. of Biotechnology and Biosciences, University of Milan-Bicocca, 20126, Milan, Italy
                [4 ] Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
                [5 ] Dept. of Statistics and Quantitative Methods, University of Milan-Bicocca, 20126, Milan, Italy
                [6 ] Dept. of Molecular Cell Physiology, Faculty of Earth and Life Sciences, VU University, Amsterdam, The Netherlands
                [7 ] Manchester Centre for Integrative Systems Biology, School of Chemical Engineering and Analytical Science, University of Manchester, Manchester, United Kingdom
                [8 ] Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands
                Christian Albrechts Universitat zu Kiel, GERMANY
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-5742-8302
                http://orcid.org/0000-0001-8519-4331
                http://orcid.org/0000-0002-7428-0522
                http://orcid.org/0000-0002-9499-0126
                http://orcid.org/0000-0002-3090-4823
                http://orcid.org/0000-0001-5452-1918
                http://orcid.org/0000-0002-0443-6114
                http://orcid.org/0000-0003-1694-931X
                http://orcid.org/0000-0003-3520-4022
                Article
                PCOMPBIOL-D-18-01038
                10.1371/journal.pcbi.1006733
                6413955
                30818329
                7551f082-9763-4ced-aa97-cf945c1a9d94
                © 2019 Damiani et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 June 2018
                : 22 December 2018
                Page count
                Figures: 5, Tables: 0, Pages: 25
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100003407, Ministero dell’Istruzione, dell’Università e della Ricerca;
                Award ID: ITFoC
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003407, Ministero dell’Istruzione, dell’Università e della Ricerca;
                Award ID: SYSBIO
                Award Recipient :
                Funded by: EU
                Award ID: 311815
                Award Recipient :
                Funded by: EU
                Award ID: 64269
                Award Recipient :
                Funded by: EU
                Award ID: 654248
                Award Recipient :
                Funded by: WOTRO
                Award ID: W01.65.324.00/4
                Award Recipient :
                This work is supported with FOE funds to SYSBIO Italian Centre of Systems Biology, from the Italian Ministry of Education, Universities and Research (MIUR, http://www.istruzione.it/) - within the Italian Roadmap for ESFRI Research Infrastructures. GM, LA, CD and MV received funding from FLAG-ERA grant ITFoC. HVW received EU (311815; 642691; 654248) and WOTRO (W01.65.324.00/4) funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Cell Biology
                Cell Physiology
                Cell Metabolism
                Medicine and Health Sciences
                Pharmacology
                Pharmacokinetics
                Drug Metabolism
                Computer and Information Sciences
                Network Analysis
                Metabolic Networks
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolic Pathways
                Biology and Life Sciences
                Physiology
                Physiological Processes
                Secretion
                Medicine and Health Sciences
                Physiology
                Physiological Processes
                Secretion
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolites
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Oxygen Metabolism
                Custom metadata
                vor-update-to-uncorrected-proof
                2019-03-12
                All RNA-seq data are available from the Gene Expression Omnibus database (accession numbers GSE69405, GSE75688).

                Quantitative & Systems biology
                Quantitative & Systems biology

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