Spatially-resolved metabolic cooperativity within dense bacterial colonies

BMC Systems Biology

Zaida Luthey-Schulten³  Jamila Hedhli²  Lars Kohler³  John A Cole¹ 

[1] Department of Physics, University of Illinois, 1110 W. Green St., Urbana 61801, IL, USA;Department of Bioengineering, University of Illinois, 1304 W. Springfield Ave., Urbana 61801, IL, USA;Department of Chemistry, University of Illinois, 600 S. Matthews Ave., Urbana 61801, IL, USA

关键词: Colony modeling;    Crossfeeding;    Metabolic cooperativity;    Reaction-diffusion modeling;    Flux balance analysis;   

Others  :  1159549
DOI  :  10.1186/s12918-015-0155-1

 received in 2014-10-31, accepted in 2015-02-24,  发布年份 2015

【 摘 要 】

Background

The exchange of metabolites and the reprogramming of metabolism in response to shifting microenvironmental conditions can drive subpopulations of cells within colonies toward divergent behaviors. Understanding the interactions of these subpopulations—their potential for competition as well as cooperation—requires both a metabolic model capable of accounting for a wide range of environmental conditions, and a detailed dynamic description of the cells’ shared extracellular space.

Results

Here we show that a cell’s position within an in silicoEscherichia coli colony grown on glucose minimal agar can drastically affect its metabolism: “pioneer” cells at the outer edge engage in rapid growth that expands the colony, while dormant cells in the interior separate two spatially distinct subpopulations linked by a cooperative form of acetate crossfeeding that has so far gone unnoticed. Our hybrid simulation technique integrates 3D reaction-diffusion modeling with genome-scale flux balance analysis (FBA) to describe the position-dependent metabolism and growth of cells within a colony. Our results are supported by imaging experiments involving strains of fluorescently-labeled E. coli. The spatial patterns of fluorescence within these experimental colonies identify cells with upregulated genes associated with acetate crossfeeding and are in excellent agreement with the predictions. Furthermore, the height-to-width ratios of both the experimental and simulated colonies are in good agreement over a growth period of 48 hours.

Conclusions

Our modeling paradigm can accurately reproduce a number of known features of E. coli colony growth, as well as predict a novel one that had until now gone unrecognized. The acetate crossfeeding we see has a direct analogue in a form of lactate crossfeeding observed in certain forms of cancer, and we anticipate future application of our methodology to models of tissues and tumors.

【 授权许可】

   
2015 Cole et al.; licensee BioMed Central.

【 图 表 】

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【 参考文献 】

• [1]Ben-Jacob E, Cohen I, Levine H: Cooperative self-organization of microorganisms. Adv Phys. 2000, 49(4):395-554.
• [2]Hellweger FL, Bucci V: A bunch of tiny individuals—individual-based modeling for microbes. Ecol Modell 2009, 220(1):8-22.
• [3]Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM, et al.: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc. 2011, 6(9):1290-307.
• [4]Orth JD, Conrad TM, Na J, Lerman JA, Nam H, Feist AM, et al. A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011. Mol Syst Biol. 2011;7(1). doi:10.1038/msb.2011.65.
• [5]Bordbar A, Monk JM, King ZA, Palsson BØ: Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet. 2014, 15(2):107-20.
• [6]Labhsetwar P, Cole JA, Roberts E, Price ND, Luthey-Schulten ZA: Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population. Proc Natl Acad Sci U S A. 2013, 110(34):14006-11.
• [7]Cole AJ, Hallock JM, Labhsetwar P, Peterson RJ, Stone EJ, Luthey Schulten Z: Stochastic simulations of cellular processes: from single cells to colonies. In Computational Systems Biology, Second Edition: From Molecular Mechanisms to Disease. Edited by Kriete A, Eils R. Academic Press, San Diego; 2014.
• [8]Roberts E, Stone JE, Luthey-Schulten Z. Lattice microbes: high-performance stochastic simulation method for the reaction-diffusion master equation: 2013. p 245–55.
• [9]Peterson JR, Hallock MJ, Cole JA, Luthey-Schulten Z. A problem solving environment for stochastic biological simulations. In: PyHPC 2013 at Supercomputing 2013. 445 Hoes Lane Piscataway, NJ: IEEE: 2013. p. 1–11.
• [10]Cole AJ, Luthey-Schulten AZ: Whole-cell modelling: from single cells to colonies. Isr J Chem 2014, 54(8):1219-29. Available online. doi:10.1002/ijch.201300147
• [11]Beuling EE, Van den Heuvel JC, Ottengraf SP: Determination of biofilm diffusion coefficients using micro-electrodes. Prog Biotechnol. 1996, 11:31-38.
• [12]Holt E, Lyons P: Diffusion in dilute aqueous acetic acid solutions. J Phys Chem. 1965, 69(7):2341-4.
• [13]Peters A, Wimpenny J, Coombs J: Oxygen profiles in, and in the agar beneath, colonies of Bacillus cereus, Staphylococcus albus and Escherichia coli. J Gen Microbiol. 1987, 133(5):1257-63.
• [14]CRC Handbook of Chemistry and Physics. CRC Press, Inc., Boca Raton, FL; 1995.
• [15]Grimson MJ, Barker GC: Continuum model for the spatiotemporal growth of bacterial colonies. Phys Rev E Stat Nonlin Soft Matter Phys. 1994, 49(2):1680.
• [16]Schulze KL, Lipe RS: Relationship between substrate concentration, growth rate, and respiration rate of Escherichia coli in continuous culture. Arch Mikrobiol. 1964, 48(1):1-20.
• [17]Edwards JS, Ibarra RU, Palsson BØ: In silico predictions of escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol. 2001, 19(2):125-30.
• [18]Peterson JR, Labhsetwar P, Ellermeier JR, Kohler PR, Jain A, Ha T, et al. Towards a computational model of a methane producing Archaeum. Archaea. 2014; 2014. doi:10.1155/2014/898453.
• [19]Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, et al.: A whole-cell computational model predicts phenotype from genotype. Cell 2012, 150(2):389-401.
• [20]Mahadevan R, Edwards J. S, Doyle III F. J: Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J. 2002, 83(3):1331-40.
• [21]Harcombe WR, Riehl WJ, Dukovski I, Granger BR, Betts A, Lang AH, et al.: Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep. 2014, 7(4):1104-15.
• [22]Marrink SJ, Berendsen HJ: Permeation process of small molecules across lipid membranes studied by molecular dynamics simulations. J Phys Chem. 1996, 100(41):16729-38.
• [23]Cooper GM, Hausman RE: The cell: a molecular approach. Sinauer Associates, Sunderland Massachusetts; 2000.
• [24]Lebenhaft JR, Kapral R: Diffusion-controlled processes among partially absorbing stationary sinks. J Stat Phys. 1979, 20(1):25-56.
• [25]Monod J: The growth of bacterial cultures. Annu Rev Microbiol. 1949, 3(1):371-94.
• [26]Varma A, Palsson BØ: Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol. 1994, 60(10):3724-31.
• [27]O’Beirne D, Hamer G: The utilisation of glucose/acetate mixtures by Escherichia coli w3110 under aerobic growth conditions. Bioprocess Eng. 2000, 23(4):375-80.
• [28]Pirt S: A kinetic study of the mode of growth of surface colonies of bacteria and fungi. J Gen Microbiol. 1967, 47(2):181-97.
• [29]Adams J: Microbial evolution in laboratory environments. Res Microbiol. 2004, 155(5):311-8.
• [30]Wolfe AJ: The acetate switch. Microbiol Mol Biol Rev. 2005, 69(1):12-50.
• [31]Beg QK, Vazquez A, Ernst J, de Menezes MA, Bar-Joseph Z, Barabási A-L, et al.: Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc Natl Acad Aci U S A. 2007, 104(31):12663-8.
• [32]Vazquez A, Beg QK, Ernst J, Bar-Joseph Z, Barabási A-L, Boros LG, et al.: Impact of the solvent capacity constraint on E. coli metabolism. BMC Syst Biol. 2008, 2(1):7. BioMed Central Full Text
• [33]Shlomi T, Benyamini T, Gottlieb E, Sharan R, Ruppin E: Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the Warburg effect. PLoS Comput Biol. 2011, 7(3):1002018.
• [34]Zhou Y, Vazquez A, Wise A, Warita T, Warita K, Bar-Joseph Z, Oltvai ZN: Carbon catabolite repression correlates with the maintenance of near invariant molecular crowding in proliferating E. coli cells. BMC Syst Biol. 2013, 7(1):138. BioMed Central Full Text
• [35]Sivaguru M, Mander L, Fried G, Punyasena SW: Capturing the surface texture and shape of pollen: a comparison of microscopy techniques. PloS One 2012, 7(6):39129.
• [36]Wang M, Weiss M, Simonovic M, Haertinger G, Schrimpf S. P, Hengartner M. O, et al.: Paxdb, a database of protein abundance averages across all three domains of life. Mol Cell Proteomics. 2012, 11(8):492-500.
• [37]Taniguchi Y, Choi PJ, Li G-W, Chen H, Babu M, Hearn J, et al.: Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 2010, 329(5991):533-8.
• [38]Reger AS, Carney JM, Gulick AM: Biochemical and crystallographic analysis of substrate binding and conformational changes in acetyl-CoA synthetase. Biochemistry 2007, 46(22):6536-46.
• [39]Scheer M, Grote A, Chang A, Schomburg I, Munaretto C, Rother M, et al.: BRENDA, the enzyme information system in 2011. Nucl Acids Res. 2011, 39:670-6.
• [40]Anstrom DM, Kallio K, Remington SJ: Structure of the Escherichia coli malate synthase G:pyruvate:acetyl-coenzyme A abortive ternary complex at 1.95 Å resolution. Protein Sci. 2003, 12(9):1822-32.
• [41]Chapman S, Faulkner C, Kaiserli E, Garcia-Mata C, Savenkov EI, Roberts AG, et al.: The photoreversible fluorescent protein iLOV outperforms GFP as a reporter of plant virus infection. Proc Natl Acad Aci U S A. 2008, 105(50):20038-43.
• [42]Mukherjee A, Walker J, Weyant KB, Schroeder CM: Characterization of flavin-based fluorescent proteins: an emerging class of fluorescent reporters. PloS One 2013, 8(5):64753.
• [43]Hallatschek O, Hersen P, Ramanathan S, Nelson DR: Genetic drift at expanding frontiers promotes gene segregation. Proc Natl Acad Sci U S A. 2007, 104(50):19926-30.
• [44]Grant MA, Wacław B, Allen RJ, Cicuta P. The role of mechanical forces in the planar-to-bulk transition in growing Escherichia coli microcolonies. J R Soc Interface.2014;11(97). http://dx.doi.org/10.1098/rsif.2014.0400.
• [45]Su P-T, Liao C-T, Roan J-R, Wang S-H, Chiou A, Syu W-J: Bacterial colony from two-dimensional division to three-dimensional development. PloS One 2012, 7(11):48098.
• [46]Sawada T, Nakamura Y: Growth inhibitory and lethal effects of ethanol on Escherichia coli. Biotechnol Bioeng. 1987, 29(6):742-6.
• [47]Schulze A, Harris AL: How cancer metabolism is tuned for proliferation and vulnerable to disruption. Nature 2012, 491(7424):364-73.
• [48]Guillaumond F, Leca J, Olivares O, Lavaut M-N, Vidal N, et al.: Strengthened glycolysis under hypoxia supports tumor symbiosis and hexosamine biosynthesis in pancreatic adenocarcinoma. Proc Natl Acad Aci U S A. 2013, 110(10):3919-24.
• [49]Sonveaux P, Végran F, Schroeder T, Wergin MC, Verrax J, et al.: Targeting lactate-fueled respiration selectively kills hypoxic tumor cells in mice. J Clin Invest. 2008, 118(12):3930.
• [50]Folger O, Jerby L, Frezza C, Gottlieb E, Ruppin E, Shlomi T. Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol. 2011; 7(1). doi:10.1038/msb.2011.35.
• [51]Agren R, Bordel S, Mardinoglu A, Pornputtapong N, Nookaew I, Nielsen J: Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using init. PLoS Comput Biol. 2012, 8(5):1002518.
• [52]Chang RL, Andrews K, Kim D, Li Z, Godzik A, Palsson BØ: Structural systems biology evaluation of metabolic thermotolerance in Escherichia coli. Science 2013, 340(6137):1220-3.
• [53]Green MR, Sambrook J: Molecular Cloning: a Laboratory Manual. Cold Springs Harbor Laboratory Press, Cold Spring Harbor, New York; 2012.