期刊论文详细信息
BMC Systems Biology
Age-correlated stress resistance improves fitness of yeast: support from agent-based simulations
John A Berges1  Neil D Fredrick2  Ferdi L Hellweger2 
[1] Department of Biological Sciences and School of Freshwater Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA;Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, USA
关键词: Yeast;    Stress resistance;    Bet hedging;    Agent-based modeling;   
Others  :  1141450
DOI  :  10.1186/1752-0509-8-18
 received in 2013-11-22, accepted in 2014-02-12,  发布年份 2014
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【 摘 要 】

Background

Resistance to stress is often heterogeneous among individuals within a population, which helps protect against intermittent stress (bet hedging). This is also the case for heat shock resistance in the budding yeast Saccharomyces cerevisiae. Interestingly, the resistance appears to be continuously distributed (vs. binary, switch-like) and correlated with replicative age (vs. random). Older, slower-growing cells are more resistant than younger, faster-growing ones. Is there a fitness benefit to age-correlated stress resistance?

Results

Here this hypothesis is explored using a simple agent-based model, which simulates a population of individual cells that grow and replicate. Cells age by accumulating damage, which lowers their growth rate. They synthesize trehalose at a metabolic cost, which helps protect against heat shock. Proteins Tsl1 and Tps3 (trehalose synthase complex regulatory subunit TSL1 and TPS3) represent the trehalose synthesis complex and they are expressed using constant, age-dependent and stochastic terms. The model was constrained by calibration and comparison to data from the literature, including individual-based observations obtained using high-throughput microscopy and flow cytometry. A heterogeneity network was developed, which highlights the predominant sources and pathways of resistance heterogeneity. To determine the best trehalose synthesis strategy, model strains with different Tsl1/Tps3 expression parameters were placed in competition in an environment with intermittent heat shocks.

Conclusions

For high severities and low frequencies of heat shock, the winning strain used an age-dependent bet hedging strategy, which shows that there can be a benefit to age-correlated stress resistance. The study also illustrates the utility of combining individual-based observations and modeling to understand mechanisms underlying population heterogeneity, and the effect on fitness.

【 授权许可】

   
2014 Hellweger et al.; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]Avery SV: Microbial cell individuality and the underlying sources of heterogeneity. Nat Rev Microbiol 2006, 4(8):577-587.
  • [2]Ackermann M: Microbial individuality in the natural environment. ISME J 2013, 7:465-467.
  • [3]Balaban NQ, Merrin J, Chait R, Kowalik L, Leibler S: Bacterial persistence as a phenotypic switch. Science 2004, 305(5690):1622-1625.
  • [4]Lewis K: Persister cells, dormancy and infectious disease. Nat Rev Microbiol 2007, 5(1):48-56.
  • [5]Kussell E, Kishony R, Balaban NQ, Leibler S: Bacterial persistence: a model of survival in changing environments. Genetics 2005, 169(4):1807-1814.
  • [6]Sinclair DA: Paradigms and pitfalls of yeast longevity research. Mech Ageing Dev 2002, 123(8):857-867.
  • [7]Raser JM, O'Shea EK: Control of stochasticity in eukaryotic gene expression. Science 2004, 304(5678):1811-1814.
  • [8]Blake WJ, Balázsi G, Kohanski MA, Isaacs FJ, Murphy KF, Kuang Y, Cantor CR, Walt DR, Collins JJ: Phenotypic consequences of promoter-mediated transcriptional noise. Mol Cell 2006, 24(6):853-865.
  • [9]Newman JRS, Ghaemmaghami S, Ihmels J, Breslow DK, Noble M, DeRisi JL, Weissman JS: Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 2006, 441(7095):840-846.
  • [10]Ghaemmaghami S, Huh W-K, Bower K, Howson RW, Belle A, Dephoure N, O'Shea EK, Weissman JS: Global analysis of protein expression in yeast. Nature 2003, 425(6959):737-741.
  • [11]Attfield PV, Choi HY, Veal DA, Bell PJL: Heterogeneity of stress gene expression and stress resistance among individual cells of Saccharomyces cerevisiae. Mol Microbiol 2001, 40(4):1000-1008.
  • [12]Bar-Even A, Paulsson J, Maheshri N, Carmi M, O'Shea E, Pilpel Y, Barkai N: Noise in protein expression scales with natural protein abundance. Nat Genet 2006, 38(6):636-643.
  • [13]Sumner ER, Avery AM, Houghton JE, Robins RA, Avery SV: Cell cycle- and age-dependent activation of Sod1p drives the formation of stress resistant cell subpopulations within clonal yeast cultures. Mol Microbiol 2003, 50(3):857-870.
  • [14]Li J, Min R, Vizeacoumar FJ, Jin K, Xin X, Zhang Z: Exploiting the determinants of stochastic gene expression in Saccharomyces cerevisiae for genome-wide prediction of expression noise. Proc Natl Acad Sci 2010, 107(23):10472-10477.
  • [15]Levy SF, Ziv N, Siegal ML: Bet hedging in yeast by Heterogeneous, age-correlated expression of a stress protectant. PLoS Biol 2012, 10(5):e1001325.
  • [16]Holland SL, Reader T, Dyer PS, Avery SV: Phenotypic heterogeneity is a selected trait in natural yeast populations subject to environmental stress. Environ Microbiol 2013. doi: 10.1111/1462-2920.12243
  • [17]Thattai M, van Oudenaarden A: Stochastic gene expression in fluctuating environments. Genetics 2004, 167(1):523-530.
  • [18]Ackermann M, Chao L, Bergstrom CT, Doebeli M: On the evolutionary origin of aging. Aging Cell 2007, 6(2):235-244.
  • [19]Hellweger FL: Carrying photosynthesis genes increases ecological fitness of cyanophage in silico. Environ Microbiol 2009, 11(6):1386-1394.
  • [20]Hellweger FL: Escherichia coli adapts to tetracycline resistance plasmid (pBR322) by mutating endogenous potassium transport: in silico hypothesis testing. FEMS Microbiol Ecol 2013, 83(3):622-631.
  • [21]Hellweger FL, Bucci V: A bunch of tiny individuals—Individual-based modeling for microbes. Ecol Model 2009, 220(1):8-22.
  • [22]Ginovart M, Cañadas J: INDISIM-YEAST: an individual-based simulator on a website for experimenting and investigating diverse dynamics of yeast populations in liquid media. J Ind Microbiol Biotechnol 2008, 35(11):1359-1366.
  • [23]Kreft J-U, Plugge CM, Grimm V, Prats C, Leveau JHJ, Banitz T, Baines S, Clark J, Ros A, Klapper I, et al.: Mighty small: observing and modeling individual microbes becomes big science. Proc Natl Acad Sci 2013, 110(45):18027-18028.
  • [24]Teusink B, Passarge J, Reijenga CA, Esgalhado E, van der Weijden CC, Schepper M, Walsh MC, Bakker BM, van Dam K, Westerhoff HV, et al.: Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Eur J Biochem 2000, 267(17):5313-5329.
  • [25]Jones KD, Kompala DS: Cybernetic model of the growth dynamics of Saccharomyces cerevisiae in batch and continuous cultures. J Biotechnol 1999, 71(1–3):105-131.
  • [26]Vargas F, Pizarro F, Perez-Correa JR, Agosin E: Expanding a dynamic flux balance model of yeast fermentation to genome-scale. BMC Syst Biol 2011, 5(1):75. BioMed Central Full Text
  • [27]Herrgard MJ, Swainston N, Dobson P, Dunn WB, Arga KY, Arvas M, Bluthgen N, Borger S, Costenoble R, Heinemann M, et al.: A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol 2008, 26(10):1155-1160.
  • [28]Smallbone K, Simeonidis E, Swainston N, Mendes P: Towards a genome-scale kinetic model of cellular metabolism. BMC Syst Biol 2010, 4(1):6. BioMed Central Full Text
  • [29]Chen KC, Calzone L, Csikasz-Nagy A, Cross FR, Novak B, Tyson JJ: Integrative analysis of cell cycle control in budding yeast. Mol Biol Cell 2004, 15(8):3841-3862.
  • [30]Vanoni M, Vai M, Popolo L, Alberghina L: Structural heterogeneity in populations of the budding yeast Saccharomyces cerevisiae. J Bacteriol 1983, 156(3):1282-1291.
  • [31]Kreft J-U, Booth G, Wimpenny JWT: BacSim, a simulator for individual-based modelling of bacterial colony growth. Microbiology 1998, 144(12):3275-3287.
  • [32]Johnson FB, Sinclair DA, Guarente L: Molecular biology of aging. Cell 1999, 96(2):291-302.
  • [33]Aguilaniu H, Gustafsson L, Rigoulet M, Nyström T: Asymmetric inheritance of oxidatively damaged proteins during cytokinesis. Science 2003, 299(5613):1751-1753.
  • [34]Erjavec N, Cvijovic M, Klipp E, Nyström T: Selective benefits of damage partitioning in unicellular systems and its effects on aging. Proc Natl Acad Sci 2008, 105(48):18764-18769.
  • [35]Erjavec N, Nyström T: Sir2p-dependent protein segregation gives rise to a superior reactive oxygen species management in the progeny of Saccharomyces cerevisiae. Proc Natl Acad Sci 2007, 104(26):10877-10881.
  • [36]Erjavec N, Larsson L, Grantham J, Nyström T: Accelerated aging and failure to segregate damaged proteins in Sir2 mutants can be suppressed by overproducing the protein aggregation-remodeling factor Hsp104p. Genes Dev 2007, 21(19):2410-2421.
  • [37]Benaroudj N, Lee DH, Goldberg AL: Trehalose accumulation during cellular stress protects cells and cellular proteins from damage by oxygen radicals. J Biol Chem 2001, 276(26):24261-24267.
  • [38]Hirsch HR: Accumulation of a senescence factor in yeast cells. Exp Gerontol 1993, 28(2):195-204.
  • [39]Proctor CJ, Sőti C, Boys RJ, Gillespie CS, Shanley DP, Wilkinson DJ, Kirkwood TBL: Modelling the actions of chaperones and their role in ageing. Mech Ageing Dev 2005, 126(1):119-131.
  • [40]Singer MA, Lindquist S: Multiple effects of trehalose on protein folding in vitro and in vivo. Mol Cell 1998, 1(5):639-648.
  • [41]Bandara A, Fraser S, Chambers PJ, Stanley GA: Trehalose promotes the survival of Saccharomyces cerevisiae during lethal ethanol stress, but does not influence growth under sublethal ethanol stress. FEMS Yeast Res 2009, 9(8):1208-1216.
  • [42]Panek A: Function of trehalose in Baker’s yeast (Saccharomyces cerevisiae). Arch Biochem Biophys 1963, 100(3):422-425.
  • [43]Küenzi MT, Fiechter A: Changes in carbohydrate composition and trehalase-activity during the budding cycle of Saccharomyces cerevisiae. Arch Microbiol 1969, 64(4):396-407.
  • [44]Silljé HH, ter Schure EG, Rommens AJ, Huls PG, Woldringh CL, Verkleij AJ, Boonstra J, Verrips CT: Effects of different carbon fluxes on G1 phase duration, cyclin expression, and reserve carbohydrate metabolism in Saccharomyces cerevisiae. J Bacteriol 1997, 179(21):6560-6565.
  • [45]Bell W, Sun W, Hohmann S, Wera S, Reinders A, De Virgilio C, Wiemken A, Thevelein JM: Composition and functional analysis of the saccharomyces cerevisiae trehalose synthase complex. J Biol Chem 1998, 273(50):33311-33319.
  • [46]Winderickx J, Winde J, Crauwels M, Hino A, Hohmann S, Dijck P, Thevelein J: Regulation of genes encoding subunits of the trehalose synthase complex in Saccharomyces cerevisiae: novel variations of STRE-mediated transcription control? Mol Gen Genet MGG 1996, 252(4):470-482.
  • [47]Lu C, Brauer MJ, Botstein D: Slow growth induces heat-shock resistance in normal and respiratory-deficient yeast. Mol Biol Cell 2009, 20(3):891-903.
  • [48]Smallbone K, Malys N, Messiha HL, Wishart JA, Simeonidis E: Chapter eighteen - Building a Kinetic Model of Trehalose Biosynthesis in Saccharomyces cerevisiae. In Methods in Enzymology. 500th edition. Edited by Daniel Jameson MV, Hans VW. San Diego, CA: Academic; 2011:355-370.
  • [49]Silljé HHW, Paalman JWG, ter Schure EG, Olsthoorn SQB, Verkleij AJ, Boonstra J, Verrips CT: Function of trehalose and glycogen in cell cycle progression and cell viability in saccharomyces cerevisiae. J Bacteriol 1999, 181(2):396-400.
  • [50]Beven K, Freer J: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. J Hydrol 2001, 249(1–4):11-29.
  • [51]Klipp E, Nordlander B, Kruger R, Gennemark P, Hohmann S: Integrative model of the response of yeast to osmotic shock. Nat Biotechnol 2005, 23(8):975-982.
  • [52]Lee SS, Vizcarra IA, Huberts DHEW, Lee LP, Heinemann M: Whole lifespan microscopic observation of budding yeast aging through a microfluidic dissection platform. Proc Natl Acad Sci 2012, 109(13):4916-4920.
  • [53]Raes J, Bork P: Molecular eco-systems biology: towards an understanding of community function. Nat Rev Microbiol 2008, 6(9):693-699.
  • [54]Klitgord N, Segrè D: Ecosystems biology of microbial metabolism. Curr Opin Biotechnol 2011, 22(4):541-546.
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