Yeasts collectively extend the limits of habitable temperatures by secreting glutathione

Abstract

The conventional view is that high temperatures cause microorganisms to replicate slowly or die. In this view, microorganisms autonomously combat heat-induced damages. However, microorganisms co-exist with each other, which raises the underexplored and timely question of whether microorganisms can cooperatively combat heat-induced damages at high temperatures. Here, we use the budding yeast Saccharomyces cerevisiae to show that cells can help each other and their future generations to survive and replicate at high temperatures. As a consequence, even at the same temperature, a yeast population can exponentially grow, never grow or grow after unpredictable durations (hours to days) of stasis, depending on its population density. Through the same mechanism, yeasts collectively delay and can eventually stop their approach to extinction, with higher population densities stopping faster. These features arise from yeasts secreting and extracellularly accumulating glutathione—a ubiquitous heat-damage-preventing antioxidant. We show that the secretion of glutathione, which eliminates harmful extracellular chemicals, is both necessary and sufficient for yeasts to collectively survive at high temperatures. A mathematical model, which is generally applicable to any cells that cooperatively replicate by secreting molecules, recapitulates all of these features. Our study demonstrates how organisms can cooperatively define and extend the boundaries of life-permitting temperatures.

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Fig. 1: Conventional cell-autonomous view of temperature-dependent cell replication.
Fig. 2: Population density determines the replicability of cells and habitability of each temperature.
Fig. 3: Cells collectively combat death to avoid extinction high temperatures.
Fig. 4: Cells secrete and extracellularly accumulate glutathione to help each other and future generations of cells to replicate at high temperatures.
Fig. 5: Mathematical model with one free parameter recapitulates all of the main experimental data.
Fig. 6: Budding yeast exports glutathione of which the extracellular, but not intracellular, role as an antioxidant enables yeast to survive high temperatures.

Data availability

All data supporting the findings of this study are available within the paper and its Supplementary Information. RNA-seq data are available at NCBI GEO (GSE137151). Source data for Figs. 24 and 6 are provided with the paper. The data that support the findings of this study are available from the corresponding author on reasonable request.

Code availability

All scripts used for simulations in this research are publicly available (GitHub diederiklt/YeastHighTemperatures).

References

  1. 1.

    Madigan, M. T., Martinko, J. M., Stahl, D. A. & Clark, D. Brock Biology of Microorganisms 13th edn, 162–163 (Pearson, 2011).

  2. 2.

    Milo, R. & Phillips, R. Cell Biology by the Numbers 1st edn (Garland Science, 2015).

  3. 3.

    Bruslind, L. Microbiology (Open Oregon State University, 2019).

  4. 4.

    Doran, P. M. Bioprocess Engineering Principles 2nd edn 653–655 (Academic, 2012).

  5. 5.

    Ghosh, K. & Dill, K. Cellular proteomes have broad distributions of protein stability. Biophys. J. 99, 3996–4002 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Leuenberger, P. et al. Cell-wide analysis of protein thermal unfolding reveals determinants of thermostability. Science 355, eaai7825 (2017).

    PubMed  Article  CAS  Google Scholar 

  7. 7.

    Verghese, J., Abrams, J., Wang, Y. & Morano, K. A. Biology of the heat shock response and protein chaperones: budding yeast (Saccharomyces cerevisiae) as a model system. Microbiol. Mol. Biol. Rev. 76, 115–158 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Richter, K., Haslbeck, M. & Buchner, J. The heat shock response: life on the verge of death. Mol. Cell 40, 253–266 (2010).

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Miller, M. B. & Bassler, B. L. Quorum sensing in bacteria. Annu. Rev. Microbiol 55, 165–199 (2001).

    CAS  PubMed  Article  Google Scholar 

  10. 10.

    Gore, J., Youk, H. & van Oudenaarden, A. Snowdrift game dynamics and facultative cheating in yeast. Nature 459, 253–256 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Koschwanez, J. H., Foster, K. R. & Murray, A. W. Sucrose utilization in budding yeast as a model for the origin of undifferentiated multicellularity. PLoS Biol. 9, e1001122 (2011).

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Koschwanez, J. H., Foster, K. R. & Murray, A. W. Improved used of a public good selects for the evolution of undifferentiated multicellularity. eLife 2, e00367 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Ratzke, C., Denk, J. & Gore, J. Ecological suicide in microbes. Nat. Ecol. Evol. 2, 867–872 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Postmus, J. et al. Quantitative analysis of the high temperature-induced glycolytic flux increase in Saccharomyces cerevisiae reveals dominant metabolic regulation. J. Biol. Chem. 283, 23524–23532 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Walsh, R. M. & Martin, P. A. Growth of Saccharomyces cerevisiae and saccharomyces uvarum in a temperature gradient incubator. J. Inst. Brew. 83, 169–172 (1977).

    Article  Google Scholar 

  16. 16.

    Ratkowsky, D. A., Lowry, R. K., McMeekin, T. A., Stokes, A. N. & Chandler, R. E. Model for bacterial culture growth rate throughout the entire biokinetic temperature range. J. Bacteriol. 154, 1222–1226 (1983).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Dekel, E. & Alon, U. Optimality and evolutionary tuning of the expression level of a protein. Nature 436, 588–592 (2005).

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Scott, M. & Hwa, T. Bacterial growth laws and their applications. Curr. Opin. Biotechnol. 22, 559–565 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Bharathi, V. et al. Use of ade1 and ade2 mutations for development of a versatile red/white colour assay of amyloid-induced oxidative stress in Saccharomyces cerevisiae. Yeast 33, 607–620 (2016).

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Charlebois, D. A., Hauser, K., Marshall, S. & Balázsi, G. Multiscale effects of heating and cooling on genes and gene networks. Proc. Natl Acad. Sci. USA 115, E10797–E10806 (2018).

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Balaban, N. Q. Persistence: mechanisms for triggering and enhancing phenotypic variability. Curr. Opin. Genet. Dev. 21, 768–775 (2011).

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Causton, H. C. et al. Remodeling of yeast genome expression in response to environmental changes. Mol. Biol. Cell 12, 323–337 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Gasch, A. P. et al. Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 11, 4241–4257 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Sugiyama, K., Kawamura, A., Izawa, S. & Inoue, Y. Role of glutathione in heat-shock-induced cell death of Saccharomyces cerevisiae. Biochem. J. 352, 71–78 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Sugiyama, K., Izawa, S. & Inoue, Y. The Yap1p-dependent induction of glutathione synthesis in heat shock response of Saccharomyces cerevisiae. J. Biol. Chem. 275, 15535–15540 (2000).

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Davidson, J. F., Whyte, B., Bissinger, P. H. & Schiestl, R. H. Oxidative stress is involved in heat-induced cell death in Saccharomyces cerevisiae. Proc. Natl Acad. Sci. USA 93, 5116–5121 (1996).

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Yakes, F. M. & van Houten, B. Mitochondrial DNA damage is more extensive and persists longer than nuclear DNA damage in human cells following oxidative stress. Proc. Natl Acad. Sci. USA 94, 514–519 (1997).

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Cabiscol, E., Piulats, E., Echave, P., Herrero, E. & Ros, J. Oxidative stress promotes specific protein damage in Saccharomyces cerevisiae. J. Biol. Chem. 275, 27393–27398 (2000).

    CAS  PubMed  Google Scholar 

  29. 29.

    Bilinski, T., Litwinska, J., Blaszczynski, M. & Bajus, A. SOD deficiency and the toxicity of the products of autoxidation of polyunsaturated fatty acids in yeast. Biochim. Biophys. Acta 1001, 102–106 (1989).

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Jamieson, D. J. Oxidative stress responses of the yeast Saccharomyces cerevisiae. Yeast 14, 1511–1527 (1998).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Zechmann, B. et al. Subcellular distribution of glutathione and its dynamic changes under oxidative stress in the yeast Saccharomyces cerevisiae. FEMS Yeast Res. 11, 631–642 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Kumar, C. et al. Glutathione revisited: a vital function in iron metabolism and ancillary role in thiol-redox control. EMBO J. 30, 2044–2056 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Toledano, M. B., Kumar, C., Le Moan, N., Spector, D. & Tacnet, F. The system biology of thiol redox system in Escherichia coli and yeast: differential functions in oxidative stress, iron metabolism and DNA synthesis. FEBS Lett. 581, 3598–3607 (2007).

    CAS  PubMed  Article  Google Scholar 

  34. 34.

    Elskens, M. T., Jaspers, C. J. & Penninckx, M. J. Glutathione as an endogenous sulfur source in the yeast Saccharomyces cerevisiae. J. Gen. Microbiol. 137, 637–644 (1991).

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Mehdi, K. & Penninckx, M. J. An important role for glutathione and γ-glutamyltranspeptidase in the supply of growth requirements during nitrogen starvation of the yeast Saccharomyces cerevisiae. Microbiology 143, 1885–1889 (1997).

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Thorsen, M. et al. Glutathione serves an extracellular defence function to decrease arsenite accumulation and toxicity in yeast. Mol. Microbiol. 84, 1177–1188 (2012).

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Perrone, G. G., Grant, C. M. & Dawes, I. W. Genetic and environmental factors influencing glutathione homeostasis in Saccharomyces cerevisiae. Mol. Biol. Cell 16, 218–230 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Giustarini, D. et al. Pitfalls in the analysis of the physiological antioxidant glutathione (GSH) and its disulphide (GSSG) in biological samples: an elephant in the room. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 1019, 21–28 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Araujo, A. R., Saraiva, M. L. & Lima, J. L. Determination of total and oxidized glutathione in human whole blood with a sequential injection analysis system. Talanta 74, 1511–1519 (2008).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Grant, C. M., MacIver, F. H. & Dawes, I. W. Glutathione is an essential metabolite required for resistance to oxidative stress in the yeast Saccharomyces cerevisiae. Curr. Genet. 29, 511–515 (1996).

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Bourbouloux, A., Shahi, P., Chakladar, A., Delrot, S. & Bachhawat, A. K. Hgt1p, a high affinity glutathione transporter from the yeast Saccharomyces cerevisiae. J. Biol. Chem. 275, 13259–13265 (2000).

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Dhaoui, M. et al. Gex1 is a yeast glutathione exchanger that interferes with pH and redox homeostasis. Mol. Biol. Cell 22, 2054–2067 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Kiriyama, K., Hara, K. Y. & Kondo, A. Extracellular glutathione fermentation using engineered Saccharomyces cerevisiae expressing a novel glutathione exporter. Appl. Microbiol. Biotechnol. 96, 1021–1027 (2012).

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Dai, L., Vorselen, D., Korolev, K. & Gore, J. Generic indicators for loss of resilience before a tipping point leading to population collapse. Science 336, 1175–1177 (2012).

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Strogatz, S. H. Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering (Westview, 1994).

  46. 46.

    Mojtahedi, M. et al. Cell fate decision as high-dimensional critical state transition. PLoS Biol. 14, e2000640 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  47. 47.

    Garcia-Ojalvo, J., Sancho, J. M. & Ramirez-Piscina, L. A nonequilibrium phase transition with colored noise. Phys. Lett. A 168, 35–39 (1992).

    Article  Google Scholar 

  48. 48.

    Youk, H. & Lim, W. A. Secreting and sensing the same molecule allows cells to achieve versatile social behaviors. Science 343, 1242782 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. 49.

    Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Riccardi, C. & Nicoletti, I. Analysis of apoptosis by propidium iodide staining and flow cytometry. Nat. Protoc. 1, 1458–1461 (2006).

    CAS  PubMed  Article  Google Scholar 

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Acknowledgements

We thank S. Itzkovitz and A. Raj for comments on our manuscript; the members of the Youk laboratory for helpful discussions; and M. Mohebbi for help with initial experiments. H.Y. was supported by the European Research Council (ERC) Starting Grant (MultiCellSysBio, no. 677972), the Netherlands Organisation for Scientific Research (NWO) Vidi award (no. 680-47-544), the CIFAR Azrieli Global Scholars Program and the EMBO Young Investigator Award.

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Contributions

H.Y. initiated this research and performed the initial growth experiments. D.S.L.T. subsequently designed additional experiments with guidance from H.Y. D.S.L.T. performed all of the experiments, developed the mathematical model and analysed the data with advice from H.Y. D.S.L.T. and H.Y. discussed and checked all of the data and wrote the manuscript.

Corresponding author

Correspondence to Hyun Youk.

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Extended data

Extended Data Fig. 1 A few cells stochastically and transiently replicate within populations that are in either no-growth or random-growth phase (Related to Fig. 2a–d).

a, The wild-type strain lacks a functional ADE2 gene for synthesizing adenine. Since we incubated yeasts in the minimal media with all the essential nutrients - including adenine - the wild-type cells were still capable of growing. But having a defective ade2 gene turns yeasts red if they have not divided for some time because they have accumulated red pigments - these are by-products of the not-fully-repressed and defective adenine-biosynthesis. The cells can only dilute away the red pigments through cell divisions. The histogram shows percentages of red cells (non-replicators) and ‘white cells’ (non-red, replicators) in a population, determined by a flow cytometer’s red-fluorescence detector that quantified redness of individual cells. b, Percentage of white and red cells over time measured with the flow cytometer for a population of wild-type yeasts. Time shows hours of incubation in 38.4 OC. These histograms show example time courses for a population that grew at a high temperature. c, Numbers of white and red cells in a population per ml, at various times for three different growth regimes indicated by the phase diagram (Fig. 2d). Random-growth phase shows two replicate populations - one growing (second row) and one not growing (third row).

Extended Data Fig. 2 The number of survivors decreasing over time as a heavy-tailed function is not due to heat-tolerant mutants or persistor-like cells existing within a population (Related to Fig. 3a, b).

a-b, The number of survivors per ml over time for populations of wild-type cells kept in the no-growth phase at 42.0 OC (Supplementary Fig. 6). The brown dashed lines represent an exponentially decaying function fitted to the data points that lie between 10 h and 50 h. The blue dashed curve is a power-law function fitted to the same data points. a, The number of surviving wild-type cells. Triangles are overestimates (i.e., the aliquots taken from the liquid culture at 42 OC did not yield any colonies on agar at 30 OC, so there could not have been more survivors in the liquid culture than values represented by the triangles). We observed eight colonies formed at the last time point (~220 h - the last circle). b, We took one of these eight colonies - progenies of the survivors from the last time point in a - and used the cells from this colony to repeat the experiment. The number of survivors in this new experiment also decreased over time as a heavy-tailed function. This result eliminates the possibility that the survivors of the high temperature (42 OC) in the first experiment a are either heat-tolerant mutants or persistor-like cells that behave similarly to persistors of antibiotic treatments. To see this, suppose that the survivors at the last time point in a were heat-tolerant mutants or persistor-like cells. Then the starting population in b must be a pure population of these heat-tolerant mutants or persistor-like cells, that would have died at a slower rate than all the other cells of the wild-type population that started in a. It then follows that the number of survivors per ml in b should decrease as a single, slowly decaying exponential function rather than decreasing over time as a heavy-tailed function. Thus, by contradiction, the survivors at high temperatures are not heat-tolerant mutants or persistor-like cells.

Extended Data Fig. 3 Mathematical model reproduces the sustained population of few replicating cells in random-growth and no-growth phases (Related to Extended Data Fig. 1).

a, The number of alive (yellow) and dead cells (red) over time. The same, fixed set of parameters was used as in Fig. 5. Depending on the initial population density, the number of alive cells grows exponentially (top row - deterministic-growth phase) or decreases exponentially until extinction (bottom row - no-growth phase). For intermediate population densities (2nd and 3rd rows - random-growth), the population is very sensitive to the stochastic transitions of very few alive cells (at ~300 h). Based on whether these cells stay alive without replicating, replicate, or die in the next time steps, the population can eventually either expand and grow exponentially or go extinct. b, The probability of replicating (blue) and the probability of dying (red) as a function of time for the same populations as in a. The probability of dying per unit time is fixed by temperature while the probability of replication is initially zero and increases over time as the alive cells always secrete glutathione (Fig. 5b). The probability of replicating quickly exceeds the probability of dying for high initial population densities (top row), leading to deterministic growth. For intermediate initial population densities (2nd and 3rd rows), the number of alive cells initially decreases over time as the probability of replicating continuously approaches - but stays smaller than - the probability of dying. Simultaneously, this decreasing pool of alive cells keeps secreting glutathione until, after ~300 h, the probability of replicating is very close to the probability of dying and very few alive cells are left in the population. Here, the probability of replicating either exceeds the probability of dying - leading to exponential growth - or remains smaller than the probability of dying - leading to extinction. This results in random-growth. For low initial population densities (bottom row), the probability of replicating remains well below the probability of dying. Here, the population goes extinct before the alive cells can secrete sufficient glutathione to increase the probability of replicating, leading to no growth.

Extended Data Fig. 4 A mutant strain that cannot synthesize glutathione detects glutathione secreted by wild-type yeasts at high temperatures (Related to Fig. 4e and Supplementary Fig. 11).

a, We constructed a mutant strain that could not synthesize glutathione by knocking out, in the wild-type strain, the GSH1 gene which is essential for glutathione biosynthesis (see “Mutant yeasts” in the Methods section). Glutathione has essential intracellular roles in yeast, so the Δgsh1-mutant can only grow in media that we supplement with glutathione40. To check this, we incubated starved the Δgsh1-mutant cells in SD-media to which we added 0 μM, 0.25 μM or 2.5 μM glutathione. These cells did not grow in medium without any glutathione (0 μM) but they grew in media with very small amounts of glutathione (i.e., more than 0.25 μM of extracellular glutathione). b, We used the Δgsh1-mutant strain to detect glutathione secreted by cells at high temperatures. We separated the growth media from cells grown at a fixed temperature by flowing the liquid cultures through 0.45μm-pore filters as previously described. We confirmed that there were no cells left behind in the filtered media by flowing the media through a flow cytometer. We then transplanted Δgsh1-mutant cells that were starved of glutathione into these filtered media at 30 OC. Subsequently, we then measured the resulting population densities over time in 30.0 OC. c-d, For example, we took the growth media from wild-type cells, just before growth at 39.2 OC (c) or during late log-phase growth at 30.0 OC (d). We then gave these filtered media to Δgsh1-cells and incubated them at 30.0 OC (green curves). As a control, we incubated populations of Δgsh1-cells at the same starting density in fresh media without any glutathione (grey curves). Only the media taken from cells incubated at high temperatures (39.2 OC) was able to induce growth of Δgsh1-cells. For all colors in each panel, there are n = 4 (a) or n = 3 (c, d) replicate populations. This result complements Supplementary Fig. 11, supporting our finding that wild-type cells secrete glutathione at micromolar concentrations at high temperatures (only above 36 OC).

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Source data for Fig. 2a–f.

Source Data Fig. 3

Source data for Fig. 3b,d.

Source Data Fig. 4

Source data for Fig. 4b–f.

Source Data Fig. 6

Source data for Fig. 6a,c–e.

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Laman Trip, D.S., Youk, H. Yeasts collectively extend the limits of habitable temperatures by secreting glutathione. Nat Microbiol 5, 943–954 (2020). https://doi.org/10.1038/s41564-020-0704-2

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