Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Metabolic rearrangement enables adaptation of microbial growth rate to temperature shifts

Nature Microbiology volume 10, pages 185–201 (2025)Cite this article

Subjects

Abstract

Temperature is a key determinant of microbial behaviour and survival in the environment and within hosts. At intermediate temperatures, growth rate varies according to the Arrhenius law of thermodynamics, which describes the effect of temperature on the rate of a chemical reaction. However, the mechanistic basis for this behaviour remains unclear. Here we use single-cell microscopy to show that Escherichia coli exhibits a gradual response to temperature upshifts with a timescale of ~1.5 doublings at the higher temperature. The response was largely independent of initial or final temperature and nutrient source. Proteomic and genomic approaches demonstrated that adaptation to temperature is independent of transcriptional, translational or membrane fluidity changes. Instead, an autocatalytic enzyme network model incorporating temperature-sensitive Michaelis–Menten kinetics recapitulates all temperature-shift dynamics through metabolome rearrangement, resulting in a transient temperature memory. The model successfully predicts alterations in the temperature response across nutrient conditions, diverse E. coli strains from hosts with different body temperatures, soil-dwelling Bacillus subtilis and fission yeast. In sum, our model provides a mechanistic framework for Arrhenius-dependent growth.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: E. coli responds asymmetrically to temperature shifts, and upshift dynamics are determined by the steady-state growth rate at the final temperature.
Fig. 2: The delayed response to a temperature upshift is not due to proteome rearrangement.
Fig. 3: Response time is unaffected by almost all genetic and chemical perturbations.
Fig. 4: A TSEN model recapitulates temperature-shift behaviours through metabolome rearrangement.
Fig. 5: The TSEN model predicts changes to upshift response during growth on simple sugars, after a downshift pulse and at low nutrient concentration.
Fig. 6: Temperature upshift response features are generally conserved across microbes and can be captured by the TSEN model.

Similar content being viewed by others

Data availability

Imaging datasets used to generate growth rate analyses are available from the corresponding author upon request. Processed and analysed imaging data sets of growth trajectories and FRAP measurements, liquid growth measurements, processed transposon sequencing and processed proteomics data are all available at the Harvard Dataverse72. Raw transposon sequencing data have been deposited in NCBI’s Sequence Read Archive (SRA) under project accession identifier PRJNA1138713. Mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE73 partner repository with data set identifier PXD048941.

Code availability

Codes for generating single-cell trajectories74 and for analysing single-cell growth rates75 are available in GitHub. Simulation code for the TSEN model is available in GitHub76.

References

  1. Zhang, Y. & Gross, C. A. Cold shock response in bacteria. Annu. Rev. Genet. 55, 377–400 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  3. Barber, M. A. The rate of multiplication of Bacillus coli at different temperatures. J. Infect. Dis. 5, 379–400 (1908).

    Article  Google Scholar 

  4. Mohr, P. W. & Krawiec, S. Temperature characteristics and Arrhenius plots for nominal psychrophiles, mesophiles and thermophiles. J. Gen. Microbiol. 121, 311–317 (1980).

    CAS  PubMed  Google Scholar 

  5. Herendeen, S. L., VanBogelen, R. A. & Neidhardt, F. C. Levels of major proteins of Escherichia coli during growth at different temperatures. J. Bacteriol. 139, 185–194 (1979).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Knapp, B. D. & Huang, K. C. The effects of temperature on cellular physiology. Annu. Rev. Biophys. 51, 499–526 (2022).

    Article  CAS  PubMed  Google Scholar 

  7. Chen, K. et al. Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation. Proc. Natl Acad. Sci. USA 114, 11548–11553 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Phillips, R., Kondev, J. & Theriot, J. Physical Biology of the Cell (Garland Science, 2009).

  9. Hinshelwood, C. N. On the chemical kinetics of autosynthetic systems. J. Chem. Soc. (Resumed) 1952, 745–755 (1952).

  10. Iyer-Biswas, S. et al. Scaling laws governing stochastic growth and division of single bacterial cells. Proc. Natl Acad. Sci. USA 111, 15912–15917 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Elias, M., Wieczorek, G., Rosenne, S. & Tawfik, D. S. The universality of enzymatic rate-temperature dependency. Trends Biochem. Sci. 39, 1–7 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Dill, K. A., Ghosh, K. & Schmit, J. D. Physical limits of cells and proteomes. Proc. Natl Acad. Sci. USA 108, 17876–17882 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lemaux, P. G., Herendeen, S. L., Bloch, P. L. & Neidhardt, F. C. Transient rates of synthesis of individual polypeptides in E. coli following temperature shifts. Cell 13, 427–434 (1978).

    Article  CAS  PubMed  Google Scholar 

  14. Gadgil, M., Kapur, V. & Hu, W. S. Transcriptional response of Escherichia coli to temperature shift. Biotechnol. Prog. 21, 689–699 (2005).

    Article  CAS  PubMed  Google Scholar 

  15. Tagkopoulos, I., Liu, Y. C. & Tavazoie, S. Predictive behavior within microbial genetic networks. Science 320, 1313–1317 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhou, Y. N., Kusukawa, N., Erickson, J. W., Gross, C. A. & Yura, T. Isolation and characterization of Escherichia coli mutants that lack the heat shock sigma factor sigma 32. J. Bacteriol. 170, 3640–3649 (1988).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Chohji, T., Sawada, T. & Kuno, S. Effects of temperature shift on growth rate of Escherichia coli BB at lower glucose concentration. Biotechnol. Bioeng. 25, 2991–3003 (1983).

    Article  CAS  PubMed  Google Scholar 

  18. Sinensky, M. Homeoviscous adaptation—a homeostatic process that regulates the viscosity of membrane lipids in Escherichia coli. Proc. Natl Acad. Sci. USA 71, 522–525 (1974).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Budin, I. et al. Viscous control of cellular respiration by membrane lipid composition. Science 362, 1186–1189 (2018).

    Article  CAS  PubMed  Google Scholar 

  20. Scott, M., Klumpp, S., Mateescu, E. M. & Hwa, T. Emergence of robust growth laws from optimal regulation of ribosome synthesis. Mol. Syst. Biol. 10, 747 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Bremer, H. & Dennis, P. P. Modulation of chemical composition and other parameters of the cell at different exponential growth rates. EcoSal Plus https://doi.org/10.1128/ecosal.5.2.3 (2008).

  22. Belliveau, N. M. et al. Fundamental limits on the rate of bacterial growth and their influence on proteomic composition. Cell Syst. 12, 924–944.e2 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zaritsky, A. Effects of growth temperature on ribosomes and other physiological properties of Escherichia coli. J. Bacteriol. 151, 485–486 (1982).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ram, J. L., Ritchie, R. P., Fang, J., Gonzales, F. S. & Selegean, J. P. Sequence-based source tracking of Escherichia coli based on genetic diversity of beta-glucuronidase. J. Environ. Qual. 33, 1024–1032 (2004).

    Article  CAS  PubMed  Google Scholar 

  25. Ram, J. L. et al. Identification of pets and raccoons as sources of bacterial contamination of urban storm sewers using a sequence-based bacterial source tracking method. Water Res. 41, 3605–3614 (2007).

    Article  CAS  PubMed  Google Scholar 

  26. Arcus, V. L. & Mulholland, A. J. Temperature, dynamics, and enzyme-catalyzed reaction rates. Annu. Rev. Biophys. 49, 163–180 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Nguyen, V. et al. Evolutionary drivers of thermoadaptation in enzyme catalysis. Science 355, 289–294 (2017).

    Article  CAS  PubMed  Google Scholar 

  28. Atolia, E. et al. Environmental and physiological factors affecting high-throughput measurements of bacterial growth. mBio 11, e01378-20 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Knapp, B. D., Zhu, L. & Huang, K. C. SiCTeC: an inexpensive, easily assembled Peltier device for rapid temperature shifting during single-cell imaging. PLoS Biol. 18, e3000786 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cashel, M. & Gallant, J. Two compounds implicated in the function of the RC gene of Escherichia coli. Nature 221, 838–841 (1969).

    Article  CAS  PubMed  Google Scholar 

  31. Irving, S. E., Choudhury, N. R. & Corrigan, R. M. The stringent response and physiological roles of (pp)pGpp in bacteria. Nat. Rev. Microbiol. 19, 256–271 (2021).

    Article  CAS  PubMed  Google Scholar 

  32. Mackow, E. R. & Chang, F. N. Correlation between RNA synthesis and ppGpp content in Escherichia coli during temperature shifts. Mol. Gen. Genet. 192, 5–9 (1983).

    Article  CAS  PubMed  Google Scholar 

  33. Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z. & Hwa, T. Interdependence of cell growth and gene expression: origins and consequences. Science 330, 1099–1102 (2010).

    Article  CAS  PubMed  Google Scholar 

  34. Maitra, A. & Dill, K. A. Bacterial growth laws reflect the evolutionary importance of energy efficiency. Proc. Natl Acad. Sci. USA 112, 406–411 (2015).

    Article  CAS  PubMed  Google Scholar 

  35. Reuveni, S., Ehrenberg, M. & Paulsson, J. Ribosomes are optimized for autocatalytic production. Nature 547, 293–297 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Hui, S. et al. Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria. Mol. Syst. Biol. 11, 784 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Schmidt, A. et al. The quantitative and condition-dependent Escherichia coli proteome. Nat. Biotechnol. 34, 104–110 (2016).

    Article  CAS  PubMed  Google Scholar 

  38. Li, G. W., Burkhardt, D., Gross, C. & Weissman, J. S. Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157, 624–635 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Jones, P. G., VanBogelen, R. A. & Neidhardt, F. C. Induction of proteins in response to low temperature in Escherichia coli. J. Bacteriol. 169, 2092–2095 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Grossberger, R. et al. Influence of RNA structural stability on the RNA chaperone activity of the Escherichia coli protein StpA. Nucleic Acids Res. 33, 2280–2289 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wetmore, K. M. et al. Rapid quantification of mutant fitness in diverse bacteria by sequencing randomly bar-coded transposons. mBio 6, e00306–e00315 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Murina, V. et al. ABCF ATPases involved in protein synthesis, ribosome assembly and antibiotic resistance: structural and functional diversification across the Tree of Life. J. Mol. Biol. 431, 3568–3590 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Cochrane, K. Elucidating Ribosomes—Genetic Studies of the ATPase Uup and the Ribosomal Protein L1 (Univ. of Michigan, 2015).

  44. Sulavik, M. C. et al. Antibiotic susceptibility profiles of Escherichia coli strains lacking multidrug efflux pump genes. Antimicrob. Agents Chemother. 45, 1126–1136 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wilson, D. N. Ribosome-targeting antibiotics and mechanisms of bacterial resistance. Nat. Rev. Microbiol. 12, 35–48 (2014).

    Article  CAS  PubMed  Google Scholar 

  46. Ikeuchi, Y., Shigi, N., Kato, J., Nishimura, A. & Suzuki, T. Mechanistic insights into sulfur relay by multiple sulfur mediators involved in thiouridine biosynthesis at tRNA wobble positions. Mol. Cell 21, 97–108 (2006).

    Article  CAS  PubMed  Google Scholar 

  47. Campbell, J. W. & Cronan, J. E. Jr. Escherichia coli FadR positively regulates transcription of the fabB fatty acid biosynthetic gene. J. Bacteriol. 183, 5982–5990 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Borgaro, J. G., Chang, A., Machutta, C. A., Zhang, X. & Tonge, P. J. Substrate recognition by beta-ketoacyl-ACP synthases. Biochemistry 50, 10678–10686 (2011).

    Article  CAS  PubMed  Google Scholar 

  49. Sorensen, T. H. et al. Temperature effects on kinetic parameters and substrate affinity of Cel7A cellobiohydrolases. J. Biol. Chem. 290, 22193–22202 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Sizer, I. W. in Advances in Enzymology and Related Areas of Molecular Biology (eds Nord, F. F. & Werkman, C. H.) 35–62 (Wiley, 1943).

  51. Ehmann, J. D. & Hultin, H. O. Temperature dependence of the Michaelis constant of chicken breast muscle lactate dehydrogenase. J. Food Sci. 38, 1119–1121 (1973).

    Article  Google Scholar 

  52. Bennett, B. D. et al. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat. Chem. Biol. 5, 593–599 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Taymaz-Nikerel, H. et al. Changes in substrate availability in Escherichia coli lead to rapid metabolite, flux and growth rate responses. Metab. Eng. 16, 115–129 (2013).

    Article  CAS  PubMed  Google Scholar 

  54. Link, H., Fuhrer, T., Gerosa, L., Zamboni, N. & Sauer, U. Real-time metabolome profiling of the metabolic switch between starvation and growth. Nat. Methods 12, 1091–1097 (2015).

    Article  CAS  PubMed  Google Scholar 

  55. Monod, J. The growth of bacterial cultures. Annu. Rev. Microbiol. 3, 371–394 (1949).

    Article  CAS  Google Scholar 

  56. Petersen, J. & Russell, P. Growth and the environment of Schizosaccharomyces pombe. Cold Spring Harb. Protoc. https://doi.org/10.1101/pdb.top079764 (2016).

  57. Chang, A. et al. BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 49, D498–D508 (2021).

    Article  CAS  PubMed  Google Scholar 

  58. Mairet, F., Gouze, J. L. & de Jong, H. Optimal proteome allocation and the temperature dependence of microbial growth laws. npj Syst. Biol. Appl. 7, 14 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Walk, S. T. et al. Cryptic lineages of the genus Escherichia. Appl. Environ. Microbiol. 75, 6534–6544 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Reas, C. & Fry, B. Processing: programming for the media arts. AI Soc. 20, 526–538 (2006).

    Article  Google Scholar 

  61. Edelstein, A., Amodaj, N., Hoover, K., Vale, R. & Stuurman, N. Computer control of microscopes using µManager. Curr. Protoc. Mol. Biol. https://doi.org/10.1002/0471142727.mb1420s92 (2010).

  62. Van Valen, D. A. et al. Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLoS Comput. Biol. 12, e1005177 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Tseng, Q. et al. A new micropatterning method of soft substrates reveals that different tumorigenic signals can promote or reduce cell contraction levels. Lab Chip 11, 2231–2240 (2011).

    Article  CAS  PubMed  Google Scholar 

  64. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  65. Knapp, B. D. et al. Decoupling of rates of protein synthesis from cell expansion leads to supergrowth. Cell Syst. 9, 434–445.e6 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Ursell, T. et al. Rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library. BMC Biol. 15, 17 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Traub, W. H. & Leonhard, B. Heat stability of the antimicrobial activity of sixty-two antibacterial agents. J. Antimicrob. Chemother. 35, 149–154 (1995).

    Article  CAS  PubMed  Google Scholar 

  68. Shi, H. et al. Precise regulation of the relative rates of surface area and volume synthesis in bacterial cells growing in dynamic environments. Nat. Commun. 12, 1975 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Galperin, M. Y., Makarova, K. S., Wolf, Y. I. & Koonin, E. V. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res. 43, D261–D269 (2015).

    Article  CAS  PubMed  Google Scholar 

  70. Karp, P. D. et al. The EcoCyc Database (2023). EcoSal Plus 11, eesp00022023 (2023).

    Article  PubMed  Google Scholar 

  71. Axelrod, D., Koppel, D. E., Schlessinger, J., Elson, E. & Webb, W. W. Mobility measurement by analysis of fluorescence photobleaching recovery kinetics. Biophys. J. 16, 1055–1069 (1976).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Knapp, B. D. Replication Data for: Metabolic rearrangement enables adaptation of microbial growth rates to temperature shifts. Harvard Dataverse https://doi.org/10.7910/DVN/SC2KXZ (2024).

  73. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    Article  CAS  PubMed  Google Scholar 

  74. MATLAB functions for generating single-cell trajectories from Morphometrics contours. GitHub https://github.com/bknapp8/cell_tracking_bacteria (2024).

  75. Temperature-shift growth rate analysis. GitHub https://github.com/bknapp8/temperature_shift_growthrate (2024).

  76. Temperature-sensitive enzyme network (TSEN) model package. GitHub https://github.com/bknapp8/TSEN_models (2024).

Download references

Acknowledgements

We thank members of the Huang lab for helpful discussions. The E. coli BW25113 transposon mutant library was provided by the Deutschbauer lab (Lawrence Berkeley National Laboratory), with help from V. Trotter. This work was funded by a Stanford Interdisciplinary Graduate Fellowship (to B.D.K.), NIH RM1 GM135102 (to K.C.H.) and NSF Awards EF-2125383 and IOS-2032985 (to K.C.H.). K.C.H. is a Chan Zuckerberg Biohub Investigator.

Author information

Authors and Affiliations

  1. Biophysics Program, Stanford University, Stanford, CA, USA

    Benjamin D. Knapp & Kerwyn Casey Huang

  2. Department of Bioengineering, Stanford University, Stanford, CA, USA

    Lisa Willis & Kerwyn Casey Huang

  3. Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA

    Carlos Gonzalez

  4. Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA, USA

    Harsh Vashistha, Joanna Jammal-Touma & Hanna Salman

  5. Department of Physics, Washington University in St Louis, St Louis, MO, USA

    Mikhail Tikhonov

  6. Department of Physiology, Wayne State University, Detroit, MI, USA

    Jeffrey Ram

  7. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Josh E. Elias & Kerwyn Casey Huang

  8. Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA

    Kerwyn Casey Huang

Authors
  1. Benjamin D. Knapp
    View author publications

    Search author on:PubMed Google Scholar

  2. Lisa Willis
    View author publications

    Search author on:PubMed Google Scholar

  3. Carlos Gonzalez
    View author publications

    Search author on:PubMed Google Scholar

  4. Harsh Vashistha
    View author publications

    Search author on:PubMed Google Scholar

  5. Joanna Jammal-Touma
    View author publications

    Search author on:PubMed Google Scholar

  6. Mikhail Tikhonov
    View author publications

    Search author on:PubMed Google Scholar

  7. Jeffrey Ram
    View author publications

    Search author on:PubMed Google Scholar

  8. Hanna Salman
    View author publications

    Search author on:PubMed Google Scholar

  9. Josh E. Elias
    View author publications

    Search author on:PubMed Google Scholar

  10. Kerwyn Casey Huang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

B.D.K. and K.C.H. conceived the study and designed the experiments. B.D.K. and C.G. performed experimental bench work. H.V., J.J.T. and H.S. helped design temperature-control devices. B.D.K., M.T. and K.C.H. analysed the data. B.D.K., L.W. and K.C.H. developed the TSEN model. J.R. contributed strains. H.S., J.E.E. and K.C.H. supervised the research. B.D.K. and K.C.H. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Kerwyn Casey Huang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Microbiology thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 E. coli exhibits robust Arrhenius behavior with a highly conserved activation energy.

a) Liquid-culture maximal growth rates across temperatures of diverse E. coli strains from various animal hosts (Methods). Each strain is labeled with its laboratory accession number (Supplementary Table 1), host source, and estimated host body temperature. Each data point represents the mean of eight biological replicates, with error bars representing the standard deviation. b) Arrhenius plots of growth rates from (a). The natural logarithm of maximal growth rate is plotted against the inverse absolute temperature for temperatures between 27 °C and 37 °C, along with weighted linear fits for each strain. c) Activation energies measured as the slope of the linear fit to the data in (b) for each E. coli strain, with errors reported as the standard error of the mean (SEM) from the weighted fit. Each strain is grouped according to host body temperature (blue: Ectothermic, green: Mammalian, red: Avian, gray: Laboratory (MG1655, BW25113)). Each bar represents the mean of eight biological replicates. d) Steady-state maximum growth rates in rich medium (LB) of E. coli BW25113, CS109, and BL21 between 18 °C and 47 °C (Methods). Each maximal growth rate is reported as the mean±1 standard deviation of eight biological replicates. e) Arrhenius plot of growth rates from (d). The natural logarithm of maximal growth rate is plotted against the inverse absolute temperature. Growth rates measured at temperatures between 25 °C and 37 °C were used for measuring the activation energy (slope, Ea).

Extended Data Fig. 2 Temperature upshifts to mild heat-shock temperatures are characterized by slower final growth rates but similar normalized response times.

a) Single-cell growth rates of E. coli MG1655 on rich medium (LB) undergoing temperature upshifts from 27 °C to 37 °C (blue, n=792 cells), 40 °C (purple, n=278 cells), or 42 °C (red, n=474 cells). Data are the mean±1 SEM (shaded region) at each time point. b) Normalized growth rate versus thermal time follows a common trajectory for each upshift in (a).

Extended Data Fig. 3 Effect of chaperones, oxygen, and the stringent response on temperature upshift responses.

a) Single-cell growth rates of E. coli MG1655 on rich medium (LB) undergoing a temperature upshift from 25 °C to 37 °C in aerobic (blue, n=773 cells) or anaerobic (orange, n=319 cells) conditions. Data are the mean±1 SEM (shaded region) at each time point. b) Normalized growth rate versus thermal time for each trajectory in (a). c) Single-cell growth rates of ΔdnaK (red, n=433 cells) and its parent BW25113 (blue, n=1011 cells) on rich medium (LB) undergoing a temperature upshift from 37 °C to 42 °C. Data are the mean±1 SEM (shaded region) at each time point. d) Single-cell growth rates of ΔdnaK (red, n=318 cells) and its parent BW25113 (blue, n=734 cells) on rich medium (LB) undergoing a temperature upshift from 27 °C to 37 °C. Data are the mean±1 SEM (shaded region) at each time point. e) Normalized growth rate versus thermal time for each trajectory in (d). f) Single-cell growth rates of a ppGppnull strain (ΔrelA ΔspoT) (purple, n=648 cells) and its parent MG1655 (blue, n=792 cells) on rich medium (LB) undergoing a temperature upshift from 27 °C to 37 °C. Data are the mean±1 SEM (shaded region) at each time point. g) Normalized growth rate versus thermal time for each trajectory in (f). h) Single-cell growth rates of ppGppnull (purple, n=47 cells) and its parent MG1655 (blue, n=474 cells) on rich medium (LB) undergoing a temperature upshift from 27 °C to 42 °C. Data are the mean±1 SEM (shaded region) at each time point.

Extended Data Fig. 4 Downshift pulses reveal temperature history.

a) Single-cell growth rates of E. coli MG1655 on rich medium (LB) starting at 37 °C subjected to 27 °C pulses for 2 min (red, n=519 cells), 5 min (purple, n=397 cells), 12 min (green, n=451 cells), or 17 min (dark red, n=422 cells). Dotted lines represent the time at which cells were subjected to a 27 °C downshift. The shift from steady-state growth at 27 °C to 37 °C is also shown for comparison (blue, n=773 cells). Data are the mean±1 SEM (shaded region). b) Single-cell growth rates of E. coli MG1655 on rich medium (LB) starting at 37 °C subjected to a 23 °C pulse for 5 min before an upshift to the intermediate temperature 30 °C (red, n=330 cells). The vertical dashed line indicates the start of cooling, which required ~2 min to reach 23 °C. The shift from steady-state growth at 23 °C to 30 °C is also shown for comparison (blue, n=396 cells).

Extended Data Fig. 5 E. coli growth rate responds rapidly to heat-shock and cold-shock pulses.

a) Single-cell growth rates of E. coli MG1655 on rich medium (LB) undergoing temperature upshifts from 37 °C to 40 °C (purple, n=479 cells), 42 °C (orange, n=1249 cells), 43 °C (red, n=819 cells), or 47 °C (dark red, n=474 cells). Data are the mean±1 SEM (shaded region) at each time point. b) Single-cell growth rates of E. coli MG1655 on rich medium (LB) starting at 37 °C and subjected to heat-shock pulses at 47 °C for 5 min (orange, n=381 cells) or 35 min (dark red, n=474 cells). Vertical dashed lines represent the times at which cells were shifted back to 37 °C. Data are the mean±1 SEM (shaded region) at each time point. c) Single-cell growth rates of E. coli MG1655 on rich medium (LB) starting at 37 °C and subjected to ~10-min cold-shock pulses at 18 °C (light blue, n=361 cells) or 12 °C (dark blue, n=390 cells). Data are the mean±1 SEM (shaded region) at each time point. d) Temperature readout of a 5-min pulse at 0 °C (Methods) starting from 37 °C. t=0 is when cells were shifted back to 37 °C. e) Single-cell growth rates of E. coli MG1655 on rich medium (LB) starting at 37 °C and subjected to a 5-min cold-shock pulse at 0 °C (purple, n=283 cells) shown in (d). Horizontal dashed line represents cell shrinkage defined as growth rate <0 h−1. Data are the mean±1 SEM (shaded region) at each time point. f) Images of E. coli MG1655 on rich medium (LB) during a 10-min pulse at 0 °C starting from 37 °C. At 37 °C, cells exhibited normal morphologies and growth (top). At 0 °C, cells shrank, as exemplified by the cell whose length at t=-10 min represented by a double-arrowed line extends beyond the cell boundary at t=0 (middle). Growth resumed quickly after the sample was heated back to 37 °C, without loss in cell viability (bottom).

Extended Data Fig. 6 Changes in membrane fluidity do not alter the response to temperature upshift but increase lysis at high temperatures.

a) FRAP recovery dynamics of the membrane dye MitoTracker in the parent strain BW25113 (blue, n=19 cells), the high-fluidity mutant ΔfabR (red, n=15 cells), and the low-fluidity mutant ΔfadR (purple, n=14 cells) grown on LB (Methods). Curves show the mean recovery and shaded regions represent ±1 SEM. b) Membrane viscosity (Methods) was significantly lower and higher in ΔfabR and ΔfadR, respectively, compared with the parent. Significance was determined using a two-sample t-test (two-sided); *: p=0.013, **: p<1.8×10−8. c) Membrane viscosity of E. coli BW25113 cells after an upshift at t=0 from 27 °C to 37 °C in LB (Methods). Points are estimates from a best fit (Methods) and error bars represent 1 standard error. Dotted line is the mean viscosity from steady-state measurements (b), and the shaded region represents ±1 standard deviation. d) Growth rate responses of E. coli BW25113 (blue, n=686 cells), ΔfabR (red, n=924 cells), and ΔfadR (purple, n=409 cells) to a temperature upshift from 27 °C to 37 °C in LB. Curves show mean growth rate and shaded regions represent ±1 SEM. e) Normalized growth rate followed a similar trajectory versus thermal time among the mutants and parent for the data in (d). f) Growth curves of wild-type (BW25113) (blue) and ΔfabR (red) cells grown in LB at 37 °C (n=3 replicates). Optical density (OD) was corrected for non-linearity at high OD values (Methods). Maximum growth rates are the mean across replicates and the error is ±1 standard deviation. g) Growth rate response to a temperature upshift from 37 °C to 42 °C in E. coli BW25113 (blue, n=1011 cells) and ΔfabR (red, n=554 cells). Both strains exhibited an initial decrease in growth rate followed by recovery to the steady-state growth rate at 37 °C, but recovery was more delayed for the high-fluidity ΔfabR mutant. Curves show mean growth rate and shaded regions represent ±1 SEM. h) (Left) Representative images of ΔfabR cells throughout a temperature upshift from 37 °C to 44 °C. At 37 °C, morphology and growth were wild-type-like (top left). Growth halted immediately after the shift to 44 °C (top right), with loss of turgor and cell death occurring within 30–40 min after the shift (bottom left, right). (Right) Representative images of wild-type MG1655 cells before and after an upshift from 37 °C to 44 °C. Cells maintained growth and shape.

Extended Data Fig. 7 Effects of changes in TSEN model parameters on temperature-shift response dynamics.

a) Increasing the catalytic rate (\({k}_{i}\)) for each reaction in a bottlenecked minimal TSEN model (gray box) from 1 min−1 to 1000 min−1 has virtually no effect on the response. b) Effect of model parameters on the normalized response time to a temperature upshift from 27 °C to 37 °C in the minimal TSEN model (gray box) for each reaction (import, production, growth). The definitions of each parameter are provided in Fig. 4a. All other parameters were set to default values (vertical gray bars) in each simulation. Notably, increases in the activation energy of the \({K}_{M}\) of the production reaction produced the largest increase in response times across all activation energies (right). c) The analytically tractable production-less TSEN model (gray box, Supplementary Text) predicts a non-zero response time. The simulation used default parameters (\({k}_{i}\)= 1 min−1, \({K}_{M}\) = 1 mM, \({E}_{a}^{{cat}}\) = 15 kcal/mol, \({E}_{a}^{M}\)= 15 kcal/mol), with the exception of the Michaelis-Menten constant of the second reaction \({K}_{1}\,\)= 20 mM. d) Normalized response time increases with increased activation energy of \({K}_{1}\) in the production-less TSEN model (gray box). e) Normalized response time increases with increased \({K}_{1}\) in the production-less TSEN model (gray box). f) Normalized response time was between 1 and 2 doublings when the cutoff used to define the adaptation was increased from 95% to 99% of the steady-state growth rate difference in the production-less TSEN model (gray box). g) Arrhenius plot of steady-state growth rate across temperatures predicted by the minimal TSEN model (gray box) exhibits slightly non-linear behavior.

Extended Data Fig. 8 TSEN model is compatible with reversible enzyme kinetics and predicts nutrient perturbation responses.

a) Predictions of the minimal TSEN (3 total reactions, single intermediate) with a reversible intermediate reaction for a temperature upshift from 27 °C to 37 °C (Supplementary Text). Simulations are shown for various values of the Michaelis-Menten constants for the reversible reaction. The standard bottleneck is defined as \({K}_{M}=20\) mM, which has an activation energy of \({E}_{a}=22.5\) kcal/mol. Standard reactions have \({K}_{M}=1\) mM and \({E}_{a}=15\) kcal/mol. Simulations were conducted with saturating external nutrients (\({c}_{0}=100\) mM). Other parameters of the minimal TSEN can be found in Fig. 4a, b. Green: with a forward bottleneck only, described by a very large reverse \({K}_{M}=1000\) mM. Red: with bottlenecks in both the forward and reverse reactions. Blue: with a forward bottleneck and standard reverse reaction. Purple: with forward and reverse reactions both possessing standard \({K}_{M}\) values. b) The TSEN model responds to a nutrient pulse from a steady state with low nutrient concentration through increased metabolite production. The full TSEN (5 intermediate reactions) with a single bottleneck (standard kinetic values in black, bottleneck values in red on left) was simulated under sub-saturating, low-nutrient conditions (\({c}_{0}=\,\)0.1 mM) until steady state was reached, and then an instantaneous nutrient pulse of 28 mM was added at \(t=\) 0 min, with no nutrients subsequently provided. Simulations were performed in a 1 L container. Left: growth rate dynamics predicted by the bottlenecked TSEN after the nutrient pulse. Standard (black) and bottleneck (red) parameter values are shown. Middle: predicted intracellular metabolite concentrations, colored by location in network during the nutrient pulse and subsequent depletion. Dynamics depend on network position. Right: External nutrient concentration throughout the simulation. c) The TSEN model responds to starvation exit through slow metabolite production. The full TSEN (5 intermediate reactions) with a single bottleneck (standard kinetic values in black, bottleneck values in red on left) was simulated under starvation-like conditions (\({c}_{0}=\,\)0.01 mM) until steady state was reached, and then the nutrient concentration was shifted to a saturating condition (\({c}_{0}=\)100 mM) at \(t=\) 0 min. Left: growth rate dynamics after the nutrient shift predicted by the bottlenecked TSEN. Standard (black) and bottleneck (red) parameter values are shown. Right: intracellular metabolite concentrations, colored by position in network, during the shift.

Extended Data Fig. 9 E. coli exhibits Michaelis-Menten kinetics across substrates.

a) Left: Liquid-culture growth rates of E. coli MG1655 grown on a variety of substrates at 37 °C (Methods). Data are the mean of the maximum growth rate extracted from three biological replicate growth curves and error bars represent ±1 standard deviation (SD). Right: Expanded view of growth rates versus concentration from the outlined box on the left. b) Liquid-culture growth rates of E. coli MG1655 in MOPS minimal medium supplemented with various concentrations of D-glucose at 25 °C (blue), 30 °C (black), or 37 °C (red). Data are the mean of the maximum growth rate extracted from three biological replicate growth curves and error bars represent ±1 SD. c) Michaelis-Menten constants (\({K}_{M}\)) of E. coli MG1655 growth rates across growth media and temperatures. Data are estimates from a non-linear weighted fit and error bars represent ±1 SEM. d) Arrhenius plots of ln(growth rate) versus 1/(absolute temperature) for E. coli MG1655 grown in MOPS minimal medium supplemented with glucose at concentrations between 0.17 mM and 11 mM (blue-to-red). Data are the mean across three biological replicates and error bars represent ±1 SD. Weighted linear fits were performed for each concentration. e) Arrhenius plots of ln(growth rate) versus 1/(absolute temperature) for E. coli MG1655 grown in MOPS minimal medium supplemented with casamino acids at concentrations between 0.9 mM and 923 mM (blue-to-red). Data are the mean across three biological replicates and error bars represent ±1 SD. Weighted linear fits were performed for each concentration. f) Activation energy versus substrate concentration of E. coli MG1655 grown in minimal media without amino acids supplemented with glucose, fructose, acetate, succinate, or maltose. Activation energies are estimates from a linear weighted fit of Arrhenius plots and error bars represent ±1 SEM. g) Activation energy versus substrate concentration of E. coli grown in LB (red) or MOPS minimal medium supplemented with casamino acids (green). Activation energies are estimates from a linear weighted fit of Arrhenius plots and error bars represent ±1 SEM.

Extended Data Fig. 10 Growth rate response to a temperature downshift is rapid across organisms.

Single-cell growth rate response to a temperature downshift on rich medium (LB) of laboratory-evolved (blue, CS109, n=911 cells) and naturally isolated E. coli strains (orange to red, n=417-1186 cells), Escherichia fergusonii (purple, n=902 cells), and Bacillus subtilis (green, n=117 cells). All downshifts were from 37 °C to 25 °C, except for B. subtilis (37 °C to 27 °C). Data are the mean±1 SEM (shaded region) at each time point.

Supplementary information

Supplementary Information

Supplementary Table 1, Figs. 1–8 and Text.

Reporting Summary

Supplementary Table 1

Strains used in this study.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Knapp, B.D., Willis, L., Gonzalez, C. et al. Metabolic rearrangement enables adaptation of microbial growth rate to temperature shifts. Nat Microbiol 10, 185–201 (2025). https://doi.org/10.1038/s41564-024-01841-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41564-024-01841-4

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing