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.

  • Letter
  • Published:

Macromolecular crowding limits growth under pressure

Nature Physics volume 18, pages 411–416 (2022)Cite this article

Subjects

Abstract

Cells that grow in confined spaces eventually build up mechanical compressive stress. This growth-induced pressure decreases cell growth. Growth-induced pressure is important in a multitude of contexts, including cancer1,2,3, microbial infections4 and biofouling5; yet, our understanding of its origin and molecular consequences remains limited. Here we combine microfluidic confinement of the yeast Saccharomyces cerevisiae6 with rheological measurements using genetically encoded multimeric nanoparticles7 to reveal that growth-induced pressure is accompanied with an increase in a key cellular physical property: macromolecular crowding. We develop a fully calibrated model that predicts how increased macromolecular crowding hinders protein expression and thus diminishes cell growth. This model is sufficient to explain the coupling of growth rate to pressure without the need for specific molecular sensors or signalling cascades. As molecular crowding is similar across all domains of life, this could be a deeply conserved mechanism of biomechanical feedback that allows environmental sensing originating from the fundamental physical properties of cells.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Confined growth leads to the intracellular accumulation of osmolytes and macromolecules.
Fig. 2: Confinement decreases growth and protein production rates.
Fig. 3: Protein production and growth are diffusion-limited processes.
Fig. 4: A physical feedback model in which crowding limits protein production and predicts the dynamics of confined cell growth.

Similar content being viewed by others

Data availability

Source data are available for this paper. All other data that support the plots within this paper and other findings of this study are available from the corresponding authors upon reasonable request.

Code availability

All the codes used in this paper are available from the corresponding authors upon reasonable request.

References

  1. Helmlinger, G., Netti, P. A., Lichtenbeld, H. C., Melder, R. J. & Jain, R. K. Solid stress inhibits the growth of multicellular tumor spheroids. Nat. Biotechnol. 15, 778–783 (1997).

    Article  Google Scholar 

  2. Alessandri, K. et al. Cellular capsules as a tool for multicellular spheroid production and for investigating the mechanics of tumor progression in vitro. Proc. Natl Acad. Sci. USA 110, 14843–14848 (2013).

    Article  ADS  Google Scholar 

  3. Rizzuti, I. et al. Mechanical control of cell proliferation increases resistance to chemotherapeutic agents. Phys. Rev. Lett. 125, 128103 (2020).

    Article  ADS  Google Scholar 

  4. Bonazzi, D. et al. Intermittent pili-mediated forces fluidize Neisseria meningitidis aggregates promoting vascular colonization. Cell 174, 143–155.e16 (2018).

    Article  Google Scholar 

  5. Delarue, M. et al. Self-driven jamming in growing microbial populations. Nat. Phys. 12, 762–766 (2016).

    Article  Google Scholar 

  6. Holt, L. J., Hallatschek, O. & Delarue, M. Mechano-chemostats to study the effects of compressive stress on yeast. Methods Cell. Biol. 147, 215–231 (2018).

    Article  Google Scholar 

  7. Delarue, M. et al. mTORC1 controls phase separation and the biophysical properties of the cytoplasm by tuning crowding. Cell 174, 338–349.e20 (2018).

  8. Streichan, S. J., Hoerner, C. R., Schneidt, T., Holzer, D. & Hufnagel, L. Spatial constraints control cell proliferation in tissues. Proc. Natl Acad. Sci. USA 111, 5586–5591 (2014).

    Article  ADS  Google Scholar 

  9. Bengough, A. G., Croser, C. & Pritchard, J. A biophysical analysis of root growth under mechanical stress. Plant Soil 189, 155–164 (1997).

    Article  Google Scholar 

  10. Fal, K. et al. Tissue folding at the organ–meristem boundary results in nuclear compression and chromatin compaction. Proc. Natl Acad. Sci. USA 118, e2017859118 (2021).

    Article  Google Scholar 

  11. Rivera-Yoshida, N., Arias Del Angel, J. A. & Benítez, M. Microbial multicellular development: mechanical forces in action. Curr. Opin. Genet. Dev. 51, 37–45 (2018).

    Article  Google Scholar 

  12. Asally, M. et al. Localized cell death focuses mechanical forces during 3D patterning in a biofilm. Proc. Natl Acad. Sci. USA 109, 18891–18896 (2012).

    Article  ADS  Google Scholar 

  13. Trejo, M. et al. Elasticity and wrinkled morphology of Bacillus subtilis pellicles. Proc. Natl Acad. Sci. USA 110, 2011–2016 (2013).

    Article  ADS  Google Scholar 

  14. Stewart, P. S. & Robertson, C. R. Microbial growth in a fixed volume: studies with entrapped Escherichia coli. Appl. Microbiol. Biotechnol. 30, 34–40 (1989).

  15. Chu, E. K., Kilic, O., Cho, H., Groisman, A. & Levchenko, A. Self-induced mechanical stress can trigger biofilm formation in uropathogenic Escherichia coli. Nat. Commun. 9, 4087 (2018).

    Article  ADS  Google Scholar 

  16. Nam, S. et al. Cell cycle progression in confining microenvironments is regulated by a growth-responsive TRPV4-PI3K/Akt-p27Kip1 signaling axis. Sci. Adv. 5, eaaw6171 (2019).

    Article  ADS  Google Scholar 

  17. Delarue, M. et al. SCWISh network is essential for survival under mechanical pressure. Proc. Natl Acad. Sci. USA 114, 13465–13470 (2017).

    Article  Google Scholar 

  18. Miermont, A. et al. Severe osmotic compression triggers a slowdown of intracellular signaling, which can be explained by molecular crowding. Proc. Natl Acad. Sci. USA 110, 5725–5730 (2013).

    Article  ADS  Google Scholar 

  19. Vibhute, M. A. et al. Transcription and translation in cytomimetic protocells perform most efficiently at distinct macromolecular crowding conditions. ACS Synth. Biol. 9, 2797–2807 (2020).

  20. Nettesheim, G. et al. Macromolecular crowding acts as a physical regulator of intracellular transport. Nat. Phys. 16, 1144–1151 (2020).

  21. Fernandez-De-Cossio-DIaz, J. & Vazquez, A. A physical model of cell metabolism. Sci. Rep. 8, 8349 (2018).

  22. Petelenz-Kurdziel, E. et al. Quantification of cell volume changes upon hyperosmotic stress in Saccharomyces cerevisiae. Integr. Biol. 3, 1120–1126 (2011).

    Article  Google Scholar 

  23. Görlich, D. & Kutay, U. Transport between the cell nucleus and the cytoplasm. Annu. Rev. Cell Dev. Biol. 15, 607–660 (2003).

  24. de, I. M., Marechal, P.-A. & Gervais, P. Passive response of Saccharomyces cerevisiae to osmotic shifts: cell volume variations depending on the physiological state. Biochem. Biophys. Res. Commun. 227, 519–523 (1996).

    Article  Google Scholar 

  25. Borodavka, A. et al. Sizes of long RNA molecules are determined by the branching patterns of their secondary structures. Biophys. J. 111, 2077–2085 (2016).

    Article  ADS  Google Scholar 

  26. Busti, S., Coccetti, P., Alberghina, L. & Vanoni, M. Glucose signaling-mediated coordination of cell growth and cell cycle in Saccharomyces cerevisiae. Sensors 10, 6195–6240 (2010).

    Article  ADS  Google Scholar 

  27. Tamás, M. J. & Hohmann, S. The osmotic stress response of Saccharomyces cerevisiae. in Yeast Stress Responses. Topics in Current Genetics Vol. 1 (eds Hohmann S. & Mager W. H.) 121–200 (Springer, 2003).

  28. Maayan, I. & Engelberg, D. The yeast MAPK Hog1 is not essential for immediate survival under osmostress. FEBS Lett. 583, 2015–2020 (2009).

    Article  Google Scholar 

  29. Rojas, E. R., Huang, K. C. & Theriot, J. A. Homeostatic cell growth is accomplished mechanically through membrane tension inhibition of cell-wall synthesis. Cell Syst. 5, 578–590.e6 (2017).

    Article  Google Scholar 

  30. Puchner, E. M., Walter, J. M., Kasper, R., Huang, B. & Lim, W. A. Counting molecules in single organelles with superresolution microscopy allows tracking of the endosome maturation trajectory. Proc. Natl Acad. Sci. USA 110, 16015–16020 (2013).

    Article  ADS  Google Scholar 

  31. Takamori, S. et al. Molecular anatomy of a trafficking organelle. Cell 127, 831–846 (2006).

    Article  Google Scholar 

  32. Vella, D., Ajdari, A., Vaziri, A. & Boudaoud, A. The indentation of pressurized elastic shells: from polymeric capsules to yeast cells. J. R. Soc. Interface 9, 448–455 (2012).

    Article  Google Scholar 

  33. Albersheim, P., Darvill, A., Roberts, K., Sederoff, R. & Staehelin A. Plant Cell Walls (Garland Science, 2010).

  34. Schaber, J. et al. Biophysical properties of Saccharomyces cerevisiae and their relationship with HOG pathway activation. Eur. Biophys. J. 39, 1547–1556 (2010).

    Article  ADS  Google Scholar 

  35. Levin, D. E. Cell wall integrity signaling in Saccharomyces cerevisiae. Microbiol. Mol. Biol. Rev. 69, 262–291 (2005).

    Article  Google Scholar 

  36. Ellis, R. J. Macromolecular crowding: obvious but underappreciated. Trends Biochem. Sci 26, 597–604 (2001).

    Article  Google Scholar 

  37. Sbalzarini, I. F. & Koumoutsakos, P. Feature point tracking and trajectory analysis for video imaging in cell biology. J. Struct. Biol. 151, 182–195 (2005).

    Article  Google Scholar 

  38. Griffith, J., Mari, M., De Mazière, A. & Reggiori, F. A cryosectioning procedure for the ultrastructural analysis and the immunogold labelling of yeast Saccharomyces cerevisiae. Traffic 9, 1060–1072 (2008).

    Article  Google Scholar 

  39. Hutter, J. L., & Bechhoefer, J. Calibration of atomic‐force microscope tips. Rev. Sci. Instrum. 64, 1868 (1993).

    Article  ADS  Google Scholar 

Download references

Acknowledgements

We thank E. Kassianidou for the initial help with the laser ablation experiments. We thank A. Liang, C. Petzold and K. Dancel-Manning at the NYULH DART Microscopy Laboratory for consultation and assistance with the transmission electron microscopy work; this core is partially funded by the NYU Cancer Center Support Grant NIH/NCI P30CA016087. The technological realizations and associated research works were partly supported by the French RENATECH network (M.D.). L.J.H. was funded by NIH grants R01 GM132447 and R37 CA240765, the American Cancer Society, the Pershing Square Sohn Cancer Research Award and Chan Zuckerberg Initiative. We thank E. Rojas for fruitful discussions. L.J.H. and M.D. thank the FACE foundation for travel support.

Author information

Authors and Affiliations

  1. LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France

    Baptiste Alric, Etienne Dague & Morgan Delarue

  2. TBI, Université de Toulouse, CNRS, INRAE, INSA, Toulouse, France

    Cécile Formosa-Dague

  3. New York University Grossman School of Medicine, Institute for Systems Genetics, New York, NY, USA

    Liam J. Holt

Authors
  1. Baptiste Alric
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cécile Formosa-Dague
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Etienne Dague
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liam J. Holt
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Morgan Delarue
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B.A. and M.D. designed and performed the experiments and data analysis. C.F.-D. and E.D. performed the AFM experiments. L.J.H. designed the strains used in the study. B.A., L.J.H. and M.D. wrote the manuscript.

Corresponding authors

Correspondence to Liam J. Holt or Morgan Delarue.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Physics thanks Pascal Hersen and the other, anonymous, reviewer(s) 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 Device used in the microfluidic experiments.

a. Cells are loaded in a culture chamber connected on its sides to narrow channels that are used to set the chemical environment. Confined growth lead to the buildup of GIP, which is measured through the deformation of the elastic chamber. b. The culture chamber is, similar to the device presented in a., connected to a set of narrow channels to set the chemical environment. A valve is actuated to confine the cell population and allow it to build up GIP. We estimate GIP by measuring the deformation of the PDMS membrane. Opening of the valve leads to a relaxation of GIP.

Extended Data Fig. 2 Impact of osmotic shock.

Ratio of nucleus and cytoplasm volume under osmotic shock and growth induced pressure.

Source data

Extended Data Fig. 3 Linear and exponential fits on the diffusion as a function of GIP data.

The score for each fit is presented. We superimposed the prediction of diffusion as a function of GIP for the 40nm-GEMs, as well as the corresponding score.

Source data

Extended Data Fig. 4 40nm-GEMs diffusion as a function of cell volume.

Model fit (Eq. (13)) of the experimental data to extract \(\xi _{40} = 7.4 \pm 2.5\) (r2 = 0.99).

Source data

Extended Data Fig. 5 Contribution of density and chamber volume change in the growth rate.

For simplicity, we denoted kg as the growth rate, \(k_\rho = \partial _t\rho /\rho\) as the contribution of cell density ρ to growth rate, and \(k_V = \partial _PV/V\;\partial _tP\) the contribution of the volume of the chamber V.

Source data

Extended Data Fig. 6 Induction time of PADH2-mCherry.

The induction time is plotted as a function of time. Inset: induction time plotted as a function of growth rate.

Source data

Extended Data Fig. 7 Protein production rate as a function of GIP.

In orange: data from the PADH2-mCherry promoter. In blue: data from the PHIS3-GFP promoter.

Source data

Extended Data Fig. 8 Laser ablation experiments and calibration of turgor pressure.

a. We used a high-intensity laser pulse to make a hole in a cell, forcing its deflation. The cell radius changes as a function of turgor pressure, cell wall elasticity, and thickness of the cell wall. b. Cells were punctured with a laser, resulting in a decrease in cell radius proportional to turgor pressure. The similar decrease in radius of WT and hog1Δ cells indicates that, absent osmotic perturbation, these cells develop similar amounts of turgor pressure. The decrease in radius of osmotically compressed (c = 1 M sorbitol) hog1Δ cells indicates that these cells are still pressurized, albeit to a reduced extent. c. We used transmission electron microscopy to measure the cell wall thickness. d. We performed AFM experiments, using small deformations (below 0.2 μm) to extract the effective elasticity of the cell. This elasticity provided a mathematical function of turgor pressure, cell wall elasticity, and cell wall thickness.

Extended Data Fig. 9 Growth induced pressure as a function of time.

In blue, for the WT cells. In orange, for the hog1Δ cells.

Source data

Extended Data Fig. 10 Measurement of the fluorescence intensity in the center versus the edge of the chamber prior to induction of PADH2-mCherry.

The data shows an insignificant (p-value = 0.16) 2.3% difference.

Supplementary information

Supplementary Information

Supplementary information containing a table of strains used along with the mathematical modelling.

Source data

Source Data Fig. 1

Raw data for Fig. 1b–e.

Source Data Fig. 2

Raw data for Fig. 2a,c,d.

Source Data Fig. 3

Raw data for Fig. 3a–c.

Source Data Fig. 4

Raw data for Fig. 4b–e.

Source Data Extended Data Fig. 2

Raw data for Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Raw data for Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Raw data for Extended Data Fig. 4.

Source Data Extended Data Fig. 5

Raw data for Extended Data Fig. 5.

Source Data Extended Data Fig. 6

Raw data for Extended Data Fig. 6.

Source Data Extended Data Fig. 7

Raw data for Extended Data Fig. 7.

Source Data Extended Data Fig. 9

Raw data for Extended Data Fig. 9.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alric, B., Formosa-Dague, C., Dague, E. et al. Macromolecular crowding limits growth under pressure. Nat. Phys. 18, 411–416 (2022). https://doi.org/10.1038/s41567-022-01506-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41567-022-01506-1

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

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