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:

Phase transition of GvpU regulates gas vesicle clustering in bacteria

Nature Microbiology volume 9, pages 1021–1035 (2024)Cite this article

Subjects

Abstract

Gas vesicles (GVs) are microbial protein organelles that support cellular buoyancy. GV engineering has multiple applications, including reporter gene imaging, acoustic control and payload delivery. GVs often cluster into a honeycomb pattern to minimize occupancy of the cytosol. The underlying molecular mechanism and the influence on cellular physiology remain unknown. Using genetic, biochemical and imaging approaches, here we identify GvpU from Priestia megaterium as a protein that regulates GV clustering in vitro and upon expression in Escherichia coli. GvpU binds to the C-terminal tail of the core GV shell protein and undergoes a phase transition to form clusters in subsaturated solution. These properties of GvpU tune GV clustering and directly modulate bacterial fitness. GV variants can be designed with controllable sensitivity to GvpU-mediated clustering, enabling design of genetically tunable biosensors. Our findings elucidate the molecular mechanisms and functional roles of GV clustering, enabling its programmability for biomedical applications.

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: Gene knockout screening determines GvpU is essential for the clustering of GVs, and purified GvpU reconstitutes the GV cluster.
Fig. 2: GvpU-mediated clustering is selective to the genotype of the GV major shell protein, and the C-terminal region of the GV major shell protein is the binding site for GvpU.
Fig. 3: GvpU can drive phase separation through homotypic interactions, which mediates the clustering of GVs.
Fig. 4: GvpU drives GV assembly, which modulates cellular fitness.
Fig. 5: GvpC-based biosensors can operate orthogonally to GvpU-mediated clustering and engineering GV particles with controllable sensitivity to GvpU-mediated clustering.
Fig. 6: Proposed model of the assembly of GV clusters.

Similar content being viewed by others

Data availability

The supporting data for findings of this study are available as source data. Models of docking are available at https://github.com/lab-of-george-lu/Li_2024_NMICROBIOL67. Raw data of disorder profile of GvpU and structure modelling of GvpU are available at https://github.com/holehouse-lab/supportingdata/tree/master/2024/Li_202468. Macromolecular structural data are available from the RCSB Protein Data Bank (PDB). GvpAAna: PDB 8GBS. GvpA2Mega: PDB 7R1C. Source data are provided with this paper.

Code availability

The computational script used for generating disorder profile of GvpU is available at https://github.com/holehouse-lab/supportingdata/tree/master/2024/Li_2024.

References

  1. Greening, C. & Lithgow, T. Formation and function of bacterial organelles. Nat. Rev. Microbiol. 18, 677–689 (2020).

    Article  CAS  PubMed  Google Scholar 

  2. Uebe, R. & Schüler, D. Magnetosome biogenesis in magnetotactic bacteria. Nat. Rev. Microbiol. 14, 621–637 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Mauriello, E. Carboxysomes: how bacteria arrange their organelles. Elife 8, e43777 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Komeili, A., Li, Z., Newman, D. K. & Jensen, G. J. Magnetosomes are cell membrane invaginations organized by the actin-like protein MamK. Science 311, 242–245 (2006).

    Article  CAS  PubMed  Google Scholar 

  5. Scheffel, A. et al. An acidic protein aligns magnetosomes along a filamentous structure in magnetotactic bacteria. Nature 440, 110–114 (2005).

    Article  PubMed  Google Scholar 

  6. Savage, D. F., Afonso, B., Chen, A. H. & Silver, P. A. Spatially ordered dynamics of the bacterial carbon fixation machinery. Science 327, 1258–1261 (2010).

    Article  CAS  PubMed  Google Scholar 

  7. Pfeifer, F. Distribution, formation and regulation of gas vesicles. Nat. Rev. Microbiol. 10, 705–715 (2012).

    Article  CAS  PubMed  Google Scholar 

  8. Walsby, A. E. Gas vesicles. Microbiol. Rev. 58, 94–144 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Bourdeau, R. W. et al. Acoustic reporter genes for non-invasive imaging of microorganisms in mammalian hosts. Nature 553, 86–90 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Farhadi, A., Ho, G. H., Sawyer, D. P., Bourdeau, R. W. & Shapiro, M. G. Ultrasound imaging of gene expression in mammalian cells. Science 365, 1469–1475 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sawyer, D. P. et al. Ultrasensitive ultrasound imaging of gene expression with signal unmixing. Nat. Methods 18, 945–952 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lu, G. J. et al. Acoustically modulated magnetic resonance imaging of gas-filled protein nanostructures. Nat. Mater. 17, 456–463 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Shapiro, M. G. et al. Biogenic gas nanostructures as ultrasonic molecular reporters. Nat. Nanotechnol. 9, 311–316 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Shapiro, M. G. et al. Genetically encoded reporters for hyperpolarized xenon magnetic resonance imaging. Nat. Chem. 6, 629–634 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Lu, G. J. et al. Genetically encodable contrast agents for optical coherence tomography. ACS Nano 14, 7823–7831 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hurt, R. C. et al. Genomically mined acoustic reporter genes for real-time in vivo monitoring of tumors and tumor-homing bacteria. Nat. Biotechnol. 41, 919–931 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lakshmanan, A. et al. Acoustic biosensors for ultrasound imaging of enzyme activity. Nat. Chem. Biol. 16, 988–996 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Kim, W. S. et al. Magneto-acoustic protein nanostructures for non-invasive imaging of tissue mechanics in vivo. Nat. Mater. 23, 290–300 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Bar-Zion, A. et al. Acoustically triggered mechanotherapy using genetically encoded gas vesicles. Nat. Nanotechnol. 16, 1403–1412 (2021).

    Article  CAS  PubMed  Google Scholar 

  20. Wu, D. et al. Biomolecular actuators for genetically selective acoustic manipulation of cells. Sci. Adv. 9, eadd9186 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Yang, Y. et al. In-vivo programmable acoustic manipulation of genetically engineered bacteria. Nat. Commun. 14, 3297 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Shen, Q. et al. 50-nm gas-filled protein nanostructures to enable the access of lymphatic cells by ultrasound technologies. Preprint at bioRxiv https://doi.org/10.1101/2023.06.27.546433 (2023).

  23. Ling, B. et al. Truly tiny acoustic biomolecules for ultrasound imaging and therapy. Adv. Mater https://doi.org/10.1002/adma.202307106 (2024).

  24. Xie, L., Wang, J., Song, L., Jiang, T. & Yan, F. Cell-cycle dependent nuclear gene delivery enhances the effects of E-cadherin against tumor invasion and metastasis. Signal Transduct. Target. Ther. 8, 182 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Song, L. et al. Biogenic nanobubbles for effective oxygen delivery and enhanced photodynamic therapy of cancer. Acta Biomater. 108, 313–325 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Fernando, A. & Gariépy, J. Coupling chlorin e6 to the surface of nanoscale gas vesicles strongly enhance their intracellular delivery and photodynamic killing of cancer cells. Sci. Rep. 10, 2802 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hao, Y., Li, Z., Luo, J., Li, L. & Yan, F. Ultrasound molecular imaging of epithelial mesenchymal transition for evaluating tumor metastatic potential via targeted biosynthetic gas vesicles. Small 19, e2207940 (2023).

    Article  PubMed  Google Scholar 

  28. Anthis, A. H. C. et al. Modular stimuli-responsive hydrogel sealants for early gastrointestinal leak detection and containment. Nat. Commun. 13, 7311 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Li, N. & Cannon, M. C. Gas vesicle genes identified in Bacillus megaterium and functional expression in Escherichia coli. J. Bacteriol. 180, 2450–2458 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Huber, S. T., Terwiel, D., Evers, W. H., Maresca, D. & Jakobi, A. J. Cryo-EM structure of gas vesicles for buoyancy-controlled motility. Cell 186, 975–986 e913 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lakshmanan, A. et al. Preparation of biogenic gas vesicle nanostructures for use as contrast agents for ultrasound and MRI. Nat. Protoc. 12, 2050–2080 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Offner, S., Hofacker, A., Wanner, G. & Pfeifer, F. Eight of fourteen gvp genes are sufficient for formation of gas vesicles in halophilic archaea. J. Bacteriol. 182, 4328–4336 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Dzuricky, M., Rogers, B. A., Shahid, A., Cremer, P. S. & Chilkoti, A. De novo engineering of intracellular condensates using artificial disordered proteins. Nat. Chem. 12, 814–825 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Molliex, A. et al. Phase separation by low complexity domains promotes stress granule assembly and drives pathological fibrillization. Cell 163, 123–133 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Bremer, A. et al. Deciphering how naturally occurring sequence features impact the phase behaviours of disordered prion-like domains. Nat. Chem. 14, 196–207 (2022).

    Article  CAS  PubMed  Google Scholar 

  36. Shin, Y. & Brangwynne, C. P. Liquid phase condensation in cell physiology and disease. Science 357, eaaf4382 (2017).

    Article  PubMed  Google Scholar 

  37. Quiroz, F. G. & Chilkoti, A. Sequence heuristics to encode phase behaviour in intrinsically disordered protein polymers. Nat. Mater. 14, 1164–1171 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ruff, K. M., Roberts, S., Chilkoti, A. & Pappu, R. V. Advances in understanding stimulus-responsive phase behavior of intrinsically disordered protein polymers. J. Mol. Biol. 430, 4619–4635 (2018).

    Article  CAS  PubMed  Google Scholar 

  39. Milo, R. What is the total number of protein molecules per cell volume? A call to rethink some published values. Bioessays 35, 1050–1055 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kar, M. et al. Phase-separating RNA-binding proteins form heterogeneous distributions of clusters in subsaturated solutions. Proc. Natl Acad. Sci. USA 119, e2202222119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Krainer, G. et al. Reentrant liquid condensate phase of proteins is stabilized by hydrophobic and non-ionic interactions. Nat. Commun. 12, 1085 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Nott, T. J. et al. Phase transition of a disordered nuage protein generates environmentally responsive membraneless organelles. Mol. Cell 57, 936–947 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bouchard, J. J. et al. Cancer mutations of the tumor suppressor SPOP disrupt the formation of active, phase-separated compartments. Mol. Cell 72, 19–36 e18 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Franzmann, T. M. et al. Phase separation of a yeast prion protein promotes cellular fitness. Science 359, eaao5654 (2018).

    Article  PubMed  Google Scholar 

  45. Atkinson, J. T. et al. Real-time bioelectronic sensing of environmental contaminants. Nature 611, 548–553 (2022).

    Article  CAS  PubMed  Google Scholar 

  46. Herud-Sikimić, O. et al. A biosensor for the direct visualization of auxin. Nature 592, 768–772 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Quijano-Rubio, A. et al. De novo design of modular and tunable protein biosensors. Nature 591, 482–487 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Jin, Z. et al. Ultrasonic reporters of calcium for deep tissue imaging of cellular signals. Preprint at bioRxiv https://doi.org/10.1101/2023.11.09.566364 (2023).

  49. Dutka, P. et al. Structure of Anabaena flos-aquae gas vesicles revealed by cryo-ET. Structure 31, 518–528 e516 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285–298 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Dai, Y. et al. Programmable synthetic biomolecular condensates for cellular control. Nat. Chem. Biol. 119, 518–528 (2023).

    Article  Google Scholar 

  52. Mittag, T. & Pappu, R. V. A conceptual framework for understanding phase separation and addressing open questions and challenges. Mol. Cell 82, 2201–2214 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Yao, Y. X., Jin, Z. Y., Ling, B., Malounda, D. & Shapiro, M. G. Self-assembly of protein superstructures by physical interactions under cytoplasm-like conditions. Biophys. J. 120, 2701–2709 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Perez, J. M., Josephson, L., O’Loughlin, T., Hogemann, D. & Weissleder, R. Magnetic relaxation switches capable of sensing molecular interactions. Nat. Biotechnol. 20, 816–820 (2002).

    Article  CAS  PubMed  Google Scholar 

  55. Bender, B. J. et al. Protocols for molecular modeling with Rosetta3 and RosettaScripts. Biochemistry 55, 4748–4763 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. Kozakov, D. et al. The ClusPro web server for protein–protein docking. Nat. Protoc. 12, 255–278 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Desta, I. T., Porter, K. A., Xia, B., Kozakov, D. & Vajda, S. Performance and its limits in rigid body protein–protein docking. Structure 28, 1071–1081.e1073 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Vajda, S. et al. New additions to the ClusPro server motivated by CAPRI. Proteins Struct. Funct. Bioinforma. 85, 435–444 (2017).

    Article  CAS  Google Scholar 

  59. Kozakov, D. et al. How good is automated protein docking? Proteins Struct. Funct. Bioinforma. 81, 2159–2166 (2013).

    Article  CAS  Google Scholar 

  60. Strunk, T. et al. Structural model of the gas vesicle protein GvpA and analysis of GvpA mutants in vivo. Mol. Microbiol. 81, 56–68 (2011).

    Article  CAS  PubMed  Google Scholar 

  61. Ezzeldin, H. M., Klauda, J. B. & Solares, S. D. Modeling of the major gas vesicle protein, GvpA: from protein sequence to vesicle wall structure. J. Struct. Biol. 179, 18–28 (2012).

    Article  CAS  PubMed  Google Scholar 

  62. Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Sievers, F. & Higgins, D. G. Clustal Omega for making accurate alignments of many protein sequences. Protein Sci. 27, 135–145 (2018).

    Article  CAS  PubMed  Google Scholar 

  65. Zakeri, B. et al. Peptide tag forming a rapid covalent bond to a protein, through engineering a bacterial adhesin. Proc. Natl Acad. Sci. USA 109, E690–E697 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Milkovic, N. M. & Mittag, T. Determination of protein phase diagrams by centrifugation. Methods Mol. Biol. 2141, 685–702 (2020).

    Article  CAS  PubMed  Google Scholar 

  67. Lu, G. J. lab-of-george-lu/Li_2024_NMICROBIOL. GitHub https://github.com/lab-of-george-lu/Li_2024_NMICROBIOL (2024).

  68. Holehouse, A. S. holehouse-lab/supportingdata. GitHub https://github.com/holehouse-lab/supportingdata/tree/master/2024/Li_2024 (2024).

  69. Del Conte, A. et al. CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins. Nucleic Acids Res. 51, W62–W69 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Tunyasuvunakool, K. et al. Highly accurate protein structure prediction for the human proteome. Nature 596, 590–596 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Joshi, V., Shivach, T., Yadav, N. & Rathore, A. S. Circular dichroism spectroscopy as a tool for monitoring aggregation in monoclonal antibody therapeutics. Anal. Chem. 86, 11606–11613 (2014).

    Article  CAS  PubMed  Google Scholar 

  72. Zhang, H. et al. In situ monitoring of molecular aggregation using circular dichroism. Nat. Commun. 9, 4961 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Grishina, I. B. & Woody, R. W. Contributions of tryptophan side chains to the circular dichroism of globular proteins: exciton couplets and coupled oscillators. Faraday Discuss. 99, 245–262 (1994).

    Article  CAS  Google Scholar 

  74. Woody, R. W. Circular dichroism. Methods Enzymol. 246, 34–71 (1995).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the Shared Equipment Authority at Rice University for the access to core facilities and instruments and W. Guo for the training. We thank the Thyer Lab at Rice University for providing template plasmids encoding the spectinomycin resistance gene and the p15A and CloDF13 origins of replication. This work was supported by the Cancer Prevention and Research Institute of Texas, the National Institutes of Health (R00 EB024600 and R21 EB033607), the Welch Foundation, G. Harold and Leila Y. Mathers Foundation, Hearing Health Foundation and John S. Dunn Foundation. M.I. acknowledges support from German Research Foundation (DFG) Postdoctoral Fellowship; E.T.U. is supported by a W.M. Keck Fellowship, and Z.L. acknowledges support from Edgar O’Rear and Mary F.D. Morse Travel Award from the Institute of Biosciences and Bioengineering at Rice University. This work was supported by the Air Force Office of Scientific Research (FA9550-20-1-0241 to L.Y. and A.C).

Author information

Authors and Affiliations

  1. Department of Bioengineering, Rice University, Houston, TX, USA

    Zongru Li, Qionghua Shen, Andrew P. Anderson, Manuel Iburg, Richard Lin, Brandon Zimmer & George J. Lu

  2. Department of Biomedical Engineering, Center for Biomolecular Condensates, Washington University in St. Louis, Saint Louis, MO, USA

    Emery T. Usher, Alex S. Holehouse & Yifan Dai

  3. Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, Saint Louis, MO, USA

    Emery T. Usher & Alex S. Holehouse

  4. Shared Equipment Authority, Rice University, Houston, TX, USA

    Matthew D. Meyer

  5. Department of Biomedical Engineering, Duke University, Durham, NC, USA

    Lingchong You, Ashutosh Chilkoti & Yifan Dai

  6. Center for Quantitative BioDesign, Duke University, Durham, NC, USA

    Lingchong You

Authors
  1. Zongru Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qionghua Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emery T. Usher
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrew P. Anderson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Manuel Iburg
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Richard Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Brandon Zimmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Matthew D. Meyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Alex S. Holehouse
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lingchong You
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ashutosh Chilkoti
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yifan Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. George J. Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, G.J.L., A.C., L.Y., Z.L., A.S.H and Y.D.; methodology, G.J.L., A.C., L.Y., Z.L., Q.S., E.T.U., A.S.H. and Y.D.; investigation, Z.L., Q.S., Y.D., A.S.H., E.T.U., A.P.A., M.I., R.L., B.Z. and M.D.M.; formal analysis, Z.L., Q.S., Y.D., A.S.H., E.T.U., A.P.A., M.I., R.L., B.Z. and M.D.M.; writing—original draft, G.J.L., A.C., L.Y., Z.L., Q.S., A.S.H. and Y.D.; supervision and funding acquisition, G.J.L., Y.D., A.C. and L.Y.

Corresponding authors

Correspondence to Lingchong You, Ashutosh Chilkoti, Yifan Dai or George J. Lu.

Ethics declarations

Competing interests

Z.L., Q.S, and G.J.L. are co-inventors on a US provisional patent application that incorporates discoveries described in this paper. Their interests are reviewed and managed by Rice University in accordance with their conflict of interest policies. The other authors declare no competing interests.

Peer review

Peer review information

Nature Microbiology thanks Jon Marles-Wright 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 Mass spectrum of GvpU::His6-tag obtained from MALDI TOF MS.

The x-axis displays the mass-to-charge ratio (m/z) of the ionized species, while the Y-axis represents the ion intensity. The presence of the peak with 15681.311 m/z in the spectrum confirms the identity of GvpU::His6-tag.

Source data

Extended Data Fig. 2 Alignment of Cryo-EM structures of major shell protein GvpAAna and GvpA2Mega.

GvpAAna is depicted in yellow and GvpA2Mega in grey. The secondary structures, adapted from cryo-EM structures for GvpAAna (PDB: 8GBS)49 and GvpA2Mega (PDB: 7R1C)30, are segmented into the N-terminal (Nt) and C-terminal (Ct) regions, two α-helices (α1 and α2), and two β-strands (β1 and β2). Notably, both cryo-EM structures did not resolve the C-terminal tail due to its disordered conformations, and we labeled the last residue visible from the cryo-EM structures on the figure.

Extended Data Fig. 3

Phase diagram of purified GvpU proteins, which shows a typical UCST phase transition behavior.

Source data

Extended Data Fig. 4 mCherry does not form time-dependent puncta in E. coli.

E. coli induced with 0.05 mM IPTG grown for a) 5 and b) 8 hours from inoculation (2 and 5 hours after induction). Scale bar = 5 µm.

Extended Data Fig. 5 GvpU is predicted to assemble into defined homo-oligomers of 5-7 units.

a) The average overall pLDDT70 score for all chains in different oligomeric states. In general, regardless of oligomeric state, the monomeric protein fold is predicted with relatively high confidence and is essentially identical, although trimer, tetramer, and octamers show lower pLDDT than pentamer, hexamer, and heptamers. The data are boxplots of the pLDDT scores for all residues in the various oligomeric complexes; thus, there is one pLDDT score for each residue in the complex. n = 138 (monomer), 276 (dimer), 414 (trimer), 552 (tetramer), 690 (pentamer), 828 (hexamer), 966 (heptamer), and 1104 (octamer). The minima is the lower adjacent value; the maxima is the upper adjacent value; the box is drawn from the 25th and 75th percentiles with the median as the horizontal line in center. Whiskers are truncated at min/max of data if they would overstep the data. b) The predicted 3D structure obtained from AlphaFold2 for monomeric GvpU. The structure is colored according to the pLDDT score (blue = more confident, red = less confident). c–i) Predicted 3D structure of dimers-thru-octamers and the Predicted Alignment Error (PAE). These data do not enable us to confidently assess which of pentamer, hexamer, and heptamer is most likely, although naively, the pentamer is the most confident overall prediction in terms of pLDDT and PAE. All models predict the same overall topology regardless of assembly start with a short (8-residue) hydrophobic low-confidence N-terminal region extending upwards (with respect to the right-hand-side representation) and a second short (8-residue) positively-charged low-confidence C-terminal region extending downwards. Both these regions are predicted to be disordered (Fig. 4a). In both cases, these short extensions are positioned adjacent to the equivalent region from the next protomer. Taken together, our structural model suggests these short disordered regions form a meshwork on the top and bottom of the oligomer.

Extended Data Fig. 6 Concentration-dependent assembly of GvpU yields anomalous spectroscopic features.

a) Far-UV circular dichroism (CD) spectra of three concentrations of GvpU. At the lowest concentration (1.27 μM = 0.02 mg/mL, lightest purple) of GvpU, noise below 220 nm precludes certainty in secondary structure assignment. Qualitatively, the local minimum at ~222 nm (denoted by vertical dashed line) may reflect some α-helical character at 1.27 μM. Notably, the spectra for the two higher concentrations do not trend as expected for a system of non-interacting chromophores71,72. Presented as molar ellipticity73,74, which is normalized to sample concentration and size, the spectra differ in shape and intensity. Specifically, as GvpU concentration increases, the minima of the spectra decrease in negative intensity and undergo a modest red shift (higher wavelength), despite a linear increase in absorbance signal with increasing concentration. b) Considering evidence from dynamic light scattering for the concentration-dependent formation of soluble oligomers of GvpU, the emergent CD spectral features at 12.7 and 22.9 μM likely reflect interactions between chromophores in separate GvpU subunits. In particular, the AF2 model for the GvpU homomeric assembly implicates hydrophobic packing at the subunit interface; drawing from exciton coupling theory, we speculate that the interactions between Phe and Tyr at the hydrophobic interface could be responsible for the observed hypochromic shift in the CD signal at concentrations that support self-assembly.

Source data

Extended Data Fig. 7 Representative TEM images of GV variants.

a) wildtype GvpA2Mega -based GVs (WT), b) WT GVs with gvpU middle region Phe to Ser mutation (F → S), c) gvpU middle region Glu to Ser/Gly mutations (E → S, G), d) and gvpU middle region Phe/Glu to Ser/Gly mutations (F, E → S, G). GV clustering in WT and E → S, G variants are zoomed in. Scale bar = 500 nm.

Source data

Extended Data Fig. 8 Inter-subunit interface of the predicted GvpU pentamer.

The predicted structure of the pentameric oligomer with the hydrophobic interface between two protomers highlighted. A network of aromatic and aliphatic residues (with one threonine) line the interior of the interface, with F71 and F57 forming a central pair of residues to connect the two protomers via hydrophobic interactions with the adjacent F26, L28, and F17 of the next protomers.

Extended Data Fig. 9 GvpC does not interfere with GvpU-mediated GV clustering.

a) Representative DLS measurement of A2Mega,2C∆gvpU constructs (n = 3 for biologically independent samples). b) Representative TEM images of A2Mega,2C∆gvpU constructs (scale bar, 500 nm).

Source data

Supplementary information

Supplementary Information

Supplementary Table 1 and Methods (sequences of oligonucleotides and reagent or kit catalogue number).

Reporting Summary

Supplementary Table 1

Sequences of oligonucleotides and reagent or kit catalogue number.

Source data

Source Data Fig. 1

Statistical source data for Fig. 1c,d,h,j.

Source Data Fig. 1

Unprocessed SDS–PAGE gel.

Source Data Fig. 1

Unprocessed TEM micrographs.

Source Data Fig. 2

Statistical source data for Fig. 2c,d,g,j,l.

Source Data Fig. 2

Unprocessed SDS–PAGE gel and western blot.

Source Data Fig. 2

Unprocessed TEM micrographs.

Source Data Fig. 3

Statistical source data for Fig. 3d,e,f,g,h,i.

Source Data Fig. 3

Unprocessed micrographs.

Source Data Fig. 4

Statistical source data for Fig. 4d,f.

Source Data Fig. 4

Unprocessed TEM micrographs.

Source Data Fig. 5

Statistical source data for Fig. 5d,e,g,i,k.

Source Data Fig. 5

Unprocessed SDS–PAGE gel (labelled).

Source Data Fig. 5

Unprocessed TEM micrographs.

Source Data Extended Data Fig. 1

Statistical source data for Extended Data Fig. 1.

Source Data Extended Data Fig. 3

Statistical source data for Extended Data Fig. 3.

Source Data Extended Data Fig. 6

Statistical source data for Extended Data Fig. 6.

Source Data Extended Data Fig. 7

Unprocessed TEM micrographs.

Source Data Extended Data Fig. 9

Statistical source data for Extended Data Fig. 9a.

Source Data Extended Data Fig. 9

Unprocessed TEM micrograph.

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

Li, Z., Shen, Q., Usher, E.T. et al. Phase transition of GvpU regulates gas vesicle clustering in bacteria. Nat Microbiol 9, 1021–1035 (2024). https://doi.org/10.1038/s41564-024-01648-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-024-01648-3

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