Biological condensates form percolated networks with molecular motion properties distinctly different from dilute solutions

  1. Zeyu Shen
  2. Bowen Jia
  3. Yang Xu
  4. Jonas Wessén
  5. Tanmoy Pal
  6. Hue Sun Chan
  7. Shengwang Du
  8. Mingjie Zhang  Is a corresponding author
  1. Hong Kong University of Science and Technology, China
  2. University of Toronto, Canada
  3. Southern University of Science and Technology, China

Abstract

Formation of membraneless organelles or biological condensates via phase separation and related processes hugely expands the cellular organelle repertoire. Biological condensates are dense and viscoelastic soft matters instead of canonical dilute solutions. To date, numerous different biological condensates have been discovered; but mechanistic understanding of biological condensates remains scarce. In this study, we developed an adaptive single molecule imaging method that allows simultaneous tracking of individual molecules and their motion trajectories in both condensed and dilute phases of various biological condensates. The method enables quantitative measurements of concentrations, phase boundary, motion behavior and speed of molecules in both condensed and dilute phases as well as the scale and speed of molecular exchanges between the two phases. Notably, molecules in the condensed phase do not undergo uniform Brownian motion, but instead constantly switch between a (class of) confined state(s) and a random diffusion-like motion state. Transient confinement is consistent with strong interactions associated with large molecular networks (i.e., percolation) in the condensed phase. In this way, molecules in biological condensates behave distinctly different from those in dilute solutions. The methods and findings described herein should be generally applicable for deciphering the molecular mechanisms underlying the assembly, dynamics and consequently functional implications of biological condensates.

Data availability

The home-written codes and use of the codes for data analysis described in this manuscript have been uploaded in the GitHub database with unrestricted access:https://github.com/NeoLShen/Code-for-phase-simulation-and-HMM-analysis.git

Article and author information

Author details

  1. Zeyu Shen

    Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1057-7191
  2. Bowen Jia

    Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China
    Competing interests
    The authors declare that no competing interests exist.
  3. Yang Xu

    Division of Life Science, Hong Kong University of Science and Technology, Hong Kong, China
    Competing interests
    The authors declare that no competing interests exist.
  4. Jonas Wessén

    Department of Biochemistry, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5904-8442
  5. Tanmoy Pal

    Department of Biochemistry, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
  6. Hue Sun Chan

    Department of Biochemistry, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1381-923X
  7. Shengwang Du

    Department of Physics, Hong Kong University of Science and Technology, Hong Kong, China
    Competing interests
    The authors declare that no competing interests exist.
  8. Mingjie Zhang

    School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
    For correspondence
    zhangmj@sustech.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9404-0190

Funding

National Natural Science Foundation of China ((82188101)

  • Mingjie Zhang

Ministry of Science and Technology (2019YFA0508402)

  • Mingjie Zhang

Shenzhen Bay Laboratory (S201101002)

  • Mingjie Zhang

University Grants Committee (AoE-M09-12,16104518 and 16101419)

  • Mingjie Zhang

Human Frontier Science Program (RGP0020/2019)

  • Mingjie Zhang

Canadian Institutes of Health Research (PJT-155930)

  • Hue Sun Chan

Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-04351)

  • Hue Sun Chan

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2023, Shen et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,956
    views
  • 323
    downloads
  • 22
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Zeyu Shen
  2. Bowen Jia
  3. Yang Xu
  4. Jonas Wessén
  5. Tanmoy Pal
  6. Hue Sun Chan
  7. Shengwang Du
  8. Mingjie Zhang
(2023)
Biological condensates form percolated networks with molecular motion properties distinctly different from dilute solutions
eLife 12:e81907.
https://doi.org/10.7554/eLife.81907

Share this article

https://doi.org/10.7554/eLife.81907

Further reading

    1. Structural Biology and Molecular Biophysics
    Parveen Goyal, KanagaVijayan Dhanabalan ... Subramanian Ramaswamy
    Research Advance

    N -Acetylneuraminic acid (Neu5Ac) is a negatively charged nine-carbon amino sugar that is often the peripheral sugar in human cell-surface glycoconjugates. Some bacteria scavenge, import, and metabolize Neu5Ac or redeploy it on their cell surfaces for immune evasion. The import of Neu5Ac by many bacteria is mediated by tripartite ATP-independent periplasmic (TRAP) transporters. We have previously reported the structures of SiaQM, a membrane-embedded component of the Haemophilus influenzae TRAP transport system, (Currie et al., 2024). However, none of the published structures contain Neu5Ac bound to SiaQM. This information is critical for defining the transport mechanism and for further structure-activity relationship studies. Here, we report the structures of Fusobacterium nucleatum SiaQM with and without Neu5Ac. Both structures are in an inward (cytoplasmic side) facing conformation. The Neu5Ac-bound structure reveals the interactions of Neu5Ac with the transporter and its relationship with the Na+ binding sites. Two of the Na+-binding sites are similar to those described previously. We identify a third metal-binding site that is further away and buried in the elevator domain. Ser300 and Ser345 interact with the C1-carboxylate group of Neu5Ac. Proteoliposome-based transport assays showed that Ser300-Neu5Ac interaction is critical for transport, whereas Ser345 is dispensable. Neu5Ac primarily interacts with residues in the elevator domain of the protein, thereby supporting the elevator with an operator mechanism. The residues interacting with Neu5Ac are conserved, providing fundamental information required to design inhibitors against this class of proteins.

    1. Computational and Systems Biology
    2. Structural Biology and Molecular Biophysics
    Bin Zheng, Meimei Duan ... Peng Zheng
    Research Article

    Viral adhesion to host cells is a critical step in infection for many viruses, including monkeypox virus (MPXV). In MPXV, the H3 protein mediates viral adhesion through its interaction with heparan sulfate (HS), yet the structural details of this interaction have remained elusive. Using AI-based structural prediction tools and molecular dynamics (MD) simulations, we identified a novel, positively charged α-helical domain in H3 that is essential for HS binding. This conserved domain, found across orthopoxviruses, was experimentally validated and shown to be critical for viral adhesion, making it an ideal target for antiviral drug development. Targeting this domain, we designed a protein inhibitor, which disrupted the H3-HS interaction, inhibited viral infection in vitro and viral replication in vivo, offering a promising antiviral candidate. Our findings reveal a novel therapeutic target of MPXV, demonstrating the potential of combination of AI-driven methods and MD simulations to accelerate antiviral drug discovery.