The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Cytoplasmic Fluidity and the Cold Life: Proteome Stability is Decoupled from Viability in Psychrophiles
Open accessPreprint — published as 10.1038/s41467-025-65270-5
Abstract
Protein diffusion, critical for cellular metabolism, occurs in the highly crowded cytoplasm. Understanding how this dynamics changes when organisms are adapted to different thermal niches is a fundamental challenge in microbiology and biophysics. In Escherichia coli , protein diffusion undergoes a pronounced slowdown at temperatures near cellular death, coinciding with the early stages of unfolding. To determine whether this phenomenon is universal, we investigated psychrophilic and hyperthermophilic bacteria. In both species, a marked diffusion slowdown takes place at the onset of proteome melting. However, while the dynamic arrest is associated with the thermal death point for the hyperthermophilic proteome, the psychrophilic proteome maintains substantial mobility well beyond the cellular inactivation. The decoupling between metabolic viability and proteome dynamics and stability suggests that the functional processes of psychrophilic bacteria are extremely temperature sensitive. This finding echoes the behaviour of psychrophilic enzymes, manifesting a large temperature gap between optimal activity and unfolding. Protein diffusion is optimised to maintain functional fluidity at the organism’s working conditions, but its temperature dependence is controlled by the proteome folded state. Our findings redefine the relationship between cytoplasmic dynamics, proteome stability, and bacterial survival in cold environments.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Research works printed but not linked: 81
- 10.3389/fmicb.2019.00780 · DOI · Paper page
- 10.1038/ismej.2013.8 · DOI · Paper page
- 10.1073/pnas.0712334105 · DOI · Paper page
- 10.1073/pnas.85.24.9436 · DOI
- 10.1007/s00253-013-5290-2 · DOI
- Yi, G. Y. , Kim, M. J. , Kim, H. I. , Park, J. & Baek, S. H. Hyperthermia treatment as a promising anti-cancer strategy: therapeutic targets, perspective mechanisms and synergistic combinations in experimental approaches. Antioxidants 11, (2022). · DOI
- 10.1007/s41745-023-00382-9 · DOI
- 10.1089/ast.2016.1608 · DOI
- 2022, Association between antibiotic resistance and increasing ambient temperature in china: an ecological study with nationwide panel data, Lancet Reg. Health West. Pac, 30, 100628
- 10.1021/acs.chemrev.3c00432 · DOI
- 10.3184/003685003783238699 · DOI
- 10.1099/00221287-137-10-2361 · DOI
- 10.1016/j.bbamem.2004.08.002 · DOI · Paper page
- 10.1021/bi500344j · DOI
- Milo, R. & Phillips, R. Cell Biology by the Numbers (Garland Science, Taylor & Francis Group, New York, 2016). · DOI · Paper page
- 10.1021/acscentsci.2c01078 · DOI
- 10.3389/fmolb.2018.00093 · DOI
- 10.1002/prot.21679 · DOI
- 10.1021/cr800373w · DOI · Paper page
- 10.1529/biophysj.107.124420 · DOI
- 10.1524/zpch.2010.6091 · DOI
- 10.1073/pnas.1107287108 · DOI
- 10.1103/PhysRev.119.863 · DOI
- 10.7554/eLife.82654 · DOI
- 10.7554/eLife.30084 · DOI
- 10.1039/C1SM06609A · DOI
- 10.1039/C4CP04944F · DOI
- 2008, Molecular dynamics study on nanoparticle diffusion in polymer melts: A test of the stokes–einstein law, J. Phys. Chem. C, 112, 6653, 10.1021/jp800474t · DOI
- 10.1038/sj.embor.7400049 · DOI
- 10.1021/acs.jpclett.3c03351 · DOI
- de Gennes, P.-G. Scaling Concepts in Polymer Physics (Cornell University Press, Ithaca, 1979).
- 2017, Slow-down in diffusion in crowded protein solutions correlates with transient cluster formation, J. Phys. Chem. B, 121, 11072, 10.1021/acs.jpcb.7b08785 · DOI
- 10.1073/pnas.1817564116 · DOI
- 10.1126/science.aai7825 · DOI · Paper page
- 10.15252/msb.20188242 · DOI
- 10.1021/jz1012142 · DOI
- 10.1099/00221287-137-10-2361 · DOI
- 10.1128/AEM.68.11.5379-5386.2002 · DOI
- 10.7717/peerj.1516 · DOI
- 10.1016/j.ymeth.2004.08.002 · DOI
- 10.1016/j.pep.2018.05.012 · DOI
- Nowak, M. , Olszewski, M. , Śpibida, M. & Kur, J. Characterization of single-stranded dna-binding proteins from the psychrophilic bacteria Desulfotalea psychrophila, Flavobacterium psychrophilum, Psychrobacter arcticus, Psychrobacter cryohalolentis, Psychromonas ingrahamii, Psychroflexus torquis, and Photobacterium profundum. BMC Microbiol. 14 (2014). · DOI
- Jung, F. , Frey, K. , Zimmer, D. & Mühlhaus, T. Deepstabp: a deep learning approach for the prediction of thermal protein stability. Int. J. Mol. Sci. 24 (2023). · DOI
- 10.1046/j.1365-2672.1998.00519.x · DOI
- 10.1128/JB.01377-08 · DOI
- 10.1016/j.bpj.2010.10.036 · DOI
- 10.1073/pnas.1114477108 · DOI · Paper page
- 10.1038/nmeth.4067 · DOI
- 10.1073/pnas.1800690115 · DOI
- 10.1016/j.bpj.2016.07.024 · DOI
- 10.1073/pnas.2122957119 · DOI
- 10.1016/j.sbi.2023.102625 · DOI
- 2025, Single-molecule diffusivity quantification in Xenopus egg extracts elucidates physicochemical properties of the cytoplasm, Proc. Natl. Acad. Sci, 121, e2411402121
- 10.1021/jz3019379 · DOI
- 10.1007/s11157-017-9443-0 · DOI · Paper page
- 10.1016/j.bpj.2009.11.048 · DOI
- 10.1038/sj.embor.7400662 · DOI · Paper page
- Nowak, J. S. & Otzen, D. E. Helping proteins come in from the cold: 5 burning questions about cold-active enzymes. BBA Adv. 100104 (2024). · DOI
- 10.1016/j.biochi.2012.05.013 · DOI
- Gault, S. , Higgins, P. , Cockell, C. & Gillies, K. A meta-analysis of the activity, stability, and mutational characteristics of temperature-adapted enzymes. Biosci. Rep. 41 (2021). · DOI
- 10.1016/j.semcdb.2017.09.003 · DOI
- 2018, How to calculate thermostability of enzymes using a simple approach, Biochem. Mol. Biol. Educ, 46, 398, 10.1002/bmb.21127 · DOI
- 10.1016/j.tibs.2010.05.001 · DOI
- 10.1038/s41467-020-16341-2 · DOI
- 10.1002/chem.202001973 · DOI
- 10.1128/jb.178.11.2999-3007.1996 · DOI
- 10.3389/fmicb.2022.841359 · DOI
- 10.1038/srep12711 · DOI
- 10.1074/jbc.M508034200 · DOI
- 10.1002/jcc.10216 · DOI · Paper page
- 10.1002/jcc.21224 · DOI · Paper page
- 10.1063/1.445869 · DOI · Paper page
- 10.1063/1.2408420 · DOI · Paper page
- 10.1063/1.448118 · DOI · Paper page
- 10.1063/1.328693 · DOI · Paper page
- 10.1063/1.464397 · DOI · Paper page
- Hess, B. , Bekker, H. , Berendsen, H. & Fraaije, J. Lincs: a linear constraint solver for molecular simulations. J. Comput. Chem. 18, (1998). · DOI
- Miyamoto, S. & Kollman, P. Settle: an analytical version of the shake and rattle algorithm for rigid water models. J. Comput. Chem. 13, (1992). · DOI
- Yeh, I.-C. & Hummer, G. System-size dependence of diffusion coefficients and viscosities from molecular dynamics simulations with periodic boundary conditions. J. Phys. Chem. B 108 (2004). · DOI · Paper page
- 10.1021/acs.jpclett.8b01090 · DOI
- Cisse, A. , Peters, J. , Lazzara, G. & Chiappisi, L. PyDSC: a simple tool to treat differential scanning calorimetry data. J. Therm. Anal. Calorim. 145 (2020). · DOI
Metadata
- Authors
- Caviglia, B., Timr, S., Guiral, M., Giudici-Orticoni, M.-T., Seydel, T., Beck, C., Peters, J., Sterpone, F., & Paciaroni, A.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- J. Heyrovský Institute of Physical Chemistry, Czech Academy of Sciences,182 00 Prague, Czechia
- Laboratoire de Bioénergétique et Ingénierie des Protéines, BIP, CNRS, Aix-Marseille Université, 13400 Marseille, France
- Institut Laue-Langevin (FR)
- University of Perugia (IT)
- Corresponding author
- Peters, Judith
- Sterpone, Fabio
- Paciaroni, Alessandro
- ORCID
- Venue
- Nature Communications
- Publisher
- openRxiv
- Published
- 2025-06-27
- Volume
- 16
- Issue
- 1
- Pages
- 10345-10345
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4416576149
- Cited by
- 5
- References
- 1
- Citations by year
- 2026: 5
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- www.biorxiv.org/about/FAQ#license (vor)
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (CUP B43C22000470005, CUP J97G22000170005, ECS00000041)
- Agence Nationale de la Recherche (ANR-22-CE95-0002, LABX-0011-01, ANR-11-EQPX-0008, ANR-11-LABX-0011)
- Akademie Věd České Republiky (LQ200402301)
- Centre National de la Recherche Scientifique (ANR-11-LABX-0011-01, ANR-11-LABX-0011, 11-LABX-0011-01)
- Grand Équipement National De Calcul Intensif
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Physical Sciences › Materials Science › Materials Chemistry › Material Dynamics and Properties
- Keywords
- psychrophiles, hyperthermophiles, protein diffusion, thermal death point, protein unfolding, cytoplasmic crowding, Escherichia coli, bacterial viability, cold adaptation, temperature dependence of diffusion
- MeSH terms
- Cold Temperature, Cytoplasm, Diffusion, Escherichia coli, Proteome, Escherichia coli Proteins, Microbial Viability, Protein Stability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.