Macromolecular condensation buffers intracellular water potential

Creators: Watson, Joseph L.¹; Seinkmane, Estere; Styles, Christine T.; Mihut, Andrei; Krüger, Lara K.; McNally, Kerrie E.; Planelles-Herrero, Vicente Jose; Dudek, Michal; McCall, Patrick M.; Barbiero, Silvia; Vanden Oever, Michael; Peak-Chew, Sew Yeu; Porebski, Benjamin T.; Zeng, Aiwei; Rzechorzek, Nina M.; Wong, David C. S.; Beale, Andrew D.; Stangherlin, Alessandra; Riggi, Margot; Iwasa, Janet; Morf, Jörg; Miliotis, Christos; Guna, Alina²; Inglis, Alison J.²; Brugués, Jan; Voorhees, Rebecca²; Chambers, Joseph E.; Meng, Qing-Jun; O'Neill, John S.; Edgar, Rachel S.; Derivery, Emmanuel

1. University of Cambridge
2. California Institute of Technology

Abstract

AbstractOptimum protein function and biochemical activity critically depends on water availability because solvent thermodynamics drive protein folding and macromolecular interactions1. Reciprocally, macromolecules restrict the movement of 'structured' water molecules within their hydration layers, reducing the available 'free' bulk solvent and therefore the total thermodynamic potential energy of water, or water potential. Here, within concentrated macromolecular solutions such as the cytosol, we found that modest changes in temperature greatly affect the water potential, and are counteracted by opposing changes in osmotic strength. This duality of temperature and osmotic strength enables simple manipulations of solvent thermodynamics to prevent cell death after extreme cold or heat shock. Physiologically, cells must sustain their activity against fluctuating temperature, pressure and osmotic strength, which impact water availability within seconds. Yet, established mechanisms of water homeostasis act over much slower timescales2,3; we therefore postulated the existence of a rapid compensatory response. We find that this function is performed by water potential-driven changes in macromolecular assembly, particularly biomolecular condensation of intrinsically disordered proteins. The formation and dissolution of biomolecular condensates liberates and captures free water, respectively, quickly counteracting thermal or osmotic perturbations of water potential, which is consequently robustly buffered in the cytoplasm. Our results indicate that biomolecular condensation constitutes an intrinsic biophysical feedback response that rapidly compensates for intracellular osmotic and thermal fluctuations. We suggest that preserving water availability within the concentrated cytosol is an overlooked evolutionary driver of protein (dis)order and function.

Copyright and License

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Data Availability

The MS proteomics and phosphoproteomics data have been deposited at the ProteomeXchange Consortium through the PRIDE partner repository⁹¹ under dataset identifier PXD044481. Proteomics analysis was performed using the Mus musculus UniProt Fasta database from March 2019. The list of phase-separating proteins was taken from PhaSepDB v1. All other data supporting the findings of this study are available from the corresponding authors on reasonable request. Source data are provided with this paper.

Source data:
Source Data Fig. 1
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Source Data Fig. 4
Source Data Fig. 5
Source Data Extended Data Fig. 1
Source Data Extended Data Fig. 2
Source Data Extended Data Fig. 3
Source Data Extended Data Fig. 5
Source Data Extended Data Fig. 7
Source Data Extended Data Fig. 9
Source Data Extended Data Fig. 10
Source Data Extended Data Fig. 11

Code Availability

Custom image-processing codes specific to this paper have been deposited at our GitHub page (https://github.com/deriverylab/granulosityindex and https://github.com/deriverylab/nuclearsegmentation). Similarly, our general image registration and wavelet filtering codes can also be found at GitHub (https://github.com/deriverylab/GPU_registration and https://github.com/deriverylab/GPU_wavelet_a_trous). All lookup tables applied to images in this paper come from the collection from J. Manton (https://github.com/jdmanton/ImageJ_LUTs).

Acknowledgement

The order of the second and corresponding authors is arbitrary and these authors can change the order of their respective names to suit their own interests. This work has been supported by the Medical Research Council, as part of United Kingdom Research and Innovation (MC_UP_1201/13 to E.D.; MC_UP_1201/4 to J.S.O. and MCMB MR/V028669/1 to J.E.C.), the Human Frontier Science Program (Career Development Award CDA00034/2017 to E.D.), a Versus Arthritis Senior Research Fellowship Award (20875 to Q.-J.M.) and an MRC project grant (MR/K019392/1 to Q.-J.M.), a Grifols ‘ALTA’ Alpha-1-Antitrypsin Laurell’s Training Award and an Alpha-1-Foundation (grant number 614939) to J.E.C., and by a Wellcome Trust Sir Henry Dale Fellowship (208790/Z/17/Z to R.S.E.). N.M.R. is supported by a Medical Research Council Clinician Scientist Fellowship (MR/S022023/1). L.K.K. and V.J.P.-H. are recipients of EMBO Postdoctoral fellowships (ALTF 876-2021 and ALTF 577-2018, respectively). K.E.M. is supported by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellowship (220480/Z/20/Z). P.M.M. and J.B. were supported by Volkswagen ‘Life’ grant number 96827 and the DFG Excellence Cluster Physics of Life. We thank H. Andreas for frog maintenance; C. Godlee and M. Kaksonen for the gift of unpublished S. cerevisiae yeast strains and initial discussion of yeast experiments about temperature; P. Tran for S. pombe yeast strains; L. Miller for help with yeast work; A. Bertolotti for the kind gift of SH-SY5Y cells; and C. Russo, F. Jülicher, M. Gonzalez-Gaitan, K. Kruse, L. Blanchoin, J. Löwe, R. Hegde, P. Farrell and P. Crosby for discussion and suggestions; the staff at the companies Cherry Biotech and Elvesys, in particular T. Guérinier, for their help in designing and assembling the custom microfluidics system required for this project; the members of the Electronics and Mechanical workshops of the LMB for key support; the staff at the LMB Mass Spectrometry facility for performing and analysing MS data; and A. Prasad and T. Stevens for sharing the scripts for protein disorder and kinase motif predictions, respectively. Cartoons were created using BioRender. For the purpose of open access, the MRC Laboratory of Molecular Biology has applied a CC BY public copyright licence to any author accepted manuscript version arising.

Additional Information

Supplementary Information:

Supplementary Information:
Supplementary Discussion and references specific to the Supplementary Information.
Reporting Summary
Supplemental Fig. 1
Full gels for all western blot, Coomassie-stained and autoradiography gels presented in this study.
Supplementary Table 1
Proteome adaptation to temperature and external osmolarity challenges. This table was used to make the diagrams in Fig. 2b (see also Extended Data Fig. 6a–d). Quiescent primary fibroblasts were cultured in duplicate for 14 days in the indicated conditions, corresponding to adaptation to increased or decreased temperature/osmolarity and subjected to quantitative proteomics (TMT-MS/MS). The table shows the variation in abundance for the 7,634 proteins detected in all conditions. Also includes volcano plots of proteins that were upregulated (slope > 0) or downregulated (slope < 0) with increasing temperature or external osmolarity. Thresholds for slope (log₂[FC], x axis) and non-adjusted P value (y axis) are for visualization purposes only; for the downstream analysis (determining which proteins change significantly in either direction), the threshold was set at a Benjamini–Hochberg-adjusted P value of 0.05.
Supplementary Table 2
Phosphoproteome adaptation to temperature and external osmolarity challenges. This table was used to make the diagram in Fig. 2d (see also Extended Data Fig. 6e–g). The same cell population used for Supplementary Table 1 also subjected to quantitative phosphoproteomics (TMT-MS/MS). The table shows the variation in abundance for the 14,530 phosphosites detected in all conditions. Also includes volcano plots of phosphoproteins that were upregulated (slope > 0) or downregulated (slope < 0) regulated with increasing external osmolarity or temperature. Thresholds for slope (log₂[FC], x axis) and non-adjusted P value (y axis) are for visualization purposes only; for the downstream analysis (determining which phosphoproteins change significantly in either direction), the threshold was set at a Benjamini–Hochberg-adjusted P value of 0.05.
Supplementary Video 1
FusLC–GFP condensation in response to temperature and osmotic changes. SH-SY5Y transiently expressing FusLC–GFP were plated onto fibronectin-coated coverslips and imaged by SDCM (21 z-planes, Δz = 0.5 µm), during fast changes of temperature (ΔT = 17 °C) or external osmolarity (Δ = −162.5 mOsm l⁻¹) using dual-layer microfluidic chips (Methods). The images correspond to maximum-intensity z-projections. Scale bars, 10 µm (middle) and 1 µm (right).
Supplementary Video 2
Fast dynamics of FusLC–GFP condensation in response to hyperosmotic changes. Left, U2OS cells transiently expressing FusLC–GFP were plated onto fibronectin-coated coverslips and imaged by fast SDCM. After 100 frames (14 s), a +67 mOsm l⁻¹ osmotic shock was induced (Methods). Images correspond to a single confocal plane. Scale bar, 10 µm. Right, dynamics of condensation automatically quantified using the granulosity index (Methods and Extended Data Figs. 7 and 8).
Supplementary Video 3
Narrated animation summarizing the paradigm proposed by this study, namely that macromolecular condensation buffers intracellular water potential.

Errata

An Author Correction to this article was published on 08 April 2024

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