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Mechanical force application to the nucleus regulates nucleocytoplasmic transport

Nature Cell Biology volume 24, pages 896–905 (2022)Cite this article

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Abstract

Mechanical force controls fundamental cellular processes in health and disease, and increasing evidence shows that the nucleus both experiences and senses applied forces. Such forces can lead to the nuclear translocation of proteins, but whether force controls nucleocytoplasmic transport, and how, remains unknown. Here we show that nuclear forces differentially control passive and facilitated nucleocytoplasmic transport, setting the rules for the mechanosensitivity of shuttling proteins. We demonstrate that nuclear force increases permeability across nuclear pore complexes, with a dependence on molecular weight that is stronger for passive than for facilitated diffusion. Owing to this differential effect, force leads to the translocation of cargoes into or out of the nucleus within a given range of molecular weight and affinity for nuclear transport receptors. Further, we show that the mechanosensitivity of several transcriptional regulators can be both explained by this mechanism and engineered exogenously by introducing appropriate nuclear localization signals. Our work unveils a mechanism of mechanically induced signalling, probably operating in parallel with others, with potential applicability across signalling pathways.

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Fig. 1: Nucleocytoplasmic transport is mechanosensitive.
Fig. 2: Passive diffusion through NPCs is mechanosensitive for small MWs.
Fig. 3: Differential mechanosensitivity of facilitated import versus passive diffusion explains force-induced nuclear translocation.
Fig. 4: Balance between affinity to importins and MW defines the mechanosensitivity of nuclear localization.
Fig. 5: Balance between affinity to exportin 1 and MW defines the mechanosensitivity of nuclear localization in constructs containing NES signals.
Fig. 6: The mechanosensitivity of twist1 can be re-engineered with exogenous NLS sequences.

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Data availability

Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

All codes generated during the current study are available from the corresponding authors on reasonable request. The kinetic mathematical model of transport is available at https://github.com/ravehlab/npctransport_kinetic.

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Acknowledgements

We thank S. Usieto for technical support, and G. Ceada, S. Conti, M. González, S. Garcia-Manyes, M. Rout and the members of the Roca-Cusachs and Trepat lab for useful feedback and discussions. We acknowledge funding from the Spanish Ministry of Science and Innovation (PGC2018-099645-B-I00 to X.T., BFU2016-79916-P and PID2019-110298GB-I00 to P.R.-C.), the European Commission (H2020-FETPROACT-01-2016-731957 to X.T. and P.R.-C.), the European Research Council (Adv-883739 to X.T.), the Generalitat de Catalunya (2017-SGR-1602 to X.T. and P.R.-C.), The prize ‘ICREA Academia’ for excellence in research to P.R.-C., Fundació la Marató de TV3 (201936-30-31 to P.R.-C.) and ‘la Caixa’ Foundation (grant LCF/PR/HR20/52400004 to X.T. and P.R.-C. and fellowships LCF/BQ/DR19/11740009 to I.G.-M. and LCF/BQ/DR19/11740009 to M.M.J.). A.E.M.B. is recipient of a Sir Henry Wellcome fellowship (210887/Z/18/Z). The Institue for Bioengineering of Catalonia (IBEC) is a recipient of a Severo Ochoa Award of Excellence from MINCIN.

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Author notes

  1. These authors contributed equally: Ion Andreu, Ignasi Granero-Moya.

Authors and Affiliations

  1. Institute for Bioengineering of Catalonia (IBEC), the Barcelona Institute of Technology (BIST), Barcelona, Spain

    Ion Andreu, Ignasi Granero-Moya, Nimesh R. Chahare, Marc Molina-Jordán, Amy E. M. Beedle, Alberto Elosegui-Artola, Juan F. Abenza, Leone Rossetti, Xavier Trepat & Pere Roca-Cusachs

  2. Universidad de Navarra, TECNUN Escuela de Ingeniería, Donostia-San Sebastián, Spain

    Ion Andreu

  3. Universitat de Barcelona, Barcelona, Spain

    Ignasi Granero-Moya, Marc Molina-Jordán, Xavier Trepat & Pere Roca-Cusachs

  4. School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel

    Kessem Clein & Barak Raveh

  5. Department of Physics, King’s College London, London, UK

    Amy E. M. Beedle & Alberto Elosegui-Artola

  6. Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Alberto Elosegui-Artola

  7. Cell and Tissue Mechanobiology Laboratory, The Francis Crick Institute, London, UK

    Alberto Elosegui-Artola

  8. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

    Xavier Trepat

  9. Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain

    Xavier Trepat

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  1. Ion Andreu
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Contributions

I.A. and P.R.-C. conceived the study; I.A. and P.R.-C. designed the experiments; I.A., I.G.-M., M.M.J., A.E.M.B. and A.E.-A. performed the experiments; I.A., I.G.-M., N.R.C., J.F.A., L.R., X.T., B.R. and P.R.-C. analysed the experiments; I.G.-M. designed and cloned all constructs, N.R.C. implemented the FLIP analysis software, K.C. and B.R. implemented the kinetic model of transport, and I.A., I.G.-M. and P.R.-C. wrote the manuscript. All authors commented on the manuscript and contributed to it.

Corresponding authors

Correspondence to Ion Andreu or Pere Roca-Cusachs.

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Nature Cell Biology thanks Dennis Discher, Michael Elbaum, André Hoelz and the other, anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Fluorescence Loss In Photobleaching (FLIP) technique.

a, b) Examples of curves showing fluorescence intensity as a function of time in the nucleus and cytoplasm in FLIP experiments on two example cells transfected with the diffusive 41 kDa construct and seeded on a) 30 kPa in control condition and b) 30kPa with DN-KASH overexpression. Data represent the mean fluorescence intensity of the compartments (nucleus/cytoplasm), normalized with the mean of the whole cell before the beginning of photobleaching, and corrected for background signal. Each curve depicts a representative experiment of one cell each. c, d) Cartoon and equations describing the model used for fitting curves as in A,B, and calculating influx and efflux rates. The model considers the molecules to freely diffuse inside the nuclear and cytoplasmic compartments (see methods). e) Mobile fraction of the L_NLS 41 kDa construct in the nucleus (Nuc) and cytoplasm (Cyt) of cells seeded on 1.5/30 kPa gels. N = 19 cells from 3 independent experiments, lines show mean ± SEM f) For cells seeded on 1.5 and 30 kPa gels, correlation between nuclear to cytosolic ratios of volume, and of areas as measured in confocal slices used for FLIP measurements; regression equation y = 0,6075 x + 0,05375. N = 20 (1.5kPa) and N = 14 (30kPa) cells from 2 independent experiments. Black line shows the linear regression. Source numerical data are available in source data.

Source data

Extended Data Fig. 2 Blocking nuclear to cytoskeletal force transmission with DN-KASH recapitulates the effects of substrate stiffness on transport rates.

a, b) Influx and efflux rates of diffusive constructs for cells seeded on 30 kPa gels, with or without DN-KASH overexpression. In a, both MW (p < 1e-15) and DN KASH (p = 1e-6) effects tested significant. In b, both MW (p < 1e-15) and DN KASH (p = 0,0002) effects tested significant. c, d) Influx and efflux rates of constructs containing L_NLS for cells seeded on 30 kPa gels, with or without DN-KASH overexpression. In c, both MW (p = 0,0025) and DN KASH (p < 1e-15) effects tested significant. In d, both MW (p < 1e-15) and DN KASH (p = 3.4e-10) effects tested significant. In all panels, N = 30 cells from 3 independent experiments. Two-way ANOVA, Šídák’s multiple comparisons test was used to obtain p-values between conditions. Data are mean ± SEM. Source numerical data are available in source data.

Source data

Extended Data Fig. 3 Control experiments on importins and AFM.

a-c) Average fluorescence intensities of nuclear and cytoplasmic areas of cells seeded on substrates of 1.5 or 30 kPa stiffness and immunostained for importin α3 (imp α3) importin α1 (imp α1), and importin β1 (imp β1). N = 90 cells from 3 independent experiments. The effect of substrate stiffness tested significant for importin α3 (p = 7.2e-8) and importin α1 (p = 1.7e-5), but not for importin β1 (p = 0.4971). p-values from Two-way ANOVA d-e) Corresponding example images showing the nucleus (Hoechst) and the distribution of the different importins. f) Corresponding quantification of nuclear to cytoplasmic ratio of importin localization. N = 91,98, 91, 98, 90, 90 cells (from left to right) from 3 independent experiments. p-values from independent two-tailed Mann-Whitney tests. g) N/C ratios of L_NLS-41 kDa or BFP constructs in cells seeded on 1.5 kPa gels before, during, and after nuclear deformation with AFM. h) L_NLS-41 kDa ratios normalized by BFP ratios, from panel g) paired measures. i,j) from g, corresponding paired dot plots of the time points right before and after force application. k) from g, corresponding % change in N/C ratios right after force application for both constructs. In g,h,i,j,k N = 15 cells from 3 independent experiments, p-values were calculated with a two-tailed paired t-test. l) N/C ratios of H_NLS-27 kDa construct in cells seeded on 1.5 kPa gels before, during, and after nuclear deformation with AFM. m) from l, corresponding paired dot plots of the time points right before and after force application. In l, m, N = 15 cells from 3 independent experiments. p-values were calculated with a two-tailed paired t-test. n) Corresponding images of constructs before and during force application, dotted line marks nucleus outline. o) N/C ratios of the L_NLS-41 kDa construct in cells co-transfected with DN-KASH and seeded on 1.5 or 30 kPa gels before, during, and after nuclear deformation with AFM. Data are mean ± SEM. p,q) from o, corresponding paired dot plots of the time points right before and after force application. In o,p,q, N = 15 cells from 3 independent experiments. p-values were calculated with a two-tailed paired t-test, traces of all cells are shown in Extended Data Fig. 8. r) Corresponding images of constructs before and during force application, dotted line marks nucleus outline. Scale bars, 20 µm. Note: in AFM experiments, non-mechanosensitive constructs (BFP and H_NLS) still show a small increase with force, likely due to lensing effects caused by changes in cell shape during indentation. This increase (~6% for BFP, ~2% for H_NLS) is much smaller than that of the mechanosensitive construct (L_NLS 41 kDa, ~14%), see panel k. Panel h in fact shows the response of the L_NLS construct after factoring out the response of BFP. Data are mean ± SEM in all panels. Source numerical data are available in source data.

Source data

Extended Data Fig. 4 Noise levels in N/C ratio measurements.

Relationship between mean N/C ratio as reported in figures, and corresponding coefficient of variation (standard deviation divided by the mean). The different points show all different constructs and conditions reported in the manuscript. Black dots indicate values of overexpressed engineered constructs, red squares indicate values of stained endogenous proteins. Source numerical data are available in source data.

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Extended Data Fig. 5 Effect of the affinity of the NLS signal in influx and efflux rates.

(a-d) Model predictions for N/C ratios (a), mechanosensitivities (b), influx rates (c) and efflux rates (d) for 41 kDa constructs as a function of NLS affinity (modelled by the binding rate kon between the NLS and importin α). e,f) Experimental Influx and efflux rates of 41 kDa constructs containing NLS signals of different affinity for importin β. In both cases (e,f), NLS strength and substrate stiffness effects tested significant (respectively: e) p < 1e-15, p < 1e-15, f) p < 1e-15, p = 2.4e-10). N = 30 cells from 3 independent experiments. p-values from Two-way ANOVA. Data are mean ± SEM. Source numerical data are available in source data.

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Extended Data Fig. 6 Further experiments and modelling results regarding NES constructs.

For M_NES constructs, influx rates (mediated by passive transport) and efflux rates (mediated by facilitated transport) as a function of molecular weight. N = 30 cells from 3 independent experiments. Substrate stiffness effects tested significative in both cases (a) p = 5.1e-13; b) p < 1e-15); MW only tested significative for influx, a) p < 1e-15; b) p = 0.2138). Two-way ANOVA, Šídák’s multiple comparisons test was used to obtain p-values between conditions. Data presented as mean ± SEM. c, d) Model predictions of N/C ratios (c) and mechanosensitivities (d) for an NLS with a binding rate kon of 54 ms−1 as a function of MW. Data are shown for experimentally measured N/C volume ratios (0.29) and for inverted volume ratios (3.5). e, f) Same predictions as in c,d for an NLS with a binding rate kon of 205 ms−1. Note that these predictions simply evaluate the role of N/C volumes on import, they do not explicitly model the export cycle (and hence mechanosensitivities are above and not below 1). Source numerical data are available in source data.

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Extended Data Fig. 7 Mechanosensitivity of transcriptional Regulators.

a-c) For Snail stainings at different conditions, quantifications of N/C ratios on 1.5/30 kPa substrates (a, N = 100 cells from 3 independent repeats), corresponding mechanosensitivities for the 3 different repeats (b), and representative images (c). d-f) For SMAD3 stainings at different conditions, quantifications of N/C ratios on 1.5/30 kPa substrates (d, N = 100 cells from 3 different repeats), corresponding mechanosensitivities for the 3 different repeats (e), and representative images (f). g-i) For GATA2 stainings at different conditions, quantifications of N/C ratios on 1.5/30 kPa substrates (g, N = 90 cells from 3 independent repeats), Corresponding mechanosensitivities for the 3 different repeats (h), and representative images (i). j-l) For NF-κβ stainings at different conditions, quantifications of N/C ratios on 1.5/30 kPa substrates, (j, N = 90 cells from 3 independent repeats), corresponding mechanosensitivities for the 3 different repeats (k), and representative images (l). For a-l, data are presented as mean ± SEM, scale bars correspond to 20 µm, and p-values from corrected multiple two-tailed Mann-Whitney (a,d) and two-tailed Mann-Whitney (g,j) tests. m) Relative gene expression of different genes as assessed with qPCR. Conditions are cells seeded on 1.5 or 30 kPa substrates, overexpressing or not a WT twist1 construct (Ctrl V5-twist1). Gene expression is shown relative to the 1.5 kPa condition without overexpression. n = 2 independent experimental repeats. Source numerical data are available in source data.

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Extended Data Fig. 8 Cell-by-cell fluorescence curves of all AFM experiments.

Plots showing the evolution with time of N/C ratios before, during and after force application to the cell nucleus for all cells measured. a, b) AFM experiments reported in Fig. 3, c) Fig. 5, and d-h) Extended Data Fig. 3. Source numerical data are available in source data.

Source data

Supplementary information

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Modelling of mechanosensitive nucleocytoplasmic transport: initial conceptual model, kinetic mathematical model of transport (Model Tables 1–3 and Model Fig. 1)

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Andreu, I., Granero-Moya, I., Chahare, N.R. et al. Mechanical force application to the nucleus regulates nucleocytoplasmic transport. Nat Cell Biol 24, 896–905 (2022). https://doi.org/10.1038/s41556-022-00927-7

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