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TorsinA is essential for neuronal nuclear pore complex localization and maturation

Nature Cell Biology volume 26, pages 1482–1495 (2024)Cite this article

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Abstract

As lifelong interphase cells, neurons face an array of unique challenges. A key challenge is regulating nuclear pore complex (NPC) biogenesis and localization, the mechanisms of which are largely unknown. Here we identify neuronal maturation as a period of strongly upregulated NPC biogenesis. We demonstrate that the AAA+ protein torsinA, whose dysfunction causes the neurodevelopmental movement disorder DYT-TOR1A dystonia, co-ordinates NPC spatial organization without impacting total NPC density. We generated an endogenous Nup107-HaloTag mouse line to directly visualize NPC organization in developing neurons and find that torsinA is essential for proper NPC localization. In the absence of torsinA, the inner nuclear membrane buds excessively at sites of mislocalized nascent NPCs, and the formation of complete NPCs is delayed. Our work demonstrates that NPC spatial organization and number are independently determined and identifies NPC biogenesis as a process vulnerable to neurodevelopmental disease insults.

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Fig. 1: NPC biogenesis is upregulated during neuronal maturation.
Fig. 2: TorsinA is essential for uniform NPC distribution but not upregulation of NPC biogenesis.
Fig. 3: Neurons bearing HaloTag-Nup107 KI alleles demonstrate endogenous NPC upregulation.
Fig. 4: Sites of NPC biogenesis are abnormal in torsinA-KO neurons.
Fig. 5: NE blebs spatially and temporally coincide with NPC biogenesis.
Fig. 6: NPC assembly completes following NE bleb resolution.

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Data availability

STEM volumes are available on the Cell Image Library (http://www.cellimagelibrary.org/groups/57193). 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

FIJI, MATLAB and Python scripts for SIM and confocal analyses are available on GitHub at https://github.com/suminkim/NPC_NE_measurements. Scripts for dSTORM analyses are available on GitHub at https://github.com/VeatchLab/smlm-analysis.

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Acknowledgements

We thank the University of Michigan Microscopy Core facility; D. Levin and X. Li for animal husbandry; A. Taylor for assistance with MATLAB codes; S. Mosalaganti, A. Erwin, D. Boassa and A. Moore for feedback and experimental discussions; A. Diehl for illustrations; and H. Worman and D. Yellajoshyula for critical reviews. This work was supported by NIH R01 NS110853, R01 DK118480, R01 NS077730 to W.T.D., NIH R01 NS097542, R01 NS113943, R56 NS128110 to S.J.B., NIH GM129347 and NSF MCB1552439 to S.L.V., NIH T32 GM007315 and UM Rackham Predoctoral Fellowship to S.K. and NIH U24 NS120055, 1S10OD021784, R01 GM082949 and NSF2014862-UTA20-000890 to M.H.E.

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  1. Sumin Kim

    Present address: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Authors and Affiliations

  1. Cellular and Molecular Biology Graduate Program, University of Michigan, Ann Arbor, MI, USA

    Sumin Kim & Sami J. Barmada

  2. Department of Neurology, University of Michigan, Ann Arbor, MI, USA

    Sumin Kim & Sami J. Barmada

  3. National Center for Microscopy and Imaging Research, Center for Research on Biological Systems, Department of Neurosciences, School of Medicine, University of California San Diego, La Jolla, CA, USA

    Sébastien Phan & Mark H. Ellisman

  4. Peter O’Donnell Jr. Brain Institute, UT Southwestern, Dallas, TX, USA

    Hung Tri Tran, Sarah H. Shahmoradian, Samuel S. Pappas & William T. Dauer

  5. Center for Alzheimer’s and Neurodegenerative Diseases, UT Southwestern, Dallas, TX, USA

    Hung Tri Tran & Sarah H. Shahmoradian

  6. Department of Biophysics, University of Michigan, Ann Arbor, MI, USA

    Thomas R. Shaw & Sarah L. Veatch

  7. Program in Applied Biophysics, University of Michigan, Ann Arbor, MI, USA

    Thomas R. Shaw & Sarah L. Veatch

  8. Department of Biophysics, UT Southwestern, Dallas, TX, USA

    Sarah H. Shahmoradian

  9. Department of Neurology, UT Southwestern, Dallas, TX, USA

    Samuel S. Pappas & William T. Dauer

  10. Department of Neuroscience, UT Southwestern, Dallas, TX, USA

    William T. Dauer

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Contributions

S.K., S.S.P., S.J.B. and W.T.D. conceived the study. S.K., H.T.T. and S.S.P. performed experiments. STEM tomograms were acquired by S.P. in M.H.E. laboratory. S.K., S.L.V., T.R.S. and S.P. analysed data. S.K. and S.S.P. wrote the manuscript. S.K. prepared the figures. S.L.V., S.J.B., S.H.S. and W.T.D. contributed to revisions. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Sami J. Barmada, Samuel S. Pappas or William T. Dauer.

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Extended data

Extended Data Fig. 1 Identification and analysis of nucleoporin puncta.

a, SIM images of primary neurons aged DIV4, 6, 8, 10, 14, 18 and 24 labeled with anti-Nup153 and anti-Nup98 antibodies. Peaks column shows peaks identified from Nup153 (green) and Nup98 (magenta) puncta. Scale bar = 2μm. b, Schematic of image analysis pipeline. The flattest part of the nuclear envelope was imaged. Nuclear ROIs were determined by manually outlining each nucleus. ROIs were eroded to account for nuclear envelope curvature at the edge, then overlayed onto Otsu thresholded images. Local maxima were identified with extended-maxima transform and used to establish peaks corresponding to each Nup. c, Superplots of nuclear ROI area as determined by Nup153/Nup98 staining in maturing neurons aged DIV4, 6, 8, 10, 14, 18 and 24. Plots show mean ± SD, with color coding indicative of biological replicates. One-way ANOVA with Dunnett’s multiple comparisons test on 3 biological replicates was performed, with DIV4 as the reference condition. Only DIV14 showed statistical significance; *P = 0.0331. d,e, Frequency distribution of nearest neighbor distances of Nup153 (d) and Nup98 (e) puncta. For each Nup153 and Nup98 peak, the distance to its nearest neighbor was calculated. The results from three biological replicates were combined for each timepoint. Source numerical data are available in source data.

Source data

Extended Data Fig. 2 Multiple NPC components exhibit density increase during neuronal maturation.

a, Confocal images of DIV4 and DIV10 primary neurons labeled with anti-Nup153 and anti-Nup98 antibodies. Scale bar = 10μm. b, c, Normalized nuclear rim fluorescence intensity of Nup153 and Nup98 in DIV4 and DIV10 primary neurons. Plots show mean ± SD, with color coding indicative of biological replicates. *P = 0.0196 (b), *P = 0.0139 (c) using two-tailed paired t-test from 4 biological replicates. d, Confocal images of DIV4 and DIV10 primary neurons labeled with anti-FG nucleoporin (mab414) and anti-Nup210 antibodies. Scale bar = 10μm. e, f, Normalized nuclear rim fluorescence intensity of FG-Nups (mab414) and Nup210 in DIV4 and DIV10 primary neurons. Plots show mean ± SD, with color coding indicative of biological replicates. **P = 0.0076 (e), ****P < 0.0001(f), two-tailed paired t-test from 4 biological replicates. Source numerical data are available in source data.

Source data

Extended Data Fig. 3 Comparison of WT and torsinA-KO neuronal NPC spatial organization and nuclear morphology.

a, Autocorrelation plot of WT and torsinA-KO neurons calculated from Nup98 WT and torsinA-KO SIM images. b, Normalized autocorrelation plot of WT and torsinA-KO neurons calculated from Nup98 WT and torsinA-KO SIM images. Autocorrelations were normalized to account for amplitude dependency on NPC density. c, Autocorrelation plot of DIV10 WT and torsinA-KO neurons calculated from Nup210 dSTORM images. d, Nearest neighbor distance of segmented DIV10 WT and torsinA-KO NPC centroids. e, f, WT and torsinA-KO Nup153 (e) and Nup98 (f) puncta density in maturing primary neurons aged DIV4, 6, 8, 10, 14. Plots show mean ± SD, with color coding indicative of biological replicates. Timepoints from the same biological replicate were matched. Repeated measures two-way ANOVA with Dunnett’s multiple comparisons test was performed on 3 biological replicates, with DIV4 as the reference condition. ns, not significant; P values are reported on the plots, ****P < 0.0001. g, Nuclear area of WT and torsinA-KO neurons calculated from manually drawn ROIs from SIM images. Plots show mean ± SD. Repeated measures two-way ANOVA with Sidak’s multiple comparisons test was used to compare genotypes at each timepoint, and no comparisons reached statistical significance. Repeated measures two-way ANOVA with Dunnett’s multiple comparisons test was used to compare all timepoints to DIV4 within each genotype, and only torsinA-KO DIV4 versus DIV14 reached significance (*P=0.0102). h, Nuclear ROI circularity of WT and torsinA-KO neurons calculated from manually drawn ROIs from SIM images. Plots show mean ± SD. Repeated measures two-way ANOVA with Sidak’s multiple comparisons test was used to compare genotypes at each timepoint. No statistical significance was detected. i, Expansion microscopy of DIV10 WT and torsinA-KO neurons labeled with Nup210 and Lamin A/C antibodies. Representative images from 3 biological replicates per genotype are shown. Scale bar = 20μm. Source numerical data are available in source data.

Source data

Extended Data Fig. 4 Validation of HaloTag-Nup107 mouse line.

a, Schematic of HaloTag-Nup107 fusion, including 5’ untranslated region (UTR, light grey), HaloTag open reading frame (magenta), flexible linker (dark grey), and Nup107 coding sequence (cyan). b, Immunoblot of P0 cortical lysates from Nup107+/+, Nup107KI/+, and Nup107KI/KI mice probed with anti-Nup107 and anti-HaloTag antibodies. GAPDH was blotted as a loading control. Each band represents an independent biological sample (3 animals each). c, Distribution of genotypes in litters derived from intercrossing Nup107KI/+ mice. No deviation from the expected Mendelian ratio was observed (P = 0.3729, Chi-squared test). 74 pups from 9 litters were analyzed. d, Superplots of nuclear ROI area from Nup153 and JFX554 SIM images. Plots show mean ± SD, with color coding indicative of biological replicates. ns, not significant; repeated measures one-way ANOVA with Dunnett’s multiple comparisons test on 3 biological replicates, with DIV4 as a reference condition. e, Representative image of Nup153 and HaloTag-Nup107 colocalization in DIV10 HaloTag-Nup107 neurons from 3 biological replicates. Scale bar = 2 μm. f, Violin plot showing the percent of Nup153 and JFX554 puncta that colocalize with JFX554 and Nup153, respectively, in DIV10 neurons. Nup153 colocalization with JFX554 was measured by calculating the % of Nup153 peaks that overlap with thresholded JFX554 puncta, and vice versa. g, Frequency distribution of nearest neighbor distances of JFX554 puncta. For each JFX554 peak, the distance to its nearest neighbor was calculated. Results from three biological replicates were combined. h, Frequency distribution of JFX554 puncta within two-pore diameter (240 nm) distance. For each JFX554 peak, the number of neighbouring puncta within a radius of 240 nm was calculated. Results from three biological replicates were combined. i, Distribution of genotypes in litters derived from intercrossing Tor1a+/-; Nup107KI/KI mice. All genotypes were born at the expected Mendelian ratio (P-0.4880, Chi-squared test). 46 pups from 6 litters were analyzed. Source numerical data and unprocessed western blot are available in source data.

Source data

Extended Data Fig. 5 Comparison of NPC density and distribution from HaloTag-Nup107 pulse-chase.

a, b, c, Nup153 (a), JFX554 (b), and new JF646 (c) puncta density in maturing WT and torsinA-KO primary neurons aged DIV4, 6, 8, 10. Plots show mean ± SD from 3 biological replicates. Repeated measures two-way ANOVA with Sidak’s multiple comparisons test was used to compare genotypes at each timepoint. No comparisons reached statistical significance. Repeated measures two-way ANOVA with Dunnett’s multiple comparisons test was used to compare all timepoints to DIV4, with ****P < 0.0001. d, Autocorrelation of JFX554 images over 0-500 nm separation distance. Similar starting amplitudes reflect constant JFX554 density. e, Autocorrelation of JF646 images over 0-500 nm separation distance. Decreasing starting amplitudes over neuronal maturation reflect increasing JF646 density. Broadening of the curve in torsinA-KO neurons indicates spatial correlation of NPCs over larger distances. Source numerical data are available in source data.

Source data

Extended Data Fig. 6 Bleb-associated pores are narrow and exhibit nucleoplasmic central plugs.

a, Slices from a DIV10 torsinA-KO tomogram oriented as an en-face view of the neuronal nuclear membrane. Pores at clusters of blebs, isolated blebs, or regular NPCs without blebs are marked by color-coded spheres centred around the middle plane of the pore channel, denoted by larger circles. Tomogram shown here is representative of 8 tomograms from two biological replicates. b, Projection of all marked pores from the tomogram shown in (a). c, Superplot of WT, KO regular (non-bleb-associated), and KO bleb-associated pore diameter (nm). Plots show mean ± SD, with color coding indicative of biological replicates. Swarmplot of individual pore diameters (small points) is overlayed with the mean of pore diameters for each biological replicate (larger points). Pores from two WT and two torsinA-KO tomograms were analyzed. KO regular and bleb-associated pores were identified from the same torsinA-KO tomograms. *P = 0.0128, **P = 0.0068; two-tailed unpaired t-test from two biological replicates. d, Blinded quantitation of the percentage of pores that have dense central plugs. Plots show mean ± SD with each point representing a cell. For KO regular and bleb-associated pores, points are color-coded to reflect quantitation from the same tomogram. Blinded quantitation was performed on 593 pores from 8 WT and 8 torsinA-KO tomograms obtained from two biological replicates each, with each tomogram from a different cell. *P = 0.0302; two-tailed unpaired t-test with Welch’s correction, ****P < 0.0001 two-tailed paired t-test, using values from 8 tomograms. e, Slices showing individual pores from DIV10 WT and torsinA-KO tomograms. Regular and bleb-associated torsinA-KO pores were sampled from the same tomogram. From top to bottom, the slices progress from the cytoplasmic side of the pore channel towards the nucleoplasm. Center plane of the pore channel is marked with a horizontal arrow. Scale bar = 100 nm. Source numerical data are available in source data.

Source data

Extended Data Fig. 7 NE blebs spatially correlate with NPC clusters.

Line scan analyses of 8 WT and 8 torsinA-KO DIV10 nuclei from two biological replicates labeled with anti-Nup153 and anti-Ubiquitin-K48 antibodies.

Extended Data Fig. 8 Nup358 is recruited to persisting NPC clusters in DIV18 torsinA-KO neurons.

a, SIM images of DIV10 and DIV18 WT and torsinA-KO neurons labeled with Nup153 and Nup358 antibodies. Scale bar = 2μm. b, dSTORM images of Nup210 in DIV18 WT and torsinA-KO neurons. Scale bar = 2 μm. Right panels show zoomed in view. Scale bar for right panels = 200 nm. c, Autocorrelation plot of DIV18 WT and torsinA-KO neurons, determined from Nup210 dSTORM images. 18 WT and 19 torsinA-KO cells from 3 biological replicates each were analyzed. d, Normalized localization density along radial distance in averaged WT and torsinA-KO NPCs from DIV18 dSTORM images. Plots show mean ± SEM from eight bootstrapping rounds with 250 randomly selected pores each. *P = 0.019. e, Confocal images from DIV10–18 pulse-chase of WT and torsinA-KO neurons expressing HaloTag-Nup107. Scale bar = 5μm. f, Plot of Nup358 nuclear rim fluorescence intensity from DIV10–18 pulse-chase. Plots show mean ± SD from two biological replicates with color coding to indicate replicates. All values were normalized to the mean DIV10 WT fluorescence intensity. Reported P values are from a two-way ANOVA test. “n” denotes the number of analyzed nuclei. g, Plot of JFX554 nuclear rim fluorescence intensity from DIV10–18 pulse-chase. Plots show mean ± SD from two biological replicates with color coding to indicate replicates. All values were normalized to the mean DIV10 WT fluorescence intensity. Reported P values are from a two-way ANOVA test. Same number of nuclei were analyzed as in (f). Source numerical data are available in source data.

Source data

Extended Data Fig. 9 Summary of the effects of torsinA loss on NPC spatial organization and maturation.

a, Model of interphase NPC assembly in WT and torsinA-KO neurons. Onset of NPC assembly is not affected by torsinA deletion. Nuclear basket, inner ring, and transmembrane nucleoporins are recruited to the nascent NPC as the INM starts to bud. Instead of the normal INM-ONM fusion found in WT neurons, excessive INM extrusion causes NE blebs to emerge and enlarge in torsinA-KO neurons. These blebs stall torsinA-KO NPCs at an intermediate stage while NPC assembly completes in WT neurons. As torsinA-KO neurons continue to mature, NE blebs resolve and INM-ONM fusion occurs. Completion of NPC biogenesis is delayed in torsinA-KO neurons. b, Model of NPC localization in maturing WT (top) and torsinA-KO (bottom) neurons. In WT neurons, newly forming NPCs (blue) localize to empty spaces between existing NPCs (red), thereby maintaining uniform spatial organization. In maturing torsinA-KO neurons, newly forming NPCs (blue) localize abnormally close to each other or to existing NPCs (red), causing aberrant clusters. NPC biogenesis is upregulated in both genotypes and total NPC number is not affected by the absence of torsinA.

Supplementary information

Supplementary Information

Supplementary video legends.

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Supplementary Video 1

A 3D volumetric tomogram of torsinA-KO DIV10 nuclear membrane. Movie starts from the cytoplasm and progresses towards the nucleoplasm. Pores occurring where clusters of blebs, single isolated blebs or regular NPCs connect to the nuclear membrane are colour-coded and annotated. Slices from the same tomogram are shown in Extended Data Fig. 6a.

Supplementary Video 2

A 3D volume of the torsinA-KO DIV10 tomogram shown in Fig. 5d–f. INM (magenta), ONM (cyan), membranes inside the blebs (green) and central plugs of pores (yellow) are segmented.

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Kim, S., Phan, S., Tran, H.T. et al. TorsinA is essential for neuronal nuclear pore complex localization and maturation. Nat Cell Biol 26, 1482–1495 (2024). https://doi.org/10.1038/s41556-024-01480-1

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