OSCR

State-dependent binding of the wedge domain controls inactivation of the mechanosensitive ion channel PIEZO1.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › PIEZO1 remains in a flat conformation after inactivation ↔ FitDistributionInterbladeDist_Wedge.m, the whole file · a weak match · score 0.81 · Gaussian mixture model, probability densities, distribution histograms, interblade distance, PIEZO1 ALFA, components
  2. [2] § Methods › 3D-Minflux data analysis ↔ FitDistributionInterbladeDist_Wedge.m, the whole file · a weak match · score 0.79 · probability density, interblade distance, standard deviation, bound, iterations, fit
  3. [3] § Methods › 3D-Minflux data analysis ↔ subroutines/PIEZO1Superparticle.m, the whole file · a weak match · score 0.62 · interblade distance, closest, cloud, locations, algorithm, iterations
  4. [4] § Results › PIEZO1 remains in a flat conformation after inactivation ↔ PIEZO1_ALFA_wedge_analysis_2.m, lines 1–8 · score 0.55 · ALFA tags, DNA PAINT, labelling, signals, MINFLUX, trimers

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 124 lines · 5.2 KB · no license · 2 matches

  1. %% Script for fitting the interblade distribution histogram with a Gaussian mixture model with six peaks
  2. % version 1.1 (25/07/2025)
  3. % written by Clement Verkest & Stefan G Lechner
  4. % INPUT: requires the 'result' structure generated by 'MFX_PIEZO1_ALFA_01_main_script.m'
  7. % function FitInterbladeDistribution(result, DataSource)
  9. %load data
  10. data = result.InterBlades(:,4);
  11. % data = myinter;
  12. DataSource = 1;
  14. % Define individual Gaussian function
  15. gaussian = @(amp, mean, sigma, y) amp * exp(-((y - mean).^2) / (2 * sigma^2));
  17. % set limits
  18. MaxSigma =[2 2 1 3 1 1];
  19. if DataSource == 6
  20. MaxSigma =[2 2 2 1 2 2];
  21. end
  23. if DataSource == 5
  24. MaxSigma =[2 1 0.25 2 1.5 1];
  25. end
  27. if DataSource == 1
  28. MaxSigma =[2 2 2 1 3 2];
  29. end
  31. if DataSource == 0
  32. MaxSigma =[2 2 1 3 1 1];
  33. end
  35. % Define histogram edges
  36. bin_edges = 5:1:40;
  37. bin_centers = bin_edges(1:end-1) + diff(bin_edges)/2;
  39. % Estimate histogram density
  40. [counts, ~] = histcounts(data, bin_edges);
  41. normalized_counts = counts / trapz(bin_centers, counts); % Normalize to make it a probability density
  43. % Initial guesses for means, amplitudes, and standard deviations
  44. initial_params = [max(normalized_counts), 10, 1, max(normalized_counts), 18, 1, max(normalized_counts), 19.5, 1, max(normalized_counts), 22.5, 1, max(normalized_counts), 27, 1, max(normalized_counts), 32, 1];
  46. if DataSource == 1
  47. initial_params = [max(normalized_counts), 12, 1, max(normalized_counts), 17, 1, max(normalized_counts), 21.5, 1, max(normalized_counts), 25.5, 1, max(normalized_counts), 28, 1, max(normalized_counts), 32, 1];
  48. end
  50. if DataSource == 0
  51. initial_params = [max(normalized_counts), 10, 1, max(normalized_counts), 18, 1, max(normalized_counts), 19.5, 1, max(normalized_counts), 22.5, 1, max(normalized_counts), 27, 1, max(normalized_counts), 32, 1];
  52. end
  54. % Define bounds
  55. lower_bounds = [0, 0, 0.1, 0, 0, 0.1, 0, 0, 0.1, 0, 0, 0.1, 0, 0, 0.1, 0, 0, 0.1];
  56. upper_bounds = [Inf, 40, MaxSigma(1), Inf, 40, MaxSigma(2), Inf, 40, MaxSigma(3), Inf, 40, MaxSigma(4), Inf, 40, MaxSigma(5), Inf, 40, MaxSigma(6)];
  58. % Define Gaussian sum function with variable means and sigmas
  59. gaussianSum = @(params, y) gaussian(params(1), params(2), params(3), y) + ...
  60. gaussian(params(4), params(5), params(6), y) + ...
  61. gaussian(params(7), params(8), params(9), y) + ...
  62. gaussian(params(10), params(11), params(12), y) + ...
  63. gaussian(params(13), params(14), params(15), y) + ...
  64. gaussian(params(16), params(17), params(18), y);
  67. % Fit the function using lsqcurvefit with max iterations
  68. opts = optimset('Display', 'off', 'MaxIter', 500); % Suppress output and set max iterations
  69. gaus_params_fit = lsqcurvefit(@(p, x) gaussianSum(p, x), initial_params, bin_centers, normalized_counts, lower_bounds, upper_bounds, opts);
  71. % Extract fitted parameters
  72. fitted_amplitudes = [gaus_params_fit(1), gaus_params_fit(4), gaus_params_fit(7), gaus_params_fit(10), gaus_params_fit(13), gaus_params_fit(16)];
  73. fitted_means = [gaus_params_fit(2), gaus_params_fit(5), gaus_params_fit(8), gaus_params_fit(11), gaus_params_fit(14), gaus_params_fit(17)];
  74. fitted_sigmas = [gaus_params_fit(3), gaus_params_fit(6), gaus_params_fit(9), gaus_params_fit(12), gaus_params_fit(15), gaus_params_fit(18)];
  76. % Calculate proportions of each Gaussian component
  77. total_area = sum(fitted_amplitudes .* fitted_sigmas * sqrt(2 * pi));
  78. proportions = (fitted_amplitudes .* fitted_sigmas * sqrt(2 * pi)) / total_area;
  80. % Display proportions
  81. disp('Proportion of each Gaussian component:');
  82. disp(proportions);
  84. % Display means
  85. disp('Mean of each Gaussian component:');
  86. disp(fitted_means);
  88. % Calculate and display goodness of fit (R-squared)
  89. predicted_counts = gaussianSum(gaus_params_fit, bin_centers);
  90. SS_res = sum((normalized_counts - predicted_counts).^2);
  91. SS_tot = sum((normalized_counts - mean(normalized_counts)).^2);
  92. R_squared = 1 - (SS_res / SS_tot);
  94. disp('Goodness of fit (R-squared):');
  95. disp(R_squared);
  97. % Plot histogram and fitted curve
  98. screenSize = get(0, 'ScreenSize');
  99. figure('Position',[0 (screenSize(4)/2) 500 200]);;
  100. bar(bin_centers, normalized_counts, 'FaceColor', [0.6 0.6 0.6],'FaceAlpha', 0.5, 'EdgeColor', 'k', 'DisplayName', 'Histogram'); hold on;
  101. ax = gca;
  102. ax.TickDir = 'out';
  103. ax.Box = 'off';
  104. ax.YLim = [0 0.15];
  106. if DataSource == 1 || DataSource == 5 || DataSource == 6 || DataSource == 0
  107. % Generate smooth x values for plotting
  108. smooth_x = linspace(min(bin_centers), max(bin_centers), 1000);
  109. smooth_y = gaussianSum(gaus_params_fit, smooth_x);
  110. plot(smooth_x, smooth_y, 'k', 'LineWidth', 1, 'DisplayName', 'Fitted Gaussians');
  112. % Plot individual Gaussians
  113. for i = 1:6
  114. plot(smooth_x, gaussian(fitted_amplitudes(i), fitted_means(i), fitted_sigmas(i), smooth_x), 'Color', [0.8 0 1], 'LineWidth', 1.5, 'DisplayName', ['Gaussian ' num2str(i)]);
  115. end
  116. end
  118. % legend;
  119. xlabel('interblade distance (nm)'); ylabel('probability');
  121. % clean up
  122. clearvars -except myinter IndivTrimersRAW IndivTrimersRAW_avg MFXdata MFX_data_files result traces_AVG traces_AVG_noMerge traces_FILT traces_RAW
  124. % end

FitDistributionInterbladeDist_Wedge.m at commit 051948e, no license · at the source

Overview

Authors: Lucas Roettger1, Nadja Zeitzschel1, Christian Gorzelanny2, Clement Verkest1, Stefan G. Lechner1
ORCID iDs: Lucas Roettger, Nadja Zeitzschel, Clement Verkest, Stefan G. Lechner
  1. Department of Anesthesiology, University Medical Center Hamburg-Eppendorf,Hamburg, Germany
  2. Department of Dermatology and Venereology, University Medical Center Hamburg-Eppendorf,Hamburg, Germany
Institutions: University Medical Center Hamburg-Eppendorf (Germany)
Journal: Nature communications, volume 17, issue 1, article 9057
Dates: received 10 February 2026; accepted 5 August 2026; published online 26 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-76927-0 · PMID 42649207 · PMCID PMC13518315 · OpenAlex W7123592987
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.64898/2026.01.12.699060
Status: code verified
Categories: human (organism), cellular / molecular (subfield)
Methods: Statistics, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Ion channels in the nervous system, Neurophysiology, Ion channels, Permeation and transport
MeSH: Ion Channels*, Mechanotransduction, Cellular*, Animals, HEK293 Cells, Humans, Ion Channel Gating, Mutagenesis, Site-Directed, Protein Binding, Protein Domains (* major topic)
Topic: Erythrocyte Function and Pathophysiology (Physiology, Medicine), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (LE3210/3-3, GO2528/10-1)
Citations: cited by 1 paper (Europe PMC); 71 references in the paper
Research resources: RRID:AB_3075981

Abstract

The mechanically activated ion channel PIEZO1 transduces membrane tension into intracellular calcium signals and is critical for a wide range of physiological processes. Recent structural and functional studies have established a detailed framework for PIEZO1 activation, but the molecular mechanisms governing its rapid inactivation remain incompletely understood. Here, we examine the contribution of the intracellular wedge domain to PIEZO1 inactivation using site-directed mutagenesis, electrophysiological recordings and MINFLUX nanoscopy. We show that wedge deletion and disruption of specific π-π and cation-π interactions between the wedge α1-helix and the pore module diminishes inactivation without impairing channel activation. Moreover, MINFLUX nanoscopy suggests that the wedge stabilizes a flat inactivated conformation of PIEZO1 and suggests that wedge dissociation is required for recovery from inactivation. Together, our data support a mechanism with the wedge acting as a state-dependent inactivation particle that docks to the pore module to terminate channel activity during sustained mechanical stimulation.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

StefanLechnerUKE/PIEZO1_wedge_analysis

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 051948e15a5109143a835c58e32fa726551a3e95, 14 July 2026
Languages: MATLAB (13)
Size: 34 files, 13 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files), Optimization Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
14 files
At the source: github.com/StefanLechnerUKE/PIEZO1_wedge_analysis

Zenodo 21363175

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files), Optimization Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
14 files
At the source:

Code availability

The custom Matlab codes for Minflux analysis are available at https://github.com/StefanLechnerUKE/PIEZO1_wedge_analysis and at Zenodo [10.5281/zenodo.21363175]71.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 26 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The MINFLUX raw data generated in this study and corresponding confocal images have been deposited together with the custom written Matlab analysis code on Github [https://github.com/StefanLechnerUKE/PIEZO1_wedge_analysis] and at Zenodo [10.5281/zenodo.21363175]71. The patch-clamp, calcium imaging and AFM data generated in this study are provided in the Source Data File. Previously published PDB files that were used for wedge interaction analysis are available at: 7WLT (https://doi.org/10.2210/pdb7WLT/pdb), 7WLU (https://doi.org/10.2210/pdb7WLU/pdb), 6B3R (https://doi.org/10.2210/pdb6B3R/pdb), 5Z10 (https://doi.org/10.2210/pdb5Z10/pdb), 6BPZ (https://doi.org/10.2210/pdb6BPZ/pdb). Source data are provided in this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 4 keywords, 9 MeSH terms, 1 funder, 70 references, 1 RRID.

Cite

This paper

Roettger, L., Zeitzschel, N., Gorzelanny, C., Verkest, C., & Lechner, S. G. (2026). State-dependent binding of the wedge domain controls inactivation of the mechanosensitive ion channel PIEZO1. Nature communications, 17(1), 9057. https://doi.org/10.1038/s41467-026-76927-0

BibTeX

@article{roettger2026state,
author = {Roettger, Lucas and Zeitzschel, Nadja and Gorzelanny, Christian and Verkest, Clement and Lechner, Stefan G.},
title = {{State-dependent binding of the wedge domain controls inactivation of the mechanosensitive ion channel PIEZO1}},
journal = {Nature communications},
year = {2026},
month = aug,
volume = {17},
number = {1},
pages = {9057},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-76927-0},
url = {https://doi.org/10.1038/s41467-026-76927-0},
pmid = {42649207},
pmcid = {PMC13518315}
}

RIS

TY - JOUR
AU - Roettger, Lucas
AU - Zeitzschel, Nadja
AU - Gorzelanny, Christian
AU - Verkest, Clement
AU - Lechner, Stefan G.
TI - State-dependent binding of the wedge domain controls inactivation of the mechanosensitive ion channel PIEZO1
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/08/26
VL - 17
IS - 1
SP - 9057
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-76927-0
UR - https://doi.org/10.1038/s41467-026-76927-0
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-76927-0",
"type": "article-journal",
"title": "State-dependent binding of the wedge domain controls inactivation of the mechanosensitive ion channel PIEZO1",
"container-title": "Nature communications",
"author": [
{
"family": "Roettger",
"given": "Lucas"
},
{
"family": "Zeitzschel",
"given": "Nadja"
},
{
"family": "Gorzelanny",
"given": "Christian"
},
{
"family": "Verkest",
"given": "Clement"
},
{
"family": "Lechner",
"given": "Stefan G."
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "9057",
"DOI": "10.1038/s41467-026-76927-0",
"PMID": "42649207",
"PMCID": "PMC13518315",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-76927-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
26
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41586-026-10182-7 [code]
The molecular basis of force selectivity by PIEZO2.
Journal: Nature
In common: Statistics and Machine Learning Toolbox, cellular / molecular, 12 references
[2] doi:10.1016/j.isci.2026.115209
Ether phospholipids modulate somatosensory responses by tuning multiple receptor functions in <i>Drosophila</i>.
Journal: iScience
In common: cellular / molecular, 5 references
[3] doi:10.1038/s41592-026-03066-1 [code]
A multimodal adaptive optical microscope for in vivo imaging from molecules to organisms.
Journal: Nature methods
In common: Optimization Toolbox, Statistics and Machine Learning Toolbox, 1 reference
[4] doi:10.1038/s41467-026-71614-6 [code]
Interferometric ultra-high resolution 3D imaging through brain sections.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, cellular / molecular, 2 references
[5] doi:10.1038/s41467-026-76146-7 [code]
Resolving endogenous protein organization in cells with nanometer resolution.
Journal: Nature communications
In common: cellular / molecular, 2 references
[6] doi:10.1038/s41467-026-74888-y
Engineered zwitterion-nanodelivery for precision targeting of brain metastases.
Journal: Nature communications
In common: 2 references
[7] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: Optimization Toolbox, Statistics and Machine Learning Toolbox, cellular / molecular
[8] doi:10.1109/tmi.2026.3664328 [code]
Axon Diameter Mapping From Myelin Water Diffusion MRI.
Journal: IEEE transactions on medical imaging
In common: Optimization Toolbox, Statistics and Machine Learning Toolbox, cellular / molecular
[9] doi:10.1038/s41467-026-71458-0 [code]
Early differential impact of MeCP2 mutations on functional networks in Rett syndrome patient-derived human cortical organoids.
Journal: Nature communications
In common: Optimization Toolbox, Statistics and Machine Learning Toolbox, cellular / molecular
[10] doi:10.1111/jnc.70551 [code]
Synaptobrevin-2 Containing Extracellular Vesicles Are Rapidly Incorporated Into Mammalian Neurons via a Dynamin-Dependent Pathway.
Journal: Journal of neurochemistry
In common: Optimization Toolbox, Statistics and Machine Learning Toolbox, cellular / molecular

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.