OSCR

Endosome maturation is orchestrated by inside-out proton signaling through a Na<sup>+</sup>/H<sup>+</sup> exchanger and pH-dependent Rab GTPase cycling.

Code ↔ Paper

3 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 3 matches
  1. [1] § Methods › Measurement of the catalytic activity of TBC1D5 › Statistical assessment of parameter uncertainty ↔ Dela App Scripts/Python Scripts/parameter_variation_kcatKm.py, lines 268–348 · score 0.66 · Gaussian noise, standard deviation, variation, kcat, KM, models
  2. [2] § Methods › Measurement of the catalytic activity of TBC1D5 › Statistical assessment of parameter uncertainty ↔ Dela App Scripts/Python Scripts/parameter_accuracy_kcatKm.py, lines 665–728 · score 0.64 · Gaussian noise, standard deviation, kcat, KM, fitting, models
  3. [3] § Results › The GAP activity of TBC1D5 for Rab7 is pH-dependent ↔ diffusion/Diffusion3D_spherical.ipynb, lines 259–345 · score 0.61 · proton diffusion, bulk, thermodynamics, diffusing, mathematical, TBC1D5

Paper

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The authors' code

Python · 364 lines · 14 KB · GPL-3.0 · 1 match

  1. #
  2. # --------------------------------------------------------------------------------------------
  3. # DELA Python script for generating kcat/Km model data with gaussian variation of enzyme or
  4. # substrate concentrations and/or noise
  5. #
  6. # Input:
  7. # Empty document
  8. #
  9. # Output:
  10. # 1. New plots with model data
  11. # 2. New sheet with model data plus optional normalization and/or noise
  12. # --------------------------------------------------------------------------------------------
  13. #
  15. from math import *
  17. #
  18. # ------------------------------------------------------------------------------
  19. # function for generating inital time grid data and model
  20. # ------------------------------------------------------------------------------
  21. #
  22. def timeGridDataAndModel(npts, Xo, Xincr, So, Eo, kcatKm, kintr, constant, basSlope, xlabel, ylabel):
  24. DelaData.newData(npts, u"Time grid")
  26. ndxLastData = DelaDocument.dataCount() - 1
  27. indicesForPlot = list()
  28. indicesForPlot.append(ndxLastData)
  30. xn = DelaData.xValues(ndxLastData)
  32. k = 0
  33. while k < npts:
  34. xn[k] = Xo + Xincr * k
  35. k += 1
  37. # set x array in new data object with new x values
  38. DelaData.setXValues(ndxLastData, xn)
  39. DelaData.setXDescription(ndxLastData, xlabel)
  40. DelaData.setYDescription(ndxLastData, ylabel)
  42. DelaLog.appendString(u"<font color:systemTealColor>%s:</font> %s <font color:systemTealColor># data points:</font> %d\n" % (DelaData.label(ndxLastData), DelaData.description(ndxLastData), npts))
  44. description = u"Time grid"
  46. # generate new sheet
  47. DelaSheet.newSheet(indicesForPlot, description)
  48. ndxLastSheet = DelaDocument.sheetCount() - 1
  50. # generate new plot
  51. DelaPlot.newPlot(indicesForPlot)
  52. ndxTimeGridPlot = DelaDocument.plotCount() - 1
  53. DelaPlot.setTitle(ndxTimeGridPlot, description)
  54. DelaPlot.setXAxisLabel(ndxTimeGridPlot, xlabel)
  55. DelaPlot.setYAxisLabel(ndxTimeGridPlot, ylabel)
  57. # update the document views to reflect changes
  58. DelaDocument.updateViews()
  60. # select the most recent plot
  61. DelaDocument.selectPlot(ndxTimeGridPlot)
  63. # select initial model
  64. DelaModel.select("Exponential Decay", 1)
  65. DelaModel.viewParameters(0)
  67. # set input parameters
  68. p = DelaParameters.values(ndxLastData)
  69. p[0] = constant
  70. p[1] = basSlope
  71. p[2] = So
  72. p[3] = kcatKm * Eo + kintr
  73. DelaParameters.setValues(ndxLastData, p)
  75. # set fit selections
  76. s = DelaParameters.fitSelections(ndxLastData)
  77. s[0] = 1
  78. s[1] = 0
  79. s[2] = 1
  80. s[3] = 1
  81. DelaParameters.setFitSelections(ndxLastData, s)
  83. # calculate model and copy to data
  84. DelaModel.setSize(0)
  85. DelaModel.calculate()
  86. DelaModel.modelToData()
  88. ndxLastData = DelaDocument.dataCount() - 1
  89. DelaLog.appendString(u"<font color:systemTealColor>Plot initial model</font>")
  90. DelaLog.appendString(u"<font color:systemTealColor>%s:</font> %s <font color:systemTealColor># data points:</font> %d\n" % (DelaData.label(ndxLastData), DelaData.description(ndxLastData), npts))
  92. # generate new plot for model
  93. ndxLastSheet = DelaDocument.sheetCount() - 1
  94. indicesForPlot = DelaSheet.dataIndices(ndxLastSheet)
  95. DelaPlot.newPlot(indicesForPlot)
  96. ndxPlot = DelaDocument.plotCount() - 1
  97. DelaPlot.setTitle(ndxPlot, u"Initial Model")
  98. DelaPlot.setXAxisLabel(ndxPlot, xlabel)
  99. DelaPlot.setYAxisLabel(ndxPlot, ylabel)
  101. # update the document views to reflect changes
  102. DelaDocument.updateViews()
  104. # select plot with time grid
  105. DelaDocument.selectPlot(ndxTimeGridPlot)
  107. #
  108. # --------------------------------------------------------------------------------
  109. # function for generating data with variable concentrations, optional normalization
  110. # and/or noise addition
  111. # --------------------------------------------------------------------------------
  112. #
  114. def dataWithVariableConcentrationsNoise(ncopies, varyConcentrations, stdDevConcentrations, varyConstant, stdDevConstant, varyBaselineSlope, stdDevBaselineSlope, addNoise, stdDevNoise, seed, xlabel, ylabel, npts, Xo, Xincr, So, Eo, EoMultiplier, nEo, kcatKm, kintr, constant, basSlope):
  116. DelaLog.appendString(u"<font color:systemTealColor># model copies:</font> " + str(ncopies) + "\n")
  117. DelaLog.appendString(u"<font color:systemTealColor># initial enzyme concentrations:</font> " + str(nEo) + "\n")
  119. ndxTimeGridData = DelaDocument.dataCount() - 2
  120. ndxTimeGridPlot = DelaDocument.plotCount() - 2
  122. # get input parameters
  123. p = DelaParameters.values(ndxTimeGridData)
  124. p[0] = constant
  125. p[1] = basSlope
  126. p[2] = So
  127. p[3] = kcatKm * Eo + kintr
  129. # generate model data with or without variable concentrations and/or noise
  130. j = 0
  131. while j < ncopies:
  132. # list for indices of transformed data to be plotted
  133. indicesForPlot = list()
  135. Eon = Eo
  136. n = 0
  137. while n < nEo:
  138. p[0] = constant
  139. p[1] = basSlope
  140. p[2] = So
  141. p[3] = kcatKm * Eon + kintr
  142. if varyConcentrations:
  143. p[2] += So * stdDevConcentrations * DelaStatistics.gaussianNoise(seed)
  144. p[3] += kcatKm * Eon * stdDevConcentrations * DelaStatistics.gaussianNoise(seed)
  145. if varyConstant:
  146. p[0] += constant * stdDevConstant * DelaStatistics.gaussianNoise(seed)
  147. if varyBaselineSlope:
  148. p[1] += basSlope * stdDevBaselineSlope * DelaStatistics.gaussianNoise(seed)
  149. DelaParameters.setValues(ndxTimeGridData, p)
  150. DelaModel.calculate()
  151. xm = DelaModel.xValues(ndxTimeGridData)
  152. ym = DelaModel.yValues(ndxTimeGridData)
  154. # new data array
  155. DelaData.newData(npts, u"Model data Eo = " + str(Eon))
  157. ndxLastData = DelaDocument.dataCount() - 1
  158. indicesForPlot.append(ndxLastData)
  160. xn = DelaData.xValues(ndxLastData)
  161. yn = DelaData.yValues(ndxLastData)
  162. en = DelaData.errorValues(ndxLastData)
  163. mn = DelaData.maskValues(ndxLastData)
  165. k = 0
  166. while k < npts:
  167. xn[k] = xm[k]
  168. yn[k] = ym[k]
  169. if addNoise:
  170. yn[k] += stdDevNoise * DelaStatistics.gaussianNoise(seed)
  171. en[k] = stdDevNoise
  172. else:
  173. en[k] = 1.0
  174. mn[k] = 0
  175. k += 1
  177. # set x and y arrays in new data object with the new x and y values (undoable as a single coalesced action)
  178. DelaData.setXValues(ndxLastData, xn)
  179. DelaData.setYValues(ndxLastData, yn)
  180. DelaData.setErrorValues(ndxLastData, en)
  181. DelaData.setMaskValues(ndxLastData, mn)
  182. DelaData.setXDescription(ndxLastData, xlabel)
  183. DelaData.setYDescription(ndxLastData, ylabel)
  185. Eon *= EoMultiplier
  186. n += 1
  188. description = u"Model data"
  189. if varyConcentrations and varyConstant and varyBaselineSlope:
  190. description += u" with variable [Eo, So], cons, slope"
  191. elif varyConcentrations and varyConstant:
  192. description += u" with variable [Eo, So], cons"
  193. elif varyConcentrations and varyBaselineSlope:
  194. description += u" with variable [Eo, So], slope"
  195. elif varyConstant and varyBaselineSlope:
  196. description += u" with variable cons, slope"
  197. elif varyConcentrations:
  198. description += u" with variable [Eo, So]"
  199. elif varyConstant:
  200. description += u" with variable cons"
  201. elif varyBaselineSlope:
  202. description += u" with variable slope"
  203. if addNoise:
  204. description += u" + noise"
  206. # generate new sheet
  207. DelaSheet.newSheet(indicesForPlot, description)
  208. ndxLastSheet = DelaDocument.sheetCount() - 1
  210. # generate new plot
  211. DelaPlot.newPlot(indicesForPlot)
  212. ndxPlot = DelaDocument.plotCount() - 1
  213. DelaPlot.setTitle(ndxPlot, description)
  214. DelaPlot.setXAxisLabel(ndxPlot, xlabel)
  215. DelaPlot.setYAxisLabel(ndxPlot, ylabel)
  217. # select plot with time grid
  218. DelaDocument.selectPlot(ndxTimeGridPlot)
  220. j += 1
  222. # restore original model parameters
  223. p[0] = constant
  224. p[1] = basSlope
  225. p[2] = So
  226. p[3] = kcatKm * Eo + kintr
  227. DelaParameters.setValues(ndxTimeGridData, p)
  228. DelaModel.calculate()
  230. # update the document views to reflect changes
  231. DelaDocument.updateViews()
  233. # select the most recent plot
  234. ndxPlot = DelaDocument.plotCount() - 1
  235. DelaDocument.selectPlot(ndxPlot)
  237. #
  238. # -----------------
  239. # main script
  240. # -----------------
  241. #
  243. # default values
  244. npts = 100
  245. Xo = 0.0
  246. Xincr = 1.0
  247. So = 1.0
  248. Eo = 1.0
  249. EoMultiplier = 2.0
  250. nEo = 5
  251. kcatKm = 0.1
  252. kintr = 0.0
  253. constant = 0.1
  254. basSlope = 0.0
  255. ncopies = 25
  256. varyConcentrations = 1
  257. stdDevConcentrations = 0.1
  258. varyConstant = 1
  259. stdDevConstant = 0.1
  260. varyBaselineSlope = 0
  261. stdDevBaselineSlope = 0.1
  262. addNoise = 1
  263. stdDevNoise = 0.01
  264. seed = 843967
  265. xlabel = u"Time (s)"
  266. ylabel = u"Fluorescence (a.u.)"
  268. # get user input
  269. DelaUI.beginSheet()
  270. DelaUI.appendHeader(u"Initial Model")
  271. nptsField = DelaUI.appendTextField(u"Number of points", npts)
  272. XoField = DelaUI.appendTextField(u"Inital time", Xo)
  273. XincrField = DelaUI.appendTextField(u"Time increment", Xincr)
  274. SoField = DelaUI.appendTextField(u"Initial [Substrate]", So)
  275. EoField = DelaUI.appendTextField(u"Initial [Enzyme]", Eo)
  276. EoMultiplierField = DelaUI.appendTextField(u"Initial [Enzyme] multiplier", EoMultiplier)
  277. nEoField = DelaUI.appendTextField(u"Number of Initial [Enzyme]", nEo)
  278. kcatKmField = DelaUI.appendTextField(u"kcat/Km", kcatKm)
  279. constantField = DelaUI.appendTextField(u"Constant", constant)
  280. basSlopeField = DelaUI.appendTextField(u"Baseline Slope", basSlope)
  281. DelaUI.appendHeader(u"Copies")
  282. ncopiesField = DelaUI.appendTextField(u"# copies", ncopies)
  283. DelaUI.appendHeader(u"Concentrations")
  284. varyConcentrationsButton = DelaUI.appendCheckButton(u"Vary concentrations", varyConcentrations)
  285. stdDevConcentrationsField = DelaUI.appendTextField(u"Standard deviation", stdDevConcentrations)
  286. DelaUI.appendHeader(u"Constant")
  287. varyConstantButton = DelaUI.appendCheckButton(u"Vary constant", varyConstant)
  288. stdDevConstantField = DelaUI.appendTextField(u"Standard deviation", stdDevConstant)
  289. DelaUI.appendHeader(u"BaselineSlope")
  290. varyBaselineSlopeButton = DelaUI.appendCheckButton(u"Vary baseline slope", varyBaselineSlope)
  291. stdDevBaselineSlopeField = DelaUI.appendTextField(u"Standard deviation", stdDevBaselineSlope)
  292. DelaUI.appendHeader(u"Gaussian noise")
  293. addNoiseButton = DelaUI.appendCheckButton(u"Add noise", addNoise)
  294. stdDevNoiseField = DelaUI.appendTextField(u"Standard deviation", stdDevNoise)
  295. DelaUI.appendHeader(u"Random number generator")
  296. seedField = DelaUI.appendTextField(u"Seed", seed)
  297. DelaUI.appendHeader(u"Labels")
  298. xlabelField = DelaUI.appendTextField(u"X description", xlabel)
  299. ylabelField = DelaUI.appendTextField(u"Y description", ylabel)
  301. DelaUI.setToolTip(nptsField, u"Number of model points")
  302. DelaUI.setToolTip(XoField, u"Initial time")
  303. DelaUI.setToolTip(XincrField, u"Time increment")
  304. DelaUI.setToolTip(SoField, u"Initial substrate concentration")
  305. DelaUI.setToolTip(EoField, u"Initial enzyme concentration")
  306. DelaUI.setToolTip(EoMultiplierField, u"Initial enzyme concentration multiplier")
  307. DelaUI.setToolTip(nEoField, u"Number of initial enzyme concentrations")
  308. DelaUI.setToolTip(kcatKmField, u"kcat/Km in 1/Initial [Enzyme] 1/second units")
  309. DelaUI.setToolTip(constantField, u"Constant in y-axis units")
  310. DelaUI.setToolTip(basSlopeField, u"Baseline slope in y-axis units")
  311. DelaUI.setToolTip(ncopiesField, u"Number of copies")
  312. DelaUI.setToolTip(varyConcentrationsButton, u"Vary substrate and enzyme concentrations")
  313. DelaUI.setToolTip(stdDevConcentrationsField, u"Fractional standard deviation for concentrations")
  314. DelaUI.setToolTip(varyConstant, u"Vary the constant")
  315. DelaUI.setToolTip(stdDevConstantField, u"Fractional standard deviation for constant")
  316. DelaUI.setToolTip(varyBaselineSlopeButton, u"Vary the baseline slope")
  317. DelaUI.setToolTip(stdDevBaselineSlopeField, u"Fractional standard deviation for baseline slope")
  318. DelaUI.setToolTip(addNoiseButton, u"Add Gaussian noise")
  319. DelaUI.setToolTip(stdDevNoiseField, u"Standard deviation for Gaussian noise")
  320. DelaUI.setToolTip(seedField, u"Seed for Gaussian noise generator")
  321. DelaUI.setToolTip(xlabelField, u"Description for X values")
  322. DelaUI.setToolTip(ylabelField, u"Description for Y values")
  324. if DelaUI.displaySheet(u"Parameter Accuracy"):
  325. DelaError.quit(u"Script execution cancelled")
  327. npts = DelaUI.textFieldInteger(nptsField)
  328. Xo = DelaUI.textFieldDouble(XoField)
  329. Xincr = DelaUI.textFieldDouble(XincrField)
  330. So = DelaUI.textFieldDouble(SoField)
  331. Eo = DelaUI.textFieldDouble(EoField)
  332. EoMultiplier = DelaUI.textFieldDouble(EoMultiplierField)
  333. nEo = DelaUI.textFieldDouble(nEoField)
  334. kcatKm = DelaUI.textFieldDouble(kcatKmField)
  335. constant = DelaUI.textFieldDouble(constantField)
  336. basSlope = DelaUI.textFieldDouble(basSlopeField)
  337. ncopies = DelaUI.textFieldInteger(ncopiesField)
  338. varyConcentrations = DelaUI.checkButtonValue(varyConcentrationsButton)
  339. stdDevConcentrations = DelaUI.textFieldDouble(stdDevConcentrationsField)
  340. varyConstant = DelaUI.checkButtonValue(varyConstantButton)
  341. stdDevConstant = DelaUI.textFieldDouble(stdDevConstantField)
  342. varyBaselineSlope = DelaUI.checkButtonValue(varyBaselineSlopeButton)
  343. stdDevBaselineSlope = DelaUI.textFieldDouble(stdDevBaselineSlopeField)
  344. addNoise = DelaUI.checkButtonValue(addNoiseButton)
  345. stdDevNoise = DelaUI.textFieldDouble(stdDevNoiseField)
  346. seed = DelaUI.textFieldInteger(seedField)
  347. xlabel = DelaUI.textFieldString(xlabelField)
  348. ylabel = DelaUI.textFieldString(ylabelField)
  350. # generate time grid and initial model
  351. DelaLog.appendString(u"<bi><font color:systemTealColor>Generate time grid and initial model</font></bi>\n")
  352. timeGridDataAndModel(npts, Xo, Xincr, So, Eo, kcatKm, kintr, constant, basSlope, xlabel, ylabel)
  353. DelaLog.appendString("\n")
  355. DelaLog.appendString(u"<bi><font color:systemTealColor>Generate data with variable concentrations and noise</font></bi>\n")
  356. dataWithVariableConcentrationsNoise(ncopies, varyConcentrations, stdDevConcentrations, varyConstant, stdDevConstant, varyBaselineSlope, stdDevBaselineSlope, addNoise, stdDevNoise, seed, xlabel, ylabel, npts, Xo, Xincr, So, Eo, EoMultiplier, nEo, kcatKm, kintr, constant, basSlope)
  357. DelaLog.appendString(u"\n")
  359. # update the document views to reflect changes
  360. DelaDocument.updateViews()
  362. # select the most recent plot
  363. ndxPlot = DelaDocument.plotCount() - 1
  364. DelaDocument.selectPlot(ndxPlot)

parameter_variation_kcatKm.py, under GPL-3.0 · at the source

Overview

Authors: YouJin Lee1,2, Qing Ouyang1,2, Li Ma1,2, Morgan Fleishman1,2, Hasib Aamir Riaz1,2, Michael Schmidt1,2, Jeffrey L. Dupree3,4, Anupam Mondal5, Priyesh Mohanty5, Jeetain Mittal5,6,7, Oliver Beckstein8,9, David G. Lambright10, Eric M. Morrow1,2
ORCID iDs: YouJin Lee, Michael Schmidt, Anupam Mondal, Jeetain Mittal, Oliver Beckstein, Eric M. Morrow
  1. Department of Molecular Biology, Cell Biology and Biochemistry, Brown University,Providence, RI USA
  2. Center for Translational Neuroscience, Carney Institute for Brain Science, Brown University,Providence, RI USA
  3. Department of Anatomy and Neurobiology, Virginia Commonwealth University,Richmond, VA USA
  4. Research Service, McGuire Veterans Affairs Medical Center,Richmond, VA USA
  5. Artie McFerrin Department of Chemical Engineering, Texas A&M University,College Station, TX USA
  6. Department of Chemistry, Texas A&M University,College Station, TX USA
  7. Interdisciplinary Graduate Program in Genetics and Genomics, Texas A&M University,College Station, TX USA
  8. Department of Physics, Arizona State University,Tempe, AZ USA
  9. Center for Biological Physics, Arizona State University,Tempe, AZ USA
  10. Program in Molecular Medicine, University of Massachusetts Chan Medical School,Worcester, MA USA
Institutions: Brown University (United States); Virginia Commonwealth University (United States); Hunter Holmes McGuire VA Medical Center (United States); Texas A&M University (United States); Arizona State University (United States); University of Massachusetts Chan Medical School (United States)
Journal: Nature communications, volume 17, issue 1, article 6208
Dates: received 2 February 2026; accepted 17 April 2026; published online 8 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-72568-5 · PMID 42098086 · PMCID PMC13369854 · OpenAlex W4405385421
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.12.09.627558
Status: code verified
Categories: human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Evoked potentials, fMRI & imaging
Keywords: Cellular neuroscience, Molecular biophysics, Ion pumps, GTP-binding protein regulators
MeSH: Endosomes*, GTPase-Activating Proteins*, Protons*, rab GTP-Binding Proteins*, Sodium-Hydrogen Exchangers*, Animals, Humans, Hydrogen-Ion Concentration, Mice, Neurons, rab7 GTP-Binding Proteins, Signal Transduction (* major topic)
Topic: Cellular transport and secretion (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institute on Aging (R01AG087455, K99AG076868); NINDS (R01NS113141); National Institute of Mental Health (R01MH137004); NIGMS (R35GM153388)
Citations: not cited yet (Europe PMC); 115 references in the paper
Research resources: RRID:Addgene_54244

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

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Becksteinlab/TBC1D5-Rab7-NHE6-proton-diffusion-model

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: b3c6e44cdd0ea91dc6638440572042729edd9db6, 27 July 2026
Languages: Jupyter (5), Shell (1)
Size: 22 files, 6 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, CITATION.cff, 5 notebooks
Not found: environment file, tests, continuous integration, documentation
Tools: Matplotlib (5 files), NumPy (5 files), MDAnalysis (3 files), SciPy (2 files), SymPy (2 files), NetworkX (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
8 files
At the source: github.com/Becksteinlab/TBC1D5-Rab7-NHE6-proton-diffusion-model

Zenodo 19054649

License: GPL-3.0
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Size: 1 file
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Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
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Zenodo 19011503

License: MIT
State: the link answers, verified on 28 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: Matplotlib (5 files), NumPy (5 files), MDAnalysis (3 files), SciPy (2 files), SymPy (2 files), NetworkX (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
8 files
At the source:

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Data

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The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-72568-5.

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Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 4 keywords, 12 MeSH terms, 4 funders, 111 references, 1 RRID.

Cite

This paper

Lee, Y., Ouyang, Q., Ma, L., Fleishman, M., Riaz, H. A., Schmidt, M., Dupree, J. L., Mondal, A., Mohanty, P., Mittal, J., Beckstein, O., Lambright, D. G., & Morrow, E. M. (2026). Endosome maturation is orchestrated by inside-out proton signaling through a Na&lt;sup&gt;+&lt;/sup&gt;/H&lt;sup&gt;+&lt;/sup&gt; exchanger and pH-dependent Rab GTPase cycling. Nature communications, 17(1), 6208. https://doi.org/10.1038/s41467-026-72568-5

BibTeX

@article{lee2026endosome,
author = {Lee, YouJin and Ouyang, Qing and Ma, Li and Fleishman, Morgan and Riaz, Hasib Aamir and Schmidt, Michael and Dupree, Jeffrey L. and Mondal, Anupam and Mohanty, Priyesh and Mittal, Jeetain and Beckstein, Oliver and Lambright, David G. and Morrow, Eric M.},
title = {{Endosome maturation is orchestrated by inside-out proton signaling through a Na\&lt;sup\&gt;+\&lt;/sup\&gt;/H\&lt;sup\&gt;+\&lt;/sup\&gt; exchanger and pH-dependent Rab GTPase cycling}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6208},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72568-5},
url = {https://doi.org/10.1038/s41467-026-72568-5},
pmid = {42098086},
pmcid = {PMC13369854}
}

RIS

TY - JOUR
AU - Lee, YouJin
AU - Ouyang, Qing
AU - Ma, Li
AU - Fleishman, Morgan
AU - Riaz, Hasib Aamir
AU - Schmidt, Michael
AU - Dupree, Jeffrey L.
AU - Mondal, Anupam
AU - Mohanty, Priyesh
AU - Mittal, Jeetain
AU - Beckstein, Oliver
AU - Lambright, David G.
AU - Morrow, Eric M.
TI - Endosome maturation is orchestrated by inside-out proton signaling through a Na&lt;sup&gt;+&lt;/sup&gt;/H&lt;sup&gt;+&lt;/sup&gt; exchanger and pH-dependent Rab GTPase cycling
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/08
VL - 17
IS - 1
SP - 6208
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72568-5
UR - https://doi.org/10.1038/s41467-026-72568-5
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-72568-5",
"type": "article-journal",
"title": "Endosome maturation is orchestrated by inside-out proton signaling through a Na&lt;sup&gt;+&lt;/sup&gt;/H&lt;sup&gt;+&lt;/sup&gt; exchanger and pH-dependent Rab GTPase cycling",
"container-title": "Nature communications",
"author": [
{
"family": "Lee",
"given": "YouJin"
},
{
"family": "Ouyang",
"given": "Qing"
},
{
"family": "Ma",
"given": "Li"
},
{
"family": "Fleishman",
"given": "Morgan"
},
{
"family": "Riaz",
"given": "Hasib Aamir"
},
{
"family": "Schmidt",
"given": "Michael"
},
{
"family": "Dupree",
"given": "Jeffrey L."
},
{
"family": "Mondal",
"given": "Anupam"
},
{
"family": "Mohanty",
"given": "Priyesh"
},
{
"family": "Mittal",
"given": "Jeetain"
},
{
"family": "Beckstein",
"given": "Oliver"
},
{
"family": "Lambright",
"given": "David G."
},
{
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"given": "Eric M."
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "6208",
"DOI": "10.1038/s41467-026-72568-5",
"PMID": "42098086",
"PMCID": "PMC13369854",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-72568-5",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
8
]
]
}
}

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