OSCR

Leucine-rich repeat kinase 2 impairs the release sites of Parkinson's disease vulnerable dopamine axons.

Code ↔ Paper

7 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 7 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › In vivo evoked dopamine release › Fiber photometry signal processing ↔ preprocess_and_binning.m, lines 63–181 · score 0.88 · sliding window, camera triggers, fluorescence traces, binned, velocity, baseline
  2. [2] § Methods › Pipelines for proteome analysis and data visualization › Gene ontology ↔ analysis code.zip/phosphopeptidesGSMLi2vsGSvehpublication.Rmd, lines 36–111 · score 0.81 · clusterProfiler, Cellular Components, Biological Process, db, mm, Pathways
  3. [3] § Methods › In vivo evoked dopamine release › Spontaneous dopamine transient selection ↔ triggered_analysis.m, the whole file · a weak match · score 0.80 · spontaneous transients, double threshold, low threshold, high threshold, onset, trace
  4. [4] § Methods › In vivo evoked dopamine release › Spontaneous dopamine transient selection ↔ snippets/transient_by_threshold.m, the whole file · a weak match · score 0.72 · double threshold, low threshold, high threshold, Spontaneous, transients, trace
  5. [5] § Results › LRRK2 phosphorylates RAB3 isoforms in vivo ↔ analysis code.zip/phosphopeptidesGSMLi2vsGSvehpublication.Rmd, lines 36–111 · score 0.57 · biological processes, log2FC, pathway, GO, enrichment, enriched
  6. [6] § Results › LRRK2 phosphorylates RAB3 isoforms in vivo ↔ analysis code.zip/volcanoPlots.Rmd, lines 17–78 · score 0.53 · log2FC, Rab3a, Volcano, GS, vehicle, MLi
  7. [7] § Results › Phosphorylation of RAB3A decreases its binding to the effector proteins RIM1 and RIM2 from mouse brain ↔ analysis code.zip/volcanoPlots.Rmd, lines 17–78 · score 0.52 · log2FC, Rab3a, volcano, MLi, phosphorylated, proteins

Paper

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The authors' code

R Markdown · 178 lines · 5.6 KB · CC-BY-4.0 · 2 matches

  1. ---
  2. title: "Phosphopeptides (GSMLI-2vsGSveh) GO Analysis"
  3. author: "Hannah Serio"
  4. date: "2025-06-09"
  5. output: html_document
  6. ---
  8. ```{r libraries, include=FALSE}
  10. library(clusterProfiler)
  11. library(org.Mm.eg.db)
  12. library(ggplot2)
  13. library(DOSE)
  14. library(readxl)
  15. library(openxlsx)
  16. library(dplyr)
  18. ```
  20. # GO Analysis - Synaptosome Phos Quant Phosphopeptides
  21. ```{r GO Analysis, warning=FALSE}
  23. excel_file <- "Anton-SPM-GSMLi2vsGSVeh-volcano-phosphopeptide-20250508 LP (3).xlsx" # table was provided by Parisiadou Lab
  24. gene_data <- read_excel(excel_file)
  26. # filter for p-value <= 0.05, |log2FC| > 0.58
  27. gene_data_filtered_abs <- gene_data %>%
  28. filter(logpvalue >= -log10(0.05), log2FC > 0.58 | log2FC < -0.58)
  30. # extract the genes of interest
  31. genesPeptideGroupsABS <- gene_data_filtered_abs$"...1"
  32. genesPeptideGroupsABS <- unique(genesPeptideGroupsABS)
  34. ```
  36. # Run GO analysis with dotplot visualization
  37. ```{r PhosphopeptidesGSMLI2, warning=FALSE, fig.width=9, fig.height=6, fig.path='R_plots_PhosphopeptidesGSMLI2publication/', dev=c('png', 'pdf')}
  38. # function to perform GO analysis and generate boxplot
  39. run_go_plot <- function(genes,
  40. ont = "BP",
  41. orgdb = org.Mm.eg.db,
  42. keytype = "SYMBOL",
  43. plot_title = "GO Analysis") {
  45. # run GO enrichment
  46. go_result <- enrichGO(gene = genes,
  47. OrgDb = orgdb,
  48. keyType = keytype,
  49. ont = ont,
  50. pAdjustMethod = "BH",
  51. pvalueCutoff = 0.05)
  53. # convert to data frame and calculate -log10(pvalue)
  54. go_df <- as.data.frame(go_result)
  55. go_df$logP <- -log10(go_df$pvalue)
  57. # function to generate a dotplot
  58. make_plot <- function(df, top_n, suffix) {
  59. df_top <- df %>% arrange(desc(logP)) %>% head(top_n)
  61. ggplot(df_top, aes(x = logP, y = reorder(Description, logP), size = Count, color = p.adjust)) +
  62. geom_point() +
  63. scale_color_gradient(low = "blue", high = "red") +
  64. labs(
  65. x = "-log10(p-value)",
  66. y = NULL,
  67. title = paste0(plot_title, " (Top ", top_n, ")")
  68. ) +
  69. theme_minimal()
  70. }
  72. # create plot with 15 top categories showing
  73. plot_top15 <- make_plot(go_df, 15, "Top 15")
  75. return(list(top15 = plot_top15))
  76. }
  78. ### PeptideGroups - BIOLOGICAL PROCESS
  79. dotplotPeptideGroupsbpABS <- run_go_plot(genes = genesPeptideGroupsABS,
  80. ont = "BP",
  81. plot_title = "GO Analysis (BP): Phosphopeptides GSMLI2 (|log2FC| > 0.58)")
  83. dotplotPeptideGroupsbpABS
  85. ### PeptideGroups - Cellular Component
  86. dotplotPeptideGroupsccABS <- run_go_plot(genes = genesPeptideGroupsABS,
  87. ont = "CC",
  88. plot_title = "GO Analysis (CC): Phosphopeptides GSMLI2 (|log2FC| > 0.58)")
  90. dotplotPeptideGroupsccABS
  92. ## KEGG Pathway Analysis
  93. # need to reformat gene list to be Entrez ID's for KEGG analysis
  94. entrezPeptideGroups <- bitr(genesPeptideGroupsABS, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Mm.eg.db)
  96. # extract only the Entrez ID column
  97. entrezPeptideGroups <- entrezPeptideGroups$ENTREZID
  99. # run clusterProfiler KEGG with dotplot visualization
  100. keggResultPeptideGroups <- enrichKEGG(gene = entrezPeptideGroups,
  101. organism = "mmu",
  102. pAdjustMethod = "BH",
  103. pvalueCutoff = 1)
  105. # extract results as a dataframe
  106. keggPeptideGroups <- as.data.frame(keggResultPeptideGroups)
  108. # convert p-value to -log10(p-value)
  109. keggPeptideGroups$logP <- -log10(keggPeptideGroups$pvalue)
  111. ```
  113. ```{r Saving Results, eval=FALSE}
  114. run_go_plot <- function(genes,
  115. ont = "BP",
  116. orgdb = org.Mm.eg.db,
  117. keytype = "SYMBOL") {
  119. # run GO enrichment
  120. go_result <- enrichGO(gene = genes,
  121. OrgDb = orgdb,
  122. keyType = keytype,
  123. ont = ont,
  124. pAdjustMethod = "BH",
  125. pvalueCutoff = 1) # keep all results
  127. # convert to data frame and calculate -log10(pvalue)
  128. go_df <- as.data.frame(go_result)
  129. go_df$logP <- -log10(go_df$pvalue)
  131. return(list(result = go_df))
  132. }
  134. # create workbook
  135. wb <- createWorkbook()
  137. # list of results
  138. go_results <- list(
  139. "PeptideGroups_BP" = run_go_plot(genesPeptideGroupsABS, ont = "BP"),
  140. "PeptideGroups_MF" = run_go_plot(genesPeptideGroupsABS, ont = "MF"),
  141. "PeptideGroups_CC" = run_go_plot(genesPeptideGroupsABS, ont = "CC"),
  142. "PeptideGroups_KEGG GSMLI2" = list(result = keggPeptideGroups)
  143. )
  145. # write results to excel sheet
  146. for (name in names(go_results)) {
  147. df <- go_results[[name]]$result
  149. addWorksheet(wb, name)
  150. writeData(wb, name, df)
  152. # Style: yellow fill for significant rows
  153. yellowStyle <- createStyle(bgFill = "#FFE599")
  155. # Style: black header row with white text (optional: bold)
  156. headerStyle <- createStyle(fontColour = "#000000", halign = "center", textDecoration = "bold")
  157. addStyle(wb, sheet = name, style = headerStyle, rows = 1, cols = 1:ncol(df), gridExpand = TRUE)
  159. # Highlight full rows where p.adjust < 0.05
  160. rows_to_highlight <- which(df$p.adjust < 0.05) + 1 # data starts at row 2
  161. if (length(rows_to_highlight) > 0) {
  162. conditionalFormatting(
  163. wb, sheet = name,
  164. cols = 1:ncol(df),
  165. rows = rows_to_highlight,
  166. rule = 'TRUE',
  167. type = "expression",
  168. style = yellowStyle
  169. )
  170. }
  172. setColWidths(wb, sheet = name, cols = 2, widths = "auto")
  174. }
  176. # save the workbook
  177. saveWorkbook(wb, "phosphopeptidesGSMLI2_Results.xlsx", overwrite = TRUE)
  178. ```

phosphopeptidesGSMLi2vsGSvehpublication.Rmd, under CC-BY-4.0 · at the source

Overview

Authors: Chuyu Chen1,2, Qianzi He2,3, Giulia Tombesi1,2, Eve Napier4, Matthew Jaconelli2,5, Oscar Andrés Moreno-Ramos2,6, Hannah Serio1, Yahaira Naaldijk7, Vanessa Promes1,2, Amanda Schneeweis2,6, Kaitlyn Quinn2,3, Christopher Nasios1,2, Elisa Greggio8,9, Yevgenia Kozorovitskiy3, Daniel Arango1, Amir R Khan4, Dario R Alessi2,5, Daniel A Dombeck2,3, Sabine Hilfiker7, Rajeshwar Awatramani2,6, Loukia Parisiadou1,2
ORCID iDs: Matthew Jaconelli, Oscar Andrés Moreno-Ramos, Vanessa Promes, Amanda Schneeweis, Elisa Greggio, Yevgenia Kozorovitskiy, Daniel Arango, Dario R Alessi, Daniel A Dombeck, Rajeshwar Awatramani, Loukia Parisiadou
  1. Department of Pharmacology, Northwestern University, Chicago, IL USA
  2. Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD USA
  3. Department of Neurobiology, Northwestern University, Evanston, IL USA
  4. School of Biochemistry and Immunology, Trinity College Dublin, Dublin, Ireland
  5. Medical Research Council Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dundee, UK
  6. Department of Neurology, Northwestern University, Chicago, IL USA
  7. Department of Anesthesiology, Rutgers, New Jersey Medical School, Newark, NJ USA
  8. Department of Biology, University of Padova, Padova, Italy
  9. Centro Studi per la Neurodegenerazione (CESNE), University of Padova, Padova, Italy
Institutions: Northwestern University (United States); Northwestern University (Philippines); Aligning Science Across Parkinson's (United States); Trinity College Dublin (Ireland); University of Dundee (United Kingdom); MRC Protein Phosphorylation and Ubiquitylation Unit (United Kingdom); Medical Research Council (United Kingdom); Rutgers, The State University of New Jersey (United States); Rutgers New Jersey Medical School (United States); University of Padua (Italy)
Journal: Nature communications, volume 17, issue 1, article 9165
Dates: received 20 February 2026; accepted 25 June 2026; published online 28 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-75194-3 · PMID 42660892 · PMCID PMC13522591 · OpenAlex W4413835653
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.28.672006
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), Parkinson's (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging, Connectivity
Keywords: Parkinson's disease, Cellular neuroscience
MeSH: Axons*, Dopamine*, Dopaminergic Neurons*, Leucine-Rich Repeat Serine-Threonine Protein Kinase-2*, Parkinson Disease*, Animals, Disease Models, Animal, Humans, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Mutation, Pars Compacta, Phosphorylation, Proteomics, rab3 GTP-Binding Proteins, Synapses (* major topic)
Topic: Parkinson's Disease Mechanisms and Treatments (Neurology, Medicine), according to OpenAlex
Funding: Michael J. Fox Foundation for Parkinson's Research (Michael J. Fox Foundation) (ASAP- 020600, ASAP-000463); NINDS NIH HHS (R01 NS147081, R01 NS119690); NIA NIH HHS (R21 AG089563); U.S. Department of Health &amp; Human Services | National Institutes of Health (NIH) (R21AG089563, R01NS119690); NIH HHS (S10 OD032270)
Citations: cited by 1 paper (Europe PMC); 99 references in the paper
Research resources: Feature plots were generated using R RRID:SCR_001905, ShinyCell package99 RRID:SCR_022756, RRID:SRC_020996

Abstract

Parkinson’s disease (PD) is defined pathologically by loss of dopamine-producing neurons in the substantia nigra pars compacta (SNc). Yet synaptic dysfunction emerges much earlier, making it essential to define the mechanisms that drive early nigrostriatal deregulation. In the SNc, molecularly distinct dopamine neuron subtypes show differential susceptibility to PD. Here, we used intersectional genetic mouse models to determine how the PD-linked kinase LRRK2 affects vulnerable dopamine subtypes. Immunofluorescence and proximity-labeling proteomics revealed enriched LRRK2 expression in vulnerable dopamine neuron subclusters. High-resolution imaging showed that pathogenic LRRK2 disrupts presynaptic release-site organization in vulnerable dopamine axons, leading to reduced spontaneous and evoked striatal dopamine release in vivo. Proteomic analyses further showed that mutant LRRK2 increases phosphorylation of RAB3 proteins, impairing their interaction with the active-zone effectors RIM1 and RIM2. Together, these findings highlight a subtype-specific, cell-autonomous mechanism by which pathogenic LRRK2 impairs PD-vulnerable nigrostriatal synapses and provide a framework for therapeutic strategies targeting early synaptic deficits in PD.

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 7 matches between paragraphs and lines of code.

curtain.proteo.info

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Statistical analysis and data visualization”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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At the source: curtain.proteo.info/#/

Zenodo 19930567

License: CC-BY-4.0
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Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (2 files), tidyverse (2 files), clusterProfiler (1 file)
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2 files
At the source:

Zenodo 20244434

License: CC-BY-4.0
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Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), Violinplot-Matlab (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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At the source:

DombeckLab/lrrk2_photometry_analysis

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d34558fdde4b6665bd631c8fe47b8a1ef18f6a73, 30 July 2026
Languages: MATLAB (7)
Size: 10 files, 7 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), Violinplot-Matlab (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/DombeckLab/lrrk2_photometry_analysis

Code availability

The codes generated in this study have been deposited on Zenodo, https://doi.org/10.5281/zenodo.19930567; https://doi.org/10.5281/zenodo.20244434, and GitHub, https://github.com/DombeckLab/lrrk2_photometry_analysis.

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 16 scripts, each with its path and the digest of its content;
  • 7 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

Datasets cited

Data availability

The mass spectrometry proteomics data generated in this study have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository, with dataset identifiers PXD065003 (https://www.ebi.ac.uk/pride/archive/projects/PXD065003), PXD065006 (https://www.ebi.ac.uk/pride/archive/projects/PXD065006), and PXD0651255 (https://www.ebi.ac.uk/pride/archive/projects/PXD065125). The processed proteomics data generated in this study are provided in the Supplementary data 1-3, and Gene Ontology analysis results are provided in the Supplementary data 4-5. Imaging data with metadata files generated in this study have been deposited in the BioStudies/BioImage Archive, with data indentifiers S-BIAD2070 (https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD2070), S-BIAD2071 (https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD2071), S-BIAD2072 (https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD2072), and S-BIAD2074 (https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD2074). Raw tubular Data generated in this study from all experiments, and uncropped western blot images with metadata files in this study are available in source data file via the open-access option on the Zenodo repository (https://doi.org/10.5281/zenodo.19930567). The protocols used in this study were uploaded to protocols.io; their DOIs are listed in the Supplementary Table 1. Source data are provided with this paper.

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Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 21 authors, 2 keywords, 18 MeSH terms, 5 funders, 97 references, 3 RRIDs.

Cite

This paper

Chen, C., He, Q., Tombesi, G., Napier, E., Jaconelli, M., Moreno-Ramos, O. A., Serio, H., Naaldijk, Y., Promes, V., Schneeweis, A., Quinn, K., Nasios, C., Greggio, E., Kozorovitskiy, Y., Arango, D., Khan, A. R., Alessi, D. R., Dombeck, D. A., Hilfiker, S., . . . Parisiadou, L. (2026). Leucine-rich repeat kinase 2 impairs the release sites of Parkinson's disease vulnerable dopamine axons. Nature communications, 17(1), 9165. https://doi.org/10.1038/s41467-026-75194-3

BibTeX

@article{chen2026leucine,
author = {Chen, Chuyu and He, Qianzi and Tombesi, Giulia and Napier, Eve and Jaconelli, Matthew and Moreno-Ramos, Oscar Andrés and Serio, Hannah and Naaldijk, Yahaira and Promes, Vanessa and Schneeweis, Amanda and Quinn, Kaitlyn and Nasios, Christopher and Greggio, Elisa and Kozorovitskiy, Yevgenia and Arango, Daniel and Khan, Amir R and Alessi, Dario R and Dombeck, Daniel A and Hilfiker, Sabine and Awatramani, Rajeshwar and Parisiadou, Loukia},
title = {{Leucine-rich repeat kinase 2 impairs the release sites of Parkinson's disease vulnerable dopamine axons}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {9165},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75194-3},
url = {https://doi.org/10.1038/s41467-026-75194-3},
pmid = {42660892},
pmcid = {PMC13522591}
}

RIS

TY - JOUR
AU - Chen, Chuyu
AU - He, Qianzi
AU - Tombesi, Giulia
AU - Napier, Eve
AU - Jaconelli, Matthew
AU - Moreno-Ramos, Oscar Andrés
AU - Serio, Hannah
AU - Naaldijk, Yahaira
AU - Promes, Vanessa
AU - Schneeweis, Amanda
AU - Quinn, Kaitlyn
AU - Nasios, Christopher
AU - Greggio, Elisa
AU - Kozorovitskiy, Yevgenia
AU - Arango, Daniel
AU - Khan, Amir R
AU - Alessi, Dario R
AU - Dombeck, Daniel A
AU - Hilfiker, Sabine
AU - Awatramani, Rajeshwar
AU - Parisiadou, Loukia
TI - Leucine-rich repeat kinase 2 impairs the release sites of Parkinson's disease vulnerable dopamine axons
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/28
VL - 17
IS - 1
SP - 9165
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75194-3
UR - https://doi.org/10.1038/s41467-026-75194-3
LA - en
ER -

CSL-JSON

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[8] doi:10.1038/s41467-026-72935-2 [code]
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