OSCR

Phosphoproteomic profiling reveals post-translational dysregulation in Huntington's disease patient-derived neurons.

Code ↔ Paper

8 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 8 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Phosphosite enrichment analysis ↔ src/R/04_psea_input.R, lines 64–91 · score 0.75 · log2 fold change, PhosphositePlus, Kinase Library, uploaded, PSEA, sequences
  2. [2] § Methods › Co-immunoprecipitation (co-IP), mass spectrometry, and data analysis ↔ src/R/07_co_ip_mxra8.R, the whole file · a weak match · score 0.68 · High confidence, abundance ratios, contaminants, FDR, background, IP
  3. [3] § Results › MXRA8-associated interactome and kinases suggest a role in autophagy and neurovascular function ↔ src/R/07_co_ip_mxra8.R, the whole file · a weak match · score 0.66 · co IP, high confidence, abundance ratio, FDR, MXRA8, proteins
  4. [4] § Results › HD-iNs display a distinct phosphoproteomic profile ↔ src/R/03_pms_diff_exp_analysis.R, lines 47–105 · score 0.59 · Shapiro Wilk, upregulated phosphopeptides, downregulated phosphopeptides, unpaired, fold, log2
  5. [5] § Results › MXRA8-associated interactome and kinases suggest a role in autophagy and neurovascular function ↔ Main.Rmd, lines 815–844 · score 0.57 · site score, pS377, kinase prediction, MXRA8, phosphosites
  6. [6] § Methods › Phosphosite enrichment analysis ↔ src/R/05_on_off_phosphopeptides.R, the whole file · a weak match · score 0.56 · log2 fold change, abundant phosphopeptides, sequences
  7. [7] § Results › MXRA8-associated interactome and kinases suggest a role in autophagy and neurovascular function ↔ Main.Rmd, lines 775–813 · score 0.55 · CYP1B1, ESYT1, LAMP1, PFKP, TGM2, binding
  8. [8] § Methods › Kinase prediction ↔ src/R/06_on_off_kinases.R, lines 19–90 · score 0.52 · PhosphositePlus, kinase prediction, score, proteomic

Paper

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The authors' code

R · 70 lines · 4 KB · no license · 2 matches

  1. ### Co-IP MXRA8 HD/Ctrl Abundance Ratio Analysis ###
  3. #
  4. # Importing the data
  5. #
  6. co_ip_igG <- fread(paste(dir_list$data, "Supplementary_Table14.csv", sep = "/"), data.table = FALSE)
  7. co_ip_mxra8 <- fread(paste(dir_list$data, "Supplementary_Table15.csv", sep = "/"), data.table = FALSE)
  9. #
  10. # Formatting the data
  11. #
  13. co_ip_mxra8 <- co_ip_mxra8 %>%
  14. select(`Protein FDR Confidence: Combined`, Contaminant, Accession, `Gene Symbol`, `Ensembl Gene ID`, `Coverage [%]`, `Abundance Ratio: (HD) / (CTRL)`, `Abundance Ratio Adj. P-Value: (HD) / (CTRL)`, `Abundance Ratio Variability [%]: (HD) / (CTRL)`, `Abundances (Normalized): F8: Sample, nonP, CTRL, C12`, `Abundances (Normalized): F10: Sample, nonP, CTRL, CKK`, `Abundances (Normalized): F12: Sample, nonP, CTRL, CKP`, `Abundances (Normalized): F14: Sample, nonP, CTRL, CBS`, `Abundances (Normalized): F2: Sample, nonP, HD, HDRH`, `Abundances (Normalized): F4: Sample, nonP, HD, HDSD`, `Abundances (Normalized): F6: Sample, nonP, HD, HD4RV`, `Abundances (Normalized): F16: Sample, nonP, HD, HDKP`, `Found in Sample: F8: Sample, nonP, CTRL, C12`, `Found in Sample: F10: Sample, nonP, CTRL, CKK`, `Found in Sample: F12: Sample, nonP, CTRL, CKP`, `Found in Sample: F14: Sample, nonP, CTRL, CBS`, `Found in Sample: F2: Sample, nonP, HD, HDRH`, `Found in Sample: F4: Sample, nonP, HD, HDSD`, `Found in Sample: F6: Sample, nonP, HD, HD4RV`, `Found in Sample: F16: Sample, nonP, HD, HDKP`) %>%
  15. rename("prot_confidence" = "Protein FDR Confidence: Combined",
  16. "contaminant" = "Contaminant",
  17. "accession" = "Accession",
  18. "gene" = "Gene Symbol",
  19. "gene_id" = "Ensembl Gene ID",
  20. "coverage" = "Coverage [%]",
  21. "abundance_ratio" = "Abundance Ratio: (HD) / (CTRL)",
  22. "p_adj" = "Abundance Ratio Adj. P-Value: (HD) / (CTRL)",
  23. "abundance_variability" = "Abundance Ratio Variability [%]: (HD) / (CTRL)",
  24. "C12_IN" = "Abundances (Normalized): F8: Sample, nonP, CTRL, C12",
  25. "CKK_IN" = "Abundances (Normalized): F10: Sample, nonP, CTRL, CKK",
  26. "CKP_IN" = "Abundances (Normalized): F12: Sample, nonP, CTRL, CKP",
  27. "CBS_IN" = "Abundances (Normalized): F14: Sample, nonP, CTRL, CBS",
  28. "HDRH_IN" = "Abundances (Normalized): F2: Sample, nonP, HD, HDRH",
  29. "HDSD_IN" = "Abundances (Normalized): F4: Sample, nonP, HD, HDSD",
  30. "HD4RV_IN" = "Abundances (Normalized): F6: Sample, nonP, HD, HD4RV",
  31. "HDKP_IN" = "Abundances (Normalized): F16: Sample, nonP, HD, HDKP",
  32. "C12_IN_Conf" = "Found in Sample: F8: Sample, nonP, CTRL, C12",
  33. "CKK_IN_Conf" = "Found in Sample: F10: Sample, nonP, CTRL, CKK",
  34. "CKP_IN_Conf" = "Found in Sample: F12: Sample, nonP, CTRL, CKP",
  35. "CBS_IN_Conf" = "Found in Sample: F14: Sample, nonP, CTRL, CBS",
  36. "HDRH_IN_Conf" = "Found in Sample: F2: Sample, nonP, HD, HDRH",
  37. "HDSD_IN_Conf" = "Found in Sample: F4: Sample, nonP, HD, HDSD",
  38. "HD4RV_IN_Conf" = "Found in Sample: F6: Sample, nonP, HD, HD4RV",
  39. "HDKP_IN_Conf" = "Found in Sample: F16: Sample, nonP, HD, HDKP")
  41. #
  42. # Filtering the data
  43. #
  45. co_ip_mxra8_filt <- co_ip_mxra8 %>%
  46. # excluding background proteins found in the fb igG data
  47. filter(!(accession %in% co_ip_igG$fb_igG_accession)) %>%
  48. # setting significance at 0.05 for the abundance ratio
  49. filter(p_adj < 0.05) %>%
  50. # only including proteins found in at least 3 samples in at least 1 sample group
  51. rowwise() %>%
  52. filter(sum(!is.na(c_across(matches("^C.*_IN$")))) >= 3 |
  53. sum(!is.na(c_across(matches("^HD.*_IN$")))) >= 3) %>%
  54. ungroup() %>%
  55. # removing identified contaminants
  56. filter(!contaminant) %>%
  57. # including only proteins with High confidence and non-NA values for abundance ratio
  58. filter(prot_confidence == "High" & !is.na(abundance_ratio)) %>%
  59. as.data.frame()
  61. saveRDS(co_ip_mxra8, paste(dir_list$rds, "co_ip_mxra8.rds", sep = "/"))
  62. saveRDS(co_ip_mxra8_filt, paste(dir_list$rds, "co_ip_mxra8_filt.rds", sep = "/"))
  63. rm(co_ip_igG)
  65. co_ip_mxra8_filt %>%
  66. fwrite(paste(dir_list$docs, "Supplementary_Data_16.csv", sep = "/"))

07_co_ip_mxra8.R at commit 4d1dbd9, no license · at the source

Overview

Authors: Lea Danics1,2,3, Chandramouli Muralidharan2,4, Ágnes Varga1,2,5, Melinda Rezeli6,7, Jeovanis Gil8, Anna A. Abbas1,2, Ádám Pap9,10, Andrew S. Park11,12, Marcell Cserhalmi13, Emilie M. Legault11,12, Ármin Sőth1,2, Dorina Jamniczky1,2, Roland Zsoldos1,2,5, Roger A. Barker14, Gergely Róna13,15, Janelle Drouin-Ouellet11,12, György Markó-Varga7, Zsuzsanna Darula9,10, Karolina Pircs1,2,4,5
ORCID iDs: Anna A. Abbas
15 affiliations
  1. Institute of Clinical Pathophysiology, Semmelweis University,Budapest, Hungary
  2. Hungarian Centre of Excellence for Molecular Medicine—Semmelweis University (HCEMM-SU), Neurobiology and Neurodegenerative Diseases Research Group,Budapest, Hungary
  3. HUN-REN-SU Cerebrovascular and Neurocognitive Diseases Research Group, Budapest, Hungary
  4. Laboratory of Molecular Neurogenetics, Department of Experimental Medical Science, Wallenberg Neuroscience Center and Lund Stem Cell Center, Lund University,Lund, Sweden
  5. HUN-REN-SZTAKI-SU Rejuvenation Research Group, HUN-REN Office for Supported Research Groups (TKI), Budapest, Hungary
  6. Division for Biomedical Engineering, Department of Biomedical Engineering, Lund University,Lund, Sweden
  7. BioMS−Swedish National Infrastructure for Biological Mass Spectrometry, Lund University,Lund, Sweden
  8. Clinical Chemistry, Department of Translational Medicine, Lund University,Lund, Sweden
  9. Single Cell Omics Advanced Core Facility, Hungarian Centre of Excellence for Molecular Medicine, Szeged, Hungary
  10. Laboratory of Proteomics, Complex Molecular and Cell Biology Service Centre, HUN-REN Biological Research Centre,Szeged, Hungary
  11. Faculty of Pharmacy, University of Montreal,Montreal, QC Canada
  12. Centre de Recherche Sur Le Cerveau Et L’apprentissage (CIRCA), University of Montreal,Montreal, QC Canada
  13. MTA-HUN-REN RCNS Lendület “Momentum” DNA Repair Research Group, Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences,Budapest, Hungary
  14. Cambridge Stem Cell Institute and John Van Geest Centre for Brain Repair, Department of Clinical Neurosciences, University of Cambridge,Forvie Site, Cambridge, UK
  15. Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine,New York, NY USA
Institutions: Semmelweis University (Hungary); Lund University (Sweden); HUN-REN Szegedi Biológiai Kutatóközpont (Hungary); Université de Montréal (Canada); HUN-REN Research Centre for Natural Sciences (Hungary); University of Cambridge (United Kingdom); New York University (United States)
Journal: Cellular & molecular biology letters, volume 31, issue 1, article 129
Dates: received 9 December 2025; accepted 27 April 2026; published online 2 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s11658-026-00948-2 · PMID 42231151 · PMCID PMC13445741 · OpenAlex W4415552527
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.10.24.684372
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, fMRI & imaging
Keywords: Huntington’s disease, Phosphorylation, Induced neurons, Post-translational modification, MXRA8, Autophagy
MeSH: Huntington Disease*, Neurons*, Phosphoproteins*, Protein Processing, Post-Translational*, Proteomics*, Autophagy, Humans, Phosphorylation (* major topic)
Topic: Genetic Neurodegenerative Diseases (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Semmelweis University
Citations: not cited yet (Europe PMC); 110 references in the paper

Abstract

Huntington’s disease (HD) is a fatal neurodegenerative disorder caused by a CAG repeat expansion in the Huntingtin gene. Although transcriptomic and proteomic changes have been characterized in patient-derived neurons, the contribution of post-translational modifications, such as phosphorylation, remains poorly understood. Here, we present the first phosphoproteomic analysis by mass spectrometry (P-MS) of human induced neurons (iNs) directly reprogrammed from HD patient fibroblasts. We identified 177 phosphopeptides with significantly altered abundance in HD-iNs, mapping to phosphoproteins associated with key signaling pathways known to be affected in HD, such as splicing and autophagy. By integrating P-MS data with previously published proteomic and transcriptomic data from the same donors, we identified distinct subsets of ON–OFF phosphopeptides that exhibited a complete loss of phosphorylation in either HD- or control-iNs, without corresponding changes at the RNA or protein level. An exception was MXRA8, previously described in glial cells as a mediator of blood–brain barrier integrity and astrocyte-mediated neuroinflammation. This protein showed increased protein abundance despite the absence of phosphorylation in HD-iNs, suggesting a compensatory mechanism. In addition, MXRA8 showed altered protein–protein interactions with lysosomal and metabolic regulators in HD-iNs, highlighting its potential role in autophagy impairment as well as in neurovascular dysfunction. These findings uncover a distinct layer of post-translational dysregulation in HD, suggesting that phospho-switch proteins such as MXRA8 may be candidate effectors of pathology, and thus, site-specific phosphorylation loss may contribute to impaired signaling and proteostasis in human HD neurons.

Supplementary Information: The online version contains supplementary material available at 10.1186/s11658-026-00948-2.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

pircs-lab/iN_HD_PMS_analysis_2025

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 4d1dbd9802da8426c50ee4b241160c7218c8a88e, 24 October 2025
Languages: R (9)
Size: 50 files, 9 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, environment (renv.lock), 1 notebook
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (3 files), pheatmap (2 files), circlize (1 file), data.table (1 file), ggplot2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
10 files
At the source: github.com/pircs-lab/iN_HD_PMS_analysis_2025

The paper's code and data availability statement is in the Data section.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 9 scripts, each with its path and the digest of its content;
  • 8 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

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials section. The mass spectrometry phosphoproteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [110] partner repository with the dataset identifier PXD055857 and 10.6019/PXD055857. The mass spectrometry dataset of the co-IP experiment was uploaded to the Proteomics Identifications Database (PRIDE, [https://www.ebi.ac.uk/pride/login]). Project accession: PXD068326The code used for the data analysis and for generation of plots can be found at (https://github.com/pircs-lab/iN_HD_PMS_analysis_2025).

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 19 authors, 6 keywords, 8 MeSH terms, 1 funder, 110 references.

Cite

This paper

Danics, L., Muralidharan, C., Varga, Á., Rezeli, M., Gil, J., Abbas, A. A., Pap, Á., Park, A. S., Cserhalmi, M., Legault, E. M., Sőth, Á., Jamniczky, D., Zsoldos, R., Barker, R. A., Róna, G., Drouin-Ouellet, J., Markó-Varga, G., Darula, Z., & Pircs, K. (2026). Phosphoproteomic profiling reveals post-translational dysregulation in Huntington's disease patient-derived neurons. Cellular & molecular biology letters, 31(1), 129. https://doi.org/10.1186/s11658-026-00948-2

BibTeX

@article{danics2026phosphoproteomic,
author = {Danics, Lea and Muralidharan, Chandramouli and Varga, Ágnes and Rezeli, Melinda and Gil, Jeovanis and Abbas, Anna A. and Pap, Ádám and Park, Andrew S. and Cserhalmi, Marcell and Legault, Emilie M. and Sőth, Ármin and Jamniczky, Dorina and Zsoldos, Roland and Barker, Roger A. and Róna, Gergely and Drouin-Ouellet, Janelle and Markó-Varga, György and Darula, Zsuzsanna and Pircs, Karolina},
title = {{Phosphoproteomic profiling reveals post-translational dysregulation in Huntington's disease patient-derived neurons}},
journal = {Cellular \& molecular biology letters},
year = {2026},
month = jun,
volume = {31},
number = {1},
pages = {129},
publisher = {BMC},
issn = {1425-8153},
doi = {10.1186/s11658-026-00948-2},
url = {https://doi.org/10.1186/s11658-026-00948-2},
pmid = {42231151},
pmcid = {PMC13445741}
}

RIS

TY - JOUR
AU - Danics, Lea
AU - Muralidharan, Chandramouli
AU - Varga, Ágnes
AU - Rezeli, Melinda
AU - Gil, Jeovanis
AU - Abbas, Anna A.
AU - Pap, Ádám
AU - Park, Andrew S.
AU - Cserhalmi, Marcell
AU - Legault, Emilie M.
AU - Sőth, Ármin
AU - Jamniczky, Dorina
AU - Zsoldos, Roland
AU - Barker, Roger A.
AU - Róna, Gergely
AU - Drouin-Ouellet, Janelle
AU - Markó-Varga, György
AU - Darula, Zsuzsanna
AU - Pircs, Karolina
TI - Phosphoproteomic profiling reveals post-translational dysregulation in Huntington's disease patient-derived neurons
T2 - Cellular & molecular biology letters
J2 - Cell Mol Biol Lett
PY - 2026
DA - 2026/06/02
VL - 31
IS - 1
SP - 129
SN - 1425-8153
PB - BMC
DO - 10.1186/s11658-026-00948-2
UR - https://doi.org/10.1186/s11658-026-00948-2
LA - en
ER -

CSL-JSON

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