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Activity-dependent ribosome profiling reveals the landscape of canonical and non-canonical translation in brain tissue.

A correction to this paper has been published: the notice, 42624858, from Europe PMC.

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The authors' code

R Markdown · 108 lines · 3.7 KB · no license

  1. ---
  2. title: "MLseq analysis IP vs Benchmark"
  3. output: html_notebook
  4. ---
  6. ```{r}
  7. library(MLSeq)
  8. library(DESeq2)
  9. library(edgeR)
  10. library(VennDiagram)
  11. library(pamr) #do not reload, otherwise restarting R might be required
  12. library(caret)
  13. library(xtable)
  14. library(e1071)
  15. library(edgeR)
  16. library(boot)
  17. library(knitr)
  18. library(rmarkdown)
  19. library(grid)
  20. library(gridExtra)
  21. library(gtable)
  22. library(Metrics)
  23. library(pROC)
  24. library(e1071)
  25. library(knitr)
  26. library(ranger)
  27. library(iml)
  28. library(rpart)
  29. library(rpart.plot)
  30. library(data.table)
  31. library(ipred)
  32. library(gbm)
  33. library(mlbench)
  34. library(adabag)
  35. library(eulerr)
  36. library(reticulate)
  37. library(venn)
  38. library(RColorBrewer)
  39. library(ggplotify)
  40. library(ggforce)
  41. library(factoextra)
  42. library(readxl)
  43. library(ggplot2)
  44. library(dplyr)
  45. ```
  47. ```{r}
  48. #data is columns containing training data (first 6 columns) and testing data (rest of the columns)
  49. #Note training was only IP or benchmark data at a time
  50. class <- data.frame(condition = factor(c(rep(c(rep("Control", 3), rep("Treated", 3), rep("Control", 13), rep("Treated", 7)), 1))))
  51. condition <- factor(c(rep(c(rep("Control", 3), rep("Treated", 3), rep("Control", 13), rep("Treated", 7)), 1)))
  53. ind <- c(1,2,3,4,5,6)
  54. data.train <- as.matrix(data[ ,ind] + 1)
  55. data.test <- as.matrix(data[ ,-ind] + 1)
  56. classtr <- DataFrame(condition = class[ind, ])
  57. classts <- DataFrame(condition = class[-ind, ])
  58. data.trainS4 = DESeqDataSetFromMatrix(countData = data.train, colData = classtr,
  59. design = formula(~condition))
  60. data.testS4 = DESeqDataSetFromMatrix(countData = data.test, colData = classts,
  61. design = formula(~condition))
  63. #Create output dataframe with ML models
  64. i <- 1
  65. MLresult <- data.frame(Classifier=as.character(), Preprocessing=as.character(), Accuracy=as.numeric(),Sensitivity=as.numeric(), Specificity=as.numeric(), Pos_Pred_Value=as.numeric(), Neg_Pred_Value=as.numeric(), Precision=as.numeric(), Recall=as.numeric(), F1=as.numeric(), Prevalence=as.numeric(), Detection_Rate=as.numeric(), Detection_Prevalence=as.numeric(), Balanced_Accuracy=as.numeric(), MSE=as.numeric(), AUC=as.numeric(), AccuracyPValue=as.numeric(), Best_tune1=as.numeric(), Best_tune2=as.numeric(), Cross_entropy=as.numeric())
  66. ```
  67. Model
  68. Random forest
  69. ```{r}
  70. set.seed(2140)
  71. fit.rf <- classify(data = data.trainS4, method = "rf",
  72. preProcessing = "deseq-vst",normalize = "none", ref = "Treated", tuneLength = 10,
  73. control = trainControl(method = "repeatedcv", number = 5,
  74. repeats = 10, classProbs = TRUE)) #vst was only option apart from rlog in MLseq
  75. show(fit.rf)
  76. trained(fit.rf) -> tr
  77. pred.rf <- predict(fit.rf, data.testS4)
  79. pred.rf <- relevel(pred.rf, ref = "Treated")
  80. actual <- relevel(classts$condition, ref = "Treated")
  82. tbl <- table(Predicted = pred.rf, Actual = actual)
  83. confusionMatrix(tbl, positive = "Treated")
  84. confusionMatrix(tbl, positive = "Treated") -> cm
  86. roc_obj <- roc(as.numeric(actual), as.numeric(pred.rf)); auc <- auc(roc_obj) #AUC
  87. rmse <- RMSE(as.numeric(actual), as.numeric(pred.rf)); mse <- rmse^2 #MSE
  89. predprob.rf <- predict(fit.rf, data.testS4, type="prob")
  90. num.actual <- ifelse(actual == "Control", 0, 1)
  91. cross_entropy <- logLoss(num.actual,predprob.rf$Treated) #CrossEntropy
  93. list <- c(Classifier=method(fit.rf), preProcessing = transformation(fit.rf),cm$overall["Accuracy"], cm$byClass, MSE=mse, AUC=auc, cm$overall["AccuracyPValue"], Sigma=tr$bestTune["mtry"], C=tr$bestTune["mtry"],Cross_entropy=cross_entropy)
  94. MLresult[i,] <- list
  95. i=i+1
  97. #Note only IP or benchmark data was ran at a time
  98. roc_obj
  99. fit.rf
  100. #Variable Importance
  101. impo <- data.frame(tr$finalModel$importance)
  102. #Probabilities
  103. prob <- predict(fit.rf, data.testS4, type = "prob")
  104. ```
  105. session info
  106. ```{r}
  107. sessionInfo()
  108. ```

MLSeq.Rmd at commit 628a21c, no license · at the source

Overview

Authors: Nayan Suryawanshi1,2, Hitoshi Uchida3,4, Ryo Endo1, Kai Sato1, Daisuke Satoh3, Kazuya Tsumagari5, Koshi Imami5, Takayasu Mikuni3, Motomasa Tanaka1,2
ORCID iDs: Nayan Suryawanshi, Hitoshi Uchida, Kai Sato, Kazuya Tsumagari, Koshi Imami, Takayasu Mikuni, Motomasa Tanaka
  1. Laboratory for Protein Conformation Diseases, RIKEN Center for Brain Science, Wako,Saitama, Japan
  2. Department of Biomedical Sciences and Engineering, Graduate School of Medical and Dental Sciences, Institute of Science Tokyo,Tokyo, Japan
  3. Department of Cellular Neuropathology, Brain Research Institute, Niigata University,Niigata, Japan
  4. Department of System Pathology for Neurological Disorders, Brain Research Institute, Niigata University,Niigata, Japan
  5. Proteome Homeostasis Research Unit, RIKEN Center for Integrative Medical Sciences,Yokohama, Japan
Institutions: RIKEN Center for Brain Science (Japan); Institute of Science Tokyo (Japan); Niigata University (Japan); RIKEN Center for Integrative Medical Sciences (Japan)
Journal: Nature communications, volume 17, issue 1, article 6179
Dates: received 6 May 2025; accepted 15 June 2026; published online 23 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-74968-z · PMID 42493509 · PMCID PMC13396407 · OpenAlex W7170142401
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Evoked potentials
Keywords: Cellular neuroscience, Gene expression analysis
MeSH: Hippocampus*, Long-Term Potentiation*, Neurons*, Protein Biosynthesis*, Ribosome Profiling*, Animals, Early Growth Response Protein 1, Female, Male, Mice, Mice, Inbred C57BL, Peroxisomes, Ribosomes (* major topic)
Topic: RNA and protein synthesis mechanisms (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Grants-in-Aid for Scientific Research (21H05257, 24H00603), Japan Agency for Medical Research and Development (AMED-CREST 21gm1410009; JP21wm0525014), the RIKEN Pioneering Projects (Biology of Intracellular Environments, Long-timescale Molecular Chronobiology), Takeda Science Foundation; JST FOREST (JPMJFR214L); Grants-in-Aid for Scientific Research (22K21353, 23H04672, 23K27265, 24H01229, 24K22000, and 25H02490), Japan Agency for Medical Research and Development (JP24wm0625117 to T.M.; JP21wm0525014), Takeda Science Foundation
Citations: not cited yet (Europe PMC); 77 references in the paper
Notices: A correction to this paper has been published (42624858, from Europe PMC)

Abstract

Neural activity-dependent translation is essential for synaptic plasticity and diverse brain functions. Translation involves not only canonical main open reading frames (mORFs) but also upstream ORFs (uORFs), which may regulate mORF expression. However, due to technical limitations, systematic investigation of activity-dependent uORFs and mORFs in brain tissues remains challenging. Here, we developed a ribosome tagging and purification strategy that bypasses the prolonged turnover of ribosomal proteins, enabling ribosome profiling with one-hour temporal resolution after neural stimulation. Applying this strategy to mouse hippocampal slices undergoing long-term potentiation, we identify hundreds of activity-induced mORFs and uORFs, including a previously unknown uORF from Egr1. We demonstrate that this Egr1-uORF translation is tightly regulated by neuronal activity, and its encoded peptide interacts with peroxisomal machinery, suggesting a potential link between synaptic stimulus and peroxisome biology. This study provides a useful technique and resources for deciphering molecular mechanisms underlying activity- and translation-dependent brain functions in health and disease.

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

Repositories

Its files are read in the Code ↔ Paper reader above.

nayanvs/Suryawanshi_et_al._2026

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 628a21cf09622b10e7bcb3144631d704143ae089, 26 December 2025
Languages: R (2)
Size: 3 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: caret (1 file), data.table (1 file), DESeq2 (1 file), edgeR (1 file), ggplot2 (1 file), pROC (1 file), reticulate (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/nayanvs/Suryawanshi_et_al._2026

Zenodo 21273040

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: caret (1 file), data.table (1 file), DESeq2 (1 file), edgeR (1 file), ggplot2 (1 file), pROC (1 file), reticulate (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
3 files
At the source:

Code availability

All software and code used in this study were previously published, are cited in the Methods, and are available at: https://github.com/nayanvs/Suryawanshi_et_al._2026. The version of the code has been archived on Zenodo at 10.5281/zenodo.21273040.

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;
  • 4 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

Bulk RNA-seq and IP Ribo-seq data from this study are available in the Gene Expression Omnibus (GEO) under accession GSE295656. Bulk Ribo-seq and IP RNA-seq datasets are available under accessions GSE317973 and GSE317975, respectively. MS proteomics data from this study are available in the ProteomeXchange Consortium via the jPOST77 partner repository with the dataset identifier: PXD074666. Datasets linked to GEO accession GSE72064, Cho. J. et al.19, GSE111899, Tyssowski K. et al.75, GSE103667, Namkoong S. et al.39, GSE180240, Duffy E. et al.5, bioproject linked to PRJNA550323, Biever A. et al.7, PRJNA634994, Glock et al.8, GSE167197, Jinoh Kim et al.55, and Mendeley data linked to 10.17632/8hrj49fthr.2, Simbriger K. et al.52, were analyzed. Source data are provided with 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, 9 authors, 2 keywords, 13 MeSH terms, 3 funders, 72 references, 1 integrity notice.

Cite

This paper

Suryawanshi, N., Uchida, H., Endo, R., Sato, K., Satoh, D., Tsumagari, K., Imami, K., Mikuni, T., & Tanaka, M. (2026). Activity-dependent ribosome profiling reveals the landscape of canonical and non-canonical translation in brain tissue. Nature communications, 17(1), 6179. https://doi.org/10.1038/s41467-026-74968-z

BibTeX

@article{suryawanshi2026activity,
author = {Suryawanshi, Nayan and Uchida, Hitoshi and Endo, Ryo and Sato, Kai and Satoh, Daisuke and Tsumagari, Kazuya and Imami, Koshi and Mikuni, Takayasu and Tanaka, Motomasa},
title = {{Activity-dependent ribosome profiling reveals the landscape of canonical and non-canonical translation in brain tissue}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {6179},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74968-z},
url = {https://doi.org/10.1038/s41467-026-74968-z},
pmid = {42493509},
pmcid = {PMC13396407}
}

RIS

TY - JOUR
AU - Suryawanshi, Nayan
AU - Uchida, Hitoshi
AU - Endo, Ryo
AU - Sato, Kai
AU - Satoh, Daisuke
AU - Tsumagari, Kazuya
AU - Imami, Koshi
AU - Mikuni, Takayasu
AU - Tanaka, Motomasa
TI - Activity-dependent ribosome profiling reveals the landscape of canonical and non-canonical translation in brain tissue
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/23
VL - 17
IS - 1
SP - 6179
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74968-z
UR - https://doi.org/10.1038/s41467-026-74968-z
LA - en
ER -

CSL-JSON

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