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Auxin-inducible degradation of UNC-116 in C. elegans inhibits bidirectional dense core vesicle transport and worm locomotion on different timescales.

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  1. [1] § MATERIALS AND METHODS › Analysis of steady-state images ↔ snb1/Puncta_analysis_normalised_alldata_snb1_v2_raw.R, lines 100 to 155 · score 0.61 · pracma package, peak intensity, raw, puncta, v2, median · Report a mismatch
  2. [2] § MATERIALS AND METHODS › Analysis of steady-state images ↔ ida1/Puncta_analysis_normalised_alldata_functions.R, lines 78 to 123 · score 0.58 · pracma package, peak intensity, raw, puncta, median, um · Report a mismatch

Paper

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The authors' code

R · 159 lines · 6 KB · no license · 1 match

  1. library(readxl)
  2. library(writexl)
  3. library(ggplot2)
  4. library(pracma)
  5. library(magrittr)
  6. library(tidyverse)
  7. library(gsignal)
  8. library(patchwork)
  10. ### NOTE:
  11. # for this script, the sheet name to be analysed has to be manually set in the lapply call at the bottom
  12. # typically this is 'axon' or 'dendrite'
  14. # define functions to detect peaks and plot the outputs
  15. detect_peaks <- function(x,intensities){
  17. target_worm <- intensities %>%
  18. dplyr::filter(Worm == x) %>%
  19. drop_na()
  21. sd_y <- sd(target_worm$Grey_Value_norm, na.rm = TRUE) # calculate standard deviation
  23. # we need all positive values for peak detection so make a grey_value column normalised to 0
  24. # min_value <- abs(min(target_worm$Grey_Value_norm))
  25. # target_worm$Grey_Value_norm_abs <- target_worm$Grey_Value_norm + min_value
  27. # peaks_median <- tibble(findpeaks(target_worm$Grey_Value_norm, minpeakheight = median(target_worm$Grey_Value))) # note we use the median of the un-normalised Grey Value - equivalent to using a threshold of median * 2
  28. peaks_sd <- as_tibble(pracma::findpeaks(target_worm$Grey_Value_norm, minpeakheight = sd_y))
  30. # colnames(peaks_median) <- c("peak_height","peak_max","peak_start","peak_end")
  31. colnames(peaks_sd) <- c("peak_height","peak_max","peak_start","peak_end")
  33. # for every identified peak pull the relevant intensity data and calculate FWHM and AUC
  34. fwhm_vec <- vector(length = nrow(peaks_sd))
  35. auc_vec <- vector(length = nrow(peaks_sd))
  36. peak_vec <- vector(length = nrow(peaks_sd))
  37. start_vec <- vector(length = nrow(peaks_sd))
  38. end_vec <- vector(length = nrow(peaks_sd))
  40. for(i in 1:length(fwhm_vec)){
  41. peak <- target_worm[peaks_sd$peak_start[i]:peaks_sd$peak_end[i],]
  43. fwhm_vec[i] <- fwhm(peak$Distance,peak$Grey_Value_norm)
  44. auc_vec[i] <- trapz(peak$Distance,peak$Grey_Value_norm)
  45. peak_vec[i] <- target_worm$Distance[peaks_sd$peak_max[i]]
  46. start_vec[i] <- target_worm$Distance[peaks_sd$peak_start[i]]
  47. end_vec[i] <- target_worm$Distance[peaks_sd$peak_end[i]]
  48. }
  50. peaks_sd$fwhm <- fwhm_vec
  51. peaks_sd$auc <- auc_vec
  52. peaks_sd$worm <- x
  53. peaks_sd$peak_max_dist <- peak_vec
  54. peaks_sd$peak_start_dist <- start_vec
  55. peaks_sd$peak_end_dist <- end_vec
  56. # calculate the puncta intensity - median ratio
  57. # note we add median value back to the puncta intensity to undo the normalisation carried out in the master function
  58. peaks_sd$median_ratio <- (peaks_sd$peak_height + median(target_worm$Grey_Value))/median(target_worm$Grey_Value)
  60. return(peaks_sd)
  61. }
  63. puncta_per_um <- function(x,intensities,peaks){
  65. target_worm <- intensities %>%
  66. dplyr::filter(Worm == x) %>%
  67. drop_na()
  69. target_peaks <- peaks %>%
  70. dplyr::filter(worm == x) %>%
  71. drop_na()
  73. peak_density <- nrow(target_peaks)/max(target_worm$Distance)
  75. return(peak_density)
  76. }
  78. plotting_func <- function(x,intensities,peaks){
  80. target_worm <- intensities %>%
  81. dplyr::filter(Worm == x) %>%
  82. drop_na()
  84. target_peak <- peaks %>%
  85. dplyr::filter(worm == x) %>%
  86. drop_na()
  88. plot_worm <- ggplot(target_worm, aes(x = Distance, y = Grey_Value_norm)) +
  89. geom_line() +
  90. geom_point(data = target_peak, aes(x = peak_max_dist, y = peak_height), color = "black",fill = "firebrick2", size = 2,shape=21,alpha = 0.5) +
  91. labs(title = paste0(str_to_title(str_extract(x,pattern="[a-z]+"))," ",str_extract(x,pattern="\\d+")),
  92. x = "Distance (µm)",
  93. y = "Normalised Intensity (A.U.)") +
  94. scale_y_continuous(limits = c(-1000,4000)) +
  95. theme_minimal(base_size = 12)
  97. return(plot_worm)
  98. }
  100. # define master function to apply analysis to every source_data
  101. analyse_intensities <- function(source_data,sheet){
  103. # load data and normalise
  104. raw_intensities <- read_xlsx(source_data,sheet = sheet)
  106. source_data_name <- str_sub(source_data,start = 1, end = -6)
  108. num_worms <- ncol(raw_intensities)/2
  110. intensities <- raw_intensities %>%
  111. pivot_longer(cols = everything(),names_to = c(".value","Distance"),names_sep = "e_")
  113. colnames(intensities) <- c("Worm","Distance","Grey_Value")
  115. # arrange worm names in numeric order to allow handling of any number of non-consecutive worm numbers
  116. worm_names <- tibble(worm = unique(intensities$Worm),worm_num = as.numeric(str_extract(unique(intensities$Worm),"\\d+")))
  118. worm_names %<>%
  119. arrange(worm_num)
  121. intensities$Worm <- factor(intensities$Worm,levels = worm_names$worm)
  123. intensities %<>%
  124. group_by(Worm) %>%
  125. mutate(Grey_Value_norm = Grey_Value - median(Grey_Value,na.rm=T))
  127. # # quick visualisation of intensity over neuron length
  128. # ggplot(intensities,aes(x = Distance, y = Grey_Value_norm)) +
  129. # geom_line() +
  130. # scale_y_continuous(breaks = c(-4000,0,4000,8000,12000,16000))+
  131. # theme_minimal()+
  132. # facet_wrap(~Worm)
  133. #
  134. # ggsave(paste0(source_data_name,"_normalised_intensity_plot.png"))
  136. # detect peaks using pracma package
  137. peaks <- do.call(rbind,lapply(unique(intensities$Worm),detect_peaks,intensities=intensities))
  139. write_xlsx(peaks,paste0("output_data/",source_data_name,"_",sheet,"_peaks_raw.xlsx"))
  141. peaks_summary <- peaks %>%
  142. group_by(worm) %>%
  143. summarise(n_peaks = n(),mean_peak_intensity = mean(peak_height),mean_peak_auc = mean(auc),mean_peak_median_ratio = mean(median_ratio))
  145. peaks_summary$puncta_per_um <- do.call(c,lapply(unique(intensities$Worm),puncta_per_um,intensities=intensities,peaks=peaks))
  147. write_xlsx(peaks_summary,paste0("output_data/",source_data_name,"_",sheet,"_peaks_summary.xlsx"))
  150. plot_list <- lapply(unique(intensities$Worm),plotting_func,intensities=intensities,peaks=peaks)
  152. wrap_plots(plot_list,nrow=5, ncol=4)
  154. ggsave(paste0("plots/all_peak_plots_",source_data_name,"_",sheet,"_4000cutoff_24h.pdf"),width = 6500, height = 3000, units = "px")
  155. }
  157. filelist_all <- list.files(pattern = paste(".*_snb1.xlsx",sep="|"))
  159. lapply(filelist_all,analyse_intensities,sheet="axon")

Puncta_analysis_normalised_alldata_snb1_v2_raw.R at commit 2dc1579, no license · at the source

Overview

Authors: Astrid H Boström1, Anna Gavrilova1,2, James C Herzig3, Gino B Poulin1, Victoria J Allan1
ORCID iDs: Astrid H Boström, Anna Gavrilova, James C Herzig, Gino B Poulin, Victoria J Allan
  1. School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, The Michael Smith Building, Rumford St, Manchester M13 9PT, UK
  2. Department of Mathematics, Faculty of Science and Engineering, The University of Manchester, Manchester M13 9PL, UK
  3. MRC-University of Glasgow Centre for Virus Research, Garscube Campus, Switchback Road, Glasgow G61 1AF, UK
Institutions: University of Manchester (United Kingdom); MRC University of Glasgow Centre for Virus Research (United Kingdom)
Journal: Journal of cell science, volume 139, issue 6, article jcs264245
Dates: received 25 June 2025; accepted 12 February 2026; published online 25 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1242/jcs.264245 · PMID 41725581 · PMCID PMC13070256 · OpenAlex W7131160077
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: C. elegans (organism), cellular / molecular (subfield)
Methods: Statistics, Evoked potentials
Keywords: Kinesin, Dense core vesicle, Neuron, C. elegans, Degron, Locomotion
MeSH: Caenorhabditis elegans*, Caenorhabditis elegans Proteins*, Indoleacetic Acids*, Kinesins*, Locomotion*, Secretory Vesicles*, Animals, Biological Transport, Cell Cycle Proteins, Neurons, Proteolysis (* major topic)
Topic: Microtubule and mitosis dynamics (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Biotechnology and Biological Sciences Research Council (BB/T008725/1, BB/Z517574/1); University of Manchester; 3i; Wellcome Trust (108867/Z/15/Z)
Citations: not cited yet (Europe PMC); 145 references in the paper

Abstract

The microtubule motor kinesin-1 is vital in neurons, with mutations being associated with neurological diseases. Deletion of the Caenorhabditis elegans kinesin-1 gene unc-116 is lethal, and viable mutants are uncoordinated. Here, we perform auxin-mediated degradation of the UNC-116 protein and monitor effects on worm locomotion and dense core vesicle (DCV) transport. UNC-116 is degraded within 1–3 h of auxin treatment. Impaired swimming and crawling are detected after 6–14 h, depending on organismal age, and degradation of UNC-116 in neurons alone disrupts locomotion. Effects on DCV transport are observed sooner, with 4 h of degradation strongly inhibiting both anterograde and retrograde movement. This motility recovers when worms are transferred to auxin-free plates for 24 h, even though protein level recovery is incomplete. Rescue from a 10 h auxin treatment completely restores crawling speed, but although swimming is initially unaffected, it becomes strongly inhibited during the rescue period, suggesting that UNC-116 has distinct roles in these locomotive gaits. Overall, by bypassing early developmental UNC-116 functions, we reveal that UNC-116 is essential for bidirectional DCV transport and crucial for worm locomotion.

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 2 matches between paragraphs and lines of code.

umkich/organelle_transport_analysis

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: dd740bba10119f4fec7b13e3bcf71e112a670821, 25 September 2024
Languages: Python (5), Jupyter (1)
Size: 12 files, 6 scripts
Software Heritage: not archived
Found in: the text, “Kymograph analysis”
Holds: README, license file, environment (pyproject.toml), 1 notebook
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
8 files
At the source: github.com/umkich/organelle_transport_analysis

cran.r-project.org/web/packages

License: none: the authors keep all their rights
State: the link answers, verified on 29 October 2026
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Found in: the text, “Analysis of steady-state images”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
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At the source: cran.r-project.org/web/packages/pracma/index.html

jcherzig/ida1_vesicle_analysis

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 2dc1579383710d3e3279a830321acd6a8a818cbe, 17 December 2025
Languages: R (9)
Size: 18 files, 9 scripts
Software Heritage: not archived
Found in: the text, “Analysis of steady-state images”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (9 files), ggplot2 (5 files), patchwork (5 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
10 files
At the source: github.com/jcherzig/ida1_vesicle_analysis/tree/main

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 6 keywords, 11 MeSH terms, 4 funders, 144 references.

Cite

This paper

Boström, A. H., Gavrilova, A., Herzig, J. C., Poulin, G. B., & Allan, V. J. (2026). Auxin-inducible degradation of UNC-116 in C. elegans inhibits bidirectional dense core vesicle transport and worm locomotion on different timescales. Journal of cell science, 139(6), jcs264245. https://doi.org/10.1242/jcs.264245

BibTeX

@article{bostrom2026auxin,
author = {Boström, Astrid H and Gavrilova, Anna and Herzig, James C and Poulin, Gino B and Allan, Victoria J},
title = {{Auxin-inducible degradation of UNC-116 in C. elegans inhibits bidirectional dense core vesicle transport and worm locomotion on different timescales}},
journal = {Journal of cell science},
year = {2026},
month = mar,
volume = {139},
number = {6},
pages = {jcs264245},
publisher = {Company of Biologists},
issn = {0021-9533},
doi = {10.1242/jcs.264245},
url = {https://doi.org/10.1242/jcs.264245},
pmid = {41725581},
pmcid = {PMC13070256}
}

RIS

TY - JOUR
AU - Boström, Astrid H
AU - Gavrilova, Anna
AU - Herzig, James C
AU - Poulin, Gino B
AU - Allan, Victoria J
TI - Auxin-inducible degradation of UNC-116 in C. elegans inhibits bidirectional dense core vesicle transport and worm locomotion on different timescales
T2 - Journal of cell science
J2 - J Cell Sci
PY - 2026
DA - 2026/03/25
VL - 139
IS - 6
SP - jcs264245
SN - 0021-9533
PB - Company of Biologists
DO - 10.1242/jcs.264245
UR - https://doi.org/10.1242/jcs.264245
LA - en
ER -

CSL-JSON

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"id": "10.1242/jcs.264245",
"type": "article-journal",
"title": "Auxin-inducible degradation of UNC-116 in C. elegans inhibits bidirectional dense core vesicle transport and worm locomotion on different timescales",
"container-title": "Journal of cell science",
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"family": "Boström",
"given": "Astrid H"
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{
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{
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"page": "jcs264245",
"DOI": "10.1242/jcs.264245",
"PMID": "41725581",
"PMCID": "PMC13070256",
"ISSN": "0021-9533",
"publisher": "Company of Biologists",
"URL": "https://doi.org/10.1242/jcs.264245",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
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}
}

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