OSCR

PrP turnover in vivo and the time to effect of prion disease therapeutics.

Code ↔ Paper

12 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 12 matches
  1. [1] § Methods › Mixture-of-rates models ↔ src/mixture_of_rates/TurnoverModels.jl, lines 367–424 · score 0.89 · Bayesian Information Criterion, Akaike Information Criterion, residual sum, Mixture models, squares, RSS
  2. [2] § Methods › Exponential decay model ↔ src/mixture_of_rates/fig3_mixture_analysis.jl, lines 83–131 · score 0.67 · linearly interpolated, RNA measurements, protein measurements, decay, numerically, residual
  3. [3] § Methods › Labeled peptide accumulation models ↔ src/utils/funcs.R, lines 214–229 · score 0.67 · chow_days, betareg, prop_labeled, grp, interaction, isotopically
  4. [4] § Results ↔ src/mixture_of_rates/TurnoverModels.jl, lines 367–424 · score 0.64 · Akaike Information Criterion, exponential decay, mixture models, component, AIC
  5. [5] § Results ↔ src/figs/fig4.R, lines 2–59 · score 0.62 · F4 WT, chow days, F4 FFI, C57BL, log2, ki
  6. [6] § Methods › Isotopic labeling of mice ↔ src/figs/figS9.jl, lines 48–89 · score 0.61 · F4 WT, F4 FFI, C57BL, young, aged, ki
  7. [7] § Results ↔ src/figs/fig2.R, lines 37–86 · score 0.60 · relative isotope abundance, protein abundance, bins, HH, LH, RIA
  8. [8] § Methods › Isotopic labeling of mice ↔ src/mixture_of_rates/fig4_mixture_analysis.jl, lines 124–197 · score 0.57 · F4 WT, F4 FFI, C57BL, ki, 60 days, ages
  9. [9] § Methods › Animals ↔ src/figs/fig2.R, lines 1–30 · score 0.57 · HuPrP, C57BL, Tg25109, ZH3, Tga20, gene
  10. [10] § Methods › Animals ↔ src/figs/figS2.R, lines 55–107 · score 0.57 · HuPrP, C57BL, Tg25109, ZH3, Tga20, gene
  11. [11] § Results ↔ src/figs/figS9.jl, lines 48–89 · score 0.57 · F4 WT, F4 FFI, C57BL, Young, aged, ki
  12. [12] § Results ↔ src/figs/figS9.jl, lines 139–217 · score 0.55 · error bars, rates fit, model fit, curve, mixture, day

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Julia · 217 lines · 7.4 KB · CC-BY-4.0 · 3 matches

  1. """
  2. Generate comparison figure showing label accumulation curves for both models
  3. Side-by-side comparison: Single (left) vs Mixture (right)
  4. """
  6. using CairoMakie, LaTeXStrings, DataFrames, CSV, Colors, Printf
  7. using Statistics: mean,std
  8. using Distributions: TDist, quantile
  9. include("TurnoverModels.jl")
  10. using .TurnoverModels
  12. """
  13. Calculate 95% confidence interval lower bound using t-distribution
  14. """
  15. function lower(x)
  16. n = length(x)
  17. if n < 2
  18. return mean(x)
  19. end
  20. m = mean(x)
  21. s = std(x)
  22. t_val = quantile(TDist(n-1), 0.975)
  23. return m - t_val * s / sqrt(n)
  24. end
  26. """
  27. Calculate 95% confidence interval upper bound using t-distribution
  28. """
  29. function upper(x)
  30. n = length(x)
  31. if n < 2
  32. return mean(x)
  33. end
  34. m = mean(x)
  35. s = std(x)
  36. t_val = quantile(TDist(n-1), 0.975)
  37. return m + t_val * s / sqrt(n)
  38. end
  41. """
  42. Generate comparison figure showing label accumulation curves for both models
  43. Side-by-side comparison: Single (left) vs Mixture (right)
  44. """
  45. function generate_figure_model_comparison()
  46. println("Creating model comparison figure...")
  48. # Load data
  49. ffi_all = CSV.read("data/iqp/ffi_turnover.tsv", DataFrame)
  50. ffi_all[!, :total] = ffi_all.light .+ ffi_all.heavy
  51. ffi_all[!, :prop_labeled] = ffi_all.heavy ./ ffi_all.total
  53. # Genotype legend
  54. leg = DataFrame(
  55. genotype = ["129(TT-3F4-FFI)HOZ", "129(TT-3F4WT)", "B6/N"],
  56. disp = ["ki-3F4-FFI", "ki-3F4-WT", "C57BL/6N WT"],
  57. color = [colorant"#D95F02", colorant"#22127A", colorant"#77127A"],
  58. xgeno = [1, 2, 3],
  59. ttest_grouping = ["test", "control", "control"]
  60. )
  62. use_peptides = ["GENFTETDVK", "VVEQMCVTQYQK"]
  63. ages = ["young", "aged"]
  65. fig = Figure(size=(1400, 1000), fontsize=11)
  67. # Create 2×2 GridLayouts for each genotype (2 ages × 2 peptides)
  68. gl = [GridLayout() for _ in 1:3]
  70. #Label(fig[1, :, Top()], "Young", fontsize=13, font=:bold, padding=(0, 0, 20, 0))
  72. fig[1, 1] = gl[1] # geno 1
  73. fig[1, 2] = gl[2] # geno 2
  74. fig[1, 3] = gl[3] # geno 3
  76. #Label(fig[2, :, Top()], "Aged", fontsize=13, font=:bold, padding=(0, 0, 20, 0))
  78. gl_aged = [GridLayout() for _ in 1:3]
  79. fig[2, 1] = gl_aged[1] # geno 1
  80. fig[2, 2] = gl_aged[2] # geno 2
  81. fig[2, 3] = gl_aged[3] # geno 3
  83. # Panel labels (A, B, C for the three genotypes)
  84. avails_func = free_lysine
  86. # Loop through genotypes (3 columns)
  87. for (geno_idx, geno_row) in enumerate(eachrow(leg))
  88. # Add panel label for this genotype
  89. panel_letter = string(Char('A' + geno_idx - 1))
  91. # Create 2×2 grid: rows=ages, cols=peptides
  92. for (age_idx, this_age) in enumerate(ages)
  93. # Select appropriate grid layout
  94. current_gl = age_idx == 1 ? gl[geno_idx] : gl_aged[geno_idx]
  95. row_in_gl = 1 # Always row 1 within each GridLayout
  97. for (pep_idx, this_peptide) in enumerate(use_peptides)
  99. # Create axis
  100. ax = Axis(current_gl[row_in_gl, pep_idx],
  101. xlabel= "day",
  102. ylabel=pep_idx == 1 ? "proportion labeled" : "",
  103. title=age_idx == 1 ? "Young " * this_peptide[1:4] : "Aged " * this_peptide[1:4],
  104. titlesize=11,
  105. width = 125,
  106. height = 125,
  107. yticks=0:0.1:0.5
  108. )
  109. hidespines!(ax, :t, :r)
  110. limits!(ax, 0, 8.5, 0, 0.55)
  112. # Add panel label to top-left of first subplot in each genotype column (young only)
  113. if age_idx == 1 && pep_idx == 1
  114. Label(current_gl[row_in_gl, pep_idx, TopLeft()],
  115. panel_letter * ")",
  116. fontsize=14, font=:bold, padding=(0, 5, 5, 0))
  117. end
  119. t_fine = collect(0:0.1:8)
  120. t_fine = collect(0:0.1:8)
  122. curve_data = filter(row -> row.genotype == geno_row.genotype &&
  123. row.peptide == this_peptide &&
  124. row.age == this_age, ffi_all)
  126. if nrow(curve_data) == 0
  127. continue
  128. end
  130. summary_df = combine(groupby(curve_data, :chow_days)) do df
  131. DataFrame(
  132. mean_prop = mean(df.prop_labeled),
  133. l95 = lower(df.prop_labeled),
  134. u95 = upper(df.prop_labeled)
  135. )
  136. end
  137. sort!(summary_df, :chow_days)
  139. # SINGLE MODEL FIT (dashed)
  140. if nrow(curve_data) > 0
  141. thalf_single = fit_single_isotopic(
  142. curve_data.chow_days,
  143. curve_data.prop_labeled,
  144. avails_func
  145. )
  147. if !isnan(thalf_single)
  148. pred_single = proportion_labeled_single(thalf_single, t_fine, avails_func)
  149. lines!(ax, t_fine, pred_single,
  150. color=geno_row.color, linewidth=2, linestyle=:dash)
  151. end
  152. end
  154. # MIXTURE MODEL FIT (solid)
  155. if nrow(curve_data) > 0
  156. mixture_fit = fit_mixture_isotopic(
  157. curve_data.chow_days,
  158. curve_data.prop_labeled,
  159. avails_func
  160. )
  162. if !isnan(mixture_fit.effective_thalf)
  163. pred_mixture = proportion_labeled_mixture(
  164. mixture_fit.thalves,
  165. mixture_fit.proportions,
  166. t_fine,
  167. avails_func
  168. )
  169. lines!(ax, t_fine, pred_mixture,
  170. color=geno_row.color, linewidth=2, linestyle=:solid)
  171. end
  172. end
  174. # Plot observed data with error bars
  175. scatter!(ax, summary_df.chow_days, summary_df.mean_prop,
  176. color=geno_row.color, markersize=8,
  177. marker=:circle, strokewidth=2, strokecolor=geno_row.color)
  178. errorbars!(ax, summary_df.chow_days, summary_df.mean_prop,
  179. summary_df.mean_prop .- summary_df.l95,
  180. summary_df.u95 .- summary_df.mean_prop,
  181. color=geno_row.color, linewidth=2,
  182. whiskerwidth=10)
  183. end # end peptide loop
  184. end # end age loop
  185. end # end genotype loop
  187. # Create legend with both genotype colors and model types
  188. legend_elements = [
  189. [LineElement(color=leg[i, :color], linewidth=2) for i in 1:3]...,
  190. LineElement(color=:black, linewidth=2, linestyle=:dash),
  191. LineElement(color=:black, linewidth=2, linestyle=:solid)
  192. ]
  193. legend_labels = [
  194. leg[1, :disp],
  195. leg[2, :disp],
  196. leg[3, :disp],
  197. "single-rate fit",
  198. "mixture fit"
  199. ]
  201. Legend(fig[3, :],
  202. legend_elements, legend_labels,
  203. orientation=:horizontal,
  204. framevisible=false,
  205. halign=:center,
  206. nbanks=1)
  208. filename = "display_items/figure-s9.png"
  209. resize_to_layout!(fig)
  211. save(filename, fig, px_per_unit=2)
  212. println("done.\nModel comparison figure saved to $(filename)")
  213. return fig
  214. end
  216. # Generate model comparison figure
  217. generate_figure_model_comparison()

figS9.jl at commit 1dfa9ba, under CC-BY-4.0 · at the source

Overview

Authors: Taylor L. Corridon1, Jill O’Moore2, Alyssa Seerley2, Daniel A. Sprague1, Yuan Lian1, Vanessa Laversenne1, Briana Noble3, Nikita G. Kamath1, Fiona E. Serack1, Abdul Basit Shaikh4, Brian Erickson5, Craig Braun5, Kendrick DeSouza-Lenz6, Michael Howard6, Nathan Chan6, Walker S. Jackson7, Andrew G. Reidenbach1, Deborah E. Cabin2, Sonia M. Vallabh1,8,9,10, Andrea Grindeland Panter2, Nina Oberbeck11, Hien T. Zhao3, Eric Vallabh Minikel1,8,9,10
ORCID iDs: Taylor L. Corridon, Alyssa Seerley, Yuan Lian, Nikita G. Kamath, Walker S. Jackson, Sonia M. Vallabh, Eric Vallabh Minikel
  1. Program in Brain Health, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
  2. Weissman Hood Institute, Great Falls, Montana, United States of America
  3. Ionis Pharmaceuticals, Carlsbad, California, United States of America
  4. Charles River Laboratories, Worcester, Massachusetts, United States of America
  5. IQ Proteomics, Framingham, Massachusetts, United States of America
  6. Comparative Medicine, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
  7. Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
  8. McCance Center for Brain Health and Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
  9. Department of Neurology, Harvard Medical School, Boston, Massachusetts, United States of America
  10. Prion Alliance, Cambridge, Massachusetts, United States of America
  11. Gate Bio, Brisbane, California, United States of America
Institutions: Broad Institute (United States); Harvard University (United States); Ionis Pharmaceuticals (United States) (United States); Charles River Laboratories (United States) (United States); Linköping University (Sweden); Massachusetts General Hospital (United States); Prion Alliance (United States)
Journal: PLoS pathogens, volume 22, issue 5, article e1014263
Dates: received 7 February 2026; accepted 14 May 2026; published online 26 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.ppat.1014263 · PMID 42189860 · PMCID PMC13221148 · OpenAlex W4404431263
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.11.12.623215
Status: code verified
Categories: human (organism), mouse (organism), rat (organism), other condition (population), clinical / translational (subfield)
Methods: Statistics
MeSH: Brain*, Prion Diseases*, Prions*, Animals, Disease Models, Animal, Half-Life, Humans, Mice, Mice, Transgenic, Rats (* major topic)
Journal subjects: Biology and Life Sciences, Anatomy, Digestive System, Gastrointestinal Tract, Colon, Medicine and Health Sciences, Research and Analysis Methods, Animal Studies, Experimental Organism Systems, Model Organisms, Mouse Models, Animal Models, Physical Sciences, Chemistry, Chemical Compounds, Organic Compounds, Amino Acids, Basic Amino Acids, Lysine, Organic Chemistry, Biochemistry, Proteins, Medical Conditions, Infectious Diseases, Prion Diseases, Zoonoses, Body Fluids, Cerebrospinal Fluid, Physiology, Nervous System, Bioengineering, Biotechnology, Genetic Engineering, Genetically Modified Organisms, Genetically Modified Animals, Engineering and Technology, Genetics, Genomics, Animal Genomics, Mammalian Genomics, Immunologic Techniques, Immunoassays, Enzyme-Linked Immunoassays
Topic: Prion Diseases and Protein Misfolding (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIH (R01 NS132022, COBRE P20 GM152335); Prion Alliance (N/A); Ionis Pharmaceuticals; Gate Bio
Citations: cited by 2 papers (Europe PMC); 47 references in the paper

Abstract

PrP lowering is effective against prion disease in animal models and is being tested clinically. Therapies in the current pipeline lower PrP production, leaving pre-existing PrP to be cleared according to its own half-life. We hypothesized that PrP’s half-life may be a rate-limiting factor for the time to effect of PrP-lowering drugs, and one reason why late treatment of prion-infected mice is not as effective as early treatment. Using isotopically labeled diet with targeted mass spectrometry, as well as antisense oligonucleotide treatment followed by timed PrP measurement, we estimate a half-life of 5–6 days for PrP in the brain. PrP turnover is not affected by over- or under-expression. Mouse PrP and human PrP have similar turnover rates measured in wild-type or humanized knock-in mice. CSF PrP appears to mirror brain PrP in real time in rats. PrP in the colon is readily quantifiable and has a half-life just slightly shorter than in brain. An under-expressed pathogenic mutant PrP, corresponding to D178N in humans, exhibits an accelerated turnover rate. Our data may inform the design of both preclinical and clinical studies of PrP-lowering drugs.

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 12 matches between paragraphs and lines of code.

vallabhminikel/halflife

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 1dfa9ba5de19ff263afbfe9f30fe3d67129e13ba, 11 June 2026
Languages: R (14), Julia (4)
Size: 140 files, 18 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file, environment (env.yml, Manifest.toml, Project.toml)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: DataFrames.jl (4 files), Distributions.jl (3 files), Makie (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
20 files
At the source: github.com/vallabhminikel/halflife

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;
  • 18 scripts, each with its path and the digest of its content;
  • 12 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

Raw data and source code sufficient to reproduce all figures and statistics in this manuscript are available at github.com/vallabhminikel/halflife.

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 2, 28 September 2026

  • Authors: added Taylor L. Corridon (0009-0002-4167-8291); Yuan Lian (0009-0001-9565-2292); Walker S. Jackson (0000-0002-3003-5509); Sonia M. Vallabh (0000-0003-3824-2702); removed Taylor L. Corridon; Yuan Lian; Walker S. Jackson; Sonia M. Vallabh

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 23 authors, 10 MeSH terms, 4 funders, 46 references.

Cite

This paper

Corridon, T. L., O’Moore, J., Seerley, A., Sprague, D. A., Lian, Y., Laversenne, V., Noble, B., Kamath, N. G., Serack, F. E., Shaikh, A. B., Erickson, B., Braun, C., DeSouza-Lenz, K., Howard, M., Chan, N., Jackson, W. S., Reidenbach, A. G., Cabin, D. E., Vallabh, S. M., . . . Minikel, E. V. (2026). PrP turnover in vivo and the time to effect of prion disease therapeutics. PLoS pathogens, 22(5), e1014263. https://doi.org/10.1371/journal.ppat.1014263

BibTeX

@article{corridon2026prp,
author = {Corridon, Taylor L. and O’Moore, Jill and Seerley, Alyssa and Sprague, Daniel A. and Lian, Yuan and Laversenne, Vanessa and Noble, Briana and Kamath, Nikita G. and Serack, Fiona E. and Shaikh, Abdul Basit and Erickson, Brian and Braun, Craig and DeSouza-Lenz, Kendrick and Howard, Michael and Chan, Nathan and Jackson, Walker S. and Reidenbach, Andrew G. and Cabin, Deborah E. and Vallabh, Sonia M. and Grindeland Panter, Andrea and Oberbeck, Nina and Zhao, Hien T. and Minikel, Eric Vallabh},
title = {{PrP turnover in vivo and the time to effect of prion disease therapeutics}},
journal = {PLoS pathogens},
year = {2026},
month = may,
volume = {22},
number = {5},
pages = {e1014263},
publisher = {PLOS},
issn = {1553-7366},
doi = {10.1371/journal.ppat.1014263},
url = {https://doi.org/10.1371/journal.ppat.1014263},
pmid = {42189860},
pmcid = {PMC13221148}
}

RIS

TY - JOUR
AU - Corridon, Taylor L.
AU - O’Moore, Jill
AU - Seerley, Alyssa
AU - Sprague, Daniel A.
AU - Lian, Yuan
AU - Laversenne, Vanessa
AU - Noble, Briana
AU - Kamath, Nikita G.
AU - Serack, Fiona E.
AU - Shaikh, Abdul Basit
AU - Erickson, Brian
AU - Braun, Craig
AU - DeSouza-Lenz, Kendrick
AU - Howard, Michael
AU - Chan, Nathan
AU - Jackson, Walker S.
AU - Reidenbach, Andrew G.
AU - Cabin, Deborah E.
AU - Vallabh, Sonia M.
AU - Grindeland Panter, Andrea
AU - Oberbeck, Nina
AU - Zhao, Hien T.
AU - Minikel, Eric Vallabh
TI - PrP turnover in vivo and the time to effect of prion disease therapeutics
T2 - PLoS pathogens
J2 - PLoS Pathog
PY - 2026
DA - 2026/05/26
VL - 22
IS - 5
SP - e1014263
SN - 1553-7366
PB - PLOS
DO - 10.1371/journal.ppat.1014263
UR - https://doi.org/10.1371/journal.ppat.1014263
LA - en
ER -

CSL-JSON

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