OSCR

Characterization of atypical Ebola virus disease in ferrets.

Code ↔ Paper

3 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 3 matches
  1. [1] § Materials and methods › Transcriptomics ↔ Scripts/Transcriptomic_Analysis.R, lines 90–133 · score 0.71 · log transformation, highest variance, DESeq2, pheatmap, transcriptomic, heatmaps
  2. [2] § Materials and methods › Proteomics ↔ Scripts/Transcriptomic_Analysis.R, lines 1–47 · score 0.61 · Mustela putorius, GO Term, database, transcriptomic, ID
  3. [3] § Results › Animals dying of atypical EVD had high levels of virus in the brain ↔ Scripts/SNP_Heatmaps.R, lines 1–63 · score 0.61 · E180V, t9267c, VP24, VP30, GP, mutations

Paper

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The authors' code

R · 283 lines · 9.3 KB · Apache-2.0 · 2 matches

  1. # Transcriptomic Analysis for Ferret Brain samples
  3. # Jordan Wight, PhD
  4. # [email hidden] / [email hidden]
  6. # Public Health Agency of Canada, National Microbiology Lab, Special Pathogens
  8. ###############################################################################
  10. # Load required packages
  12. library("BRGenomics")
  13. library("DESeq2")
  14. library("rtracklayer")
  15. library("dplyr")
  16. library("AnnotationHub")
  17. library("ggplot2")
  18. library("pheatmap")
  19. library("RColorBrewer")
  20. library("tibble")
  21. library("GO.db")
  24. # Import the ferret gff database that was used to align
  26. genelist <- import.gff2("../Data/Transcriptomics/genes.gtf")
  28. id2name <- genelist@elementMetadata %>%
  29. as_tibble() %>%
  30. dplyr::select(gene_id, gene_name) %>%
  31. distinct() %>%
  32. filter(!is.na(gene_name))
  34. # Import ferret annotations and GO terms
  36. ah <- AnnotationHub()
  37. # R asks if you want to create the directory, type yes if no snapshot exists
  40. orgs <- query(ah, "OrgDb")
  42. mput.db <- query(ah,'org.Mustela_putorius_furo.eg.sqlite')
  44. ferretGOdb <- mput.db[["AH120337"]]
  45. # If AnnotationHub updates, may have to change this accession number,
  46. #if so, run the below to see current accession number
  47. query(ah,'org.Mustela_putorius_furo.eg.sqlite')
  50. # Read in aligned data (checking first if merged file already exists)
  52. if(file.exists("../Data/Transcriptomics/br_data.Rdata")){
  53. br_data <- readRDS("../Data/Transcriptomics/br_data.Rdata")
  54. } else {
  56. br_data <- list(Acute_rep1 = import_bam("../Data/Transcriptomics/141M-BR.bam",
  57. revcomp = T, mapq = 20, paired_end = F),
  58. Acute_rep2 = import_bam("../Data/Transcriptomics/451F-BR.bam",
  59. revcomp = T, mapq = 20, paired_end = F),
  60. Atypical_rep1 = import_bam("../Data/Transcriptomics/698F-BR.bam",
  61. revcomp = T, mapq = 20, paired_end = F),
  62. Survivor_rep1 = import_bam("../Data/Transcriptomics/701F-BR.bam",
  63. revcomp = T, mapq = 20, paired_end = F),
  64. Acute_rep3 = import_bam("../Data/Transcriptomics/736M-BR.bam",
  65. revcomp = T, mapq = 20, paired_end = F),
  66. Atypical_rep2 = import_bam("../Data/Transcriptomics/744M-BR.bam",
  67. revcomp = T, mapq = 20, paired_end = F),
  68. Atypical_rep3 = import_bam("../Data/Transcriptomics/965M-BR.bam",
  69. revcomp = T, mapq = 20, paired_end = F),
  70. Survivor_rep2 = import_bam("../Data/Transcriptomics/993M-BR.bam",
  71. revcomp = T, mapq = 20, paired_end = F))
  72. }
  75. # Calculate normalization factors
  76. brain_NF <- getSpikeInNFs(br_data, si_pattern = "spikein", ctrl_pattern = "Survivor",
  77. ncores = 1, batch_norm = F)
  78. # Prepare the dataset for analysis
  79. Brain_dsd <- getDESeqDataSet(br_data, genelist, gene_names = genelist$gene_id,
  80. ncores = 1, sizeFactors = 1/brain_NF)
  82. # Estimate size factors
  83. dds <- estimateSizeFactors(Brain_dsd)
  86. ################### Remove ERCC and EBOV genes
  88. dds1<-dds
  90. # Make list of EBOV genes (8 b/c GP and sGP)
  91. genesToRemove<-c("gene-VP30","nbis-gene-2","nbis-gene-1","gene-VP40","gene-VP35",
  92. "gene-NP","gene-VP24","gene-L")
  94. # Remove them from DESeqDataSet
  95. dds1.1<-dds1[setdiff(rownames(dds1), genesToRemove),]
  97. # R log transformation of this data with the EBOV genes removed
  98. rld1.1 <- rlog(dds1.1, blind = FALSE)
  101. ######### Make heatmap of the top 50 genes (highest variance)
  103. # Pull top 50 genes with the highest variance across samples
  104. topVarGenes3.1 <- head(order(rowVars(assay(rld1.1)), decreasing = TRUE), 50)
  106. # Build matrices
  107. mat <- assay(rld1.1)[ topVarGenes3.1, ]
  108. mat <- mat - rowMeans(mat)
  109. anno <- as.data.frame(colData(rld1.1)[, c("condition","replicate")])
  110. pheatmap(mat, annotation_col = anno) # base plot
  112. # Real plot with good and nice color scale
  113. color.divisions<-100
  115. pheatmap(mat,
  116. breaks = seq(-8,8, length.out=(color.divisions + 1)),
  117. color = rev(hcl.colors(101,"RdBu")))
  119. ################# Associate gene names with these
  121. # Turn the matrix of top 50 most variable genes to data frame
  122. mat2<-as.data.frame(mat)
  124. # Make row name (gene ID) the first column
  125. mat2 <- tibble::rownames_to_column(mat2, "gene_id")
  127. # Join and match the gene names to the matrix
  128. top50genes <- mat2 %>%
  129. left_join(id2name,by="gene_id")
  131. top50genes.2<-top50genes %>%
  132. remove_rownames %>%
  133. column_to_rownames(var="gene_id")
  135. # Plot top50 genes, including those with no names
  136. pheatmap(mat,
  137. breaks = seq(-8,8, length.out=(color.divisions + 1)),
  138. color = rev(hcl.colors(101,"RdBu")),
  139. labels_row = top50genes.2$gene_name)
  141. # Reorder animal IDs, sorting based on fold change per outcome
  143. top50all_reorder<- mat[,c("Survivor_rep1",
  144. "Survivor_rep2",
  145. "Atypical_rep2",
  146. "Atypical_rep3",
  147. "Atypical_rep1",
  148. "Acute_rep2",
  149. "Acute_rep1",
  150. "Acute_rep3")]
  152. top50all_reorder<-as.data.frame(top50all_reorder)
  154. # Rename column names to animal IDs
  155. top50all_reorder<-rename(top50all_reorder,
  156. "701F" = Survivor_rep2,
  157. "993M" = Survivor_rep1,
  158. "744M" = Atypical_rep3,
  159. "965M" = Atypical_rep1,
  160. "698F" = Atypical_rep2,
  161. "451F" = Acute_rep1,
  162. "141M" = Acute_rep3,
  163. "736M" = Acute_rep2)
  165. names(top50all_reorder)<-c("701F","993M",
  166. "744M","965M","698F",
  167. "451F","141M","736M")
  170. # With ENSEMBL IDs
  171. p5<-pheatmap(top50all_reorder,
  172. breaks = seq(-8,8, length.out=(color.divisions + 1)),
  173. color = rev(hcl.colors(101,"RdBu")),
  174. cluster_cols = FALSE,
  175. fontsize = 22,
  176. angle_col = "90")
  179. # Renamed with gene names/NAs if no name
  180. p5.1<-pheatmap(top50all_reorder,
  181. breaks = seq(-8,8, length.out=(color.divisions + 1)),
  182. color = rev(hcl.colors(101,"RdBu")),
  183. cluster_cols = FALSE,
  184. labels_row = top50genes.2$gene_name,
  185. fontsize = 22,
  186. angle_col = "90")
  189. ggsave("Ferret_Transcriptomics_top50named_DE_genes_2026-03-26.tiff",
  190. plot=p5.1,
  191. width=15,
  192. height=19,
  193. units=c("in"),
  194. dpi=600)
  196. # These are used to make the figure for the supplement.
  200. ########### Now get the top 50 named genes, since their roles/functions can be discussed
  202. # First pull top 1000 most variable
  203. topVarGenes3.2 <- head(order(rowVars(assay(rld1.1)), decreasing = TRUE), 1000)
  205. # Build matrices
  206. mat.1 <- assay(rld1.1)[ topVarGenes3.2, ]
  207. mat.1 <- mat.1 - rowMeans(mat.1)
  208. mat.1a<-as.data.frame(mat.1)
  209. mat.1b <- tibble::rownames_to_column(mat.1a, "gene_id")
  211. # Join and match the gene names to the matrix
  212. top1000genes <- mat.1b %>%
  213. left_join(id2name,by="gene_id")
  215. # Remove rows that have NA for the gene_name
  217. top803genes<-dplyr::filter(top1000genes,!is.na(gene_name))
  219. # Make new ID file for ENSEMBL ID and gene names, top 803
  220. ENS_plus_names<-data.frame(top803genes$gene_id,top803genes$gene_name)
  222. names(ENS_plus_names)[1]<-"gene_id"
  223. names(ENS_plus_names)[2]<-"gene_name"
  225. # Put ENSEMBL ID back to row names
  226. top803<-top803genes %>%
  227. remove_rownames %>%
  228. column_to_rownames(var="gene_id")
  230. # Remove gene names
  231. top803$gene_name<-NULL
  233. # Already ranked as the most variable, now just pull the top 50
  234. top803variable50 <- head(top803,50)
  236. top803variable50.1<-tibble::rownames_to_column(top803variable50, "gene_id")
  238. top50namedgenes <- top803variable50.1 %>%
  239. left_join(ENS_plus_names,by="gene_id")
  241. # Check with a heatmap
  242. pheatmap(top803variable50,
  243. breaks = seq(-8,8, length.out=(color.divisions + 1)),
  244. color = rev(hcl.colors(101,"RdBu")))
  246. # Renaming worked correctly, confirmed
  247. pheatmap(top803variable50,
  248. breaks = seq(-8,8, length.out=(color.divisions + 1)),
  249. color = rev(hcl.colors(101,"RdBu")),
  250. labels_row = ENS_plus_names$gene_name)
  252. # Reorder animal IDs, sorting based on fold change per outcome
  254. top803variable50_reorder<- top803variable50[,c("Survivor_rep1",
  255. "Survivor_rep2",
  256. "Atypical_rep2",
  257. "Atypical_rep3",
  258. "Atypical_rep1",
  259. "Acute_rep2",
  260. "Acute_rep1",
  261. "Acute_rep3")]
  263. names(top803variable50_reorder)<-c("701F","993M",
  264. "744M","965M","698F",
  265. "451F","141M","736M")
  267. ##### Here's the top50 named genes, ordered by outcome and intensity
  268. p6<-pheatmap(top803variable50_reorder,
  269. breaks = seq(-8,8, length.out=(color.divisions + 1)),
  270. color = rev(hcl.colors(101,"RdBu")),
  271. labels_row = ENS_plus_names$gene_name,
  272. cluster_cols = FALSE,
  273. fontsize = 22,
  274. angle_col = "90")
  276. ggsave("Ferret_Transcriptomics_top50_with_names_DE_genes.tiff",
  277. plot=p6,
  278. width=15,
  279. height=19,
  280. units=c("in"),
  281. dpi=600)
  283. # This is Figure 4A in the manuscript.

Transcriptomic_Analysis.R at commit ef8dac5, under Apache-2.0 · at the source

Overview

Authors: Wenguang Cao1, Shihua He1, Helene Schulz1, Jordan Wight1, Michael Chan1, Karla Emeterio1, Guodong Liu1, Jonathan Audet1, Kevin Tierney1, Kimberly Azaransky1, Kathy Frost2, Lilianne Gee3, Peter McQueen3, Patrick Chong3, Sarah B Sulkosky4, Stephanie Booth2, Garrett Westmacott3, Erica Ollmann Saphire5,6, Xiankun Zeng4, Logan Banadyga1,7
ORCID iDs: Helene Schulz, Logan Banadyga
  1. Special Pathogens Program, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
  2. Mycobacteriology, Vector-Borne and Prion Diseases Division, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
  3. Mass Spectrometry and Proteomics Core, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
  4. United States Army Medical Research Institute of Infectious Diseases, Fort Detrick, Frederick, Maryland, United States of America
  5. Center for Vaccine Innovation, La Jolla Institute for Immunology, La Jolla, California, United States of America
  6. Deptartment of Medicine, University of California San Diego, La Jolla, California, United States of America
  7. Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada
Institutions: Public Health Agency of Canada (Canada); United States Army Medical Research Institute of Infectious Diseases (United States); La Jolla Institute for Immunology (United States); University of California San Diego (United States); University of Manitoba (Canada)
Journal: PLoS pathogens, volume 22, issue 5, article e1013916
Dates: received 16 January 2026; accepted 10 April 2026; published online 4 May 2026
Type: Research article · Language: English
License: CC0
Identifiers: DOI 10.1371/journal.ppat.1013916 · PMID 42081574 · PMCID PMC13155671 · OpenAlex W7160232064
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (organism)
MeSH: Ebolavirus*, Hemorrhagic Fever, Ebola*, Animals, Antibodies, Monoclonal, Antibodies, Viral, Disease Models, Animal, Female, Ferrets, Viremia (* major topic)
Topic: Viral Infections and Outbreaks Research (Infectious Diseases, Medicine), according to OpenAlex
Funding: Public Health Agency of Canada (N/A); National Institute of Allergy and Infectious Diseases (U19 AI142790-03); Defense Threat Reduction Agency (CB11408)
Citations: not cited yet (Europe PMC); 60 references in the paper

Abstract

Ebola virus (EBOV) infection typically results in severe—and often lethal—acute disease. However, increasing evidence suggests that EBOV can persist in certain immune-privileged tissues, which may then serve as reservoirs for the later reemergence of EBOV and disease recrudescence. Here, we report atypical EVD recrudescence in a ferret model inoculated with an otherwise lethal dose of EBOV and treated with low doses of a highly potent monoclonal antibody cocktail. Among 32 antibody-treated ferrets, 14 animals survived, while 8 succumbed to acute EVD within about 5–8 days. The remaining 10 animals succumbed to atypical EVD between 12 and 18 days post-infection (DPI) despite having shown no, or very minor, signs of illness during the acute phase of disease. While viremia disappeared by 14 DPI in most animals that succumbed to atypical EVD, it rebounded modestly just prior to death. Unlike animals that died of acute EVD, those that died of atypical EVD showed only a moderate systemic inflammatory response and few signs of organ dysfunction, in line with low levels of virus in the liver and spleen. Interestingly, however, ferrets that died of atypical EVD showed high levels of virus in the brain, consistent with increased markers of inflammation in the central nervous system and significant pathological changes, including a breakdown in the blood-brain barrier and severe meningoencephalitis. Not only does this study shed important light on the atypical and underappreciated manifestations of EVD, but it also establishes the ferret as a valuable model of EBOV recrudescence.

Reproduced under the paper's license (CC0), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

jaudetnml/Atypical_EBOV_ferrets

License: Apache-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: ef8dac5ec1eecf35ec2ac648d6b5d22da6d8c0d3, 17 April 2026
Languages: R (3)
Size: 17 files, 3 scripts
Software Heritage: not archived
Found in: the text, “Proteomics”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (3 files), pheatmap (2 files), cowplot (1 file), data.table (1 file), DESeq2 (1 file), ggplot2 (1 file), ggpubr (1 file), patchwork (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
5 files
At the source: github.com/jaudetnml/Atypical_EBOV_ferrets

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;
  • 3 scripts, each with its path and the digest of its content;
  • 3 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

Data links

Data Availability

The raw sequencing data are available on the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra): PRJNA1454895 and PRJNA1454531. The R scripts used to generate the figures are available on GitHub: https://github.com/jaudetnml/Atypical_EBOV_ferrets.

Reproduced under the paper's license (CC0), 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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 20 authors, 9 MeSH terms, 3 funders, 51 references.

Cite

This paper

Cao, W., He, S., Schulz, H., Wight, J., Chan, M., Emeterio, K., Liu, G., Audet, J., Tierney, K., Azaransky, K., Frost, K., Gee, L., McQueen, P., Chong, P., Sulkosky, S. B., Booth, S., Westmacott, G., Saphire, E. O., Zeng, X., & Banadyga, L. (2026). Characterization of atypical Ebola virus disease in ferrets. PLoS pathogens, 22(5), e1013916. https://doi.org/10.1371/journal.ppat.1013916

BibTeX

@article{cao2026characterization,
author = {Cao, Wenguang and He, Shihua and Schulz, Helene and Wight, Jordan and Chan, Michael and Emeterio, Karla and Liu, Guodong and Audet, Jonathan and Tierney, Kevin and Azaransky, Kimberly and Frost, Kathy and Gee, Lilianne and McQueen, Peter and Chong, Patrick and Sulkosky, Sarah B and Booth, Stephanie and Westmacott, Garrett and Saphire, Erica Ollmann and Zeng, Xiankun and Banadyga, Logan},
title = {{Characterization of atypical Ebola virus disease in ferrets}},
journal = {PLoS pathogens},
year = {2026},
month = may,
volume = {22},
number = {5},
pages = {e1013916},
publisher = {PLOS},
issn = {1553-7366},
doi = {10.1371/journal.ppat.1013916},
url = {https://doi.org/10.1371/journal.ppat.1013916},
pmid = {42081574},
pmcid = {PMC13155671}
}

RIS

TY - JOUR
AU - Cao, Wenguang
AU - He, Shihua
AU - Schulz, Helene
AU - Wight, Jordan
AU - Chan, Michael
AU - Emeterio, Karla
AU - Liu, Guodong
AU - Audet, Jonathan
AU - Tierney, Kevin
AU - Azaransky, Kimberly
AU - Frost, Kathy
AU - Gee, Lilianne
AU - McQueen, Peter
AU - Chong, Patrick
AU - Sulkosky, Sarah B
AU - Booth, Stephanie
AU - Westmacott, Garrett
AU - Saphire, Erica Ollmann
AU - Zeng, Xiankun
AU - Banadyga, Logan
TI - Characterization of atypical Ebola virus disease in ferrets
T2 - PLoS pathogens
J2 - PLoS Pathog
PY - 2026
DA - 2026/05/04
VL - 22
IS - 5
SP - e1013916
SN - 1553-7366
PB - PLOS
DO - 10.1371/journal.ppat.1013916
UR - https://doi.org/10.1371/journal.ppat.1013916
LA - en
ER -

CSL-JSON

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