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Species-specific brain transcriptomic responses to clothianidin and sulfoxaflor in pollinators.

Code ↔ Paper

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  1. [1] § STAR★Methods › Quantification and statistical analysis › Weighted gene co-expression network analysis (WGCNA) ↔ 08_vcar_wgcna.R, lines 43–88 · score 0.92 · soft thresholding power, mergeCutHeight, random seed, scale free topology, WGCNA, connectivity
  2. [2] § STAR★Methods › Quantification and statistical analysis › Comparison to other bumble bee tissues ↔ 09_tissue_species_conparison.R, lines 126–209 · score 0.81 · removeBatchEffect, Surrogate Variable, limma, SVA, leg, PCA
  3. [3] § STAR★Methods › Quantification and statistical analysis › Differential gene expression analysis ↔ 01_feature_count.R, the whole file · a weak match · score 0.67 · STAR alignments, featureCounts, quality, BAM, genomes, butterfly
  4. [4] § Results › Variation in statistical power across species influences differentially expressed gene detection ↔ 04_bter_iterations.R, lines 185–247 · score 0.64 · Kolmogorov Smirnov, positively skewed, skewness, median
  5. [5] § STAR★Methods › Quantification and statistical analysis › Differential gene expression analysis ↔ 04_bter_iterations.R, lines 56–131 · score 0.59 · betaPrior, DESeq2, Wald, colony, bter, filtered
  6. [6] § STAR★Methods › Method details › Insecticide treatment preparation ↔ 06-kegg_enrichment.R, lines 121–194 · score 0.57 · amino acid, sugar, degradation, protein, sucrose, treatments
  7. [7] § Results › The effects of insecticides differ between species ↔ 09_tissue_species_conparison.R, lines 126–209 · score 0.53 · biological signals, PC2, PC1, tissues, PCA, tubule

Paper

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The authors' code

R · 210 lines · 9.7 KB · no license · 2 matches

  1. # Uplad species
  2. vcar_matrix <- read.csv("~/2023-species_comparisons/tables/star_counts/vcar_star_featureCounts.csv")
  3. obic_matrix <- read.csv("~/2023-species_comparisons/tables/star_counts/FULL_SET_obic_star_featureCounts_notfilt.csv")
  4. bter_matrix <- read.csv("~/2023-species_comparisons/tables/star_counts/bter_star_featureCounts.csv")
  5. lser_matrix <- read_csv("~/2022-Lsericata_brainDGE/results_update/lser_FULL_STAR_counts_290324.csv")
  7. colnames(vcar_matrix) <- gsub(".Aligned.out.sorted.bam", "", colnames(vcar_matrix))
  8. colnames(obic_matrix) <- gsub(".Aligned.out.sorted.bam", "", colnames(obic_matrix))
  9. colnames(lser_matrix) <- gsub(".Aligned.out.sorted.bam", "", colnames(lser_matrix))
  10. colnames(lser_matrix) <- sub("_S.*", "", colnames(lser_matrix))
  11. colnames(bter_matrix) <- gsub(".Aligned.out.sorted.bam", "", colnames(bter_matrix))
  13. bter_meta <- read.csv("~/2023-species_comparisons/tables/metadata/bter_meta.csv")
  14. vcar_meta <- read.csv("~/2023-species_comparisons/tables/metadata/vcar_meta.csv")
  15. obic_meta <- read.csv("~/2023-species_comparisons/tables/metadata/obic_meta.csv")
  16. lser_meta <- read.csv("~/2023-species_comparisons/tables/metadata/lser_meta_update.csv")
  18. bter_dds <- read_rds("~/2023-species_comparisons/DGE_pipelines/bter_dds_filtered_052024.rds")
  19. vcar_dds <- read_rds("~/2023-species_comparisons/DGE_pipelines/vcar_dds_filtered_052024.rds")
  20. lser_dds <- read_rds("~/2023-species_comparisons/DGE_pipelines/lser_dds_filtered_052024.rds")
  21. obic_dds <- read_rds("~/2023-species_comparisons/DGE_pipelines/obic_dds_filtered_052024.rds")
  23. # Subset samples in the raw matrices (only controls)
  24. bter_g <- bter_matrix$X
  25. vcar_g <- vcar_matrix$X
  26. lser_g <- lser_matrix$...1
  28. vcar_matrix_cont <- vcar_matrix[, grep("CONT", colnames(vcar_matrix))]
  29. lser_matrix_cont <- lser_matrix[, grep("CONT", colnames(lser_matrix))]
  30. bter_matrix_cont <- bter_matrix[, grep("CONT", colnames(bter_matrix))]
  32. colnames(vcar_matrix_cont) <- gsub(".CONT", "-CONT", colnames(vcar_matrix_cont))
  33. vcar_matrix_cont <- vcar_matrix_cont[, colnames(vcar_matrix_cont) %in% vcar_meta$SAMPLE_CODE]
  34. colnames(bter_matrix_cont) <- gsub("\\.", "-", colnames(bter_matrix_cont))
  35. bter_matrix_cont <- bter_matrix_cont[, colnames(bter_matrix_cont) %in% bter_meta$SAMPLE_NAME]
  36. colnames(lser_matrix_cont) <- gsub("-", ".", colnames(lser_matrix_cont))
  37. lser_matrix_cont <- lser_matrix_cont[, colnames(lser_matrix_cont) %in% colnames(lser_dds)]
  39. lser_matrix_cont$X <- lser_g
  40. vcar_matrix_cont$X <- vcar_g
  41. bter_matrix_cont$X <- bter_g
  43. obic_g <- obic_matrix$X
  44. colnames(obic_matrix) <- gsub("\\.", "-", colnames(obic_matrix))
  45. obic_matrix <- obic_matrix[, colnames(obic_matrix) %in% obic_meta$SAMPLE_SEQ]
  46. sample_code_mapping <- setNames(obic_meta$SAMPLE_CODE, obic_meta$SAMPLE_SEQ)
  47. colnames(obic_matrix) <- sample_code_mapping[colnames(obic_matrix)]
  49. obic_matrix <- obic_matrix[, grep("cont", colnames(obic_matrix))]
  50. obic_matrix$X <- obic_g
  52. obic_matrix_cont <- obic_matrix
  54. # Upload tissue ban files
  55. # bamFiles <- list.files(pattern="*.bam",
  56. # path = "~/2022-bter_tissues_chronic_exposure/results/2022-10-07-star/update/tmp/star_combined_run/")
  57. # bamFiles <- bamFiles[grep("FCONT", bamFiles)]
  58. # bamFiles <- paste("~/2022-bter_tissues_chronic_exposure/results/2022-10-07-star/update/tmp/star_combined_run", bamFiles, sep = "/")
  59. # annotationFile <- "~/2023-species_comparisons/genomes/bter/bter.gtf"
  60. # outDir <- ""
  61. #
  62. # counts_bter <- featureCounts(
  63. # files = bamFiles,
  64. # annot.ext = annotationFile,
  65. # isGTFAnnotationFile = TRUE,
  66. # isPairedEnd = TRUE,
  67. # minMQS = 0,
  68. # )
  69. #
  70. # bter_tissues_matrix <- counts_bter$counts
  72. muscle_matrix <- bter_tissues_matrix[, grep("LEG", colnames(bter_tissues_matrix))]
  73. tubules_matrix <- bter_tissues_matrix[, grep("TUBULES", colnames(bter_tissues_matrix))]
  75. colnames(muscle_matrix) <- gsub(".Aligned.out.sorted.bam", "", colnames(muscle_matrix))
  76. colnames(tubules_matrix) <- gsub(".Aligned.out.sorted.bam", "", colnames(tubules_matrix))
  79. # Subset genes that are conserved 1-1 orthologs: n = 2956
  80. muscle_matrix_subset <- muscle_matrix[rownames(muscle_matrix) %in% ortholog_list$all_species_orthologs$Bter_gene, ]
  81. tubules_matrix_subset <- tubules_matrix[rownames(tubules_matrix) %in% ortholog_list$all_species_orthologs$Bter_gene, ]
  82. bter_matrix_subset <- bter_matrix_cont[bter_matrix_cont$X %in% ortholog_list$all_species_orthologs$Bter_gene, ]
  83. vcar_matrix_cont$X <- gsub("gene-", "", vcar_matrix_cont$X)
  84. vcar_matrix_subset <- vcar_matrix_cont[vcar_matrix_cont$X %in% ortholog_list$all_species_orthologs$Vcar_gene, ]
  85. lser_matrix_subset <- lser_matrix_cont[lser_matrix_cont$X %in% ortholog_list$all_species_orthologs$Lser_gene, ]
  86. obic_matrix_subset <- obic_matrix_cont[obic_matrix_cont$X %in% ortholog_list$all_species_orthologs$Obic_gene, ]
  88. # Change names to bter gene IDs
  89. ##Ensure 'X' column exists and not in rownames, and convert to data frames if necessary
  90. muscle_matrix_subset <- as.data.frame(muscle_matrix_subset) # Ensure it's a data frame
  91. muscle_matrix_subset$X <- rownames(muscle_matrix_subset) # Move rownames to a column 'X'
  92. rownames(muscle_matrix_subset) <- NULL # Remove rownames
  93. tubules_matrix_subset <- as.data.frame(tubules_matrix_subset) # Ensure it's a data frame
  94. tubules_matrix_subset$X <- rownames(tubules_matrix_subset) # Move rownames to a column 'X'
  95. rownames(tubules_matrix_subset) <- NULL # Remove rownames
  96. bter_matrix_subset <- as.data.frame(bter_matrix_subset) # Ensure it's a data frame
  97. vcar_matrix_subset <- as.data.frame(vcar_matrix_subset) # Ensure it's a data frame
  98. lser_matrix_subset <- as.data.frame(lser_matrix_subset) # Ensure it's a data frame
  99. obic_matrix_subset <- as.data.frame(obic_matrix_subset)
  101. # Replace gene IDs
  102. lser_gene_mapping <- setNames(ortholog_list$all_species_orthologs$Bter_gene, ortholog_list$all_species_orthologs$Lser_gene)
  103. lser_matrix_subset$X <- lser_gene_mapping[lser_matrix_subset$X] # Replace Lser gene IDs with Bter_gene IDs
  104. vcar_gene_mapping <- setNames(ortholog_list$all_species_orthologs$Bter_gene, ortholog_list$all_species_orthologs$Vcar_gene)
  105. vcar_matrix_subset$X <- vcar_gene_mapping[vcar_matrix_subset$X] # Replace Vcar gene IDs with Bter_gene IDs
  106. obic_gene_mapping <- setNames(ortholog_list$all_species_orthologs$Bter_gene, ortholog_list$all_species_orthologs$Obic_gene)
  107. obic_matrix_subset$X <- obic_gene_mapping[obic_matrix_subset$X] # Replace Obic gene IDs with Bter_gene IDs
  109. ### SVA batch correction
  110. ### Combine the data frames
  111. ### Run PCA
  113. # combined_matrix <- Reduce(function(x, y) merge(x, y, by = "X"),
  114. # list(muscle_matrix_scaled, tubules_matrix_scaled, bter_matrix_scaled,
  115. # vcar_matrix_scaled, lser_matrix_scaled, obic_matrix_scaled))
  117. # OR:
  118. combined_matrix <- Reduce(function(x, y) merge(x, y, by = "X"),
  119. list(muscle_matrix_subset, tubules_matrix_subset, bter_matrix_subset,
  120. vcar_matrix_subset, lser_matrix_subset, obic_matrix_subset))
  122. scale_0_to_1 <- function(x) {
  123. return((x - min(x)) / (max(x) - min(x)))
  124. }
  126. # Apply the scaling function to each column (except the 'X' column)
  127. combined_matrix[, -1] <- as.data.frame(lapply(combined_matrix[, -1], scale_0_to_1))
  131. # Create metadata
  132. all_meta <- data.frame(sample = colnames(combined_matrix))
  133. all_meta <- all_meta %>%
  134. mutate(
  135. tissue = case_when(
  136. grepl("TUBULES", sample) ~ "tubules",
  137. grepl("LEG", sample) ~ "muscle",
  138. TRUE ~ "brain"
  139. ),
  140. species = case_when(
  141. grepl("FCONT", sample) ~ "bter",
  142. grepl("cont_", sample) ~ "obic",
  143. grepl("X", sample) ~ "lser",
  144. grepl("AU", sample) ~ "vcar",
  145. TRUE ~ "unknown" # Use this to catch any unexpected cases
  146. )
  147. ) %>%
  148. mutate(batch = case_when(
  149. species == "bter" & tissue == "brain" ~ "batch_a", # bter brain
  150. species == "obic" ~ "batch_b", # obic species
  151. species == "lser" ~ "batch_c", # lser species
  152. species == "vcar" ~ "batch_d", # vcar species
  153. species == "bter" & (tissue == "tubules" | tissue == "muscle") ~ "batch_e", # tubules and muscle (bter)
  154. TRUE ~ "unknown" # Fallback in case there are any unhandled cases
  155. ))
  157. all_meta <- all_meta %>% filter(sample != "X")
  159. # Batch effect
  160. numeric_expression_data <- as.matrix(combined_matrix[, -1])
  162. mod <- model.matrix(~ tissue, data = all_meta)
  164. # Create a null model for the null hypothesis (no biological signal)
  165. mod0 <- model.matrix(~ 1, data = all_meta)
  167. # Estimate surrogate variables (SVs) using sva()
  168. svobj <- sva(numeric_expression_data, mod, mod0)
  170. batch_corrected <- limma::removeBatchEffect(numeric_expression_data, covariates = svobj$sv, design = mod0)
  172. # batch_corrected <- ComBat(dat = batch_corrected, batch = all_meta$batch, mod = NULL)
  174. data_for_pca <- t(batch_corrected)
  176. # Perform PCA
  177. pca_result <- prcomp(data_for_pca, scale. = TRUE)
  179. # Summary of PCA (optional)
  180. summary(pca_result)
  182. # Create a data frame of PCA results (scores)
  183. pca_scores <- as.data.frame(pca_result$x)
  185. # Add sample names as a column to the PCA scores
  186. pca_scores$sample <- rownames(pca_scores)
  188. pca_scores <- pca_scores %>%
  189. mutate(
  190. tissue = case_when(
  191. grepl("TUBULES", sample) ~ "tubules",
  192. grepl("LEG", sample) ~ "muscle",
  193. TRUE ~ "brain"
  194. ),
  195. species = case_when(
  196. grepl("FCONT", sample) ~ "bter",
  197. grepl("cont_", sample) ~ "obic",
  198. grepl("X", sample) ~ "lser",
  199. grepl("AU", sample) ~ "vcar",
  200. TRUE ~ "unknown" # Use this to catch any unexpected cases
  201. )
  202. )
  204. # Plot the PCA using ggplot2 with color indicating species and shape indicating tissue
  205. ggplot(pca_scores, aes(x = PC1, y = PC2, color = species, shape = tissue)) +
  206. geom_point(size = 3) +
  207. #geom_text(aes(label = sample), vjust = -1, size = 3) +
  208. labs(x = "PC1 [38.257%]", y = "PC2 [25.494%]") +
  209. scale_shape_manual(values = c(0,1,23))

09_tissue_species_conparison.R at commit 78331a5, no license · at the source

Overview

Authors: Alicja Witwicka1,2, Federico López-Osorio1,3, Courtney May1, Yeahji Jeong1, Yannick Wurm1,4
ORCID iDs: Courtney May
  1. Biology Department, Queen Mary University of London, London, UK
  2. Tree of Life, Wellcome Sanger Institute, Hinxton, UK
  3. Institute of Developmental Biology and Neurobiology, Johannes Gutenberg University Mainz, Mainz, Germany
  4. Digital Environment Research Institute, Queen Mary University of London, London, UK
Institutions: Queen Mary University of London (United Kingdom); Wellcome Sanger Institute (United Kingdom); Johannes Gutenberg University Mainz (Germany)
Journal: iScience, volume 29, issue 8, article 116878
Dates: received 29 October 2025; accepted 6 July 2026; published online 22 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.isci.2026.116878 · PMID 42540610 · PMCID PMC13426221 · OpenAlex W7170061677
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), other (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, Spectral & time-frequency
Keywords: pesticide assessment, neonicotinoids, acetamiprid, species-specific effects, toxicogenomics, butterflies, bees, flies
Topic: Insect and Pesticide Research (Insect Science, Agricultural and Biological Sciences), according to OpenAlex
Funding: Natural Environment Research Council (NE/L00626X/1, NE/S007229/1); European Union (H2020-MSCA-IF-2018-840185); RCUK | Biotechnology and Biological Sciences Research Council (BBSRC) (BB/T015683/1)
Citations: not cited yet (Europe PMC); 102 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

aswitwicka/species_insecticide_exposure

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 78331a5cd779535fc5c33e0d4547f76ab73a67c1, 22 October 2025
Languages: R (9)
Size: 11 files, 9 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: DESeq2 (3 files), tidyverse (3 files), clusterProfiler (1 file), igraph (1 file), limma (1 file), WGCNA (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/aswitwicka/species_insecticide_exposure

Zenodo 21206760

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: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: DESeq2 (3 files), tidyverse (3 files), clusterProfiler (1 file), igraph (1 file), limma (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
10 files
At the source: doi.org/10.5281/zenodo.21206760

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

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Read it in the paper: doi.org/10.1016/j.isci.2026.116878.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 8 keywords, 3 funders, 100 references.

Cite

This paper

Witwicka, A., López-Osorio, F., May, C., Jeong, Y., & Wurm, Y. (2026). Species-specific brain transcriptomic responses to clothianidin and sulfoxaflor in pollinators. iScience, 29(8), 116878. https://doi.org/10.1016/j.isci.2026.116878

BibTeX

@article{witwicka2026species,
author = {Witwicka, Alicja and López-Osorio, Federico and May, Courtney and Jeong, Yeahji and Wurm, Yannick},
title = {{Species-specific brain transcriptomic responses to clothianidin and sulfoxaflor in pollinators}},
journal = {iScience},
year = {2026},
month = jul,
volume = {29},
number = {8},
pages = {116878},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116878},
url = {https://doi.org/10.1016/j.isci.2026.116878},
pmid = {42540610},
pmcid = {PMC13426221}
}

RIS

TY - JOUR
AU - Witwicka, Alicja
AU - López-Osorio, Federico
AU - May, Courtney
AU - Jeong, Yeahji
AU - Wurm, Yannick
TI - Species-specific brain transcriptomic responses to clothianidin and sulfoxaflor in pollinators
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/07/22
VL - 29
IS - 8
SP - 116878
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116878
UR - https://doi.org/10.1016/j.isci.2026.116878
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Species-specific brain transcriptomic responses to clothianidin and sulfoxaflor in pollinators",
"container-title": "iScience",
"author": [
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"given": "Alicja"
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{
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"container-title-short": "iScience",
"volume": "29",
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"page": "116878",
"DOI": "10.1016/j.isci.2026.116878",
"PMID": "42540610",
"PMCID": "PMC13426221",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.116878",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
22
]
]
}
}

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