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Exposure to non-nestmate odors changes the odorant receptor profile in <i>Acromyrmex echinatior</i> leaf-cutting ants.

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  1. [1] § STAR★Methods › Quantification and statistical analysis › Transcriptome data ↔ Bey-habituation-transcriptome-analysis.R, lines 122–170 · score 0.60 · plotPCA, rlogTransformation, DEGs, rows, DESeq2, transforms

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The authors' code

R · 326 lines · 13 KB · no license · 1 match

  1. ##########Bey et al. 2026 transcriptome analysis
  3. #############functions to be used later
  4. filterreadcounts<-function(counts,design,minreads,minsamples){
  5. row.names(design) <- colnames(counts[0,])
  6. library(dplyr)
  7. Design_ant <- design %>% mutate_if(is.character,as.factor)
  9. all(rownames(design) %in% colnames(counts)) #TRUE
  10. all(rownames(design) == colnames(counts)) #TRUE
  12. filtered <- counts
  13. filtered[filtered < minreads] <- NA #mark gene/sample combination with <minreads counts with NA
  15. # remove genes that have reads in fewer than $minsamples samples
  16. filtermist=rowSums(is.na(filtered))
  17. filtermist[filtermist > minsamples] <- NA#mark genes with fewer than minsamples samples with < minreads reads
  19. counts_filtered <- counts[!is.na(filtermist),]#remove genes
  20. rm(filtermist,filtered)
  22. return(counts_filtered)
  23. }
  27. allgenesconsistent<-function(counts,design){
  28. #plots pca of the expression of all genes; tests for consistent treatment effects
  29. library(DESeq2)
  30. library(ggplot2)
  31. dds <- DESeqDataSetFromMatrix(countData = counts, colData = design, design = ~ colony+habituation)
  32. dds <- DESeq(dds)
  33. rld <- rlogTransformation(dds)
  34. allpca <- plotPCA(rld, intgroup=c("habituation"), returnData=TRUE)
  35. plot(allpca[,1:2],col=design$colony,pch=19)
  37. ana=DESeq(dds,test="LRT",reduced=~colony)
  38. exposure_additive=na.omit(results(ana))
  39. return(exposure_additive)
  40. }
  44. plotcolonyeffect <- function (pca,COLOR=0){
  45. #PCA plot
  46. percentVar <- round(100 * attr(pca, "percentVar"))
  48. p1<- ggplot(pca, aes(PC1, PC2, shape=colony)) +
  49. geom_point(size=12) +
  50. scale_shape_manual(values=c(1, 19))+
  51. xlab(paste0("PC1: ",percentVar[1],"% variance")) +
  52. ylab(paste0("PC2: ",percentVar[2],"% variance"))+
  53. theme(axis.text=element_text(size=20),
  54. axis.title=element_text(size=20,face="bold"),
  55. panel.background=element_rect(fill = COLOR, colour = "black"),
  56. panel.grid.major = element_blank(),
  57. panel.grid.minor = element_blank(),
  58. legend.position = "none")+
  59. coord_cartesian(ylim = c(-10, 10))+
  60. stat_ellipse()
  62. return(p1)
  63. }
  66. library(MASS)
  67. library(ggplot2)
  68. library(ggbeeswarm)
  70. #####correlation of OR and orco reads
  72. orcof<-function(orcocor){
  74. orcocor$habituation<-as.character(orcocor$habituation)
  75. orcocor$habituation[orcocor$habituation=="Pentan"]<-"control"
  76. orcocor$habituation[orcocor$habituation=="Ae"]<-"conspecific"
  77. orcocor$habituation[orcocor$habituation=="Ao"]<-"allospecific"
  78. orcocor$habituation<-as.factor(orcocor$habituation)
  79. orcocor$habituation<-relevel(orcocor$habituation,ref="conspecific")
  80. orcocor$habituation<-relevel(orcocor$habituation,ref="control")
  82. ggplot(orcocor, aes(x=orco, y=ORsum)) +
  83. geom_point(aes(color=habituation, shape=colony), size=12) +
  84. geom_smooth(method=lm, se=FALSE, fullrange=TRUE,aes(color=habituation)) +
  85. scale_shape_manual(values=c(1, 19))+
  86. scale_color_manual(values = c("yellow2", "blue4","lightgreen")) +
  87. labs(x = "Orco reads", y = "Sum OR reads")+
  88. theme(axis.text=element_text(size=54, face="bold"),
  89. axis.title=element_text(size=54,face="bold"),
  90. panel.background=element_rect(fill = "white", colour = "black"),
  91. legend.position = "none",
  92. legend.text = element_text(size = 40),
  93. plot.margin = margin(r = 50, t=10,l=10,unit = "pt") # Extend margin
  94. )+
  95. labs(colour = "", shape = "")
  96. ggsave(filename ="FigS3E-orco-correlation.pdf",width = 20, height = 12, dpi = 1200)
  100. ggplot(orcocor, aes(x = habituation, y = ratio)) +
  101. geom_beeswarm(aes(color=habituation, shape=colony),size=12)+
  102. scale_shape_manual(values=c(1, 19))+
  103. scale_color_manual(values = c("yellow2", "blue4","lightgreen")) +
  104. labs(x = "", y = "Ratio OR reads / ORco reads") +
  105. theme(axis.text=element_text(size=54, face="bold"),
  106. axis.title=element_text(size=54,face="bold"),
  107. panel.background=element_rect(fill = "white", colour = "black"),
  108. legend.position = "none",
  109. legend.text = element_text(size = 40))+
  110. labs(colour = "", shape = "") +
  111. ylim(0, 17) +
  112. geom_segment(aes(x = 1, y = 12, xend = 2, yend = 12), linewidth = 1)+
  113. geom_text(aes(x = 1.5, y = 12.5, label = "**"), size = 40)+
  114. geom_segment(aes(x = 1, y = 15, xend = 3, yend = 15), linewidth = 1) +
  115. geom_text(aes(x = 2, y = 15.5, label = "*"), size = 40)
  118. ggsave(filename ="Fig2E-orco.pdf",width = 16, height = 12, dpi = 1200)
  119. }
  122. #reading read count table and design table
  124. counts <- read.table("readcounttable_habituation_allsamples.csv",
  125. header=TRUE,row.names = 1, sep=";", na.strings="NA", dec=".", strip.white=TRUE)
  127. design <- read.table("design_habituation.csv",
  128. header=TRUE, sep=";", na.strings="NA", dec=".", strip.white=TRUE,stringsAsFactors = T)
  130. rownames(design)<-colnames(counts)
  131. summary(design)
  135. #filtering read counts:
  136. frcounts=filterreadcounts(counts=counts,design=design,minreads=10,minsamples=8)#removing all genes that have fewer than 10 reads in more than 8 samples (smallest treatment group size is 9)
  137. dim(frcounts)
  138. dim(counts)
  140. ##############testing for consistent effects of odour exposure
  141. exposure_additive=allgenesconsistent(counts,design)
  142. write.csv(exposure_additive,file="SI-table-7-DEGs-allgenes-bothcolonies_exposure_effect.csv")
  143. exposure_additive[exposure_additive$padj<=0.05,]#list of DEGS
  144. nrow(exposure_additive[exposure_additive$padj<=0.05,])#number of DEGs for exposure
  147. gg=rownames(exposure_additive[exposure_additive$padj<=0.05,])#list of DEGS
  148. for (i in 1:length(gg)){
  149. boxplot(as.integer(counts[rownames(counts)==gg[i],])~design$habituation
  150. ,main=gg[i])
  151. }
  155. #####creating a read count table for ORs only:
  156. ORcounts=counts [grepl("AechOR", rownames(counts)),]
  157. dim(ORcounts)#number of OR genes and samples
  160. ####Analyse colony differences for each treatment separately
  161. #Pentane:
  162. ddsPen <- DESeqDataSetFromMatrix(
  163. countData = ORcounts[design$habituation=="Pentan"]
  164. , colData = design[design$habituation=="Pentan",]
  165. , design = ~ colony)
  166. write.csv(results(DESeq(ddsPen), contrast = c("colony", "Ae21","Ae32"))
  167. ,file="SI-table-5-OR-colony-effect-Pentane.csv")
  169. pcaPen <- plotPCA(rlogTransformation(ddsPen), intgroup=c("colony"), returnData=TRUE)
  170. summary(manova(as.matrix(pcaPen[,1:2])~pcaPen$colony),test="Pillai")#testing if colonies differ
  173. #A. echinatior exposure:
  174. ddsAe <- DESeqDataSetFromMatrix(
  175. countData = ORcounts[design$habituation=="Ae"]
  176. , colData = design[design$habituation=="Ae",]
  177. , design = ~ colony)
  178. write.csv(results(DESeq(ddsAe), contrast = c("colony", "Ae21","Ae32"))
  179. ,file="SI-table-5-OR-colony-effect-Ae.csv")
  181. pcaAe <- plotPCA(rlogTransformation(ddsAe), intgroup=c("colony"), returnData=TRUE)
  183. summary(manova(as.matrix(pcaAe[,1:2])~pcaAe$colony),test="Pillai")#testing if colonies differ
  185. #A. octospinosus exposure:
  186. ddsAo <- DESeqDataSetFromMatrix(
  187. countData = ORcounts[design$habituation=="Ao"]
  188. , colData = design[design$habituation=="Ao",]
  189. , design = ~ colony)
  190. write.csv(results(DESeq(ddsAo), contrast = c("colony", "Ae21","Ae32"))
  191. ,file="SI-table-5-OR-colony-effect-Ao.csv")
  193. pcaAo <- plotPCA(rlogTransformation(ddsAo), intgroup=c("colony"), returnData=TRUE)
  194. summary(manova(as.matrix(pcaAo[,1:2])~pcaAo$colony),test="Pillai")#testing if colonies differ
  196. sigPen=nrow(subset(results(DESeq(ddsPen), contrast = c("colony", "Ae21","Ae32")),padj < 0.05))
  197. sigAe=nrow(subset(results(DESeq(ddsAe), contrast = c("colony", "Ae21","Ae32")),padj < 0.05))
  198. sigAo=nrow(subset(results(DESeq(ddsAo), contrast = c("colony", "Ae21","Ae32")),padj < 0.05))
  199. chisq.test(matrix(c(sigPen,sigAe,sigAo,435-sigPen,435-sigAe,435-sigAo),nrow=3))
  201. #Figure 2A-C:
  202. library(egg)
  203. pPen=plotcolonyeffect(pcaPen,COLOR="lightyellow")#PCA plot
  204. pAe=plotcolonyeffect(pcaAe,COLOR="#BFB8FF")#PCA plot
  205. pAo=plotcolonyeffect(pcaAo,COLOR="#B8FFC0")#PCA plot
  206. ggarrange(pPen, pAe,pAo, ncol=3)
  211. ######Analyse which ORs are affected by treatment, separately for each colony
  212. dds21 <- DESeqDataSetFromMatrix(
  213. countData = ORcounts[design$colony=="Ae21"]
  214. , colData = design[design$colony=="Ae21",]
  215. , design = ~ habituation)
  216. ana21=DESeq(dds21)
  217. write.csv(results(ana21, contrast = c("habituation", "Ae", "Pentan"))
  218. ,file="SI-table-6-OR-habituationPenAe-colony21.csv")
  219. write.csv(results(ana21, contrast = c("habituation", "Ao", "Pentan"))
  220. ,file="SI-table-6-OR-habituationPenAo-colony21.csv")
  222. dds32 <- DESeqDataSetFromMatrix(
  223. countData = ORcounts[design$colony=="Ae32"]
  224. , colData = design[design$colony=="Ae32",]
  225. , design = ~ habituation)
  226. ana32=DESeq(dds32)
  227. write.csv(results(ana32, contrast = c("habituation", "Ae", "Pentan"))
  228. ,file="SI-table-6-OR-habituationPenAe-colony32.csv")
  229. write.csv(results(ana32, contrast = c("habituation", "Ao", "Pentan"))
  230. ,file="SI-table-6-OR-habituationPenAo-colony32.csv")
  235. #####correlation of OR and orco reads
  236. ORsum=apply(ORcounts,2,sum)#sum of all OR reads per sample
  237. orco=as.integer(counts[grepl("AechOrco", rownames(counts)),])
  238. orcocor=data.frame(ORsum, orco,habituation = design$habituation,colony=design$colony,ratio=ORsum/ orco)
  241. orcof(orcocor)#figures 2E & S3
  243. #effect of orco on sum of OR reads
  244. anova(glm.nb(ORsum~colony+habituation*orco,data=orcocor))
  246. #total OR / orco ratio:
  247. wilcox.test(ratio~habituation,data=orcocor[orcocor$habituation!="Ae",])#Pentane vs Ao
  248. wilcox.test(ratio~habituation,data=orcocor[orcocor$habituation!="Ao",])#Pentane vs Ae
  251. #####distribution of ORs affected by colony or treatment over the OR subfamilies
  252. annotation<- read.csv("SI-table-8-AechORsubfamilies.csv",stringsAsFactors = T)
  254. colony <- read.csv("SI-table-5-OR-colony-effect-Pentane.csv")#Deseq on colony difference
  255. expressed=colony$X#all ORs that are expressed
  256. #colonydiff=na.omit(colony[colony$padj<0.05,])$X#only DE ORs
  257. colonydiff=na.omit(colony[colony$pvalue<0.05,])$X#all ORs with unadjusted p < 0.05
  259. length(expressed)
  260. length(colonydiff)
  261. length(annotation$OR)
  264. unknownfamily=setdiff(expressed,annotation$OR)#OR genes without family annotation
  266. new=cbind(unknownfamily,"unknown")#annotating unannotated ORs with "unknown"
  267. colnames(new)<-c("OR","Subfamily")
  269. allors=rbind(annotation,new)# all ORs with their subfamily (including an "unknown" group)
  270. colonydifference=as.vector(table(allors[allors$OR %in% colonydiff,]$Subfamily))#count which ORs are in colonydiff list
  272. ##pulling up ORs with habituation effects
  273. Ae21 <- read.csv("SI-table-6-OR-habituationPenAe-colony21.csv")
  274. Ae32 <- read.csv("SI-table-6-OR-habituationPenAe-colony32.csv")
  275. Ao21 <- read.csv("SI-table-6-OR-habituationPenAo-colony21.csv")
  276. Ao32 <- read.csv("SI-table-6-OR-habituationPenAo-colony32.csv")
  277. allhabituation=rbind(Ae21,Ao21,Ae32,Ao32)
  278. #habituationors=na.omit(allhabituation[allhabituation$padj<0.05,])$X#only DE ORs
  279. habituationors=na.omit(allhabituation[allhabituation$pvalue<0.05,])$X#pre-correction
  280. habituationors=unique(habituationors)#all OR genes that were affected by odour exposure in any combination of colony and odour
  281. habituationdifference=as.vector(table(allors[allors$OR %in% habituationors,]$Subfamily))#count which ORs are in habituation diff list
  284. ##############combining data and testing
  286. all=data.frame(table(allors$Subfamily),colonydifference,habituationdifference) #subfamily names and distribution in the genome and DEG lists
  287. colnames(all)[1:2]<-c("subfamily","genome")
  290. mat=as.matrix(t(all[,-1]))#transforming subfamily count table into matrix
  291. colnames(mat)<-all[,1]
  292. sums=apply(mat,1,sum)#total number of ORs in genome and number od DE ORs for the effects
  294. fisher.test(mat,simulate.p.value = T)#genome vs. colony difference vs habituation overall
  295. fisher.test(mat[1:2,],simulate.p.value = T)#genome vs. colony difference overall
  296. fisher.test(mat[c(1,3),],simulate.p.value = T)#genome vs. colony difference overall
  298. #significance testing using fisher's exact test
  299. fishtest=data.frame(family="",overall=0,colony=0,habituation=0)
  300. for (i in 1:ncol(mat)){
  301. oc=sum(mat[2:3,i]>0)# with habituation
  302. fishtest[i,]=c(
  303. colnames(mat)[i]
  304. ,fisher.test(matrix(c(sums-mat[,i],mat[,i]),nrow=3))$p#overall
  305. ,fisher.test(matrix(c(sums[c(1,2)]-mat[c(1,2),i],mat[c(1,2),i]),nrow=2))$p#colony difference
  306. ,fisher.test(matrix(c(sums[c(1,3)]-mat[c(1,3),i],mat[c(1,3),i]),nrow=2))$p#habituation
  307. )
  308. if (mat[1,i]<3){ fishtest[i,2:4]<-NA }
  309. }
  312. all=data.frame(all,pcolony=fishtest$colony,phabituation=fishtest$habituation)
  313. write.csv(all, file="SI-table-8-ORspersubfamily-pre-correction.csv")
  315. pdf("Fig3B.pdf",width=4,height=3)
  316. par(mar=c(4.3,4,0.5,0.2))
  317. used=na.omit(all)
  318. used=used[used$subfamily!="unknown",]
  319. mx=data.frame(used$genome/sums[1]*100,used$habituation/sums[3]*100)
  320. xx=barplot(as.matrix(t(mx)),beside = T,col=c("black","steelblue"),ylab="% of total genes",las=1,ylim=c(0,50))
  321. xxx=apply(xx,2,mean)
  322. mtext(side=1, line=1,at=xxx,text = used$subfamily)
  323. legend(x="topright",col=c("black","steelblue"),pch=15,legend=c("in genome","affected by exposure"))
  324. text(x=xxx[c(1,7)],y=c(48,20),labels=c("**","*"),cex=3)
  325. mtext(side=1,line=3,"OR subfamily")
  326. dev.off()

Bey-habituation-transcriptome-analysis.R, no license · at the source

Overview

Authors: Mélanie Bey1, Naomi Jeanne Luna Alex1, Lisa Maczkowicz1, Yoann Pellen2, Joel Vizueta3, Volker Nehring1
ORCID iDs: Volker Nehring
  1. Department of Evolutionary Biology and Ecology, Institute of Biology I, University of Freiburg, Freiburg, Germany
  2. Institute of Evolution, Department of Evolutionary and Environmental Biology, University of Haifa, Haifa, Israel
  3. Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark
Institutions: University of Freiburg (Germany); University of Haifa (Israel); University of Copenhagen (Denmark)
Journal: iScience, volume 29, issue 8, article 116665
Dates: received 11 December 2025; accepted 17 June 2026; published online 13 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116665 · PMID 42519063 · PMCID PMC13382326 · OpenAlex W7168165173
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: chemoreception, habituation, learning, nestmate recognition
Topic: Insect and Arachnid Ecology and Behavior (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Smithsonian Tropical Research Institute; German Research Foundation (NE 1969/6-1)
Citations: not cited yet (Europe PMC); 67 references in the paper

Abstract

Social insects recognize their nestmates by colony-specific olfactory labels stored as neural templates in the memory. The template is often considered a product of learning in higher brain centers. However, some evidence suggests that the template may be stored in the neural periphery, i.e., the antennae or antennal lobes. We investigated a potential mechanism for peripheral nestmate recognition templates: the composition and plasticity of the antennal odorant receptor (OR) profile. We found that OR gene expression in leaf-cutting ants is colony-specific, mirroring the colony-specific recognition labels. After exposure to non-nestmate odors, the ants exhibited reduced aggression toward the non-nestmate label, indicating habituation. When we exposed two different colonies to the same non-nestmate odor, their OR profiles converged. These results suggest that the olfactory system can adapt to the current nest-specific olfactory environment and may explain how habituation plays a role in nestmate recognition.

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

Repository

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supp:PMC13382326/mmc6.zip

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 1 file, 1 script
Software Heritage: not checked
Found in: the supplementary material
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: DESeq2 (1 file), ggplot2 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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1 file
At the source: pmc-oa-opendata.s3.amazonaws.com/PMC13382326.1/mmc6.zip

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

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Data

Datasets cited

Data and code availability

Raw behavioral data are available from Table S9. Raw sequences are available from NCBI under the BioProject: PRJNA1147124 (https://ncbi.nlm.nih.gov/sra?term=PRJNA1147124).

All original code is available in File S1 of the manuscript.

Any additional information required to reanalyze the data reported in this study is available from the lead contact upon request.

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, 6 authors, 4 keywords, 2 funders, 57 references.

Cite

This paper

Bey, M., Alex, N. J. L., Maczkowicz, L., Pellen, Y., Vizueta, J., & Nehring, V. (2026). Exposure to non-nestmate odors changes the odorant receptor profile in &lt;i&gt;Acromyrmex echinatior&lt;/i&gt; leaf-cutting ants. iScience, 29(8), 116665. https://doi.org/10.1016/j.isci.2026.116665

BibTeX

@article{bey2026exposure,
author = {Bey, Mélanie and Alex, Naomi Jeanne Luna and Maczkowicz, Lisa and Pellen, Yoann and Vizueta, Joel and Nehring, Volker},
title = {{Exposure to non-nestmate odors changes the odorant receptor profile in \&lt;i\&gt;Acromyrmex echinatior\&lt;/i\&gt; leaf-cutting ants}},
journal = {iScience},
year = {2026},
month = jul,
volume = {29},
number = {8},
pages = {116665},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116665},
url = {https://doi.org/10.1016/j.isci.2026.116665},
pmid = {42519063},
pmcid = {PMC13382326}
}

RIS

TY - JOUR
AU - Bey, Mélanie
AU - Alex, Naomi Jeanne Luna
AU - Maczkowicz, Lisa
AU - Pellen, Yoann
AU - Vizueta, Joel
AU - Nehring, Volker
TI - Exposure to non-nestmate odors changes the odorant receptor profile in &lt;i&gt;Acromyrmex echinatior&lt;/i&gt; leaf-cutting ants
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/07/13
VL - 29
IS - 8
SP - 116665
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116665
UR - https://doi.org/10.1016/j.isci.2026.116665
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Exposure to non-nestmate odors changes the odorant receptor profile in &lt;i&gt;Acromyrmex echinatior&lt;/i&gt; leaf-cutting ants",
"container-title": "iScience",
"author": [
{
"family": "Bey",
"given": "Mélanie"
},
{
"family": "Alex",
"given": "Naomi Jeanne Luna"
},
{
"family": "Maczkowicz",
"given": "Lisa"
},
{
"family": "Pellen",
"given": "Yoann"
},
{
"family": "Vizueta",
"given": "Joel"
},
{
"family": "Nehring",
"given": "Volker"
}
],
"container-title-short": "iScience",
"volume": "29",
"issue": "8",
"page": "116665",
"DOI": "10.1016/j.isci.2026.116665",
"PMID": "42519063",
"PMCID": "PMC13382326",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.116665",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
13
]
]
}
}

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