Brain and antennal transcriptomes of host ants reveal potential links between behaviour and the functioning of socially parasitic colonies

Published: 25 July 2023| Version 1 | DOI: 10.17632/32kr9b2bk7.1
Marah Stoldt,
, Maide Macit,


Social ant parasites are characterised by exploiting the social behaviour of their hosts. Why exactly hosts direct their altruistic behaviour towards these parasites and their offspring remains largely unstudied. One hypothesis is that hosts do not perceive their social environment as altered and accept the parasitic colony as their own. We used the ant Leptothorax acervorum, host of the dulotic, obligate social parasite Harpagoxenus sublaevis to shed light on molecular mechanisms underlying behavioural exploitation by contrasting tissue-specific gene expression in young host ants. Therefore, host pupae were experimentally (re-)introduced into parasitic colonies, their natal, or another conspecific colony. After the emerged host workers were about 10 weeks old, we recorded their behaviour and then sampled them for sequencing of antennal and brain gene expression to analyse genes affected by the social environment the ants lived in as well as those genes that correlate with the behaviours performed the hour before sampling.


Steps to reproduce

About 10 weeks (63-69 days) after the emergence of the first worker, colonies were transferred to 22°C and the red foil from their glass nest was removed to allow workers to adapt to light. The next day, the slide nest was transferred to a fluon-treated arena (3 cm x 7.5 cm) and each colony was filmed for 100 min in 4k using a SONY FDR-AX33 camera under a Leica KL1500 LED light (resulting in video files available under Zenodo) Ant behaviour was analysed by scan sampling (1 scan every 2 min, 30 scans in total) for the last 60 min of video recording using QuickTime Player 7.6.6. (resulting in file Ethogram-scan.xlsx). The recorded behaviours and the location were then analysed using Principal Component Analyses (PCA) separately using the packages FactoMineR, factoextra, and missMDA with R version 4.1.2. Individual Principal Components (PCs), which explain at least ten percent of the variance, were extracted (resulting in files PCA_location_short.txt and PCA_behavior_short.txt). Additionally, both antennal and brain transcriptomes were sequenced (RNA reads available under SRA Bioproject PRJNA865882) and processed using the scripts within the scripts folder in the following steps: -removing contaminations using FastQScreen v0.14.0 (see Supplementary Information for details about databases used for FastQSCreen) -adapter- and quality-trimmed using Trimmomatic v0.39 -reads were mapped against the genome assembly of L. acervorum (Jongepier et al., 2022) using HISAT2 v2.1.0 -StringTie v1.3.6 was used to create a genome-guided transcriptome assembly -transcript assemblies were then filtered to only retain contigs which contained an open-reading frame (ORF) of more than 149 bp as identified by TransRate v1.0.3 -transcripts were translated to their most likely protein sequence using TransDecoder v5.5.0 -Gene Ontology terms were assigned to these predicted proteins based on the presence of functional domains identified using InterProScan v5.51-85.0 (GO to gene ID maps can be found under transcripts_lace_antenna_filtered.fs.transdecoder_edit.fasta.onlyGO_edit.tsv and transcripts_lace_brain_filtered.fs.transdecoder_edit.fasta.onlyGO_edit.tsv). -quantified gene counts using the script retrieved from (resulted in files gene_count_matrix_antennae_lace.csv and gene_count_matrix_brain_lace.csv) -removed genes with less than ten reads in at least five samples, irrespective of treatment, as they likely represent noise This resulted in a separate gene count matrix for each tissue (see gene_count_matrix_antennae_lace.csv and gene_count_matrix_brain_lace.csv). The statistical analyses described in the workflow file resulted in differentially expressed genes between the three treatments for each tissue (see Summary_DEGs) using DESeq2, modules of genes which are coexpressed using WGCNA (see WGCNA_modules_both_tissues.xlsx) as well as the corresponding enriched GO terms (see Summary_TopGO.xlsx) using topGO.


Johannes Gutenberg Universitat Mainz


Animal Behavior, RNA Sequencing, Differential Gene Expression, Gene Ontology