DOI: 10.5061/dryad.7h44j107x
title: Data from: DNA methylation reflects tissue and caste identity but not parasitism-induced changes in a social insect

description: The role of DNA methylation in regulating transcriptional activity and
 post-transcriptional modifications in insects, particularly in social
 insects, remains debated. Social insect castes, primarily determined by
 differential gene expression, exhibit pronounced phenotypic differences in
 morphology, life history traits, and behaviour. In some ants, parasites
 induce divergent phenotypes, but the role of DNA methylation in this
 remains poorly understood. In this study, we examined DNA methylation
 patterns across different tissues (brain and fat body) and castes (queens
 and workers) in the ant Temnothorax nylanderi. We also compared healthy
 workers to those infected with the cestode Anomotaenia brevis, which is
 associated with changes in lifespan, morphology, and behaviour. Our
 results reveal significant differences in gene body methylation across
 tissues, which were positively linked to gene expression. We also observed
 caste-specific differences in DNA methylation, particularly in the fat
 body. Surprisingly, however, we detected no differences in DNA methylation
 between cestode-infected and healthy workers, despite pronounced
 differences in transcriptional activity, suggesting that other epigenetic
 mechanisms exert stronger regulatory control over parasite-induced
 transcriptional changes. The pronounced differences in DNA methylation
 between tissues and castes, partly associated with transcriptional
 variation, warrant further investigation into their functional role,
 including the modulation of alternative splicing.
description: # Data from: DNA methylation reflects tissue and caste identity but not
 parasitism-induced changes in a social insect Dataset DOI:
 [10.5061/dryad.7h44j107x](https://doi.org/10.5061/dryad.7h44j107x) ##
 Description of the data and file structure The data were generated to
 quantify genome-wide DNA methylation patterns and gene expression
 differences across castes, tissues, and infection states in the ant
 *Temnothorax nylanderi*. Whole-genome bisulfite sequencing and RNA-seq
 were performed on brain and fat body samples from queens, uninfected
 workers, and parasite-infected workers to assess how caste identity and
 parasite manipulation influence epigenetic and transcriptional profiles.
 ### Files and variables ### File: Supplementary_File_1-Table_1.csv
 **Description:** Overview of colony-level information for samples used in
 Whole-Genome Bisulfite Sequencing (WGBS), Whole-Genome Sequencing (WGS),
 and RNA sequencing (RNA-seq). For each colony, the table reports the
 sample collection date, count date, colony ID, and the number of
 individuals of each caste or developmental stage, including queens,
 workers (infected and uninfected), pupae, larvae, and males. Queens
 belonging to the colonies that start with "Q" are only included
 in RNA-seq analysis. ### File: Supplementary_File_1-Table_2.csv
 **Description:** Summary of sequencing and alignment quality metrics for
 all samples from WGBS, including duplication rate, GC content, number of
 methylation-informative reads, and alignment performance. Note: “NA”
 indicates that the metric was not applicable for a given sample (e.g.,
 sequencing-only metrics vs alignment-only metrics). NA does not indicate
 missing data. ### File: Supplementary_File_1-Table_3.csv **Description:**
 Summary of sequencing and alignment quality metrics for all samples from
 WGS, including duplication rate, GC content, number of informative reads,
 and alignment performance. Note: “NA” indicates that the metric was not
 applicable for a given sample (e.g., sequencing-only metrics vs
 alignment-only metrics). NA does not indicate missing data. ### File:
 Supplementary_File_1-Table_4.csv **Description:** Summary of sequencing
 and alignment quality metrics for all samples from RNA-seq, including
 duplication rate, GC content, number of uniquely mapped reads, and
 alignment performance.Note: “NA” indicates that the metric was not
 applicable for a given sample (e.g., sequencing-only metrics vs
 alignment-only metrics). NA does not indicate missing data. ### File:
 Supplementary_File_2-Table_1.csv **Description:** Methylation quality
 metrics per sample. Lambda phage bisulfite conversion rates and mean
 methylation levels for each sample, with experimental group labels and
 relevant metadata for quality assessment. ### File:
 Supplementary_File_2-Table_2.csv **Description:** CpG colocalization data.
 Summary of CpG sites and their genomic colocalization information using
 two annotation levels: "Basic" at exon and "Detailed"
 at CDS, 5′UTR and 3′UTR levels. Each row provides the region identity and
 the corresponding methylated and all CpG counts. ### File:
 Supplementary_File_2-Table_3.csv **Description:** Classification of genes
 based on DNA methylation consistency across samples.\ For each gene, the
 table reports the number of CpG sites that were classified as methylated
 in every sample. ### File: Supplementary_File_2-Table_4.csv
 **Description:** Classification of genes based on DNA methylation
 consistency across samples. Gene-level expression values were used to
 compare the expression profiles of consistently methylated genes
 (Housekeeping) versus other genes in the genome. For each gene, the raw
 median expression across samples was length-normalized (per kilobase) and
 log₂-transformed. ### File: Supplementary_File_2-Table_5.csv
 **Description:** Classification of genes based on DNA methylation
 consistency across samples. For each gene, expression values were
 log₂-transformed and used to compute the median absolute deviation (MAD)
 and standard deviation (SD), allowing comparison between consistently
 methylated (Housekeeping) genes and all other genes. ### File:
 Supplementary_File_2-Table_6.csv **Description:** DMLs with genomic
 position, Q-value, methylation difference, and classification
 (hyper/hypomethylated) for each tissue/caste comparison. DMLs were
 considered hypo or hypermethylated if they have a methylation difference
 of more than 10% and qvalue &lt; 0.01 in the genome between tissue and
 castes. ### File: Supplementary_File_2-Table_7.csv
 **Description:** Summary of differential methylation metrics (average
 difference, number of DMLs, weighted Scores) by tissue and caste
 comparisons. ### File: Supplementary_File_3-Table_1.csv
 **Description:** Raw gene-level read counts obtained from featureCounts,
 with genes listed in rows and samples in columns. These counts represent
 the input data for DESeq2 differential expression analysis and include all
 samples used in the study. ### File: Supplementary_File_3-Table_2.csv
 **Description:** Differential gene expression results from multiple
 pairwise RNA-seq comparisons generated with DESeq2 are presented in this
 table. For each gene and comparison, the table reports the gene identifier
 (GeneID), average normalised expression (baseMean), log2 fold change
 (log2FoldChange), standard error of the fold change estimate (lfcSE), Wald
 test statistic (stat), raw p-value (pvalue), and adjusted p-value (padj).
 ### File: Supplementary_File_4.zip **Description:** **GO Enrichment
 Background Set**\ This file provides the complete list of Gene Ontology
 (GO) terms used as the background universe for GO enrichment analyses. ###
 **File: Supplementary_File_5-Table_1.csv**  **Description:** GO enrichment
 terms for differentially expressed genes processed with REVIGO to reduce
 semantic redundancy based on GO term similarity. The table includes
 results from comparisons across tissue types, castes, and infection
 status. ### File: Supplementary_File_5-Table_2.csv
 **Description:** REVIGO-processed GO enrichment terms for genes grouped by
 DNA methylation status, including differentially methylated genes and
 genes with balanced methylation between brain and fat body. The table
 includes results from comparisons across tissue types, castes, and
 infection status. ### File: Supplementary_File_6.html
 **Description:** **Full Analysis Workflow (HTML Format)**:\ A complete,
 reproducible report of the analysis pipeline, including both Bash and R
 code. The workflow spans all steps from raw data processing to figure
 generation: quality control, alignment, methylation calling,
 preprocessing, differential analysis, normalisation, dataset integration,
 GO enrichment, and visualisation. ### File: Supplementary_Figures.pdf
 **Description:** Supplementary histograms and density plots showing
 genome-wide distributions of weighted gene-level methylation differences
 across tissues and castes. ## Code/software File 6. Full analysis workflow
 (HTML).\ This HTML report contains the complete, reproducible workflow
 used in the study. It documents all Bash and R steps executed on a Linux
 HPC environment, including quality control, alignment, methylation
 calling, preprocessing, differential analysis, normalisation, dataset
 integration, GO enrichment, and visualisation. All software versions (R,
 Bismark/Bowtie2, SAMtools, BEDTools, and R packages) match those reported
 in the Methods section of the manuscript. The file can be opened in any
 standard web browser. ## Access information Other publicly accessible
 locations of the data: * None. All data associated with this study are
 deposited exclusively in Dryad, Zenodo, and NCBI SRA as
 described. BioProject accession: PRJNA1297301 Data was derived from the
 following sources: * All datasets were generated specifically for this
 study; no external datasets were used.