DOI: 10.5061/dryad.m0cfxpp9b
title: Data from: Epigenetic signatures of social status in wild spotted hyenas (Crocuta crocuta)

description: In mammalian societies, dominance hierarchies translate into inequalities
 in health, reproductive performance and survival. DNA methylation is
 thought to mediate the effects of social status on gene expression and
 phenotypic outcomes, yet, a study of social status-specific DNA
 methylation profiles in different age classes in a wild social mammal is
 missing. We tested for social status signatures in DNA methylation
 profiles in wild female spotted hyenas (Crocuta crocuta) in cubs and
 adults using non-invasively collected gut epithelium samples. In spotted
 hyena clans, female social status influences access to resources, foraging
 behaviour, health, reproductive performance and survival. We identified
 149 differentially methylated regions between 42 high- and low-ranking
 spotted hyena females (cubs and adults). Differentially methylated genes
 were associated with energy conversion, immune function and glutamate
 receptor signalling. Our results provide evidence that socio-environmental
 inequalities are reflected at the molecular level in cubs and adults in a
 wild mammal.
description: In the Serengeti National Park in Tanzania, we collected gut
 epithelium cells from faecal samples of 42 free-ranging female hyenas (24
 cubs and 18 adults) of either low (15 cubs, 9 adults) or high (9 cubs, 9
 adults) social status. In spotted hyenas, social status is determined by
 the animals’ interactions within their social group (clan). We extracted
 DNA and RNA from each sample. Epithelium cells contain mainly one
 cell-type, reducing the effect of cell-type specific methylation. We first
 performed whole genome bisulfite sequencing (WGBS), but mapping yielded in
 up to 99% bacterial data and parasitic DNA and the analysis with MACAU did
 not give coherent and in trustworthy results (Supplementary Table 7).
 Using methylation capture targeting CpG-methylation (with MBD2,
 MethylMiner, Invitrogen), we enriched methylated DNA fragments of hyenas.
 After Illumina sequencing (HiSeqX 150 bp PE, Macrogen), we mapped the
 quality-filtered reads to the novel spotted hyena reference
 sequence<sup>50 </sup>with the bowtie2
 mapper<sup>74</sup>. We identified rank-associated
 differentially methylated regions (DMRs), by splitting the genome in 300bp
 windows (selecting windows with 10 reads per position and a minimum of 10
 different individuals) and fitting a negative-binomial model using the
 EdgeR package<sup>75 </sup>with age class (cub, adult) and
 social status class (high-ranking, low-ranking) as fixed effects. We
 estimated the variance using the weighted likelihood empirical Bayes
 method. P-values were computed with Likelihood Ratio Tests, and False
 Discovery Rate (FDR) controlled with the Benjamini-Hochberg
 procedure<sup>76</sup>. Windows with a FDR ≤ 0.05 were
 considered significantly different and adjacent significant windows were
 merged to create DMRs (‘rankDMRs’). We annotated the rankDMRs by using the
 striped hyena annotation (<em>Hyena hyena, </em>RefSeq-
 Assembly ID: GCF_003009895.1) and by adding Transcription Start Sites
 (TSS) and promoter regions. We identified coherent DMRs using a
 subsampling procedure and estimated the discriminative power of rankDMRs
 and mean methylation using a random-forest
 approach<sup>77</sup>. Further details for all experimental,
 methodical and statistical and reproducibility can be found in
 Supplementary Material. We conducted this study under research permits
 from the Tanzania Commission for Science and Technology and permission
 from the Tanzanian National Parks Authority and Tanzanian Wildlife
 Research Institute. All procedures were performed in accordance with the
 Leibniz Institute for Zoo and Wildlife Research Ethics Committee on Animal
 Welfare (permit number: 2017-11-02).
description: # Data from: Epigenetic signatures of social status in free-ranging
 spotted hyenas (Crocuta crocuta) The MBD-Seq data are uploaded to NCBI
 BioProject ID PRJNA1036526, including reference genome
 "crocuta.fasta". The statistical analysis is available as a
 R-package “Weyrich23” at
 [https://github.com/vullioud/Weyrich23](https://github.com/vullioud/Weyrich23). Dryad data contain the novel annotation file and read count tables for our study DOI xxx. ## Description of the data and file structure **Annotation file "crocuta_liftoff_Hhy_ASM300989v1_addPromoter.gtf":** We performed the custom annotation file in which we incorporated relevant positions for epigenetic regulation (i.e. gene annotations, Transcription Start Sites (TSS), promoters). In a first step, we mapped the annotation of the recently published striped hyena (*Hyena hyena)* annotation (RefSeq Assembly ID: GCF_003009895.1) to the spotted hyena genome as closest relative using the Liftoff software (v1.6.1). This provided the annotation information "exon", "gene", "transcript", "start_codon", "CDS", "stop_codon". In a second step, we annotated transcription start sites (TSS) as the first position of the first exon of a gene, and the “promoter” regions as 2,000bp upstream the “TSS”. The annotation file includes the following functional regions (features): "promoter", "TSS”, "exon", "gene", "transcript", "start_codon", "CDS", "stop_codon". Exons for mitochondrial genes were added. Introns were calculated by subtraction of gene minus exon. Annotation was done with bash commands and R version 3.6. ***MBD-Seq table "full_300_ws_tables.rda"*** The MBD protein binds to methylated DNA fragments in genomic regions. More methylation in this region results in higher numbers of sequenced reads in this region and thus higher read counts (more read counts = more methylated, less reads counts = less methylated). All analyses were performed using R version 4.2.1. Data preparation was done using the Bioconductor package MEDIPS, and modelling was performed using the EdgeR package. We first created genomic windows of 300bp and normalized libraries using the method trimmed mean of M-values (TMM) (edgeR:normLibSizes) which normalizes the library sizes to minimize the log-fold changes between the samples (edgeR users guide). Second, windows were filtered for a minimal total coverage of 10 reads per position and a minimum of 10 different individuals with positive read count values. These windows represented our regions of interest, unless stated otherwise. Third, we fitted a negative-binomial model using the edgeR::glmFit() function with age class (cub, adult) and social status class (high-ranking, low-ranking) as fixed effects. The variance was estimated using the weighted likelihood empirical Bayes method (edgeR::EstimateDisp()). P-values were computed with Likelihood Ratio Tests (edgeR::glmLRT), and False Discovery Rate (FDR) controlled with the Benjamini-Hochberg procedure <sup>35</sup>. Windows with a FDR ≤ 0.05 were considered significantly different and adjacent significant windows were gathered to create DMRs. Finally, differentially methylated regions (DMRs) were annotated to their functional genomic region, using the novel custom-made annotation, including promoters, exons, introns, splice sites, 3’- and 5’-untranslated regions (3’UTRs, and 5’UTRs) of genes and intergenic regions. For four uncharacterized genes (with LOC ids) we identified gene names using the NCBI database search function. The STRING online tool <sup>36,37</sup>; [https://string-db.org](https://string-db.org), accessed on 03 January 2023; *Felis catus* genome used as reference) was used for network analysis with default settings and robust differentially methylated protein-encoding genes for detecting protein–protein interactions. **RNA table "RNA_Tables.rda"** We compared mitochondrial and nuclear gene expression (RNA-Seq). For the analysis of gene expression, we used RNA from samples from 16 different adult females (10 high-ranking and 6 low-ranking). Expression data were processed using the same pipeline as for the DNA methylation analysis. We used the region of interest consisted of transcript length per gene. We then computed the mean Count Per Million (cpm; function edgeR::CPM()) for all transcripts and all individuals in mitochondrial genes (n = 8) and nuclear genes (n = 7056). ## Sharing/Access information The MBD-Seq data are uploaded to NCBI BioProject ID PRJNA1036526, including reference genome "crocuta.fasta". ## Code/Software The statistical analysis is available as a R-package “Weyrich23” at [https://github.com/vullioud/Weyrich23](https://github.com/vullioud/Weyrich23).