Data processing and analysis
We used the nf-core-chipseq pipeline version 1.2.2 (Ewels et al., 2020; Patel et al., 2021) to identify differentially enriched peaks. The pipeline includes Trimgalore(https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) for trimming and adapter removal. We also used BWA-mem (Li, 2013) to map the reads to a high-contiguity genome assembly of the painted lady (Lohse et al., 2021). The Model based Analysis of ChIP-Seq (MACS2) package (Zhang et al., 2008) was applied to identify read coverage significantly higher than the random genome-wide variation and to construct consensus peaks. For determining number and location of enrichment of histone marks we used only consensus peaks called in all four groups (treatment*replicates). The analysis of differential activation with hostplant availability as contrast was performed with this requirement relaxed to account for differential enrichment of peaks absent in one of the treatments. The count data for the peaks were transformed with Voom (Law et al., 2014), for linear models in the R-package Limma (Ritchie et al., 2015) to detect differentially enriched peaks between the two treatment groups. To correct for multiple testing, the p-values were adjusted with the Benjamini-Hochberg false discovery rate as implemented in Limma . We used previously available annotation information (Shipilina et al., 2022) to identify the gene located closest to each differentially activated region. Potential functions of candidate genes were obtained from the annotation in combination with homology searches withBLAST to the NCBI database using the nucleotide sequence of each candidate gene (Altschul et al., 1990). Additional functional information was extracted from Flybase (https://flybase.org).
Results