DOI: 10.5061/dryad.j9kd51crw
title: Data from: Divergent sensory transcriptomic profiles in positive and negative learning in <em>Bicyclus anynana</em> butterflies

description: Mate preference learning, where individuals learn to prefer or avoid
 specific phenotypes during mate selection, is pervasive across animal taxa
 and influences reproductive isolation and trait evolution. Despite its
 significance, the genetic basis underlying the associated valence
 attribution (whether to prefer or avoid) remains largely unclear. Both in
 terms of what genes are associated with attributing valence, and whether
 valence attribution is associated with transcriptional changes in both
 sensory tissues and neural circuits in the brain, or is restricted to the
 brain.  Here, we investigate the neurogenomic basis of positive
 and negative mate preference learning using Bicyclus anynana butterflies.
 We put females in positive or negative learning scenarios, as well as a
 naïve control, and compared their transcriptomic profiles across three
 tissues: antennae, eyes, and brain using RNA-sequencing. Our results
 reveal tissue-specific transcriptional responses, with the antennae
 showing higher transcriptomic changes during negative learning and the
 eyes showing higher transcriptomic changes during positive learning,
 relative to the naïve control, indicating that valence attribution during
 learning may not be restricted to transcriptional changes in the brain. We
 identified a subset of genes in each tissue whose expression patterns
 changed with valence, as well as genes previously linked to classical
 conditioning pathways, supporting the hypothesis that imprinting-like
 learning and classical conditioning share molecular mechanisms. Our
 findings suggest that valence attribution in mate preference learning
 involves tissue-specific transcriptional responses in sensory and brain
 tissues, emphasising the role of peripheral sensory systems in modulating
 learned mate preferences.
description: To determine the gene expression associated with different types
 of social learning, we exposed female <em>B. anynana</em> to
 different training/ exposure treatments for one hour (n = 10 per
 treatment) from August to October 2022. Exposure assays were conducted one
 hour after sunrise. Every individual used in our exposure assays were
 size- and age-matched: females were Day 0, ensuring sexual immaturity
 (Costanzo and Monteiro 2007), and males were Day 3, when their sex
 pheromones are known to be attractive to females (Nieberding et al. 2012).
 Naïve females were isolated and were not exposed to training stimuli (i.e.
 males), females that were exposed to a randomly selected 4-spotted male
 with undisrupted male sex pheromones were considered given a “positive
 training exposure”, and females that were exposed to a randomly selected
 2-spotted male with disrupted male sex pheromones were considered given a
 “negative training exposure”, based on prior studies (Westerman et al.
 2012; Westerman and Monteiro 2013). After one hour of exposure (training),
 the females were decapitated with RNA-free scissors. Each head was placed
 into individual RNA-free 1.5ml LoBind tubes, immediately flash frozen in
 liquid nitrogen, and stored in -80⁰C until dissection. Sensory (antennae
 and eyes) and brain tissues were dissected from these females, RNA was
 extracted, libraries prepared, and sent for RNA-seq. Sequencing data was
 analysed using DESeq2 analyses and permutation tests. 
description: # Data from: Divergent sensory transcriptomic profiles in positive and
 negative learning in *Bicyclus anynana* butterflies Dataset DOI:
 [10.5061/dryad.j9kd51crw](10.5061/dryad.j9kd51crw) ## Description of the
 data and file structure Naïve females were isolated and were not exposed
 to training stimuli (i.e. males), females that were exposed to a randomly
 selected 4-spotted male with undisrupted male sex pheromones were
 considered given a “positive training exposure”, and females that were
 exposed to a randomly selected 2-spotted male with disrupted male sex
 pheromones were considered given a “negative training exposure” (n = 10
 per training exposure).  After one hour of exposure (training), the
 females were decapitated with RNA-free scissors and flash frozen. Three
 tissues: antennae, eyes and brain, were dissected and their RNA was
 extracted. Libraries were prepared and sent for RNA-seq. Sequencing data
 was analysed using DESeq2 and permutation tests.  ### Files and variables
 #### For all "perm_.csv" files: * perm_ant_ap_cont.csv *
 perm_ant_av_ap.csv * perm_ant_av_cont.csv * perm_br_ap_cont.csv *
 perm_br_av_ap.csv * perm_br_av_cont.csv * perm_ey_ap_cont.csv *
 perm_ey_av_ap.csv * perm_ey_av_cont.csv "perm_ant..." files
 refer to antennae data, "perm_br..." files refer to brain data,
 and "perm_ey..." files refer to eye data. "ap_cont"
 refers to DEGs between positively-trained and naive females,
 "av_cont" refers to DEGs between negative-trained and naive
 females, and "av_ap" refers to DEGs between positively-trained
 and negatively-trained females.  Empty cells and NA cells are used
 interchangeably. Usually, NAs are populated from genome alignments (i.e.
 NA when B. anynana genome is BLAST-ed to D. melanogaster genome), while
 empty cells were populated from manual annotation from FlyBase (from
 author). Regardless, these csv files have been prefiltered for gene counts
 &lt; 10 (including NA gene counts), and are viable DEGs. The NAs/empty
 cells represent the lack of complete mapping or unknown functions. 
 **Column descriptions in all "perm_"files:**  ##### Variables *
 SeqName: Gene ID for Bicyclus anynana based on v1.2's reference
 genome (e.g. BANY...) * perc_5: 5% tail of the permutated data p-value
 distribution * perc_1: 1% tail of the permutated data p-value distribution
 * baseMean: average normalised count for each gene across all samples *
 log2FoldChange: effect size estimate of gene expression change between two
 treatments * lfcSE: standard error estimate for log2fc * pvalue: p-value
 derived from DESeq2 analysis * padj: p-adjusted value derived from DESeq2
 analysis * Description_c_1.2: Gene name after blasting B. anynana
 reference genome to NCBI using BLASTX * Description_p_1.2: Gene name after
 blasting B. anynana reference genome to NCBI using BLASTP * Name_dmel:
 Gene/ortholog name in D. melanogaster * Symbol_dmel: Gene/ortholog ID in
 D. melanogaster * Description_dmel: Similar to Name_dmel, except it has a
 RefSeq accession number prefix. * Protein.accession_dmel: Protein
 accession number of D. melanogaster ortholog * Locus.tag_dmel: Annotation
 symbol of D. melanogaster ortholog * Description_dmel2: Very similar to
 Description_dmel, except this was based on common orthologs/genes after
 blasting B. anynana genome to D. melanogaster genome for exact ortholog
 blast hits.  * Gene.Type: "protein-coding" or not. *
 Transcripts_accession_bany: Transcription accession number of B. anynana
 transcript * Protein_accession_bany: Protein accession number of B.
 anynana transcript * GO.IDs: GO IDs derived from BLAST2GO's mapping *
 GO.Names: GO terms derived from BLAST2GO's mapping * Accession_bany:
 RefSeq's accession number for B. anynana * Drosophila_ortholog:
 Ortholog name in D. melanogaster for easy searching in FlyBase *
 GO_terms_flybase: GO terms associated with ortholog based on FlyBase -
 more specific than BLAST2GO's mapping since this was done manually. *
 comments: some comments regarding the functions of ortholog * category:
 which gene category it was classified under (out of eight: learning and
 memory, vision, olfaction, mechanosensory, nervous system,
 neurotransmitter receptor, uncharacterised and other) * dave_gene_list:
 Genes shared with Dave et al. (2023), as well as what was classified as
 "vision" or "chemosensory". #### For all dds_.RData: *
 dds_ant_all.RData * dds_br_all.RData * dds_ey_all.RData dds_ant_all refers
 to RData containing DESeq data for antennae (i.e. dds file needed to
 replicate DESeq2 analysis) dds_ey_all refers to RData containing DESeq
 data for eyes (i.e. dds file needed to replicate DESeq2 analysis)
 dds_br_all refers to RData containing DESeq data for brain (i.e. dds file
 needed to replicate DESeq2 analysis) Note: dds files were produced using
 the DESeq2 analysis/ codes below, with no modifications. Essentially,
 after producing these dds files, you can run all subsequent analyses
 (bargraphs, heatmaps, WGCNA, boxplots and venn diagrams). One can
 replicate all figures using these files. ## Code/software Software: R (or
 Rstudio), version 4.2.1+ Packages: DESeq2, dplyr, Rmisc, ggplot, WGCNA All
 raw input files (.fastq) can be found in SRA Bioproject PRJNA1257361. The
 resulting ReadsPerGene.out.tab files are used as input files in the
 genereads.R script. #### File: avap_ant_genereads.R
 **Description:** Rscript for DESeq2 and permutation tests in antennae, for
 Pos, Neg and Pos-Neg comparisons #### File: avap_ey_genereads.R
 **Description:** Rscript for DESeq2 and permutation tests in eyes, for
 Pos, Neg and Pos-Neg comparisons #### File: avap_br_genereads.R
 **Description:**  Rscript for DESeq2 and permutation tests in brain, for
 Pos, Neg and Pos-Neg comparisons #### File: avap_activity_females.R
 **Description:** Rscript for comparing activity levels of
 positively-trained females with naive females during exposure to male
 Input file for activity level can be found in: Westerman, Erica; Ernst,
 David; Agcaoili, Gabrielle; Merrill, Abbigail (2023). Behavioral data for:
 A learning experience elicits sex-dependent neurogenomic responses in
 Bicyclus anynana butterflies [Dataset]. Dryad.
 [https://doi.org/10.5061/dryad.612jm647d](https://doi.org/10.5061/dryad.612jm647d) #### File: avap_bargraphs_only.R **Description:** Rscript for plotting bar graphs of DEGs across comparisons #### File: avap_heatmaps_collated.R **Description:** R script for plotting collated z-scored heatmaps #### File: avap_ant_binarise_wgcna.R **Description:** Rscript for WGCNA analysis in antennae #### File: avap_br_binarise_wgcna.R **Description:** Rscript for WGCNA analysis in brain #### File: avap_ey_binarise_wgcna.R **Description:** Rscript for WGCNA analysis in eyes #### File: avap_deg_boxplots.R **Description:** Rscript for plotting DEG boxplots #### File: avap_venn_diagram.R **Description:** Rscript for plotting venn diagrams of DEGs ## Access information Other publicly accessible locations of the data: * Raw sequence data can be found in NCBI SRA's BioProject PRJNA1257361