DOI: 10.5061/dryad.05qfttf3c
title: Supplementary data for: Transcriptomics of mosaic brain differentiation underlying complex division of labor in a social insect

description: Concerted developmental programming may constrain changes in component
 structures of the brain, thus limiting the ability of selection acting on
 individual brain compartments to form an adaptive mosaic independent of
 total brain size or body size. Measuring patterns of gene expression
 underpinning brain scaling in conjunction with anatomical brain atlases
 can aid in identifying influences of concerted and/or mosaic evolution.
 Species exhibiting exceptional size and behavioral polyphenisms provide
 excellent systems to test predictions of brain evolution models by
 quantifying brain gene expression. We examined patterns of brain gene
 expression in a remarkably polymorphic and behaviorally complex social
 insect, the leafcutter ant Atta cephalotes. Approximately ~50% of
 differential gene expression observed among three morphologically,
 behaviorally, and neuroanatomically differentiated worker size groups was
 attributable to body size, but we also found strong evidence of
 differential brain gene expression unexplained by worker morphological
 variation. Transcriptomic analysis identified patterns of gene expression
 not linearly correlated with worker size but rather, in some cases,
 mirroring neuropil scaling. Additionally, we observed enriched gene
 ontology terms associated with nucleic acid regulation, metabolism,
 neurotransmission, and sensory perception, further supporting a
 relationship between brain gene expression and worker social role. These
 findings demonstrate that differential brain gene expression among
 polymorphic workers is linked to behavioral and neuroanatomical
 differentiation underpinning complex agrarian division of labor in A.
 cephalotes.
description: Brains of mature, fully sclerotized <em>Atta cephalotes
 </em>workers categorized by size group as minims (0.5–0.7mm in head
 width), medias (1.7–1.9mm in head width), and majors (≥3mm in head width)
 were sampled from three mature colonies for gene expression analyses. We
 prepared between nine and 11 samples from each worker size group, composed
 of three to 10 pooled brains depending on worker size group, distributed
 across three colonies of origin, for a total of 30 samples.
 Total RNA was extracted from worker brains using a ThermoFisher
 PicoPure kit. Sample quality and quantity, as well as lack of protein or
 DNA contaminants, were assessed using a Thermo Scientific Nanodrop
 spectrophotometer and an Agilent Bioanalyzer 2100, respectively.
 Libraries were sequenced using a combination of Illumina NextSeq
 and MiSeq with SE 75 reads. RNAseq unstranded libraries with mRNA poly-A
 selection were prepared by Harvard BioPolymers using a KAPA mRNA HyperPrep
 kit. mRNA sequence libraries were individually barcoded and multiplexed in
 equal proportions and all libraries were sequenced across four
 lanes. <em>Kallisto</em> (Bray et al.,
 2016) was used to pseudoalign sequenced reads to the available
 transcriptome and to quantify transcript abundance. The <em>kallisto
 </em>index file was created using the <em>A.
 cephalotes</em> version 1.0 cDNA set accessed through Ensembl
 Metazoa Genomes (Howe et al., 2020). <em>DESeq2 </em>(Love et
 al., 2014) was used to statistically assess the significance of
 differential gene expression based on transcript abundance counts
 generated by <em>kallisto</em>. Weighted
 gene coexpression network analysis (WGNCA) was performed using the
 <em>WGCNA</em> R package (Langfelder & Horvath, 2008).
 biomaRt was used to assign GO categories to all expressed genes in our
 data set (Smedley et al., 2009).