Supplementary data for: Transcriptomics of mosaic brain differentiation underlying complex division of labor in a social insect

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.

Methods
Brains of mature, fully sclerotized Atta cephalotes 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.

Kallisto (Bray et al., 2016) was used to pseudoalign sequenced reads to the available transcriptome and to quantify transcript abundance. The kallisto index file was created using the A. cephalotes version 1.0 cDNA set accessed through Ensembl Metazoa Genomes (Howe et al., 2020). DESeq2 (Love et al., 2014) was used to statistically assess the significance of differential gene expression based on transcript abundance counts generated by kallisto.

Weighted gene coexpression network analysis (WGNCA) was performed using the WGCNA R package (Langfelder & Horvath, 2008). biomaRt was used to assign GO categories to all expressed genes in our data set (Smedley et al., 2009).

协同发育程序可能会约束大脑各组成结构的变化，进而限制了选择作用于单个脑区以形成独立于全脑尺寸或躯体尺寸的适应性镶嵌结构的能力。结合大脑解剖图谱，解析支撑大脑缩放的基因表达模式，有助于识别协同演化和/或镶嵌演化的影响。展现出显著体型与行为多型性的物种，为通过量化脑基因表达来验证脑演化模型的预测提供了极佳的研究体系。本研究以一种极具多型性且行为复杂的社会性昆虫——切叶蚁（Atta cephalotes）为对象，解析其脑基因表达模式。在三个形态、行为及神经解剖结构均存在分化的工蚁体型类群中，观测到的差异基因表达约有50%可归因于躯体尺寸，但本研究同时发现了大量无法用工蚁形态变异解释的脑差异基因表达现象。转录组分析（Transcriptomic analysis）显示，部分基因表达模式与工蚁体型并非线性相关，在某些情况下反而能够反映神经纤维丛的缩放规律。此外，本研究观测到与核酸调控、代谢、神经传递及感官感知相关的基因本体（Gene Ontology, GO）富集术语，进一步佐证了脑基因表达与工蚁社会角色之间的关联。上述研究结果表明，多型工蚁之间的脑差异基因表达，与支撑切叶蚁（A. cephalotes）复杂农耕分工行为的神经解剖分化及行为分化存在关联。

方法
从3个成熟蚁巢中采集成熟且完全骨化的切叶蚁工蚁脑组织，按照体型类群分为小型工蚁（minims，头宽0.5–0.7mm）、中型工蚁（medias，头宽1.7–1.9mm）以及大型工蚁（majors，头宽≥3mm），用于基因表达分析。每个体型类群的工蚁样本量为9至11份，每份样本由3至10个混合脑组织组成（混合数量依工蚁体型类群而定），样本均来自3个供试蚁巢，最终共获得30份样本。
使用ThermoFisher PicoPure试剂盒提取工蚁脑组织的总RNA。分别使用Thermo Scientific Nanodrop分光光度计与Agilent Bioanalyzer 2100检测仪，评估样本的质量、浓度以及是否存在蛋白质或DNA污染物。
采用Illumina NextSeq与MiSeq平台组合进行单端75bp测序。mRNA poly-A富集的非链特异性RNAseq文库由哈佛大学生物聚合物实验室（Harvard BioPolymers）使用KAPA mRNA HyperPrep试剂盒构建。所有mRNA序列文库均进行了单独条形码标记，并以等比例进行多重化处理，随后在4个测序泳道上完成测序。
使用Kallisto（Bray等人，2016）将测序reads伪比对至已发表的转录组，并定量转录本丰度。本研究通过Ensembl Metazoa基因组数据库（Howe等人，2020）获取切叶蚁（A. cephalotes）v1.0版cDNA序列集，用于构建Kallisto索引文件。基于Kallisto生成的转录本丰度计数结果，使用DESeq2（Love等人，2014）对差异基因表达的显著性进行统计学评估。
使用WGCNA R软件包（Langfelder与Horvath，2008）进行加权基因共表达网络分析（Weighted Gene Coexpression Network Analysis, WGCNA）。使用biomaRt工具为数据集内所有表达基因分配GO类别（Smedley等人，2009）。