New Results

Colour polymorphism associated with a gene duplication in male wood tiger moths

Melanie N. Brien, Anna Orteu, Eugenie C. Yen, Juan A. Galarza, Jimi Kirvesoja, Hannu Pakkanen, Kazumasa Wakamatsu, Chris D. Jiggins, Johanna Mappes
doi: https://doi.org/10.1101/2022.04.29.490025

Abstract

Colour is often used as an aposematic warning signal, with predator learning expected to lead to a single colour pattern within a population. However, there are many puzzling cases where aposematic signals are also polymorphic. The wood tiger moth, Arctia plantaginis, uses bright hindwing colours as a signal of unpalatability, and males have discrete colour morphs which vary in frequency geographically. In Finland, both white and yellow morphs can be found, and these colour morphs also differ in behavioural and life-history traits. Complex polymorphisms such as these are often explained by supergenes. Here, we show that male colour is linked to an extra copy of a yellow family gene that is only present in the white morphs. This white-specific duplication, which we name valkea, is highly upregulated during wing development, and could act to reduce recombination, thus potentially representing a supergene. We also characterise the pigments responsible for yellow, white and black colouration, showing that yellow is partly produced by pheomelanins, while black is dopamine-derived eumelanin. The yellow family genes have been linked to melanin synthesis and behavioural traits in other insect species. Our results add to only a few examples of seemingly paradoxical and complex polymorphisms which are associated with single genes.

Introduction

Colour polymorphisms, defined as the presence of multiple discrete colour phenotypes within a population (Huxley, 1955), provide an ideal trait to study natural and sexual selection. Colour phenotypes are linked to fitness in many contexts including camouflage, mimicry and mating success. Colour is often associated with aposematism, warning predators of unpalatability (Cott, 1940; Cuthill et al., 2017), and in such cases, predator learning should favour the most common colour pattern, leading to positive frequency dependent selection (Endler, 1988). Despite this, aposematic polymorphisms can be stable when selection is context-dependent (Briolat et al., 2019), especially where genetic correlations between colour phenotypes and other traits lead to complex fitness landscapes (reviewed by McKinnon and Pierotti, 2010).

A variety of genetic mechanisms can underpin these types of complex polymorphisms involving multiple associated traits (Orteu and Jiggins, 2020). In many cases, such complex polymorphisms are controlled by ‘supergenes’ in which divergent alleles at several linked genes are maintained in strong linkage disequilibrium by reduced recombination. The most common mechanism for locally reduced recombination are inversions, which range from single inversions involving a small number of genes, to multiple nested inversions covering large genomic regions (Wang et al., 2013; Küpper et al., 2016; Tuttle et al., 2016). Nonetheless, other mechanisms for reducing recombination, such as centromeres or large genomic deletions, may also play a role. An alternative mechanism is that a single regulatory gene could act in the same way as a supergene, by controlling variation via multiple downstream effects (Thompson and Jiggins, 2014). While there are fewer instances in which multiple phenotypes seem to be controlled by a single gene, one potential example is the common wall lizard, where colour genes have pleiotropic effects on behavioural and reproductive traits (Andrade et al., 2019). Multiple mutations within a single gene can also lead to variation in multiple traits (Linnen et al., 2013).

The wood tiger moth, Arctia plantaginis, has a complex polymorphism that has been well studied in an ecological context. Males show polymorphic aposematic hindwing colouration with discrete yellow, white or red hindwing colour morphs found at varying frequencies in different geographic locations. In Finland, for example, both yellow and white morphs can be found, with white morphs varying in frequency from 40 to 75% (Galarza et al., 2014). In Estonia, white morphs make up 97% of the population, while yellows morphs form a completely monomorphic population in Scotland (Hegna, Galarza and Mappes, 2015) (Figure 1). Long term breeding studies of these moths have shown that male hindwing colour is a Mendelian trait controlled by a single locus with two alleles (Suomalainen, 1938; Nokelainen et al., 2022). White alleles (W) are dominant over the yellow (y). These colour genotypes also covary with behavioural and life-history traits, contributing to the maintenance of this polymorphism. Yellow males are subject to lower levels of predation in the wild (Nokelainen et al., 2012, 2014), while white males have a positive frequency-dependent mating advantage (Gordon et al., 2015). Yellow morphs have stronger chemical defences (Rojas et al., 2017), but show reduced flight activity compared to white males, although yellows may fly at more selective times, i.e. at peak female calling periods (Rojas, Gordon and Mappes, 2015). In summary, there is a trade-off between natural selection through predation and reproductive success, which contributes to the maintenance of this polymorphism (Rönkä et al., 2020).

Figure 1:
Figure 1:

(A) Sampling locations and frequencies of yellow and white Arctia plantaginis in Finland, Scotland and Estonia. (B) Males of the white and yellow colour morphs (Credit: Samuel Waldron).

Despite the large body of research on A. plantaginis colour morphs, the genetic basis of this polymorphism is unknown. Here, we explore the genetic basis of male hindwing colour using wild populations and lab crosses to test whether the polymorphism is associated with large structural rearrangements controlling multiple phenotypic elements, or the result of a single gene mutation. We then narrow down the candidate genes using a differential gene expression analysis and identify the pigments producing yellow, white and black colouration on the wings of male A. plantaginis.

Results

A narrow genomic region is associated with hindwing colour

To investigate the genetic basis of male hindwing colouration in A. plantaginis, we carried out a quantitative trait locus (QTL) mapping analysis using crosses between heterozygous Wy males and homozygous yy females. We used RADseq data aligned to the yellow A. plantaginis reference genome from 172 male offspring (90 white and 82 yellow) from four families. The QTL analysis identified a single marker associated with male hindwing colour (Figure 2). This marker was found on scaffold YY_tarseq_206_arrow at position 9,887,968bp (95% confidence intervals 9,349,978-9,888,009bp) and had a LOD score of 32.8 (p<0.001). The significant marker explains around 75% of the phenotypic variation and, with one exception, yellow individuals all had a homozygous yy genotype at this marker. This is consistent with tight linkage of this marker to the causal SNP.

Figure 2:
Figure 2:

(A) QTL analysis reveals a 500kb region significant on scaffold 206, part of linkage group 9. (B) A GWAS of wild samples showed SNPs associated with hindwing colour along the same scaffold. The dotted line shows the Bonferroni corrected significance threshold. (C) An insertion in the white reference sequence contains a copy of the yellow-e gene, which we named valkea, in addition to the yellow-e present in both white and yellow morphs. (D) Read depth across the candidate region in all Finnish white and yellow samples.

To further narrow down this region, we ran a genome wide association study (GWAS) using whole genome sequences of males from four populations: Southern Finland (5 white, 5 yellow) and Central Finland (10 w, 10 y), where male morphs are either white or yellow, Estonia (4 w), where males are mostly white, and Scotland (12 y), where all males are yellow. This identified a region of associated SNPs also on scaffold 206 (Figure 2). Two SNPs, 137bp apart (at positions 9,885,384 and 9,885,521), were significant above a strict Bonferroni corrected threshold. 162 SNPs were over the threshold of p<0.0001 and, of these, 155 are within a 99kb region on scaffold 206 (9,833,387-9,932,264bp). The top SNPs are within 2.5kb from the top QTL marker, and the SNP at this marker has a p-value <0.0001.

The 538Kb QTL interval contains 21 genes (Table S1) which were annotated with reference to D. melanogaster. Of these genes, four are part of the yellow gene family. The top two SNPs from the GWAS, and the top marker from the QTL, fall in a non-coding region upstream of the gene, yellow-e, and are also close to an additional yellow gene, yellow-g.

Identifying structural variation in this region

The trio binning method used by Yen et al. (2020) to assemble the A. plantaginis reference genome produced two reference sequences, one for a white allele and one for a yellow allele. We extracted the region containing the QTL interval from the yellow reference and aligned it against the white reference. The alignment showed a duplicated region approximately 117kb long on scaffold 419 of the white reference from around 6,941,000-7,058,000bp (Figure S1). The yellow-e gene and its flanking regions are within this sequence and are therefore duplicated in the white reference (Figure 2). One copy of the gene (named jg1310 in the W annotation) has 7 exons and is similar to the yellow-e gene in the yellow reference (99.7% identity in coding sequences). The second copy unique to the white scaffold (jg1308) has only the first 5 exons (81.8% identical to the gene in the yellow reference), possibly due to a stop codon mutation in the 5th exon. For clarity, we named this duplicated white-specific copy valkea, in reference to a Finnish term for ‘white’. While all white samples had consistent coverage of reads across the duplicated region, coverage was patchy in yellow samples, with many regions having no coverage in yellow samples (Figure 2). Those reads that map in the valkea region in yellow samples are likely to be mapping errors.

To confirm that both of these gene copies are related to yellow-e, we compared them to yellow-e orthologs found in Bombyx mori, Heliconius melpomene and Drosophila melanogaster, along with other yellow genes from A. plantaginis and B. mori. Both of the tiger moth genes were most closely related to the H. melpomene yellow-e (Figure S2). Between valkea and yellow-e there is an additional gene which showed highest similarity to Drosophila yellow-g2 (when extracted from both the white and yellow references). This gene is not part of the duplicated sequence and is present as a single copy in both morphs. Coverage across yellow-g and yellow-e genome regions in wild samples is similar in both morphs (Figure 2).

Valkea is differentially expressed between morphs

To pinpoint which of these candidate genes is associated with male wing polymorphism in A. plantaginis, we next performed gene expression analyses across several developmental stages. Based on knowledge of the expression patterns of yellow genes and other colour pattern genes such as optix (Reed et al., 2011) and cortex (Nadeau et al., 2016) in Lepidoptera, we hypothesised that changes in gene regulation that control the development of wing colour morphs in the tiger moth most likely occur during pupal development. Pupal development in the wood tiger moth lasts for approximately 8 days, and no colour is present in the wings until day 7, when the yellow pigment appears. A few hours later, black melanin pigmentation is deposited. We sampled two stages early in development when no colouration is present in the wings (72 hours post-pupation, and 5 day old pupae), and two stages later in development: the point when yellow appears in yy morphs (Pre-mel, 7 day old pupae) and the other after black melanin has also been deposited (Mel, 7-8 days old). Five individuals per genotype and stage were sampled.

We analysed differential gene expression using the W reference genome, which contains the duplicated region that includes valkea. Developmental stage was the main factor explaining most of the genome-wide variation in gene expression between samples (Figure S3). Such a pattern would be expected as many genes are involved in development and thus are likely to be differentially expressed (DE) between developmental stages. No apparent clustering can be observed among samples of the same colour morph.

We next compared gene expression between yy and WW individuals at each of the developmental stages. From the 22 genes identified in the GWAS and QTL analysis, only two, yellow-e and valkea, were differentially expressed in any of the comparisons. Valkea was overexpressed in white individuals in the pre-melanin stage with a Log Fold Change of 10.32 and a p-value of 2.18e-06. As valkea is only fully present in the W genome, it is not expected to be expressed at all in the Y genome. Yellow-e was also overexpressed in white individuals with a Log Fold Change of 3.86 and adjusted p-value of 5.62e-06. In other developmental stages, neither valkea nor yellow-e showed differences in expression between morphs (Figure 3).

Figure 3:
Figure 3:

(A) RNA-seq of developing wings shows 99 genes that are differentially expressed between morphs. (B) Valkea is highly expressed in the white morph at the pre-melanin stage. Expression of valkea in yellow morphs is around 0.

The GWAS and QTL peaks of association are situated in scaffold 419 of the WW reference assembly, which in a WW linkage map forms a linkage group along with 6 more scaffolds (472, 487, 515, 531, 540 and 609). We found that 12 genes present in this linkage group were differentially expressed, including valkea and yellow-e (Table S2), and identified their orthologues in Drosophila melanogaster.

We next conducted a genome-wide analysis of the RNA-seq data. Lowly expressed genes were filtered out with 10,920 genes retained. Overall, 99 genes were differentially expressed (FDR < 0.05) between the two morphs. Of these, 49 were upregulated in the yy morph, while the remaining 50 were upregulated in WW individuals. The earliest developmental stage, 72-hours, was the stage with the highest number of DE genes (n = 48), while the 5-day old stage had the fewest (n=7). One gene which encodes a C2H2 zinc finger transcription factor in D. melanogaster, ‘jg15945’, was over-expressed in yy in the first three stages (Figure S4).

Yellow-e is not associated with colour

We next used a tree topology approach to explore the relationships between alleles around the duplicated sites among the populations. For four populations (Finnish white and yellow, Estonian and Scottish populations), three topologies are possible; grouping populations by colour, by geography and one grouping neither by colour nor geography. At yellow-e, the highest weighting is towards the geography grouping, suggesting that this gene is not associated with the colour switch (Figure S5).

Pigment analysis

Since the yellow gene family, to which valkea is related, is known to be responsible for the production of melanin pigments, we further investigated the identity of the wing pigmentation. First, we ruled out the presence of several non-melanin pigment types in the hindwings, including pterins and carotenoids. Pterins are commonly found in insects and, along with purine derivatives, papiliochromes and flavonoids, are soluble in strong acids and bases or in organic solvents (Umebachi, 1975; Kayser, 1985; Shamim et al., 2014). We placed wing samples from each morph in NaOH overnight, then measured the absorbance of the supernatant using a spectrophotometer. We also left wings in methanol overnight before measuring the supernatant. The spectra did not show any peaks indicative of any pigment dissolved in the sample. Similarly, we found no evidence for carotenoid pigments after dissolving in a hexane:tert-butyl methyl ether solution (Figure S6). Wings did not fluoresce under UV light, providing further evidence for the lack of fluorescent pigments including pterins, flavonoids, flavins and papiliochromes (Umebachi, 1975; Kayser, 1985). Ommochromes are red and yellow pigments; high performance liquid chromatography (HPLC) ruled out the presence of these pigments on the moth wings, which we compared to data from ommochrome-containing Heliconius wings and a Xanthurenic acid standard (Figure S7).

HPLC analysis showed peaks characteristic of pheomelanin (Figure S8). Pheomelanins produce red-brown colour in grasshoppers and wasps (Galván et al., 2015; Jorge García, Polidori and Nieves-Aldrey, 2016), and orange-red colours in ants and bumblebees (Hines et al., 2017; Polidori, Jorge and Ornosa, 2017). Insects generally have dopamine-derived pheomelanin and a breakdown product of this is 4-Amino-3-hydroxyphenylethylamine (4-AHPEA) (Barek et al., 2018). Yellow wings showed large peaks for 4-AHPEA. White wings had around 27% of the 4-AHPEA levels seen in yellow wings, and black sections of the wings had 16%. Hydrogen iodide hydrolysis of wings produced the isomer 3-AHPEA, which may come from 3-nitrotyramine originating from the decarboxylation of 3-nitrotyrosine. Reduction of 3-nitrotyrosine produces 3-AHP, another marker of pheomelanin (Wakamatsu, Ito and Rees, 2002).

Analysis of the black portions of the wing found pyrrole-2,3-dicarboxylic acid (PDCA) and pyrrole-2,3,5-tricarboxylic acid (PTCA) (Figure S9). Both are components of eumelanin (Barek et al., 2018), suggesting that the black colouration seen in the wood tiger moth is predominantly eumelanin derived from dopamine. This is common in producing black colouration and providing structural components of the exoskeleton. In addition, dopamine is acylated to both N-β-alanyldopamine (NBAD) and N-acetyldopamine (NADA) sclerotins. NADA sclerotins are colourless and likely to be present on the white wings. This analysis of pigmentation is therefore consistent with a role for yellow family genes in regulating the colour polymorphism.

Discussion

Hindwing colouration of male Arctia plantaginis is polymorphic and these colour morphs vary in multiple behavioural and life-history traits, providing an example of a complex polymorphism. Here, we have shown that variation in male hindwing colour is associated with a narrow region in the genome, characterised by a duplicated sequence found only in white morphs and containing a gene from the yellow gene family. The white-specific copy, valkea, is highly expressed during pupal development, consistent with genetic dominance of the white allele. The presence of only one gene is in contrast to ‘classical’ supergenes which often explain the genetic architecture of complex polymorphisms (Thompson and Jiggins, 2014) and involve chromosomal rearrangements containing multiple genes that allow the maintenance of co-adapted haplotypes (Joron et al., 2006; Lowry and Willis, 2010; Küpper et al., 2016; Funk et al., 2021).

Although we did not find evidence for a supergene involving multiple loci, this morph-specific duplication still provides a region of reduced recombination between morphs. This is similar to the genetic architecture of the Primula supergene controlling heterostyly, which involves a large duplication containing five genes (Huu et al., 2020). The duplicated region is effectively hemizygous and cannot recombine except in homozygote genotypes, which could contribute to the maintenance of the complex polymorphism and the linkage of multiple traits. In polymorphic Papilio dardanus, one colour pattern morph is associated with a duplicated region, again providing physical constraints on recombination (Timmermans et al., 2014). Nonetheless, in the case of the wood tiger moth, it remains unclear how a single gene, such as valkea, can control the development of a broad array of phenotypic traits.

One possible mechanism is that there is a regulatory element along the scaffold which is controlling colour via the valkea gene, but also regulating other genes to control different phenotypic traits. We found the most significant markers and SNPs located in a non-coding region close to the yellow genes, which likely contains a cis-regulatory element (CRE) controlling transcription of valkea. In cichlids, for example, a CRE at the gene encoding agouti-related peptide 2 controls variation in strip patterning in two closely related species (Kratochwil et al., 2018).

Differential expression of other genes on the same chromosome controlled by the CRE could explain variation in covarying traits. The over-expression of another gene, possibly encoding a zinc transcription factor, in yellow individuals in the early pupal stages suggests that there is differential expression of unlinked genes as a result of the polymorphism, although since this gene is on a different chromosome to valkea it is unlikely to be directly controlled by the CRE. Another hypothesis is that somehow valkea itself regulates other genes. However, yellow family genes are not known to regulate transcription of other genes, unlike for example doublesex, a transcription factor that undergoes alternative splicing and female mimetic wing pattern polymorphism in Papilio polytes (Kunte et al., 2014; Nishikawa et al., 2015).

The yellow family genes are highly conserved throughout insects (Ferguson et al., 2011). They have been widely linked to colouration (Wittkopp, Vaccaro and Carroll, 2002; Miyazaki et al., 2014; Zhang et al., 2017; Zhang, Mazo-Vargas and Reed, 2017), as well as behaviour, sex-specific phenotypes and reproductive maturation (Wilson et al., 1976). These genes share a common origin with the major royal jelly protein (MRJP) genes (Drapeau et al., 2006) which are crucial in caste development in honeybees. Like the MRJP genes, yellow genes in honeybees have diverse spatial and temporal expression patterns. As our focus in A. plantaginis has been on wing tissue, we are missing expression of genes in other tissues that could be linked to other traits. Thus, it is not impossible to imagine that a yellow gene could have a similar function to a MRJP in regulating the development of a complex phenotype. Recent work with Bicyclus anynana showed that yellow functions as a repressor of male courtship (Connahs et al., 2022). On the other hand, sex specific behavioural phenotypes of yellow mutants in Drosophila were found to be due to pigmentation effects (Massey et al., 2019), so more evidence is needed to suggest a functional role for yellow genes outside of pigmentation.

The duplication of yellow-e and surrounding regions in the white morphs suggests that the yellow morph is the ancestral form. Valkea could have evolved in a stepwise fashion, first as a tandem duplication then with a stop codon mutation altering the gene structure. Gene duplications can facilitate adaptation and, in some examples, lead to polymorphism. Although both valkea and yellow-e are overexpressed in the white morph, the topology weighting suggests that yellow-e is not closely associated with the colour change. Valkea likely represents an example of neofunctionalization, where the duplicated gene gains a different function to the original gene copy.

Contrary to previous work that attributed red and yellow colours to pterins in another tiger moth species (Gawne and Nijhout, 2019), we found high levels of 4-AHPEA in the yellow wings confirming the presence of pheomelanins. These pigments have been widely associated with red and yellow colours in mammals (e.g. Mcgraw and Wakamatsu, 2004), but only relatively recently described in insects and likely to be more widespread than previously thought. Yellow colours can also be produced by NBAD sclerotins which are sclerotizing precursor molecules made from dopamine and these have an important role in the sclerotization pathway for hardening the insect cuticle (Andersen, 2007; Barek, Evans and Sugumaran, 2017) before becoming involved in melanisation (Barek et al., 2018). Thus, we suggest that the yellow colour arises partly from the NBAD sclerotins and partly from the presence of pheomelanin pigments, which has been proposed in other Lepidoptera (Matsuoka and Monteiro, 2018). While some 4-AHPEA also occurred in white wings, this may be due to its role in production of cross-linking cuticular proteins and chitin during sclerotization (Sugumaran, 2010). Upregulation of genes on the white allele could be acting as a repressor of the generation of yellow colour. We suspect that yellow family genes play multiple roles within the melanin production pathway. In the wood tiger moth, yellow affects the conversion of DOPA into black dopamine melanin (Galarza, 2021). Yellow-e in particular has been linked to larval colouration in Bombyx mori (Ito et al., 2010) and adult colour in beetles (Wang et al., 2022), while another gene,yellow-f, has a role in eumelanin production (Barek et al., 2018).

In summary, we identified a structural variant containing a previously undescribed gene, valkea, which is only present in white morphs of A. plantaginis. While functional studies are needed to determine the exact function of this locus, the presence of a regulatory element controlling wing colour and other traits via multiple downstream effects could explain how multiple traits are linked to wing colouration. This complex polymorphism allows multiple beneficial phenotypes to be inherited together, whereas recombination would separate multiple loci leading to maladapted individuals. Our results provide the basis for further exploration of the genetic basis of covarying behavioural and life-history traits, and offer an intriguing example of how complex polymorphisms are maintained.

Methods

Sampling

Homozygous lines of white (WW) and yellow (yy) Arctia plantaginis moths were created from Finnish populations at the University of Jyväskylä, Finland. Larvae were fed with wild dandelion (Taraxacum sp.) and reared under natural light conditions, with an average day temperature of 25°C and night temperature between 15-20°C. For the crosses, a heterozygous male, created from crossing a heterozygous male with a homozygous yy female, was backcrossed with a yy female. This was repeated to obtain four families totalling 172 offspring and 8 parents (Table S3). Samples from wild populations were caught in Southern Finland (n=10) and Central Finland (n=20), where male morphs are either white or yellow, Estonia (n=4), where males are mostly white, and Scotland (n=4), where males are yellow (Table S4). In addition, we included 8 samples which are F1 offspring of wild Scottish samples. Forty pupae with known genotypes from lab populations (20 WW and 20 yy) were used for the RNA extractions.

DNA extraction and sequencing

For the lab crosses, DNA was extracted from two legs crushed with sterilised PVC pestles using a Qiagen DNeasy Blood & Tissue kit, following the manufacturer’s instructions. Library preparation and GBS sequencing were performed by BGI Genomics on an Illumina HiSeq X Ten. For the wild samples, DNA was extracted from the thoraces also with a Qiagen kit. Library preparation and sequencing were performed by Novogene (Hong Kong, China). 150bp paired-end reads were sequenced on an Illumina NovaSeq 6000 platform.

Linkage mapping analysis

FASTQ reads were mapped using bowtie v2.3.2 (Langmead and Salzberg, 2012) to the yellow Arctia plantaginis scaffold-level genome assembly (Yen et al., 2020). BAM files were sorted and indexed using SAMtools v.1.9 (Li et al., 2009) and duplicates removed using PicardTools MarkDuplicates (broadinstitute.github.io/picard). Twelve samples which had aligned <30% were removed. Reads of the remaining samples had an average alignment of 94%. SNPs were called using SAMtools mpileup with minimum mapping quality set to 20 and bcftools call function. Lep-MAP3 (Rastas, 2017) was used for linkage map construction and we ran the following modules: ParentCall2 which called 105,622 markers, Filtering2, SeparateChromosomes2 with lodLimit=5 and sizeLimit=100, JoinSingles2All and OrderMarkers2 with recombination2=0 to denote the lack of female recombination. Genotypes were phased using the map2genotypes.awk script included with Lep-MAP3. Markers were named based on the genomic positions of the SNPs in the reference genome and the map.awk script, and this was used to further order the markers within the linkage groups. This resolved 30 linkage groups. Although we expect that there are 31 chromosomes in the moth genome, we suspect that the sex chromosome is missing in this assembly as the yy individual used in the genome assembly was female (Yen et al., 2020). A small number of markers which caused long gaps at the beginning or end of linkage groups were manually removed, leaving the final map 948.7cM long with 19,803 markers. Markers were well distributed so we began the first analyses with this map. A linkage map was also assembled using sequences aligned to the white reference and this separated into 31 linkage groups.

The QTL analysis was carried out in R/qtl (Broman et al., 2003). Genotype probabilities were calculated before running a genome scan using the scanone function with the Haley-Knott method and binary model parameters, and including family as an additive covariate. The phenotype was labelled as either 0 (Wy) or 1 (yy). We ran 5000 permutations to determine the significance level for the QTL LOD scores. The bayesint function calculated the 95% Bayesian confidence intervals around the significant marker.

Analysis of whole genome sequences

FASTQ reads were mapped to the yellow A. plantaginis genome assembly (Yen et al., 2020) using BWA-MEM v7.17 (Li, 2013). As before, BAM files were sorted and indexed, and duplicates were removed. Genotyping and variant calling was carried out with the Genome Analysis Toolkit (GATK) (McKenna et al., 2010). Variants were called using HaplotypeCaller (v.3.7) in GVCF mode, then gVCFs combined with GenomicsDBImport (v.4.0). Joint genotyping was run with GenotypeGVCFs, set with a heterozygosity of 0.01, and SNPs were called using SelectVariants. Finally, the set of 20,787,772 raw SNPs were filtered using VariantFiltration and thresholds: quality by depth (QD > 2.0), root mean square mapping quality (MQ > 50.0), mapping quality rank sum test (MQRankSum > −12.5), read position rank sum test (ReadPosRankSum > −8.0), Fisher strand bias (FS < 60.0), and strand odds ratio (SOR < 3.0). A set of 5,227,288 SNPs passed the filtering.

We carried out a Genome Wide Association Study (GWAS) using the R package GenABEL v.1.8 (Aulchenko et al., 2007). The set of filtered SNPs were converted to BED format with PLINK2, keeping only biallelic SNPs (cog-genomics.org/plink/2.0/). Sites which were not in Hardy-Weinberg equilibrium (p<0.01), or had a call rate of <0.5, were excluded. Following this, 381,266 sites were retained across 40 individuals (out of 57). To account for population stratification, we performed multidimensional scaling on kinship and identity-by-state (IBS) information estimated from the data, and included this as a covariate in the association test. Significance levels were calculated using Bonferroni corrected thresholds to account for multiple testing. Central and Southern Finnish populations were pooled for this analysis, based on a previous principal component analysis of these samples (Yen et al., 2020).

In Yen et al. (2020), many of these samples were processed in the same way but aligned to the white genome assembly (based on a white individual). We used the VCF file from this study for a Twisst analysis (Martin and Van Belleghem, 2017) which included samples from the Finnish, Estonian and Scottish populations. A minor allele frequency of 3 was applied to the VCF with VCFtools, and this was phased using Beagle v5.1 (Browning, Zhou and Browning, 2018). We generated neighbour joining trees with a window size of 50 using PhyML and ran Twisst using scripts from github.com/simonhmartin/twisst. Read depth of the W-mapped samples was calculated in 1kb windows across the candidate region using BEDtools (v.2.20.1) multicov (Quinlan and Hall, 2010).

For analysis of structural variants, sequences from the white and yellow genome assemblies were aligned using MAFFT v7.450 (Katoh and Standley, 2013) and viewed with Geneious. Our focal sequence, scaffold 419 in the white genome, is the reverse complement of scaffold 206 in the yellow genome.

Identification of candidate genes and tree construction

To identify candidate genes in the QTL interval and GWAS region, we ran a protein BLASTP v.2.4.0 search to identify Heliconius melpomene (Hmel2.5) proteins homologous to predicted A. plantaginis proteins in the region from the genome annotation. Informative gene names were obtained by performing a BLASTP search with the H. melpomene proteins against all Drosophila melanogaster proteins in FlyBase v.FB2020_01 (flybase.org/blast).

For the yellow gene tree, we used Lepbase (Challi et al., 2016) to search for yellow genes in Bombyx mori (ASM15162v1). We identified yellow-e in H. melpomene by searching for major royal jelly proteins, then comparing protein sequences of these against Drosophila proteins in Flybase. The sequence for Dmel yellow-e was downloaded from Flybase. To make the tree, coding sequences of all genes were aligned in Geneious using MAFFT v7.450 (Katoh and Standley, 2013), then the tree was constructed with PhyML using 10 bootstraps (Guindon et al., 2010).

Differential gene expression

We dissected the wings out of the pupae in Cambridge, UK. Pupae and larvae were sent to Cambridge from Jyväskylä and were kept between 22 and 30°C. Pupae were sexed and only males were used. Dissections were made at 4 different stages: 72 hours after pupation (72h), 5 days after pupation (5d; counting 0-24 first hours after pupation as day 1), pre melanin deposition (Pre-mel) and post melanin deposition (Mel). We sampled 5 individuals per stage and genotype. Hindwings and forewings were stored separately in RNA-later (Sigma-Aldrich) at 4 °C for 2 weeks and later transferred to −20 °C, while the rest of the body was stored in pure ethanol.

Total RNA was extracted from hindwing tissue using a standard hybrid protocol. First, we transferred the wing tissue into Trizol Reagent (Invitrogen) and homogenised it using dounce tissue grinders (Sigma-Aldrich). Then, we performed a chloroform phase extraction, followed by DNase treatment (Ambion) for 30 mins at 37°C. We measured the concentration of total RNA using Qubit Fluorometric Quantitation (Thermofisher) and performed a quality check using an Agilent 4200 TapeStation (Agilent). The extracted total RNA was stored at −20°C before being sent to Novogene UK for sequencing.

We performed quality control and low-quality base and adapter trimming of the sequence data using TrimGalore! We then mapped the trimmed reads to the two A. plantaginis genomes using STAR. We performed a second round of mapping (2pass) including as input the output splice junctions from the first round. The A. plantaginis genome annotations WW and YY were included in each round of mapping respectively. We then used FeatureCounts to count the mapped reads. Finally, we used DESeq2 to analyse the counts and perform the DE analysis.

To identify the gene or genes controlling the development of wing colour in A. plantaginis, we performed a genome wide differential expression analysis using limma-voom (Ritchie et al., 2015). First, we defined a categorical variable, ‘GenStage’, with 8 levels containing the genotype and stage information of every individual sample (e.g. YY72h, WW72h, YY5days, etc.). Then, we built the design matrix fitting a model with GenStage as the only fixed effect factor contributing to the variance in gene expression and included family as a random effect factor (gene expression ~ 0 + GenStage + (1|Family)). We then filtered lowly expressed genes, which resulted in a reduction of the number of tested genes from 17,615 to 11,330 genes in the Y-mapped analysis and 17,930 to 10,920 in the W-mapped one. Finally, the list of genes that are differentially expressed in each stage were extracted using the Benjamini-Hochberg procedure to correct for multiple testing.

Orthology assignment

To infer genome wide orthology between A. plantaginis and D. melanogaster, we used OrthoFinder (v2.5.4) (Emms and Kelly, 2019). We used proteomes from 6 Lepidoptera species, Plutela xylostella (GCA_905116875_2), Bombyx mori (GCF_014905235_1), Spodoptera frugiperda (GCF_011064685_1), Parnassius apollo (GCA_907164705_1), Pieris macdunnoughi (GCA_905332375_1) amd Pararge aegeria (GCF_905163445_1) and Drosophila melanogaster (GCF_000001215_4). We ran the primary_transcript.py utility from OrthoFinder to extract only one transcript per protein, and then ran OrthoFinder with default settings.

Pigment analysis

Solubility and fluorescence tests

Five hindwings from each morph were placed in two separate solvents (0.1M NaOH and 90% MeOH) and left overnight. The supernatant was analysed with an Agilent Cary 8454 UV-Visible spectrophotometer and the spectra compared to known spectra for various pigments. A UV lamp (Philips TL8W/08 F8T5/BLB) used to test for fluorescence on the wings. The presence of carotenoids was tested by placing wings into 1ml of pyridine and leaving at 95°C for 4 hours (McGraw et al., 2002). To these we added 1ml of 1:1 hexane:tert-butyl methyl ether and 2ml of water before shaking and leaving overnight. Again, the supernatant was measured with the spectrometer.

High-Performance Liquid Chromatography (HPLC) to test to eumelanin and ommochrome pigments

To determine the type of melanin producing the black colour on the wings, we cut out approximately 5mg of the black sections of the wings, from both females and males. Eumelanin analysis was carried out according to (Borges et al., 2001). Each sample was added to a tube containing 820μl 0.5 M NaOH, 80μl 3% H2O2 and an internal standard (48nmol phthalic acid) and heated in a boiling water bath for 20 minutes. Once cool, 20μl of 10% Na2SO3 and 250μl of 6M HCl was added. Samples were then extracted twice with 7ml of ethyl acetate. Ethyl acetate was dried at 45 °C under a stream of nitrogen. The residue was dissolved into 0.5ml of 0.1% formic acid.

We carried out high-performance liquid chromatography on an Agilent 1100 HPLC. 20μl of the sample was injected into a Waters Atlantis T3, 100 x 3.0mm i.d. analytical column (Waters, Milford, MA, USA). The column was set to 25°C and analytes were detected at wavelength 280nm. The HPLC mobile phase consisted of two eluents: UHQ-water/MeOH (98/2; v/v) with 0.1% formic acid and UHQ-water/MeOH (40/60; v/v) with 0.1% formic acid. Flow rate was 0.4 ml/min and the used gradient started with 100% of eluent A and ramped evenly from time 0 to 15 minutes to 40:60 (A:B; v/v), held at 40:60 for 6 minutes and ramped evenly back to initial eluent composition (100% A) over 5 minutes. We compared chromatograms obtained from the samples to the chromatograms obtained from synthetic melanin, ink from sepia officinalis and black human hair.

HPLC was also applied to observe the possible presence of ommochrome pigments. Injection volume was 10μl and for the separation we used the same Waters Atlantis T3 column (100 x 3.0 mm i.d.) set to 30°C. Solvent A was UHQ-water and B was acetonitrile (ACN), both containing 0.1% formic acid. Flow rate was 0.4ml/min and the used gradient was as follows: Initial flow ratio was 98/2 water/ACN (v/v) ramping then evenly from time 1 to 15 min to 30:70 water:ACN (v/v), held for 1.5 min and then ramped evenly back to initial eluent composition over 0.5 minutes. The column was stabilised for 7 minutes before a new run.

Pheomelanin analysis

Samples were analysed for pheomelanin content according to the method of Kolb et al. (1997) with modifications. A 2mg sample was placed in a screw-capped tube with 100μl water, 500μl ~55-58% hydrogen iodide (HI), and 20μl 50% hypophosphorous acid (H3PO2).

Samples were capped tightly and hydrolysed for 20 hours at 130°. After cooling, samples were evaporated under nitrogen flow, then dissolved in 1ml of 0.1M HCl and purified with solid phase extraction. Strata SCX cartridges were preconditioned with 2ml of methanol, 3ml of water and 1ml of 0.1M HCl. Sample was then applied to the cartridge, washed with 1ml of 0.1M HCl, and finally eluted with 1ml of methanol (MeOH): 0.5M ammonium acetate (NH4CH3CO2) (20:80 v/v).

Hydrogen Iodide hydrolysis products were determined by a Dionex high-performance liquid chromatograph equipped with pulsed amperometric detection (HPLC/PAD). A Phenomenex Kinetex C18 column (150 x 4.6 mm i.d.; 5μm particle size) with a gradient elution (Table S5) at a flow rate of 0.9 ml min-1 with the eluents: (A) sodium citrate buffer (Hines et al., 2017) in ultra-high-quality water (internal resistance ≥ 18.2 MΩ cm; Milli-Q Plus; Millipore, Bedford, MA, USA) and (B) methanol were used for the separation. Dionex ED-50 pulsed amperometric detector (Dionex, Sunnyvale, CA, USA) equipped with a disposable working electrode by using a Dionex waveform A with potentials presented in Table S6 was used for detection. 4-AHP, 3-AHPEA and 4-AHPEA standards used in calibration were kindly offered from the laboratory of Emeritus Professor Wakamatsu and the preparation method is described in (Wakamatsu et al., 2014).

Data availability

All the scripts used for mapping and DE analysis will be uploaded to an online repository. Scripts and data for the QTL and GWAS analyses can currently be found at github.com/mnbrien/Aplantaginis-male-colour. Raw sequencing data of wild samples has previously been deposited in ENA, study accession No. PRJEB36595.

Acknowledgements

We thank Kaisa Suisto and the greenhouse staff for insect rearing, and Elisa Salmivirta and Sari Viinikainen for lab assistance. Thanks to Emeritus Prof. Shosuke Ito for providing pheomelanin standards and to Bodo Wilts for advice on the pigment analyses.

This work was supported by Academy of Finland grants to MB (#343356) and JM (projects 345091 and 328474).

Footnotes

  • ↵** Co-senior authors.

References

  1. ↵
    Andersen, S. O. (2007) ‘Involvement of tyrosine residues, N-terminal amino acids, and β-alanine in insect cuticular sclerotization’, Insect Biochemistry and Molecular Biology, 37(9), pp. 969–974. doi: https://doi.org/10.1016/j.ibmb.2007.05.002.
    OpenUrlCrossRefPubMed
  2. ↵
    Andrade, P. et al. (2019) ‘Regulatory changes in pterin and carotenoid genes underlie balanced color polymorphisms in the wall lizard’, Proceedings of the National Academy of Sciences of the United States of America, 116(12), pp. 5633–5642. doi: 10.1073/pnas.1820320116.
    OpenUrlAbstract/FREE Full Text
  3. ↵
    Aulchenko, Y. S. et al. (2007) ‘GenABEL: an R library for genome-wide association analysis’, Bioinformatics, 23(10), pp. 1294–1296. doi: 10.1093/bioinformatics/btm108.
    OpenUrlCrossRefPubMedWeb of Science
  4. ↵
    Barek, H. et al. (2018) ‘Insect cuticular melanins are distinctly different from those of mammalian epidermal melanins’, Pigment Cell and Melanoma Research, 31(3), pp. 384–392. doi: 10.1111/pcmr.12672.
    OpenUrlCrossRefPubMed
  5. ↵
    Barek, H., Evans, J. and Sugumaran, M. (2017) ‘Unraveling complex molecular transformations of N-β-alanyldopamine that account for brown coloration of insect cuticle’, Rapid Communications in Mass Spectrometry, 31(16), pp. 1363–1373. doi: https://doi.org/10.1002/rcm.7914.
    OpenUrlCrossRef
  6. ↵
    Borges, C. R. et al. (2001) ‘Relationship of Melanin Degradation Products to Actual Melanin Content: Application to Human Hair’, Analytical Biochemistry, 290(1), pp. 116–125. doi: https://doi.org/10.1006/abio.2000.4976.
    OpenUrlCrossRefPubMedWeb of Science
  7. ↵
    Briolat, E. S. et al. (2019) ‘Diversity in warning coloration: selective paradox or the norm?’, Biological Reviews, 94(2), pp. 388–414. doi: 10.1111/brv.12460.
    OpenUrlCrossRef
  8. ↵
    Broman, K. W. et al. (2003) ‘R/qtl: QTL mapping in experimental crosses’, Bioinformatics, 19(7), pp. 889–890. doi: 10.1093/bioinformatics/btg112.
    OpenUrlCrossRefPubMedWeb of Science
  9. ↵
    Browning, B. L., Zhou, Y. and Browning, S. R. (2018) ‘A One-Penny Imputed Genome from Next-Generation Reference Panels’, The American Journal of Human Genetics, 103(3), pp. 338–348. doi: https://doi.org/10.1016/j.ajhg.2018.07.015.
    OpenUrlCrossRefPubMed
  10. ↵
    Challi, R. J. et al. (2016) ‘Lepbase: the Lepidopteran genome database’, bioRxiv, p. 56994. doi: 10.1101/056994.
    OpenUrlAbstract/FREE Full Text
  11. ↵
    Connahs, H. et al. (2022) ‘The yellow gene regulates behavioural plasticity by repressing male courtship in Bicyclus anynana butterflies’, Proceedings of the Royal Society B: Biological Sciences, 289(1972), p. 20212665. doi: 10.1098/rspb.2021.2665.
    OpenUrlCrossRef
  12. ↵
    Cott, H. B. (1940) Adaptive coloration in animals. London: Methuen.
  13. ↵
    Cuthill, I. C. et al. (2017) ‘The biology of color’, Science, 357(6350). doi: 10.1126/science.aan0221.
    OpenUrlAbstract/FREE Full Text
  14. ↵
    Drapeau, M. D. et al. (2006) ‘Evolution of the Yellow/Major Royal Jelly Protein family and the emergence of social behavior in honey bees’, Genome Research, 16(11), pp. 1385–1394. doi: 10.1101/gr.5012006.
    OpenUrlAbstract/FREE Full Text
  15. ↵
    Emms, D. M. and Kelly, S. (2019) ‘OrthoFinder: phylogenetic orthology inference for comparative genomics’, Genome Biology, 20(1), p. 238. doi: 10.1186/s13059-019-1832-y.
    OpenUrlCrossRefPubMed
  16. ↵
    Endler, J. A. (1988) ‘Frequency-dependent predation, crypsis and aposematic coloration’, Philosophical Transactions of the Royal Society B: Biological Sciences, 319, pp. 505–523.
    OpenUrlCrossRefPubMed
  17. ↵
    Ferguson, L. C. et al. (2011) ‘Evolution of the insect yellow gene family’, Molecular Biology and Evolution, 28(1), pp. 257–272. doi: 10.1093/molbev/msq192.
    OpenUrlCrossRefPubMedWeb of Science
  18. ↵
    Funk, E. R. et al. (2021) ‘A supergene underlies linked variation in color and morphology in a Holarctic songbird’, Nature Communications, 12(1), p. 6833. doi: 10.1038/s41467-021-27173-z.
    OpenUrlCrossRef
  19. ↵
    Galarza, J. A. et al. (2014) ‘Temporal relationship between genetic and warning signal variation in the aposematic wood tiger moth (Parasemia plantaginis)’, Molecular Ecology, 23(20), pp. 4939–4957. doi: 10.1111/mec.12913.
    OpenUrlCrossRef
  20. ↵
    Galarza, J. A. (2021) ‘Comparative transcriptomics of albino and warningly-coloured caterpillars’, Ecology and Evolution, 11(12), pp. 7507–7517. doi: https://doi.org/10.1002/ece3.7581.
    OpenUrl
  21. ↵
    Galván, I. et al. (2015) ‘Insects synthesize pheomelanin’, Pigment Cell and Melanoma Research, 28(5), pp. 599–602. doi: 10.1111/pcmr.12397.
    OpenUrlCrossRefPubMed
  22. ↵
    Gawne, R. and Nijhout, H. F. (2019) ‘Expanding the nymphalid groundplan’s domain of applicability: pattern homologies in an arctiid moth (Utetheisa ornatrix)’, Biological Journal of the Linnean Society, 126(4), pp. 912–924. doi: 10.1093/biolinnean/bly193.
    OpenUrlCrossRef
  23. ↵
    Gordon, S. P. et al. (2015) ‘Colour polymorphism torn apart by opposing positive frequency-dependent selection, yet maintained in space’, Journal of Animal Ecology, 84(6), pp. 1555–1564. doi: 10.1111/1365-2656.12416.
    OpenUrlCrossRef
  24. ↵
    Guindon, S. et al. (2010) ‘New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0’, Systematic Biology, 59(3), pp. 307–321. doi: 10.1093/sysbio/syq010.
    OpenUrlCrossRefPubMedWeb of Science
  25. ↵
    Hegna, R. H., Galarza, J. A. and Mappes, J. (2015) ‘Global phylogeography and geographical variation in warning coloration of the wood tiger moth (Parasemia plantaginis)’, Journal of Biogeography, 42(8), pp. 1469–1481. doi: 10.1111/jbi.12513.
    OpenUrlCrossRef
  26. ↵
    Hines, H. M. et al. (2017) ‘Melanic variation underlies aposematic color variation in two hymenopteran mimicry systems’, PLoS ONE, 12(7), pp. 1–17. doi: 10.1371/journal.pone.0182135.
    OpenUrlCrossRefPubMed
  27. ↵
    Huu, C. N. et al. (2020) ‘Supergene evolution via stepwise duplications and neofunctionalization of a floral-organ identity gene’, Proceedings of the National Academy of Sciences of the United States of America, 117(37), pp. 23148–23157. doi: 10.1073/pnas.2006296117.
    OpenUrlAbstract/FREE Full Text
  28. ↵
    Huxley, J. (1955) ‘Morphism and evolution’, Heredity, 9(1), pp. 1–52. doi: 10.1038/hdy.1955.1.
    OpenUrlCrossRef
  29. ↵
    Ito, K. et al. (2010) ‘Yellow-e determines the color pattern of larval head and tail spots of the silkworm Bombyx mori’, Journal of Biological Chemistry, 285(8), pp. 5624–5629. doi: 10.1074/jbc.M109.035741.
    OpenUrlAbstract/FREE Full Text
  30. ↵
    Jorge García, A., Polidori, C. and Nieves-Aldrey, J. L. (2016) ‘Pheomelanin in the secondary sexual characters of male parasitoid wasps (Hymenoptera: Pteromalidae)’, Arthropod Structure & Development, 45(4), pp. 311–319. doi: https://doi.org/10.1016/j.asd.2016.05.001.
    OpenUrlCrossRefPubMed
  31. ↵
    Joron, M. et al. (2006) ‘A Conserved Supergene Locus Controls Colour Pattern Diversity in Heliconius Butterflies’, PLoS Biology, 4(10), pp. 1831–1840. doi: 10.1371/journal.pbio.0040303.
    OpenUrlCrossRefWeb of Science
  32. ↵
    Katoh, K. and Standley, D. M. (2013) ‘MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability’, Molecular Biology and Evolution, 30(4), pp. 772–780. doi: 10.1093/molbev/mst010.
    OpenUrlCrossRefPubMedWeb of Science
  33. ↵
    1. Kerkut, G. A. and
    2. Gilbert, L. I.
    Kayser, H. (1985) ‘Pigments’, in Kerkut, G. A. and Gilbert, L. I. (eds) Comparative Insect Physiology, Biochemistry, and Pharmacology. Oxford: Pergamon Press, pp. 367–415.
  34. ↵
    Kolb, A. M. et al. (1997) ‘Determination of Pheomelanin by Measurement of Aminohydroxyphenylalanine Isomers with High-Performance Liquid Chromatography’, Analytical Biochemistry, 252(2), pp. 293–298. doi: https://doi.org/10.1006/abio.1997.2342.
    OpenUrlPubMed
  35. ↵
    Kratochwil, C. et al. (2018) ‘Agouti-related peptide 2 facilitates convergent evolution of stripe patterns across cichlid fish radiations’, Science, 362(6413), pp. 457–460. doi: 10.1126/science.aao6809.
    OpenUrlAbstract/FREE Full Text
  36. ↵
    Kunte, K. et al. (2014) ‘doublesex is a mimicry supergene’, Nature, 507(7491), pp. 229–232. doi: 10.1038/nature13112.
    OpenUrlCrossRefPubMedWeb of Science
  37. ↵
    Küpper, C. et al. (2016) ‘A supergene determines highly divergent male reproductive morphs in the ruff’, Nature Genetics, 48(1), pp. 79–83. doi: 10.1038/ng.3443.
    OpenUrlCrossRef
  38. ↵
    Langmead, B. and Salzberg, S. L. (2012) ‘Fast gapped-read alignment with Bowtie 2’, Nature Methods, 9, p. 357. Available at: http://dx.doi.org/10.1038/nmeth.1923.
    OpenUrl
  39. ↵
    Li, H. et al. (2009) ‘The Sequence Alignment/Map format and SAMtools’, Bioinformatics, 25(16), pp. 2078–2079. doi: 10.1093/bioinformatics/btp352.
    OpenUrlCrossRefPubMedWeb of Science
  40. ↵
    Li, H. (2013) ‘Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM’, arXiv, p. 1303.3997.
  41. ↵
    Linnen, C. R. et al. (2013) ‘Adaptive Evolution of Multiple Traits Through Multiple Mutations at a Single Gene’, Science, 339(6125), pp. 1312–1316. doi: 10.1126/science.1233213.
    OpenUrlAbstract/FREE Full Text
  42. ↵
    Lowry, D. B. and Willis, J. H. (2010) ‘A Widespread Chromosomal Inversion Polymorphism Contributes to a Major Life-History Transition, Local Adaptation, and Reproductive Isolation’, PLOS Biology, 8(9), p. e1000500. Available at: https://doi.org/10.1371/journal.pbio.1000500.
    OpenUrlCrossRefPubMed
  43. ↵
    Martin, S. H. and Van Belleghem, S. M. (2017) ‘Exploring Evolutionary Relationships Across the Genome Using Topology Weighting’, Genetics, 206(1), pp. 429 LP–438. doi: 10.1534/genetics.116.194720.
    OpenUrlAbstract/FREE Full Text
  44. ↵
    1. K. VijayRaghavan and
    2. C. L. Peichel
    Massey, J. H. et al. (2019) ‘The yellow gene influences Drosophila male mating success through sex comb melanization’, eLife. Edited by K. VijayRaghavan and C. L. Peichel, 8, p. e49388. doi: 10.7554/eLife.49388.
    OpenUrlCrossRef
  45. ↵
    Matsuoka, Y. and Monteiro, A. (2018) ‘Melanin Pathway Genes Regulate Color and Morphology of Butterfly Wing Scales’, Cell Reports, 24(1), pp. 56–65. doi: 10.1016/j.celrep.2018.05.092.
    OpenUrlCrossRef
  46. ↵
    McGraw, K. J. et al. (2002) ‘The effect of dietary carotenoid access on sexual dichromatism and plumage pigment composition in the American goldfinch’, Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology, 131(2), pp. 261–269. doi: https://doi.org/10.1016/S1096-4959(01)00500-0.
    OpenUrl
  47. ↵
    Mcgraw, K. J. and Wakamatsu, K. (2004) ‘Melanin Basis of Ornamental Feather Colors in Male Zebra Finches’, The Condor, 106(3), pp. 686–690. doi: 10.1093/condor/106.3.686.
    OpenUrlCrossRef
  48. ↵
    McKenna, A. et al. (2010) ‘The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data’, Genome Research, 20, pp. 1297–1303. doi: 10.1101/gr.107524.110.
    OpenUrlAbstract/FREE Full Text
  49. ↵
    McKinnon, J. S. and Pierotti, M. E. R. (2010) ‘Colour polymorphism and correlated characters: Genetic mechanisms and evolution’, Molecular Ecology, 19(23), pp. 5101–5125. doi: 10.1111/j.1365-294X.2010.04846.x.
    OpenUrlCrossRefPubMedWeb of Science
  50. ↵
    Miyazaki, S. et al. (2014) ‘Sexually Dimorphic Body Color Is Regulated by Sex-Specific Expression of Yellow Gene in Ponerine Ant, Diacamma Sp’, PLOS ONE, 9(3), p. e92875. Available at: https://doi.org/10.1371/journal.pone.0092875.
    OpenUrlCrossRef
  51. ↵
    Nadeau, N. J. et al. (2016) ‘The gene cortex controls mimicry and crypsis in butterflies and moths’, Nature, 534(7605), pp. 106–110. doi: 10.1038/nature17961.
    OpenUrlCrossRefPubMed
  52. ↵
    Nishikawa, H. et al. (2015) ‘A genetic mechanism for female-limited Batesian mimicry in Papilio butterfly’, Nature Genetics, 47(4), pp. 405–409. doi: 10.1038/ng.3241.
    OpenUrlCrossRefPubMed
  53. ↵
    Nokelainen, O. et al. (2012) ‘Trade-off between warning signal efficacy and mating success in the wood tiger moth’, Proceedings of the Royal Society B: Biological Sciences, 279(1727), pp. 257–265. doi: 10.1098/rspb.2011.0880.
    OpenUrlCrossRefPubMed
  54. ↵
    Nokelainen, O. et al. (2014) ‘Changes in predator community structure shifts the efficacy of two warning signals in Arctiid moths’, Journal of Animal Ecology, 83(3), pp. 598–605. doi: 10.1111/1365-2656.12169.
    OpenUrlCrossRefPubMed
  55. ↵
    Nokelainen, O. et al. (2022) ‘Genetic colour variation visible for predators and conspecifics is concealed from humans in a polymorphic moth’, Journal of Evolutionary Biology, (February), pp. 467–478. doi: 10.1111/jeb.13994.
    OpenUrlCrossRef
  56. ↵
    Orteu, A. and Jiggins, C. D. (2020) ‘The genomics of coloration provides insights into adaptive evolution’, Nature Reviews Genetics, 21(8), pp. 461–475. doi: 10.1038/s41576-020-0234-z.
    OpenUrlCrossRef
  57. ↵
    Polidori, C., Jorge, A. and Ornosa, C. (2017) ‘Eumelanin and pheomelanin are predominant pigments in bumblebee (Apidae: Bombus) pubescence’, PeerJ, 2017(5), pp. 1–21. doi: 10.7717/peerj.3300.
    OpenUrlCrossRef
  58. ↵
    Quinlan, A. R. and Hall, I. M. (2010) ‘BEDTools: a flexible suite of utilities for comparing genomic features’, Bioinformatics, 26(6), pp. 841–842. doi: 10.1093/bioinformatics/btq033.
    OpenUrlCrossRefPubMedWeb of Science
  59. ↵
    Rastas, P. (2017) ‘Lep-MAP3: robust linkage mapping even for low-coverage whole genome sequencing data’, Bioinformatics, 33, pp. 3726–3732. doi: 10.1093/bioinformatics/btx494.
    OpenUrlCrossRef
  60. ↵
    Reed, R. D. et al. (2011) ‘optix drives the repeated convergent evolution of butterfly wing pattern mimicry’, Science, 333, pp. 1137–1141. doi: 10.1126/science.1208227.
    OpenUrlAbstract/FREE Full Text
  61. ↵
    Ritchie, M. E. et al. (2015) ‘limma powers differential expression analyses for RNA-sequencing and microarray studies’, Nucleic Acids Research, 43(7), pp. e47–e47. doi: 10.1093/nar/gkv007.
    OpenUrlCrossRefPubMed
  62. ↵
    Rojas, B. et al. (2017) ‘How to fight multiple enemies: Target-specific chemical defences in an aposematic moth’, Proceedings of the Royal Society B: Biological Sciences, 284(1863). doi: 10.1098/rspb.2017.1424.
    OpenUrlCrossRefPubMed
  63. ↵
    Rojas, B., Gordon, S. P. and Mappes, J. (2015) ‘Frequency-dependent flight activity in the colour polymorphic wood tiger moth’, Current Zoology, 61(4), pp. 765–772. doi: 10.1093/czoolo/61.4.765.
    OpenUrlCrossRef
  64. ↵
    Rönkä, K. et al. (2020) ‘Geographic mosaic of selection by avian predators on hindwing warning colour in a polymorphic aposematic moth’, Ecology Letters, 23(11), pp. 1654–1663. doi: https://doi.org/10.1111/ele.13597.
    OpenUrl
  65. ↵
    Shamim, G. et al. (2014) ‘Biochemistry and biosynthesis of insect pigments’, European Journal of Entomology, 111(2), pp. 149–164. doi: 10.14411/eje.2014.021.
    OpenUrlCrossRef
  66. ↵
    1. Simpson, S. J. B. T.-A. in I. P
    Sugumaran, M. (2010) ‘Chapter 5 - Chemistry of Cuticular Sclerotization’, in Simpson, S. J. B. T.-A. in I. P. (ed.). Academic Press, pp. 151–209. doi: https://doi.org/10.1016/B978-0-12-381387-9.00005-1.
  67. ↵
    Suomalainen, E. (1938) ‘Die erblichkeitsverhältnisse des männlichen dimorphismus bei parasemia plantaginis’, Hereditas, 24, pp. 386–390.
    OpenUrl
  68. ↵
    Thompson, M. J. and Jiggins, C. D. (2014) ‘Supergenes and their role in evolution’, Heredity, 113(1), pp. 1–8. doi: 10.1038/hdy.2014.20.
    OpenUrlCrossRefPubMed
  69. ↵
    Timmermans, M. J. T. N. et al. (2014) ‘Comparative genomics of the mimicry switch in Papilio dardanus’, Proceedings of the Royal Society B: Biological Sciences, 281(1787). doi: 10.1098/rspb.2014.0465.
    OpenUrlCrossRefPubMed
  70. ↵
    Tuttle, E. M. et al. (2016) ‘Divergence and Functional Degradation of a Sex Chromosome-like Supergene’, Current Biology, 26(3), pp. 344–350. doi: https://doi.org/10.1016/j.cub.2015.11.069.
    OpenUrlCrossRefPubMed
  71. ↵
    Umebachi, Y. (1975) ‘Further studies on the dopamine derivative, SN-1 derived from the yellow pigments of Papilio xuthus’, Insect Biochemistry, 5, pp. 73–92.
    OpenUrl
  72. ↵
    Wakamatsu, K. et al. (2014) ‘Reduction of the Nitro Group to Amine by Hydroiodic Acid to Synthesize o-Aminophenol Derivatives as Putative Degradative Markers of Neuromelanin’, Molecules. doi: 10.3390/molecules19068039.
    OpenUrlCrossRef
  73. ↵
    Wakamatsu, K., Ito, S. and Rees, J. L. (2002) ‘The Usefulness of 4-Amino-3-hydroxyphenylalanine as a Specific Marker of Pheomelanin’, Pigment Cell Research, 15(3), pp. 225–232. doi: https://doi.org/10.1034/j.1600-0749.2002.02009.x.
    OpenUrlCrossRefPubMedWeb of Science
  74. ↵
    Wang, J. et al. (2013) ‘A Y-like social chromosome causes alternative colony organization in fire ants’, Nature, 493(7434), pp. 664–668. doi: 10.1038/nature11832.
    OpenUrlCrossRefPubMedWeb of Science
  75. ↵
    Wang, P. et al. (2022) ‘Yellow-b, -c, -e and -h are required for normal body coloration in Henosepilachna vigintioctopunctata’, Archives of Insect Biochemistry and Physiology, 109(2), p. e21856. doi: https://doi.org/10.1002/arch.21856.
    OpenUrl
  76. ↵
    Wilson, R. et al. (1976) ‘Behavioural pleiotropy of the yellow gene in Drosophila melanogaster’, Genetical Research. 2009/04/14, 28(1), pp. 75–88. doi: DOI: 10.1017/S0016672300016748.
    OpenUrlCrossRefPubMedWeb of Science
  77. ↵
    Wittkopp, P. J., Vaccaro, K. and Carroll, S. B. (2002) ‘Evolution of yellow gene regulation and pigmentation in Drosophila’, Current Biology, 12(18), pp. 1547–1556. doi: 10.1016/S0960-9822(02)01113-2.
    OpenUrlCrossRefPubMedWeb of Science
  78. ↵
    Yen, E. C. et al. (2020) ‘A haplotype-resolved, de novo genome assembly for the wood tiger moth (Arctia plantaginis) through trio binning’, GigaScience, 9(8), pp. 1–12. doi: 10.1093/gigascience/giaa088.
    OpenUrlCrossRef
  79. ↵
    Zhang, L. et al. (2017) ‘Genetic basis of melanin pigmentation in butterfly wings’, Genetics, 205(4), pp. 1537–1550. doi: 10.1534/genetics.116.196451.
    OpenUrlAbstract/FREE Full Text
  80. ↵
    Zhang, L., Mazo-Vargas, A. and Reed, R. D. (2017) ‘Single master regulatory gene coordinates the evolution and development of butterfly color and iridescence’, Proceedings of the National Academy of Sciences, 114, pp. 10707–10712. doi: 10.1073/pnas.1709058114.
    OpenUrlAbstract/FREE Full Text
Back to top
Posted May 01, 2022.
Download PDF

Supplementary Material

Email
Colour polymorphism associated with a gene duplication in male wood tiger moths
Melanie N. Brien, Anna Orteu, Eugenie C. Yen, Juan A. Galarza, Jimi Kirvesoja, Hannu Pakkanen, Kazumasa Wakamatsu, Chris D. Jiggins, Johanna Mappes
bioRxiv 2022.04.29.490025; doi: https://doi.org/10.1101/2022.04.29.490025
Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools

Subject Area