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Proceedings of the Royal Society B: Biological Sciences logoLink to Proceedings of the Royal Society B: Biological Sciences
. 2022 Jun 8;289(1976):20220336. doi: 10.1098/rspb.2022.0336

Genetic and genomic architecture of species-specific cuticular hydrocarbon variation in parasitoid wasps

Jan Buellesbach 1,✉, Henrietta Holze 1, Lukas Schrader 1, Jürgen Liebig 2, Thomas Schmitt 3, Juergen Gadau 1, Oliver Niehuis 4
PMCID: PMC9174729  PMID: 35673870

Abstract

Cuticular hydrocarbons (CHCs) serve two fundamental functions in insects: protection against desiccation and chemical signalling. How the interaction of genes shapes CHC profiles, which are essential for insect survival, adaptation and reproductive success, is still poorly understood. Here we investigate the genetic and genomic basis of CHC biosynthesis and variation in parasitoid wasps of the genus Nasonia. We mapped 91 quantitative trait loci (QTL) explaining the variation of a total of 43 CHCs in F2 hybrid males from interspecific crosses between three Nasonia species. To identify candidate genes, we localized orthologues of CHC biosynthesis-related genes in the Nasonia genomes. We discovered multiple genomic regions where the location of QTL coincides with the location of CHC biosynthesis-related candidate genes. Most conspicuously, on a region close to the centromere of chromosome 1, multiple CHC biosynthesis-related candidate genes co-localize with several QTL explaining variation in methyl-branched alkanes. The genetic underpinnings behind this compound class are not well understood so far, despite their high potential for encoding chemical information as well as their prevalence in hymenopteran CHC profiles. Our study considerably extends our knowledge on the genetic architecture governing this important compound class, establishing a model for methyl-branched alkane genetics in the Hymenoptera in general.

Keywords: comparative genomics, gene mapping, orthologue inference, quantitative trait loci, Nasonia, hymenoptera

1. Introduction

Understanding the genetic basis of quantitative phenotypic traits remains one of the central challenges in population genetics and evolutionary biology [1,2]. Genes and regulatory sequences governing natural variation in a set of phenotypic traits can be mapped (and potentially identified) in a genome by searching for phenotypic covariation with polymorphic genetic markers, i.e. quantitative trait locus (QTL) mapping [3,4]. Evolutionary genetic studies addressing variation and divergence of traits between closely related species particularly benefit from QTL mapping techniques [2,5]. Two components are of paramount importance for these kinds of studies: first, the availability of genetically tractable and crossable species with comparably short generation times and, ideally, established phylogenetic relationships [1,6]. Second, polygenic traits of interest that differ between the crossed species [1,5]. Insects have proven to be particularly useful for QTL studies, combining various beneficial attributes: a multitude of quantifiable phenotypes, short generation times, large offspring numbers and, most commonly, relatively small genomes [7,8]. Cuticular hydrocarbons (CHCs), lipids constituting a major part of the waxy layer covering the insects' epicuticle, are particularly promising traits to dissect genetically via QTL mapping, as they are comparatively easily quantifiable, polygenic and phenotypically as well as genotypically traceable [9,10].

CHCs are long-chained hydrocarbon molecules whose primary function is to protect terrestrial insects from desiccation [11,12]. As versatile semiochemicals, CHCs have also been demonstrated to function in a wide array of chemical communication systems integral to insect survival, reproduction and adaptation [9,13]. For instance, CHCs have been shown to be major sex pheromonal compounds [14,15], capable of signalling species affiliation and contributing to reproductive isolation [16,17], as well as primary nestmate and caste recognition cues in eusocial insects [18,19].

Differences between CHC profiles arise from presence, absence and abundance of individual CHCs, which can differ in their chain length as well as in the presence and position of double bonds and methyl groups [9,20]. The most commonly occurring compound classes are saturated straight-chain alkanes (n-alkanes), unsaturated olefins (mostly n-alkenes with one and alkadienes with two double bonds), and methyl-branched alkanes [9,13]. Despite considerable diversity of CHC profiles across insects, to the current stage of knowledge, the basic pathway of CHC biosynthesis appears to be mostly conserved [9,10]. Briefly, the biosynthetic pathway consists of the elongation of fatty-acyl-coenzyme A units to produce very long-chain fatty acids that are subsequently converted to hydrocarbons by subducting the carboxyl group ([10,13], figure 1).

Figure 1.

Figure 1.

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Overview of CHC biosynthesis, with the main enzymes catalysing the intermediate reactions highlighted (compare to figure 3 for localization of the corresponding genes). The pathway branches at different stages, eventually synthesizing the main CHC compound classes: Methyl-branched alkanes (mono-, di-, tri- and tetra-), straight-chain n-alkanes and unsaturated olefins. Respective percentages of the individual compound classes found on the cuticle of Nasonia males (averaged over our three study species) are indicated as well (electronic supplementary material, table S8). Abbreviations: CoA: coenzyme A, ACC: acetyl-CoA carboxylase, FAS: fatty acid synthase (m: microsomal, c: cytosolic, f: fat body associated). Adapted from Holze et al. [10]. (Online version in colour.)

Most of what we currently know about the genetics governing CHC biosynthesis is based on studies on the insect model organism Drosophila (e.g. [14,16]), but knowledge on the genetic underpinnings of this fundamental process in other insect taxa is far more scarce [10,13]. Particularly little is known about the genetic factors governing the variation of methyl-branched alkanes, a dominant compound class in many insect taxa with high potential for encoding chemical information through a multitude of possible methyl-branch positions and numbers [10,21]. In contrast to Drosophila, where methyl-branched alkanes only comprise a small fraction of their CHC profiles, this particular compound class dominates the CHC profiles in most species of the extensively investigated insect order Hymenoptera [22,23]. However, knowledge on the genetics governing CHC variation in the Hymenoptera is mostly lacking so far except for a few single case studies on Apis mellifera [24,25]. To address this knowledge gap, more suitable model organisms are required to provide a basis for studying the genetic background of CHC production, variation and divergence with particular emphasis on the predominant methyl-branched alkanes.

The parasitoid jewel wasp genus Nasonia (Hymenoptera: Pteromalidae) has emerged as a model system well suited to study quantitative as well as polygenic phenotypic traits in the Hymenoptera. It combines ease of maintenance, the possibility to generate hybrids between its four described species, haplo-diploid genetics and a growing genetic tool kit [26,27]. This has already proven to be particularly useful for QTL mapping of interspecific differences in a variety of complex traits [27,28]. For instance, comparing CHC variation through QTL mapping between two Nasonia species shed some first light on the genetic basis of CHC profile differences between them [29]. A study on CHC variation between all four so far described Nasonia species, N. vitripennis, N. longicornis, N. giraulti and N. oneida, established clearly distinguishable species- and sex-specific CHC blends, with the potential to function as female sex pheromones [30,31].

Here we dissect the genomic and genetic architecture of CHC variation in hybrid cross-comparisons between three Nasonia species. We conduct comparative QTL analyses on CHC variation in recombinant F2 hybrid males obtained by crossing N. vitripennis, N. longicornis and N. giraulti and simultaneously map candidate genes for CHC biosynthesis in the Nasonia genome. Further extending beyond the genus Nasonia, we additionally include two more distantly related parasitoid wasp genera in a comprehensive screen for candidate gene orthologues and selection signatures. Finally, we discuss how CHC profile divergence between the three Nasonia species can be explained phenotypically when considering the newly gained knowledge on the genetic architecture of CHC profile differences.

2. Methods

(a) . Nasonia strains and crossing experiments

We used the standard laboratory strains AsymCX, RV2x(U) and IV7(U) of the species N. vitripennis, N. giraulti and N. longicornis, respectively, to conduct the cross experiments [26]. All strains were maintained in an incubator at 25°C under permanent light and were provided with pupae of the flesh fly Sarcophaga bullata as hosts. Cross experiments were conducted as described by Niehuis et al. [28]. We generated two types of F1 hybrid females: (i) by crossing virgin N. vitripennis females with N. longicornis males and (ii) by crossing virgin N. giraulti females with N. longicornis males. All F1 hybrid females were kept unmated and thus generated recombinant haploid F2 hybrid males, which were collected 36 h after they had eclosed, freeze-killed and stored at −80°C before characterizing their genotypes and CHC phenotypes.

(b) . Chemical analyses

We analysed the CHC profiles of 100 N. vitripennis x N. longicornis (LV) and 101 N. longicornis × N. giraulti (LG) recombinant hybrid F2 males. We additionally characterized the CHC profiles of 12 N. giraulti, 11 N. longicornis and 32 N. vitripennis males of the investigated strains for comparison. CHCs were extracted by submersing individual wasps for 10 min in 10 µl n-hexane (≥99.0%; Sigma-Aldrich, St Louis, MO, USA) using 1 ml glass vials equipped with a 0.1 ml micro insert (Alltech, Deerfield, IL, USA). The CHC extracts were subsequently transferred to new vials and concentrated under a stream of nitrogen to approximately 1 μl total volume. The CHC extracts were then injected into a gas chromatograph coupled with a mass spectrometer (GC: 6890 N; MS: 5975C; Agilent, Santa Clara, CA, USA) for analysis. The GC-MS was operated in splitless mode with an injector temperature of 250°C. Separation of compounds was performed on a J&W DB-5MS fused silica capillary column (30 m × 0.25 mm ID × 0.25 µm; Agilent, Santa Clara, CA, USA) and applying the following temperature program: 60°C start temperature, temperature increase by 40°C per min to 200°C and followed by an increase of 5°C per min to 320°C. Helium with a constant flow of 1 ml min–1 was used as carrier gas. The obtained chromatograms were analysed with the software Enhanced Chemstation E.01.00.237 (Agilent, Santa Clara, CA, USA). CHC compounds were identified according to their retention indices, diagnostic ions and mass spectra [30,32]. The data were standardized by dividing each peak area through the sum of all integrated peaks, obtaining relative peak ratios [29,30].

(c) . Cuticular hydrocarbon profile discriminant analysis

Discriminant analysis (DA) was performed with the R package ‘MASS’ [33] to test whether CHC profiles statistically differ between the five investigated groups of male wasps (i.e. those of the three parental strains and those of the F2 hybrids) and to visualize the degree of separation between these groups based entirely on CHC profile differences. To standardize the peak area values for the DA, the normalization method of the function ‘decostand’ of the community ecology R package ‘vegan’ was used based on the following formula [34]: Tx,y = Px,y/√Σ P2y, where Tx,y is the transformed peak area × of individual y, Px,y is the absolute peak area × of individual y and Σ Py2 to the squared sums of all absolute peak areas of individual y. This widely applied method for normalizing phenotypic data was chosen to make the peak areas comparable between our groups and to highlight the relative peak area differences. To visualize the data by plotting the first three discriminant functions simultaneously, the R package ‘scatterplot3d’ was used [35]. Wilk's λ was calculated to measure the quality of the DA.

(d) . DNA extraction and quantitative trait loci analysis

We extracted genomic DNA from all wasps after extraction of their CHCs. DNA extraction was done as outlined by Niehuis et al. [36]. Genotype data were collected for 29 length-polymorphic markers with known positions in the Nasonia nuclear genome (electronic supplementary material, table S1, [29]). The species-specific length of the markers was assessed on a LI-COR 4300 DNA analysis system (LI-COR, Lincoln, NE, USA) and analysed using the SAGA Generation 2 software (LI-COR) following the procedure given by Niehuis et al. [28,36].

QTL analyses were conducted with the R/qtl package v. 1.14-2 [37] on a 64-bit-build of R v. 2.10.1 [38]. Significant phenotypic differences in the relative abundances of CHC compounds were assessed with Bonferroni-corrected Wilcoxon rank-sum tests with continuity correction [39,40]. Each CHC compound was subjected to a one-dimensional one-QTL and to a two-dimensional two-QTL scan using Haley–Knott regression [3] with an assumed genotypic error probability of 0.001 and a step width of 1 cM. We additionally applied a multiple QTL model to each CHC trait with forward/backward model selection as implemented in R/qtl. The maximum number of QTL for each trait was set to 10, with QTL positions refined after each step of the forward and backward selection. Penalties for model selection were set based on the permutation from the two-dimensional two-QTL genome scan of the respective CHC trait with a significance level of 0.01. Significance thresholds for QTL presence were estimated from 10 000 permutations of each respective phenotypic CHC abundance. Since our QTL mapping was based on the Nasonia linkage map inferred by Niehuis et al. [41] and on genome assembly 1.0 (Nvit 1.0, [26]), we transferred marker positions and QTL to the most recent high-resolution linkage map of Nasonia (Nvit 2.1, [42]) where we performed the mapping of candidate CHC biosynthesis genes (electronic supplementary material, table S2, see below). A fixed confidence interval of 20 cM was chosen for each QTL, since this distance was equivalent to the average distance between markers.

(e) . Search for candidate cuticular hydrocarbon biosynthesis genes

Thirty-eight well-characterized candidate genes with a demonstrated impact on CHC variation via targeted knockdown studies were selected from Drosophila melanogaster (electronic supplementary material, table S2, see also [10]) via FlyBase (v. FB2019_02, http://flybase.org/). To screen for orthologues of these candidate genes in the Nasonia Nvit 2.0 reference genome, three complementary methods were used: first, the amino acid sequences, including all possible isoforms encoded by D. melanogaster CHC biosynthesis genes of interest, were searched against the N. vitripennis proteome available on NCBI (https://www.ncbi.nlm.nih.gov/) using the BLAST+ software suite (v. 2.8.1, [43]). Amino acid sequences with an e-value smaller than 10−10 were then again searched reciprocally against the D. melanogaster proteome. Reciprocal best hits were considered as orthologues. Second, the program OrthoFinder (v. 2.3.1, [44]) was used to identify orthologue groups at the hierarchical level of the last common ancestor of the investigated species. The software DIAMOND [45] was used to align the amino acid sequences of a given orthologue group. Third, we queried the N. vitripennis WaspAtlas database (http://cyverse.warwick.ac.uk:3000/) for orthologue groups.

We were unable to identify unambiguous orthologues of some of the D. melanogaster CHC biosynthesis candidate genes in the N. vitripennis genome with the methods outlined above despite partially high sequence similarities (electronic supplementary material, table S2). Therefore, BLAST+-inferred hits in the N. vitripennis proteome with amino acid sequence similarity thresholds of greater than 30% identity, less than 10−10 e-value and greater than 85% query coverage to D. melanogaster were additionally considered, although these may not necessarily constitute direct orthologues. The identification of orthologous candidate proteins of two protein families posed particular problems: fatty acid elongases (ELOs) and fatty-acyl-CoA reductases (FARs). Candidate ELOs and FARs in the N. vitripennis proteome with the highest amino acid sequence similarity (lowest e-value and highest bit score) to the D. melanogaster ELOs and FARs with demonstrated impact on CHC variation (electronic supplementary material, table S2) were used as queries to search for homologues in the Nasonia proteome using blastp of the BLAST+ software suite. Hits above a similarity threshold of an e-value less than 10−20 were considered to belong to the same protein/gene family, and domain structure of the respective hits was taken into account to ensure that the derived gene sequences encode functional proteins. This was achieved with hmmscan of the HMMER software (v. 3.1b2, http://hmmer.org/) and use of the Pfam-A database (v. 32, [46]). For visualization of the QTL regions and candidate gene positions on the N. vitripennis linkage map Nvit 2.1, the R package LinkageMapView was used [47].

(f) . Gene orthology and screens for signatures of positive selection

To test for signatures of positive selection in our candidate genes, we extended our orthologue inference analysis to the fourth Nasonia species, N. oneida, as well as to two closely related parasitoid wasp genera, Trichomalopsis and Muscidifurax (electronic supplementary material, figure S1). The nucleotide sequences of the candidate genes from the N. vitripennis genome were extracted with SAMtools [48] and aligned with the software BLAT (v. 35, [49]) to the nucleotide sequences of the latest genome assemblies of N. giraulti, N. longicornis, N. oneida and T. sarcophagae kindly provided by Xiaozhu Wang (unpublished data). Nucleotide sequences that shared at least 50 identical nucleotide positions and that were aligned along at least 80% of the query nucleotide sequence length were considered as homologous. To ensure these were also orthologous to the candidate genes of the N. vitripennis genome, their amino acid sequences were reciprocally searched against the N. vitripennis proteome with BLAST+ (v. 2.8.1, [43]). Only genes that fulfilled the best reciprocal hit criterion were considered as orthologous. To structurally annotate candidate genes in the draft genomes of N. giraulti, N. oneida, N. longicornis and T. sarcophagae, we used the program GeMoMa (v. 1.6, [50]) with nucleotide sequences of the orthologous N. vitripennis genes as references. Since BLAT was not sensitive enough to identify orthologues in M. raptorellus (comparatively distantly related to N. vitripennis; see electronic supplementary material, figure S1), we de novo-inferred gene models in a draft genome of this species, kindly provided by Eva Jongepier (unpublished data), with the software GeMoMa. To obtain the respective coding nucleotide sequences from the GeMoMa-predicted annotations, the program BEDtools (v. 2.28.0, [51]) was used with either the nucleotide sequences of the gene regions or the whole genome assembly.

Candidate genes were screened for signatures of positive selection by analysing the ratio of non-synonymous to synonymous substitutions (ω = dN/dS). The nucleotide sequences of each candidate gene orthologue group containing the orthologues of all six wasp species were aligned with the codon-sensitive alignment tool PRANK (v. 170 427, [52]). The alignment process was supported by a phylogenetic tree (electronic supplementary material, figure S1) of the study species whose evolutionary distances are based on the D2 expansion region of their 28S rDNA sequences [53] and mitochondrial DNA sequences [26]. Poorly aligned regions were trimmed with Gblocks (v. 0.91b, [54]). Each sequence alignment was screened for signatures of positive selection on at least one site from at least one branch of the given phylogeny with the BUSTED (Branch-site Unrestricted Statistical Test for Episodic Diversification) algorithm, implemented in HyPhy (v. 2.5, http://hyphy.org). Resulting p-values were Benjamini–Hochberg-corrected for multiple testing.

3. Results

(a) . Cuticular hydrocarbon divergence between F0 parental and F2 hybrid males

To assess whether CHC profiles differ statistically between males of the parental strains and the recombinant F2 hybrid males, we conducted a DA on the CHC profile data of 256 male wasps belonging to five groups: N. giraulti (NG), N. longicornis (NL), N. vitripennis (NV), N. longicornis × N. vitripennis F2 hybrids (LV) and N. longicornis × N. giraulti F2 hybrids (LG) (figure 2). All groups differed in their CHC profiles significantly from each other (Wilk's λ < 0.001, χ2 = 23.22, p < 0.001), and the recombinant F2 males consistently clustered in between their respective F0 parental males. Discriminant function 1 accounted for 63.03%, function 2 for 31.54% and function 3 for 4.7% of the total variation, amounting to 99.27% of total variance explained by the first three functions.

Figure 2.

Figure 2.

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Plot of the first three discriminant functions showing the divergence between CHC profiles of F0 parental and F2 hybrid males between the three investigated Nasonia species in three dimensions. F0 NG: N. giraulti males (n = 12), F0 NL: N. longicornis males (n = 11), F0 NV: N. vitripennis males (n = 32), F2 LV: N. longicornis/N. vitripennis hybrid males (n = 101), F2 LG: N. longicornis/N. giraulti hybrid males (n = 100), total n = 256. All groups were significantly differentiated from each other (Wilk's λ < 0.001, χ2 = 23.22, p < 0.001); the variation each function explains is indicated in percentages. (Online version in colour.)

(b) . Quantitative trait loci explaining cuticular hydrocarbon variation in Nasonia F2 hybrid males

We detected a total of 91 QTL explaining CHC variation in recombinant F2 hybrid males. Of those, 80 were found in the recombinant F2 hybrid males obtained from crossing N. longicornis (♂) and N. vitripennis (♀) and 11 were found in the recombinant F2 hybrid males obtained from crossing N. longicornis (♂) and N. giraulti (♀) (figure 3; electronic supplementary material, table S3). The detected QTL explain variation in 43 (of a total of 53 analysed) CHCs. The QTL of seven CHCs (two mono-methyl-branched, three di-methyl-branched and two tetra-methyl-branched alkanes) were found in hybrids from both crosses, but corresponding QTL are located on different chromosomes or on different chromosomal regions (electronic supplementary material, table S3). The QTL of one single CHC compound, 15,17-DiMeC29 (RI: 2982), was detected exclusively in hybrids that originated from crossing N. longicornis and N. giraulti. Sixty-nine QTL, explaining variation of 35 CHCs, were exclusively detected in hybrids that originated from crossing N. longicornis and N. vitripennis (figure 3; electronic supplementary material, table S3). The QTL detected in F2 hybrid males of the latter cross are spread across all five chromosomes and encompass all six compound classes identified in Nasonia CHC profiles (i.e. n-alkanes, n-alkenes, mono-, di-, tri- and tetra-methyl-branched alkanes). Several QTL explaining quantitative variation of structurally similar CHCs (mostly methyl-branched alkanes) clustered together, with two clusters each on chromosomes 1 and 2, and one cluster each on chromosomes 3, 4 and 5 (figure 3; electronic supplementary material, table S3). Eleven QTL were found in recombinant F2 hybrid males obtained from crossing N. longicornis and N. giraulti. All of them explain variation of methyl-branched alkanes (mono-, di- and tetra-methy-branched alkanes). These QTL are located on chromosomes 2, 4 and 5, with clusters of them detected on chromosomes 4 and 5 (figure 3). Although these QTL clusters are in spatial proximity to the ones detected on the same two chromosomes in the F2 male hybrids obtained from crossing N. longicornis and N. vitripennis, they explain variation of different CHCs (electronic supplementary material, table S3).

Figure 3.

Figure 3.

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Linkage map based on F2 hybrid males from a cross between Nasonia longicornis (♂) and Nasonia vitripennis (♀) depicting positions of 80 QTL for 42 individual CHCs to the right of the chromosomes, and from a cross between N. longicornis (♂) and N. giraulti (♀), depicting 11 QTL for nine CHCs to the left of the chromosomes. CHCs are indicated by symbols corresponding to their respective compound class (n-alkanes, n-alkenes, mono-, di-, tri- and tetra-methyl-branched alkanes), their RI and numbers inside the symbols reflect their carbon chain length (see also electronic supplementary material, table S3). Black horizontal lines on the chromosomes (1–5) depict the positions of the molecular markers used for determining the QTL positions. Additionally, the positions of CHC biosynthesis-related candidate genes are shown; colour coded according to their inferred position in the CHC biosynthesis pathway where possible (figure 1). Regions of comparatively low recombination (greater than 0.8 Mb/cM) and centromeric regions [42] are marked in light and dark grey, respectively. (Online version in colour.)

(c) . Cuticular hydrocarbon biosynthesis-related candidate genes

Of the candidate genes from Drosophila melanogaster with a demonstrated impact on CHC variation through targeted knockdown studies (electronic supplementary material, table S2), we found 15 to be represented by exactly one orthologue in the Nasonia reference genome, irrespective of the applied orthologue inference method (i.e. reciprocal BLAST search (RBS), OrthoFinder and WaspAtlas) (electronic supplementary material, table S4). Orthologue inference of the remaining D. melanogaster candidate genes generated inconsistent results when comparing the three orthologue inference methods. For instance, RBS of three fatty acid synthase (FAS) genes with a demonstrated impact on Drosophila CHC biosynthesis identified nine putative fas orthologues in the Nasonia reference genome, whereas OrthoFinder found five. However, upon closer inspection, several of the putative fas orthologues identified by RBS are actually comprised of very shortnucleotide sequences that unlikely encode fully functional proteins. These potential pseudogenes were excluded as CHC biosynthesis candidate genes in Nasonia. Moreover, three desaturases in D. melanogaster, among the first genes to be functionally characterized in the CHC biosynthesis pathway [10,14], yielded no consistent orthologues in the Nasonia genome by any of the three orthologue inference methods, and were therefore not included in the orthologous candidate gene list. Nevertheless, we included 11 further candidate genes in addition to the 15 unambiguously characterized D. melanogaster orthologues due to strong evidence for orthology from a subset of the applied orthologue inference methods (electronic supplementary material, table S4). Thus, we identified a total of 26 candidate genes for CHC biosynthesis and variation in the Nasonia reference genome.

Two further gene families emerged in our analysis with high nucleotide sequence similarity to Drosophila CHC biosynthesis candidate genes despite no evidence for direct orthology: elongases (elos) and fatty-acyl-CoA reductases (fars). Of the four elos and the two fars in the D. melanogaster genome with a clear impact on CHC profile composition, we were able to identify 12 and 22 homologous genes with high nucleotide sequence similarity in the Nasonia reference genome (electronic supplementary material, table S5; figure 4). Mapping of those genes on the Nasonia linkage map revealed a region on chromosome 1 around the centromere where eight elos and 13 fars cluster together in close spatial vicinity (figure 4). Two additional candidate genes, orthologous to the Drosophila CHC biosynthesis genes cyp4g1 and CG14688, also map in this chromosomal region.

Figure 4.

Figure 4.

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Nasonia linkage map of chromosome 1 with an enlarged region densely packed with CHC biosynthesis-related candidate genes. Eight elongase (elo) and 13 fatty-acyl-reductase (far) genes cluster together in close spatial vicinity (see also electronic supplementary material, table S5) in addition to two further CHC biosynthesis genes (cyp4g1 and CG14688) in this chromosomal region. Note that this region maps around the centromere and is characterized by a particularly low recombination rate (greater than 0.8 Mb/cM). Molecular markers and chromosomal regions are indicated as in figure 3. (Online version in colour.)

(d) . Correlation in the genomic localization of candidate genes and quantitative trait loci

QTL from the second cluster on chromosome 1 explaining variation of CHCs in recombinant F2 male hybrids of N. longicornis and N. vitripennis co-localize with CHC biosynthesis-related candidate genes in close spatial vicinity to the centromere (figure 3). Intriguingly, this also encompasses the conspicuous chromosomal region with a high density of elo and far genes (figure 4). Of the two detected QTL clusters on chromosome 2 from the cross mentioned above, the first one co-localizes with an orthologue of the candidate gene spidey that codes for a component of the elongation enzyme complex ([10], see figures 1 and 3, [55]). Some QTL from the second cluster co-localize with several far genes (compare figure 3 and figure 4). On chromosome 3, the detected QTL cluster maps closely to the position of a gene orthologous to CG16979, a candidate gene with an impact on CHC variation in D. melanogaster despite an unclear role in the CHC biosynthesis pathway [10,56]. On chromosome 4, several QTL map closely to a hacd1-orthologue, another gene that codes for an elongation enzyme complex component [10,55]. On chromosome 5, a particularly large cluster, consisting almost entirely of QTL explaining variation in methyl-branched alkanes, co-localizes with a fas gene orthologue (fas5). Another QTL cluster identified in male F2 hybrids from the cross between N. longicornis and N. giraulti on chromosome 4 maps close to the hacd1-orthologue, in close proximity to the above-mentioned QTL cluster from the cross between N. vitripennis and N. longicornis on the same chromosome (figure 3).

(e) . Orthology and signatures of positive selection for cuticular hydrocarbon candidate genes in other parasitoid wasps

We screened all 26 CHC biosynthesis-related candidate genes identified in the N. vitripennis reference genome (see electronic supplementary material, table S2) for orthologues in the respective genomes of the other three Nasonia species and in the genomes of two additional parasitoid wasp species: Trichomalopsis sarcophagae and Muscidifurax raptorellus (electronic supplementary material, figure S1). In the genome of the latter, we were unable to identify orthologues of seven of the 26 N. vitripennis CHC biosynthesis candidate genes (CG14688, fas2, fas4, fas5, hacd2 and nrt). Of these seven, two (fas5 and hacd2) remained unidentified in T. sarcophagae and three remained unidentified in N. giraulti (fatp1, nrt and hacd2). Interestingly, hacd2 was the only N. vitripennis-exclusive CHC biosynthesis-related candidate gene with no orthologues in any of the other investigated parasitoid wasp species (electronic supplementary material, table S6). Orthologues of the remaining 19 CHC biosynthesis-related candidate genes were found in all investigated species and were screened for signatures of positive selection based on the ratios of non-synonymous (dN) to synonymous (dS) nucleotide substitutions. After Benjamini–Hochberg correction for multiple testing, the signature for positive selection in one gene, fas1, was considered statistically significant.

4. Discussion

(a) . Genomic regions harbouring candidate genes and quantitative trait loci explaining cuticular hydrocarbon variation

By analysing recombinant F2 hybrid males from crosses between N. longicornis and N. vitripennis (LV) and between N. longicornis and N. giraulti (LG), we identified several QTL clusters mapping to genomic regions that harbour CHC biosynthesis candidate genes. Four of these regions additionally coincide with the location of QTL explaining CHC variation in recombinant F2 hybrid males of N. vitripennis and N. giraulti (VG) obtained in a previous study ([29], electronic supplementary material, table S7). Interestingly, QTL explaining variation of identical compounds in different hybrid crosses map only in very few instances to the same genomic regions. Conversely, in the majority of cases, QTL explaining variation of the same CHC compounds map to different chromosomal regions, or to different chromosomes entirely (compare electronic supplementary material, tables S3 and S7). This illustrates the genetic complexity and potential pleiotropy of the governance of most investigated CHC compounds. However, specifically comparing our cross between N. vitripennis and N. longicornis with the previously analysed cross between N. vitripennis and N. giraulti [29], we identified overlapping QTL clusters governing the variation of structurally similar CHC compounds. The three most obvious of these clusters map close to the centromeric regions of chromosomes 1, 2 and 5 (figure 3; electronic supplementary material, table S7). The respective CHC compounds are all methyl-branched alkanes, partially sharing methyl-branching patterns and chain lengths. Intriguingly, all of the above-mentioned QTL clusters co-localize with orthologues of candidate genes implicated with CHC biosynthesis. Particularly, the shared cluster on chromosome 1 mapped to the identified chromosomal region with the highest density of orthologues of CHC biosynthesis candidate genes (compare figure 3 and figure 4). As the recombination rate around the centromeres is very low, the close proximity of these gene orthologues is concordant with a higher likelihood of passing the whole unaltered gene cluster to the next generation, rendering gene shuffling in this region rare [41]. The other two clusters co-localize with the positions of the gene orthologues spidey and fas5 on chromosome 2 and 5, respectively. Spidey encodes a 3-hydroxy-acyl-CoA-dehydratase, constituting a part of the elongation enzyme complex [55], whereas fas5 encodes a fatty acid synthase, instrumental for the formation of long-chain fatty acids as precursors for the elongation process ([10,55], see figure 1). These findings render these three distinct chromosomal regions particularly promising targets for further investigating the impact of the candidate genes mapped in these regions on CHC biosynthesis.

(b) . Cuticular hydrocarbon compound class variation and gene orthology between Nasonia and Drosophila

The vast majority of QTL that we detected in the hybrids from both crosses explained variation of methyl-branched alkanes (79 out of 91 in total). This compound class dominates in CHC profiles of Nasonia males and accounts for 87.3% of the total amount of CHCs when averaged across all three investigated species (figure 1; electronic supplementary material, table S8). This result is in accordance with those of most studies on CHC profiles in Hymenoptera, consistently reporting methyl-branched alkanes as the dominant CHC class (e.g. [22,23]). In species of other insect orders, however, methyl-branched alkanes seem to be less dominant, which is particularly true for Drosophila melanogaster: analysing CHC profiles in lines of the D. melanogaster reference panel, Dembeck et al. [56] detected only mono-methyl-branched alkanes in D. melanogaster CHC profiles, and these account for 16.2% and 24.1% of the total CHCs in CHC profiles of males and females, respectively (electronic supplementary material, table S9). This might be one of the reasons why our understanding of the genetics governing the variation in methyl-branched alkanes is comparatively limited.

The split between Hymenoptera and other holometabolous insects, including Diptera, is estimated to have occurred around 327 Ma [57]. Thus, it is not surprising that we were unable to reliably identify orthologues of several genes with an impact on CHC profile composition in Drosophila in the genome of Nasonia. For instance, the potentially best-studied CHC biosynthesis-related genes in Drosophila are desaturases, catalysing the introduction of double bounds in hydrocarbon chains ([10,21], figure 1). Although several desaturase genes have been identified in the Nasonia genome [29], we were not able to clearly confirm their orthology to Drosophila desaturases in the present study. However, the proportions of unsaturated CHCs vastly differ between the CHC profiles of Drosophila flies and those of Nasonia wasps (on average 59.2% versus 1.4% in males; electronic supplementary material, tables S8 and S9). Thus, since the proportion of unsaturated compounds is considerably lower in Nasonia CHC profiles, the main genes governing their biosynthesis and variation might have strongly deviated after more than 300 Ma of estimated evolutionary divergence between Nasonia and Drosophila [57].

Similar in their unresolved orthology to Drosophila, we found numerous elo and far genes across the Nasonia genome (electronic supplementary material, table S5). In D. melanogaster, 19 elo genes have been identified in total [21], but only five have been clearly associated with CHC biosynthesis and variation [56,58]. Concerning far genes, from 17 identified in the D. melanogster genome [59], two have a demonstrated impact on CHC biosynthesis [56]. This already indicates the difficulty in associating members of these two large and diverse gene families with CHC biosynthesis and variation. However, there are two further properties of Nasonia CHC profiles that greatly differentiate them from their Drosophila counterparts: first, in Drosophila, CHC chain lengths apparently do not exceed beyond C31 [56], whereas in Nasonia, CHC compounds with chain lengths of up to C37 have been identified, and additional CHCs with chain lengths of up to C52 have recently been described [60]. Since ELOs contribute to the elongation of long-chain fatty acids (figure 1), the longer and more diverse chain lengths in the Nasonia profiles could mean that a larger and more diverse set of elongase genes is functionally involved in CHC biosynthesis in Nasonia wasps. Second, Nasonia CHC profiles appear to be more complex, generally containing more compound classes, including di-, tri- and tetra-methyl-branched alkanes, in contrast to CHC profiles of D. melanogaster (compare electronic supplementary material, tables S8 and S9). It has been demonstrated that the far gene family shows particularly high evolutionary turnover rates, which could allow, in turn, for a concordant rapid diversification of CHC profiles [59]. This also argues for the involvement of a larger set of far genes in CHC biosynthesis in Nasonia than in Drosophila due to the more diverse and complex nature of Nasonia CHC profiles. Hence, the region on chromosome 1 containing eight elo and 11 far genes while harbouring QTL mostly explaining methyl-branched alkane variation in two of our analysed hybrid crosses constitutes a promising target-rich region for future functional genetic studies. Nasonia might prove to be a particularly well-suited model system for investigating the genetic underpinnings of methyl-branched alkane biosynthesis and diversification, with our current study providing a comprehensive foundation.

(c) . Phylogenetic divergence in relation to cuticular hydrocarbon differentiation and selection signatures

It has been estimated that N. vitripennis diverged around 1 Ma from the lineage from which the remaining Nasonia species evolved [26,53]. The divergences of the remaining Nasonia lineages occurred approximately between 500 000 and 400 000 years ago ([26], electronic supplementary material, figure S1). We discovered the smallest number of QTL governing CHC variation between the more recently diverged species pair studied by us: N. longicornis and N. giraulti. This finding matches well with the considerably shorter evolutionary time frame to accumulate genetic differences compared to the longer divergence time between each of the two species and N. vitripennis. Therefore, it is all the more surprising that phenotypically, the overall CHC profile divergence sufficiently discriminates all species with F2 hybrid males clustering as intermediate phenotypes between the respective F0 males (figure 2). As N. longicornis and N. giraulti males also differ considerable in their CHC composition from each other, this obvious phenotypic divergence is not reflected in the comparably low genomic divergence between these two species hinted at by our QTL comparison. A potential explanation for this might be other mechanisms, e.g. cis-regulatory changes [58] or phenotypic plasticity [61], that factor into CHC profile divergence between these phylogenetically less distant species.

Concerning CHC biosynthesis candidate gene orthology, we detected the fewest orthologues in the most distantly related species, Muscidifurax raptorellus, in comparison to the N. vitripennis reference genome (19 out of 26; electronic supplementary material, table S6). Furthermore, taking into account all shared 19 candidate genes in our six investigated wasp genomes, we detected signatures of positive selection in only one of them, fas1. The gene fas1 is an orthologue to FASN1 and FASN2 in Drosophila which play important roles in the early steps of CHC biosynthesis, the latter of which specifically impacting the abundance of methyl-branched alkanes despite their relatively low abundance in this genus [55,62]. Additionally, fas1 maps in close proximity to the conspicuous cluster of CHC biosynthesis gene orthologues (figure 4) that also co-localizes with QTL explaining variation of mostly methyl-branched alkanes between N. vitripennis and N. giraulti and between N. longicornis and N. giraulti (figure 3; electronic supplementary material, table S7). This renders fas1 a particularly promising candidate gene for further functional genetic studies investigating its impact on CHC profiles not only in Nasonia, but also in other Hymenoptera harbouring genes orthologous to fas1.

5. Conclusion and outlook

With our study on the genetic and genomic architecture of CHC variation between parasitoid wasps of the genus Nasonia, we uncovered several genomic regions where QTL explaining variation in similar CHC compounds and CHC biosynthesis-related candidate genes clustered. Especially one region on chromosome 1 close to the centromere is conspicuous in this regard, as it harbours many CHC biosynthesis-related candidate genes and QTL explaining variation of methyl-branched alkanes, the prevalent CHC compound class in Hymenoptera in general and in Nasonia in particular. This underlines the considerable potential of Nasonia as a model system to further investigate the little-know genetic and genomic architecture of methyl-branched alkane biosynthesis and variation. The experimental basis established here will allow future forward genetic studies to unravel the direct impact of these candidate genes as well as investigate their potential interactions and hierarchy within the CHC biosynthesis pathway.

Acknowledgements

Thanks to Andrea K. Judson, Nadine Brehm and Sylvia Geeritsma for their help in acquiring the chemical and QTL data, Joshua D. Gibson for his valuable input in conceptualizing the manuscript in its current form, and Valerio Vitali for helpful suggestions on the first draft of the manuscript.

Data accessibility

All data necessary for full comprehension of the presented study are made available either directly as figures and tables in the manuscript or in the electronic supplementary material [63]. All raw data underlying the manuscript are available from the Dryad data repository under: https://doi.org/10.6078/D1XD8T [64].

Authors' contributions

J.B.: conceptualization, data curation, formal analysis, investigation, methodology, project administration, supervision, validation, visualization, writing—original draft and writing—review and editing; H.H.: formal analysis, investigation, methodology, validation and visualization; L.S.: formal analysis, investigation, methodology, supervision and writing—review and editing; J.L.: funding acquisition, project administration, resources, supervision and writing—review and editing; T.S.: conceptualization, funding acquisition, investigation, methodology, project administration, resources, validation and writing—review and editing; J.G.: conceptualization, funding acquisition, investigation, methodology, project administration, resources, supervision, validation and writing—review and editing; O.N.: conceptualization, data curation, formal analysis, investigation, methodology, project administration, supervision, validation and writing—review and editing.

All authors gave final approval for publication and agreed to be held accountable for the work performed therein.

Conflict of interest declaration

We declare we have no competing interests.

Funding

This study was partially funded by the Excellence Initiative of the German Research Foundation (GSC-4, Spemann Graduate School) and individual German Research Foundation grants 427879779 and 403980864.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Buellesbach J, Holze H, Schrader L, Liebig J, Schmitt T, Gadau J, Niehuis O. 2022. Genetic and genomic architecture of species-specific cuticular hydrocarbon variation in parasitoid wasps. FigShare. ( 10.6084/m9.figshare.c.6000527) [DOI] [PMC free article] [PubMed]
  2. Buellesbach J, Holze H, Schrader L, Liebig J, Schmitt T, Gadau J, Niehuis O. 2022. Data from: Genetic and genomic architecture of species-specific cuticular hydrocarbon variation in parasitoid wasps. Dryad Digital Repository. ( 10.6078/D1XD8T) [DOI] [PMC free article] [PubMed]

Data Availability Statement

All data necessary for full comprehension of the presented study are made available either directly as figures and tables in the manuscript or in the electronic supplementary material [63]. All raw data underlying the manuscript are available from the Dryad data repository under: https://doi.org/10.6078/D1XD8T [64].

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