Genetic divergence and phenotypic plasticity contribute to variation in cuticular hydrocarbons in the seaweed fly Coelopa frigida

Host Plant Effects on Sexual Selection Dynamics in Phytophagous Insects

Natural selection is notoriously dynamic in nature, and so, too, is sexual selection. The interactions between phytophagous insects and their host plants have provided valuable insights into the many ways in which ecological factors can influence sexual selection. In this review, we highlight recent discoveries and provide guidance for future work in this area. Importantly, host plants can affect both the agents of sexual selection (e.g., mate choice and male-male competition) and the traits under selection (e.g., ornaments and weapons). Furthermore, in our rapidly changing world, insects now routinely encounter new potential host plants. The process of adaptation to a new host may be hindered or accelerated by sexual selection, and the unexplored evolutionary trajectories that emerge from these dynamics are relevant to pest management and insect conservation strategies. Examining the effects of host plants on sexual selection has the potential to advance our fundamental understanding of sexual conflict, host range evolution, and speciation, with relevance across taxa.

A supergene in seaweed flies modulates male traits and female perception

Supergenes, tightly linked sets of alleles, offer some of the most spectacular examples of polymorphism persisting under long-term balancing selection. However, we still do not understand their evolution and persistence, especially in the face of accumulation of deleterious elements. Here, we show that an overdominant supergene in seaweed flies, Coelopa frigida, modulates male traits, potentially facilitating disassortative mating and promoting intraspecific polymorphism. Across two continents, the Cf-Inv(1) supergene strongly affected the composition of male cuticular hydrocarbons (CHCs) but only weakly affected CHC composition in females. Using gas chromatography-electroantennographic detection, we show that females can sense male CHCs and that there may be differential perception between genotypes. Combining our phenotypic results with RNA-seq data, we show that candidate genes for CHC biosynthesis primarily show differential expression for Cf-Inv(1) in males but not females. Conversely, candidate genes for odorant detection were differentially expressed in both sexes but showed high levels of divergence between supergene haplotypes. We suggest that the reduced recombination between supergene haplotypes may have led to rapid divergence in mate preferences as well as increasing linkage between male traits, and overdominant loci. Together this probably helped to maintain the polymorphism despite deleterious effects in homozygotes.

The Relative Power of Structural Genomic Variation versus SNPs in Explaining the Quantitative Trait Growth in the Marine Teleost Chrysophrys auratus

Background: Genetic diversity provides the basic substrate for evolution. Genetic variation consists of changes ranging from single base pairs (single-nucleotide polymorphisms, or SNPs) to larger-scale structural variants, such as inversions, deletions, and duplications. SNPs have long been used as the general currency for investigations into how genetic diversity fuels evolution. However, structural variants can affect more base pairs in the genome than SNPs and can be responsible for adaptive phenotypes due to their impact on linkage and recombination. In this study, we investigate the first steps needed to explore the genetic basis of an economically important growth trait in the marine teleost finfish Chrysophrys auratus using both SNP and structural variant data. Specifically, we use feature selection methods in machine learning to explore the relative predictive power of both types of genetic variants in explaining growth and discuss the feature selection results of the evaluated methods. Methods: SNP and structural variant callers were used to generate catalogues of variant data from 32 individual fish at ages 1 and 3 years. Three feature selection algorithms (ReliefF, Chi-square, and a mutual-information-based method) were used to reduce the dataset by selecting the most informative features. Following this selection process, the subset of variants was used as features to classify fish into small, medium, or large size categories using KNN, naïve Bayes, random forest, and logistic regression. The top-scoring features in each feature selection method were subsequently mapped to annotated genomic regions in the zebrafish genome, and a permutation test was conducted to see if the number of mapped regions was greater than when random sampling was applied. Results: Without feature selection, the prediction accuracies ranged from 0 to 0.5 for both structural variants and SNPs. Following feature selection, the prediction accuracy increased only slightly to between 0 and 0.65 for structural variants and between 0 and 0.75 for SNPs. The highest prediction accuracy for the logistic regression was achieved for age 3 fish using SNPs, although generally predictions for age 1 and 3 fish were very similar (ranging from 0–0.65 for both SNPs and structural variants). The Chi-square feature selection of SNP data was the only method that had a significantly higher number of matches to annotated genomic regions of zebrafish than would be explained by chance alone. Conclusions: Predicting a complex polygenic trait such as growth using data collected from a low number of individuals remains challenging. While we demonstrate that both SNPs and structural variants provide important information to help understand the genetic basis of phenotypic traits such as fish growth, the full complexities that exist within a genome cannot be easily captured by classical machine learning techniques. When using high-dimensional data, feature selection shows some increase in the prediction accuracy of classification models and provides the potential to identify unknown genomic correlates with growth. Our results show that both SNPs and structural variants significantly impact growth, and we therefore recommend that researchers interested in the genotype–phenotype map should strive to go beyond SNPs and incorporate structural variants in their studies as well. We discuss how our machine learning models can be further expanded to serve as a test bed to inform evolutionary studies and the applied management of species.

Divergent selection on behavioural and chemical traits between reproductively isolated populations of Drosophila melanogaster

Speciation is driven by traits that can act to prevent mating between nascent lineages, including male courtship and female preference for male traits. Mating barriers involving these traits evolve quickly because there is strong selection on males and females to maximize reproductive success, and the tight co-evolution of mating interactions can lead to rapid diversification of sexual behaviour. Populations of Drosophila melanogaster show strong asymmetrical reproductive isolation that is correlated with geographic origin. Using strains that capture natural variation in mating traits, we ask two key questions: which specific male traits are females selecting, and are these traits under divergent sexual selection? These questions have proven extremely challenging to answer, because even in closely related lineages males often differ in multiple traits related to mating behaviour. We address these questions by estimating selection gradients for male courtship and cuticular hydrocarbons for two different female genotypes. We identify specific behaviours and particular cuticular hydrocarbons that are under divergent sexual selection and could potentially contribute to premating reproductive isolation. Additionally, we report that a subset of these traits are plastic; males adjust these traits based on the identity of the female genotype they interact with. These results suggest that even when male courtship is not fixed between lineages, ongoing selection can act on traits that are important for reproductive isolation.

Candidate genes involved in cuticular hydrocarbon differentiation between cryptic, parabiotic ant species

Insect cuticular hydrocarbons (CHCs) are highly diverse and have multiple functions, including communication and waterproofing. CHC profiles form species-specific, complex blends of up to 150 compounds. Especially in ants, even closely related species can have largely different profiles, raising the question how CHC differences are mirrored in the regulation of biosynthetic pathways. The neotropical ants Crematogaster levior and Camponotus femoratus both consist of two cryptic species each that are morphologically similar, but express strongly different CHC profiles. This is ideal to study the molecular basis of CHC differences. We thus investigated gene expression differences in fat-body transcriptomes of these ants. Despite common garden conditions, we found several thousand differentially expressed transcripts within each cryptic species pair. Many of these were related to metabolic processes, probably accounting for physiological differences. Moreover, we identified candidate genes from five gene families involved in CHC biosynthesis. By assigning candidate transcripts to orthologs in Drosophila, we inferred which CHCs might be influenced by differential gene expression. Expression of these candidate genes was often mirrored in the CHC profiles. For example, Cr. levior A, which has longer CHCs than its cryptic sister species, had a higher expression of elongases and a lower expression of fatty acyl- CoA reductases. This study is one of the first to identify CHC candidate genes in ants and will provide a basis for further research on the genetic basis of CHC biosynthesis.

Advances in deciphering the genetic basis of insect cuticular hydrocarbon biosynthesis and variation

Cuticular hydrocarbons (CHCs) have two fundamental functions in insects. They protect terrestrial insects against desiccation and serve as signaling molecules in a wide variety of chemical communication systems. It has been hypothesized that these pivotal dual traits for adaptation to both desiccation and signaling have contributed to the considerable evolutionary success of insects. CHCs have been extensively studied concerning their variation, behavioral impact, physiological properties, and chemical compositions. However, our understanding of the genetic underpinnings of CHC biosynthesis has remained limited and mostly biased towards one particular model organism (Drosophila). This rather narrow focus has hampered the establishment of a comprehensive view of CHC genetics across wider phylogenetic boundaries. This review attempts to integrate new insights and recent knowledge gained in the genetics of CHC biosynthesis, which is just beginning to incorporate work on more insect taxa beyond Drosophila. It is intended to provide a stepping stone towards a wider and more general understanding of the genetic mechanisms that gave rise to the astonishing diversity of CHC compounds across different insect taxa. Further research in this field is encouraged to aim at better discriminating conserved versus taxon-specific genetic elements underlying CHC variation. This will be instrumental in greatly expanding our knowledge of the origins and variation of genes governing the biosynthesis of these crucial phenotypic traits that have greatly impacted insect behavior, physiology, and evolution.