Insect immune response plays a crucial role in how external threats influence overall fitness through life history traits. An understudied question is how the use of different host plants might affect the ability of herbivorous insects to resist viral pathogens. The Melissa blue butterfly (Lycaeides melissa) has colonized the exotic legume Medicago sativa as a larval host within the past 200 years. Here we investigate how novel host plant use affects the immune response of L. melissa when infected with the lepidopteran virus, Junonia coenia densovirus (JcDV). We measured immune strength in response to JcDV in two ways: 1) direct measurement of phenoloxidase activity and melanization, and 2) transcriptional sequencing of individuals exposed to different viral and host plant treatments. Viral infection caused total phenoloxidase (total PO) to increase. We detected an interaction between viral infection and host plant for total PO: for control larvae, host plant use had no effect on total PO, whereas for infected larvae, total PO was significantly higher for larvae consuming the native host. Within the exotic host plant treatment, few genes were differentially regulated due to viral infection. Approximately two times more genes were differentially regulated in response to infection for larvae eating the native or exotic host, with differential expression of few putative immune genes. These results demonstrate that consumption of a novel host plant can alter both physiological and transcriptional responses to infection, emphasizing the importance of understanding diet when studying the molecular basis of immune function.

Epigenetic mechanisms, such as DNA methylation, can influence gene regulation and affect phenotypic variation, raising the possibility that they contribute to ecological adaptation. To being to address this issue requires high-resolution sequencing studies of natural populations to pinpoint epigenetic regions of potential ecological and evolutionary significance. However, such studies are still relatively uncommon, especially in insects, and are mainly restricted to a few model organisms. Here, we characterize patterns of DNA methylation for natural populations of Timema cristinae adapted to two host plant species (i.e., ecotypes). By integrating results from sequencing of whole transcriptomes, genomes, and methylomes, we investigate whether environmental, host, and genetic differences of these stick insects are associated with methylation levels of cytosine nucleotides in CpG context. We report an overall genome-wide methylation level for T. cristinae of ~14%, being enriched in gene bodies and impoverished in repetitive elements. Genome-wide DNA methylation variation was strongly positively correlated with genetic distance (relatedness), but also exhibited significant host-plant effects. Using methylome-environment association analysis, we pinpointed specific genomic regions that are differentially methylated between ecotypes, with these regions being enriched for genes with functions in membrane processes. The observed association between methylation variation with genetic relatedness and the ecologically-important variable of host plant suggest a potential role for epigenetic modification in T. cristinae adaptation. To substantiate such adaptive significance, future studies could test if methylation has a heritable component and the extent to which it responds to experimental manipulation in field and laboratory studies.

Infections by maternally inherited bacterial endosymbionts, especially Wolbachia, are common in insects and other invertebrates but infection dynamics across species ranges are largely under studied. Specifically, we lack a broad understanding of the origin of Wolbachia infections in novel hosts and the factors governing their spread. We used Genotype-by-Sequencing (GBS) data from previous population genomics studies for range-wide surveys of Wolbachia presence and genetic diversity in over 2,700 North American butterflies of the genus Lycaeides. As few as one sequence read identified by assembly to a Wolbachia pan-reference genome provided high accuracy in detecting infections as determined by confirmatory PCR tests. Using a conservative threshold of five reads, we detected Wolbachia in all but two of the 107 sampling localities spanning the continent, and with most localities having high infection frequencies (mean = 91\% infection rate). Three major lineages of Wolbachia were identified as separate strains that appear to represent three separate invasions of Lycaeides butterflies. Overall, we found extensive evidence for acquisition of Wolbachia through interspecific transfer between host lineages. Strain wLycC was confined to a single butterfly taxon, hybrid lineages derived from it, and closely adjacent populations in other taxa. While the other two strains were detected throughout the rest of the continent, strain wLycB almost always co-occurred with wLycA. Our demographic modeling suggests wLycB is a recent invasion. These results demonstrate the utility of using resequencing data from hosts to quantify Wolbachia genetic variation and provide evidence of multiple colonizations of novel hosts through hybridization between butterfly lineages and complex dynamics between Wolbachia strains.

We recently published a paper quantifying the genome-wide consequences of natural selection, including the effects of indirect selection due to the correlation of genetic regions (neutral or selected) with directly selected regions (Gompert et al. 2022). In their critique of our paper, Charlesworth & Jensen (2022) make two main points: (i) indirect selection is equivalent to hitchhiking and thus well documented (i.e., our results are not novel), and (ii) that we do not demonstrate the source of linkage disequilibrium (LD) between SNPs and the Mel-Stripe locus in the Timema cristinae experiment we analyze. As we discuss in detail below, neither of these are substantial criticisms of our work.

Non-random mating among individuals can lead to spatial clustering of genetically similar individuals and population stratification. This deviation from panmixia is commonly observed in natural populations. Consequently, individuals can have parentage in single populations or involving hybridization between differentiated populations. Accounting for this mixture and structure is important when mapping the genetics of traits and learning about the formative evolutionary processes that shape genetic variation among individuals and populations. Stratified genetic relatedness among individuals is commonly quantified using estimates of ancestry that are derived from a statistical model. Development of these models for polyploid and mixed-ploidy individuals and populations has lagged behind those for diploids. Here, we extend and test a hierarchical Bayesian model, called entropy, which can utilize low-depth sequence data to estimate genotype and ancestry parameters in autopolyploid and mixed-ploidy individuals (including sex chromosomes and autosomes within individuals). Our analysis of simulated data illustrated the trade-off between sequencing depth and genome coverage and found lower error associated with low depth sequencing across a larger fraction of the genome than with high depth sequencing across a smaller fraction of the genome. The model has high accuracy and sensitivity as verified with simulated data and through analysis of admixture among populations of diploid and tetraploid Arabidopsis arenosa.

Strong selection can cause rapid evolutionary change, but temporal fluctuations in the form, direction and intensity of selection can limit net evolutionary change over longer time periods. Fluctuating selection could affect molecular diversity levels and the evolution of plasticity and ecological specialization. Nonetheless, this phenomenon remains understudied, in part because of analytical limitations and the general difficulty of detecting selection that does not occur in a consistent manner. Herein, I fill this analytical gap by presenting an approximate Bayesian computation (ABC) method to detect and quantify fluctuating selection on polygenic traits from population-genomic time-series data. I propose a model for environment-dependent phenotypic selection. The evolutionary genetic consequences of selection are then modeled based on a genotype-phenotype map. Using simulations, I show that the proposed method generates accurate and precise estimates of selection when the generative model for the data is similar to the model assumed by the method. Performance of the method when applied to an evolve-and-resequence study of host adaptation in the cowpea seed beetle (Callosobruchus maculatus) was more idiosyncratic and depended on specific analytical choices. Despite some limitations, these results suggest the proposed method provides a powerful approach to connect causes of (variable) selection to traits and genome-wide patterns of evolution. Documentation and open source computer software (fsabc) implementing this method are available from GitHub (https://github.com/zgompert/fsabc.git).