Wolbachia have made it twice: Hybrid introgression between two sister species of Eurema butterflies.
Mai N MiyataMasashi NomuraDaisuke KageyamaPublished in: Ecology and evolution (2020)
Wolbachia, cytoplasmically inherited endosymbionts of arthropods, are known to hijack their host reproduction in various ways to increase their own vertical transmission. This may lead to the selective sweep of associated mitochondria, which can have a large impact on the evolution of mitochondrial lineages. In Japan, two different Wolbacahia strains (wCI and wFem) are found in two sister species of pierid butterflies, Eurema mandarina and Eurema hecabe. In both species, females infected with wCI (C females) produce offspring with a nearly 1:1 sex ratio, while females infected with both wCI and wFem (CF females) produce all-female offspring. Previous studies have suggested the historical occurrence of hybrid introgression in C individuals between the two species. Furthermore, hybrid introgression in CF individuals is suggested by the distinct mitochondrial lineages between C females and CF females of E. mandarina. In this study, we performed phylogenetic analyses based on nuclear DNA and mitochondrial DNA markers of E. hecabe with previously published data on E. mandarina. We found that the nuclear DNA of this species significantly diverged from that of E. mandarina. By contrast, mitochondrial DNA haplotypes comprised two clades, mostly reflecting Wolbachia infection status rather than the individual species. Collectively, our results support the previously suggested occurrence of two independent historical events wherein the cytoplasms of CF females and C females moved between E. hecabe and E. mandarina through hybrid introgression.
Keyphrases
- mitochondrial dna
- cystic fibrosis
- copy number
- risk assessment
- oxidative stress
- aedes aegypti
- escherichia coli
- high fat diet
- type diabetes
- circulating tumor
- magnetic resonance
- randomized controlled trial
- magnetic resonance imaging
- gene expression
- dna methylation
- machine learning
- electronic health record
- artificial intelligence
- contrast enhanced
- data analysis