bioRxivEvolutionary BiologyPreprint
The genetic basis for adaptation in giant sea anemones to their symbiosis with anemonefish and Symbiodiniaceae
Abstract
Sea anemones in the order Anthozoa play an integral part in marine ecosystems by providing refuge and habitat for various organisms. Despite this, much of their molecular ecology remains elusive. Sea anemones can nurture numerous symbiotic relationships; the most iconic being the one between giant sea anemones and anemonefish. However, the genes and biological processes associated with this symbiosis in the sea anemones in unknown. Additionally, it is unclear how genes can mediate interactions between sea anemones, anemonefish, and symbionts from the algal family Symbiodiniaceae. Here we compared the gene expression profiles of tentacles from several cnidarians to uncover the genetic basis for adaptations in giant sea anemones to their symbiosis with anemonefish and Symbiodiniaceae. We found that tentacle transcriptomes of cnidarians are highly diverse, with closely related species having more similar expression patterns. However, despite an overall high correlation between gene expression and phylogeny, the giant sea anemones showed distinct expression patterns. The giant sea anemones had gene co-expression clusters enriched for processes involved in nutrient exchange and metabolism. These genes were not only differentially expressed, but also experienced evolutionary shifts in expression in giant sea anemones. Using a phylogenetic multilevel model, we found that Symbiodiniaceae and anemonefish significantly affect gene expression in giant sea anemone tentacles. By characterizing gene expression patterns, we identify genes and biological processes that provide evidence for the cross-talk between Symbiodiniaceae, anemonefish, and giant sea anemones. Our study demonstrates how integrated biological processes can lead to the evolution of a successful multi-organism interaction.
- organism: non-human primate
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
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