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Retention of larval skin traits in adult amphibious killifishes: a cross-species investigation.

Louise TunnahJonathan M WilsonPatricia A Wright
Published in: Journal of comparative physiology. B, Biochemical, systemic, and environmental physiology (2022)
The gills are the primary site of exchange in fishes. However, during early life-stages or in amphibious fishes, ionoregulation and gas-exchange may be primarily cutaneous. Given the similarities between larval and amphibious fishes, we hypothesized that cutaneous larval traits are continuously expressed in amphibious fishes across all life-stages to enable the skin to be a major site of exchange on land. Alternatively, we hypothesized that cutaneous larval traits disappear in juvenile stages and are re-expressed in amphibious species in later life-stages. We surveyed six species spanning a range of amphibiousness and characterized cutaneous ionocytes and neuroepithelial cells (NECs) as representative larval skin traits at up to five stages of development. We found that skin ionocyte density remained lower and constant in exclusively water-breathing, relative to amphibious species across development, whereas in amphibious species ionocyte density generally increased. Additionally, adults of the most amphibious species had the highest cutaneous ionocyte densities. Surprisingly, cutaneous NECs were only identified in the skin of one amphibious species (Kryptolebias marmoratus), suggesting that cutaneous NECs are not a ubiquitous larval or amphibious skin trait, at least among the species we studied. Our data broadly supports the continuous-expression hypothesis, as three of four amphibious experimental species expressed cutaneous ionocytes in all examined life-stages. Further, the increasing density of cutaneous ionocytes across development in amphibious species probably facilitates the prolonged occupation of terrestrial habitats.
Keyphrases
  • soft tissue
  • genetic diversity
  • genome wide
  • aedes aegypti
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  • signaling pathway
  • zika virus
  • machine learning
  • climate change
  • deep learning
  • binding protein