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Discovery of novel representatives of bilaterian neuropeptide families and reconstruction of neuropeptide precursor evolution in ophiuroid echinoderms.

Meet ZandawalaIsmail MoghulLuis Alfonso Yañez GuerraJérôme DelroisseNikara AbylkassimovaAndrew F HugallTimothy D O'HaraMaurice R Elphick
Published in: Open biology (2018)
Neuropeptides are a diverse class of intercellular signalling molecules that mediate neuronal regulation of many physiological and behavioural processes. Recent advances in genome/transcriptome sequencing are enabling identification of neuropeptide precursor proteins in species from a growing variety of animal taxa, providing new insights into the evolution of neuropeptide signalling. Here, detailed analysis of transcriptome sequence data from three brittle star species, Ophionotus victoriae, Amphiura filiformis and Ophiopsila aranea, has enabled the first comprehensive identification of neuropeptide precursors in the class Ophiuroidea of the phylum Echinodermata. Representatives of over 30 bilaterian neuropeptide precursor families were identified, some of which occur as paralogues. Furthermore, homologues of endothelin/CCHamide, eclosion hormone, neuropeptide-F/Y and nucleobinin/nesfatin were discovered here in a deuterostome/echinoderm for the first time. The majority of ophiuroid neuropeptide precursors contain a single copy of a neuropeptide, but several precursors comprise multiple copies of identical or non-identical, but structurally related, neuropeptides. Here, we performed an unprecedented investigation of the evolution of neuropeptide copy number over a period of approximately 270 Myr by analysing sequence data from over 50 ophiuroid species, with reference to a robust phylogeny. Our analysis indicates that the composition of neuropeptide 'cocktails' is functionally important, but with plasticity over long evolutionary time scales.
Keyphrases
  • copy number
  • genome wide
  • gene expression
  • single cell
  • mitochondrial dna
  • small molecule
  • dna methylation
  • rna seq
  • machine learning
  • big data
  • subarachnoid hemorrhage
  • brain injury