Open BiologyEvolutionary BiologyPeer-reviewed journal article
The proteotranscriptomic characterization of venom in the white seafan Eunicella singularis elucidates the evolution of Octocorallia arsenal
Maria Vittoria ModicaiD, Serena LeoneiD, Marco GerdoliD, Samuele GrecoiD, Didier AurelleiD, Marco OliverioiD, Giulia FassioiD, Khadija El KoulaliiD, Célia Barrachina, Sébastien DutertreiD
Abstract
All the members of the phylum Cnidaria are characterized by the production of venom in specialized structures, the nematocysts. Venom of jellyfish (Medusozoa) and sea anemones (Anthozoa) has been investigated since the 1970s, revealing a remarkable molecular diversity. Specifically, sea anemones harbour a rich repertoire of neurotoxic peptides, some of which have been developed in drug leads. However, venoms of the vast majority of Anthozoa species remain uncharacterized, particularly in the class Octocorallia. To fill this gap, we applied a proteo-transcriptomic approach to investigate the venom composition in Eunicella singularis, a gorgonian species common in Mediterranean hard-bottom benthic communities. Our results highlighted the peculiarities of the venom of E. singularis with respect to sea anemones, which is reflected in the presence of several toxins with novel folds, worthy of functional characterization. A comparative genomic survey across the octocoral radiation allowed us to generalize these findings and provided insights into the evolutionary history, molecular diversification patterns and putative adaptive roles of venom toxins. A comparison of whole-body and nematocyst proteomes revealed the presence of different cytolytic toxins inside and outside the nematocysts. Two instances of differential maturation patterns of toxin precursors were also identified, highlighting the intricate regulatory pathways underlying toxin expression.
- organism: non-human primate
- assay: proteomics
- research focus: cell type annotation
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 2
- FWCI
- 0.62
- Field percentile
- top 41%
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