Data for "The visual system of the longest-living vertebrate, the Greenland shark"

This dataset provides the raw data for publication: "The visual system of the longest-living vertebrate, the Greenland shark". For detailed information about this dataset, please see the markdown file: README.md. Authors: Lily G. Fogg, Emily Tom, Maxime Policarpo, William Cho, Fangyuan Gao, Doreen Hii, Aaron E. Fawcett, Nicolas Boileau, Amalie Bech-Poulsen, Kirstine F. Steffensen, Cherlyn J Ng, Peter Bushnell, John Fleng Steffensen, Richard Brill, Walter Salzburger, Dorota Skowronska-Krawczyk Abstract: The Greenland shark (Somniosus microcephalus) is the longest-living vertebrate and inhabits the extremely dim waters of the deep ocean, leading to speculations that it has lost the ability to see. Using whole genome and retinal RNA sequencing, we show that dim-light (rod-based) vision genes are intact and robustly expressed. In contrast, most genes involved in bright-light (cone-based) vision were lost or pseudogenized. RNAscope confirmed the presence of key visual cell types, including rods, bipolar cells and ganglion cells. Finally, histology and in vitro opsin expression revealed visual adaptations typical of deep-sea species, including densely packed, elongated rods and a short-wavelength shift in rod visual pigment sensitivity. Despite individuals being centuries old and having parasitised corneas, no signs of retinal degeneration were observed, and corneal light transmission remained intact. Our findings provide genomic, transcriptomic, histological and functional evidence that S. microcephalus has a visual system well-adapted for its dim light environment and preserves visual functionality over centuries. General information: This dataset contains phylogenetic trees, gene expression data, and selection analysis results related to phototransduction and DNA repair genes in the Greenland shark and five other Chondrichthyes species. The data includes species trees, gene trees, dN/dS analysis outputs, and gene expression values. Licensing: This dataset is licensed under a CC BY-NC 4.0 license. Please note this prohibits commercial use without explicit permission.