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An Insight into the Chromosomal Evolution of Lebiasinidae (Teleostei, Characiformes).

Francisco de M C SassiTerumi HatanakaRenata Luiza Rosa de MoraesGustavo Akira TomaEzequiel Aguiar de OliveiraThomas LiehrPetr RabLuiz A C BertolloPatrik Ferreira VianaEliana FeldbergMauro NirchioManoela Maria F MarinhoJosé Francisco de S E SouzaMarcelo de Bello Cioffi
Published in: Genes (2020)
Lebiasinidae fishes have been historically neglected by cytogenetical studies. Here we present a genomic comparison in eleven Lebiasinidae species, in addition to a review of the ribosomal DNA sequences distribution in this family. With that, we develop ten sets of experiments in order to hybridize the genomic DNA of representative species from the genus Copeina, Copella, Nannostomus, and Pyrrhulina in metaphase plates of Lebiasina melanoguttata. Two major pathways on the chromosomal evolution of these species can be recognized: (i) conservation of 2n = 36 bi-armed chromosomes in Lebiasininae, as a basal condition, and (ii) high numeric and structural chromosomal rearrangements in Pyrrhulininae, with a notable tendency towards acrocentrization. The ribosomal DNA (rDNA) distribution also revealed a marked differentiation during the chromosomal evolution of Lebiasinidae, since both single and multiple sites, in addition to a wide range of chromosomal locations can be found. With some few exceptions, the terminal position of 18S rDNA appears as a common feature in Lebiasinidae-analyzed species. Altogether with Ctenoluciidae, this pattern can be considered a symplesiomorphism for both families. In addition to the specific repetitive DNA content that characterizes the genome of each particular species, Lebiasina also keeps inter-specific repetitive sequences, thus reinforcing its proposed basal condition in Lebiasinidae.
Keyphrases
  • copy number
  • circulating tumor
  • cell free
  • single molecule
  • genetic diversity
  • high frequency
  • genome wide
  • nucleic acid
  • machine learning
  • gene expression
  • circulating tumor cells
  • cross sectional
  • neural network