Login / Signup

Comparative chromosome painting in three Pelecaniformes species (Aves): Exploring the role of macro and microchromosome fusions in karyotypic evolution.

Igor Chamon Assumpção SeligmannIvanete de Oliveira FuroMichelly da Silva Dos SantosRicardo José GunskiAnalía Del Valle GarneroFabio Augusto Oliveira SilvaPatricia O BrienMalcolm Ferguson-SmithRafael KretschmerEdivaldo Herculano Correia de Oliveira
Published in: PloS one (2023)
Pelecaniformes is an order of waterbirds that exhibit diverse and distinct morphologies. Ibis, heron, pelican, hammerkop, and shoebill are included within the order. Despite their fascinating features, the phylogenetic relationships among the families within Pelecaniformes remain uncertain and pose challenges due to their complex evolutionary history. Their karyotypic evolution is another little-known aspect. Therefore, to shed light on the chromosomal rearrangements that have occurred during the evolution of Pelecaniformes, we have used whole macrochromosome probes from Gallus gallus (GGA) to show homologies on three species with different diploid numbers, namely Cochlearius cochlearius (2n = 74), Eudocimus ruber (2n = 66), and Syrigma sibilatrix (2n = 62). A fusion between GGA6 and GGA7 was found in C. cochlearius and S. sibilatrix. In S. sibilatrix the GGA8, GGA9 and GGA10 hybridized to the long arms of biarmed macrochromosomes, indicating fusions with microchromosomes. In E. ruber the GGA7 and GGA8 hybridized to the same chromosome pair. After comparing our painting results with previously published data, we show that distinct chromosomal rearrangements have occurred in different Pelecaniformes lineages. Our study provides new insight into the evolutionary history of Pelecaniformes and the chromosomal changes involving their macrochromosomes and microchromosomes that have taken place in different species within this order.
Keyphrases
  • copy number
  • genome wide
  • small molecule
  • randomized controlled trial
  • systematic review
  • dna methylation
  • electronic health record
  • deep learning