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Sequence analysis of wheat subtelomeres reveals a high polymorphism among homoeologous chromosomes.

Miguel AguilarPilar Prieto
Published in: The plant genome (2020)
Bread wheat, Triticum aestivum L., is one of the most important crops in the world. Understanding its genome organization (allohexaploid; AABBDD; 2n = 6x = 42) is essential for geneticists and plant breeders. Particularly, the knowledge of how homologous chromosomes (equivalent chromosomes from the same genome) specifically recognize each other to pair at the beginning of meiosis, the cellular process to generate gametes in sexually reproducing organisms, is fundamental for plant breeding and has a big influence on the fertility of wheat plants. Initial homologous chromosome interactions contribute to specific recognition and pairing between homologues at the onset of meiosis. Understanding the molecular basis of these critical processes can help to develop genetic tools in a breeding context to promote interspecific chromosome associations in hybrids or interspecific genetic crosses to facilitate the transfer of desirable agronomic traits from related species into a crop like wheat. The terminal regions of chromosomes, which include telomeres and subtelomeres, participate in chromosome recognition and pairing. We present a detailed molecular analysis of subtelomeres of wheat chromosome arms 1AS, 4AS, 7AS, 7BS and 7DS. Results showed a high polymorphism in the subtelomeric region among homoeologues (equivalent chromosomes from related genomes) for all the features analyzed, including genes, transposable elements, repeats, GC content, predicted CpG islands, recombination hotspots and targeted sequence motifs for relevant DNA-binding proteins. These polymorphisms might be the molecular basis for the specificity of homologous recognition and pairing in initial chromosome interactions at the beginning of meiosis in wheat.
Keyphrases
  • copy number
  • genome wide
  • dna repair
  • dna damage
  • dna methylation
  • gene expression
  • single molecule
  • climate change
  • machine learning
  • drug delivery
  • cancer therapy
  • gram negative