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Subgenome evolution in allotetraploid plants.

Matteo SchiavinatoAlexandrina Bodrug-SchepersJuliane C DohmHeinz Himmelbauer
Published in: The Plant journal : for cell and molecular biology (2021)
Polyploidization is a well-known speciation and adaptation mechanism. Traces of former polyploidization events were discovered within many genomes, and especially in plants. Allopolyploidization by interspecific hybridization between two species is common. Among hybrid plants, many are domesticated species of agricultural interest and many of their genomes and of their presumptive parents have been sequenced. Hybrid genomes remain challenging to analyse because of the presence of multiple subgenomes. The genomes of hybrids often undergo rearrangement and degradation over time. Based on 10 hybrid plant genomes from six different genera, with hybridization dating from 10,000 to 5 million years ago, we assessed subgenome degradation, subgenomic intermixing and biased subgenome fractionation. The restructuring of hybrid genomes does not proceed proportionally with the age of the hybrid. The oldest hybrids in our data set display completely different fates: whereas the subgenomes of the tobacco plant Nicotiana benthamiana are in an advanced stage of degradation, the subgenomes of quinoa (Chenopodium quinoa) are exceptionally well conserved by structure and sequence. We observed statistically significant biased subgenome fractionation in seven out of 10 hybrids, which had different ages and subgenomic intermixing levels. Hence, we conclude that no correlation exists between biased fractionation and subgenome intermixing. Lastly, domestication may encourage or hinder subgenome intermixing, depending on the evolutionary context. In summary, comparative analysis of hybrid genomes and their presumptive parents allowed us to determine commonalities and differences between their evolutionary fates. In order to facilitate the future analysis of further hybrid genomes, we automated the analysis steps within manticore, which is publicly available at https://github.com/MatteoSchiavinato/manticore.git.
Keyphrases
  • machine learning
  • heavy metals
  • climate change
  • transcription factor
  • gene expression
  • high throughput
  • big data
  • artificial intelligence
  • nucleic acid
  • cell wall