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The evolution history of an allotetraploid mangrove tree analysed with a new tool Allo4D.

Yuan WangYulong LiWeihong WuShao ShaoQi FangShaohua XuZixiao GuoSuhua ShiZiwen He
Published in: Plant biotechnology journal (2023)
Mangrove species are broadly classified as true mangroves and mangrove associates. The latter are amphibious plants that can survive in the intertidal zone and reproduce naturally in terrestrial environments. Their widespread distribution and extensive adaptability make them ideal research materials for exploring adaptive evolution. In this study, we de novo assembled two genomes of mangrove associates (the allotetraploid Barringtonia racemosa (2n = 4x = 52) and diploid Barringtonia asiatica (2n = 2x = 26)) to investigate the role of allopolyploidy in the evolutionary history of mangrove species. We developed a new allotetraploid-dividing tool Allo4D to distinguish between allotetraploid scaffold-scale subgenomes and verified its accuracy and reliability using real and simulated data. According to the two subgenomes of allotetraploid B. racemosa divided using Allo4D, the allopolyploidization event was estimated to have occurred approximately one million years ago (Mya). We found that B. racemosa, B. asiatica, and Diospyros lotus shared a whole genome duplication (WGD) event during the K-Pg (Cretaceous-Paleozoic) period. K-Pg WGD and recent allopolyploidization events contributed to the speciation of B. racemosa and its adaptation to coastal habitats. We found that genes in the glucosinolates (GSLs) pathway, an essential pathway in response to various biotic and abiotic stresses, expanded rapidly in B. racemosa during polyploidization. In summary, this study provides a typical example of the adaptation of allopolyploid plants to extreme environmental conditions. The newly developed tool, Allo4D, can effectively divide allotetraploid subgenomes and explore the evolutionary history of polyploid plants, especially for species whose ancestors are unknown or extinct.
Keyphrases
  • genome wide
  • climate change
  • machine learning
  • heavy metals
  • electronic health record
  • genetic diversity
  • tissue engineering
  • bioinformatics analysis