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Characterization of the cryptic interspecific hybrid Lemna×mediterranea by an integrated approach provides new insights into duckweed diversity.

Luca BragliaSimona CeschinM Adelaide IannelliManuela BogMarco FabrianiGiovanna FrugisFloriana GavazziSilvia GianìFlaminia MarianiMaurizio MuzziEmanuele PelellaLaura Morello
Published in: Journal of experimental botany (2024)
Lemnaceae taxonomy is challenged by the particular morphology of these tiny free-floating angiosperms. Although molecular taxonomy has helped clarify the phylogenetic history of this family, some inconsistency with morphological data leads to frequent misclassifications in the genus Lemna. Recently, the finding that Lemna japonica is an interspecific hybrid between Lemna minor and Lemna turionifera provided a clear explanation for one such taxonomic question. Here we demonstrated that L. minor is also capable of hybridizing with Lemna gibba, generating a cryptic but widespread taxon in the Mediterranean area. The nothotaxon Lemna ×mediterranea is described and compared with clones of the putative parental species L. minor and L. gibba. Genetic analysis by nuclear and plastid markers, as well as genome size measurement, revealed that two different cytotypes, diploid and triploid, originated by at least two independent hybridization events. Despite high overall similarity, morphometrical, physiological, and biochemical analyses showed an intermediate position of L. ×mediterranea between its parental species in most qualitative and quantitative characters, and also separation of the two hybrid cytotypes by some criteria. These data provide evidence that hybridization and polyploidization, driving forces of terrestrial plant evolution, contribute to duckweed genetic diversity and may have shaped the phylogenetic history of these mainly asexual, aquatic plants.
Keyphrases
  • genetic diversity
  • electronic health record
  • single molecule
  • risk assessment
  • systematic review
  • gene expression
  • genome wide
  • mass spectrometry
  • data analysis
  • deep learning
  • nucleic acid