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Phylogenetic relationships of philometrid nematodes (Philometridae Baylis & Daubney, 1926) inferred from 18S rRNA, with molecular characterisation of recently described species.

Diane P BartonFrantišek MoravecXiaocheng ZhuShokoofeh Shamsi
Published in: Parasitology research (2021)
Nematodes of the family Philometridae Baylis & Daubney, 1926 (Dracunculoidea Stiles, 1907) are generally poorly known, and there are many taxonomic issues within the family. Philometrids are parasites of fish and are found in various locations throughout the host, including within the subcutaneous tissues and musculature, the abdominal cavity and gonads; vast sexual dimorphism often means the males are not collected, leading to many species being described solely on female characteristics. Although there have been a number of studies utilising molecular data, the vast majority of species are yet to be sequenced. This study undertook genetic sequencing of 15 recently described species of philometrids across 4 genera, many of which were from specimens collected from waters off Australia. All of the sequences obtained were closely related with representatives of the family Philometridae. Species were found to be distributed in the phylogenetic trees within 4 clades based on a combination of site of infection within the host and host habitat. Family of host and geographical location was not as important for position within the trees. Clade A contained philometrids collected from the abdominal cavities and head tissues of South American freshwater fish. Clade B contained philometrids primarily from the abdominal cavities of freshwater European cyprinids. Clade C contained philometrids primarily from the ovaries of marine fish. Clade D contained philometrids from the body tissues of marine and freshwater fish. The potential co-evolutionary patterns between philometrids and their fish hosts are highlighted as an area of future research. This research also highlighted the importance of correct identification of any sequenced specimen.
Keyphrases
  • gene expression
  • genetic diversity
  • genome wide
  • climate change
  • mental health
  • dna methylation
  • risk assessment
  • deep learning