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"Visiting old, learn new": taxonomical overview of chiropteran trypanosomes from the morphology to the genes.

Hiroshi SatoEliakunda Mafie
Published in: Parasitology research (2022)
Bats (the order Chiroptera) account for more than 20% of all mammalian species in the world; remarkably, they are the only mammals capable of true and sustained flight using their wing-like forelimbs. Since the beginning of the twentieth century, various morphotypes (or genotypes in the last decade) of haemoflagellates in the genus Trypanosoma (Euglenozoa: Kinetoplastea: Trypanosomatidae) have been reported worldwide in the blood of bats. Of note, the latent nature of chiropteran trypanosome infection with low levels of parasitaemia, together with the apparent morphological variation of the bloodstream forms related to phenotypical plasticity and the morphological resemblance of different parasite species, has hampered the taxonomic classification of bat trypanosomes based on morphological criteria. This said, 50 years ago, Hoare (1972) provisionally divided bat trypanosomes into two major morphotypes: the megadermae group (corresponding to the subgenus Megatrypanum in the traditional taxonomic system; 8 species) and the vespertilionis group (similar to the subgenus Schizotrypanum; 5 species). Importantly, the biological and biochemical analyses of bat trypanosomes isolated by haemoculture, together with the molecular genetic characterisation using various gene markers, allowed the establishment of clear phylogenetic and taxonomic relationships of various isolates from different continents in the last two decades. Here, we review the historical taxonomic approaches used to define chiropteran trypanosomes, as well as the ones currently employed to shed light on the diversity and evolutional tracks of the globally distributed chiropteran trypanosomes.
Keyphrases
  • genome wide
  • genetic diversity
  • machine learning
  • copy number
  • magnetic resonance
  • genome wide identification
  • gene expression
  • single molecule
  • diffusion weighted imaging
  • neural network