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Dentition patterns and molecular diversity of Mastophorus muris (Gmelin, 1790) (Nematoda: Spiruroidea) support a host-associated subdivision.

Jenny JostJörg HirzmannĽudovít ĎurejeDenny MaazPeer MartinThomas StachEmanuel HeitlingerVíctor Hugo Jarquín-Díaz
Published in: Parasitology research (2024)
Mastophorus muris (Gmelin, 1790) is a globally distributed parasitic nematode of broad range mammals. The taxonomy within the genus Mastophorus and the cryptic diversity among the genus are controversial among taxonomists. This study provides a detailed morphological description of M. muris from Mus musculus combined with a molecular phylogenetic approach. Moreover, descriptions and molecular data of M. muris from non-Mus rodents and wildcats complement our findings and together provide new insights into their taxonomy. The analysis of M. muris was based on light microscopy and scanning electron microscopy. The morphological description focused on the dentition pattern of the two trilobed pseudolabia. Additionally, we described the position of the vulva, arrangement of caudal pairs of papillae, spicules and measured specimens from both sexes and the eggs. For the molecular phylogenetic approach, we amplified the small subunit ribosomal RNA gene and the internal transcribed spacer, and the cytochrome c oxidase subunit 1. Mastophorus morphotypes based on dentition patterns and phylogenetic clustering indicate a subdivision of the genus in agreement with their host. We recognize two groups without a change to formal taxonomy: One group including those specimens infecting Mus musculus, and the second group including organisms infecting non-Mus rodents. Our genetic and morphological data shed light into the cryptic diversity within the genus Mastopohorus. We identified two host-associated groups of M. muris. The described morphotypes and genotypes of M. muris allow a consistent distinction between host-associated parasites.
Keyphrases
  • electron microscopy
  • single molecule
  • genome wide
  • high resolution
  • copy number
  • single cell
  • deep learning
  • rna seq
  • transcription factor
  • machine learning
  • multidrug resistant
  • high speed