In:
Parasites & Vectors, Springer Science and Business Media LLC, Vol. 15, No. 1 ( 2022-10-19)
Abstract:
Classification of the Acanthocephala, a clade of obligate endoparasites, remains unresolved because of insufficiently strong resolution of morphological characters and scarcity of molecular data with a sufficient resolution. Mitochondrial genomes may be a suitable candidate, but they are available for a small number of species and their suitability for the task has not been tested thoroughly. Methods Herein, we sequenced the first mitogenome for the large family Rhadinorhynchidae: Micracanthorhynchina dakusuiensis . These are also the first molecular data generated for this entire genus. We conducted a series of phylogenetic analyses using concatenated nucleotides (NUC) and amino acids (AAs) of all 12 protein-coding genes, three different algorithms, and the entire available acanthocephalan mitogenomic dataset. Results We found evidence for strong compositional heterogeneity in the dataset, and Micracanthorhynchina dakusuiensis exhibited a disproportionately long branch in all analyses. This caused a long-branch attraction artefact (LBA) of M. dakusuiensis resolved at the base of the Echinorhynchida clade when the NUC dataset was used in combination with standard phylogenetic algorithms, maximum likelihood (ML) and Bayesian inference (BI). Both the use of the AA dataset (BI-AAs and ML-AAs) and the CAT-GTR model designed for suppression of LBA (CAT-GTR-AAs and CAT-GTR-NUC) at least partially attenuated this LBA artefact. The results support Illiosentidae as the basal radiation of Echinorhynchida and Rhadinorhynchidae forming a clade with Echinorhynchidae and Pomporhynchidae. The questions of the monophyly of Rhadinorhynchidae and its sister lineage remain unresolved. The order Echinorhynchida was paraphyletic in all of our analyses. Conclusions Future studies should take care to attenuate compositional heterogeneity-driven LBA artefacts when applying mitogenomic data to resolve the phylogeny of Acanthocephala. Graphical Abstract
Type of Medium:
Online Resource
ISSN:
1756-3305
DOI:
10.1186/s13071-022-05488-0
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2022
detail.hit.zdb_id:
2409480-8
