Phylogenetic signal retention across evolutionary depths in highly divergent mitovirus RNA-dependent RNA polymerases

Read full text
Authors: Andrés G. Jacquat
Cited by: 0

Abstract

The family Mitoviridae comprises a highly diverse group of positive-sense RNA viruses whose deep evolutionary relationships remain difficult to resolve due to extreme sequence divergence. Here, we investigated whether mitovirus and mitovirus-like RNA-dependent RNA polymerases (RdRps) retain sufficient phylogenetic signal for robust evolutionary inference, assessing which aspects of Mitoviridae phylogeny remain recoverable across evolutionary depths. We compiled 1,036 RdRp sequences and applied a sequential curation strategy based on conserved motif architecture and positional homology, followed by 90% amino acid identity clustering to yield a non-redundant dataset of 426 representative sequences. Three alignment strategies (MAFFT-DASH, MUSCLE5, and PROMALS) were evaluated before and after conservative trimming using complementary alignment and phylogenetic metrics, including sequence consistency, informative-site composition, branch support, and topological congruence. Major intrafamilial lineages were reproducibly recovered across alternative analytical treatments, revealing substantial topological concordance between MAFFT and MUSCLE5. However, statistical support and topological consistency progressively declined toward the deepest internal branches. These results indicate that the conserved RdRp region retains substantial recoverable phylogenetic signal for derived lineages, but that primary sequence data alone provide limited resolution of the oldest intrafamilial divergences. Our findings highlight the necessity of benchmarking alternative alignment strategies and integrating independent structural evidence when reconstructing deep RNA virus evolution.

Fields of Study & Subjects

Genomics and Phylogenetic Studies Evolution and Genetic Dynamics RNA and protein synthesis mechanisms

Publication details

Journal / source
CONICET Digital (CONICET)
Publication date
6 February 2026
Language
EN