Uncovering ciliate diversity in rumen microbiomes through computational species delimitation approaches

Herbivorous mammals host a complex microbiological community composed of archaea, bacteria, fungi, and ciliate protists. Metabarcoding sequencing approaches are among the main tools used to investigate these microbiomes. Despite their contribution to our understanding of local biodiversity, detailed taxonomic analyses are hindered by gaps in knowledge regarding ciliate diversity and fundamental taxonomic inferences. Short-reads alignment software from next-generation sequencing (NGS), along with computational species delimitation methods, offer promising tools for analyzing these data, enhancing accuracy and revealing previously undocumented lineages and diversity patterns within microbiomes. We propose a new workflow that integrates short-reads alignment with computational species delimitation, using different algorithms to analyze microbiomes from bovines and camelids. Our results showed that amplicon sequence variants (ASVs) from bovine and camelid microbiomes clustered within known families of endosymbiotic protists. At the level of taxonomic signatures, the application of different delimitation methods yielded contrasting numbers of Evolutionarily Significant Units (ESUs) across the microbiomes analyzed. In addition, the approaches were able to recover taxonomic signatures at the genus level and identify taxonomic correspondences with known species. They also demonstrated greater taxonomic resolution when applied to the bovine microbiome and revealed hidden diversity within the camelid microbiome. This work is pioneering in presenting a methodological framework for processing amplicon data generated by NGS platforms, enabling a more refined taxonomic analysis of the structure of ciliated protist communities.