The research team, led by Lalianet Abuin-Denis of the Center for Genetic Engineering and Biotechnology in Havana, with colleagues including Consuelo Almazán and Juan Mosqueda of the Autonomous University of Querétaro and Alejandro Cabezas-Cruz of INRAE in France, generated predicted MetaCyc pathway profiles with PICRUSt2 and compared taxonomic composition, alpha and beta diversity, exploratory co-occurrence network topology, and predicted functional profiles between the two datasets.

Both datasets showed recurrent bacterial taxa, including Staphylococcus, Corynebacterium, and Coxiella-like organisms.

The diversity comparisons produced a mixed picture. Pielou’s evenness was higher and observed feature richness was lower in the Comarca Lagunera dataset than in the Querétaro dataset, with Kruskal-Wallis tests returning p = 0.0001 for both measures. Shannon diversity, however, did not differ between the datasets, with a p-value of 0.888.

Beta diversity told a more complicated story. The Bray-Curtis permutational multivariate analysis of variance was significant, with p = 0.0001, but the authors caution that this result cannot be read as an unambiguous difference in group centroids. Multivariate dispersion also differed between the datasets, with a betadisper ANOVA yielding F = 10.61 and p = 0.0001, meaning the PERMANOVA signal may reflect differences in within-group spread rather than differences in the location of group centers.

The inferred co-occurrence networks differed descriptively in topology between the two datasets, and PICRUSt2 predicted pathways shared across both, including pathways related to central carbon metabolism, amino acid biosynthesis, and cofactor metabolism. The authors stress that these network and functional predictions are hypothesis-generating only; they do not establish microbiome stability, robustness, functional conservation, redundancy, or metabolic activity.

A central strength of the paper is its explicit handling of confounding. Because dataset identity was entangled with tissue type, 16S region, host replication, life stage, feeding status, and laboratory processing, the observed differences cannot be attributed to geography. The two collections were not designed as a controlled comparison, and the authors are careful not to claim that the tick microbiomes of Querétaro and Comarca Lagunera truly differ in the field.

Despite these limitations, the study provides a descriptive baseline for two independently generated O. megnini microbiome datasets, something that did not previously exist for this understudied soft tick. The recurrent taxa and shared predicted metabolic pathways offer concrete starting points for future studies that can control sampling design more tightly.

For researchers working on tick-borne disease and vector biology, the message is that soft tick microbiomes are now on the map, but interpreting the patterns reported here will require new, carefully matched collections that separate geography from the many other variables that differed between these two datasets.

Subject of Research: The microbiome of the soft tick Otobius megnini in Mexico

Article Title: Exploring the microbiome of Otobius megnini in Mexico

Article References: Abuin-Denis, L., Kratou, M., Skičková-Halajčiková, Š., Piloto-Sardiñas, E., Maitre, A., Wu-Chuang, A., Obregón, D., Almazán, C., Mosqueda, J., & Cabezas-Cruz, A. (2026). Exploring the microbiome of Otobius megnini in Mexico. Frontiers in Arachnid Science, 5, Article 1892872. https://doi.org/10.3389/frchs.2026.1892872

Image Credits: AI Generated

DOI: 10.3389/frchs.2026.1892872

Keywords: Otobius megnini, soft ticks, microbiome, 16S rRNA, Mexico, Querétaro, Comarca Lagunera, Coxiella-like organisms, PICRUSt2, beta diversity, tick microbiota, Frontiers in Arachnid Science