Exploring the microbiome of Otobius megnini in Mexico
9 affiliations
- 1Animal Biotechnology Department, Center for Genetic Engineering and Biotechnology, Havana, Cuba
- 2Laboratory of Microbiology, National School of Veterinary Medicine of Sidi Thabet, University of Manouba, Manouba, Tunisia
- 3Pavol Jozef Šafárik University in Košice, Faculty of Science, Košice, Slovakia
- 4Direction of Animal Health, National Center for Animal and Plant Health, Carretera de Tapaste y Autopista Nacional, San José de las Lajas, Mayabeque, Cuba
- 5Department of Biological and Environmental Sciences, University of Jyväskylä, Jyväskylä, Finland
- 6Universidad Nacional de Asunción, Facultad de Ciencias Químicas, San Lorenzo, Paraguay
- 7School of Environmental Sciences, University of Guelph, Guelph, ON, Canada
- 8Immunology and Vaccines Laboratory, Natural Sciences School, Autonomous University of Querétaro, Querétaro, Mexico
- 9ANSES, INRAE, Ecole Nationale Vétérinaire d’Alfort, UMR BIPAR, Laboratoire de Santé Animale, Maisons-Alfort, France
Abstract
IntroductionTicks harbor complex microbial communities that influence both their biology and their capacity for pathogen transmission. Although the microbiota of hard ticks has been extensively investigated, that of soft ticks, such as Otobius megnini, remains comparatively understudied.MethodsIn this exploratory study, we compared two independently generated 16S rRNA datasets from O. megnini collected in Querétaro and Comarca Lagunera and generated predicted MetaCyc pathway profiles with PICRUSt2. The taxonomic composition, alpha and beta diversity, exploratory co-occurrence network topology, and predicted functional profiles were compared descriptively. Differences in tissue type, 16S region, host replication, life stage, feeding status, and laboratory processing prevented causal attribution to geography.ResultsBoth datasets showed recurrent bacterial taxa, including Staphylococcus, Corynebacterium, and Coxiella-like organisms. Pielou’s evenness was higher and observed feature richness was lower in Comarca Lagunera than in Querétaro (Kruskal–Wallis: both p = 0.0001), whereas Shannon diversity did not differ (p = 0.888). The Bray–Curtis permutational multivariate analysis of variance (PERMANOVA) was significant (p = 0.0001), but multivariate dispersion also differed (betadisper ANOVA: F = 10.61, p = 0.0001). Therefore, the PERMANOVA result cannot be interpreted as an unambiguous difference in group centroids. The inferred networks differed descriptively in topology, and PICRUSt2 predicted pathways shared across the datasets, including those related to central carbon metabolism, amino acid biosynthesis, and cofactor metabolism.ConclusionsThese results provide a descriptive baseline for two independently generated O. megnini microbiome datasets. Because dataset identity was confounded with tissue type, 16S region, host replication, life stage, feeding status, and laboratory processing, the observed differences cannot be attributed to geography. The network and PICRUSt2 results are hypothesis-generating and do not establish microbiome stability, robustness, functional conservation, redundancy, or metabolic activity.
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BibTeX
@article{abuindenis2026exploring,
title = {{Exploring the microbiome of Otobius megnini in Mexico}},
author = {Lianet Abuin-Denis and Myriam Kratou and Štefánia Skičková-Halajčiková and Elianne Piloto-Sardiñas and Apolline Maitre and Alejandra Wu-Chuang and Dasiel Obregón and Consuelo Almazán and Juan Mosqueda and Alejandro Cabezas-Cruz},
journal = {Frontiers in Arachnid Science},
year = {2026},
month = oct,
doi = {10.3389/frchs.2026.1892872},
url = {https://doi.org/10.3389/frchs.2026.1892872},
note = {CC BY},
}
RIS
TY - JOUR
TI - Exploring the microbiome of Otobius megnini in Mexico
AU - Lianet Abuin-Denis
AU - Myriam Kratou
AU - Štefánia Skičková-Halajčiková
AU - Elianne Piloto-Sardiñas
AU - Apolline Maitre
AU - Alejandra Wu-Chuang
AU - Dasiel Obregón
AU - Consuelo Almazán
AU - Juan Mosqueda
AU - Alejandro Cabezas-Cruz
PY - 2026
DA - 2026/10/26
JO - Frontiers in Arachnid Science
DO - 10.3389/frchs.2026.1892872
UR - https://doi.org/10.3389/frchs.2026.1892872
AB - IntroductionTicks harbor complex microbial communities that influence both their biology and their capacity for pathogen transmission. Although the microbiota of hard ticks has been extensively investigated, that of soft ticks, such as Otobius megnini, remains comparatively understudied.MethodsIn this exploratory study, we compared two independently generated 16S rRNA datasets from O. megnini collected in Querétaro and Comarca Lagunera and generated predicted MetaCyc pathway profiles with PICRUSt2. The taxonomic composition, alpha and beta diversity, exploratory co-occurrence network topology, and predicted functional profiles were compared descriptively. Differences in tissue type, 16S region, host replication, life stage, feeding status, and laboratory processing prevented causal attribution to geography.ResultsBoth datasets showed recurrent bacterial taxa, including Staphylococcus, Corynebacterium, and Coxiella-like organisms. Pielou’s evenness was higher and observed feature richness was lower in Comarca Lagunera than in Querétaro (Kruskal–Wallis: both p = 0.0001), whereas Shannon diversity did not differ (p = 0.888). The Bray–Curtis permutational multivariate analysis of variance (PERMANOVA) was significant (p = 0.0001), but multivariate dispersion also differed (betadisper ANOVA: F = 10.61, p = 0.0001). Therefore, the PERMANOVA result cannot be interpreted as an unambiguous difference in group centroids. The inferred networks differed descriptively in topology, and PICRUSt2 predicted pathways shared across the datasets, including those related to central carbon metabolism, amino acid biosynthesis, and cofactor metabolism.ConclusionsThese results provide a descriptive baseline for two independently generated O. megnini microbiome datasets. Because dataset identity was confounded with tissue type, 16S region, host replication, life stage, feeding status, and laboratory processing, the observed differences cannot be attributed to geography. The network and PICRUSt2 results are hypothesis-generating and do not establish microbiome stability, robustness, functional conservation, redundancy, or metabolic activity.
SN - 2813-5083
ER -