Symbiotic microorganisms may provide their hosts with abilities critical to their occupation of microhabitats. Gut (intestinal) bacterial communities aid animals to digest substrates that are either innutritious or toxic, as well as support their development and physiology. The role of microbial communities associated with sibling species in the hosts’ adaptation remains largely unexplored. In this study, we examined the composition and plasticity of the bacteriomes in two sibling intertidal gastropod species, Littorina fabalis and L. obtusata, which are sympatric but differ in microhabitats. We applied 16S rRNA gene metabarcoding and shotgun sequencing to describe associated microbial communities and their spatial and temporal variation. A significant drop in the intestinal bacteriome diversity was revealed during the cold season, which may reflect temperature-related metabolic shifts and changes in snail behavior. Importantly, there were significant interspecies differences in the gut bacteriome composition in summer but not in autumn. The genera Vibrio, Aliivibrio, Moritella and Planktotalea were found to be predominantly associated with L. fabalis, while Granulosicoccus, Octadecabacter, Colwellia, Pseudomonas, Pseudoalteromonas and Maribacter were found to be mostly associated with L. obtusata. Based on these preferential associations, we analyzed the metabolic pathways’ enrichment. We hypothesized that the L. obtusata gut bacteriome contributes to decomposing algae and detoxifying polyphenols produced by fucoids. Thus, differences in the sets of associated bacteria may equip their closely phylogenetically related hosts with a unique ability to occupy specific micro-niches.

Background/Objectives: Gut dysbiosis has been implicated in multiple sclerosis (MS), but microbial signatures remain inconsistent across studies. Machine learning (ML) algorithms based on global microbiome data integration can reveal key disease-associated microbial biomarkers and new insights into MS pathogenesis. This study aimed to investigate gut microbial signatures associated with MS and to evaluate the potential of ML for diagnostic applications. Methods: Fecal samples from 29 relapsing–remitting MS patients during exacerbation and 27 healthy controls were analyzed using 16S rRNA gene sequencing. Differential abundance analysis was performed, and data were integrated with 29 published studies. Four ML models were developed to distinguish MS-associated microbiome profiles. Results: MS patients exhibited reduced levels of Eubacteriales (p = 0.037), Lachnospirales (p = 0.021), Oscillospiraceae (p = 0.013), Lachnospiraceae (p = 0.012), Parasutterella (p = 0.018), Faecalibacterium (p = 0.004), and higher abundance of Lachnospiraceae UCG-008 (p = 0.045) compared to healthy controls. The Light Gradient Boosting Machine classifier demonstrated the highest performance (accuracy: 0.88, AUC-ROC: 0.95) in distinguishing MS microbiome profiles from healthy controls. Conclusions: This study highlights specific microbiome dysbiosis in MS patients and supports the potential of ML for diagnostic applications. Further research is needed to elucidate the mechanistic role of these microbial alterations in MS progression and their therapeutic utility.

A healthy ocular surface is characterized by relatively stable and comparatively low microbial diversity. However, pathological conditions can provoke shifts in the composition of bacterial taxa, which may be associated, among other factors, with the progression of myopia. Purpose. This study compared the taxonomic diversity of the conjunctival microbiota in children with varying degrees of myopia and those without clinically confirmed myopia. Material and methods. Ocular surface samples were obtained from 29 children aged 6—17 years with myopia (58 eyes) and from 12 children of the control group aged 9—17 years (24 eyes). The taxonomic composition of the conjunctival microbiota was analyzed using 16S ribosomal RNA gene (rRNA) sequencing, followed by microbiome profiling through bioinformatics and statistical tools. Results. The ocular surface microbiota in children with myopia demonstrated higher alpha diversity compared to the control group. This was confirmed by values of the Chao (reflecting low-abundance taxa) and Shannon (reflecting overall bacterial diversity — higher diversity means higher index; optimal value; 3.1—4.2) indices. The results revealed a clear trend toward differentiation in bacterial composition between myopic and control groups. These differences were associated with changes in the relative abundance of opportunistic bacteria depending on the degree of myopia. Conclusion. The taxonomic diversity of the ocular microbiota at the genus level in patients with varying degrees of myopia was characterized by a higher number of taxonomic units compared to the control group. The general trend is an increase in the biodiversity of the bacterial composition due to an increase in the relative representation of opportunistic microorganisms. Further research on the influence of the ocular microbiota on the progression of myopia is needed.