Probiotics are known to regulate host metabolism. In randomized controlled trial we aimed to assess whether interventions with probiotic containing following strains: Bifidobacterium bifidum W23, Bifidobacterium lactis W51, Bifidobacterium lactis W52, Lactobacillus acidophilus W37, Levilactobacillus brevis W63, Lacticaseibacillus casei W56, Ligilactobacillus salivarius W24, Lactococcus lactis W19, and Lactococcus lactis W58 affect gut microbiota to promote metabolic effects. By 16S rRNA sequencing we analyzed the fecal microbiota of 56 obese, postmenopausal women randomized into three groups: (1) probiotic dose 2.5 × 109 CFU/day (n = 18), (2) 1 × 1010 CFU/day (n = 18), or (3) placebo (n = 20). In the set of linear mixed-effects models, the interaction between pre- or post-treatment bacterial abundance and time on cardiometabolic parameters was significantly (FDR-adjusted) modified by type of intervention (26 and 19 three-way interactions for the pre-treatment and post-treatment abundance, respectively), indicating the modification of the bio-physiological role of microbiota by probiotics. For example, the unfavorable effects of Erysipelotrichi, Erysipelotrichales, and Erysipelotrichaceae on BMI might be reversed, but the beneficial effect of Betaproteobacteria on BMI was diminished by probiotic treatment. Proinflammatory effect of Bacteroidaceae was alleviated by probiotic administration. However, probiotics did not affect the microbiota composition, and none of the baseline microbiota-related features could predict therapeutic response as defined by cluster analysis. Conclusions: Probiotic intervention alters the influence of microbiota on biochemical, physiological and immunological parameters, but it does not affect diversity and taxonomic composition. Baseline microbiota is not a predictor of therapeutic response to a multispecies probiotic. Further multi-omic and mechanistic studies performed on the bigger cohort of patients are needed to elucidate the cardiometabolic effect of investigated probiotics in postmenopausal obesity.

The past decade has seen advancement in high-throughput sequencing technologies resulting in rapid accumulation of genomic data from microbial communities. While this growth in sequence data and gene discovery is impressive, the majority of microbial gene functions remain uncharacterized.

The sinus microbiome in patients with chronic rhinosinusitis (CRS) is considered homogenous across the sinonasal cavity. The middle nasal meatus is the recommended sampling site for 16S rRNA sequencing. However, individuals with unusually high variability between the middle meatus and the sinuses were identified in previous studies. Patients with CRS present various mechanisms of disease evolution. Their sinuses are frequently separated from the middle meatus by pathological changes. This study aimed to identify which factors determine increased microbial heterogeneity between sampling sites in the sinuses. In this cross-sectional study samples for 16S rRNA sequencing were obtained from the middle meatus, the maxillary and frontal sinus in 50 patients with CRS. The microbiome diversity between sampling sites was analyzed in relation to the size of the sinus ostia and clinical metadata. In approximately 15% of study participants, the differences between sampling sites within one patient were greater than between the patient and other individuals. Contrary to a popular hypothesis, obstruction of the sinus ostium resulted in decreased dissimilarity between the sinus and the middle meatus. The dissimilarity between the sampling sites was patient-specific: greater between-sinus differences were associated with greater meatus-sinus differences, regardless of the drainage pathway patency. Decreased spatial variability was observed in patients with nasal polyps and extensive mucosal changes in the sinuses. Researchers and clinicians should be aware that sampling from the middle meatus is not universally representative of the sinus microbiome. The differences between sites cannot be predicted from the patency of communication pathways between them.

Comprehensive protein function annotation is key to understanding microbiome-related disease mechanisms in the host organisms. We have developed a new metagenome analysis workflow integrating de novo genome reconstruction, taxonomic profiling and deep learning-based functional annotations from DeepFRI. We validate DeepFRI functional annotations by comparing them to orthology-based annotations from eggNOG. Further, we demonstrate the usage of the workflow using 1,070 infant metagenome samples from the DIABIMMUNE cohort. We have generated a sequence catalogue comprising a collection of 7,174 metagenome-assembled genomes (MAGs), of which 2,255 are high quality near-complete bacterial genomes. These genomes encode 1.9 million non-redundant genes. We found high concordance (70%) between GO annotations predicted by DeepFRI and eggNOG. Importantly, we show that DeepFRI improved the annotation coverage, with nearly all the genes in the gene catalogue obtaining GO molecular function annotations, although the annotations are less specific compared to eggNOG. The pan-genome analysis of 42 bacterial species revealed a striking difference between DeepFRI and eggNOG annotations. eggNOG was shown to annotate more genes coming from well-studied organisms such as Escherichia coli while DeepFRI on the other hand is not sensitive to taxa. This work contributes to the understanding of the functional signature of the human gut microbiome.

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