Gut microbiome and colonic inflammation can be associated with the predisposition and progression of Parkinson’s disease (PD). The presented study aimed to compare gastrointestinal microbiota composition between patients diagnosed with PD and treated only with Levodopa to healthy controls. In this prospective study, patients were recruited in 1 academic hospital from July 2019 to July 2020. The detailed demographic data and medical history were collected using a set of questionnaires. Fecal samples were obtained from all participants. Next-Generation Sequencing was used to assess the microbiota composition. The endpoint was the difference in composition of the gut microbiota. In this study, we enrolled 27 hospitalized PD patients with well-controlled symptoms. The control group included 44 healthy subjects matched for age. Among PD patients, our results presented a higher abundance of Bacteroides phylum, class Corynebacteria among phylum Actinobacteria, class Deltaproteobacteria among phylum Proteobacteria, and genera such as Butyricimonas, Robinsoniella, and Flavonifractor. The species Akkermansia muciniphila, Eubacterium biforme, and Parabacteroides merdae were identified as more common in the gut microbiota of PD patients. In conclusion, the patients diagnosed with PD have significantly different gut microbiota profiles in comparison with healthy controls.

Background: The aim of this study was to investigate the role of urine-derived extracellular vesicles (uEVs) in diabetic kidney disease (DKD) in patients diagnosed with type 2 diabetes mellitus (T2DM). Methods: UEVs were characterized by size distribution and microRNA content by next-generation small RNA sequencing and quantitative reverse transcription PCR. Results: A subset of sixteen miRNAs enriched in T2DM patients with DKD, including hsa-miR-514a-5p, hsa-miR‑451a, hsa-miR-126-3p, hsa-miR-214, or hsa-miR‑503 was identified. Eight miRNAs as hsa-miR-21-3p, hsa-miR-4792, hsa-miR‑375, hsa-miR-1268a, hsa-miR-501-5p, or hsa-miR-582 were downregulated. Prediction of potential target genes and pathway enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) confirmed possible functions related to cellular processes such as apoptosis, inflammation, and tissue remodeling, that promote diabetic complications, such as DKD. Among them, hsa-miR-375, hsa-miR-503, and hsa-miR-451a make important contribution. Additionally, downregulated hsa-miR-582-5p has not been reported so far in any diabetes-related pathways. Conclusions: This study revealed the most significant miRNAs in uEVs of patients with T2DM. However, as this is a bioinformatic prediction that we performed based on the putative targets of the identified miRNAs. Thus, further in vitro functional studies are needed to confirm our findings. Knowing the fact that EVs are crucial in transferring miRNAs, there is a great need toto discover their involvement in the pathomechanism of T2DM-related kidney disease.

The type of diet not only affects the composition of the oral microflora but is also one of the more critical factors associated with an increased risk of Parkinson’s disease, PD. This study compared diet preferences and oral microbiota profiles in patients with PD vs. healthy controls. This study compared the oral microbiota composition of 59 patients with PD and 108 healthy controls (without neurodegeneration) using 16S rRNA gene amplicon sequencing. According to results, oral microbiota in patients with PD is different compared from healthy controls. In particular, decreased abundance of Proteobacteria, Pastescibacteria, and Tenercutes was observed. The oral cavity of patients with PD was characterized by the high relative abundance of bacteria from the genera Prevotella, Streptococcus, and Lactobaccillus. There were also differences in food preferences between patients with PD and healthy controls, which revealed significantly higher intake of margarine, fish, red meat, cereals products, avocado, and olives in the patients with PD relative to healthy controls. Strong positive and negative correlations between specific food products and microbial taxa were identified.

Background: We aimed to assess the changes in composition of bacterial microbiota at two levels of the digestive tract: oral cavity and large intestine in patients 6 months after bariatric surgery. Methods: This was a prospective cohort study including patients undergoing bariatric surgery. Before surgery and 6 months after the procedure, oral swabs were obtained and stool samples were provided. Our endpoint was the analysis of the differences in compositions of oral and fecal microbiota prior and after the surgical treatment of obesity. Results: Bacteria from phylum Bacteroidetes seemed to increase in abundance in both the oral cavity and the large intestine 6 months after surgery among patients undergoing bariatric surgery. The subgroup analysis we conducted based on the volume of weight-loss revealed that patients achieving at least 50% of excess weight loss present similar results to the entire study group. Patients with less favorable outcomes presented an increase in the population of bacteria from phylum Fusobacteria and a decrease of phylum Firmicutes in oral cavity. Conclusion: Intestinal microbiota among these patients underwent similar changes in composition to the rest of the study group. Bariatric surgery introduces a significant change in composition of oral and intestinal microbiota. Graphical

Background and Objectives: The aim of this study was to compare the differences in compositions of oral and fecal bacterial microbiota between patients with morbid obesity and normal-weight controls. Material and Methods: This was a prospective cohort study. The study included group 1 (patients with BMI ≥ 40 kg/m²) and group 2 (patients with BMI from 18.5 to 24.9 kg/m²). Our endpoint was the analysis of the differences in compositions of oral and fecal microbiota between the groups. Oral swabs and fecal samples were collected from the patients. The analysis of microbiota was conducted using next-generation sequencing. Results: Overall, the study included 96 patients; 52 (54.2%) were included in group 1, 44 (39.8%)—in group 2. In group 1, oral microbiota included significantly more bacteria from genera Veillonella, Oribacterium and Soonwooa, whereas, in group 2, Streptobacillus, Parvimonas and Rothia were more common. Fecal microbiota in group 1 included more Bacteroides, Odoribacter and Blautia and group 2 was more abundant in Ruminococcus, Christensenella and Faecalibacterium. Conclusions: Both oral and fecal gastrointestinal microbiota differs significantly among patients with severe obesity and lean individuals.

This study aimed to compare the gut and oral microbiota composition of professional male football players and amateurs. Environmental and behavioral factors are well known to modulate intestinal microbiota composition. Active lifestyle behaviors are involved in the improvement of metabolic and inflammatory parameters. Exercise promotes adaptational changes in human metabolic capacities affecting microbial homeostasis. Twenty professional football players and twelve amateurs were invited to the study groups. Fecal and oral microbiota were analyzed using next-generation sequencing of the 16S rRNA gene. Diversity in the oral microbiota composition was similar in amateurs and professionals, while the increase in training intensity reduced the number of bacterial species. In contrast, the analysis of the intestinal microbiota showed the greatest differentiation between professional football players and amateurs, especially during intensive training. Firmicutes were characterized by the largest population in all the studied groups. Intensive physical activity increases the abundance of butyrate and succinate-producing bacteria affecting host metabolic homeostasis, suggesting a very beneficial role for the host immune system's microbiome homeostasis and providing a proper function of the host immune system.