Microbiomic association between the saliva and salivary stone in patients with sialolithiasis.

Salivary stones, known as sialoliths, form within the salivary ducts due to abnormal salivary composition and cause painful symptoms, for which surgical removal is the primary treatment. This study explored the role of the salivary microbial communities in the formation of sialoliths. We conducted a comparative analysis of microbial communities present in the saliva and salivary stones, and sequenced the 16S rRNA gene in samples obtained from patients with sialoliths and from healthy individuals. Although the diversity in the saliva was high, the essential features of the microbial environment in sialoliths were low diversity and evenness. The association of microbial abundance between stones and saliva revealed a positive correlation between Peptostreptococcus and Porphyromonas, and a negative correlation for Pseudomonas in saliva. The functional potential differences between saliva and stones Bacterial chemotaxis and the citrate cycle were negatively correlated with most genera found in salivary stone samples. However, the functions required for organic compound degradation did not differ between the saliva samples. Although some microbes were shared between the sialoliths and saliva, their compositions differed significantly. Our study presents a novel comparison between salivary stones and salivary microbiomes, suggesting potential preventive strategies against sialolithiasis.

Microbiome analysis of circulating bacterial extracellular vesicles in obsessive-compulsive disorder.

AIM: The present study examined the microbiome abundance and composition of drug-naive or drug-free patients with obsessive-compulsive disorder (OCD) compared with healthy controls. In addition, in the OCD group, the microbiome composition was compared between early-onset and late-onset OCD. METHODS: Serum samples were collected from 89 patients with OCD and 107 age- and sex-matched healthy controls. Bacterial DNA was isolated from bacteria-derived extracellular vesicles in serum and then amplified and quantified using primers specific to the V3-V4 hypervariable region of the 16S ribosomal RNA gene. The 16S ribosomal DNA gene amplicon sequencing was performed. RESULTS: The pooled estimate showed that α-diversity was significantly reduced in patients with OCD compared with that in healthy controls (PShannon = 0.00015). In addition, a statistically significant difference was observed in ß-diversity between patients with OCD and healthy controls at the order (P = 0.012), family (P = 0.003), genus (P < 0.001), and species (P = 0.005) levels. In the microbiome composition, Pseudomonas, Caulobacteraceae (f), Streptococcus, Novosphingobium, and Enhydrobacter at the genus level were significantly less prevalent in patients with OCD than in controls. In addition, among patients with OCD, the microbial composition in the early-onset versus late-onset types was significantly different with respect to the genera Corynebacterium and Pelomonas. CONCLUSION: The present study showed an aberrant microbiome in patients with OCD, suggesting a role of the microbiota-brain interaction in the pathophysiology of OCD. Further longitudinal studies with larger sample sizes adjusting for various confounders are warranted.

Dietary Efficacy Evaluation by Applying a Prediction Model Using Clinical Fecal Microbiome Data of Colorectal Disease to a Controlled Animal Model from an Obesity Perspective.

Obesity associated with a Western diet such as a high-fat diet (HFD) is a known risk factor for inflammatory bowel disease (IBD) and colorectal cancer (CRC). In this study, we aimed to develop fecal microbiome data-based deep learning algorithms for the risk assessment of colorectal diseases. The effects of a HFD and a candidate food (Nypa fruticans, NF) on IBD and CRC risk reduction were also evaluated. Fecal microbiome data were obtained from 109 IBD patients, 111 CRC patients, and 395 healthy control (HC) subjects by 16S rDNA amplicon sequencing. IBD and CRC risk assessment prediction models were then constructed by deep learning algorithms. Dietary effects were evaluated based on fecal microbiome data from rats fed on a regular chow diet (RCD), HFD, and HFD plus ethanol extracts or water extracts of NF. There were significant differences in taxa when IBD and CRC were compared with HC. The diagnostic performance (area under curve, AUC) of the deep learning algorithm was 0.84 for IBD and 0.80 for CRC prediction. Based on the rat fecal microbiome data, IBD and CRC risks were increased in HFD-fed rats versus RCD-fed rats. Interestingly, in the HFD-induced obesity model, the IBD and CRC risk scores were significantly lowered by the administration of ethanol extracts of NF, but not by the administration of water extracts of NF. In conclusion, changes in the fecal microbiome of obesity by Western diet could be important risk factors for the development of IBD and CRC. The risk prediction model developed in this study could be used to evaluate dietary efficacy.