The oral microbiome in young women at different stages of periodontitis: Prevotella dominant in stage III periodontitis

Microbiological and Metabolomic Analysis of Biomarkers for Grades A and B in Stage II Periodontitis

Periodontitis is a chronic inflammatory disease characterized by inflammation of the periodontal soft tissues and loss of alveolar bone. In the oral environment, subgingival microorganisms and salivary metabolites reflect the host's health status. This study aimed to understand periodontitis severity and progression rate by analyzing subgingival microflora and salivary metabolites to identify potential biomarkers. Fifty-three volunteers with stage II periodontitis were graded using the bone loss (%)/age index into two grades: 33 in grade A (< 0.25) and 20 in grade B (0.25-1.00). Using a case-control study, simultaneously analyzed biomarkers associated with the severity and rate of progression of periodontitis. The red complex, the orange complex, Campylobacter spp., uncultured Candidatus Saccharibacteria and metabolites such as 5-Aminovaleric acid, N1-Acetylspermine showed a significant positive correlation with periodontal clinical parameters. Furthermore, we identified four of the salivary differential metabolites (DL-Leucineamide, Dodecanedioic acid, L-Tyrosine methyl ester and Phenylpyruvic acid) that may serve as potential biomarkers for predicting the rate of periodontitis progression. These results showed that the red complex significantly correlated with periodontitis severity and influenced changes in salivary metabolites. Additionally, biomarkers indicating the progression rate were predominantly amino acid derivatives, confirming that interactions between microorganisms and metabolites may exacerbate periodontitis development.

Gut microbiome, T cell subsets, and cytokine analysis identify differential biomarkers in tuberculosis

Introduction

The gut microbiota, T cell subsets, and cytokines participate in tuberculosis (TB) pathogenesis. To date, the mechanisms by which these factors interactively promote TB development at different time points remain largely unclear. In the context of this study, We looked into the microorganisms in the digestive tract, T cell types, and cytokines related to tuberculosis.

Methods

According to QIIME2, we analyzed 16SrDNA sequencing of the gut microbiome on the Illumina MiSeq. Enzyme-linked immunosorbent assay was used to measure the concentrations of cytokines.

Results

We showed the presence of 26 identifiable differential microbiomes in the gut and 44 metabolic pathways between healthy controls and the different time points in the development of TB in patients. Five bacterial genera (Bacteroides, Bifidobacterium, Faecalibacterium, Collinsella, and Clostridium) were most closely associated with CD4/CD8, whereas three bacterial taxa (Faecalibacterium, Collinsella, and Clostridium) were most closely associated with CD4. Three bacterial taxa (Faecalibacterium, Ruminococcus, and Dorea) were most closely associated with IL-4. Ruminococcus was most closely associated with IL-2 and IL-10.

Conclusion

Diverse microorganisms, subsets of T cells, and cytokines, exhibiting varying relative abundances and structural compositions, were observed in both healthy controls and patients throughout distinct phases of tuberculosis. Gaining insight into the function of the gut microbiome, T cell subsets, and cytokines may help modulate therapeutic strategies for TB.

Genetically predicted gut microbiome and risk of oral cancer

PURPOSE

Mounting evidence suggests a possible link between gut microbiome and oral cancer, pointing to some potential modifiable targets for disease prevention. In the present study, Mendelian randomization (MR) was used to explore whether there was a causal link between gut microbiome and oral cancer.

METHODS

The single nucleotide polymorphisms (SNPs) significantly associated with gut microbiome were served as instrumental variables. MR analyses were performed using genetic approaches such as inverse variance weighting (IVW), MR Egger and weighted median, with IVW as the primary approach, supplemented by MR Egger and weighted median. Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) and MR-Egger regression were used to detect the presence of horizontal pleiotropy and identify outlier SNPs.

RESULTS

Causal effect estimates indicated that genetically predicted abundance of Prevotellaceae was associated with higher risk of oral cancer (odds ratio (OR) 1.80, 95% confidence interval (CI) 1.16-2.81, p = 0.009). There was no evidence of notable heterogeneity and horizontal pleiotropy.

CONCLUSION

Genetically derived estimates suggest that Prevotellaceae may be associated with the risk of oral cancer. Such robust evidence should be given priority in future studies and explore the underlying mechanisms.

The microbiota of pregnant women with SARS-CoV-2 and their infants

BACKGROUND

Infants receive their first bacteria from their birthing parent. This newly acquired microbiome plays a pivotal role in developing a robust immune system, the cornerstone of long-term health.

RESULTS

We demonstrated that the gut, vaginal, and oral microbial diversity of pregnant women with SARS-CoV-2 infection is reduced, and women with early infections exhibit a different vaginal microbiota composition at the time of delivery compared to their healthy control counterparts. Accordingly, a low relative abundance of two Streptococcus sequence variants (SV) was predictive of infants born to pregnant women with SARS-CoV-2 infection.

CONCLUSIONS

Our data suggest that SARS-CoV-2 infections during pregnancy, particularly early infections, are associated with lasting changes in the microbiome of pregnant women, compromising the initial microbial seed of their infant. Our results highlight the importance of further exploring the impact of SARS-CoV-2 on the infant's microbiome-dependent immune programming. Video Abstract.

Oral microbiome changes associated with the menstrual cycle in healthy young adult females

The relationship between the menstrual cycle and the oral microbiome has not been clarified. The purpose of this study was to assess potential changes in the oral microbiome of healthy young adults using 16S rRNA-based sequencing. Eleven females (aged 23–36 years) with stable menstrual cycles and without any oral problems were recruited. Saliva samples were collected before brushing every morning during the menstrual period. Based on basal body temperatures, menstrual cycles were divided into four phases, namely the menstrual, follicular, early luteal, and late luteal phases. Our results showed that the follicular phase had a significantly higher abundance ratio of the Streptococcus genus than the early and late luteal phases, whereas the abundance ratios of the Prevotella 7 and Prevotella 6 genera were significantly lower in the follicular phase than those in the early and late luteal phases and that in the early luteal phase, respectively. Alpha diversity by the Simpson index was significantly lower in the follicular phase than that in the early luteal phase, and beta diversity showed significant differences among the four phases. Using the relative abundance data and copy numbers of the 16S rRNA genes in the samples, the bacterial amounts in the four phases were compared, and we observed that the follicular phase had significantly lower amounts of the Prevotella 7 and Prevotella 6 genera than the menstrual and early luteal phase, respectively. These results indicate reciprocal changes with the Streptococcus genus and Prevotella genera, particularly in the follicular phase. In the present study, we showed that the oral microbiome profiles are affected by the menstrual cycles of healthy young adult females.