Comparative evaluation of microbial profiles of oral samples obtained at different collection time points and using different methods

Changes in salivary microbiota due to gastric cancer resection and its relation to gastric fluid microbiota

Gastric cancer is one of the leading causes of death worldwide, and resections are performed to cure the disease. We have previously reported the changes in the gastric microbiota after gastric cancer resection, which may be associated with the oral microbiota; however, the changes in the oral microbiota remain uncharacterized. This study aimed to characterize the changes in the salivary microbiota caused by gastric cancer resection and to evaluate their association with the gastric fluid microbiota. Saliva and gastric fluid samples were collected from 63 patients who underwent gastrectomy before and after surgery, and a 16S rRNA metagenomic analysis was performed to compare the microbiota composition. The number of bacterial species in the salivary microbiota decreased, and the bacterial composition changed after the resection of gastric cancer. In addition, we identified several bacterial genera that varied significantly in the salivary microbiota, some of which also showed similar changes in the gastric fluid microbiota. These findings indicate that changes in the gastric environment affect the oral microbiota, emphasizing the close association between the oral and gastric fluid microbiota. Our study signifies the importance of focusing on the oral microbiota in the perioperative period of gastrectomy in patients with gastric cancer.

Factors influencing oral microbiome analysis: from saliva sampling methods to next-generation sequencing platforms

The exploration of oral microbiome has been increasing due to its relatedness with various systemic diseases, but standardization of saliva sampling for microbiome analysis has not been established, contributing to the lack of data comparability. Here, we evaluated the factors that influence the microbiome data. Saliva samples were collected by the two collection methods (passive drooling and mouthwash) using three saliva-preservation methods (OMNIgene, DNA/RNA shield, and simple collection). A total of 18 samples were sequenced by both Illumina short-read and Nanopore long-read next-generation sequencing (NGS). The component of the oral microbiome in each sample was compared with alpha and beta diversity and the taxonomic abundances, to find out the effects of factors on oral microbiome data. The alpha diversity indices of the mouthwash sample were significantly higher than that of the drooling group with both short-read and long-read NGS, while no significant differences in microbial diversities were found between the three saliva-preservation methods. Our study shows mouthwash and simple collection are not inferior to other sample collection and saliva-preservation methods, respectively. This result is promising since the convenience and cost-effectiveness of mouthwash and simple collection can simplify the saliva sample preparation, which would greatly help clinical operators and lab workers.

Characterization of Oral Microbiota Following Chemotherapy in Patients With Hematopoietic Malignancies

Oral microbiota may be associated with serious local or systemic medical conditions resulting from chemotherapy. This study was conducted to evaluate the changes in the oral microbiota following the initiation of chemotherapy in patients with hematopoietic malignancies and to identify the characteristics of the oral microbiota associated with oral mucositis. Oral samples were collected from 57 patients with hematopoietic malignancies at 2 time points: before the start of chemotherapy and 8 to 20 days after the start of chemotherapy, when chemotherapy-induced oral mucositis often occurs, and 16S rRNA metagenomic analyses were performed. Comparative and linear discriminant analysis effect size (LEfSe) analyses were used to determine the characteristic bacterial groups before and after the initiation of chemotherapy and in those who developed oral mucositis. The alpha and beta diversities of oral microbiota before and after the initiation of chemotherapy differed significantly (operational taxonomic unit index, P < .001; Shannon's index, P < .001; unweighted UniFrac distances, P = .001; and weighted UniFrac distances, P = .001). The LEfSe analysis revealed a group of bacteria whose abundance differed significantly before and after the initiation of chemotherapy. In the group of patients who developed oral mucositis, a characteristic group of bacteria was identified before the start of chemotherapy. In conclusion, we characterized the oral microbiota associated with the initiation of chemotherapy in patients with hematopoietic malignancies. In addition, our findings suggest that oral microbiota composition before the start of chemotherapy may be associated with oral mucositis. The results of this study emphasize the importance of oral management focusing on the oral microbiota during chemotherapy in patients with hematologic malignancies.

Salivary Microbiota Associated with Peripheral Microvascular Endothelial Dysfunction

AIMS

Oral health is associated with atherosclerotic cardiovascular disease (ACVD). We previously identified the salivary microbiota characteristics of patients with ACVD. However, whether salivary microbiota is characteristic under impaired vascular endothelial function before ACVD onset remains unclear. Therefore, we aimed to evaluate the characteristics of salivary microbiota associated with peripheral microvascular endothelial dysfunction.

METHODS

We collected saliva samples from 172 community-dwelling elderly individuals without a history of ACVD and performed 16S rRNA metagenomic analysis. We assessed the peripheral microvascular endothelial function using reactive hyperemia index (RHI) and compared the salivary microbiota in the groups with normal (RHI ≥ 2.10), borderline, and abnormal (RHI ＜1.67) peripheral endothelial function. Furthermore, we applied machine learning techniques to evaluate whether salivary microbiota could discriminate between individuals with normal and abnormal endothelial function.

RESULTS

The number of operational taxonomic units (OTUs) was higher in the abnormal group than in the normal group (p=0.037), and differences were found in the overall salivary microbiota structure (unweighted UniFrac distances, p=0.038). The linear discriminant analysis (LDA) effect size (LEfSe) algorithm revealed several significantly differentially abundant bacterial genera between the two groups. An Extra Trees classifier model was built to discriminate between groups with normal and abnormal vascular endothelial function based on the microbial composition at the genus level (AUC=0.810).

CONCLUSIONS

The salivary microbiota in individuals with endothelial dysfunction was distinct from that in individuals with normal endothelial function, indicating that the salivary microbiota may be related to endothelial function.

Saliva sampling methods. Cariogenic streptococci count using two different methods of saliva collection in children

The aim of this study is to compare the efficacy of two methods for collecting saliva samples from infants under 2 years of age for cariogenic streptococci (CS) count. Two collection methods were applied in 11 infants. In Method (A), saliva samples were collected by swabbing the inner cheek mucosa and floor of the mouth in figure of eight motions with a sterile cotton swab until it was soaked. In method (B), saliva samples were collected by aspiration of 1 ml of saliva with a sterile plastic syringe on the floor of the mouth, after stimulation with glove. The samples were cultured in modified Gold's broth (MSMG), and on trypticase, yeast extract, sucrose, cystine and bacitracin culture medium (TYSCB). In method (A), the swab with the sample was unloaded in situ on TYSCB and placed in PBS medium for transport. Then, 100 μl of the eluate was seeded in MSMG. In method (B) 100 μl were seeded in TYSCB and 100 μl in MSMG. Both culture media were incubatedundercapnophilicconditions for 48 hours at 37 °C. Colony forming units (CFU/ml) were counted by calibrated operators (kappa = 0.75). The presence of cariogenic streptococci (CS) (Streptococcus mutans-Streptococcus sobrinus) was determined by qPCR in the samples collected by both methods. The CFU/ml counts in MSMG differed significantly between methods (p = 0.021). In TYSCB, the recovery of CFU/ml was higher in method (A), without significant difference (p = 0.705). The molecular technique detected presence of CS, with no difference between collection methods. Collecting saliva samples by swabbing proved more effective in terms of recovery of microorganisms, and did not affect the detection of presence of CS by molecular techniques.

Healthy Oral Lifestyle Behaviours Are Associated with Favourable Composition and Function of the Oral Microbiota

Modifiable lifestyle interventions may influence dental disease by shifting the composition of the oral microbiota. This study aimed to test whether lifestyle traits are associated with oral microbiota composition and function. Swedish volunteers, aged 16 to 79 years, completed a lifestyle traits questionnaire including lifestyle characteristics and oral health behaviours. Bacterial 16S rDNA amplicons were sequenced and classified into genera and species, using salivary DNA. Microbiota functions were predicted using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States and the KO Database of Molecular Functions by ortholog annotation. Tests for association used partial least squares and linear regression analysis with correction for multiple testing. The main analysis included 401 participants and 229 common bacterial species (found in ≥10% of the participants). The overall microbiota composition was strongly associated with questions "do you think caries is a disease?" and "do you use floss or a toothpick?". Enriched relative abundance of Actinomyces, Campylobacter, Dialister, Fusobacterium, Peptidophaga and Scardovia genera (all p < 0.05 after adjustment for multiple testing), and functional profiles showing enrichment of carbohydrate related functions, were found in participants who answered "no" to these questions. Socio-demographic traits and other oral hygiene behaviours were also associated. Healthier oral microbiota composition and predicted functions are found in those with favourable oral health behaviours. Modifiable risk factors could be prioritized for possible interventions.

Characterization of salivary microbiota in elderly patients with type 2 diabetes mellitus: a matched case-control study

OBJECTIVE

The importance of oral health in type 2 diabetes mellitus (T2DM) is widely recognized; however, oral microbiota characteristics associated with T2DM in the elderly population are not well-understood. This study was conducted to evaluate the characteristics of the salivary microbiota in elderly Japanese patients with T2DM.

METHODS

Saliva samples were collected from 42 elderly Japanese patients with T2DM and 42 age- and sex-matched subjects without T2DM (control). 16S ribosomal RNA metagenomic analysis and comparative analysis of both groups were performed. Random forest classification by machine learning was performed to discriminate between the salivary microbiota in the two groups.

RESULTS

There were significant differences in the overall salivary microbiota structure between the T2DM and control groups (beta diversity; unweighted UniFrac distances, p = 0.001; weighted UniFrac distances, p = 0.001). The phylum Firmicutes was abundant in patients with T2DM, whereas the phylum Bacteroidetes was abundant in controls. The T2DM prediction model by random forest based on salivary microbiota data was verified with a high predictive potential in five cross-validation tests (area under the curve (AUC) = 0.938 (95% CI, 0.824-1.000)).

CONCLUSION

Characterization revealed that the salivary microbiota profile of the elderly patients with T2DM is significantly distinct from that of the controls.

CLINICAL RELEVANCE

These data indicate the necessity of oral health management based on the characteristics of the salivary microbiota in elderly patients with T2DM. Our findings will contribute to future research on the development of new diagnostic and therapeutic methods for this purpose.

Characterization of Salivary Microbiota in Patients with Atherosclerotic Cardiovascular Disease: A Case-Control Study

Aims: Oral bacteria have been reported to be associated with the pathogenesis of atherosclerosis; however, the relationship between the oral microbiota and atherosclerosis remains unclear. The present study aimed to investigate whether or not salivary microbiota of patients with atherosclerotic cardiovascular disease (ACVD) differs from that of subjects without ACVD, and to characterize the salivary microbiota of patients with ACVD.

Methods: This study included 43 patients with ACVD and 86 age- and sex-matched non-ACVD individuals. 16S rRNA metagenomic analysis were performed using DNA isolated from the saliva samples of the participants. To select unique operational taxonomic unit (OTU) sets of ACVD, we conducted the random forest algorithm in machine learning, followed by confirmation via 10-fold cross-validation

Results: There was no difference in richness or evenness between the ACVD and non-ACVD groups (alpha diversity; observed OTU index, p =0.503; Shannon’s index, p =0.478). However, significant differences were found in the overall salivary microbiota structure (beta diversity; unweighted UniFrac distances, p =0.001; weighted UniFrac distances, p =0.001). The Actinobacteria phylum was highly abundant in patients with ACVD, while the Bacteroidetes phylum was less abundant. The random forest classifier identified 43 OTUs as an optimal marker set of ACVD. In a 10-fold cross validation using the validation data, an area under the curve (AUC) of 0.933 (95% CI, 0.855–1.000) was obtained.

Conclusions: The salivary microbiota in patients with ACVD was distinct from that of non-ACVD individuals, indicating that the salivary microbiota may be related to ACVD.