A new approach to describe the taxonomic structure of microbiome and its application to assess the relationship between microbial niches.

BACKGROUND: Data from microbiomes from multiple niches is often collected, but methods to analyse these often ignore associations between niches. One interesting case is that of the oral microbiome. Its composition is receiving increasing attention due to reports on its associations with general health. While the oral cavity includes different niches, multi-niche microbiome data analysis is conducted using a single niche at a time and, therefore, ignores other niches that could act as confounding variables. Understanding the interaction between niches would assist interpretation of the results, and help improve our understanding of multi-niche microbiomes. METHODS: In this study, we used a machine learning technique called latent Dirichlet allocation (LDA) on two microbiome datasets consisting of several niches. LDA was used on both individual niches and all niches simultaneously. On individual niches, LDA was used to decompose each niche into bacterial sub-communities unveiling their taxonomic structure. These sub-communities were then used to assess the relationship between microbial niches using the global test. On all niches simultaneously, LDA allowed us to extract meaningful microbial patterns. Sets of co-occurring operational taxonomic units (OTUs) comprising those patterns were then used to predict the original location of each sample. RESULTS: Our approach showed that the per-niche sub-communities displayed a strong association between supragingival plaque and saliva, as well as between the anterior and posterior tongue. In addition, the LDA-derived microbial signatures were able to predict the original sample niche illustrating the meaningfulness of our sub-communities. For the multi-niche oral microbiome dataset we had an overall accuracy of 76%, and per-niche sensitivity of up to 83%. Finally, for a second multi-niche microbiome dataset from the entire body, microbial niches from the oral cavity displayed stronger associations to each other than with those from other parts of the body, such as niches within the vagina and the skin. CONCLUSION: Our LDA-based approach produces sets of co-occurring taxa that can describe niche composition. LDA-derived microbial signatures can also be instrumental in summarizing microbiome data, for both descriptions as well as prediction.

The microbiome of dental and peri-implant subgingival plaque during peri-implant mucositis therapy: A randomized clinical trial.

AIM: To assess the microbial effects of mechanical debridement in conjunction with a mouthrinse on sites with peri-implant mucositis and gingivitis. MATERIALS AND METHODS: Eighty-nine patients with peri-implant mucositis were included in a double-blinded, randomized, placebo-controlled trial with mechanical debridement and 1-month use of either delmopinol, chlorhexidine (CHX), or a placebo mouthrinse. Submucosal and subgingival plaque samples of implants and teeth were collected at baseline and after 1 and 3 months, processed for 16S V4 rRNA gene amplicon sequencing, and analysed bioinformatically. RESULTS: The sites with peri-implant mucositis presented with a less diverse and less anaerobic microbiome. Exposure to delmopinol or CHX, but not to the placebo mouthrinse resulted in microbial changes after 1 month. The healthy sites around the teeth harboured a more diverse and more anaerobe-rich microbiome than the healthy sites around the implants. CONCLUSIONS: Peri-implant sites with mucositis harbour ecologically less complex and less anaerobic biofilms with lower biomass than patient-matched dental sites with gingivitis while eliciting an equal inflammatory response. Adjunctive antimicrobial therapy in addition to mechanical debridement does affect both dental and peri-implant biofilm composition in the short term, resulting in a less dysbiotic subgingival biofilm.

Optimizing the quality of clinical studies on oral microbiome: A practical guide for planning, performing, and reporting.

With this review, we aim to increase the quality standards for clinical studies with microbiome as an output parameter. We critically address the existing body of evidence for good quality practices in oral microbiome studies based on 16S rRNA gene amplicon sequencing. First, we discuss the usefulness of microbiome profile analyses. Is a microbiome study actually the best approach for answering the research question? This is followed by addressing the criteria for the most appropriate study design, sample size, and the necessary data (study metadata) that should be collected. Next, we evaluate the available evidence for best practices in sample collection, transport, storage, and DNA isolation. Finally, an overview of possible sequencing options (eg, 16S rRNA gene hypervariable regions, sequencing platforms), processing and data interpretation approaches, as well as requirements for meaningful data storage, sharing, and reporting are provided.

Salivary microbiome profiles of oral cancer patients analyzed before and after treatment.

BACKGROUND: Treating oral squamous cell carcinoma (OSCC) introduces new ecological environments in the oral cavity. This is expected to cause changes in the oral microbiome. The purpose of this study was to gain new information on the salivary microbiome of OSCC patients in order to improve the aftercare of OSCC patients. The aims of this study were to investigate possible changes in the salivary microbiome profiles of OSCC patients before and after cancer treatment and to compare these changes with the profiles of healthy controls. PATIENTS AND METHODS: Paraffin-stimulated whole saliva samples were collected, and the salivary flow rate was measured from 99 OSCC patients prior to surgical resection of the tumor and other adjuvant therapy. After treatment, 28 OSCC patients were re-examined with a mean follow-up time of 48 months. In addition, 101 healthy controls were examined and sampled. After DNA extraction and purification, the V4 hypervariable region of the 16S rRNA gene was amplified and sequenced using Illumina MiSeq. The merged read pairs were denoised using UNOISE3, mapped to zero-radius operational taxonomic units (zOTUs), and the representative zOTU sequences were assigned a taxonomy using HOMD. Descriptive statistics were used to study the differences in the microbial profiles of OSCC patients before and after treatment and in comparison to healthy controls. RESULTS: At baseline, the OSCC patients showed a higher relative abundance of zOTUs classified as Streptococcus anginosus, Abiotrophia defectiva, and Fusobacterium nucleatum. The microbial profiles differed significantly between OSCC patients and healthy controls (F = 5.9, p < 0.001). Alpha diversity of the salivary microbiome of OSCC patients was decreased at the follow-up, and the microbial profiles differed significantly from the pre-treatment (p < 0.001) and from that of healthy controls (p < 0.001). CONCLUSIONS: OSCC patients' salivary microbiome profile had a higher abundance of potentially pathogenic bacteria compared to healthy controls. Treatment of the OSCC caused a significant decrease in alpha diversity and increase in variability of the salivary microbiome, which was still evident after several years of follow-up. OSCC patients may benefit from preventive measures, such as the use of pre- or probiotics, salivary substitutes, or dietary counseling. Video Abstract.