Comparative evaluation of peptidome and microbiota in different types of saliva samples

The salivary microbiome as a diagnostic biomarker of periodontitis: a 16S multi-batch study before and after the removal of batch effects

Introduction

Microbiome-based clinical applications that improve diagnosis related to oral health are of great interest to precision dentistry. Predictive studies on the salivary microbiome are scarce and of low methodological quality (low sample sizes, lack of biological heterogeneity, and absence of a validation process). None of them evaluates the impact of confounding factors as batch effects (BEs). This is the first 16S multi-batch study to analyze the salivary microbiome at the amplicon sequence variant (ASV) level in terms of differential abundance and machine learning models. This is done in periodontally healthy and periodontitis patients before and after removing BEs.

Methods

Saliva was collected from 124 patients (50 healthy, 74 periodontitis) in our setting. Sequencing of the V3-V4 16S rRNA gene region was performed in Illumina MiSeq. In parallel, searches were conducted on four databases to identify previous Illumina V3-V4 sequencing studies on the salivary microbiome. Investigations that met predefined criteria were included in the analysis, and the own and external sequences were processed using the same bioinformatics protocol. The statistical analysis was performed in the R-Bioconductor environment.

Results

The elimination of BEs reduced the number of ASVs with differential abundance between the groups by approximately one-third (Before=265; After=190). Before removing BEs, the model constructed using all study samples (796) comprised 16 ASVs (0.16%) and had an area under the curve (AUC) of 0.944, sensitivity of 90.73%, and specificity of 87.16%. The model built using two-thirds of the specimens (training=531) comprised 35 ASVs (0.36%) and had an AUC of 0.955, sensitivity of 86.54%, and specificity of 90.06% after being validated in the remaining one-third (test=265). After removing BEs, the models required more ASVs (all samples=200-2.03%; training=100-1.01%) to obtain slightly lower AUC (all=0.935; test=0.947), lower sensitivity (all=81.79%; test=78.85%), and similar specificity (all=91.51%; test=90.68%).

Conclusions

The removal of BEs controls false positive ASVs in the differential abundance analysis. However, their elimination implies a significantly larger number of predictor taxa to achieve optimal performance, creating less robust classifiers. As all the provided models can accurately discriminate health from periodontitis, implying good/excellent sensitivities/specificities, the salivary microbiome demonstrates potential clinical applicability as a precision diagnostic tool for periodontitis.

Saliva assay: a call for methodological standardization

The oral cavity provides an ideal environment for microorganisms, including bacteria, viruses, and fungi, to flourish. Increasing attention has been focused on the connection between the oral microbiome and both oral and systemic diseases, spurring active research into the collection and analysis of specimens for healthcare purposes. Among the various methods for analyzing the oral microbiome, saliva analysis is especially prominent. Saliva samples, which can be collected non-invasively, provide information on the systemic health and oral microbiome composition of an individual. This review was performed to evaluate the current state of the relevant research through an examination of the literature and to suggest an appropriate assay method for investigating the oral microbiome. We analyzed articles published in English in SCI(E) journals after January 1, 2000, ultimately selecting 53 articles for review. Articles were identified through keyword searches in the PubMed, Embase, Cochrane, Web of Science, and CINAHL databases. Three experienced researchers conducted full-text assessments following title and abstract screening to select appropriate papers. Subsequently, they organized and analyzed the desired data. Our review revealed that most studies utilized unstimulated saliva samples for oral microbiome analysis. Of the 53 studies examined, 29 identified relationships between the oral microbiome and various diseases, such as oral disease, Behçet disease, cancer, and oral lichen planus. However, the studies employed diverse methods of collection and analysis, which compromised the reliability and accuracy of the findings. To address the limitations caused by methodological inconsistencies, a standardized saliva assay should be established.