Meta‐analysis of caries microbiome studies can improve upon disease prediction outcomes. Issue 12 (20th September 2022)

• Record Type: Journal Article
• Authors: Butcher, Mark C.; Short, Bryn; Veena, Chandra Lekha Ramalingam; Bradshaw, Dave; Pratten, Jonathan R.; McLean, William; Shaban, Suror Mohamad Ahmad; Ramage, Gordon; Delaney, Christopher
• Other Names: Bjarnsholt Thomas guestEditor.; Ralfkiaer Ulrik guestEditor.; Malone Matthew guestEditor.
• Is Part Of: Apmis. Volume 130:Issue 12(2022)
• Page Start: 763
• Page End: 777
• Publication Date: 2022-09-20
• Subjects: 16S -- bioinformatics -- dental caries -- Microbiome -- sequencing -- tooth decay; Pathology -- Periodicals; Microbiology -- Periodicals; Immunology -- Periodicals; 572
• DOI: 10.1111/apm.13272
• Languages: English
• ISSNs: 0903-4641

Abstract

Abstract : As one of the most prevalent infective diseases worldwide, it is crucial that we not only know the constituents of the oral microbiome in dental caries but also understand its functionality. Herein, we present a reproducible meta‐analysis to effectively report the key components and the associated functional signature of the oral microbiome in dental caries. Publicly available sequencing data were downloaded from online repositories and subjected to a standardized analysis pipeline before analysis. Meta‐analyses identified significant differences in alpha and beta diversities of carious microbiomes when compared to healthy ones. Additionally, machine learning and receiver operator characteristic analysis showed an ability to discriminate between healthy and disease microbiomes. We identified from importance values, as derived from random forest analyses, a group of genera, notably containing Selenomonas, Aggregatibacter, Actinomyces and Treponema, which can be predictive of dental caries. Finally, we propose the most appropriate study design for investigating the microbiome of dental caries by synthesizing the studies, which had the most accurate differentiation based on random forest modelling. In conclusion, we have developed a non‐biased, reproducible pipeline, which can be applied to microbiome meta‐analyses of multiple diseases, but importantly we have derived from our meta‐analysis a key group of organisms that can be used to identify individuals at risk of developing dental caries based on oral microbiome inhabitants.