Metatranscriptome of the Oral Microbiome in Health and Disease

Abstract

The last few decades have witnessed an increasing interest in studying the human microbiome and its role in health and disease. The focus of those studies was mainly the characterization of changes in the composition of the microbial communities under different conditions. As a result of those studies, we now know that imbalance in the composition of the microbiome, also referred to as microbial dysbiosis, is directly linked to developing certain conditions. Dysbiosis of the oral microbiome is a prime example of how this imbalance leads to disease in the case of periodontal disease. However, there is considerable overlap in the phylogenetic profiles of microbial communities associated with active and inactive lesions, suggesting that the difference in periodontal status of those sites may not be explained solely by differences in the subgingival microbial composition. These findings suggest that differences in functional activities may be the essential elements that define the dysbiotic process. Researchers have recently begun to study gene expression of the oral microbiome in situ with the goal of identifying changes in functional activities that could explain the transition from health to disease. These initial results suggest that, rather than a specific composition, a better understanding of oral dysbiosis can be obtained from the study of functional activities of the microbial community. In this review, we give a summary of these initial studies, which have opened a new door to our understanding of the dynamics of the oral community during the dysbiotic process in the oral cavity.

Keywords: caries; dysbiosis; metagenome; microbiota; periodontitis; transcriptome.

Figure 1.

Laboratory work flow for metatranscriptome analysis. A general overview of the different kinds of analyses that can be performed to study the oral microbiome metatranscriptome.

Figure 2.

Comparison of metagenome and metatranscriptome in the oral and gut microbiome. (A) Metagenome and metatranscriptome analysis of the gut microbiome. Comparison of between-sample diversity for taxonomic and functional profiles with the Bray-Curtis metric (adapted with permissions from Franzosa et al. 2014). (B) Microbiota composition in the human oral biofilm from a 24-h dental plaque sample. Relative abundance of bacterial genera from metagenomic data differs from that obtained from metatranscriptomic data (adapted from Benítez-Páez et al. 2014). (C) Rank distribution of statistically significant relative increase in number of hits for the metagenome and metatranscriptome results in samples from healthy patients and patients with severe periodontitis. In green, species with statistical differences in both metagenome and metatranscriptome. In blue, species with statistical differences in metagenomic counts. In red, species with statistical differences in metatranscriptome counts (adapted from Duran-Pinedo et al. 2014).

Figure 3.

Overview of a general bioinformatic work flow for metatranscriptome analysis. Common steps in the bioinformatic analysis of microbial transcriptomes: quality control of the sequences obtained by next-generation sequencing, alignment against the genomes of interest, phylogenetic assignment of the transcripts and metagenome, differential expression analysis and gene set or pathway enrichment analysis based on the obtained differentially expressed genes. In purple are software packages widely used for metatranscriptome analysis. HMP, Human Microbiome Project; HOMD, Human Oral Microbiome Database; LEfSe, linear discriminant analysis effect size.

Figure 4.

Ranked species by the number of upregulated putative virulence factors in the metatranscriptome. Putative virulence factors were identified by alignment of the protein sequences from the different genomes against the Virulence Factors of Pathogenic Bacteria Database. The numbers in the graph are the absolute number of hits for the different species of the upregulated putative virulence factors identified. In red are the members of the “red complex.” In orange are members of the “orange complex.” (A) Comparison of health vs. severe periodontitis. (B) Comparison of baseline vs. progressing sites in periodontitis progression (adapted from Yost et al. 2015). (C) Comparison of baseline nonprogressing vs. baseline progressing in periodontitis progression. (Adapted from Duran-Pinedo et al. 2014 and Yost et al. 2015.)

Figure 5.

Gene Ontology (GO) enrichment analysis in severe periodontitis and periodontitis progression. Enriched terms obtained with goseq were summarized and visualized as a scatter plot via REVIGO. Summarized GO terms related to biological processes in (A) severe periodontitis (adapted from Duran-Pinedo et al. 2014) and (B) health (adapted from Duran-Pinedo et al. 2014). GO enrichment analysis comparison of baselines from progressing and nonprogressing sites: Summarized GO terms related to biological processes in baselines of (C) progressing and (D) nonprogressing sites (adapted from Yost et al. 2015). In red are activities that have been associated with pathogenesis in periodontitis. Circle size is proportional to the frequency of the GO term, whereas color indicates the log10 P value (red higher, blue lower).