Functional signatures of oral dysbiosis during periodontitis progression revealed by microbial metatranscriptome analysis

Abstract

Background: Periodontitis is a polymicrobial biofilm-induced inflammatory disease that affects 743 million people worldwide. The current model to explain periodontitis progression proposes that changes in the relative abundance of members of the oral microbiome lead to dysbiosis in the host-microbiome crosstalk and then to inflammation and bone loss. Using combined metagenome/metatranscriptome analysis of the subgingival microbiome in progressing and non-progressing sites, we have characterized the distinct molecular signatures of periodontitis progression.

Methods: Metatranscriptome analysis was conducted on samples from subgingival biofilms from progressing and stable sites from periodontitis patients. Community-wide expression profiles were obtained using Next Generation Sequencing (Illumina). Sequences were aligned using 'bowtie2' against a constructed oral microbiome database. Differential expression analysis was performed using the non-parametric algorithm implemented on the R package 'NOISeqBio'. We summarized global functional activities of the oral microbial community by set enrichment analysis based on the Gene Ontology (GO) orthology.

Results: Gene ontology enrichment analysis showed an over-representation in the baseline of active sites of terms related to cell motility, lipid A and peptidoglycan biosynthesis, and transport of iron, potassium, and amino acids. Periodontal pathogens (Tannerella forsythia and Porphyromonas gingivalis) upregulated different TonB-dependent receptors, peptidases, proteases, aerotolerance genes, iron transport genes, hemolysins, and CRISPR-associated genes. Surprisingly, organisms that have not been usually associated with the disease (Streptococcus oralis, Streptococcus mutans, Streptococcus intermedius, Streptococcus mitis, Veillonella parvula, and Pseudomonas fluorenscens) were highly active transcribing putative virulence factors. We detected patterns of activities associated with progression of clinical traits. Among those we found that the profiles of expression of cobalamin biosynthesis, proteolysis, and potassium transport were associated with the evolution towards disease.

Conclusions: We identified metabolic changes in the microbial community associated with the initial stages of dysbiosis. Regardless of the overall composition of the community, certain metabolic signatures are consistent with disease progression. Our results suggest that the whole community, and not just a handful of oral pathogens, is responsible for an increase in virulence that leads to progression.

Trial registration: NCT01489839, 6 December 2011.

Figure 1

Statistical differences in metagenome composition. Metagenome hit counts were first normalized using GASIC. Normalized counts were then analyzed using LEfSe with default parameters, to identify significant differences at species level between the microbial communities compared. (A) Comparison baseline samples from active sites vs. progressing samples from active sites (that is, samples collect at the visit when an increase in CAL ≥2 mm was detected). (B) Comparison baseline samples from stable sites vs. follow-up samples from stable sites (that is, collected 2 months after baseline). (C) Comparison baseline samples from active sites vs. baseline samples from stable sites.

Figure 2

Statistical differences in metatranscriptome normalized composition. Metatranscriptome hits were first normalized by the relative frequency of species obtained in the metagenomic analysis using GASIC. Normalized counts were then analyzed using LEfSe with default parameters to identify significant differences in activity at the species level. (A) Comparison baseline samples from active sites vs. progressing samples from active sites (that is, samples collect at the visit when an increase in CAL ≥2 mm was detected). (B) Comparison baseline samples from stable sites vs. follow-up samples from stable sites (that is, collected 2 months after baseline). (C) Comparison baseline samples from active sites vs. baseline samples from stable sites.

Figure 3

GO enrichment analysis comparing baseline in active sites to progression profiles in the same sites. Enriched terms obtained using GOseq were summarized and visualized as a scatter plot using REVIGO. (A) Summarized GO terms related to biological processes at baseline. (B) Summarized GO terms related to biological processes in progression. Circle size is proportional to the frequency of the GO term, while color indicates the log10 P value (red higher, blue lower).

Figure 4

GO enrichment analysis comparison of baselines from progressing and non-progressing sites. Enriched terms obtained using goseq were summarized and visualized as a scatter plot using REVIGO. (A) Summarized GO terms related to biological processes in baselines of progressing sites. (B) Summarized GO terms related to biological processes in baselines of non-progressing sites. Circle size is proportional to the frequency of the GO term, while color indicates the log10 P value (red higher, blue lower).

Figure 5

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 Database (VFDB) as described in the Methods section. Numbers in the graph refer to the absolute number of hits for the different species for the putative virulence factors identified. The numbers correspond to the total hits to different strains corresponding to the species in our database and they could be redundant. In red are members of the red complex. In orange are members of the orange complex. (A) Comparison of baseline to progressing. (B) Comparison of baseline non-progressing to baseline progressing.

Figure 6

GO assignment terms for genes with relevance networks associations with clinical parameters. Relevance networks were obtained using the first three sPLS dimensions. We used a threshold of r = 0.95 to select for association between progression of clinical parameters (ΔPD increase in pocket depth, ΔCAL increase in clinical attachment level) and gene expression profiles. GO terms were assigned to genes whose pattern of expression was significantly associated with the clinical parameters measured. GO terms were summarized using REVIGO. (A) GO terms associated with ΔPD. (B) GO terms associated with ΔCAL.