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

Correlation Network Analysis Applied to Complex Biofilm Communities

The complexity of the human microbiome makes it difficult to reveal organizational principles of the community and even more challenging to generate testable hypotheses. It has been suggested that in the gut microbiome species such as Bacteroides thetaiotaomicron are keystone in maintaining the stability and functional adaptability of the microbial community. In this study, we investigate the interspecies associations in a complex microbial biofilm applying systems biology principles. Using correlation network analysis we identified bacterial modules that represent important microbial associations within the oral community. We used dental plaque as a model community because of its high diversity and the well known species-species interactions that are common in the oral biofilm. We analyzed samples from healthy individuals as well as from patients with periodontitis, a polymicrobial disease. Using results obtained by checkerboard hybridization on cultivable bacteria we identified modules that correlated well with microbial complexes previously described. Furthermore, we extended our analysis using the Human Oral Microbe Identification Microarray (HOMIM), which includes a large number of bacterial species, among them uncultivated organisms present in the mouth. Two distinct microbial communities appeared in healthy individuals while there was one major type in disease. Bacterial modules in all communities did not overlap, indicating that bacteria were able to effectively re-associate with new partners depending on the environmental conditions. We then identified hubs that could act as keystone species in the bacterial modules. Based on those results we then cultured a not-yet-cultivated microorganism, Tannerella sp. OT286 (clone BU063). After two rounds of enrichment by a selected helper (Prevotella oris OT311) we obtained colonies of Tannerella sp. OT286 growing on blood agar plates. This system-level approach would open the possibility of manipulating microbial communities in a targeted fashion as well as associating certain bacterial modules to clinical traits (e.g.: obesity, Crohn's disease, periodontal disease, etc)

Potassium is a key signal in host-microbiome dysbiosis in periodontitis

<div><p>Dysbiosis, or the imbalance in the structural and/or functional properties of the microbiome, is at the origin of important infectious inflammatory diseases such as inflammatory bowel disease (IBD) and periodontal disease. Periodontitis is a polymicrobial inflammatory disease that affects a large proportion of the world's population and has been associated with a wide variety of systemic health conditions, such as diabetes, cardiovascular and respiratory diseases. Dysbiosis has been identified as a key element in the development of the disease. However, the precise mechanisms and environmental signals that lead to the initiation of dysbiosis in the human microbiome are largely unknown. In a series of previous <i>in vivo</i> studies using metatranscriptomic analysis of periodontitis and its progression we identified several functional signatures that were highly associated with the disease. Among them, potassium ion transport appeared to be key in the process of pathogenesis. To confirm its importance we performed a series of <i>in vitro</i> experiments, in which we demonstrated that potassium levels a increased the virulence of the oral community as a whole and at the same time altering the immune response of gingival epithelium, increasing the production of TNF-α and reducing the expression of IL-6 and the antimicrobial peptide human β-defensin 3 (hBD-3). These results indicate that levels of potassium in the periodontal pocket could be an important element in of dysbiosis in the oral microbiome. They are a starting point for the identification of key environmental signals that modify the behavior of the oral microbiome from a symbiotic community to a dysbiotic one.</p></div

Genetic and biochemical characterization of the Na+/H+ antiporters of Pseudomonas aeruginosa

Pseudomonas aeruginosa has four N

Effect of potassium concentration on hemolytic activity of different bacterial strains.

<p>A) Hemolytic activity of supernatants from <i>P</i>. <i>nigrescens</i> ATCC 33563 and <i>S</i>. <i>mitis</i> NCTC 1226, as the percentage of lysis of horse erythrocytes with respect to a positive control (100% of activity). Results are from 4 biological replicates for each concentration. B) Hemolytic activity on agar plates with different concentrations of K⁺ added of <i>P</i>. <i>nigrescens</i> ATCC 33563 after 48 hours of incubation. C) Hemolytic activity on agar plates with different concentrations of K⁺ added of <i>P</i>. <i>nigrescens</i> ATCC 33563 after 6 days of incubation.</p

Effect of potassium concentration on gingival cytokine expression.

<p>A three-dimensional multilayered gingival tissue model with cornified apical layers (EpiGingival, MatTek Corporation) was used to assess the effect of different concentrations of K⁺ and bacteria on the profiles of expression of different cytokines. A) Heatmap of cytokine expression measured by Luminex under different K⁺ concentrations and presence or absence of bacteria from dental plaque. B) Box plot showing the values of observed concentrations in the media of the different cytokines assayed.</p

GO enrichment analysis comparing plaque response to the presence and absence of added ion potassium to the medium.

<p>Enriched terms obtained using GOseq were summarized and visualized as a scatter plot using REVIGO. Only GO terms with FDR adjusted p-value < 0.05 in the 'GOseq' analysis were used. A) Summarized GO terms related to biological processes after addition of K⁺. B) Summarized GO terms related to biological processes with no K⁺ added. Circle size is proportional to the frequency of the GO terms, color indicates the log10 p-value (red higher, blue lower). Distance between circles represent GO terms' semantic similarities. Each of the circles represent a GO term, which depending on the similarity in the terms included in them they will be closer or more distant in the graph.</p

Statistical differences in metatranscriptome composition after addition of ion potassium.

<p>Phylogenetic assignment of the mRNA hits was performed using Kraken [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006457#ppat.1006457.ref017" target="_blank">17</a>] and were analyzed using LEfSe with default parameters (p-value < 0.05 for Kruskal-Wallis rank sum test on classes and pairwise Wilcoxon test between subclasses of different classes) to identify significant differences in transcription activity at species level between the microbial communities compared. A) Cladogram showing the taxonomic distribution of lineages whose expression levels had a LDA value of 3.0 or higher as determined by LEfSe [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006457#ppat.1006457.ref018" target="_blank">18</a>]. B) Histogram of LDA scores for differentially active taxa. In red are taxa whose activity, as determined by number of transcripts, was increased in the presence of K⁺. In green are species whose activity was higher in the absence of K⁺.</p