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Modeling Normal and Dysbiotic Subgingival Microbiomes: Effect of Nutrients.

ABSTRACT: Screening for microbiome modulators requires availability of a high-throughput in vitro model that replicates subgingival dysbiosis and normobiosis, with a tool to measure microbial dysbiosis. Here, we tested various formulations to grow health- and periodontitis-associated subgingival microbiomes in parallel, and we describe a new subgingival dysbiosis index. Subgingival plaque samples pooled from 5 healthy subjects and, separately, 5 subjects with periodontitis were used to inoculate a Calgary Biofilm Device containing saliva-conditioned, hydroxyapatite-coated pegs. Microbiomes were grown for 7 d on either nutrient-rich media-including a modification of SHI medium, brain-heart infusion (BHI) supplemented with hemin and vitamin K, and a blend of SHI and BHI, each at 3 sucrose concentrations (0%, 0.05% and 0.1%)-or nutrient-limited media (saliva with 5%, 10%, or 20% inactivated human serum). The microbiomes were assessed for biomass, viability, and 16S rRNA profiles. In addition to richness and diversity, a dysbiosis index was calculated as the ratio of the sum of relative abundances of disease-associated species to that of health-associated species. The supplemented BHI and blend of SHI and BHI resulted in the highest biomass, whereas saliva-serum maximized viability. Distinct groups of bacteria were enriched in the different media. Regardless of medium type, the periodontitis-derived microbiomes showed higher species richness and alpha diversity and clustered with their inoculum separate from the health-derived microbiomes. Microbiomes grown in saliva-serum showed the highest species richness and the highest similarity to the clinical inocula in both health and disease. However, inclusion of serum reduced alpha diversity and increased dysbiosis in healthy microbiomes in a dose-dependent manner, mainly due to overenrichment of Porphyromonas species. The modification of SHI stood second in terms of species richness and diversity but resulted in low biomass and viability and significantly worsened dysbiosis in the periodontitis-derived microbiomes. Overall, saliva with 5% human serum was optimal for replicating subgingival microbiomes from health and disease.

SUBMITTER: Baraniya D

PROVIDER: S-EPMC7243421 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundGut microbiome plays a crucial role in the formation and progression of colorectal cancer (CRC). To better identify the underlying gene-level pathogenic mechanisms of microbiome-associated CRC, we applied our newly developed Reverse Microbiomics (RM) to predict potential pathogenic factors using the data of microbiomes in CRC patients.ResultsOur literature search first identified 40 bacterial species enriched and 23 species depleted in the guts of CRC patients. These bacteria were systematically modeled and analyzed using the NCBI Taxonomy ontology. Ten species, including 6 enriched species (e.g., Bacteroides fragilis, Fusobacterium nucleatum and Streptococcus equinus) and 4 depleted species (e.g., Bacteroides uniformis and Streptococcus thermophilus) were chosen for follow-up comparative genomics analysis. Vaxign was used to comparatively analyze 47 genome sequences of these ten species. In total 18 autoantigens were predicted to contribute to CRC formation, six of which were reported with experimental evidence to be correlated with drug resistance and/or cell invasiveness of CRC. Interestingly, four human homology proteins (EDK89078.1, EDK87700.1, EDK89777.1, and EDK89145.1) are conserved among all enriched strains. Furthermore, we predicted 76 potential virulence factors without homology to human proteins, including two riboflavin synthase proteins, three ATP-binding cassettes (ABC) transporter protein family proteins, and 12 outer membrane proteins (OMPs). Riboflavin synthase is present in all the enriched strains but not in depleted species. The critical role of riboflavin synthase in CRC development was further identified from its hub role in our STRING-based protein-protein interaction (PPI) network analysis and from the finding of the riboflavin metabolism as the most significantly enriched pathway in our KEGG pathway analysis. A novel model of the CRC pathogenesis involving riboflavin synthase and other related proteins including TpiA and GrxC was further proposed.ConclusionsThe RM strategy was used to predict 18 autoantigens and 76 potential virulence factors from CRC-associated microbiome data. In addition to many of these autoantigens and virulence factors experimentally verified as reported in the literature, our study predicted many new pathogenetic factors and developed a new model of CRC pathogenesis involving the riboflavin synthase from the enriched colorectal bacteria and other associated proteins.

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Project description:Modeling subgingival microbiome in health and disease is key to identifying the drivers of dysbiosis and to studying microbiome modulation. Here, we optimize growth conditions of our previously described in vitro subgingival microbiome model. Subgingival plaque samples from healthy and periodontitis subjects were used as inocula to grow normobiotic and dysbiotic microbiomes in MBEC assay plates. Saliva supplemented with 1%, 2%, 3.5%, or 5% (v/v) heat-inactivated human serum was used as a growth medium under shaking or non-shaking conditions. The microbiomes were harvested at 4, 7, 10 or 13 days of growth (384 microbiomes in total) and analyzed by 16S rRNA gene sequencing. Biomass significantly increased as a function of serum concentration and incubation period. Independent of growth conditions, the health- and periodontitis-derived microbiomes clustered separately with their respective inocula. Species richness/diversity slightly increased with time but was adversely affected by higher serum concentrations especially in the periodontitis-derived microbiomes. Microbial dysbiosis increased with time and serum concentration. Porphyromonas and Alloprevotella were substantially enriched in higher serum concentrations at the expense of Streptococcus, Fusobacterium and Prevotella. An increase in Porphyromonas, Bacteroides and Mogibacterium accompanied by a decrease in Prevotella, Catonella, and Gemella were the most prominent changes over time. Shaking had only minor effects. Overall, the health-derived microbiomes grown for 4 days in 1% serum, and periodontitis-derived microbiomes grown for 7 days in 3.5%-5% serum were the most similar to the respective inocula. In conclusion, normobiotic and dysbiostic subgingival microbiomes can be grown reproducibly in saliva supplemented with serum, but time and serum concentration need to be adjusted differently for the health and periodontitis-derived microbiomes to maximize similarity to in vivo inocula. The optimized model could be used to identify drivers of dysbiosis, and to evaluate interventions such as microbiome modulators.

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Modeling Normal and Dysbiotic Subgingival Microbiomes: Effect of Nutrients.

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