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Proteomics, Lipidomics, Metabolomics, and 16S DNA Sequencing of Dental Plaque From Patients With Diabetes and Periodontal Disease.

ABSTRACT: Oral microbiome influences human health, specifically prediabetes and type 2 diabetes (Pre-DM/DM) and periodontal diseases (PDs), through complex microbial interactions. To explore these relations, we performed 16S rDNA sequencing, metabolomics, lipidomics, and proteomics analyses on supragingival dental plaque collected from individuals with Pre-DM/DM (n = 39), Pre-DM/DM and PD (n = 37), PD alone (n = 11), or neither (n = 10). We identified on average 2790 operational taxonomic units and 2025 microbial and host proteins per sample and quantified 110 metabolites and 415 lipids. Plaque samples from Pre-DM/DM patients contained higher abundance of Fusobacterium and Tannerella than plaques from metabolically healthy patients. Phosphatidylcholines, plasmenyl phosphatidylcholines, ceramides containing non-OH fatty acids, and host proteins related to actin filament rearrangement were elevated in plaques from PD versus non-PD samples. Cross-omic correlation analysis enabled the detection of a strong association between Lautropia and monomethyl phosphatidylethanolamine (PE-NMe), which is striking because synthesis of PE-NMe is uncommon in oral bacteria. Lipidomics analysis of in vitro cultures of Lautropia mirabilis confirmed the synthesis of PE-NMe by the bacteria. This comprehensive analysis revealed a novel microbial metabolic pathway and significant associations of host-derived proteins with PD.

SUBMITTER: Overmyer KA

PROVIDER: S-EPMC8426274 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Supragingival dental plaque samples were collected from 40 Swedish adolescents, including 20 with caries lesions (CAR) and 20 caries-free (CF). Fresh plaque samples were subjected to an ex vivo acid tolerance (AT) test where the proportion of bacteria resistant to an acid shock was evaluated through confocal microscopy and live/dead staining, and the metabolites produced were quantified by 1H Nuclear Magnetic Resonance (1H NMR). In addition, DNA was extracted and the 16S rRNA gene was sequenced by Illumina sequencing, in order to characterize bacterial composition in the same samples. There were no significant differences in AT scores between CAR and CF individuals. However, 7 out of the 10 individuals with highest AT scores belonged to the CAR group. Regarding bacterial composition, Abiotrophia, Prevotella and Veillonella were found at significantly higher levels in CAR individuals (p=0.0085, 0.026 and 0.04 respectively) and Rothia and Corynebacterium at significantly higher levels in CF individuals (p=0.026 and 0.003). The caries pathogen Streptococcus mutans was found at low frequencies and was absent in 60% of CAR individuals. Random-forest predictive models indicate that at least 4 bacterial species or 9 genera are needed to distinguish CAR from CF adolescents. The metabolomic profile obtained by NMR showed a significant clustering of organic acids with specific bacteria in CAR and/or high AT individuals, being Scardovia wiggsiae the species with strongest associations. A significant clustering of ethanol and isopropanol with health-associated bacteria such as Rothia or Corynebacterium was also found. Accordingly, several relationships involving these compounds like the Ethanol : Lactate or Succinate : Lactate ratios were significantly associated to acid tolerance and could be of predictive value for caries risk. We therefore propose that future caries risk studies would benefit from considering not only the use of multiple organisms as potential microbial biomarkers, but also their functional adaptation and metabolic output.

Dataset Information

Proteomics, Lipidomics, Metabolomics, and 16S DNA Sequencing of Dental Plaque From Patients With Diabetes and Periodontal Disease.

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