Multi-omics analysis of periodontal pocket microbial communities pre- and posttreatment

Katy J. Califf, Karen Schwarzberg-Lipson, Neha Garg, Sean M. Gibbons, J. Gregory Caporaso, Jørgen Slots, Chloe Cohen, Pieter C. Dorrestein, Scott T. Kelley

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


Periodontitis is a polymicrobial infectious disease that causes breakdown of the periodontal ligament and alveolar bone. We employed a meta-omics approach that included microbial 16S rRNA amplicon sequencing, shotgun metagenomics, and tandem mass spectrometry to analyze sub- and supragingival biofilms in adults with chronic periodontitis pre- and posttreatment with 0.25% sodium hypochlorite. Microbial samples were collected with periodontal curettes from 3- to 12- mm-deep periodontal pockets at the baseline and at 2 weeks and 3 months. All data types showed high interpersonal variability, and there was a significant correlation between phylogenetic diversity and pocket depth at the baseline and a strong correlation (rho = 0.21; P = 0.008) between metabolite diversity and maximum pocket depth (MPD). Analysis of subgingival baseline samples (16S rRNA and shotgun metagenomics) found positive correlations between abundances of particular bacterial genera and MPD, including Porphyromonas, Treponema, Tannerella, and Desulfovibrio species and unknown taxon SHD-231. At 2 weeks posttreatment, we observed an almost complete turnover in the bacterial genera (16S rRNA) and species (shotgun metagenomics) correlated with MPD. Among the metabolites detected, the medians of the 20 most abundant metabolites were significantly correlated with MPD pre- and posttreatment. Finally, tests of periodontal biofilm community instability found markedly higher taxonomic instability in patients who did not improve posttreatment than in patients who did improve (UniFrac distances; t = -3.59; P = 0.002). Interestingly, the opposite pattern occurred in the metabolic profiles (Bray- Curtis; t = 2.42; P = 0.02). Our results suggested that multi-omics approaches, and metabolomics analysis in particular, could enhance treatment prediction and reveal patients most likely to improve posttreatment.

Original languageEnglish (US)
Article numbere00016
Issue number3
StatePublished - May 1 2017


  • 16S rRNA
  • Diagnostics
  • Metabolome
  • Microbiome
  • Molecular networking
  • Periodontal disease
  • Periodontitis
  • Shotgun metagenomics

ASJC Scopus subject areas

  • Microbiology
  • Physiology
  • Biochemistry
  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computer Science Applications


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