title: Integrated metagenomic data analysis demonstrates that a loss of diversity in oral microbiota is associated with periodontitis.
abstract: Periodontitis is an inflammatory disease affecting the tissues supporting teeth (periodontium). Integrative analysis of metagenomic samples from multiple periodontitis studies is a powerful way to examine microbiota diversity and interactions within host oral cavity.
  
  
  
  A total of 43 subjects were recruited to participate in two previous studies profiling the microbial community of human subgingival plaque samples using shotgun metagenomic sequencing. We integrated metagenomic sequence data from those two studies, including six healthy controls, 14 sites representative of stable periodontitis, 16 sites representative of progressing periodontitis, and seven periodontal sites of unknown status. We applied phylogenetic diversity, differential abundance, and network analyses, as well as clustering, to the integrated dataset to compare microbiological community profiles among the different disease states.
  
  
  
  We found alpha-diversity, i.e., mean species diversity in sites or habitats at a local scale, to be the single strongest predictor of subjects' periodontitis status (P < 0.011). More specifically, healthy subjects had the highest alpha-diversity, while subjects with stable sites had the lowest alpha-diversity. From these results, we developed an alpha-diversity logistic model-based naive classifier able to perfectly predict the disease status of the seven subjects with unknown periodontal status (not used in training). Phylogenetic profiling resulted in the discovery of nine marker microbes, and these species are able to differentiate between stable and progressing periodontitis, achieving an accuracy of 94.4%. Finally, we found that the reduction of negatively correlated species is a notable signature of disease progression.
  
  
  
  Our results consistently show a strong association between the loss of oral microbiota diversity and the progression of periodontitis, suggesting that metagenomics sequencing and phylogenetic profiling are predictive of early periodontitis, leading to potential therapeutic intervention. Our results also support a keystone pathogen-mediated polymicrobial synergy and dysbiosis (PSD) model to explain the etiology of periodontitis. Apart from P. gingivalis, we identified three additional keystone species potentially mediating the progression of periodontitis progression based on pathogenic characteristics similar to those of known keystone pathogens.
authors.affiliation: School of Mathematics and Physics, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083, People's Republic of China.
authors.forename: Dongmei
authors.initials: D
authors.lastname: Ai
authors.affiliation: School of Mathematics and Physics, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083, People's Republic of China.
authors.forename: Ruocheng
authors.initials: R
authors.lastname: Huang
authors.affiliation: Department of Prosthodontics, Ninth People's Hospital Affiliated with Shanghai Jiao Tong University, School of Medicine, 639 Zhizaoju Road, Shanghai, 200011, China.|Oral Bioengineering Lab, Shanghai Research Institute of Stomatology, Ninth People's Hospital Affiliated with Shanghai Jiao Tong University, School of Medicine, Shanghai Key Laboratory of Stomatology, 639 Zhizaoju Road, Shanghai, 200011, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Wen
authors.affiliation: School of Mathematics and Physics, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083, People's Republic of China.
authors.forename: Chao
authors.initials: C
authors.lastname: Li
authors.affiliation: School of Mathematics and Physics, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing, 100083, People's Republic of China.
authors.forename: Jiangping
authors.initials: J
authors.lastname: Zhu
authors.affiliation: Department of Medicine, Stanford University School of Medicine, 269 Campus Dr., Stanford, CA, 94305, USA. lixia@stanford.edu.|Department of Statistics, The Wharton School, University of Pennsylvania, 3730 Walnut Street, Philadelphia, PA, 19014, USA. lixia@stanford.edu.
authors.forename: Li Charlie
authors.initials: LC
authors.lastname: Xia
mesh_terms: D000465:Algorithms; D044822:Biodiversity; D016022:Case-Control Studies; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003773:Dental Plaque; D005881:Gingiva / Q000382:microbiology; D006801:Humans; D054892:Metagenome; D056186:Metagenomics / Q000379:methods*; D064307:Microbiota; D009055:Mouth / Q000382:microbiology; D010518:Periodontitis / Q000382:microbiology*; D010802:Phylogeny; D057188:Workflow