Microbes are ubiquitous and impact virtually the entire biosphere including human body, plant, soil and ocean. Can we use them to diagnose and predict the state of ecosystem such as human health? A team from the Single-Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT), Chinese Academy of Sciences (CAS), collaborating with P&G Beijing Innovation Center, has made significant progress in a potential microbiome-based predictive model for the development and progression of the chronic disease, and the findings have been published online (Huang, Li, et al, ISME J, 2014). 

Microbial index of gingivitis (Image by P&G)

Based on the different microbial patters in niches of human oral ecosystem, HUANG Shi and LI Rui et al applied 16S rRNA gene and whole metagenomic sequencing approach to characterize organismal and functional compositions of microbial community in supragingival plaque from 50 adults undergoing controlled transitions from naturally occurring gingivitis (NG), to healthy gingivae (baseline), and then to experimental gingivitis (EG). Moreover, based on the 15 specific bacterial genera associated with gingivitis severity, the research proposed a novel microbial index of gingivitis (MiG), which can be a potential microbiota-based diagnostic tool for the severity of oral infectious diseases. Intriguingly, MiG distinguished healthy from diseased individuals with 95% accuracy when tested on another 41-member cohort. Furthermore, two host types were identified in the 50 subjects based on microbial compositions in plaque microbiota: the less gingivitis-sensitive Type-I and the highly sensitive Type-II. Our proposed microbial index of gingivitis sensitivity (MiG-S) classified the two types with 74% reliability, which unraveled a microbial bias for the heterogeneity of gingivitis outcome in human population. Compared with the traditional methods of gingivitis diagnose such as visual examination and periodontal probing, MiG, a novel non-invasive and examiner-independent method, might offer pronounced advantages in patient-friendliness, reproducibility and comparability.  Therefore, MiG provides a novel strategy for the monitoring and preventive intervention of gingivitis, as well as for the evaluation and development of new-generation oral care products. 

Furthermore, the research found that, unlike the gut microbiome, differences between healthy and diseased oral microbiota within a subject are larger than inter-personal differences and thus oral microbiome may take greater advantages than gut ones in early diagnosis and prediction of certain chronic infections. On the other hand, to diagnose and predict chronic disease based on human microbiome is attractive yet challenging as few case studies published are available. The findings of this study may shed new lights into commensal microorganisms inhabiting other major mucosal surfaces of the human body, including nasal passages, skin, gastrointestinal tract, and urogenital tract, and may also provide new insights into the methodology for microbiota-based environmental monitoring of marine, soil and air. 

The research was led by Professor XU Jian, Director of the Single Cell Center of QIBEBT and Dr. LIU Jiquan, Technical Director of P&G Beijing Innovation Center. It was supported by CAS-P&G Framework Agreement for Cooperation and Innovation. Professor Rob Knight from University of Colorado at Boulder and researchers from Qingdao Municipal Hospital also participated in this study. 

References: 

1. Huang, Li, et al, Predictive modeling of gingivitis severity and susceptibility via oral microbiota. ISME J, 2014. 

2. Huang, et al, Preliminary characterization of the oral microbiota of Chinese adults with and without gingivitis. BMC Oral Health, 2011 

Contact: 

Professor XU Jian 
Email: xujian (AT) qibebt.ac.cn