| 其他摘要 | One key goal of human microbiome projects worldwide is for prediction and intevention of host diseases based on human microbiota. However, few studies have yet reported successful microbiota-based prediction of future disease outcome based on uncovered genetic nature and mechanisms. Gingivitis and dental caries in the human oral cavity are the most common chronic infectious diseases, which affect most of people worldwide and incur enormous societal costs. In order to probe the mechanistic foundation of such predicting capability of oral microbiome, employing metagenomics technology, we integrated a cross-sectional and longitudinal study design to multi-dimensionally dissect the dynamic of healthy and diseased oral microbiota with particularly high temporal resolution during the homeostasis and dysbiosis course, identified and validated the specific biomarkers, and finally constrcuted a mechnistic models for disease classification and prediction. This study proposed the following specific contents: (i) Method framing. Based on metagenomics data, bioinformatic pipeline and statistic methods were to construct the relationships between microbial coommunity structure and host states. (ii) Based on the temporal dynamics of oral microbiota from naturally occurring gingivitis, to healthy gingivae (baseline), and to experimental gingivitis, a microbial index of gingivitis was proposed and then to evaluate and predict various treatment regimens. (iii) In order to probe the mechanistic foundation of such predicting capability of oral microbiome, specific “early-alarm microbes” were identified and validated, and then a predictive model was estabished to predict future disease onset. In summary, we aimed to eluciate the essential role of oral microbiota in the prediction of the onset, progression, and prognosis of the oral chronic infectious diseases. These efforts are expected to provide new ways of developing new tools, not just for etiology research but also for a new paradigm of personalized medicine for preventive intervention of gingivitis and caries.
(1) Predictive modeling of gingivitis based on the microbiota. Totally, fifty adults underwent controlled transitions from naturally occurring gingivitis, to healthy gingivae (baseline), and to experimental gingivitis. In diseased plaque microbiota, 27 bacterial genera changed in relative abundance, and functional genes including 33 flagellar-biosynthesis-related groups were enriched. Plaque microbiota structure exhibited a continuous gradient along the first principal component, reflecting transition from healthy to diseased states, which correlated with Mazza Gingival Index. We identified two host types with distinct gingivitis sensitivity. Our proposed Microbial indices of Gingivitis (MiGs) classified host types with 74% reliability, and, when tested on another 41-member cohort, distinguished healthy from diseased individuals with 95% accuracy. Furthermore, the state of the microbiota in naturally occurring gingivitis predicted the microbiota state and severity of subsequent experimental gingivitis (but not the state of the microbiota during the healthy baseline period). Because the effect of disease is greater than inter-personal variation in plaque, in contrast to the gut, plaque microbiota may provide advantages in predictive modeling of oral diseases.
(2) Microbiota-based profiling and evaluation of gingivitis treatments. Plaque-induced gingivitis can be alleviated by various treatment regimens. To probe the impacts of various anti-gingivitis treatments on plaque microflora, here a double blinded, randomized controlled trial of 91 adults with moderate gingivitis was designed with two anti-gingivitis regimens: the brush-alone treatment and the brush-plus-rinse treatment. In the later group, more reduction in both Plaque Index (TMQHI) and Gingival Index (mean MGI) at Day 3, Day 11 and Day 27 was evident, and more dramatic changes were found between baseline and other time points for both supragingival plaque microbiota structure and salivary metabonomic profiles. A comparison of plaque microbiota changes was also performed between these two treatments and a third dataset where 50 subjects received regimen of dental scaling. Only Actinobaculum, TM7 and Leptotrichia were consistently reduced by all the three treatments, whereas the different microbial signatures of the three treatments during gingivitis relieve indicate distinct mechanisms of action. Our study suggests that microbiota based signatures can serve as a valuable approach for understanding and potentially comparing the modes of action for clinical treatments and oral-care products in the future.
(3) Microbiota-based Prediction of ECC. Microbiota-based prediction of chronic infections is promising yet not well established. Early childhood caries (ECC) is the most common infection in children. Here we simultaneously tracked microbiota development at plaque and saliva in 50 4-year-old preschoolers for 2 years; children either stayed healthy, transitioned into cariogenesis, or experienced caries exacerbation. Caries onset delayed microbiota development, which is otherwise correlated with aging in healthy children. Both plaque and saliva microbiota are more correlated with changes in ECC severity (dmfs) during onset than progression. By distinguishing between aging- and disease-associated taxa and exploiting the distinct microbiota dynamics between onset and progression, we developed a model, Microbial Indicators of Caries, to diagnose ECC from healthy samples with 70% accuracy and predict, with 81% accuracy, future ECC onsets for samples clinically perceived as healthy. Thus, caries onset in apparently healthy teeth can be predicted using microbiota, when appropriately de-trended for age. |
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