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基于人体共生菌群的口腔疾病诊断和预警方法学研究
黄适
Thesis Advisor徐健
2016-06
Degree Grantor中国科学院研究生院
Place of Conferral北京
Degree Discipline生物化学与分子生物化学
Keyword口腔菌群 宏基因组学 预测模型 牙龈炎 龋病
Abstract利用人类共生菌群进行更为精准的生态诊断、预测乃至干预,是当前人类微生物组研究的前沿和终极目标之一。但是,目前成功范例和机制认识尚非常有限。牙龈炎和龋齿人类最普通的慢性感染性疾病,给社会和个人带来沉重的经济负担。本研究以宏基因组学技术为依托,采用横断面研究与纵向追踪研究相结合的方法,全面进行健康和疾病状态菌群的稳态和失衡过程的动态评估,在此基础上利用所筛选生物因子建立疾病风险模型,主要内容包括:(1)研究方法确立。通过考察微生物在时间和空间上的变化规律,选择出研究特定疾病的取样部位和时间。基于宏基因组技术,初步建立关联菌群结构变化和环境变化的生物信息学流程和统计学方法。(2)利用口腔菌群结构变化模拟牙龈炎发展和治疗过程,并预测不同口腔护理方法的治疗效果。(3)考察菌群对早期儿童龋病发生的预警作用。通过对宿主临床症状出现前后菌群的比较,归纳和提炼出具有对疾病预警作用的微生物。综上,通过深入解析口腔微生物群落信息在疾病发生、发展及预后预警中的作用,为未来个体化监测宿主健康状态,预测和评价宿主罹患龋病的风险提供理论基础。 (1)牙龈炎发展和恢复过程的菌群变化特征。本研究中,50名成年受试者经历了从自然发生牙龈炎,到健康牙龈状态(Baseline),再到实验性牙龈炎的可控转变。在疾病菌斑微生物组中,27个细菌属在相对丰度上显著改变,还包括33个鞭毛生物合成相关的功能基因富集。菌斑微生物组结构在第一个主要组分(PC1)上表现为连续性的递进变化,反映了健康到疾病状态的转变过程,并且与Mazza牙龈指数(MGI)相关。我们鉴定了具有截然不同牙龈炎敏感性的两类宿主类型。我们提出的牙龈炎微生物指数 (MiGs) 在宿主类型分类中达到74%的可信度,并且当被用于测试其余41名宿主时,鉴别出疾病与健康个体的准确率达95%。此外,基于自然发生牙龈炎微生物组状态可以预测随后发生的实验性龈炎严重性和微生物组状态(并不是健康状态时期微生物组状态)。与肠道恰恰相反,牙菌斑由于疾病状态导致的差异大于个体差异,所以菌斑微生物组在建立口腔疾病预测模型方面有诸多优势。 (2)不同牙龈炎治疗过程的菌群变化特征和评价。不同的口腔护理都可以使菌斑积累引起的牙龈炎得到缓解。为了考察不同口腔护理方法作用下菌斑菌群的变化特征,我们设计了一个双盲的,随机的临床实验:91名带有中度牙龈炎的受试者被随机接受两种不同的口腔护理治疗(刷牙以及刷牙加漱口)。在后一组中,菌斑指数(TMQHI)和牙龈炎指数(MGI)在第3,11和27天都下降的更快,而且菌斑菌群结构和唾液的代谢物组图谱变化更加显著。我们进一步将另外由50名受试者接受洗牙产生的菌群响应图谱与这两种口腔护理方法作用下菌群的动态响应进行比较。只有Actinobaculum,TM7和Leptotrichia的丰度在三者过程中都显著降低,而不同护理方法作用下不同的微生物菌群标记揭示了不同的牙龈炎治疗机制。因此,本研究得出的微生物标记物可以作为未来比较和理解临床治疗方法和口腔护理产品开发的重要手段。 (3)微生物对于龋病的预警作用。通过菌群进行疾病的生态预警乃至干预是人体微生物组计划的核心目标之一,但成功范例和机制认知尚非常有限。早期儿童龋病是菌群介导、通常不可逆的慢性感染性疾病。基于口腔内不同部位菌群分布的规律,我们监测了龋病发生、龋病进展和健康对照这三组、共五十名4到6岁儿童的龈上牙菌斑和唾液菌群,发现菌群变化与儿童年龄密切相关,而且先于龋病症状的出现。通过区分与年龄和疾病状态分别相关的微生物,本研究发明了 “龋病的菌群指数(Microbial Indicators of Caries;MiC)”。MiC能够在临床症状尚未出现时,以81%的准确率预测ECC的发病。该研究还发现,在健康儿童中,口腔菌群的发育带有明显的宿主生理年龄特征,因此,本研究提出了“口腔菌群年龄(Oral Microbiota Age)”这一概念。健康儿童的“口腔菌群年龄”与其生理年龄大体保持一致,但在ECC风险升高乃至发病的儿童中,其“口腔菌群年龄”则显著偏离了儿童生理年龄。因此“口腔菌群年龄”可用于监测和预警儿童龋齿风险。这一研究发明的基于口腔菌群的龋病风险评估和预警策略,对于其它人体部位乃至海洋、土壤等自然环境的微生物组研究具有重要的启示。
Other AbstractOne 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.
Department单细胞中心
Subject Area生物学
Date Available2026-06-30
Subtype博士 ; 学位论文
Language中文
Document Type学位论文
Identifierhttp://ir.qibebt.ac.cn/handle/337004/9771
Collection单细胞中心
Affiliation中国科学院青岛生物能源与过程研究所
Recommended Citation
GB/T 7714
黄适. 基于人体共生菌群的口腔疾病诊断和预警方法学研究[D]. 北京. 中国科学院研究生院,2016.
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