利用次世代分析比對慢性牙周炎患者之牙周炎及植體周圍炎致病菌微生物組成之臨床研究

Metagenomic Analysis of the Microbiome Diversity of Peri-implantitis and Periodontitis in Chronic Periodontitis Patients

余珮璇(Pei-Shiuan Yu)

指導教授 ： 陳漪紋

國立臺灣大學/醫學院/臨床牙醫學研究所/碩士(2021年)

https://doi.org/10.6342/NTU202103521

摘要

研究背景：植體赴周圍炎與慢性牙周炎的致病因子相似，由相似的致病菌組成，然而，植體周圍炎的致病因子及治療方法卻沒有更確切的結論。牙根整平術通常在慢性牙周炎的治療上，有益於減少炎症和牙周探測深度，被公認是牙周治療的黃金標準。文獻報導，藉由次世代分析，可以找到更多不同於以往的植體周圍炎致病微生物組成，藉由確認植體周圍炎的病因，進而找到更有效的治療方式。這項研究的目的是利用次世代分析，探討慢性牙周炎及植體周圍炎的致病菌微生物組成差異，並評估牙根整平術對已確診的慢性牙周炎及植體周圍炎患者的治療抗菌效果。
材料與方法：從台灣大學附屬醫院的牙科部招募了22名同時患有慢性牙周炎及植體周圍炎的患者，進行牙周病及植體周圍炎的常規治療及全口牙周檢查（殘留齒數、牙周囊袋深度、臨床附連喪失、牙菌斑指數、牙齦發炎指數、放射線骨高度），此研究收集患者治療前的牙周病患齒及見健康牙、植體周圍炎植牙及健康植牙等四個齒位以及治療後植體周圍炎植牙的牙齦下牙菌斑，做次世代分析微生物組成，並觀察比較非手術治療後的臨床牙周參數變化。
結果：微生物組成分析出512個菌種，且精細到菌種分枝的階層有377個菌種。在整體微生物組成中定義出47個核心菌種（每組百分之八十以上的樣本中都有，且相對豐度為百分之一以上），針對47個核心菌種做5組的數據分析比對，發現植體周圍炎組別及健康牙齒組別擁有較多種類的核心菌種；植體周圍炎與牙周病患齒之間相異的核心菌種彼此間的相關性為負相關，顯現植體周圍炎的細菌並非與牙周炎完全一致，與以往的研究不同；植體周圍炎在非手術治療後的微生物組成並無太大改變。
結論：透過次世代分析16S rDNA的全長序列，植體周圍炎的微生物組成能精細到菌種分枝，有助於比較疾病之間的相關性和交互作用、致病菌的探索及治療的的成效。未來透過更進一步的DNA分析方式（第三代分析或是全基因霰彈槍定序法），可以分析細菌的功能性基因，建立疾病診斷及預後預測模型。

關鍵字

植體周圍炎 ； 慢性牙周炎 ； 次世代分析 ； 宏基因組學分析

並列摘要

Research background: Peri-implantitis and chronic periodontitis have similar pathogenic factors and are composed of similar pathogenic bacteria. However, there is no more precise conclusion on the pathogenic factors and treatments of peri-implantitis. Root planing is usually used in the treatment of chronic periodontitis. It is beneficial to reduce inflammation and the pocket depth of teeth, and it is recognized as the gold standard for periodontal treatment.
To clarify the etiology of these two diseases, studies have characterized their respective microbiomes. The aim of this study is to use Next-Generation Sequencing (NGS) with full length of 16S rRNA gene amplification to explore the differences in the microbial composition of pathogenic bacteria in chronic periodontitis and peri-implantitis, and to evaluate the bactericidal effect of root planing therapy (non-surgical treatment) on peri-implantitis.
Materials and methods: 22 patients with chronic periodontitis and peri-implantitis were recruited from the Department of Dentistry at the National Taiwan University Hospital for routine treatment of periodontal disease and peri-implantitis and full mouth periodontal examination. Subgingival plaque was collected from four distinct clinical sites, diseased implant (DI), healthy implant (HI), diseased tooth (DT), healthy tooth (HT), and re-evaluation of diseased implant after treatment (DIRE). Metagenomics analysis using full length of 16S rDNA with NGS to analyze the microbiome of four groups and comparison of before and after non-surgical treatment.
Result: The metagenomics analysis revealed 512 OTUs, and there were 377 OTUs at strains level. Among of overall microbiome, 47 core species were defined (more than 80% of the samples in each group, and the relative abundance was more than 1%), and the union of 5 group core species constituted the core microbiome. Analysis and comparison revealed that the peri-implantitis group and the healthy tooth group had more diversity in core species. Part of DI core species shared similarity with DT core species. However, the different part from each other had negative correlation. The microbiome of peri-implantitis was similar with periodontitis but had unique part of each other. The microbiome of peri-implantitis did not have significantly different change but some core species decreased after non-surgical treatment.
Conclusion: Through the analysis of the full-length of 16S rDNA via NGS, the microbiome of peri-implantitis can be presented and detect taxa to strain level, which is helpful to compare the correlation and interaction between diseases, and exploration of pathogenic bacteria and effectiveness of treatment. In the future, different ways of NGS, third generation sequencing and whole genome shotgun sequencing are developing, which lead to more specific at species level and strain level. Strain-level profiling can be performed to retrieve from publicly available datasets to find evidence of diagnosis and prognosis prediction. In addition, functional gene can be retrieved from datasets, and further pathogenic mechanism and pathogenic bacteria of peri-implantitis will be clearer.

並列關鍵字

peri-implantitis ； chronic periodontitis ； Next-generation sequencing (NGS) ； metagenomics analysis

參考文獻

1. Jin, L.J., et al., Global oral health inequalities: task group-periodontal disease. Adv Dent Res, 2011. 23(2): p. 221-6.

2. Liu, Y., et al., Infection of microglia with Porphyromonas gingivalis promotes cell migration and an inflammatory response through the gingipain-mediated activation of protease-activated receptor-2 in mice. . Scientific Reports, 2017. 7(1): p. 11759.

3. Lai, H., et al., A prediction model for periodontal disease: modelling and validation from a National Survey of 4061 Taiwanese adults. . J Clin Periodontol, 2015. 42(5): p. 413-21.

4. Grossi, S.G., et al., Assessment of risk for periodontal disease. I. Risk indicators for attachment loss. J Periodontol, 1994. 65(3): p. 260-7.

5. Cekici, A., et al., Inflammatory and immune pathways in the pathogenesis of periodontal disease. Periodontology 2000, 2014. 64(1): p. 57-80.