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研究生: 胡博翔
Hu, Po-Hsiang
論文名稱: 在炎症性腸病患者中探索黏膜與糞便微生物群的關係
Exploring the relationship between mucosa and stool microbiota in inflammatory bowel disease patients
指導教授: 劉宗霖
Liu, Tsung-Lin
學位類別: 碩士
Master
系所名稱: 生物科學與科技學院 - 生物科技與產業科學系
Department of Biotechnology and Bioindustry Sciences
論文出版年: 2024
畢業學年度: 112
語文別: 英文
論文頁數: 68
中文關鍵詞: 炎症性腸病 、腸道組織微生物群 、糞便微生物群 、宏基因組分析 、16s rRNA測序
外文關鍵詞: Inflammatory bowel disease, gut tissue microbiota, stool microbiota, metagenomics analysis, 16s rRNA sequencing
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  • 發炎性腸道疾病(IBD)是一種慢性的疾病,包括克羅恩病(CD)和潰瘍性結腸炎(UC)。主要影響之一是腸道炎症,導致腸道組織長時間的發炎,進而引起腹痛、腹部脹氣等。最近的研究 Buffie and Pamer 也發現了IBD與腸道菌叢之間的密切關聯,且發炎會導致腸道屏障的破壞。在我們實驗室之前的研究中,我們注意到UC的糞便和組織的菌相組成較相似。為了查看UC病人的糞便和組織的菌像組成是否比較相似以及造成這個現象可能的原因,此研究中採取IBD患者和非IBD患者提供的糞便、直腸拭子和組織樣本收集進行了序列分析。在 β 多樣性方面,UC病人的糞便菌相組成和組織菌相組成較相近,且發炎程度提高,糞便和組織的菌相組成變得更相近。在豐度差異分析中,UC病人的糞便和組織有豐度差異的細菌較少且有差異細菌的差異量也較小。在FEAST分析中,UC病人的組織菌相對糞便菌相的貢獻顯著高於在非IBD病人中的貢獻,且UC嚴重發炎病人的組織菌相對糞便菌相的貢獻也顯著高於在UC輕微發炎病人的貢獻,這結果也進一步證實了發炎會使更多組織中的細菌進入到糞便中。總的來說,這些現象支持炎症導致腸道屏障的破壞,從而導致微生物從組織轉移到糞便的假說。

    Inflammatory bowel disease (IBD) is a chronic condition that includes Crohn’s disease (CD) and ulcerative colitis (UC). One of the primary effects is intestinal inflammation, leading to prolonged inflammation of the intestinal tissues and resulting in a range of gut issues such as abdominal pain and bloating. Recent research has also found a close association between IBD and gut microbiota, with inflammation causing damage to the intestinal barrier. In our laboratory’s previous studies, we observed that the microbiota of stool and tissue samples from UC patients are relatively similar. To investigate whether the microbiota composition of stool and tissue samples in UC patients is more similar and to explore potential reasons for this phenomenon, this study analyzed sequence data from stool, rectal swab, and tissue samples collected from IBD and Non-IBD patients. In terms of β-diversity, the microbiota composition of stool samples in UC patients was closer to that of tissue samples, and with increased inflammation, the microbiota composition of stool and tissue samples became even more similar. After conducting diversity analysis, we investigated the differences in bacterial abundance between different types of patients and found fewer differences in bacterial abundance between UC stool and tissue samples, with smaller differences in the types of bacteria. FEAST analysis revealed that the contribution of tissue to stool microbiota in UC patients was significantly higher compared to Non-IBD patients, and in severely inflamed UC tissues, the contribution to stool microbiota was also significantly higher compared to mildly inflamed UC tissues. This result further suggests that inflammation causes more bacteria from the tissues to enter the feces. Overall, these findings support the hypothesis that inflammation leads to damage of the intestinal barrier, therefore facilitating microbial transferring from tissue to stool.

    中文摘要 I Exploring the relationship between mucosa and stool microbiota in inflammatory bowel disease patients II Acknowledgements V Table of contents VI List of Tables VIII List of Figures IX List of Abbreviation X Main Article 1 Research background 1 1-1. Inflammatory bowel disease 1 1-2. gut microbiota 1 1-3. 16S rRNA gene & 16S rRNA sequencing 2 1-4. Microbiome analysis & Bioinformatics 3 Materials and Method 4 2-1. Sample collection 4 2-2. inflammation level 6 2-3. DNA extraction 6 2-4. Sequencing 7 2-5. Sequence data pre-processing 7 2-6. zOTU clustering 7 2-7. Taxonomy annotation 8 2-8. Beta diversity analysis 9 2-9. Linear regression 9 2-10. Abundance differential analysis 9 2-11. Source tracking analysis 10 Results 10 3-1. The microbial composition of different samples varies among different types of patients 10 3-2. The similarity of microbial composition between two sample sites from the same patient. 11 3-3. The similarity of bacterial composition between stool and tissue in UC and CD patients with different levels of inflammation. 12 3-4. Differential abundance analysis between stool and tissue in different types of patients. 13 3-5. Source tracking analysis of different types of patients. 15 3-6. Source tracking analysis of UC and CD patients with different levels of inflammation. 16 Discussion 17 4-1. The relationship between the microbial composition of stool and tissue 17 4-2. Bacteria with smaller differences in abundance 18 4-3. Source tracking analysis 21 4-4. Conclusion 23 References 25 Tables 30 Figures 40

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