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研究生: 高聖翔
Gao, Sheng-Siang
論文名稱: 腸道微生物測序與慢性腎病的關聯性分析
Association analysis of shotgun metagenomic sequencing data with chronic kidney disease
指導教授: 謝文萍
Hsieh, Wen-Ping
口試委員: 鍾仁華
Chung, Ren-Hua
蘇仕奇
Su, Shih-Chi
學位類別: 碩士
Master
系所名稱: 理學院 - 統計學研究所
Institute of Statistics
論文出版年: 2019
畢業學年度: 107
語文別: 英文
論文頁數: 35
中文關鍵詞: 腸道微生物慢性腎病關聯性
外文關鍵詞: shotgun metagenomic sequencing
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  • 慢性腎臟病患的腸道微生物越來越備受重視,許多腸道細菌會分解胺基酸產生對人體有害的物質,所以尋找腸道微生物與疾病進展的關聯性至關重要。我們有100個樣本分屬正常人或三種不同嚴重程度的慢性腎臟病患者,對他們血液中數種重要的代謝物濃度進行測量並取樣其腸道微生物進行基因組測序。在血液測量值中,尿毒症毒素在不同組別中有著非常顯著的差異。我們首先從微生物基因組數據重建基因組,標註其物種,計算腸道物種相對豐富度後進行腸道物種多樣性分析,我們利用α多樣性與β多樣性了解樣本內與樣本間的細菌多樣性,從中發現物種分配存在著很大的差異,而且許多物種豐富度都與尿毒症毒素呈現正相關。另外,我們使用canonical correspondence analysis和co-inertia analysis分析尿毒症毒素與腸道微生物豐富度的關聯性,並呈現在二維平面上。我們也使用Random forest 模型來尋找對於疾病嚴重程度較為重要的腸道物種。最後,我們在KEGG分析中,描繪出隨著疾病進展,優勢微生物基因功能的轉變。
    總結來說,隨著慢性腎臟病的進展,病患的腸道物種分配以及微生物組的功能性都隨之改變。許多腸道物種與腎毒素相關,而血液中腎毒素濃度決定了慢性腎臟病的嚴重程度,所以腸道微生物與慢性腎臟病息息相關,在未來的標靶治療扮演很重要的角色。


    Intestinal microbes in chronic kidney disease (CKD) are gaining more and more attention. Many intestinal bacteria can decompose amino acids to produce harmful substances, so it is important to find the correlation between intestinal microbes and disease progression. We have 100 samples of normal controls or CKD patients with different severity. Four metabolites in their blood were measured and their gut microbiota were shot-gun sequenced to get the microbiome data. The uremic toxins measured from blood have significant differences in different groups. We reconstructed the genome from metagenomic data, and labeled their closest species. The relative abundance of intestinal species were then obtained and used to calculate the αdiversity and βdiversity. With the analysis, we knew the diversity of intestinal species within and between samples, and found that there are great differences in species distribution, and many species abundance is positively correlated with uremic toxins. In addition, we used canonical correspondence analysis(CCA) and co-inertia analysis(CIA) to analyze the association of uremic toxins with intestinal microbial richness and present them on a two-dimensional plot. We also used the Random forest model to find intestinal species that are important in predicting the severity of the disease. In the KEGG analysis, a shift in microbial gene function is depicted as the disease progresses.
    In summary, as CKD progresses, the intestinal species distribution of the patient and the functionality of the microbiome changed. Many intestinal species are associated with nephrotoxin, and the concentration of nephrotoxin in the blood determines the severity of chronic kidney disease. Therefore, intestinal microbes are closely related to CKD, and they play an important role in future target treatment.

    Contents 1. Introduction 1 2. Materials and Methods 3 2.1 Subjects and clinical measurements 3 2.2 Sequencing and preprocessing of metagenomic data 4 2.2.1 Shotgun metagenome sequencing 4 2.2.2 Metagenomic data processing and analysis 4 2.2.3 Gene count and rarefaction curve 5 2.3 Diversity 6 2.4 Canonical correspondence analysis and Co-inertia analysis 7 2.5 Random Forest analysis 9 2.6 KEGG analysis 10 2.6.1 Construction of KO profile 10 2.6.2 Calculation of reporter score 11 3. Result 12 3.1 Annotation of bins 12 3.2 Different clinical performance between groups 13 3.3 Gut microbial alteration 13 3.4 Associations between gut microbial species and clinical indices 19 3.5 Function level analysis 22 3.6 Identification of possible microbial biomarkers 25 3.6.1 Use random forest model to select possible biomarkers 25 4. Conclusion and Discussion 27 Supplementary 29 References 35

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