根據 2017 年美國腎臟資料登錄系統 (United States Renal Data System) 報告，台灣在 2015 年，末期慢性腎臟病發生率及盛行率為全世界之冠，2015年慢性腎臟病(Chronic kidney disease) 新發人數平均每一百萬人中就有 476人，盛行率有 3,317 人。據 2016 年台灣健保資料庫統計，該年有 85,000 人洗腎，健保支出為 483 億元，位居所有疾病第一。流行病學研究指出，慢性腎臟病人血液中因尿毒素持續累積，使病程加劇，並造成血管內皮功能惡化，產生心血管疾病等其他併發症。然而透過目前洗腎病患的血液透析或是腹膜透析技術亦無法有效去除尿毒素。更有研究指出腸道細菌特別是Proteobacteria會代謝腸道內的 tryptophan 及 tyrosine 分別產生 indole 以及 p-cresol，再由肝臟酵素轉化為 indoxyl sulfate 與 p-cresyl sulfate，此代謝產物簡稱尿毒素會造成腎臟功能下降。因為腸道菌群和其代謝物會受到遺傳背景、飲食習慣和生活環境不同而有差異，本研究乃針對一個家庭中洗腎病人與未患有慢性腎臟病之三名家屬，在相近飲食習慣與生活環境下，以糞便中細菌之 DNA，擴增 16S rDNA V4 區域序列及使用高通量技術定序，利用 Quantitative Insights into Microbial Ecology pipeline 軟體對菌種組成及多樣性進行分析；在主成分分析（PCoA, Principal Co-ordinates Analysis）結果顯示同一個人相隔六個月菌種相似度高，而洗腎病人糞便與一般人糞便中發現菌種有耶微的差異性，顯示洗腎病人菌群與正常人有所差異。在病人之糞便菌群中，主要有三大菌群 Proteobacteria, Bacteroidetes 與 Firmicutes和一些比例較低的菌群Actinobacteria and Verrucomicrobia。除此之外，以液相層析質譜儀在洗腎病人糞便中定量腸道微生物代謝產物包括Indoxyl sulfate, p-cresol sulfate, trimethylamine N-oxide, kynurenic acid, indoxyl aczvetic acid, hippuric acid，在糞便中之尿毒素除了p-cresol，其他尿毒素並沒有顯著差異，但在血液中之尿毒素，健康族群有顯著性下降。糞便中短鏈脂肪酸含量亦為腸道菌群恆定之另一關鍵指標，微生物代謝產生之短鏈脂肪酸，可調控免疫路徑抑制發炎，本研究於分析前先進行衍生化反應，後以氣相層析質譜儀進行定性與定量分析，乙酸、丙酸及丁酸在糞便中比例為5:3:2，糞便中短鏈脂肪酸含量在慢性腎臟病患這中有上升趨勢，將可連結細菌代謝物與菌群之關聯。

According to the 2002 US National Kidney Foundation survey, the annual incidence of chronic kidney disease in Taiwan is 400 cases per million population, the highest rank of the world. The prevalence of CKD in Taiwan is also at the highest risk as 2,500 per million people. In 2016, about 85,000 people need to undergo dialysis and spent 48.3 billion on CKD patient based on National Health Insurance Research Database (NHIRD). Patients who lose kidney function also have a lot of toxic uremic toxins accumulated in blood which are not effectively removed by neither hemodialysis or peritoneal dialysis. The updated studies have indicated bacterial species belonged to Proteobacteria produce indole and p-cresol uremic toxins that will deteriorate kidney function and endothelia function. The uremic toxins are reported to be originated from gut microbiota. In this study, feces from CKD patient and health, were collected, and the bacterial 16S rRNA V4 region were amplified and sequenced by Illumina High-seq sequencing platform. The microbial composition was analyzed by Quantitative Insights into Microbial Ecology pipeline v 1.9.0 (Qiime). The fecal microbiome of these patients are different to that health subjects. In principal co-ordinates analysis (PCoA) ordination plot of fecal microbiota profiles at genus level were closely associated in the same person interval 6 months; and little associated between CKD patients and health subjects. The most abundant bacteria in the CKD gut are the Proteobacteria, Bacteroidetes and Firmicutes phyla,and other bacterial species mostly belong to members of the phyla Actinobacteria and Verrucomicrobia. The uremic toxin-producing bacteria and its role on gut microbiota and immune system are worth further investigation. On the other hand, the uremic toxins such as Indoxyl sulfate, p-cresol sulfate, trimethylamine N-oxide, kynurenic acid, indoxyl acetic acid, hippuric acid were measurement in feces and serum sample using liquid chromatography. Each of the solutes without p-cresol were no significant between CKD and health in feces sample, but it has significant difference in serum sample. Additionally, short-chain fatty acids are also be measured using gas chromatography, The molar ratio of acetate, propionate, and butyrate in the stool is about 5:3:2. Additionally, the total SCFAs concentration of the CKD patients was lower than those of healthy subjects. The number of microorganisms in the colon and the food consumed by the individual are some of the reasons for the above variation.

誌謝 I
中文摘要 II
Abstract IV
Table of Contents VI
List of Figures IX
List of Tables X
List of Appendix XI
Introduction 1
1 The uremic syndrome of Chronic kidney disease (CKD) 1
1.1 Background of CKD 1
1.2 Gut microbiota as a potential source of uremic toxin and the other metabolites 1
1.3 Gut microbiota and status of CKD 2
2 Uremic toxin 2
2.1 Intestinal compounds generated by gut microbiota 2
2.2 Reducing the concentration of uremic toxins 3
2.3 Biological effects of uremic toxin on inflammatory system 4
3 Short-chain fatty acids (SCFAs) in kidney diseases 7
3.1 Diet related to SCFAs 7
3.2 Absorption and Regulation routes of SCFAs 7
3.3 SCFAs play a positive role in kidney disease 7
4 Research aim 9
Flow chart used to display the whole process of the study 10
Materials and methods 11
1 Human sample collection 11
1.1 Patient selection and study population 11
1.1 Feces collection, sampling, storage, and labelling 11
1.2 Serum sample collection 12
2 Analysis of fecal short-chain fatty acid 12
2.1 Preparation of standard solutions 12
2.2 Preparation of fecal samples 12
2.3 GC-BID (Barrier Ionization Discharge) equipment 13
2.4 Identification and quantification of human fecal water metabolites 13
3 Uremic toxin concentration quantitative 13
3.1 Preparation of standard solutions 13
3.2 Measurement of serum levels of uremic toxins 14
3.3 Measurement of fecal levels of uremic toxins 14
3.4 UPLC-MS equipment 15
4 Analysis of precursor of uremic toxins: p-cresol and indole 15
4.1 Preparation of standard solutions 15
4.2 Extraction of indole and p-cresol in feces 15
4.3 HPLC-PDA equipment 16
5 Bacterial community analysis 16
5.1 DNA extraction and 16S ribosomal RNA gene sequencing 16
6 Normalization water content 16
7 Chemicals and instruments 17
7.1 Chemicals 17
7.2 Instruments 17
Results 19
1 Total uremic toxin concentration and reduction rate in serum after hemodialysis 19
2 Microbial population of the colon and the relationship with uremic toxin 20
2.1 Microbiota Diversity in Fecal Samples 20
2.2 CKD versus Healhy Controls 20
3 Uremic toxin production in gut microbiota 21
3.1 The precursor of uremic toxins: p-cresol and indole 21
3.2 Feces concentration of uremic toxins between CKD and health 21
3.3 Normalization water content of uremic toxins in feces 21
4 Comparing short-chain fatty acids in human feces using gas chromatography-barrier ionization detection 22
Discussion 23
1. Variability in Concentrations uremic toxin 23
2. Bacterial community in CKD 23
3. Comparison of uremic toxins concentration in serum and feces 24
4. Gut microbial metabolites as mediators of renal disease, how about the short-chain fatty acids? 25
5 The different analysis techniques of the SCFAs 26
Conclusion 28
Reference 29

 
List of Figures
Figure 1. Schematic overview of tyrosine and tryptophan metabolic pathways by gut microbiota 33
Figure 2. Schematic overview of trimethylamine oxide and hippuric acid metabolic pathways by gut microbiota 34
Figure 3. After collection, fecal sample were mixture and distribution for experiment. 35
Figure 4. Procedure for preparing short-chain fatty acid derivatives of fecal water. 36
Figure 5. Reaction scheme of SCFAs treated with methyl ester derivatization. 37
Figure 6. The blood clearances of protein-bound uremic toxins by dialysis are limited. 38
Figure 7. Sequencing depth of CKD and health. 39
Figure 8. Relative abundance of microbial community at the phylum level in fecal samples collected from both the CKD and health groups. 40
Figure 9. Relative abundance of microbial community at the family level in fecal samples collected from both the CKD and health groups. 41
Figure 10. Changed in normalized relative abundance of microbial community at the genus level in fecal samples collected from both the CKD and health groups ....42
Figure 11. Relative abundance of microbial community at the genus level in fecal samples collected from both the CKD and health groups. 43
Figure 12. Measurements about the uremic toxin of feces in UPLC-MS 44
Figure 13. Measurements about the precursor of uremic toxin of feces in HPLC-PDA. 45
Figure 14. Measurements about the SCFAs of feces in GC-BID. 46
Figure 15. Principal Co-ordinates Analysis (PCoA) ordination plot of fecal microbiota profiles of CKD and healthy controls at the genus level. 47
 
List of Tables
Table 1. The different classes of uremic toxins as used in this publication.. 48
Table 2. Baseline characteristics of all patients 49
Table 3. Parameters of GC-BID condition and retention times of the Short-chain fatty acids 50
Table 4. Parameters of MRM condition and retention times of the uremic toxins. 51


 
List of Appendix
Appendix 1. A proposed workflow for fecal sample collection from inpatients and outpatients and subsequent sample storage for metabolic profiling 52
Appendix 2. Indoxyl sulfate and p-cresol sulfate calibration curve. 53
Appendix 3. Trimethylamine N-oxide and kynurenic acid calibration curve 54
Appendix 4. The HPLC-PDA chromatograms of SCFAs, acetic, propionic, butyric acid. ... 55
Appendix 5. The ideally reaction scheme of SCFAs derivatization using 99% trichloroacetyl chloride in feces sample. …56

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