【摘要】：研究背景:腸道菌群的分析流程中包含了大量的環(huán)節(jié),對最終的分析結(jié)果可以起到重要的影響,尤其是在大數(shù)據(jù)分析方面,諸如不同研究數(shù)據(jù)如何合并進行分析,以及結(jié)果的穩(wěn)定性和運算的速度等,都是需要驗證和優(yōu)化的問題。另外腸道菌群的人群變異度大,可能會使腸道菌群的研究結(jié)果不穩(wěn)定,一個應(yīng)對的方案是采用大規(guī)模流行病學(xué)采樣方法研究腸道菌群和宿主關(guān)系,從而獲得更加可靠和全面的結(jié)果,而類似的研究在東方發(fā)展中地區(qū)還較少。研究目標:本論文首先對微生物組分析中實驗操作的影響進行探索,并針對分析流程的穩(wěn)定性以及運算速度等方面進行優(yōu)化,并建立完整的分析流程。進一步采用上述方法,分析廣東省慢病調(diào)查人群腸道菌群,研究腸道菌群與宿主之間的關(guān)系,并揭示其代謝綜合征特征菌譜。研究方法:第一章我們從四個角度對微生物組數(shù)據(jù)分析方法進行了研究:1.我們以不同的引物擴增,但使用同一 16SrRNA基因區(qū)段為例,觀察實驗細節(jié)對微生物組學(xué)分析結(jié)果的影響;2.我們以稀釋曲線不穩(wěn)定為切入點,闡述了聚類單元不穩(wěn)定的原因,并提出較為穩(wěn)定的聚類方法;3.我們將貪婪重頭聚類算法進行了多線程化,試圖解決微生物組學(xué)大數(shù)據(jù)分析中聚類速度的問題;4.我們基于現(xiàn)有的一些分析方法和平臺,整合了針對微生物組數(shù)據(jù)分析的流程,并在公共平臺上公開。第二章我們在廣東省采用聚類抽樣法,選擇了 14個區(qū)/縣,每個區(qū)/縣采用按規(guī)模大小成比例的方法(PPS)抽取了 3個街道/鎮(zhèn),在每個街道/鎮(zhèn)我們采用PPS方法抽取了兩個居委會/村落,并在每個居委會/村落隨機抽取了 45戶家庭進行調(diào)查。我們收集了每位參與者的糞便標本,以及其他的生理指標或社會經(jīng)濟參數(shù)。我們采用PERMANOVA法計算了各種宿主信息對腸道菌群變異的解釋度,并采用多元線性相關(guān)(MaAsLin)方法來計算各類與代謝綜合征相關(guān)的metadata和具體某些細菌分類之間的關(guān)系。研究結(jié)果:1.針對微生物組學(xué)數(shù)據(jù)分析方法的驗證與優(yōu)化:實驗細節(jié)會對微生物組學(xué)研究結(jié)果造成顯著的影響,意味著實驗方法嚴格統(tǒng)一的大規(guī)模人群調(diào)查對于研究宿主和腸道菌群關(guān)系來說是必要的。我們進一步開發(fā)了穩(wěn)定的聚類算法,并將重頭貪婪聚類算法進行多線程化來解決計算速度的問題,并基于這些結(jié)果建立并公開了一個微生物組學(xué)數(shù)據(jù)分析的流程。2.人群腸道菌群分析:我們在廣東省共納入了 8600位志愿者,并采集了超過100項背景信息。我們發(fā)現(xiàn)在廣東省地區(qū),腸道菌群與人群的背景信息普遍相關(guān),其中以地理分布的影響最大,而這種地理上的差異可能與當?shù)厝耸雏}習慣相關(guān)。其他信息中如年齡,布魯斯拓評分,體重,尿酸水平,靜坐時間,飲食等與腸道菌群變異的相關(guān)性也相對較大。在疾病信息中,代謝綜合征與腸道菌群的變異相關(guān)性最高,其具體的疾病譜特征與發(fā)達地區(qū)較為相似,但發(fā)展中地區(qū)的變形菌門細菌顯著較高,且和經(jīng)濟發(fā)展呈負相關(guān)。結(jié)論:1.由于微生物組學(xué)研究容易受到實驗細節(jié)的影響,真實實施的流程嚴格而統(tǒng)一的大規(guī)模人群研究是闡述腸菌與宿主關(guān)系的重要手段之一,同時我們優(yōu)化了相關(guān)算法,優(yōu)化了分析方法的穩(wěn)定性和運算速度,并建立了整合的分析流程。2.腸道菌群與宿主信息普遍相關(guān),其中地理分布是重要的影響因素。我們發(fā)現(xiàn)東方發(fā)展中地區(qū)有其獨特的腸道菌群特征,且可能和生活方式協(xié)同作用增加代謝性疾病的風險,針對代謝病在快速發(fā)展中地區(qū)大爆發(fā)現(xiàn)象提出了新的解釋角度和潛在的干預(yù)靶標。
[Abstract]:Research background: the analysis process of intestinal microflora contains a large number of links, which can play an important role in the final analysis, especially in large data analysis, such as how to analyze the combination of different research data, and the stability and speed of the calculation. The large population variation in the population may make the results of the intestinal flora unstable. One solution is to use a large-scale epidemiological sampling method to study the relationship between intestinal flora and host, so as to obtain more reliable and comprehensive results, and similar research is less in the eastern development area. The influence of the experimental operation in the microbiological analysis was explored, and the stability of the analysis process and the speed of operation were optimized, and a complete analysis process was established. Further, the above method was used to analyze the intestinal flora of the Guangdong slow disease survey population, to study the relationship between the intestinal microflora and the host, and to reveal its metabolism. Characteristic Bacteria Spectrum of syndrome. In the first chapter, we studied the data analysis method of microbial group from four angles: 1. we amplified by different primers, but we used the same 16SrRNA gene section as an example to observe the effect of the experimental details on the results of microbiological analysis; 2. we took the dilution curve instability as the breakthrough point. The reasons for the instability of the cluster unit and a more stable clustering method are proposed. 3. we have multithreaded the greedy and heavy head clustering algorithm to solve the problem of clustering speed in the large data analysis of microbiomics. 4., based on some existing analytical methods and platforms, we integrate the flow of data analysis for microbiological groups, and In the public platform, in the second chapter, we adopted cluster sampling in Guangdong Province, selected 14 districts / counties, each district / county took 3 streets / towns by scale and scale method (PPS). In each street / town, we took two neighborhood committees / villages by PPS method, and randomly selected 45 of each neighborhood committee / village. We collected each participant 's fecal specimens, and other physiological or socioeconomic parameters. We used the PERMANOVA method to calculate the interpretation of the diversity of the intestinal flora and the multiple linear correlation (MaAsLin) method to calculate all kinds of metadata related to metabolic syndrome. Specific relationships among certain bacterial classifications. Results: 1. verification and optimization of microbiological data analysis methods: experimental details will have a significant impact on the results of microbiological research, which means that a rigorous and unified mass survey of experimental methods is necessary for the study of the relationship between the host and the intestinal flora. We further developed a stable clustering algorithm, and multithreading the heavy head greedy clustering algorithm to solve the problem of computing speed. Based on these results, we set up and open a microbiome data analysis process for the.2. population analysis of intestinal flora: We included 8600 volunteers in Guangdong Province, and collected more than 10. 0 background information. We found that in Guangdong Province, the intestinal flora is generally related to the background information of the population, and the geographical distribution is the most influential, and the geographical difference may be related to the salt habit of the local people. Other information such as age, Bruce extension, body weight, uric acid level, sitting time, diet and other intestinal flora The correlation of variation is also relatively large. In the disease information, the metabolic syndrome has the highest correlation with the variation of intestinal flora, and the specific characteristics of the disease spectrum are similar to those in the developed areas, but the bacteria of the deformable bacteria in the developing region are significantly higher and have negative correlation with the economic development. Conclusion: 1. because of the microbiological study, the study is easy to be real. It is one of the important means to elaborate the relationship between intestinal bacteria and host, and we optimize the correlation algorithm, optimize the stability and operation speed of the analysis method, and establish an integrated analysis process,.2. intestinal flora and host information. Physical distribution is an important factor. We have found that Eastern developing regions have their unique intestinal microflora characteristics, and they may cooperate with lifestyle to increase the risk of metabolic diseases. New interpretation angles and potential intervention targets are proposed for the rapid development of metabolic diseases in the rapid development of regional outbreak.
【學(xué)位授予單位】：南方醫(yī)科大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：R371

【相似文獻】

相關(guān)期刊論文 前10條

1 繆其宏;新兵在服役適應(yīng)期的腸道菌群[J];解放軍預(yù)防醫(yī)學(xué)雜志;1992年01期

2 劉華;黃湘寧;許莉;;成都市中老年人腸道菌群比例的初步探討[J];現(xiàn)代預(yù)防醫(yī)學(xué);2007年20期

3 王新文;阿基業(yè);曹蓓;劉林生;趙春艷;陳曉虎;王廣基;;腸道菌群對某些疾病及藥物療效與毒性的影響[J];藥學(xué)進展;2012年05期

4 張曉婧;曾本華;劉智偉;廖振林;方祥;魏泓;;兩種不同品系小鼠的人源菌群模型的建立與腸道菌群的比較[J];中國微生態(tài)學(xué)雜志;2013年04期

5 陳秀琴;黃小潔;石達友;郭世寧;;中藥與腸道菌群相互作用的研究進展[J];中草藥;2014年07期

6 ;腸道菌群 你所不知的那部分“自己”[J];科學(xué)之友(上旬);2013年06期

7 阿梅;漫話腸道菌群平衡[J];中老年保健;1997年02期

8 尹軍霞,林德榮;腸道菌群與疾病[J];生物學(xué)通報;2004年03期

9 王建陽;關(guān)注住院病人的腸道菌群[J];現(xiàn)代檢驗醫(yī)學(xué)雜志;2005年04期

10 ;歐盟啟動腸道菌群基因組研究工程[J];中國食品學(xué)報;2008年03期

相關(guān)會議論文 前10條

1 曹虹;郝小燕;彭亮;方幸幸;;腸道菌群與機體代謝及相關(guān)疾病——感染微生態(tài)學(xué)的研究進展[A];新發(fā)和再發(fā)傳染病防治熱點研討會論文集[C];2011年

2 王保紅;李e，

本文編號：2131419