Bioinformatics and Biostatistics of Gastrointestinal Diseases and Geometric Statistics

本建议为宏基因组数据开发新的生物信息学和生物统计学方法。特别适用于胃肠道疾病溃疡性结肠炎（UC）和艰难梭菌（C difficile）感染（CDI）。UC是一种由结肠炎症控制的慢性疾病，而后者由侵袭性病原体引起。最近进行并完成了两项独立的临床试验，特别关注了实验性粪便微生物群移植（FMT）后的临床反应。在两项临床试验中，FMT在临床解决方面提供了强有力的统计证据。收集了大量数据，包括临床和人口数据、健康调查数据和宏基因组数据。****临床反应数据包括FMT(s)后的分辨率，以时间到事件的方式记录。供体粪便提供了材料，并与相应患者的fmt前粪便样本配对。同时对供体和fmt前患者粪便以及随后的几个粪便样本进行测序。我们注意到，并非所有fmt都是成功的。因此，特别感兴趣的是从大量宏基因组（生物信息学）协变量中选择变量的新型生物统计方法的发展，这些协变量最好地解释了临床反应。更具体地说，我们寻求发展一种生物统计学方法来确定哪些细菌，在患者和供体的结肠微生物系统中，在获得临床分辨率中发挥关键作用。脑肠轴描述了胃肠道和大脑之间的生理联系。有强有力的证据表明，大脑的日常功能，也就是中枢神经系统，可以通过调节肠道微生物群来改变。虽然UC和CDI不是神经系统疾病，但通过发展与健康调查数据对应的宏基因组协变量的变量选择，可以揭示一些有关脑肠轴的有价值的可量化信息。这可以为更好地理解中枢神经系统疾病提供一种方法，比如自闭症、多发性硬化症、帕金森氏症等等。***还将研究微生物组拓扑结构的研究方法。当前的宏基因组学以DNA序列为基础，构建基于系统分型的系统发生树。然而，如果我们采用独特的序列读取并计算距离，使用现代计算方法，我们可以计算出微生物组的拓扑结构。然后可以制定统计检验。点云数据通常具有很大的噪声，迄今为止，在计算代数拓扑中缺乏稀疏方法。这些方法将继续发展，主要应用于胃肠道疾病的生物信息学数据。* * * * *

This proposal develops novel bioinformatic and biostatistical methodology for metagenomic data. The particular application is for the gastrointestinal diseases ulcerative colitis (UC) and Clostridium difficile (C difficile) infection (CDI). UC is a chronic disease governed by inflammation of the colon while the latter is caused by an aggressive pathogen. Two separate clinical trials were recently conducted and completed with particular attention paid to the clinical response following an experimental fecal microbiota transplantation (FMT). In both clinical trials, FMT provided strong statistical evidence of its efficacy in terms of clinical resolution. A vast quantity of data was collected which includes clinical and demographic data, health survey data and metagenomic data.****The clinical response data consisting of resolution following an FMT(s) was recorded in terms of time-to-event. A donor stool provided the material and was paired to the corresponding patient's pre-FMT stool sample. The donor and pre-FMT patient stool, along with the several subsequent stool samples, were simultaneously sequenced. We note that not all FMTs were successful. Consequently, of particular interest is the development of novel biostatistical methodology for variable selection from the voluminous metagenomic (bioinformatic) covariates that best explains clinical response. More specifically we seek to development a biostatistical methodology to identify what bacteria, in patient and donor colonic microbial systems, play crucial roles in obtaining clinical resolution.***The brain-gut axis describes the physiological connection between the gastrointestinal tract and the brain. There is strong evidence that the every day function of the brain, hence the central nervous system, can be altered by modulating the gut microbiota. Although UC and CDI are not neurological diseases some valuable quantifiable information concerning the brain-gut axis could be revealed by the development of variable selection that corresponds the metagenomic covariates to the health survey data. This could provide a methodology for better understanding diseases of the central nervous system such as autism, multiple sclerosis, Parkinson's disease, to name a few. ***A methodology for the study of the topology of the microbiome will also be pursued. Current metagenomics takes DNA sequences and builds a phylogenetic tree based on phylotyping. If however, we take the unique sequence reads and calculate the distances, using modern computational methods, we can compute the topology of the microbiome. Statistical testing can then be formulated. Typically point cloud data is very noisy and to date there is a paucity in sparse methods in computational algebraic topology. The development of such methods will be pursued with the main application being the bioinformatic data from the gastrointestinal diseases. *** **