Bioinformatics and Biostatistics of Gastrointestinal Diseases and Geometric Statistics

这一建议为元基因组数据开发了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(艰难梭菌)感染(CDI)。UC是一种慢性疾病，由结肠炎引起，而后者是由侵袭性病原体引起的。最近进行并完成了两个独立的临床试验，特别关注实验性粪便微生物区系移植(FMT)后的临床反应。在这两个临床试验中，FMT提供了强有力的统计证据，证明了其在临床解决方面的有效性。收集了大量的数据，包括临床和人口学数据、健康调查数据和元基因组数据。 临床反应数据包括FMT后的分辨率(S)，根据到事件的时间记录。供体粪便提供了材料，并与相应患者FMT前的粪便样本配对。同时对供者和FMT前患者的粪便以及随后的几个粪便样本进行了测序。我们注意到，并非所有禁产措施都取得了成功。因此，特别令人感兴趣的是发展新的生物统计学方法，从最能解释临床反应的大量元基因组(生物信息学)协变量中选择变量。更具体地说，我们寻求开发一种生物统计学方法，以确定患者和捐赠者结肠微生物系统中的哪些细菌在获得临床解决方案方面发挥关键作用。 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，通过调节肠道微生物区系，可以改变大脑的日常功能，从而改变中枢神经系统。虽然UC和CDI不是神经系统疾病，但通过将元基因组协变量与健康调查数据相对应的变量选择的发展，可以揭示一些关于脑-肠轴的有价值的量化信息。这可能为更好地理解中枢神经系统疾病提供了一种方法，例如自闭症、多发性硬化症、帕金森氏症等。 还将采用一种研究微生物组拓扑学的方法。目前的元基因组学获取DNA序列，并基于系统分类建立系统发育树。然而，如果我们获得唯一的序列读数并计算距离，使用现代计算方法，我们可以计算微生物组的拓扑。然后就可以进行统计检验了。通常情况下，点云数据噪声很大，而计算代数拓扑学中的稀疏方法至今还很少。这些方法的发展将以胃肠道疾病的生物信息学数据为主要应用。

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.