Bioinformatics and Biostatistics of Gastrointestinal Diseases and Geometric Statistics

胃肠道疾病生物信息学和生物统计学与几何统计学

• 批准号: RGPIN-2016-03909
• 负责人: Kim, Peter
• 金额: $ 1.97万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=681775
• 关键词: Bioinformatics; Biostatistics; Gastrointestinal; Diseases; Geometric

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. *** **

该提案为宏基因组数据开发了新型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，特别关注实验性粪便微生物群移植（FMT）后的临床反应。 在这两项临床试验中，FMT提供了强有力的统计学证据证明其在临床消退方面的疗效。收集了大量数据，包括临床和人口统计数据，健康调查数据和宏基因组数据。根据至事件发生时间记录临床应答数据，包括FMT后的消退。 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品沿着进行测序。我们注意到，并非所有金融管理小组都取得了成功。 因此，特别感兴趣的是开发新的生物统计方法，用于从大量的宏基因组（生物信息学）协变量中选择最能解释临床反应的变量。 更具体地说，我们寻求开发一种生物统计学方法，以确定患者和供体结肠微生物系统中的哪些细菌在获得临床解决方面发挥关键作用。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，大脑的日常功能，因此中枢神经系统，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，但通过将宏基因组协变量与健康调查数据相对应的变量选择的发展，可以揭示关于脑-肠轴的一些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，例如自闭症，多发性硬化症，帕金森病，仅举几例。 * 还将寻求研究微生物组拓扑结构的方法。目前的宏基因组学采用DNA序列，并建立了一个系统发育树的基础上，基因分型。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，我们可以计算微生物组的拓扑结构。 然后可以制定统计测试。 典型的点云数据是非常嘈杂的，迄今为止，在计算代数拓扑稀疏的方法很少。 这些方法的发展将继续进行，主要应用是来自胃肠道疾病的生物信息学数据。*** **