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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=708879
• 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.

该建议开发了用于宏基因组数据的新型生物信息学和生物统计学方法。 特殊应用是胃肠道疾病溃疡性结肠炎（UC）和艰难梭菌（C艰难梭菌）感染（CDI）。 UC是一种由结肠炎症控制的慢性疾病，而后者是由侵略性病原体引起的。 最近进行了两项单独的临床试验，并在实验性粪便菌群移植（FMT）后特别注意临床反应。 在这两个临床试验中，FMT都提供了其在临床分辨率方面有效性的有力统计证据。收集了大量数据，其中包括临床和人口统计数据，健康调查数据和宏基因组数据。 根据事件的时间记录了由FMT之后分辨率组成的临床响应数据。 供体粪便提供了材料，并与相应的患者的FMT前粪便样品配对。同时对供体和FMT患者粪便以及随后的几个粪便样品进行了测序。我们注意到，并非所有FMT都成功。 因此，特别感兴趣的是从大量的元基因组（生物信息学）协变量中开发出新的生物统计学方法，从而最能解释临床反应。 更具体地说，我们试图开发一种生物统计学方法，以识别患者和供体菌落微生物系统中哪些细菌在获得临床分辨率中起着至关重要的作用。 脑脉管轴描述了胃肠道与大脑之间的生理联系。有充分的证据表明，大脑的每天功能，因此可以通过调节肠道菌群来改变中枢神经系统。尽管UC和CDI不是神经疾病，但可以通过开发可变选择的开发来揭示有关元基因组协变量与健康调查数据相对应的有价值的有关脑脉管轴的有价值的量化信息。这可以提供一种方法来更好地理解中枢神经系统的疾病，例如自闭症，多发性硬化症，帕金森氏病，仅举几例。 还将采用一种研究微生物组拓扑结构的方法。当前的宏基因组学采用DNA序列，并基于系统型生成系统发育树。但是，如果我们使用现代的计算方法进行独特的序列读取并计算距离，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。 通常，点云数据非常嘈杂，迄今为止，计算代数拓扑中的稀疏方法存在很少。 这种方法的开发将被追求，主要应用是胃肠道疾病的生物信息学数据。