Global Lipidomics Analysis Techniques for Novel Biomarker Discovery of Environmental Enteropathy

项目摘要/摘要 作为小肠的一种状况，环境性肠病会引起慢性炎症和 营养吸收不良导致其表现出类似营养不良的症状。分离和 诊断营养不良的环境肠病患者只能通过侵入性上腹部检查完成。 胃肠道内窥镜检查。为了更好地描述和了解环境肠病，全球 对一项大型临床队列的血浆、尿液和十二指肠抽吸物样本进行了脂质组学分析。 对415名巴基斯坦儿童的研究。更大的样本量带来了技术挑战，例如需要运行 批量样品，这反过来又带来了分析的计算挑战。这项提案的重点是 基于数据的非靶向脂质组学分析方法的发展 每种环境肠病和营养不良的独特特征。 这项研究的第一个目标是设计一种高效、准确的多批次分析流水线 聚合不同批次的数据。这条流水线将解决3个计算挑战， 批次数据：1)色谱保留时间对齐，2)数据缺失，3)批次效应 更正。这些挑战中的每一个都被单独分析过，但在数据分析中，每一步都是 依赖于前一个，并受前一个影响。开发集成的、依赖于数据的管道专用 质谱学数据将允许重复性的结果。管道的准确性将通过以下方式进行评估 对各种样本矩阵进行测试，并与现有算法进行比较。 提出的第二个目标是开发一种基于通路的数据依赖工具，用于推测脂质 身份证明。非靶向分析的一个瓶颈是化合物的快速鉴定。由于……的数量 数据，当前仅识别那些具有统计意义的要素的方法在以下方面造成了差距 下游工作，如路径分析。在工作流程中更早地引入路径知识将在 更有意义的结果。这种方法使用研究特有的脂类初始输入，并建立一个网络 更多相关的脂类。这些新的脂类存储在数据库中，并在 用户的数据。用这种方法鉴定血脂将导致结果的更完整的网络概况。 这个项目将识别环境肠病患者的独特的脂质体特征，并将他们分开。 来自一个更大的营养不良疾病控制队列。这第一步将为未来的验证奠定基础 研究并最终利用非侵入性诊断标记物诊断环境肠病 为了改善这些孩子的健康。随着大规模研究逐渐变得普遍，为了回答 由此带来的计算挑战，该项目将为无目标的多个 批量质谱脂组学分析，然后可以为未来的脂肪组学研究进行个性化。

Project Summary/Abstract As a condition of the small intestine, environmental enteropathy causes to chronic inflammation and malabsorption of nutrients leading it to present itself phenotypically similar to malnutrition. Separation and identification of patients with environmental enteropathy from malnutrition can only be done via an invasive upper gastrointestinal endoscopy. In an effort to better characterize and understand environmental enteropathy, global lipidomics profiling was performed on plasma, urine, and duodenal aspirate samples of a large cohort clinical study of 415 Pakistani children. Larger sample sizes introduces technical challenges, such as the need to run samples in batches, which in turn presents computational challenges of analysis. This proposal is focused on the development of data-dependent methodologies for untargeted lipidomics analysis to identify robust lipid profiles unique to each environmental enteropathy and malnutrition. The first aim of this proposed research is to design a multi-batch analysis pipeline to efficiently and accurately aggregate data across the various batches. This pipeline will address 3 computational challenges of multi- batched data: 1) chromatographic retention time alignment, 2) missing data imputation, and 3) batch effect correction. Each of these challenges have been analyzed individually, but in data analysis each step is dependent on and influenced by the prior one. Development of an integrated, data-dependent pipeline specific to mass spectrometry data will allow for reproducible results. The pipeline will be evaluated for accuracy via testing on various sample matrices and by comparison to existing algorithms. The proposed second aim is the development of a pathway-based data-dependent tool for putative lipid identification. A bottleneck of untargeted analysis is the rapid identification of compounds. Due to the volume of data, the current approach of only identifying those features which are statistically significant creates gaps in downstream work such as pathway analysis. Introducing pathway knowledge earlier in the workflow will yield in more meaningful results. This approach uses an initial input of lipids unique to the study and builds a networks of additional connected lipids. These new lipids are stored in a database and a search is performed for them in the user’s data. Identification of lipids with this methods will lead to a more complete network profile of results. This project will identify distinctive lipidome profiles of environmental enteropathy patients and separate them from a larger malnutrition disease control cohort. This initial step will lay the foundation for future validation studies and ultimately the utilization of non-invasive diagnostics markers of environmental enteropathy, leading to improved health of these children. As large-scale studies steadily become more common and to answer the resulting computational challenges, this project will produce data-dependent methodologies for untargeted multi- batch mass spectrometry lipidomics analysis which can then be personalized for future lipidomics studies.