Global Lipidomics Analysis Techniques for Novel Biomarker Discovery of Environmental Enteropathy

用于环境肠病新生物标志物发现的全球脂质组学分析技术

• 批准号: 10663197
• 负责人: Khyati Mehta
• 金额: $ 1.55万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10663197
• 关键词: Address; Algorithms; Biological Assay; Biological Markers; Cessation of life; Charge; Child; Child Health; Child Malnutrition; Childhood; Chromatography; Chronic; Clinical Research; Cohort Studies; Computing Methodologies; Data; Data Aggregation; Data Analyses; Data Set; Databases; Developing Countries; Development; Duodenum; Environmental Impact; Foundations; Future; Gastrointestinal Diseases; Gastrointestinal Endoscopy; Individual; Inflammation; Intervention; Isomerism; Knowledge; Lipids; Malabsorption Syndromes; Malnutrition; Mass Spectrum Analysis; Methodology; Methods; Modeling; Morbidity - disease rate; Morphologic artifacts; Motivation; Nutrient; Pathway Analysis; Pathway interactions; Patients; Pattern Recognition; Phenotype; Plasma; Population Study; Reporting; Reproducibility; Reproducibility of Results; Research; Running; Sample Size; Sampling; Serum; Small Intestines; Structure; Techniques; Testing; Time; Urine; Variant; Work; analysis pipeline; analytical tool; aspirate; biomarker discovery; biomarker validation; candidate identification; candidate marker; cohort; comorbidity; computational pipelines; data complexity; design; diagnostic biomarker; disease phenotype; disorder control; improved; insight; large scale data; lipidome; lipidomics; liquid chromatography mass spectrometry; mortality; noninvasive diagnosis; nonlinear regression; novel; novel marker; personalized medicine; tool; treatment planning; validation studies

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.

项目总结/摘要 作为小肠的一种状况，环境性肠病导致慢性炎症， 营养吸收不良，导致其表现为与营养不良相似。分离和 环境性肠病患者与营养不良的鉴别只能通过侵入性上消化道检查来完成， 胃肠内窥镜检查为了更好地描述和理解环境性肠病，全球 脂质组学分析是在一个大的队列临床研究中对血浆、尿液和十二指肠穿刺样本进行的。 研究对象为415名巴基斯坦儿童。更大的样本量带来了技术挑战，例如需要运行 批量样品，这反过来又提出了分析的计算挑战。该提案的重点是 开发非靶向脂质组学分析的数据依赖性方法， 每种环境性肠病和营养不良的独特特征。 本研究的第一个目的是设计一个多批次分析管道， 聚合不同批次的数据。这条管道将解决多个计算挑战， 批处理数据：1）色谱保留时间对齐，2）缺失数据插补，3）批效应 纠正一下这些挑战中的每一个都被单独分析，但在数据分析中， 受前一个的影响和影响。开发一个集成的、依赖于数据的管道， 质谱数据将允许可再现的结果。将通过以下方式评估管道的准确性： 在各种样本矩阵上进行测试，并与现有算法进行比较。 建议的第二个目标是开发一个基于途径的数据依赖工具，用于推定的脂质 识别.非靶向分析的一个瓶颈是化合物的快速鉴定。由于体积 数据，目前的方法只确定那些具有统计意义的特征， 下游工作，如路径分析。在工作流程的早期引入途径知识将产生 更有意义的结果。这种方法使用了研究中特有的脂质的初始输入，并建立了一个网络， 额外的连接脂质。这些新的脂质存储在数据库中，并在数据库中搜索它们。 用户的数据。用这种方法鉴定脂质将得到更完整的结果网络图谱。 该项目将确定环境性肠病患者的独特脂质组谱，并将其分离 来自一个更大的营养不良疾病控制队列。这一初步步骤将为今后的验证奠定基础 研究并最终利用环境肠病的非侵入性诊断标志物， 改善这些孩子的健康。随着大规模研究越来越普遍， 由此产生的计算挑战，该项目将产生数据依赖的方法，为非目标的多， 批量质谱脂质组学分析，然后可以个性化用于未来的脂质组学研究。