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.

项目概要/摘要 作为一种小肠疾病，环境性肠病会导致慢性炎症和 营养吸收不良导致其表现出与营养不良相似的表型。 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 为了更好地表征和了解全球环境性肠病。 对大型临床队列的血浆、尿液和十二指肠抽吸样本进行脂质组学分析 对 415 名巴基斯坦儿童进行的研究带来了技术挑战，例如跑步的需要。 批量采样，这反过来又带来了分析的计算挑战。 开发用于非靶向脂质组学分析的数据依赖方法，以识别稳健的脂质 每种环境性肠病和营养不良都有其独特的特征。 这项研究的首要目标是设计一个多批次分析流程，以高效、准确地 该管道将​​跨多个批次聚合数据，以解决多方面的 3 个计算挑战。 批量数据：1) 色谱保留时间对齐，2) 缺失数据插补，以及 3) 批量效应 这些挑战中的每一个都已单独分析，但在数据分析中，每个步骤都是独立的。 依赖于前一个管道并受前一个管道的影响。 质谱数据将允许获得可重复的结果，并将通过以下方式评估管道的准确性。 对各种样本矩阵进行测试并与现有算法进行比较。 拟议的第二个目标是开发一种基于途径的数据依赖工具，用于假定的脂质 由于化合物的体积，非靶向分析的一个瓶颈是快速鉴定。 数据，当前仅识别那些具有统计显着性的特征的方法在 在工作流程的早期引入路径分析等下游工作将产生效果。 这种方法使用研究特有的脂质初始输入并建立一个网络。 这些新的脂质被存储在数据库中，并在以下位置进行搜索： 使用这种方法识别脂质将产生更完整的网络结果概况。 该项目将鉴定环境性肠病患者独特的脂质组谱并将其分离 来自更大的营养不良疾病控制队列的这一初步步骤将为未来的验证奠定基础。 研究并最终利用环境性肠病的非侵入性诊断标记物，领先 随着大规模研究逐渐变得普遍，并回答这些问题，以改善这些儿童的健康。 由此产生的计算挑战，该项目将为非目标多目标产生依赖于数据的方法 批量质谱脂质组学分析，然后可以针对未来的脂质组学研究进行个性化。