Systems Metabolomics for Biomarker Discovery

用于生物标志物发现的系统代谢组学

• 批准号: 10491700
• 负责人: Habtom W Ressom
• 金额: $ 39万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10491700
• 关键词: Biological; Biological Markers; Biological databases; Computing Methodologies; Control Groups; Coupled; Data; Development; Disease; Evaluation; Future; Gas Chromatography; Genomics; Goals; Individual; Libraries; Liquid Chromatography; Machine Learning; Mass Spectrum Analysis; Methods; Multiomic Data; Network-based; Outcome; Pattern; Performance; Play; Proteomics; Research; Resources; Role; Sampling; System; Systems Biology; Validation; base; biomarker discovery; biomarker selection; biomarker validation; candidate marker; candidate selection; computerized tools; computing resources; data integration; glycoproteomics; high throughput analysis; improved; innovation; machine learning method; metabolomics; multiple omics; network models; platform-independent; programs; small molecule; statistical and machine learning; transcriptomics; validation studies

PROJECT SUMMARY Metabolomics offers a comprehensive analysis of thousands of small molecules in biological samples. It can play an indispensable role in the growing systems biology approaches to unravel the relationships between metabolites and diseases. Liquid chromatography coupled to mass spectrometry (LC-MS) and gas chromatography coupled to mass spectrometry (GC-MS) have been used for high-throughput analysis of thousands of metabolites. However, the potential values of many disease-associated metabolites discovered by using these platforms have been inadequately explored in systems biology approaches for biomarker discovery due to lack of computational tools and resources to: (1) accurately determine the identity of most of the metabolites; (2) investigate the rewiring interactions among the metabolites due to diseases; and (3) integrate metabolite profiles with those from other omics studies to evaluate the relationships between the metabolites and the diseases at the systems level. Partly due to these limitations, poor generalizability of previously identified metabolite biomarker candidates has been observed, especially when they are evaluated through independent platforms and validation sets. Therefore, new methods are sought to find more generalizable metabolite biomarker candidates. The goal of this research program is to fill the gaps in metabolite identification and multi- omics integration by using systems metabolomics approaches that will enhance the role of metabolomics in systems biology approaches for biomarker discovery. Specifically, the proposed research program will utilize multiple resources (biological databases, spectral libraries, etc.) and innovative statistical, machine learning, and network-based methods for: (1) developing a comprehensive workflow for ranking putative metabolite IDs; (2) differential analysis of metabolite profiles based on changes in the levels of individual metabolites and pairwise interactions in disease vs. control groups; and (3) integration of metabolomics data with genomics, transcriptomics, proteomics, and glycoproteomics data to identify highly promising metabolite biomarker candidates. Our recent progress has led to acquisition of multi-omics data and development of computational tools for metabolite identification and integrative analysis. The performance of the proposed metabolite identification workflow in ranking putative metabolite IDs will be evaluated through experimental methods using reference compounds. The differential and integrative analysis methods will be used for selection of candidate biomarkers via multi-omics data acquired in biomarker discovery studies. The selected candidates will be evaluated by targeted quantitation using independent samples and platforms compared to those used for discovery. The outcomes of these experimental evaluations will be used not only to help refine the computational methods but also to identify promising biomarker candidates. In summary, the proposed research program seeks to capitalize on the power of network modeling, machine learning, and multi-omics data integration to improve the ability to find disease biomarkers that are likely to succeed in future large-scale biomarker validation studies.

项目摘要 代谢组学对生物样品中数千个小分子进行了全面分析。它可以 在不断增长的系统生物学方法中起着不可或缺的作用 代谢物和疾病。液相色谱与质谱（LC-MS）和气体耦合 耦合到质谱法（GC-MS）的色谱已用于高通量分析 成千上万的代谢产物。但是，许多与疾病相关的代谢产物的潜在值 在系统生物标志物发现的系统生物学方法中，使用这些平台的探索不足 由于缺乏计算工具和资源：（1）准确确定大多数的身份 代谢物； （2）研究由于疾病引起的代谢产物之间的重新连接相互作用； （3）整合 与其他OMICS研究的代谢物谱一起评估代谢物之间的关系 以及系统层面的疾病。部分由于这些局限性，先前识别的概括性差 已经观察到了代谢物生物标志物候选物，尤其是当它们通过独立评估时 平台和验证集。因此，寻求新的方法来找到更多可概括的代谢物 生物标志物候选人。该研究计划的目的是填补代谢物识别和多种多样的空白 通过使用系统代谢组学方法的OMICS集成，以增强代谢组学在 系统生物标志物发现的系统生物学方法。具体而言，拟议的研究计划将使用 多个资源（生物数据库，光谱库等）以及创新的统计，机器学习和 基于网络的方法：（1）开发一个全面的工作流程，用于对推定的代谢物ID进行排名； （2） 基于单个代谢物和成对水平的变化的代谢产物谱分析的差异分析 疾病与对照组的相互作用； （3）代谢组学数据与基因组学的整合， 转录组学，蛋白质组学和糖蛋白质组学数据，以识别高度有希望的代谢物生物标志物 候选人。我们最近的进步导致了多摩斯数据的获取和计算的开发 代谢物识别和综合分析的工具。提议的代谢物的性能 标识工作流在排名推定的代谢物ID中，将通过实验方法评估 参考化合物。差分和综合分析方法将用于选择候选者 通过在生物标志物发现研究中获取的多摩学数据的生物标志物。选定的候选人将是 通过使用独立的样本和平台进行针对性的定量评估 发现。这些实验评估的结果将不仅用于改进计算 方法也可以识别有希望的生物标志物候选者。总而言之，拟议的研究计划寻求 利用网络建模，机器学习和多摩斯数据集成的力量以改进 在未来的大规模生物标志物验证研究中找到可能成功的疾病生物标志物的能力。