Systems Metabolomics for Biomarker Discovery

项目总结 代谢组学提供了对生物样本中数千个小分子的全面分析。它可以 在不断发展的系统生物学方法中扮演着不可或缺的角色，以解开 代谢物和疾病。液-质联用(LC-MS)和气相联用 色质联用(GC-MS)已被用于高通量分析 成千上万的代谢物。然而，许多与疾病相关的代谢物的潜在价值 利用这些平台在系统生物学方法中发现生物标记物的探索还不充分。 由于缺乏计算工具和资源：(1)准确确定大多数 代谢物；(2)研究由于疾病引起的代谢物之间的重新连接相互作用；以及(3)整合 与其他组学研究中的代谢物图谱比较，以评估代谢物之间的关系 以及系统层面的疾病。部分由于这些限制，以前确定的可概括性较差 已经观察到代谢物生物标记物候选，特别是当它们通过独立的 平台和验证集。因此，人们寻求新的方法来寻找更具普遍性的代谢物。 生物标记物候选者。这一研究计划的目标是填补代谢物鉴定和多因素分析方面的空白。 通过使用系统代谢组学方法进行组学集成，这将增强代谢组学在 生物标记物发现的系统生物学方法。具体地说，拟议的研究计划将利用 多种资源(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)开发用于对推定的代谢物ID进行排序的全面工作流程；(2) 基于个体代谢物水平变化和成对变化的代谢物谱差异分析 疾病组与对照组之间的相互作用；以及(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组学数据用于鉴定极具前景的代谢物生物标记物 候选人。我们最近的进展导致了多组学数据的获取和计算技术的发展 代谢物鉴定和综合分析工具。建议的代谢物的性能 对推定的代谢物ID进行排序的鉴定工作流程将通过实验方法进行评估，使用 参比化合物。候选人的选择将采用差异性分析和综合分析的方法 通过生物标记物发现研究中获得的多组学数据获得的生物标记物。入选的候选人将是 通过使用独立样本和平台进行有针对性的量化评估，与用于 发现号。这些实验评估的结果将不仅用于帮助改进计算 方法，也是为了确定有前途的生物标记物候选。总而言之，拟议的研究计划旨在 利用网络建模、机器学习和多组学数据集成的强大功能来改进 发现疾病生物标记物的能力，这些疾病生物标记物可能在未来的大规模生物标记物验证研究中取得成功。

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.