Developing Computational Approaches for Integration of Metabolomics into Systems Biology

开发将代谢组学整合到系统生物学中的计算方法

• 批准号: RGPIN-2016-04990
• 负责人: Xia, Jianguo
• 金额: $ 2.26万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709553
• 关键词: Developing; Computational; Approaches; Integration; Metabolomics

Metabolomics is the comprehensive study of all chemicals and metabolites (i.e. the metabolome) present in a biological system. The metabolome includes both endogenous metabolites and exogenous compounds from food, gut microbes, chemical exposures, etc., representing the final products of complex biological events governed by host genetics and environmental influences. Metabolomics is increasingly applied to study complex diseases, plant physiology, animal nutrition, host-gut microbiota interactions as well as environmental monitoring. Bioinformatics plays an essential role in metabolomics - from raw data processing to biological interpretation. Currently, metabolomics is facing three main computational challenges: i) the spectral processing challenge due to the increasing use of high-resolution mass spectrometry (MS) systems, ii) the functional interpretation challenge due to the generation of metabolomics data from complex environments such as the mammalian gut microbiota (i.e. meta-metabolome), and iii) the challenge for integrating metabolomics data with other omics data for comprehensive biological understanding. Novel computational approaches are urgently needed to address these needs. The long-term goal of my research program is to develop an integrated, comprehensive and predictive systems-biology framework to help explain metabolome variations due to different environmental perturbations. The short-term objective is to develop bioinformatics approaches to address the current data processing and integration challenges within the context of host-gut microbiota metabolic interactions. To this end, I will develop innovative computational algorithms to improve MS-based metabolomics data processing and annotation; integrate with genome mining approaches for comprehensive gut metabolome characterization; and finally model the variations of gut metabolome through community-scale metabolic networks. The performance of these tools will be evaluated using the public data sets as well as the data sets generated in-house and through collaborations. The research results, including tools, databases and algorithms will be publicly available through the latest web technologies and cloud-based computing platforms. The proposed research program addresses the major bottlenecks in current metabolomics, and offers great potential for translational applications through the identification of novel biomarkers, the discovery of key connections and the development of predictive models. The research activities described in this proposal will enable effective training and placement of HQP in a cross-disciplinary environment.

代谢组学是对生物系统中存在的所有化学物质和代谢物（即代谢物组）的综合研究。代谢物组包括来自食物、肠道微生物、化学暴露等的内源性代谢物和外源性化合物，代表了由宿主遗传学和环境影响控制的复杂生物事件的最终产物。代谢组学越来越多地应用于研究复杂疾病、植物生理学、动物营养、宿主-肠道微生物群相互作用以及环境监测。生物信息学在代谢组学中起着至关重要的作用-从原始数据处理到生物学解释。目前，代谢组学面临着三个主要的计算挑战：i）由于越来越多地使用高分辨率质谱（MS）系统而导致的光谱处理挑战，ii）由于从复杂环境（例如哺乳动物肠道微生物群）生成代谢组学数据而导致的功能解释挑战，（即元代谢组），和iii）整合代谢组学数据与其他组学数据的挑战，以全面的生物学理解。迫切需要新的计算方法来满足这些需求。我研究项目的长期目标是开发一个综合、全面和预测的系统生物学框架，以帮助解释由于不同环境扰动而导致的代谢组变化。短期目标是开发生物信息学方法，以解决当前宿主肠道微生物群代谢相互作用背景下的数据处理和集成挑战。为此，我将开发创新的计算算法，以改善基于MS的代谢组学数据处理和注释;与基因组挖掘方法相结合，以进行全面的肠道代谢组表征;并最终通过社区规模的代谢网络对肠道代谢组的变化进行建模。将使用公共数据集以及内部和通过合作生成的数据集来评估这些工具的性能。研究成果，包括工具、数据库和算法，将通过最新的网络技术和基于云的计算平台公开提供。拟议的研究计划解决了当前代谢组学的主要瓶颈，并通过识别新的生物标志物，发现关键连接和开发预测模型为翻译应用提供了巨大的潜力。本建议书中描述的研究活动将使HQP在跨学科环境中得到有效的培训和安置。