CAREER: Developing Biochemoinformatics Tools for Large-Scale Metabolomics Applications

职业：开发用于大规模代谢组学应用的生物化学信息学工具

• 批准号: 1252893
• 负责人: Hunter Moseley
• 金额: $ 110.95万
• 依托单位: University of Louisville Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1252893&HistoricalAwards=false
• 关键词: CAREER; Developing; Biochemoinformatics; Tools; Large

Research: Recent advances in stable isotope-resolved metabolomics (SIRM) are enabling orders-of-magnitude increase in the number of observable metabolic traits (a metabolic phenotype) for a given organism or community of organisms. Analytical experiments that take only a few minutes to perform can detect stable isotope-labeled variants of thousands of metabolites. Thus, unique metabolic phenotypes may be observable for almost all significant biological states, biological processes, and perturbations. Currently, the major bottleneck is the lack of data analysis that can properly organize and interpret this mountain of phenotypic data as insightful biochemical and biological information. The research goals are to develop systems-level biochemical tools as part of an integrated data analysis pipeline that will alleviate this limitation, enabling a broad application of SIRM from the discovery of specific metabolic phenotypes representing biological states of interest to a mechanism-based understanding of a wide range of biological processes with particular metabolic phenotypes. The major specific intellectual merits are developing:- Novel methods for detection and identification of metabolites that utilize the combined advantages from stable isotope labeling, chemoselective probes, ultra-high resolution/accurate MS, and NMR. Since unidentified metabolites make up the majority of detected features in current metabolomics datasets, identification of metabolites is a key focus. - Key error analyses that allow: i) rigorous quantitative evaluation of detected isotopologue intensities and their errors; ii) evaluation of error propagation through subsequent analyses; and iii) development of quality control measures derived from the detected errors. - New algorithms for isotopic non-steady state conditions of SIRM experiments, especially deconvolution methods that will aid relative flux interpretation and metabolic flux analysis. - New methods that integrate and cross-validate metabolomics with genomics, transcriptomics, and proteomics via mutually-identified metabolic, gene expression, and signaling pathways.Education: Simultaneous trends of declining student effort and declining graduation rates in STEM disciplines do not bode well for the successful education of the next generation of scientists. A more expedient approach to improving student outcomes may be to increase the effectiveness of students? effort. Using a design-based research approach, this project integrates multiple advanced teaching-learning methods into content-rich college science courses. Statistical analysis of these methods shows large effect sizes for the use of scaffolded explicit revision to improve the effectiveness of student effort and indicates a path for significant refinement of these methods, which will be pursued and implemented. The proposed research will create computational tools that analyze and derive unique mechanistic information from large datasets available from cutting-edge metabolomics technologies which track atomic level changes in the production and utilization of thousands of molecules (metabolites) inside the cells of organisms. These novel computational tools will be tested and refined in the Center for Regulatory and Environmental Analytical Metabolomics (CREAM), which provides state-of-the-art analytical services and expertise for national and international stable isotope-resolved metabolomics (SIRM) research efforts. Once these computational tools reach production-quality, they will be disseminated to the broader scientific community for a wide variety of scientific applications involving biological processes with changes in cellular metabolism. Also, these methods will integrate metabolomics datasets with genomics and other omics-level datasets, allowing new systems-level metabolic insights into a wide range of biological processes. During the execution of this proposed research, significant numbers of high school, undergraduate, and graduate students from a wide variety of STEM (Science, Technology, Engineering, & Math) disciplines will be exposed to and trained with multidisciplinary bioinformatics research projects using interdisciplinary approaches to research. In addition, the principal investigator has developed an integrated set of advanced teaching-learning methods amenable to content-rich college science courses that have statistically significant impacts on student effort and outcomes. These advanced teaching-learning methods focus students' efforts at correcting and learning from prior mistakes on assignments, quizzes, and exam questions via a series of explicit revision steps that span different levels of learning.

研究：稳定同位素分辨代谢组学（SIRM）的最新进展使得给定生物体或生物体群落的可观察代谢性状（代谢表型）的数量能够数量级增加。 只需几分钟的分析实验就可以检测出数千种代谢物的稳定同位素标记变体。 因此，独特的代谢表型可以观察到几乎所有重要的生物状态，生物过程和扰动。 目前，主要的瓶颈是缺乏数据分析，无法正确地组织和解释这些堆积如山的表型数据，使其成为有洞察力的生化和生物信息。 研究目标是开发系统级生化工具，作为集成数据分析管道的一部分，这将减轻这种限制，使SIRM的广泛应用从发现代表感兴趣的生物状态的特定代谢表型到基于机制的理解具有特定代谢表型的广泛生物过程。 主要的具体智力优势正在开发：-新的方法检测和鉴定代谢物，利用稳定的同位素标记，化学选择性探针，超高分辨率/准确的MS和NMR的组合优势。 由于未鉴定的代谢物构成了当前代谢组学数据集中检测到的大部分特征，因此代谢物的鉴定是一个关键焦点。- 关键错误分析允许：i）对检测到的同位素体强度及其误差进行严格的定量评估; ii）通过后续分析评估误差传播;以及iii）开发源自检测到的误差的质量控制措施。- SIRM实验同位素非稳态条件的新算法，特别是有助于相对通量解释和代谢通量分析的去卷积方法。 - 通过相互识别的代谢、基因表达和信号通路，将代谢组学与基因组学、转录组学和蛋白质组学进行整合和交叉验证的新方法。教育：STEM学科学生努力程度下降和毕业率下降的趋势对下一代科学家的成功教育来说并不是一个好兆头。提高学生成绩的一个更权宜之计可能是提高学生的效率？努力 本计画以设计为基础的研究方法，将多种先进的教与学方法整合到内容丰富的大学科学课程中。 这些方法的统计分析表明，使用脚手架明确的修订，以提高学生的努力的有效性，大的效果大小，并表明这些方法，这将是追求和实施的显着细化的路径。拟议的研究将创建计算工具，从尖端代谢组学技术提供的大型数据集中分析和获得独特的机制信息，这些技术跟踪生物体细胞内数千种分子（代谢物）的生产和利用中的原子水平变化。 这些新的计算工具将在监管和环境分析代谢组学中心（CREAM）进行测试和改进，该中心为国家和国际稳定同位素分辨代谢组学（SIRM）研究工作提供最先进的分析服务和专业知识。 一旦这些计算工具达到生产质量，它们将被传播到更广泛的科学界，用于涉及细胞代谢变化的生物过程的各种科学应用。 此外，这些方法将把代谢组学数据集与基因组学和其他组学水平的数据集整合在一起，从而允许对广泛的生物过程进行新的系统水平的代谢洞察。 在执行这项拟议的研究期间，来自各种STEM（科学，技术，工程，数学）学科的大量高中，本科和研究生将接触并接受使用跨学科方法进行研究的多学科生物信息学研究项目的培训。 此外，主要研究者还开发了一套先进的教学方法，适合内容丰富的大学科学课程，对学生的努力和成果有统计学上的显着影响。 这些先进的教学方法集中学生的努力，纠正和学习以前的错误，作业，测验和考试问题，通过一系列明确的修订步骤，跨越不同层次的学习。