CAREER: Developing Biochemoinformatics Tools for Large-Scale Metabolomics Applications

研究：稳定同位素分解代谢组学（SIRM）的最新进展使特定生物或生物群落的可观察代谢特征（代谢表型）的数量增加了数量级。只需几分钟的分析实验就可以检测到数千种代谢物的稳定同位素标记变体。因此，几乎所有重要的生物状态、生物过程和扰动都可以观察到独特的代谢表型。目前，主要的瓶颈是缺乏数据分析，无法将这堆积如山的表型数据适当地组织和解释为有洞察力的生化和生物学信息。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，以减轻这一限制，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对具有特定代谢表型的广泛生物过程的理解。主要的知识优势正在发展：-利用稳定同位素标记、化学选择探针、超高分辨率/精确质谱和核磁共振的综合优势，检测和鉴定代谢物的新方法。由于在目前的代谢组学数据集中，未识别的代谢物构成了检测到的大部分特征，因此代谢物的鉴定是一个关键的焦点。-关键误差分析，允许：i)对检测到的同位素强度及其误差进行严格的定量评价；Ii)通过后续分析评估误差传播；iii)根据检测到的错误制定质量控制措施。- SIRM实验中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反褶积方法。-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整合并交叉验证的新方法。教育：STEM学科学生努力程度和毕业率同时下降的趋势，对下一代科学家的成功教育来说不是好兆头。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。本项目采用基于设计的研究方法，将多种先进的教学方法整合到内容丰富的大学科学课程中。对这些方法的统计分析表明，使用支架式明确修订来提高学生努力的有效性具有很大的效果，并指出了对这些方法进行重大改进的途径，这将被追求和实施。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，这些数据集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解代谢组学（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，这些方法将代谢组学数据集与基因组学和其他组学水平数据集相结合，允许对广泛的生物过程进行新的系统级代谢见解。在本研究的执行过程中，大量来自不同STEM（科学、技术、工程和数学）学科的高中生、本科生和研究生将接触到多学科生物信息学研究项目，并使用跨学科方法进行研究。此外，首席研究员还开发了一套综合的先进的教学方法，适用于内容丰富的大学科学课程，这些课程对学生的努力和结果有统计上的显著影响。这些先进的教学方法通过一系列跨越不同学习水平的明确修订步骤，将学生的努力集中在纠正和学习以前在作业、测验和考试问题上的错误上。

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.