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体或生物群落的可观察代谢特征(代谢表型)的数量能够增加数量级。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，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来适当地组织和解释这些堆积如山的表型数据，将其作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，作为综合数据分析管道的一部分，以缓解这一限制，使SIRM能够广泛应用，从发现代表感兴趣的生物状态的特定代谢表型，到基于机制理解具有特定代谢表型的广泛生物过程。主要的特殊智能优点正在开发中：-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和核磁共振的组合优势来检测和鉴定代谢物的新方法。由于在当前的代谢组学数据集中，未识别的代谢物构成了大多数检测到的特征，因此对代谢物的识别是一个关键的焦点。-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估；ii)通过后续分析评估误差传播；以及iii)根据检测到的误差制定质量控制措施。-SIRM实验的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相结合并交叉验证的新方法。教育：学生努力程度下降和STEM学科毕业率下降的同时趋势，对下一代科学家的成功教育来说不是一个好兆头。提高学生成绩的一个更便捷的方法可能是提高学生的有效性？努力。该项目采用基于设计的研究方法，将多种先进的教与学方法整合到内容丰富的大学理科课程中。对这些方法的统计分析表明，使用脚手架显式修改来提高学生努力的有效性的效果很大，并指出了这些方法将被追求和实施的一条显著改进的途径。这项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数据集中分析并得出独特的机械信息，这些技术跟踪生物体细胞内数千个分子(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，该中心为国内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，用于广泛的科学应用，涉及细胞代谢变化的生物过程。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，允许对广泛的生物过程进行新的系统级代谢洞察。在这项拟议的研究的执行过程中，来自各种STEM(科学、技术、工程和数学)学科的大量高中、本科生和研究生将接触到多学科生物信息学研究项目，并使用跨学科的研究方法进行培训。此外，首席调查员还开发了一套先进的教与学方法，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重大影响。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的复习步骤，集中学生在作业、测验和考题上纠正和学习先前的错误的努力。

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.