Platform for in vivo Metabolism

项目摘要 现代代谢组学已经彻底改变了生物学和生物医学研究。现在可以确定具体的 与疾病或对治疗的反应相关的代谢生物标志物，其可以转化为改善的 诊断然而，关键的知识差距仍然限制了代谢组学的影响。首先， 促进代谢组学发展的分析仪器仅限于生物流体或组织提取物， 细胞代谢是一个高度动态的过程，可以随着环境条件迅速变化，但大多数 代谢组学技术不能直接在体内监测动态过程。相反，当他们 动态是通过离散采样来测量的，这导致了多个样本， 方差代谢组学的第二个限制是我们以高置信度鉴定未知代谢物的能力。 在代谢组学研究中通过LC-MS或NMR测量的许多“特征”仍然未知，限制了代谢组学的研究。 生物影响。 我们的实验室最近开发了解决这些知识差距的方法。通过NIGMS的资助， 我们已经开发了改进的NMR探针，其允许在NMR测量中具有更高的灵敏度。这是 这一点很重要，因为NMR是鉴定未知代谢物的最佳方法。我们现在的探测器 2022年2月投入使用，并针对21.1 T（900 MHz 1H）下的13 C检测进行了优化;我们预计它将 提供最高可能的13 C NMR灵敏度。这项技术允许数据， 提高我们识别未知代谢物的能力。我们还开发了代谢物“馏分库”， 从特定样品化学分离开始，然后通过1D和2D NMR测量每个级分 来自馏分库的数据将允许通过有效地连接NMR和LC-MS/MS来鉴定未知物。 和LC-MS数据。在这个MIRA中，我们将通过开发工具来连接 不同的数据集我们还开发了一种称为核磁共振连续体内代谢（CIVM-）的方法 NMR）。我们已经将CIVM-NMR应用于生长粗糙脉孢菌，这是一种丝状真菌， 将遗传学与新陈代谢联系起来我们可以监控N. crassa实时约1分钟的分辨率 超过1周。这使我们能够测量所有代谢物和脂质的定量代谢细节， 浓度大于25 μM。我们已经制作了计算工具， 一个单一的CIVM-NMR数据集，使我们能够在功能上表征代谢变化随时间的推移， 碳源、温度或氧可用性的函数。在这个MIRA项目中，我们将扩展CIMV-NMR 通过测量不同环境下的代谢突变体，并建立一个连接所有数据的网络服务器。

Project Summary Modern metabolomics have revolutionized biology and biomedical research. It is now possible to identify specific metabolic biomarkers associated with disease or response to treatment, which can translate into improved diagnostics. However, key gaps in knowledge remain that limit the impact of metabolomics. First, advances in analytical instrumentation that fueled the growth of metabolomics are limited to biofluids or extracts of tissues or cells. Metabolism is a highly dynamic process that can change rapidly with environmental conditions, but most metabolomics techniques are not able to monitor the dynamic process directly in vivo. Rather, when they are measured at all, dynamics are measured by discrete sampling, which leads to multiple samples and added variance. A second limitation in metabolomics is our ability to identify unknown metabolites with high confidence. Many of the “features” measured by LC-MS or NMR in metabolomics studies remain unknown, limiting the biological impact. Our laboratory has recently developed methods to address these gaps in knowledge. Through NIGMS funding, we have developed improved NMR probes that allow for greater sensitivity in NMR measurements. This is important because NMR is the best method for unknow metabolite identification. Our current probe will be commissioned in February 2022 and is optimized for 13C detection at 21.1 T (900 MHz 1H); we expect that it will provide the highest possible 13C NMR sensitivity available. This technology allows for data that will substantially improve our ability to identify unknown metabolites. We have also developed metabolite “fraction libraries”, which start with chemical separation of a specific sample followed by measurement of each fraction by 1D and 2D NMR and LC-MS/MS. The data from a fraction library will allow unknowns to be identified by efficiently linking the NMR and LC-MS data. In this MIRA we will make a fraction library knowledgebase by developing tools to connect the different datasets. We have also developed an approach called continuous in vivo metabolism by NMR (CIVM- NMR). We have applied CIVM-NMR to growing Neurospora crassa, a filamentous fungus that has been used to link genetics to metabolism. We can monitor the growth of N. crassa in real-time with about 1 minute resolution for over 1 week. This allows us to measure quantitative metabolic details of all the metabolites and lipids with concentrations greater than 25 µM. We have made computational tools to extract over 300 growth curves from a single CIVM-NMR dataset, allowing us to functionally characterize the metabolic changes over time as a function of carbon source, temperature, or oxygen availability. In this MIRA project, we will expand CIMV-NMR by measuring metabolic mutants under different environments and build a web server that connects all the data.