Platform for in vivo Metabolism

项目摘要 现代代谢组学已经给生物学和生物医学研究带来了革命性的变化。现在可以确定特定的 与疾病或治疗反应相关的代谢生物标志物，这可以转化为改善 诊断。然而，知识中的关键差距仍然限制了代谢组学的影响。第一，在 促进代谢组学发展的分析仪器仅限于生物液或组织或 细胞。新陈代谢是一个高度动态的过程，可以随着环境条件的变化而迅速变化，但大多数 代谢组学技术不能直接监测体内的动态过程。相反，当他们 从根本上说，动态是通过离散采样来测量的，这导致了多个样本并添加了 差异。代谢组学的第二个限制是我们能够高度自信地识别未知的代谢物。 在代谢组学研究中，LC-MS或核磁共振测量的许多“特征”仍然未知，限制了 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资金， 我们已经开发了改进的核磁共振探针，允许在核磁共振测量中具有更高的灵敏度。这是 重要的是因为核磁共振是鉴定未知代谢物的最佳方法。我们目前的调查将是 于2022年2月投入使用，针对21.1T(900 MHz 1H)的13C检测进行了优化；我们预计它将 提供最高的13C核磁共振灵敏度。这项技术允许数据实质上 提高我们识别未知代谢物的能力。我们还开发了代谢物“组分文库”， 首先对特定样品进行化学分离，然后用一维和二维核磁共振测量每一组分 和LC-MS/MS，来自分数库的数据将允许通过有效地连接核磁共振来识别未知 和LC-MS数据。在本Mira中，我们将通过开发工具来连接 不同的数据集。我们还开发了一种被称为连续体内核磁共振代谢的方法(CIVM- 核磁共振)。我们已经将CIVM-核磁共振应用于培养粗糙脉孢霉，这是一种丝状真菌，已经被用来 将遗传学与新陈代谢联系起来。我们可以以大约1分钟的分辨率实时监测粗枝藻的生长 一周以上。这使我们能够测量所有代谢物和脂类的定量代谢细节 浓度大于25微米我们已经制作了计算工具来提取300多条生长曲线 一个单一的CIVM-核磁共振数据集，使我们能够从功能上表征随着时间的推移的代谢变化 碳源、温度或氧气利用率的函数。在这个MIRA项目中，我们将扩展CIMV-核磁共振 通过测量不同环境下的新陈代谢突变，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.