Platform for in vivo Metabolism

体内代谢平台

基本信息

批准号：
10552310
负责人：
ARTHUR S EDISON
金额：
$ 55.08万
依托单位：
UNIVERSITY OF GEORGIA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2028-01-31
项目状态：
未结题

项目摘要

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.

项目概要现代代谢组学彻底改变了生物学和生物医学研究，现在可以识别特定的物质。与疾病或治疗反应相关的代谢生物标志物，可以转化为改善然而，知识方面的关键差距仍然限制了代谢组学的进步。推动代谢组学发展的分析仪器仅限于生物体液或组织提取物或细胞的新陈代谢是一个高度动态的过程，可以随着环境条件而迅速变化，但大多数。代谢组学技术无法直接监测体内的动态过程。无论如何测量，动态都是通过离散采样来测量的，这会导致多个样本并添加代谢组学的第二个限制是我们以高置信度识别未知代谢物的能力。代谢组学研究中通过 LC-MS 或 NMR 测量的许多“特征”仍然未知，限制了生物学影响。我们的实验室最近通过 NIGMS 资助开发了解决这些知识差距的方法。我们开发了改进的 NMR 探头，可以提高 NMR 测量的灵敏度。重要的是，核磁共振是鉴定未知代谢物的最佳方法。于 2022 年 2 月投入使用，针对 21.1 T (900 MHz 1H) 的 13C 检测进行了优化，我们预计它会实现这一目标；提供尽可能高的 13C NMR 灵敏度，该技术可提供显着的数据。提高我们识别未知代谢物的能力我们还开发了代谢物“组分库”。首先对特定样品进行化学分离，然后通过 1D 和 2D NMR 测量每个组分和 LC-MS/MS 馏分库中的数据将允许通过有效连接 NMR 来识别未知物。在此 MIRA 中，我们将通过开发连接数据的工具来创建分数库知识库。我们还开发了一种称为 NMR 连续体内代谢（CIVM-）的方法。我们已将 CIVM-NMR 应用于生长粗糙脉孢菌（Neurospora crassa），这是一种丝状真菌，已用于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糙猪笼草的生长。这使我们能够测量所有代谢物和脂质的定量代谢细节。我们已经制作了计算工具来提取超过 300 条生长曲线。单个 CIVM-NMR 数据集，使我们能够在功能上表征随时间变化的代谢变化碳源、温度或氧气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构建连接所有数据的网络服务器。