Platform for in vivo Metabolism

点击翻译按钮获取中文摘要

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.