Metabolomics Explorer (MetEx) for Cryptic Natural Product or Antibiotic Drug Discovery
Princeton Docket # 22-3873
Researchers in Princeton University’s Department of Chemistry have developed Metabolomics Explorer (MetEx), an application for the analysis of parallel liquid chromatography-coupled mass spectrometry (LC-MS)-based metabolomics data. MetEx is a highly interactive application that facilitates visualization and analysis of complex metabolomics datasets, consisting of features described by retention time, m/z, and MS intensity, as a function of hundreds of conditions or elicitors. The software enables prioritization of leads from three-dimensional maps, extraction of two-dimensional slices from various higher order plots, organization of datasets by elicitor chemotypes, customizable library-based dereplication, and automatically scored lead selection. Application of MetEx to analyze a large metabolomics dataset of 750 individual LC-MS datafiles from a high-throughput elicitor screen performed in Burkholderia gladioli identified dozens of novel metabolites and the associated elicitors.
Advances in next-generation DNA sequencing technologies, bioinformatics, and mass spectrometry-based metabolite detection have ushered in a new era of natural product discovery. Mass spectrometry acquired snapshots of microbial secondary metabolomes are complex, especially when otherwise silent biosynthetic genes are activated, and there is there-fore a need for data analysis software to explore and map the resulting highly dimensional datasets.
- Natural Product Drug Discovery
- Effective analysis of hundreds of HPLC-MS datasets
- Identification of new natural products synthesized by cryptic biosynthetic gene clusters
- Metabolome Visualization
- User Friendly interface, web based application
- Interactive to facilitate the visualization of potentially thousands of microbial metabolomes within a single plot
- Solves the challenge of dissecting multi-dimensional metabolomics datasets
Intellectual Property & Development Status
Patent protection is pending.
Princeton is currently seeking commercial partners for the further development and commercialization of this opportunity.
Stage of Development
The invention was evaluated in detail using an example dataset from a High-Throughput Elicitor Screen (HiTES) conducted on Burkholderia gladioli. In this screen, B. gladioli was subjected to a 750-member elicitor library, and the resulting induced metabolomes were analyzed for the first time using a rapid UPLC-Qtof-MS method. From this dataset, the invention was able to prioritize many secondary metabolites overproduced in B. gladioli through elicitor treatment including gladiobactin, gladiolin, icosalide, and burriogladin. Numerous novel metabolites were also automatically prioritized in a matter of minutes for further characterization.
Mohammad R. Seyedsayamdost is Professor in the Departments of Chemistry and Molecular Biology at Princeton University. His research focuses on the development of novel discovery approaches for natural products and potential drug candidates from microbial sources. He received a Ph.D. in Chemistry from the Massachusetts Institute of Technology and conducted postdoctoral research at Harvard Medical School before joining Princeton.
Brett Covington is a post-doc in the Seyedsayamdost at Princeton University. His research focuses on metabolomics-based discovery methods of microbial natural products. He obtained a Ph.D. in Chemistry from Vanderbilt University before joining the Seyedsayamdost lab at Princeton.
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