Advancing maldi-TOF-based microbial and metabolite identification through machine learning and multi-omics approaches
Active Project Grant Awarded Last action
- Award ID
- F31AI200270
- Recipient
-
The Regents Of The University Of California UEI MR5QC5FCAVH5
- Awarding agency
-
National Institute of Allergy and Infectious Diseases
National Institutes of Health, Department of Health and Human Services
- Obligated
- $93,576
Award facts
- Assistance listing
- 93.855 Allergy and Infectious Diseases Research
- Completion date 0% complete
- Originally
- Place of performance
- Riverside, CA 92521, USA
Award details come from USAspending.gov.
Description
- Abstract
- The successful identification of bacteria and the specialized metabolites (SMS) they produce are key challenges in understanding the ecology of the microbiome, and its interaction with its host. Matrix-assisted laser desorption-ionization mass spectrometry (maldi MS) has enabled low-cost, high throughput identification of bacteria. However, the ability to annotate bacteria remains limited to pathogenic-focused libraries and locked within vendor-specific software. Our recent introduction of the idbac-knowledgebase (idbac-KB) has facilitated the creation of a crowd-sourced library of maldi proteomic fingerprints, enabling the identification of microbes in a broader setting but has exposed new challenges in cross-platform and cross- laboratory identification. Therefore, the development of a parameter- and platform-conscious approach for identifying bacteria based on proteomic fingerprints is essential for the broad annotation of the organisms in the microbiome. This advancement will accelerate the discovery of bioactive, health- promoting supplements and the isolation of natural product scaffolds for therapeutic development. We propose to develop a novel approach for bacterial comparison and identification based on state-of-the-art machine learning methods, enhancing the utility of this new resource. Further we propose extending idbac to enable the identification of SM by integrating cutting-edge tandem MS (MS/MS) workflows on maldi-time-of- flight (TOF) instruments with machine-learning-based methods for annotating MS/MS spectra with compound libraries. These aims will enable the characterization of SMS at scale, allowing for the identification of metabolites associated with up to 384 bacterial colonies within four hours. In doing so, we will illuminate the SMS responsible for mediating microbial interactions and creating stable communities. These discoveries will inform research in the production of BIO-active supplements that improve microbiome health and allow for the isolation of compounds that enable the growth of otherwise unculturable bacteria thereby expanding the space of bacteria accessible in isolation. Downstream, this will enable the study of novel natural product space, unlocking access to novel natural product chemical space and new therapeutic scaffolds. Successful completion of these aims will constitute a major advance in microbial ecology and our understanding of the human microbiome. IT will enable researchers to explore new chemical space and identify compounds that promote microbiome-host symbiosis or contribute to dysbiosis and disease.
Transactions
| Action | Obligated | Date |
|---|---|---|
| Amendment | $46,788 | |
| Amendment | $46,788 | |
| Original award | $0 |
Quick answers
Who received award F31AI200270?
F31AI200270 was awarded to The Regents Of The University Of California by National Institute of Allergy and Infectious Diseases (National Institutes of Health) on November 21, 2026.
How much is award F31AI200270 worth?
$93,576 has been obligated on F31AI200270.
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