Summary
The problem: Much effort has been directed to identifying disease risk factors and indeed, numerous genetic, lifestyle, anthropometric and clinical risk factors are routinely used for this task. However, the clinical utility of existing risk factors is limited and most of them appear when physiological derangements have already occurred. The gut microbiome plays a role in human physiology and health and as such, combining it with existing risk factors can lead to earlier and more robust disease detection. However, very few microbiome-based markers predictive of disease onset and progression were found to date and none are currently used by healthcare systems.
The solution: In the ERC-funded project, we used our unique multi-omics and longitudinally profiled 10,000-person cohort and biobank to develop methods for metagenome-wide association studies. We found microbial genetic variation at the single nucleotide level and single bacterial genes level whose baseline levels were predictive of future disease onset. In this ERC-PoC, we propose to prioritize the bacteria found in the ERC-funded project (WP1) and use them within machine learning disease diagnostic models (WP2) that we will then license to diagnostic companies. We further propose to build a microbial biobank of the ERC-identified strains by isolating them from cohort participants, functionally characterizing them with metabolomics and combining them into bacterial consortiums (WP3). As these strains have therapeutic potential, we will license them to probiotic or pharmaceutical companies (WP4). Notably, we already validated this approach in two licensing agreements, one with a Japanese-based company who licensed bacteria that we found for treating Atopic Dermatitis, and another with a U.S. based probiotics company who licensed bacteria that we found for weight loss. If successful, our ERC-PoC may thus result in novel microbiome-based diagnostic and therapeutic products for various disease indications.
The solution: In the ERC-funded project, we used our unique multi-omics and longitudinally profiled 10,000-person cohort and biobank to develop methods for metagenome-wide association studies. We found microbial genetic variation at the single nucleotide level and single bacterial genes level whose baseline levels were predictive of future disease onset. In this ERC-PoC, we propose to prioritize the bacteria found in the ERC-funded project (WP1) and use them within machine learning disease diagnostic models (WP2) that we will then license to diagnostic companies. We further propose to build a microbial biobank of the ERC-identified strains by isolating them from cohort participants, functionally characterizing them with metabolomics and combining them into bacterial consortiums (WP3). As these strains have therapeutic potential, we will license them to probiotic or pharmaceutical companies (WP4). Notably, we already validated this approach in two licensing agreements, one with a Japanese-based company who licensed bacteria that we found for treating Atopic Dermatitis, and another with a U.S. based probiotics company who licensed bacteria that we found for weight loss. If successful, our ERC-PoC may thus result in novel microbiome-based diagnostic and therapeutic products for various disease indications.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101122655 |
| Start date: | 01-10-2023 |
| End date: | 31-03-2025 |
| Total budget - Public funding: | - 150 000,00 Euro |
Cordis data
Original description
The problem: Much effort has been directed to identifying disease risk factors and indeed, numerous genetic, lifestyle, anthropometric and clinical risk factors are routinely used for this task. However, the clinical utility of existing risk factors is limited and most of them appear when physiological derangements have already occurred. The gut microbiome plays a role in human physiology and health and as such, combining it with existing risk factors can lead to earlier and more robust disease detection. However, very few microbiome-based markers predictive of disease onset and progression were found to date and none are currently used by healthcare systems.The solution: In the ERC-funded project, we used our unique multi-omics and longitudinally profiled 10,000-person cohort and biobank to develop methods for metagenome-wide association studies. We found microbial genetic variation at the single nucleotide level and single bacterial genes level whose baseline levels were predictive of future disease onset. In this ERC-PoC, we propose to prioritize the bacteria found in the ERC-funded project (WP1) and use them within machine learning disease diagnostic models (WP2) that we will then license to diagnostic companies. We further propose to build a microbial biobank of the ERC-identified strains by isolating them from cohort participants, functionally characterizing them with metabolomics and combining them into bacterial consortiums (WP3). As these strains have therapeutic potential, we will license them to probiotic or pharmaceutical companies (WP4). Notably, we already validated this approach in two licensing agreements, one with a Japanese-based company who licensed bacteria that we found for treating Atopic Dermatitis, and another with a U.S. based probiotics company who licensed bacteria that we found for weight loss. If successful, our ERC-PoC may thus result in novel microbiome-based diagnostic and therapeutic products for various disease indications.
Status
SIGNEDCall topic
ERC-2023-POCUpdate Date
12-03-2024
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