Summary
Rising incidences of chronic disorders and cancer have been linked to reduced microbial diversity. Genetic risk factors are similarly involved in the development of chronic disorders and cancer. The effect of both microbial and genetic factors is particularly evident in inflammatory bowel disease (IBD) and colorectal cancer (CRC). However, the identification of specific bacteria that trigger/drive disease or modulate therapy efficacy in IBD and CRC lacks a comprehensive approach. Even less is known about how these bacteria mechanistically induce such effects. The paucity of so far identified disease relevant bacteria and their mode of action, likely lies in the complexity of both the microbiome and the host’s genetic risk factors. We hypothesize that specific bacteria, which do not harm the host under steady state conditions, hijack host pathways in the presence of certain genetic risk factors to drive inflammation, onset and progression of IBD and CRC. We speculate that these bacteria, termed pathobionts, have so far been overlooked due to a lack of methods to triangulate these disease relevant bacteria in multi-factorial disorders like IBD and CRC.
It is our objective to identify these pathobionts and match them to host genetic risk. To do so, we have developed an antibody coating based approach, to identify and culture pathobionts. Now, we want to overhaul our approach to develop a high throughput-pathobiont identification technology. Through machine learning, we then aim to find pathobiont-host genetic risk matches that drive disease and validate these matches in vivo. Lastly, we plan to demonstrate the potential of our new technology and gained knowledge. Through leveraging the gained knowledge, we aim to identify pathobiont-host risk matches in publicly available databases. This will unmask individuals at risk for the development of IBD, CRC and potentially other disorders. This may pave the way for future microbiome-based precision medicine approaches.
It is our objective to identify these pathobionts and match them to host genetic risk. To do so, we have developed an antibody coating based approach, to identify and culture pathobionts. Now, we want to overhaul our approach to develop a high throughput-pathobiont identification technology. Through machine learning, we then aim to find pathobiont-host genetic risk matches that drive disease and validate these matches in vivo. Lastly, we plan to demonstrate the potential of our new technology and gained knowledge. Through leveraging the gained knowledge, we aim to identify pathobiont-host risk matches in publicly available databases. This will unmask individuals at risk for the development of IBD, CRC and potentially other disorders. This may pave the way for future microbiome-based precision medicine approaches.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101115638 |
| Start date: | 01-01-2024 |
| End date: | 31-12-2028 |
| Total budget - Public funding: | 1 993 688,00 Euro - 1 993 688,00 Euro |
Cordis data
Original description
Rising incidences of chronic disorders and cancer have been linked to reduced microbial diversity. Genetic risk factors are similarly involved in the development of chronic disorders and cancer. The effect of both microbial and genetic factors is particularly evident in inflammatory bowel disease (IBD) and colorectal cancer (CRC). However, the identification of specific bacteria that trigger/drive disease or modulate therapy efficacy in IBD and CRC lacks a comprehensive approach. Even less is known about how these bacteria mechanistically induce such effects. The paucity of so far identified disease relevant bacteria and their mode of action, likely lies in the complexity of both the microbiome and the host’s genetic risk factors. We hypothesize that specific bacteria, which do not harm the host under steady state conditions, hijack host pathways in the presence of certain genetic risk factors to drive inflammation, onset and progression of IBD and CRC. We speculate that these bacteria, termed pathobionts, have so far been overlooked due to a lack of methods to triangulate these disease relevant bacteria in multi-factorial disorders like IBD and CRC.It is our objective to identify these pathobionts and match them to host genetic risk. To do so, we have developed an antibody coating based approach, to identify and culture pathobionts. Now, we want to overhaul our approach to develop a high throughput-pathobiont identification technology. Through machine learning, we then aim to find pathobiont-host genetic risk matches that drive disease and validate these matches in vivo. Lastly, we plan to demonstrate the potential of our new technology and gained knowledge. Through leveraging the gained knowledge, we aim to identify pathobiont-host risk matches in publicly available databases. This will unmask individuals at risk for the development of IBD, CRC and potentially other disorders. This may pave the way for future microbiome-based precision medicine approaches.
Status
SIGNEDCall topic
ERC-2023-STGUpdate Date
12-03-2024
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