Systematic Triangulation of Pathobiont-Host-Interactions

Introduction

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).

Problem Statement

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.

Hypothesis

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.

Objectives

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.

Methodology

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.

Future Plans

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.

Conclusion

This may pave the way for future microbiome-based precision medicine approaches.