From meta-genomics to causality: Understanding the role of colon cancer-associated bacteria in colorectal cancer

The human gastrointestinal tract is home for trillions of bacteria that influence homeostasis and health in a complex biological system: the gut microbiome. Accumulating evidence suggests that a state of pathological imbalance in the microbiome (dysbiosis) is present in patients suffering from colorectal cancer (CRC). To date, microbiome studies identified specific bacteria being associated with dysbiosis in CRC. Some of these bacteria (e.g. Fusobacteria) directly or indirectly interact with cancer and immune cells of their host. However, current studies only focused on certain microbes in detail, hence, their role in the etiology of the disease remains elusive. Accordingly, my project investigates the role of CRC-associated bacteria in tumor initiation and progression while addressing the question: which and what kind of microbes interact with, favor, or can cause CRC? In a first step, we identified CRC-associated bacteria, enriched at the tumor site of Luxembourgish CRC patients. By using Fusobacterium nucleatum as our study model, we predicted and optimized bacterial growth (media) in silico by using a genome-scale metabolic reconstruction model for a constraint-based modelling approach. Next, we assessed bacterial growth and metabolism in the optimized growth medium by using flow cytometry and mass spectrometry. Finally, we co-cultured the bacteria together with primary patient-derived cultures in the recently developed, microfluidics-based, human-microbial cross-talk model (HuMiX) [1]. As part of our ongoing validations, we infected patient-derived, healthy and cancerous 3D colonic organoids with our bacterial candidate. This workflow enables us to analyze pro-tumorigenic capacities of CRC-associated bacteria on healthy and cancerous colonocytes. It will serve as a promising tool for future analysis of host-microbial interaction mechanisms of various CRC-associated bacteria on a transcriptomic, proteomic, and metabolomic level. [1] Shah P, Fritz JV, Glaab E, Desai MS, Greenhalgh K et al. (2016) A microfluidics-based in vitro model of the gastrointestinal human-microbe interface. Nature communications 7: 11535.MiDiC