European experts in the field of clinical and experimental immunology are studying NF-kappa-B signalling dynamics and oscillations in inflammatory bowel disease (IBD). The long-term goal is to identify biomarkers that could be exploited for disease prediction, diagnosis and stratification.
 
IBD is a chronic inflammation of the gut, with ulcerative colitis and 
Crohn's disease being the most frequent disease manifestations. At 
present, there are no cures for IBD and clinicians can only provide 
disease management options.
The large EU-funded 'Systems medicine of chronic inflammatory bowel disease' (
SYSMEDIBD)
 consortium is exploring a systems medicine approach to generate new 
treatments for IBD. By focusing on NF-kappa-B signalling, the project 
aims to understand disease mechanism, and develop new biomarkers for 
patient stratification and for the design of future personalised 
treatments.
To study the human pathway, scientists have generated mice with 
bacterial artificial chromosomes carrying two NF-kappa-B–encoding human 
transgenes. These genes have been fluorescently tagged to follow pathway
 activation in 3D gut organoid cultures under normal and inflammatory 
conditions. Using this system and additional biomarkers, researchers 
will analyse pathway activation in patient cells and progression to 
chronic inflammation.
A further humanised mouse model is being developed that contains 
human gut and could be used to simulate IBD conditions. Partners are 
hopeful that they could use this in vivo model to find novel therapies 
for IBD.
The consortium has identified a number of biomarkers following 
mapping of inflammation-associated genes onto the NF-kappa-B pathway. A 
selection of these targets will be used to screen small molecule 
inhibitors, while others that are potentially released in the serum 
could be exploited for diagnostic purposes. This will offer a less 
invasive way of arriving at an IBD diagnosis.
All the information generated during SYSMEDIBD will ultimately be 
used to construct a mathematical model to predict IBD onset and 
progression. In addition, the same model could be used for predicting 
the therapeutic outcome of various regimens and following a personalised
 treatment approach.