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.