Modulating tenascin-C functions by targeting the MAtrix REgulating MOtif, "MAREMO".

The extracellular matrix molecule Tenascin-C (TNC) promotes cancer and chronic inflammation by multiple mechanisms. Recently, TNC was shown to promote an immune suppressive tumor microenvironment (TME) through binding soluble chemoattracting factors, thus retaining leukocytes in the stroma. TNC also binds to fibronectin (FN) and other molecules, raising the question of a potential common TNC binding mechanism. By sequence comparison of two TNC-interacting domains in FN, the fifth (FN5) and thirteenth (FN13) fibronectin type III domains we identified a MAtrix REgulating MOtif "MAREMO" or M-motif that is highly conserved amongst vertebrates. By sequence analysis, structural modeling and functional analysis we found also putative M-motifs in TNC itself. We showed by negative staining electron microscopic imaging that the M-motif in FN mediates interactions with FN as well as with TNC. We generated two M-motif mimetic peptides P5 and P13 resembling the M-motif in FN5 and FN13, respectively. By using structural information we modelled binding of these M-motif mimetics revealing a putative MAREMO binding site MBS in FN5 and TN3, respectively overlapping with the M-motif. We further demonstrated that the M-motif mimetic peptides blocked several functions of TNC, such as binding of TNC to FN, cell rounding on a mixed FN/TNC substratum, FN matrix expression and subsequent assembly, TNC-induced signaling and gene expression, TNC chemokine binding and dendritic cell retention, thus providing novel opportunities to inhibit TNC actions. Our results suggest that targeting the MAREMO/MBS interaction could be exploited for reducing inflammation and matrix functions in cancer and fibrosis.

Population biological principles of drug-resistance evolution in infectious diseases.

The emergence of resistant pathogens in response to selection pressure by drugs and their possible disappearance when drug use is discontinued are evolutionary processes common to many pathogens. Population biological models have been used to study the dynamics of resistance in viruses, bacteria, and eukaryotic microparasites both at the level of the individual treated host and of the treated host population. Despite the existence of generic features that underlie such evolutionary dynamics, different conclusions have been reached about the key factors affecting the rate of resistance evolution and how to best use drugs to minimise the risk of generating high levels of resistance. Improved understanding of generic versus specific population biological aspects will help to translate results between different studies, and allow development of a more rational basis for sustainable drug use than exists at present.