Back

Heterogeneity of NOTCH3 activation mechanisms uncovers therapeutic potential for targeted therapies for CADASIL.

Hosseini-Alghaderi, S. S.; Baron, M.

2024-11-18 cell biology
10.1101/2024.11.17.623993 bioRxiv
Show abstract

NOTCH3 is a developmental signalling receptor that regulates vascular smooth muscle cell (VSMC) proliferation, maintenance and phenotypic plasticity. Its misregulation is linked to both age-associated and inherited forms of small vessel disease and vascular dementia. CADASIL is a dominantly inherited condition, linked to recurrent stroke and vascular dementia, which is caused by mutations in NOTCH3. The mutations result in the accumulation of the extracellular domain (ECD) of NOTCH3 which has a toxic gain of function effect. Misregulated signalling may also play a role. ECD detachment is an obligatory step in NOTCH3 activation, but the mechanisms of CADASIL mutant NOTCH3 activation are not understood. Some CADASIL mutants affect ligand-induced activation and so ligand interactions are not a common underlying requirement for disease progression. Here we show that both wild type and CADASIL mutant NOTCH3 proteins are endocytosed as a full-length protein and then undergo dissociation and independent trafficking of the ECD and ICD in the endosome. We further show heterogeneity of activation mechanisms between different CADASIL mutants defined by dependency or not on metalloproteases. Understanding the variety of mechanisms by which NOTCH3 signalling and ECD shedding occur will inform new targeted approaches to treatments of small vessel disease. Tuning NOTCH3 activity through modulation of the endocytic pathway may offer better tolerated approaches than direct targeting of NOTCH3 signalling, important when considering the balance of risk and benefit for treating long term, chronic, progressive diseases, like vascular dementia.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.