Back

Non-canonical regulation of Endoglin by rare and common variants: new molecular and clinical perspectives for Hereditary Hemorrhagic Telangiectasia and beyond

Soukarieh, O.; Munsch, G.; Deiber, C.; Meguerditchian, C.; Proust, C.; Caro, I.; Tusseau, M.; Guilhem, A.; Mohamed, S.; INVENT consortium, ; Jaspard-Vinassa, B.; Goyenvalle, A.; Debette, S.; Dupuis-Girod, S.; Tregouet, D.-A.

2024-01-30 genetic and genomic medicine
10.1101/2024.01.28.24301864 medRxiv
Show abstract

Endoglin, encoded by ENG, is a transmembrane glycoprotein crucial for endothelial cell biology. Loss-of-function ENG variants cause Hereditary Hemorrhagic Telangiectasia (HHT). Despite advances in HHT diagnosis and management, the molecular origin of some cases and the source of clinical heterogeneity remain unclear. We propose a comprehensive in silico analysis of all 5UTR ENG single nucleotide variants that could lead to Endoglin deficiency by altering upstream Open Reading Frames (upORFs). Experimentally, we confirm that variants creating uAUG-initiated overlapping upORFs associate with reduced Endoglin levels in vitro and characterize the effect of a uCUG-creating variant identified in two suspected HHT patients. Using plasma proteogenomics resources, we identify eight loci associated with soluble Endoglin levels, including ABO and uPAR-pathway loci and experimentally demonstrate the association between uPAR and Endoglin in endothelial cells. This study provides new insights into Endoglins molecular determinants, opening avenues for improved HHT management and other diseases involving Endoglin. Key pointsO_LINew insights on the characterization of ENG non-coding variants, in particular those altering upstream Open Reading Frames in the 5UTR. C_LIO_LILeverage of large-scale plasma proteogenomics results combined with functional assays revealed new actors in Endoglin regulation. C_LI

Matching journals

The top 9 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.