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Proteolytic Remodeling of Cargo Receptor Networks by RHBDL4 Tunes Secretory Pathway Flux

Cargo receptors are central organizers of the secretory pathway, yet the mechanisms controlling their abundance remain poorly understood. The endoplasmic reticulum (ER)-resident intramembrane protease RHBDL4 promotes substrate turnover via a non-canonical branch of ER-associated degradation and has recently been implicated in regulating secretory pathway components. We previously identified the…

Cargo receptors play a crucial role in organizing the secretory pathway, but the factors influencing their abundance are still not fully understood. The endoplasmic reticulum (ER)-resident intramembrane protease RHBDL4 has been found to contribute to the regulation of secretory pathway components through a unique branch of ER-associated degradation.

Previous research identified the cargo receptor TMED7 as a substrate for RHBDL4, suggesting that the turnover of cargo receptor proteins impacts the secretory pathway's regulation.

A recent study employed quantitative proteomics to identify additional members of the ER-Golgi intermediate compartment (ERGIC) cargo receptor family as endogenous RHBDL4 substrates. This discovery demonstrates that RHBDL4 targets multiple cargo receptor families within the early secretory pathway, thereby modulating several ERGIC-dependent transport pathways. Moreover, unbiased secretome analysis revealed an increase in the secretion of lysosomal precursor proteins when RHBDL4 was removed.

The researchers further elucidated the mechanism behind this enigmatic phenomenon, showing that the increased secretion of lysosomal precursor proteins is at least partially due to RHBDL4-dependent cleavage of the lysosomal cargo receptor sortilin/SORT1. In summary, this study has revealed that cargo receptors serve as a significant class of RHBDL4 substrates, and proteolytic remodeling of cargo receptor networks emerges as a critical mechanism regulating secretory pathway flux.

Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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