To safeguard proteomic integrity, cells rely on the proteasome to degrade aberrant polypeptides, but it is unclear how cells remove defective proteins that have escaped degradation owing to proteasome insufficiency or dysfunction. Here we report a pathway termed misfolding-associated protein secretion, which uses the endoplasmic reticulum (ER)-associated deubiquitylase USP19 to preferentially export aberrant cytosolic proteins. Intriguingly, the catalytic domain of USP19 possesses an unprecedented chaperone activity, allowing recruitment of misfolded proteins to the ER surface for deubiquitylation. Deubiquitylated cargos are encapsulated into ER-associated late endosomes and secreted to the cell exterior. USP19-deficient cells cannot efficiently secrete unwanted proteins, and grow more slowly than wild-type cells following exposure to a proteasome inhibitor. Together, our findings delineate a protein quality control (PQC) pathway that, unlike degradation-based PQC mechanisms, promotes protein homeostasis by exporting misfolded proteins through an unconventional protein secretion process.

Deep neural networks have demonstrated promising potential for the field of medical image reconstruction, successfully generating high quality images for CT, PET and MRI. In this work, an MRI reconstruction algorithm, which is referred to as quantitative susceptibility mapping (QSM), has been developed using a deep neural network in order to perform dipole deconvolution, which restores magnetic susceptibility source from an MRI field map. Previous approaches of QSM require multiple orientation data (e.g. Calculation of Susceptibility through Multiple Orientation Sampling or COSMOS) or regularization terms (e.g. Truncated K-space Division or TKD; Morphology Enabled Dipole Inversion or MEDI) to solve an ill-conditioned dipole deconvolution problem. Unfortunately, they either entail challenges in data acquisition (i.e. long scan time and multiple head orientations) or suffer from image artifacts. To overcome these shortcomings, a deep neural network, which is referred to as QSMnet, is constructed to generate a high quality susceptibility source map from single orientation data. The network has a modified U-net structure and is trained using COSMOS QSM maps, which are considered as gold standard. Five head orientation datasets from five subjects were employed for patch-wise network training after doubling the training data using a model-based data augmentation. Seven additional datasets of five head orientation images (i.e. total 35 images) were used for validation (one dataset) and test (six datasets). The QSMnet maps of the test dataset were compared with the maps from TKD and MEDI for their image quality and consistency with respect to multiple head orientations. Quantitative and qualitative image quality comparisons demonstrate that the QSMnet results have superior image quality to those of TKD or MEDI results and have comparable image quality to those of COSMOS. Additionally, QSMnet maps reveal substantially better consistency across the multiple head orientation data than those from TKD or MEDI. As a preliminary application, the network was further tested for three patients, one with microbleed, another with multiple sclerosis lesions, and the third with hemorrhage. The QSMnet maps showed similar lesion contrasts with those from MEDI, demonstrating potential for future applications.

BackgroundProtein homeostasis in the endoplasmic reticulum (ER) has recently emerged as a therapeutic target for cancer treatment. Disruption of ER homeostasis results in ER stress, which is a major cause of cell death in cells exposed to the proteasome inhibitor Bortezomib, an anti-cancer drug approved for treatment of multiple myeloma and Mantle cell lymphoma. We recently reported that the ERAD inhibitor Eeyarestatin I (EerI) also disturbs ER homeostasis and has anti-cancer activities resembling that of Bortezomib.Methodology and Principal FindingsHere we developed in vitro binding and cell-based functional assays to demonstrate that a nitrofuran-containing (NFC) group in EerI is the functional domain responsible for the cytotoxicity. Using both SPR and pull down assays, we show that EerI directly binds the p97 ATPase, an essential component of the ERAD machinery, via the NFC domain. An aromatic domain in EerI, although not required for p97 interaction, can localize EerI to the ER membrane, which improves its target specificity. Substitution of the aromatic module with another benzene-containing domain that maintains membrane localization generates a structurally distinct compound that nonetheless has similar biologic activities as EerI.Conclusions and SignificanceOur findings reveal a class of bifunctional chemical agents that can preferentially inhibit membrane-bound p97 to disrupt ER homeostasis and to induce tumor cell death. These results also suggest that the AAA ATPase p97 may be a potential drug target for cancer therapeutics.