Zinc transport via ZNT5-6 and ZNT7 is critical for cell surface glycosylphosphatidylinositol-anchored protein expression

Glycosylphosphatidylinositol (GPI)-anchored proteins play crucial roles in various enzyme activities, cell signaling and adhesion, and immune responses. While the molecular mechanism underlying GPI-anchored protein biosynthesis has been well studied, the role of zinc transport in this process has not yet been elucidated. Zn transporter (ZNT) proteins mobilize cytosolic zinc to the extracellular space and to intracellular compartments. Here, we report that the early secretory pathway ZNTs [ZNT5-ZNT6 heterodimers (ZNT5-6) and ZNT7-ZNT7 homodimers (ZNT7)], which supply zinc to the lumen of the early secretory pathway compartments are essential for GPI-anchored protein expression on the cell surface. We show, using overexpression and gene disruption/re-expression strategies in cultured human cells, that loss of ZNT5-6 and ZNT7 zinc transport functions results in significant reduction in GPI-anchored protein levels similar to that in mutant cells lacking phosphatidylinositol glycan anchor biosynthesis (PIG) genes. Furthermore, medaka fish with disrupted Znt5 and Znt7 genes show touch-insensitive phenotypes similar to zebrafish Pig mutants. These findings provide a previously unappreciated insight into the regulation of GPI-anchored protein expression and protein quality control in the early secretory pathway

Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals

Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohort

Effectiveness of Sirolimus in Combination with Cyclosporine against Chronic Rejection in a Pediatric Liver Transplant Patient

The patient is a 3-year-old boy who received living-donor liver transplantation (LDLT) for hepatoblastoma, with his mother as the donor. Oral tacrolimus was started at a dose of 0.3 mg every 12 h from day 1, with the dosage adjusted on the basis of trough concentrations. The levels of aspartate aminotransferase (AST), alanine transferase (ALT), and total bilirubin (T-bil) were 110 U/L, 182 U/L, and 12.6 mg/dL, respectively, when chronic rejection (CR) was pathologically diagnosed. Then, sirolimus at a dose of 1.0 mg/d was added to the tacrolimus-based regimen. The T-bil level rapidly decreased to 5.4 mg/dL, without changes in AST and ALT. Because the intracellular receptor of sirolimus and tacrolimus is FK506-binding protein 12, we switched tacrolimus to cyclosporine at a dose of 60 mg/d to avoid competitive inhibition between these 2 drugs. The target trough concentration of sirolimus and cyclosporine was set to around 15 ng/mL and 180 ng/mL, respectively. The concentration/dose ratio of sirolimus was significantly correlated with the blood cyclosporine level (r=0.5293, p<0.05), suggesting the pharmacokinetic interaction between these 2 drugs. Thereafter, the levels of AST and ALT as well as the T-bil were successfully decreased to 73 U/L, 83 U/L, and 3.0 mg/dL, respectively. These results suggest that sirolimus therapy in combination with cyclosporine may be an effective treatment against CR after liver transplantation