Citations

Abstract GNE myopathy (GNEM) is a neuromuscular disorder caused by biallelic mutations in the gene GNE , which encodes a bifunctional enzyme essential for the biosynthesis of sialic acid. Despite extensive characterization, no effective therapies or validated biomarkers exist, and the molecular mechanisms driving disease pathology remain poorly understood. This study identifies the proteoglycan serglycin as a preliminary candidate biomarker in GNEM and investigates the mechanisms underlying its dysregulation. Proteomic analysis of serum samples from GNEM patients revealed reduced circulating serglycin levels. In contrast, increased intracellular serglycin was observed in GNE knockout and GNEM patient iPSC-derived myoblasts, suggesting impaired secretion and/or degradation. To investigate the basis of this accumulation, intracellular trafficking and autophagic pathways were examined. Proteomic profiling from HEK GNE knockout cells revealed alterations in pathways involved in membrane trafficking, Golgi function, and glycoprotein processing, consistent with broader disruptions in the secretory system that may impair serglycin export and contribute to its intracellular retention. In GNEM iPSC-derived myoblasts, serglycin overlapped with LAMP1-positive lysosomal compartments and the ER-phagy receptor FAM134B, suggesting association with lysosomal and ER-phagy related pathways. Functional assays further indicated impaired autophagic flux in mutant cells. Together, these findings support a model in which combined defects in secretion and autophagic clearance contribute to intracellular serglycin accumulation and reduced circulating levels. This study identifies serglycin as a potential circulating biomarker for GNEM and highlights dysregulation of secretory and degradative pathways as converging features of disease pathology.

Abstract Background Diabetes mellitus elevates Parkinson’s disease (PD) risk, via hypothesized cerebrovascular mediation. Whether the diabetes/prediabetes vascular-risk phenotype concentrates in cardiometabolic risk or macrovascular events across prodromal and clinically diagnosed PD remains unresolved. Objectives To quantify the vascular-risk burden associated with diabetes/prediabetes across the PPMI diagnostic cohorts to test whether this association differs by cohort. Methods Cross-sectional analysis of 413 PPMI participants (76 healthy controls, 145 prodromal PD, 192 clinically diagnosed PD) examined diabetes/prediabetes (n = 73) and seven vascular risk factors. The Vascular Burden Score (0 to 7) was a priori partitioned into microvascular and macrovascular sub-scores. Modified Poisson regression estimated adjusted prevalence ratios (aPR), adjusted for age, sex, and body mass index. A cohort-by-diabetes interaction tested cross-cohort consistency. Sensitivity analyses incorporated nigral diffusion tensor imaging (PD-risk biomarker) and FreeSurfer white matter hypointensity volume (cerebrovascular marker). Results Diabetes/prediabetes elevated Vascular Burden Score (β = 0.53, 95% CI 0.29 to 0.77, p < 0.001) versus non-diabetic participants, with a non-significant cohort-by-diabetes interaction (F = 0.29, p = 0.747). Three microvascular factors survived false discovery rate correction: obesity (aPR 2.28), hypertension (aPR 1.60), and hyperlipidemia (aPR 1.45). Macrovascular events showed no diabetic amplification (β = −0.06, p = 0.25). In the imaging-phenotyped subset, Vascular Burden Score components contributed classifier variance distinct from nigral microstructure. Conclusions Diabetes/prediabetes operationalize a microvascular cluster stable across prodromal and idiopathic PD. Cardiometabolic phenotyping may complement established PD-risk biomarkers (dopamine transporter SPECT, nigral diffusion), pending longitudinal validation linking vascular phenotype to dopaminergic markers.

Summary Functional ex vivo assays using live tumor tissues have demonstrated strong predictive accuracy for response to immune checkpoint inhibitors (ICIs) but are not scalable, requiring manual processing of large resections collected at academic centers. Here, an ex vivo live tumor fragment (LTF) platform was developed using standard-of-care biopsies from 228 patients with suspected malignancy collected across prospective, multicenter observational trials and biobanks. Hierarchical clustering of ICI-mediated changes in cytokine production identified two groups: responders and nonresponders. A binary classifier (elive index) using 8 cytokines achieved an AUC of 0.99 for cluster prediction. elive index correctly predicted clinical benefit in 93% (26/28) of patients ( P = 3.2x10 -5 ) and accurately identified 83% (10/12) of objective responders. Critically, elive responders were identified among biomarker-negative patients, highlighting the platform as a scalable approach that complements existing companion diagnostics and expands the population of patients identified to benefit from ICI therapy.