Quantitative Proteomics of Human Fibroblasts with I1061T Mutation in Niemann–Pick C1 (NPC1) Protein Provides Insights into the Disease Pathogenesis*
Bibliographic record
Abstract
Niemann-Pick type C (NPC) disease is a fatal neurodegenerative disorder characterized by the accumulation of unesterified cholesterol in the late endosomal/lysosomal compartments. Mutations in the NPC1 protein are implicated in 95% of patients with NPC disease. The most prevalent mutation is the missense mutation I1061T that occurs in ∼ 15-20% of the disease alleles. In our study, an isobaric labeling-based quantitative analysis of proteome of NPC1(I1061T) primary fibroblasts when compared with wild-type cells identified 281 differentially expressed proteins based on stringent data analysis criteria. Gene ontology enrichment analysis revealed that these proteins play important roles in diverse cellular processes such as protein maturation, energy metabolism, metabolism of reactive oxygen species, antioxidant activity, steroid metabolism, lipid localization, and apoptosis. The relative expression level of a subset of differentially expressed proteins (TOR4A, DHCR24, CLGN, SOD2, CHORDC1, HSPB7, and GAA) was independently and successfully substantiated by Western blotting. We observed that treating NPC1(I1061T) cells with four classes of seven different compounds that are potential NPC drugs increased the expression level of SOD2 and DHCR24. We have also shown an abnormal accumulation of glycogen in NPC1(I1061T) fibroblasts possibly triggered by defective processing of lysosomal alpha-glucosidase. Our study provides a starting point for future more focused investigations to better understand the mechanisms by which the reported dysregulated proteins triggers the pathological cascade in NPC, and furthermore, their effect upon therapeutic interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".