Prion Protein Deficiency Causes Diverse Proteome Shifts in Cell Models That Escape Detection in Brain Tissue
Bibliographic record
Abstract
A popular method for studying the function of a given protein is to generate and characterize a suitable model deficient for its expression. For the prion protein (PrP), best known for its role in several invariably fatal neurodegenerative diseases, a natural choice, therefore, would be to undertake such studies with brain samples. We recently documented the surprising observation that PrP deficiency caused a loss or enhancement of NCAM1 polysialylation, dependent on the cell model used. To identify possible causes for this disparity, we set out to systematically investigate the consequence of PrP deficiency on the global proteome in brain tissue and in four distinct cell models. Here we report that PrP deficiency causes robust but surprisingly divergent changes to the global proteomes of cell models but has no discernible impact on the global brain proteome. Amongst >1,500 proteins whose levels were compared in wild-type and PrP-deficient models, members of the MARCKS protein family exhibited pronounced, yet cell model-dependent changes to their steady-state levels. Follow-up experiments revealed that PrP collaborates with members of the MARCKS protein family in its control of NCAM1 polysialylation. We conclude that the physiological function of PrP may be masked in analyses of complex brain samples but its cell-type specific influence on a lipid raft-based NCAM1-related cell biology comes to the fore in investigations of specific cell types.
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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) | 0.001 | 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 | 0.001 |
| 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".