The prion protein is embedded in a molecular environment that modulates transforming growth factor β and integrin signaling
Bibliographic record
Abstract
At times, it can be difficult to discern if a lack of overlap in reported interactions for a protein-of-interest reflects differences in methodology or biology. In such instances, systematic analyses of protein-protein networks across diverse paradigms can provide valuable insights. Here, we interrogated the interactome of the prion protein (PrP), best known for its central role in prion diseases, in four mouse cell lines. Analyses made use of identical affinity capture and sample processing workflows. Negative controls were generated from PrP knockout lines of the respective cell models, and the relative levels of peptides were quantified using isobaric labels. The study uncovered 26 proteins that reside in proximity to PrP. All of these proteins are predicted to have access to the outer face of the plasma membrane, and approximately half of them were not reported to interact with PrP before. Strikingly, although several proteins exhibited profound co-enrichment with PrP in a given model, except for the neural cell adhesion molecule 1, no protein was highly enriched in all PrP-specific interactomes. However, Gene Ontology analyses revealed a shared association of the majority of PrP candidate interactors with cellular events at the intersection of transforming growth factor β and integrin signaling.
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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) | 0.000 | 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".