Approaches to imaging unfolded secretory protein stress in living cells
Bibliographic record
Abstract
The endoplasmic reticulum (ER) is the point of entry of proteins into the secretory pathway. Nascent peptides interact with the ER quality control machinery that ensures correct folding of the nascent proteins. Failure to properly fold proteins can lead to loss of protein function and cytotoxic aggregation of misfolded proteins that can lead to cell death. To cope with increases in the ER unfolded secretory protein burden, cells have evolved the Unfolded Protein Response (UPR). The UPR is the primary signaling pathway that monitors the state of the ER folding environment. When the unfolded protein burden overwhelms the capacity of the ER quality control machinery, a state termed ER stress, sensor proteins detect accumulation of misfolded peptides and trigger the UPR transcriptional response. The UPR, which is conserved from yeast to mammals, consists of an ensemble of complex signaling pathways that aims at adapting the ER to the new misfolded protein load. To determine how different factors impact the ER folding environment, various tools and assays have been developed. In this review, we discuss recent advances in live cell imaging reporters and model systems that enable researchers to monitor changes in the unfolded secretory protein burden and activation of the UPR and its associated signaling pathways.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".