Imbalance of heterologous protein folding and disulfide bond formation rates yields runaway oxidative stress
Bibliographic record
Abstract
BACKGROUND: The protein secretory pathway must process a wide assortment of native proteins for eukaryotic cells to function. As well, recombinant protein secretion is used extensively to produce many biologics and industrial enzymes. Therefore, secretory pathway dysfunction can be highly detrimental to the cell and can drastically inhibit product titers in biochemical production. Because the secretory pathway is a highly-integrated, multi-organelle system, dysfunction can happen at many levels and dissecting the root cause can be challenging. In this study, we apply a systems biology approach to analyze secretory pathway dysfunctions resulting from heterologous production of a small protein (insulin precursor) or a larger protein (α-amylase). RESULTS: HAC1-dependent and independent dysfunctions and cellular responses were apparent across multiple datasets. In particular, processes involving (a) degradation of protein/recycling amino acids, (b) overall transcription/translation repression, and (c) oxidative stress were broadly associated with secretory stress. CONCLUSIONS: Apparent runaway oxidative stress due to radical production observed here and elsewhere can be explained by a futile cycle of disulfide formation and breaking that consumes reduced glutathione and produces reactive oxygen species. The futile cycle is dominating when protein folding rates are low relative to disulfide bond formation rates. While not strictly conclusive with the present data, this insight does provide a molecular interpretation to an, until now, largely empirical understanding of optimizing heterologous protein secretion. This molecular insight has direct implications on engineering a broad range of recombinant proteins for secretion and provides potential hypotheses for the root causes of several secretory-associated diseases.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".