Genome-wide screen identifies new set of genes for improved heterologous laccase expression in Saccharomyces cerevisiae
Bibliographic record
Abstract
The yeast Saccharomyces cerevisiae is widely used as a host cell for recombinant protein production due to its fast growth, cost-effective culturing, and ability to secrete large and complex proteins. However, one major drawback is the relatively low yield of produced proteins compared to other host systems. To address this issue, we developed an overlay assay to screen the yeast knockout collection and identify mutants that enhance recombinant protein production, specifically focusing on the secretion of the Trametes trogii fungal laccase enzyme. Gene ontology analysis of these mutants revealed an enrichment of processes including vacuolar targeting, vesicle trafficking, proteolysis, and glycolipid metabolism. We confirmed that a significant portion of these mutants also showed increased activity of the secreted laccase when grown in liquid culture. Notably, we found that the combination of deletions of OCA6, a tyrosine phosphatase gene, along with PMT1 or PMT2, two genes encoding ER membrane protein-O-mannosyltransferases involved in ER quality control, and SKI3, which encode for a component of the SKI complex responsible for mRNA degradation, further increased secreted laccase activity. Conversely, we also identified over 200 gene deletions that resulted in decreased secreted laccase activity, including many genes that encode for mitochondrial proteins and components of the ER-associated degradation pathway. Intriguingly, the deletion of the ER DNAJ co-chaperone gene SCJ1 led to almost no secreted laccase activity. When we expressed SCJ1 from a low-copy plasmid, laccase secretion was restored. However, overexpression of SCJ1 had a detrimental effect, indicating that precise dosing of key chaperone proteins is crucial for optimal recombinant protein expression. This study offers potential strategies for enhancing the overall yield of recombinant proteins and provides new avenues for further research in optimizing protein production systems.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".