A streamlined strain engineering workflow with genome-wide screening detects enhanced protein secretion in Komagataella phaffii
Bibliographic record
Abstract
Expression of secreted recombinant proteins burdens the protein secretion machinery, limiting production. Here, we describe an approach to improving protein production by the non-conventional yeast Komagataella phaffii comprised of genome-wide screening for effective gene disruptions, combining them in a single strain, and recovering growth reduction by adaptive evolution. For the screen, we designed a multiwell-formatted, streamlined workflow to high-throughput assay of secretion of a single-chain small antibody, which is cumbersome to detect but serves as a good model of proteins that are difficult to secrete. Using the consolidated screening system, we evaluated >19,000 mutant strains from a mutant library prepared by a modified random gene-disruption method, and identified six factors for which disruption led to increased antibody production. We then combined the disruptions, up to quadruple gene knockouts, which appeared to contribute independently, in a single strain and observed an additive effect. Target protein and promoter were basically interchangeable for the effects of knockout genes screened. We finally used adaptive evolution to recover reduced cell growth by multiple gene knockouts and examine the possibility for further enhancing protein secretion. Our successful, three-part approach holds promise as a method for improving protein production by non-conventional microorganisms.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".