An Engineered Survival-Selection Assay for Extracellular Protein Expression Uncovers Hypersecretory Phenotypes in <i>Escherichia coli</i>
Bibliographic record
Abstract
The extracellular expression of recombinant proteins using laboratory strains of Escherichia coli is now routinely achieved using naturally secreted substrates, such as YebF or the osmotically inducible protein Y (OsmY), as carrier molecules. However, secretion efficiency through these pathways needs to be improved for most synthetic biology and metabolic engineering applications. To address this challenge, we developed a generalizable survival-based selection strategy that effectively couples extracellular protein secretion to antibiotic resistance and enables facile isolation of rare mutants from very large populations ( i.e., 10 10–12 clones) based simply on cell growth. Using this strategy in the context of the YebF pathway, a comprehensive library of E. coli single-gene knockout mutants was screened and several gain-of-function mutations were isolated that increased the efficiency of extracellular expression without compromising the integrity of the outer membrane. We anticipate that this user-friendly strategy could be leveraged to better understand the YebF pathway and other secretory mechanisms—enabling the exploration of protein secretion in pathogenesis as well as the creation of designer E. coli strains with greatly expanded secretomes—all without the need for expensive exogenous reagents, assay instruments, or robotic automation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".