Faculty Opinions recommendation of Outer membrane vesicle production by Escherichia coli is independent of membrane instability.
Bibliographic record
Abstract
It has been long noted that gram-negative bacteria produce outer membrane vesicles, and recent data demonstrate that vesicles released by pathogenic strains can transmit virulence factors to host cells.However, the mechanism of vesicle release has remained undetermined.This genetic study addresses whether these structures are merely a result of membrane instability or are formed by a more directed process.To elucidate the regulatory mechanisms and physiological basis of vesiculation, we conducted a screen in Escherichia coli to identify gene disruptions that caused vesicle over-or underproduction.Only a few low-vesiculation mutants and no null mutants were recovered, suggesting that vesiculation may be a fundamental characteristic of gram-negative bacterial growth.Gene disruptions were identified that caused differences in vesicle production ranging from a 5-fold decrease to a 200-fold increase relative to wild-type levels.These disruptions included loci governing outer membrane components and peptidoglycan synthesis as well as the E cell envelope stress response.Mutations causing vesicle overproduction did not result in upregulation of the ompC gene encoding a major outer membrane protein.Detergent sensitivity, leakiness, and growth characteristics of the novel vesiculation mutant strains did not correlate with vesiculation levels, demonstrating that vesicle production is not predictive of envelope instability.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.021 |
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".