Fitness cost of the green fluorescent protein in gastrointestinal bacteria
Bibliographic record
Abstract
There are surprisingly few studies that have successfully used the green fluorescent protein (GFP) as a quantitative reporter in selection experiments screening for inducible bacterial promoters. One explanation is that GFP expression may confer a fitness cost for bacteria. To test this possibility, we monitored the doubling time in enteric bacteria expressing GFP. Four bacterial species, Escherichia coli, enterohaemorrhagic E. coli, Shigella flexneri, Salmonella typhi, and Vibrio cholerae, were examined. The level of GFP expression was varied by using a salt-inducible promoter. After accounting for the increase in doubling time resulting from elevated osmolarity, the doubling time of all bacteria was found to increase proportionally with GFP expression, and some strains were more affected than others. Cultures of the bacteria most affected by GFP exhibited a proportion of elongated cells, which suggests that GFP production could interfere with cell division in these strains. The results in this study show that GFP is costly to bacteria and suggest that overly active promoters should be difficult to obtain from a genomic promoter library. They also suggest that the chances of succeeding in using GFP as a reporter in selection experiments are increased by growing the bacteria for the fewest number of generations and by subduing the expression of GFP whenever possible, such as by using a low copy vector to clone the library.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".