In vivo hydrolysis of<i>S</i>-adenosyl-L-methionine in<i>Escherichia coli</i>increases export of 5-methylthioribose
Bibliographic record
Abstract
Escherichia coli can not synthesize methionine from 5-methylthioribose (MTR) but instead exports this sulfur-containing, energy-rich molecule into the surrounding medium. Transforming E. coli with plasmids that direct expression of the cloned coliphage T3 S-adenosyl-L-methionine (SAM) hydrolase (SAMase) induces the met regulon by cleaving the SAM co-repressor to form 5'-methylthioadenosine, which is then cleaved to produce MTR. To test the effect of in vivo SAMase activity on MTR production and its fate, cultures were incubated in the presence of [35S]methionine and [methyl-3H]methionine. Cells with SAMase activity produced significantly enhanced levels (up to 40-fold in some trials) of extracellular MTR -- the only radiolabeled compound released in significant amounts -- when compared with controls. SAM synthetase (metK) mutants transformed with SAMase expression vectors did not show this increase, verifying the path through SAM as the sole route to MTR production. SAMase expression had little or no effect on intracellular MTR pools, levels of radiolabeled macromolecules, or the transfer of methyl groups to compounds that could be precipitated by trichloroacetic acid. Thus, MTR appears to be a dead-end metabolite in E. coli, begging questions about how this has evolved, the mechanism of MTR export for the cell, and whether the release of MTR is important for some other activity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".