The <i>Escherichia coli</i> FadR transcription factor: Too much of a good thing?
Bibliographic record
Abstract
Escherichia coli FadR is a transcription factor regulated by acyl-CoA thioester binding that optimizes fatty acid (FA) metabolism in response to environmental FAs. FadR represses the fad genes of FA degradation (β-oxidation) and activates the fab genes of FA synthesis thereby allowing E. coli to have its cake (acyl chains for phospholipid synthesis) and eat it (degrade acyl chains to acetyl-CoA). Acyl-CoA binding of FadR derepresses the transcription of the fad genes and cancels fab gene transcriptional activation. Activation of fab genes was thought restricted to the fabA and fabB genes of unsaturated FA synthesis, but FadR overproduction markedly increases yields of all FA acyl chains. Subsequently, almost all of the remaining fab genes were shown to be transcriptionally activated by FadR binding, but binding was very weak. Why are the low-affinity sites retained? What effects on cell physiology would result from their conversion to high-affinity sites (thereby mimicking FadR overproduction)? Investigations of E. coli cell size determinants showed that FA synthesis primarily determines E. coli cell size. Upon modest induction of FadR, cell size increases, but at the cost of growth rate and accumulation of intracellular membranes. Greater induction resulted in further growth rate decreases and abnormal cells. Hence, too much FadR is bad. FadR is extraordinarily conserved in γ-proteobacteria but has migrated. Mycobacterium tuberculosis encodes FadR orthologs one of which is functional in E. coli. Strikingly, the FadR theme of acyl-CoA-dependent transcriptional regulation is found in a different transcription factor family where two Bacillus species plus bacterial and archaeal thermophiles contain related proteins of similar function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".