The Thiamin Pyrophosphate Riboswitch is Affected by Lysine-derived Metabolic Conversion Products in Escherichia coli
Bibliographic record
Abstract
Riboswitches are 5' untranslated regulators that control gene expression by specifically monitoring cellular metabolites. Metabolite binding to the riboswitch triggers the genetic regulation at the transcriptional or translational level. Riboswitches typically exhibit high affinities and strong discrimination against non-cognate metabolites, making them well suited to regulate gene expression. Importantly, despite the well characterized cellular processes ensuring metabolic conversion and recycling in bacteria, there is little information about how these processes influence riboswitch regulation mechanisms. Here, we characterize the regulation mechanisms of the lysine-sensing and thiamin pyrophosphate (TPP)-sensing riboswitches in E. coli. In agreement with previous results, our study indicates that the addition of lysine or TPP to the growth medium significantly reduces the expression of the respective riboswitch-regulated mRNAs. Surprisingly, we find that the addition of lysine also leads to a significant decrease in TPP-regulated mRNAs, suggesting that lysine indirectly affects TPP riboswitches. Using mutant strains from the Keio collection, we observe that the effect of lysine on TPP riboswitches is lost when perturbing the lysine degradation process. These data suggest that lysine degradation products may be used to generate TPP through metabolic conversion. In contrast, our results indicate that TPP does not modulate the regulation of the lysine riboswitch, suggesting that TPP does not indirectly affect the lysine riboswitch genetic control. Together, our results indicate that intracellular changes in lysine concentrations can be detected by TPP riboswitches, thus suggesting that riboswitches may be sensitive to cellular stress that are not directly related to their cognate metabolite.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".