Fluorescent riboswitch-controlled biosensors for the genome scale analysis of metabolic pathways
Bibliographic record
Abstract
Fluorescent detection in cells has been tremendously developed over the years and now benefits from a large array of reporters that can provide sensitive and specific detection in real time. However, the intracellular monitoring of metabolite levels still poses great challenges due to the often complex nature of detected metabolites. Here, we provide a systematic analysis of thiamin pyrophosphate (TPP) metabolism in Escherichia coli by using a TPP-sensing riboswitch that controls the expression of the fluorescent gfp reporter. By comparing different combinations of reporter fusions and TPP-sensing riboswitches, we determine key elements that are associated with strong TPP-dependent sensing. Furthermore, by using the Keio collection as a proxy for growth conditions differing in TPP levels, we perform a high-throughput screen analysis using high-density solid agar plates. Our study reveals several genes whose deletion leads to increased or decreased TPP levels. The approach developed here could be applicable to other riboswitches and reporter genes, thus representing a framework onto which further development could lead to highly sophisticated detection platforms allowing metabolic screens and identification of orphan riboswitches.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".