Ribosome binding site libraries and pathway modules for shikimic acid synthesis with Corynebacterium glutamicum
Bibliographic record
Abstract
BACKGROUND: The shikimic acid (SA) pathway is a fundamental route to synthesize aromatic building blocks for cell growth and metabolic processes, as well as for fermentative production of various aromatic compounds. Genes encoding enzymes of SA pathway are not continuous on genome and they are differently regulated. RESULTS: In this study, efforts were made to construct continuous genetic modules of SA pathway that are regulated by a same Ptac promoter. Firstly, aro genes [aroG (NCgl2098), aroB (NCgl1559), aroD (NCgl0408) and aroE (NCgl1567)] from Corynebacterium glutamicum and ribosome binding site (RBS) libraries that were tailored for the above genes were obtained, and the strength of each RBS in the 4 libraries was quantified. Secondly, 9 genetic modules were built up from the RBS libraries, a previously characterized ribozyme insulator (RiboJ) and transcriptional promoter (Ptac) and terminator, and aroG, aroB, aroD and aroE. The functionality and efficiency of the constructed genetic modules were evaluated in C. glutamicum by determination of SA synthesis. Results showed that C. glutamicum RES167ΔaroK carrying a genetic module produced 4.3 g/L of SA, which was 54 folds higher compared to that of strain RES167ΔaroK (80 mg/L, without the genetic module) during fermentation in 250-mL flasks. The same strain produced 7.4, and 11.3 g/L of SA during 5-L batch and fed-batch fermentations, respectively, which corresponding to SA molar yields of 0.39 and 0.24 per mole sucrose consumption. CONCLUSION: These results demonstrated that the constructed SA pathway modules are effective in increasing SA synthesis in C. glutamicum, and they might be useful for fermentative production of aromatic compounds derived from SA pathway.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".