Isolation, evaluation and use of two strong, carbon source-inducible promoters from<i>Corynebacterium glutamicum</i>
Bibliographic record
Abstract
AIMS: To obtain strong, carbon source-inducible promoters useful for industrial applications of Corynebacterium glutamicum. METHODS AND RESULTS: DNA microarray and qRT-PCR enabled identification of the promoters of cgR_2367 (malE1) and cgR_2459 (git1) as strong, maltose- and gluconate-inducible promoters, respectively, in C. glutamicum. Promoter probe assays revealed that in the presence of the inducing sugars, PmalE1 and Pgit1, respectively, facilitated 3.4- and 4.2-fold increased beta-galactosidase activities compared to the same activity induced by glucose. In addition, PmalE1 was not functional in Escherichia coli, in which Pgit1 function was repressible, which enabled the cloning of a hitherto 'difficult-to-clone' heterologous gene of a lignocellulolytic enzyme, whose secretion was consequently induced by the carbon sources. CONCLUSIONS: PmalE1 and Pgit1 are strong, carbon source-inducible promoters of C. glutamicum whose characteristics in E. coli are integral to the secretion ability of C. glutamicum to secrete lignocellulolytic enzyme. SIGNIFICANCE AND IMPACT OF THE STUDY: Corynebacterium glutamicum, like its counterpart industrial workhorses E. coli and Bacillus subtilis, does exhibit strong, carbon source-inducible promoters, and the functionality of two of which was demonstrated in this study. While this study may be most relevant in the ongoing efforts to establish technologies of the biorefinery, it should also be of interest to general microbiologists exploring the versatility of industrial micro-organisms. In so doing, the study should impact future advances in industrial microbiology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".