Construction and evaluation of food-grade vectors for Lactococcus lactis using aspartate aminotransferase and alpha-galactosidase as selectable markers
Bibliographic record
Abstract
AIMS: We report development of two food-grade cloning vectors for Lactococcus lactis, which utilize either a lactococcal aspartate aminotransferase gene (aspC), or Bifidobacterium longumalpha-galactosidase gene (aglL) as selectable markers. METHODS AND RESULTS: The theta-replicon of lactococcal plasmid, pW563, was combined with aspC and a multiple cloning site. When electroporated into L. lactis JLS400 (AspC-), the resulting vector, pSUW611 (3.9 kbp), restores ability of the mutant to grow in milk thus allowing for selection of the transformants. The vector is stable during 100 generations of nonselective growth (0.2% loss per generation). The second vector, pSUW711 (5.1 kbp), was constructed by exchanging aspC with aglL under the control of usp45 promoter. Lactococcus lactis transformed with pSUW711 produced distinctive colonies within 48-72 h on melibiose-containing plates. Expression of two Lactobacillus helveticus peptidases was attempted using these new vehicles. Introduction of pepN on pSUW611 and pSUW711 into L. lactis led to a sixfold, or 27-fold increase in aminopeptidase activity, respectively. However, no changes in endopeptidase activity were recorded upon transformation with pSUW611 carrying pepO2 under control of three different promoters. Attempts were also made to construct high copy variants of pSUW711. CONCLUSIONS: The aspC and aglL can be employed as food-grade genetic markers for L. lactis. The vectors, pSUW611 and pSUW711, were successfully used to express Lact. helveticus PepN in L. lactis. SIGNIFICANCE AND IMPACT OF THE STUDY: Two novel food-grade vectors were developed which provide simple and convenient selection and maintenance in L. lactis.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".