Use of an α-Galactosidase Gene as a Food-Grade Selection Marker for Streptococcus thermophilus
Bibliographic record
Abstract
The alpha-galactosidase gene (aga) of Lactococcus raffinolactis ATCC 43920 was previously shown to be an efficient food-grade selection marker in Lactococcus lactis and Pediococcus acidilactici but not in Streptococcus thermophilus. In this study, we demonstrated that the alpha-galactosidase of L. raffinolactis is thermolabile and inoperative at 42 degrees C, the optimal growth temperature of S. thermophilus. An in vitro assay indicated that the activity of this alpha-galactosidase at 42 degrees C was only 3% of that at 30 degrees C, whereas the enzyme retained 23% of its activity at 37 degrees C. Transformation of Strep. thermophilus RD733 with the shuttle-vector pNZ123 bearing the aga gene of L. raffinolactis (pRAF301) generated transformants that were stable and able to grow on melibiose and raffinose at 37 degrees C or below. The transformed cells possessed 6-fold more alpha-galactosidase activity after growth on melibiose than cells grown on lactose. Slot-blot analyses of aga mRNA indicated that repression by lactose occurred at the transcriptional level. The presence of pRAF301 did not interfere with the lactic acid production when the transformed cells of Strep. thermophilus were grown at the optimal temperature in milk. Using the recombinant plasmid pRAF301, which carries a chloramphenicol resistance gene in addition to aga, we showed that both markers were equally efficient at differentiating transformed from nontransformed cells. The aga gene of L. raffinolactis can be used as a highly efficient selection marker in Strep. thermophilus.
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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) | 0.000 | 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 | 0.001 |
| 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".