Purification and characterization of two phospho-β-galactosidases, LacG1 and LacG2, from Lactobacillus gasseri ATCC33323T
Bibliographic record
Abstract
Lactobacillus gasseri ATCC33323(T) expresses four enzymes showing phospho-β-galactosidase activity (LacG1, LacG2, Pbg1 and Pbg2). We previously reported the purification and characterization of two phospho-β-galactosidases (Pbg1 and Pbg2) from Lactobacillus gasseri JCM1031 cultured in lactose medium. Here we aimed to characterize LacG1 and LacG2, and classify the four enzymes into 'phospho-β-galactosidase' or 'phospho-β-glucosidase.' LacG1 and recombinant LacG2 (rLacG2), from Lb. gasseri ATCC33323(T), were purified to homogeneity using column chromatography. Kinetic experiments were performed using sugar substrates, o-nitrophenyl-β-D-galactopyranoside 6-phosphate (ONPGal-6P) and o-nitrophenyl-β-D-glucopyranoside 6-phosphate (ONPGlc-6P), synthesized in our laboratory. LacG1 and rLacG2 exhibited high k(cat)/K(m) values for ONPGal-6P as compared with Pbg1 and Pbg2. The V(max) values for ONPGal-6P were higher than phospho-β-galactosidases previously purified and characterized from several lactic acid bacteria. A phylogenetic tree analysis showed that LacG1 and LacG2 belong to the phospho-β-galactosidase cluster and Pbg1 and Pbg2 belong to the phospho-β-glucosidase cluster. Our data suggest two phospho-β-galactosidase, LacG1 and LacG2, are the primary enzymes for lactose utilization in Lb. gasseri ATCC33323(T). We propose a reclassification of Pbg1 and Pbg2 as phospho-β-glucosidase.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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".