Conversion of Phytochemicals by Lactobacilli: (Phospho)-β-glucosidases Are Specific for Glucosylated Phytochemicals Rather than Disaccharides
Bibliographic record
Abstract
Food-fermenting lactobacilli convert glycosylated phytochemicals to glycosyl hydrolases and thereby alter their biological activity. This study aimed to investigate the microbial transformation of β-glucosides of phytochemicals in comparison with utilization of cellobiose. Four homofermentative and four heterofermentative lactobacilli were selected to represent the metabolic diversity of Lactobacillaceae . The genomes of Lactobacillus crispatus, Companilactobacillus paralimentarius, Lacticaseibacillus paracasei, and Lactiplantibacillus plantarum encoded for 8 to 22 enzymes, predominantly phospho-β-glucosidases, with predicted activity on β-glucosides. Levilactobacillus hammesii and Furfurilactobacillus milii encoded for 3 β-glucosidases, Furfurilactobacillus rossiae for one, and Fructilactobacillus sanfranciscensis for none. The hydrolysis of amygdalin, esculin, salicin, glucosides of quercetin and genistein, and ginsenosides demonstrated that several strains hydrolyzed β-glucosides of phytochemicals but not cellobiose. Taken together, several of the carbohydrate-active enzymes of food-fermenting lactobacilli are specific for glycosides of phytochemicals.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".