<i>Lactiplantibacillus plantarum</i> Interstrain Variability in the Production of Bioactive Phenolic Metabolites from Flavan-3-ols
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Flavan-3-ols intake is associated with numerous health benefits, but these are influenced by their conversion into smaller phenolic metabolites by the gut microbiota. Thus, the identification of bacteria that metabolize flavan-3-ols could lead to targeted interventions to enhance their benefits. To this end, we screened 47 Lactiplantibacillus plantarum strains for their ability to metabolize (+)-catechin, a flavan-3-ol. Then, we assessed these strains for their capacity to convert various flavan-3-ol structures. Out of the 47 isolates, 12 released 3-(3′,4′-dihydroxyphenyl)-1-(2,4,6-trihydroxyphenyl)-propan-2-ol (a form of diphenylpropan-2-ol) from (+)-catechin. All strains metabolized (+)-catechin, (−)-epicatechin, (−)-epigallocatechin, but only a subset transformed (−)-gallocatechin. Among these simple flavan-3-ol structures, (−)-epicatechin was metabolized the most. A hierarchical cluster analysis identified two groups of flavan-3-ol-metabolizing strains categorized as having “high” and “low” production of diphenylpropan-2-ols. Notably, the strains that produced higher levels of diphenylpropan-2-ol from (+)-gallocatechin and (+)-catechin also performed better with a camu–camu extract, which was studied as a complex source of flavan-3-ols and predominantly contained these two flavan-3-ols. These results demonstrate the interstrain variability in L. plantarum metabolism, which may be useful for developing tailored formulations to enhance the production of flavan-3-ols bioactive metabolites.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".