Discovery and mechanistic characterization of a probiotic-origin 3β-OH-Δ <sup>5-6</sup> -cholesterol-5β-reductase directly converting cholesterol to coprostanol
Bibliographic record
Abstract
Abstract On the one hand, cholesterol is a foundational molecule for various structural and biochemical pathways, while elevated cholesterol levels are associated with cardiovascular diseases. Some selected strains of Lactobacilli are also known for modulating cholesterol levels. However, the molecular mechanism of cholesterol transformation by lactobacilli has remained challenging to reveal. This study reports the discovery and role of a microbial 3β-OH-Δ 5-6 -cholesterol-5β-reductase from Limosilactobacillus fermentum NKN51, which directly converts cholesterol to coprostanol, thereby resolving the enigma. Protein engineering of the reductase enzyme identified the cholesterol and NADP + interacting amino acids, detailing the catalytic mechanism of 5βChR. Phylogenetic studies emphasize the abundance of 5βChRs in gut commensal lactobacilli, which shares a common ancestor with plant 5β reductases. Meta-analysis results of healthy participant microbiomes underline the significance of 5βChR homologs, while a cohort study reveals an association between higher 5βChR abundance and diabetes. The discovery of the 5βChR enzyme and its molecular mechanism in cholesterol metabolism pave the way for a better understanding of the gut-associated microbiome and the design of practical applications to ameliorate dyslipidemia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".