Discovery and mechanistic characterization of a probiotic‐origin 3β‐ <scp>OH</scp> ‐Δ <sup>5‐6</sup> ‐cholesterol‐5β‐reductase directly converting cholesterol to coprostanol
Bibliographic record
Abstract
Cholesterol serves as a fundamental molecule in various structural and biochemical pathways; however, high cholesterol levels are linked to cardiovascular diseases. Some selected strains of Lactobacilli are known for modulating cholesterol levels. However, the molecular mechanism underlying cholesterol transformation by lactobacilli has remained elusive. This study describes the discovery and function of a microbial 3β‐OH‐Δ 5‐6 ‐cholesterol‐5β‐reductase (5βChR) from Limosilactobacillus fermentum NKN51, which directly converts cholesterol to coprostanol, thereby unraveling this longstanding mystery. Protein engineering of the reductase enzyme identified the cholesterol and NADPH interacting amino acid residues, detailing the catalytic mechanism of 5βChR. Phylogenetic analyses highlight the prevalence of 5βChRs in gut commensal lactobacilli, which share a common evolutionary origin with plant 5β reductases. Meta‐analysis of microbiomes from healthy individuals underscores the importance of 5βChR homologs, while a cohort study demonstrates an inverse association between 5βChR abundance and diabetes. The discovery of the 5βChR enzyme and its molecular mechanism in cholesterol metabolism paves 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".