A novel pathway of levodopa metabolism by commensal Bifidobacteria
Bibliographic record
Abstract
The gold-standard treatment for Parkinson's disease is levodopa (L-DOPA), which is taken orally and absorbed intestinally. L-DOPA must reach the brain intact to exert its clinical effect; peripheral metabolism by host and microbial enzymes is a clinical management issue. The gut microbiota is altered in PD, with one consistent and unexplained observation being an increase in Bifidobacterium abundance among patients. Recently, certain Bifidobacterium species were shown to have the ability to metabolize L-tyrosine, an L-DOPA structural analog. Using both clinical cohort data and in vitro experimentation, we investigated the potential for commensal Bifidobacteria to metabolize this drug. In PD patients, Bifidobacterium abundance was positively correlated with L-DOPA dose and negatively with serum tyrosine concentration. In vitro experiments revealed that certain species, including B. bifidum, B. breve, and B. longum, were able to metabolize this drug via deamination followed by reduction to the compound 3,4-dihydroxyphenyl lactic acid (DHPLA) using existing tyrosine-metabolising genes. DHPLA appears to be a waste product generated during regeneration of NAD +. This metabolism occurs at low levels in rich medium, but is significantly upregulated in nutrient-limited minimal medium. Discovery of this novel metabolism of L-DOPA to DHPLA by a common commensal may help inform medication management in PD.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".