Microbiota-derived aromatic amino acid decarboxylases: linking microbial fitness and host neurochemical communication
Bibliographic record
Abstract
ABSTRACT The human microbiota produces a diverse array of bioactive molecules, including classic neurotransmitters (dopamine and serotonin) and trace amines (tryptamine, tyramine, and phenylethylamine). Although long considered products of host metabolism, these aromatic monoamines are now also known to originate in part from the microbiota, where they are synthesized by bacterial aromatic L-amino acid decarboxylases (AADCs). This review explores the distribution, biochemical diversity, and host interactions of microbiota-encoded AADCs, highlighting their roles in gut and skin ecosystems. Bacterial AADCs vary in gene organization, substrate range, and expression patterns across taxa like Ruminococcus gnavus , Clostridium sporogenes , Enterococcus spp., and Staphylococcus spp. These enzymes contribute to microbial fitness through acid stress resistance, energy generation via proton motive force, epithelial adherence and internalization, and niche dominance. Critically, their products modulate host physiology via trace amine-associated receptors (TAARs) and other signaling pathways, influencing neurotransmission, immune response, barrier integrity, and metabolism. Microbiota-derived monoamines can enter systemic circulation and cross the blood–brain barrier, implicating them in disorders ranging from irritable bowel syndrome to neurodegeneration. Emerging data also reveal their impact on wound healing and drug efficacy, notably in Parkinson’s disease. By positioning microbial AADCs as key players in host-microbe chemical communication, this review underscores their relevance for health and disease and highlights them as potential therapeutic targets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".