Biosynthesis of kratom opioids
Bibliographic record
Abstract
Mitragynine, an analgesic alkaloid from the plant Mitragyna speciosa (kratom), offers a safer alternative to clinical opioids such as morphine, owing to its more favorable side effect profile. Although kratom has been traditionally used for stimulation and pain management in Southeast Asia, the mitragynine biosynthesis pathway has remained elusive. We embarked on a search for mitragynine biosynthetic genes from the transcriptomes of kratom and other members of the Rubiaceae family. We studied their functions in vitro and in vivo. Our investigations led to the identification of several reductases and an enol methyltransferase that forms a new clade within the SABATH methyltransferase family. Furthermore, we discovered a methyltransferase from Hamelia patens (firebush), which catalyzes the final step. With the tryptamine 4-hydroxylase from the psychedelic mushroom Psilocybe cubensis, we accomplished the four-step biosynthesis for mitragynine and its stereoisomer, speciogynine in both yeast and Escherichia coli when supplied with tryptamine and secologanin. Although we have yet to pinpoint the authentic hydroxylase and methyltransferase in kratom, our discovery completes the mitragynine biosynthesis. Through these breakthroughs, we achieved the microbial biosynthesis of kratom opioids for the first time. The remarkable enzyme promiscuity suggests the possibility of generating derivatives and analogs of kratom opioids in heterologous systems.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".