Detailed Phytochemical Analysis of High- and Low Artemisinin-Producing Chemotypes of Artemisia annua
Bibliographic record
Abstract
Chemical derivatives of aArtemisinin, a sesquiterpene lactone produced by Artemisia annua, is are the active ingredient in the most effective treatment for malaria. Comprehensive phytochemical analysis of two contrasting chemotypes of A. annua resulted in the characterisation of over 80 natural products by NMR, more than 20 of which are novel and described here for the first time. Analysis of high- and low-artemisinin producing (HAP and LAP) chemotypes of A. annua confirmed the latter to have a low level of DBR2 (artemisinic aldehyde Δ11 (13) reductase) gene expression. Here we show that the LAP chemotype accumulates high levels of artemisinic acid, arteannuin B, epi-deoxyarteannuin B and other amorpha-4,11-diene derived sesquiterpenes which are unsaturated at the 11,13-position. By contrast, the HAP chemotype is rich in sesquiterpenes saturated at the 11,13-position (dihydroartemisinic acid, artemisinin and dihydro-epi-deoxyarteannunin B), which is consistent with higher expression levels of DBR2, and also with the presence of a HAP-chemotype version of CYP71AV1 (amorpha-4,11-diene C-12 oxidase). Our results indicate that the conversion steps from artemisinic acid to arteannuin B, epi-deoxyarteannuin B and artemisitene in the LAP chemotype are non-enzymatic and parallel the non-enzymatic conversion of DHAA to artemisinin and dihyro-epi-deoxyarteannuin B in the HAP chemotype. Interestingly, artemisinic acid in the LAP chemotype preferentially converts to arteannuin B rather than the endoperoxide bridge containing artemisitene. In contrast, in the HAP chemotype, DHAA preferentially converts to artemisinin. Broader metabolomic and transcriptomic profiling revealed significantly different terpenoid profiles and related terpenoid gene expression in these two morphologically distinct chemotypes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".