Indigenous community-guided chemical genomic insight into synergy of rapamycin with nerolidol derived from a leaf extract of kānuka (Kunzea robusta) in Tairāwhiti, Aotearoa New Zealand
Bibliographic record
Abstract
Maximizing efficacy and minimizing toxicity are essential in drug discovery. Utilizing a natural product combinatorial approach can offer a promising strategy to preserve bioactivity while reducing the dosage of potentially toxic drugs in patient treatment. Kānuka ( Kunzea robusta ) is a well-known endemic plant of Aotearoa New Zealand that has therapeutic value with traditional knowledge and preliminary laboratory studies indicating antimicrobial bioactivities, yet combinatorial bioactivity studies of this plant are non-existent. Via a research collaboration led by an Indigenous Māori social enterprise that ensured Indigenous landowners contributed biological material and knowledge with free, prior and informed consent at each stage of the study, we show that a steam extract of kānuka leaves has synergistic activity with the well-known immunosuppressive agent rapamycin resulting in amplified bioactivity in the model organism Saccharomyces cerevisiae. Network analysis of a genome-wide gene deletion screen suggested the extract increased the bioavailability of rapamycin. Subsequent fluorescent microscopy analyses revealed the importance of endocytic and oxidative stress pathways. Bioassay-guided metabolomic analyses elucidated the main component of the steam extract, α-pinene, to not be responsible for the synergistic bioactivity, rather, highlighting nerolidol as a strong synergistic candidate. Given rapamycin is an advanced compound in many clinical studies involving anti-cancer, anti-ageing, and anti-microbial activities, albeit associated with several toxic side-effects, this study provides mechanistic insight into this previously unknown bioactivity of kānuka leaves and illustrates how a lower, and safer, concentration of rapamycin could be potentially used in these diverse clinical settings.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".