Hypostatin, a new small molecule inhibitor of plant cell expansion, is glyco-activated in vivo
Bibliographic record
Abstract
Natural variation in humans in the response to a bioactive or drug-like small organic molecule is defined as pharmacogenetic variation. Studies have shown that variation in both drug metabolism genes and drug target loci can cause inter-individual variation in drug response. For example, sequence differences in UDP-glucuronosyl transferases (UGTs), which glycosylate xenobiotics, can affect drug sensitivity by affecting rates of drug detoxification. Although this subject has been explored extensively in humans, the biological pervasiveness of pharmacogenetic variation had not been systematically examined. If pervasive, pharmacogenetic variation in model systems could be used for both functional studies and to gain deeper insight into the mechanisms of this important form of natural variation. To examine this question, 8 geographically diverse Arabidopsis accessions were screened on a 10,000 member chemical library. I focused on new inhibitors of cell expansion in the etiolated hypocotyl. In total, 742 chemicals (7.4%) caused greater than 20% inhibition of hypocotyl cell expansion. 11 weak polymorphic chemicals were uncovered, as well as a strong polymorphic hypocotyl cell expansion inhibitor, named hypostatin. My work has firmly established the existence of pharmacogenetic variation in Arabidopsis.
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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) | 0.002 | 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".