New insights into natural rubber biosynthesis from rubber‐deficient lettuce mutants expressing goldenrod or guayule <i>cis</i>‐prenyltransferase
Bibliographic record
Abstract
Summary Lettuce produces natural rubber (NR) with an average M w of > 1 million Da in laticifers, similar to NR from rubber trees. As lettuce is an annual, self‐pollinating, and easily transformable plant, it is an excellent model for molecular genetic studies of NR biosynthesis. CRISPR/Cas9 mutagenesis was optimized using lettuce hairy roots, and NR‐deficient lettuce was generated via bi‐allelic mutations in cis ‐prenyltransferase ( CPT ). This is the first null mutant of NR deficiency in plants. In the CPT mutant, orthologous CPT counterparts from guayule ( Parthenium argentatum ) and goldenrod ( Solidago canadensis ) were expressed under a laticifer‐specific promoter to examine how the average M w of NR is affected. No developmental defects were observed in the NR‐deficient mutants. The lettuce mutants expressing guayule and goldenrod CPT produced 1.8 and 14.5 times longer NR, respectively, than the plants of their origin. This suggests that, although goldenrod cannot synthesize a sufficiently lengthy NR, goldenrod CPT has the catalytic competence to produce high‐quality NR in the cellular context of lettuce laticifers. Thus, CPT alone does not determine the length of NR. Other factors, such as substrate concentration, additional proteins, and/or the nature of protein complexes including CPT‐binding proteins, influence CPT activity in determining NR length.
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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) | 0.001 | 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 | 0.001 |
| 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".