Spider dragline silk proteins in transgenic tobacco leaves: accumulation and field production
Bibliographic record
Abstract
Spider dragline silk is a unique biomaterial and represents nature's strongest known fibre. As it is almost as strong as many commercial synthetic fibres, it is suitable for use in many industrial and medical applications. The prerequisite for such a widespread use is the cost-effective production in sufficient quantities for commercial fibre manufacturing. Agricultural biotechnology and the production of recombinant dragline silk proteins in transgenic plants offer the potential for low-cost, large-scale production. The purpose of this work was to examine the feasibility of producing the two protein components of dragline silk (MaSp1 and MaSp2) from Nephila clavipes in transgenic tobacco. Two different promoters, the enhanced CaMV 35S promoter (Kay et al., 1987) and a new tobacco cryptic constitutive promoter, tCUP (Foster et al., 1999) were used, in conjunction with a plant secretory signal (PR1b), a translational enhancer (alfalfa mosaic virus, AMV) and an endoplasmic reticulum (ER) retention signal (KDEL), to express the MaSp1 and MaSp2 genes in the leaves of transgenic plants. Both genes expressed successfully and recombinant protein accumulated in transgenic plants grown in both greenhouse and field trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".