Characterization of a Cruciferin Deficient Mutant of Arabidopsis and Its Utility for Overexpression of Foreign Proteins in Plants
Bibliographic record
Abstract
Plant seeds naturally accumulate storage reserves (proteins, carbohydrates, lipids) that are mobilized during germination to provide energy and raw materials to support early seedling growth. Seeds have been exploited as bioreactors for the production to foreign materials, but stable, high level expression has been elusive, in part due to the intrinsic bias for producing the natural reserves in their typical proportions. To identify mutants governing seed filling, we screened a population of mutagenized Arabidopsis plants for a mutant that failed to fill its seeds. Here we report the identification of ssp1, a recessive, viable mutant that accumulates approximately 15% less protein than wildtype seeds. Molecular analyses revealed that ssp1 is due to the introduction of a premature stop codon in CRU3, one of the major cruciferin genes. Unlike many other reserve mutants or transgenic lines in which seed storage protein levels are reduced by antisense/RNAi technologies, ssp1 exhibits low level compensation by other reserves, and represents a mutant background that might prove useful for high level expression of foreign proteins. To test this hypothesis, we used a bean phytohemagglutinin (PHA) gene as a reporter and compared PHA expression levels in single copy insertion lines in ssp1 vs. wildtype. These near isogenic lines allow reporter protein levels to be compared without the confounding and sometimes unknown influences of transgene copy number and position effects on gene expression. The ssp1 lines consistently accumulated more PHA than the backcrossed counterparts, with increases ranging from 12% to 126%. This proof of principle study suggests that similar strategies in crop plants may improve the yield of foreign proteins of agronomic and economic interest.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".