Antisense RNA-Mediated Suppression of Benzophenanthridine Alkaloid Biosynthesis in Transgenic Cell Cultures of California Poppy
Bibliographic record
Abstract
California poppy (Eschscholzia californicaCham.) cell cultures produce several benzophenanthridine alkaloids, such as sanguinarine, chelirubine, and macarpine, with potent pharmacological activity. Antisense constructs of genes encoding two enzymes involved in benzophenanthridine alkaloid biosynthesis, the berberine bridge enzyme (BBE) and N-methylcoclaurine 3′-hydroxylase (CYP80B1), were introduced separately into California poppy cell cultures. Transformed cell lines expressing antisenseBBE or antisense CYP80B1 constructs and displaying low levels of BBE or CYP80B1 mRNAs, respectively, showed reduced accumulation of benzophenanthridine alkaloids compared with control cultures transformed with a β-glucuronidase gene. Pathway intermediates were not detected in any of the transformed cell lines. The suppression of benzophenanthridine alkaloid biosynthesis using BBE or CYP80B1 antisense RNA constructs also reduced the growth rate of the cultures. Two-dimensional 1H-nuclear magnetic resonance and in vivo 15N-nuclear magnetic resonance spectroscopy showed no difference in the abundance of carbohydrate metabolites in the various transgenic cell lines. However, transformed cells with reduced benzophenanthridine alkaloid levels contained larger cellular pools of several amino acids including alanine, leucine, phenylalanine, threonine, and valine compared with controls. The relative abundance of tyrosine, from which benzophenanthridine alkaloids are derived, was less than 2-fold higher in antisense-suppressed cells relative to controls. These results show that alterations in the metabolic flux through benzophenanthridine alkaloid biosynthesis can affect the regulation of amino acid pools. These data provide new insight into the metabolic engineering of benzophenanthridine alkaloid pathways.
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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 | 0.000 |
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".