Metabolic engineering for the production of plant isoquinoline alkaloids
Bibliographic record
Abstract
Several plant isoquinoline alkaloids (PIAs) possess powerful pharmaceutical and biotechnological properties. Thus, PIA metabolism and its fascinating molecules, including morphine, colchicine and galanthamine, have attracted the attention of both the industry and researchers involved in plant science, biochemistry, chemical bioengineering and medicine. Currently, access and availability of high-value PIAs [commercialized (e.g. galanthamine) or not (e.g. narciclasine)] is limited by low concentration in nature, lack of cultivation or geographic access, seasonal production and risk of overharvesting wild plant species. Nevertheless, most commercial PIAs are still extracted from plant sources. Efforts to improve the production of PIA have largely been impaired by the lack of knowledge on PIA metabolism. With the development and integration of next-generation sequencing technologies, high-throughput proteomics and metabolomics analyses and bioinformatics, systems biology was used to unravel metabolic pathways allowing the use of metabolic engineering and synthetic biology approaches to increase production of valuable PIAs. Metabolic engineering provides opportunity to overcome issues related to restricted availability, diversification and productivity of plant alkaloids. Engineered plant, plant cells and microbial cell cultures can act as biofactories by offering their metabolic machinery for the purpose of optimizing the conditions and increasing the productivity of a specific alkaloid. In this article, is presented an update on the production of PIA in engineered plant, plant cell cultures and heterologous micro-organisms.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".