Plants as factories for production of biopharmaceutical and bioindustrial proteins: lessons from cell biologyThis review is one of a selection of papers published in the Special Issue on Plant Cell Biology.
Bibliographic record
Abstract
Transgenic plants, seeds, and cultured plant cells are potentially one of the most economical systems for large-scale production of recombinant proteins for industrial and pharmaceutical uses. Biochemical, technical, and economic concerns with current production systems have generated enormous interest in developing plants as alternative production systems. However, various challenges must be met before plant systems can fully emerge as suitable, viable alternatives to current animal-based systems for large-scale production of biopharmaceuticals and other products. Aside from regulatory issues and developing efficient methods for downstream processing of recombinant proteins, there are at least two areas of challenge: (1) Can we engineer plant cells to accumulate recombinant proteins to sufficient levels? (2) Can we engineer plant cells to post-translationally modify recombinant proteins so that they are structurally and functionally similar to the native proteins? Attempts to improve the accumulation of a recombinant protein in plant cells require an appreciation of the processes of gene transcription, mRNA stability, processing, and export, and translation initiation and efficiency. Likewise, many post-translational factors must be considered, including protein stability, protein function and activity, and protein targeting. Moreover, we need to understand how the various processes leading from the gene to the functional protein are interdependent and functionally linked. Manipulation of the post-translational processing machinery of plant cells, especially that for N-linked glycosylation and glycan processing, is a challenging and exciting area. The functions of N-glycan heterogeneity and microheterogeneity, especially with respect to protein function, stability, and transport, are poorly understood and this represents an important area of cell biology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".