Large‐scale plasmid DNA processing: evidence that cell harvesting and storage methods affect yield of supercoiled plasmid DNA
Bibliographic record
Abstract
The effect of bacterial-cell centrifugation and handling on the initial stages of plasmid processing was investigated. Escherichia coli cells containing either a 6 or 20 kb plasmid were grown in 75- and 450-litre bioreactors, and the process yield of the early recovery stages was characterized in terms of SC pDNA (supercoiled plasmid DNA) recovered. In all cases, the cells were totally recovered using either a continuous-feed, intermittent-solids-discharge, disc-stack centrifuge or a continuous-feed, batch-discharge, solid-bowl centrifuge. The cells were then either processed immediately or stored frozen. The centrifugation method considerably affected the yield of SC pDNA, and there was evidence that the intermittent discharge of cells from a centrifuge operating at high speed led to a sediment containing lysed cells and degraded pDNA. This led to estimated plasmid yield losses of up to 40% as compared with cells recovered from laboratory or solid-bowl centrifuges, where there is evidently no cell stress on discharge. By inference, the cell stress on feed to either of the continuous centrifuges studied was not implicated in product loss. Freezing of the recovered cells gives a convenient hold stage prior to further processing. In all cases, this extra freeze-thaw stage led to loss of SC pDNA, and this was in addition to the loss attributed to cell lysis during centrifugation discharge. Only average yields can be gained from pilot plant-scale studies; separate laboratory-based experiments indicated that this loss of SC pDNA is determined by the time and temperature for which the resuspended cells are held.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".