SLICER: A Seamless Gene Deletion Method for Deinococcus radiodurans
Bibliographic record
Abstract
Deinococcus radiodurans’ high resistance to various stressors combined with its ability to utilize sustainable carbon sources makes it an attractive bacterial chassis for synthetic biology and industrial bioproduction. However, to fully harness the capabilities of this microbe, further strain engineering and tool development are required. Methods for creating seamless genome modifications are an essential part of the microbial genetic toolkit to enable strain engineering. Here, we report the development of the SLICER method, which can be used to create seamless gene deletions in D. radiodurans. This process involves (a) integration of a seamless deletion cassette replacing a target gene, (b) introduction of the pSLICER plasmid to mediate cassette excision by I-SceI endonuclease cleavage and homologous recombination, and (c) curing of the helper plasmid. We demonstrate the utility of SLICER for creating multiple gene deletions in D. radiodurans by sequentially targeting 5 putative restriction-modification system genes, recycling the same selective and screening markers for each subsequent deletion. While we observed no significant increase in transformation efficiency for most of the knockout strains, we demonstrated SLICER as a promising method to create a fully restriction-minus strain to expand the synthetic biology applications of D. radiodurans, including its potential as an in vivo DNA assembly platform.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".