A Toolkit for Effective and Successive Genome Engineering of Escherichia coli
Bibliographic record
Abstract
The bacterium Escherichia coli has been well-justified as an effective workhorse for industrial applications. In this study, we developed a toolkit for flexible genome engineering of this microorganism, including site-specific insertion of heterologous genes and inactivation of endogenous genes, such that bacterial hosts can be effectively engineered for biomanufacturing. We first constructed a base strain by genomic implementation of the cas9 and λRed recombineering genes. Then, we constructed plasmids for expressing gRNA, DNA cargo, and the Vibrio cholerae Tn6677 transposon and type I-F CRISPR-Cas machinery. Genomic insertion of a DNA cargo up to 5.5 kb was conducted using a transposon-associated CRISPR-Cas system, whereas gene inactivation was mediated by a classic CRISPR-Cas9 system coupled with λRed recombineering. With this toolkit, we can exploit the synergistic functions of CRISPR-Cas, λRed recombineering, and Tn6677 transposon for successive genomic manipulations. As a demonstration, we used the developed toolkit to derive a plasmid-free strain for heterologous production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) by genomic knock-in and knockout of several key genes with high editing efficiencies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".