Transcription of foreign DNA in <i>Escherichia coli</i>
Bibliographic record
Abstract
Propagation of heterologous DNA in E. coli host cells is central to molecular biology. DNA constructs are often engineered for expression of recombinant protein in E. coli, but the extent of incidental transcription arising from natural regulatory sequences in cloned DNA remains underexplored. Here, we have used programmable microarrays and RT-PCR to measure, comprehensively, the transcription of H. influenzae, P. aeruginosa, and human DNA propagating in E. coli as bacterial artificial chromosomes. We find evidence that at least half of all H. influenzae genes are transcribed in E. coli. Highly transcribed genes are principally involved in energy metabolism, and their proximal promoter regions are significantly enriched with E. coli sigma(70) (also known as RpoD) binding sites. H. influenzae genes acquired from an ancient bacteriophage Mu insertion are also highly transcribed. Compared with H. influenzae, a smaller proportion of P. aeruginosa genes are transcribed in E. coli, and in E. coli there is punctuated transcription of human DNA. The presence of foreign DNA in E. coli disturbs the host transcriptional profile, with expression of the E. coli phage shock protein operon and the flagellar gene cluster being particularly strongly up-regulated. While cross-species transcriptional activation is expected to be enabling for horizontal gene transfer in bacteria, incidental expression of toxic genes can be problematic for DNA cloning. Ongoing characterization of cross-expression will help inform the design of biosynthetic gene clusters and synthetic microbial genomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".