Mono and diterpene production in <i>Escherichia coli</i>
Bibliographic record
Abstract
Mono- and diterpenoids are of great industrial and medical value as specialty chemicals and pharmaceuticals. Production of these compounds in microbial hosts, such as Escherichia coli, can be limited by intracellular levels of the polyprenyl diphosphate precursors, geranyl diphosphate (GPP), and geranylgeranyl diphosphate (GGPP). To alleviate this limitation, we constructed synthetic operons that express three key enzymes for biosynthesis of these precursors: (1). DXS,1-deoxy-d-xylulose-5-phosphate synthase; (2). IPPHp, IPP isomerase from Haematococcus pluvialis; and (3). one of two variants of IspA, FPP synthase that produces either GPP or GGPP. The reporter plasmids pAC-LYC and pACYC-IB, which encode enzymes that convert either FPP or GGPP, respectively, to the pigment lycopene, were used to demonstrate that at full induction, the operon encoding the wild-type FPP synthase and mutant GGPP synthase produced similar levels of lycopene. To synthesize di- or monoterpenes in E. coli using the GGPP and GPP encoding operons either a diterpene cyclase [casbene cyclase (Ricinus communis L) and ent-kaurene cyclase (Phaeosphaeria sp. L487)] or a monoterpene cyclase [3-carene cyclase (Picea abies)] was coexpressed with their respective precursor production operon. Analysis of culture extracts or headspace by gas chromatography-mass spectrometry confirmed the in vivo production of the diterpenes casbene, kaur-15-ene, and kaur-16-ene and the monoterpenes alpha-pinene, myrcene, sabinene, 3-carene, alpha-terpinene, limonene, beta-phellandrene, alpha-terpinene, and terpinolene. Construction and functional expression of GGPP and GPP operons provides an in vivo precursor platform host for the future engineering of di- and monoterpene cyclases and the overproduction of terpenes in bacteria.
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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) | 0.001 | 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 | 0.000 |
| 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".