Deletion of <i>arcA</i>, <i>iclR</i>, and <i>tdcC</i> in <i>Escherichia coli</i> to improve <scp>l</scp>‐threonine production
Bibliographic record
Abstract
l-Threonine is an important amino acid supplemented in food, medicine, or feed. Starting from glucose, l-threonine production in Escherichia coli involves the glycolysis, TCA cycle, and the l-threonine biosynthetic pathway. In this study, how the l-threonine production in an l-threonine producing E. coli TWF001 is controlled by the three regulators ArcA, Cra, and IclR, which control the expression of genes involved in the glycolysis and TCA cycle, has been investigated. Ten mutant strains were constructed from TWF001 by different combinations of deletion or overexpression of arcA, cra, iclR, and tdcC. l-Threonine production was increased in the mutants TWF015 (ΔarcAΔcra), TWF016 (ΔarcAPcra::Ptrc), TWF017 (ΔarcAΔiclR), TWF018 (ΔarcAΔiclRΔtdcC), and TWF019 (ΔarcAΔcraΔiclRΔtdcC). Among these mutant strains, the highest l-threonine production (26.0 g/L) was obtained in TWF018, which was a 109.7% increase compared with the control TWF001. In addition, TWF018 could consume glucose more efficiently than TWF001 and produce less acetate. The results suggest that deletion of arcA, iclR, and tdcC could efficiently increase l-threonine production in E. coli.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".