Metabolic engineering of <i>Bacillus subtilis</i> for<scp>l</scp>‐valine overproduction
Bibliographic record
Abstract
Abstract Bacillus subtilis has been commonly applied to industrial enzyme production due to its genetic tractability, “generally recognized as safe (GRAS)” status, and robust growth characteristics. In spite of its ideal attributes as a biomanufacturing platform, B. subtilis has seen limited use in the production of other value‐added biochemicals. Here, we report the derivation of engineered strains of B. subtilis for l ‐valine overproduction using our recently developed CRISPR (clustered regularly interspaced palindromic repeats)‐Cas9 (CRISPR‐associated [protein] 9) toolkit. We first manipulate the native l ‐valine biosynthetic pathway by relieving transcriptional and allosteric regulation, resulting in a >14‐fold increase in the l ‐valine titer, compared to the wild‐type strain. We subsequently identify and eliminate factors limiting l ‐valine overproduction, specifically increasing pyruvate availability and blocking the competing l ‐leucine and l ‐isoleucine biosynthetic pathways. By inactivating (a) pdhA , encoding the E1α subunit of the pyruvate dehydrogenase complex, to increase the intracellular pyruvate pool, and (b) leuA and ilvA , respectively encoding 2‐isopropylmalate synthase and l ‐threonine dehydratase, to abolish the competing pathways, the l ‐valine titer reached 4.61 g/L in shake flask cultures. Our engineered l ‐valine‐overproducing strains of B. subtilis are devoid of plasmids and do not sporulate due to the inactivation of sigF , encoding the sporulation‐specific transcription factor σ F , making them attractive for large‐scale l ‐valine production. However, acetate dissimilation was identified as limiting l ‐valine overproduction in Δ pdhA B. subtilis strains, and improving acetate dissimilation or identifying alternate modes of increasing pyruvate pools to enhance l ‐valine‐overproduction should be explored.
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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 | 0.000 |
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".