Metabolic pathway of α-ketoglutarate in Lactobacillus sanfranciscensis and Lactobacillus reuteri during sourdough fermentation
Bibliographic record
Abstract
AIM: To identify metabolites of α-ketoglutarate (α-KG) in Lactobacillus sanfranciscensis and Lactobacillus reuteri in modified MRS and sourdough. METHODS AND RESULTS: Lactobacillus sanfranciscensis and L. reuteri were grown with additional α-KG in mMRS and in wheat sourdough. In mMRS, α-KG was used as an electron acceptor and converted to 2-hydroxyglutarate (2-OHG) by both organisms. Production of 2-OHG was identified by high performance liquid chromatography (HPLC) and confirmed by gas chromatography (GC). Crude cell extracts of L. sanfranciscensis and L. reuteri grown with or without α-KG exhibited OHG dehydrogenase activity of 6.3 ± 0.3, 2.3 ± 0.9, 1.2 ± 0.2, and 1.1 ± 0.1 mmol l(-1) NADH (min x mg protein)(-1), respectively. The presence of phenylalanine and citrate in addition to α-KG partially redirected the use of α-KG from electron acceptor to amino group acceptor. In wheat sourdoughs, α-KG was predominantly used as electron acceptor and converted to 2-OHG. CONCLUSIONS: Lactobacillus sanfranciscensis and L. reuteri utilize α-KG as electron acceptor. Alternative use of α-KG as amino group acceptor occurs in the presence of abundant amino donors and citrate. SIGNIFICANCE AND IMPACT OF THE STUDY: The use of α-KG as electron acceptor in heterofermentative lactobacilli impacts the formation of flavour volatiles through the transamination pathway.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".