Isocitrate Lyase and Succinate Semialdehyde Dehydrogenase Mediate the Synthesis of α-Ketoglutarate in Pseudomonas fluorescens
Bibliographic record
Abstract
Glycerol is an important by-product of the biodiesel industry and its transformation into value-added products is being actively pursued in order to improve the efficacy of this renewable energy sector. Here we report that the enhanced production of α-ketoglutarate (KG) effected by Pseudomonas fluorescens in a mineral medium supplemented with manganese (Mn) is propelled by the increased activities of succinate semialdehyde dehydrogenase (SSADH), γ-aminobutyric acid aminotransaminase (GABAT), and isocitrate lyase (ICL). The latter generates glyoxylate and succinate two key metabolites involved in this process. Fumarate reductase (FRD) also aids in augmenting the pool of succinate, a precursor of succinate semialdehyde (SSA). The latter is then carboxylated to KG with the assistance of α ketoglutarate decarboxylase (KDC). These enzymes work in tandem to ensure copious secretion of the keto-acid. When incubated with glycerol in the presence of bicarbonate (HCO3-), cell-free extracts readily produce KG with a metabolite fingerprint attributed to glutamate, γ-aminobutyric acid (GABA), succinate and succinate semialdehyde. Further targeted metabolomic and functional proteomic studies with HPLC, NMR and gel electrophoresis techniques provided molecular insights into this KG-generating machinery. RT-qPCR analyses revealed the transcripts responsible for ICL and SSADH were elevated in the Mn-supplemented cultures. This is the first report of a metabolic network where ICL and SSADH orchestrate the enhanced production of KG from glycerol. It affords a biochemical process that can be harnessed to improve the efficiency of the biodiesel industry.
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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 | 0.000 |
| 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".