A Metabolic Network Mediating the Cycling of Succinate, a Product of ROS Detoxification into α-Ketoglutarate, an Antioxidant
Bibliographic record
Abstract
can survive in a low sulfur environment. When cultured in a sulfur-deficient medium, the bacterium reprograms its metabolic pathways to produce α-ketoglutarate (KG) and regenerate this keto-acid from succinate, a by-product of ROS detoxification. Succinate semialdehyde dehydrogenase (SSADH) and KG decarboxylase (KGDC) work in partnership to synthesize KG. This process is further aided by the increased activity of the enzymes glutamate decarboxylase (GDC) and γ-amino-butyrate transaminase (GABAT). The pool of succinate semialdehyde (SSA) generated is further channeled towards the formation of the antioxidant. Spectrophotometric analyses, HPLC experiments and electrophoretic studies with intact cells and cell-free extracts (CFE) pointed to the metabolites (succinate, SSA, GABA) and enzymes (SSADH, GDC, KGDC) contributing to this KG-forming metabolic machinery. Real-time polymerase chain reaction (RT-qPCR) revealed significant increase in transcripts of such enzymes as SSADH, GDC and KGDC. The findings of this study highlight a novel pathway involving keto-acids in ROS scavenging. The cycling of succinate into KG provides an efficient means of combatting an oxidative environment. Considering the central role of KG in biological processes, this metabolic network may be operative in other living systems.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".