α-Ketoglutarate Dehydrogenase and Glutamate Dehydrogenase Work in Tandem To Modulate the Antioxidant α-Ketoglutarate during Oxidative Stress in <i>Pseudomonas fluorescens</i>
Bibliographic record
Abstract
Alpha-ketoglutarate (KG) is a crucial metabolite in all living organisms, as it participates in a variety of biochemical processes. We have previously shown that this keto acid is an antioxidant and plays a key role in the detoxification of reactive oxygen species (ROS). In an effort to further confirm this intriguing phenomenon, Pseudomonas fluorescens was exposed to menadione-containing media, with various amino acids as the sources of nitrogen. Here, we demonstrate that KG dehydrogenase (KGDH) and NAD-dependent glutamate dehydrogenase (GDH) work in tandem to modulate KG homeostasis. While KGDH was sharply decreased in cells challenged with menadione, GDH was markedly increased in cultures containing arginine (Arg), glutamate (Glu), and proline (Pro). When ammonium (NH(4)) was utilized as the nitrogen source, both KGDH and GDH levels were diminished. These enzymatic profiles were reversed when control cells were incubated in menadione media. (13)C nuclear magnetic resonance and high-performance liquid chromatography studies revealed how KG was utilized to eliminate ROS with the concomitant formation of succinate. The accumulation of KG in the menadione-treated cells was dependent on the redox status of the lipoic acid residue in KGDH. Indeed, the treatment of cellular extracts from the menadione-exposed cells with dithiothreitol, a reducing agent, partially restored the activity of KGDH. Taken together, these data reveal that KG is pivotal to the antioxidative defense strategy of P. fluorescens and also point to the ROS-sensing role for KGDH.
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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 | 0.000 | 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".