The Impact of Nitric Oxide Toxicity on the Evolution of the Glutathione Transferase Superfamily
Bibliographic record
Abstract
Glutathione transferases (GSTs) are protection enzymes capable of conjugating glutathione (GSH) to toxic compounds. During evolution an important catalytic cysteine residue involved in GSH activation was replaced by serine or, more recently, by tyrosine. The utility of these replacements represents an enigma because they yield no improvements in the affinity toward GSH or in its reactivity. Here we show that these changes better protect the cell from nitric oxide (NO) insults. In fact the dinitrosyl·diglutathionyl·iron complex (DNDGIC), which is formed spontaneously when NO enters the cell, is highly toxic when free in solution but completely harmless when bound to GSTs. By examining 42 different GSTs we discovered that only the more recently evolved Tyr-based GSTs display enough affinity for DNDGIC ( K D < 10 −9 m) to sequester the complex efficiently. Ser-based GSTs and Cys-based GSTs show affinities 10 2 –10 4 times lower, not sufficient for this purpose. The NO sensitivity of bacteria that express only Cys-based GSTs could be related to the low or null affinity of their GSTs for DNDGIC. GSTs with the highest affinity (Tyr-based GSTs) are also over-represented in the perinuclear region of mammalian cells, possibly for nucleus protection. On the basis of these results we propose that GST evolution in higher organisms could be linked to the defense against NO. Background: Why do ancestral GSTs utilize cysteine/serine as catalytic residues, whereas more recently evolved GSTs utilize tyrosine? Results: Only the more recently evolved GSTs display enough affinity to bind and make harmless the toxic DNDGIC (a natural NO carrier). Conclusion: GST evolution could be linked to the defense against NO. Significance: This represents a further piece in the puzzle of evolutive adaptation to NO toxicity.
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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 | 0.001 | 0.001 |
| 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".