Role of metallothionein in nitric oxide signaling as revealed by a green fluorescent fusion protein
Bibliographic record
Abstract
Although the function of metallothionein (MT), a 6- to 7-kDa cysteine-rich metal binding protein, remains unclear, it has been suggested from in vitro studies that MT is an important component of intracellular redox signaling, including being a target for nitric oxide (NO). To directly study the interaction between MT and NO in live cells, we generated a fusion protein consisting of MT sandwiched between two mutant green fluorescent proteins (GFPs). In vitro studies with this chimera (FRET-MT) demonstrate that fluorescent resonance energy transfer (FRET) can be used to follow conformational changes indicative of metal release from MT. Imaging experiments with live endothelial cells show that agents that increase cytoplasmic Ca(2+) act via endogenously generated NO to rapidly and persistently release metal from MT. A role for this interaction in intact tissue is supported by the finding that the myogenic reflex of mesenteric arteries is absent in MT knockout mice (MT(-/-)) unless endogenous NO synthesis is blocked. These results are the first application of intramolecular green fluorescent protein (GFP)-based FRET in a native protein and demonstrate the utility of FRET-MT as an intracellular surrogate indicator of NO production. In addition, an important role of metal thiolate clusters of MT in NO signaling in vascular tissue is revealed.
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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".