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Record W1533731827 · doi:10.1038/sj.bjp.0706725

<i>S</i>‐nitrosylation of cysteine 289 of the AT<sub>1</sub>receptor decreases its binding affinity for angiotensin II

2006· article· en· W1533731827 on OpenAlexaff
Patrice Leclerc, Pascal M. Lanctôt, Mannix Auger‐Messier, Emanuel Escher, Richard Leduc, Gaétan Guillemette

Bibliographic record

VenueBritish Journal of Pharmacology · 2006
Typearticle
Languageen
FieldMedicine
TopicRenin-Angiotensin System Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCysteineReceptorChemistryAngiotensin IIS-NitrosylationSodium nitroprussideNitrosylationMutantNitric oxideTransmembrane proteinBiochemistryPharmacologyBiologyEnzymeGene

Abstract

fetched live from OpenAlex

1. Nitric oxide (NO) is known to affect the properties of various proteins via the S-nitrosylation of cysteine residues. This study evaluated the direct effects of the NO donor sodium nitroprusside (SNP) on the pharmacological properties of the AT1 receptor for angiotensin II expressed in HEK-293 cells. 2. SNP dose-dependently decreased the binding affinity of the AT1 receptor without affecting its total binding capacity. This modulatory effect was reversed within 5 min of removing SNP. 3. The effect of SNP was not modified in the presence of the G protein uncoupling agent GTPgammaS or the soluble guanylyl cyclase inhibitor 1H-[1,2,4]oxadiazolo[4,3-a]quinoxalin-1-one. 4. The binding properties of a mutant AT1 receptor in which all five cysteine residues within the transmembrane domains had been replaced by serine was not affected by SNP. Systematic analysis of mutant AT1 receptors revealed that cysteine 289 conferred the sensitivity to SNP. 5. These results suggest that NO decreased the binding affinity of the AT1 receptor by S-nitrosylation of cysteine 289. This modulatory mechanism may be particularly relevant in pathophysiological situations where the beneficial effects of NO oppose the deleterious effects of angiotensin II.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.268
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations55
Published2006
Admission routes1
Has abstractyes

Explore more

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