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Mitogen‐activated protein kinase phosphatase‐1 (MKP‐1) preferentially dephosphorylates p42/44MAPK but not p38MAPK in rat pinealocytes

2007· article· en· W2033305975 on OpenAlexafffund
Donald M. Price, M. T. Wloka, Constance L. Chik, Anthony K. Ho

Bibliographic record

VenueJournal of Neurochemistry · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Tyrosine Phosphatases
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsPinealocyteProtein kinase AMAPK/ERK pathwayPhosphataseBiologyKinaseMitogen-activated protein kinasePineal glandMolecular biologyCell biologyMitogen-activated protein kinase kinaseEndocrinologyPhosphorylationMelatonin

Abstract

fetched live from OpenAlex

We recently reported a diurnal and norepinephrine (NE) -induced expression of mitogen-activated protein kinase (MAPK) phosphatase-1 (MKP-1) in the rat pineal gland and postulated that this MKP-1 expression might impact adrenergic-regulated arylalkylamine-N-acetyltransferase (AA-NAT) activity via modulation of MAPKs. In this study, we investigated the effect of depletion of MKP-1 expression by using doxorubicin, a topoisomerase inhibitor that suppresses the expression of MKP-1 in other cell types and small interfering RNA targeted against Mkp1 in NE-stimulated pinealocytes. We found that both treatments were effective in inhibiting NE induction of MKP-1 expression. Moreover, both treatments also resulted in a prolonged activation of p42/44MAPK and an increase in AA-NAT induction by NE. In contrast, treatment of pinealocytes with PD98059, an inhibitor of MAPK kinase, reduced NE-stimulated AA-NAT activity. Interestingly, suppressing MKP-1 expression had no effect on the time profile of NE-stimulated p38MAPK activation. These results indicate that MKP-1 modulates the profile of AA-NAT activity by selectively shaping the activation profile of p42/44MAPK but not that of p38MAPK.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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.

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

Citations11
Published2007
Admission routes2
Has abstractyes

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