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Record W2148140273 · doi:10.1194/jlr.m000398

Sphingosine kinase regulates oxidized low density lipoprotein-mediated calcium oscillations and macrophage survival

2009· article· en· W2148140273 on OpenAlexaff
Johnny H. Chen, Maziar Riazy, Shih Wei Wang, Jiazhen Dai, Vincent Duronio, Urs P. Steinbrecher

Bibliographic record

VenueJournal of Lipid Research · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSphingolipid Metabolism and Signaling
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSERCASphingosine kinaseCell biologyIntracellularSphingosineChemistryThapsigarginSignal transductionSphingosine-1-phosphateExtracellularCalcium signalingKinaseBiochemistryBiologyReceptorATPaseEnzyme

Abstract

fetched live from OpenAlex

We recently reported that oxidized LDL (oxLDL) induces an oscillatory increase in intracellular calcium ([Ca(2+)](i)) levels in macrophages. Furthermore, we have shown that these [Ca(2+)](i) oscillations mediate oxLDL's ability to inhibit macrophage apoptosis in response to growth factor deprivation. However, the signal transduction pathways by which oxLDL induces [Ca(2+)](i) oscillations have not been elucidated. In this study, we show that these oscillations are mediated in part by intracellular mechanisms, as depleting extracellular Ca(2+) did not completely abolish the effect. Inhibiting sarco-endoplasmic reticulum ATPase (SERCA) completely blocked [Ca(2+)](i) oscillations, suggesting a role for Ca(2+) reuptake by the ER. The addition of oxLDL resulted in an almost immediate activation of sphingosine kinase (SK), which can increase sphingosine-1-phosphate (S1P) levels by phosphorylating sphingosine. Moreover, S1P was shown to be as effective as oxLDL in blocking macrophage apoptosis and producing [Ca(2+)](i) oscillations. This suggests that the mechanism in which oxLDL generates [Ca(2+)](i) oscillations may be 1) activation of SK, 2) SK-mediated increase in S1P levels, 3) S1P-mediated Ca(2+) release from intracellular stores, and 4) SERCA-mediated Ca(2+) reuptake back into the ER.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.038
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 science0.0000.000
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 head0.034
GPT teacher head0.333
Teacher spread0.300 · 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

Citations8
Published2009
Admission routes1
Has abstractyes

Explore more

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