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Record W2809945838 · doi:10.1073/pnas.1721245115

Disrupting LXRα phosphorylation promotes FoxM1 expression and modulates atherosclerosis by inducing macrophage proliferation

2018· article· en· W2809945838 on OpenAlexfundno aff
Matthew Gage, Natalia Bécares, Rikah Louie, Kirsty E. Waddington, Yu Zhang, Thaís Tittanegro, Sabela Rodríguez‐Lorenzo, A. Jathanna, Benoît Pourcet, Óscar M. Pello, Juan Vladimir de la Rosa, Antonio Castrillo, Inès Pineda‐Torra

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldMedicine
TopicCholesterol and Lipid Metabolism
Canadian institutionsnot available
FundersMedical Research CouncilSchool of Medicine, New York UniversityMinisterio de Economía y CompetitividadUniversity College LondonYork UniversityBritish Heart FoundationFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsLiver X receptorPhosphorylationCell biologyBiologyDownregulation and upregulationCancer researchCell growthGeneTranscription factorNuclear receptorBiochemistry

Abstract

fetched live from OpenAlex

Significance To date, the importance of liver X receptors (LXRs) in atherosclerosis development has been gleaned from their pharmacological or genetic manipulation. Here, we show that altering LXRα phosphorylation can shape proatherogenic responses to fat-rich diets, uncovering previously unrecognized mechanisms. Disrupting LXRα phosphorylation in myeloid cells triggers global changes in gene expression in macrophages, including the up-regulation of proliferation-promoting factors, consistent with increased proliferation of lesion-resident cells. This leads to an enhanced atherosclerotic plaque burden and plaques with altered phenotypic features. Notably, novel LXRα-regulated targets revealed by impaired LXRα phosphorylation are markedly distinct from those promoted by LXR ligand activation. Overall, this work reveals LXRα phosphorylation as an important determinant of atherosclerosis development. This could be exploited for the design of novel antiatherosclerotic strategies.

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 score0.164
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.302
Teacher spread0.269 · 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

Citations57
Published2018
Admission routes1
Has abstractyes

Explore more

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