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Epigenetic control of RGS5 expression following femoral artery injury in mice

2009· article· en· W2261284416 on OpenAlexaffabout
Steven Gu, Basil Al‐Sabeq, Roxy Chis, Scott P. Heximer

Bibliographic record

VenueThe FASEB Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicRenin-Angiotensin System Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDNA methylationEpigeneticsMethylationBiologyGene expressionGeneMedicineGenetics

Abstract

fetched live from OpenAlex

RGS5 is involved in blood pressure regulation and tumor vascularization. Its expression is dynamic varying with age and disease, however, the specific pathways of regulation are unknown. Thus, the objective of this study is to identify the key modulators that regulate RGS5 expression. To assess the activity of the RGS5 promoter, which like other VSMC genes contains degenerate CArG boxes [CC(A/T) 6 GG], we generated a construct with luciferase under the control of the RGS5 promoter. This construct is 3‐fold more active in the presence of myocardin than with YFP control. Previous work in mouse carotid arteries show that degenerate CArGs are important in the post‐injury down‐regulation of several genes. In a model of femoral artery injury, we show that RGS5 expression decreases and remains so for up to 4 weeks. To understand pathways involved in this loss of expression, we looked at possible epigenetic changes. Using pyro‐sequencing and clonal analysis, we show that there is hypermethylation at 3 CpG sites in injured arteries compared to control. Interestingly, one site is within a degenerate CArG box suggesting that methylation could impede SRF binding and transcription activation. Thus, DNA methylation at sites important for promoter activity can be an effective way to silence RGS5 expression. This work was supported by the Canadian Institute of Health Research and the Heart and Stroke Foundation of Ontario.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.315

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 studies0.0000.000
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 head0.016
GPT teacher head0.274
Teacher spread0.258 · 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

Citations0
Published2009
Admission routes2
Has abstractyes

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