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Record W4400952981 · doi:10.1161/atvb.44.suppl_1.1133

Abstract 1133: Docosahexaenoic Acid’s Impact On The HMG CoA Reductase Pathway: Endothelial Cell Growth State-dependency And Implications For Cardiovascular Health

2024· article· en· W4400952981 on OpenAlexaff
Shiqi Huang, Peter Zahradka, Carla G. Taylor

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEicosanoids and Hypertension Pharmacology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHMG-CoA reductaseDocosahexaenoic acidReductaseDependency (UML)Endothelial stem cellCell growthCell biologyBiochemistryBiologyEnzymeChemistryMedicineInternal medicineEndocrinologyFatty acidComputer sciencePolyunsaturated fatty acid

Abstract

fetched live from OpenAlex

Docosahexaenoic acid (DHA), widely assumed to be atheroprotective, has recently faced scrutiny due to divergent findings in clinical trials studying omega-3 fatty acids and cardiovascular disease (CVD). Our prior work unveiled nuanced actions of DHA on endothelial cells, especially demonstrating its differential regulation of endothelial nitric oxide synthase in growing versus quiescent cells, which approximate the dysfunctional and healthy states in vivo , respectively. These findings led to the hypothesis that DHA benefits healthy endothelial cells but not dysfunctional ones. To elucidate novel pathways, RNA-seq was done on DHA-treated (20 or 125 μM for 8 h) and control human EA.hy926 cells in both states. The data were processed by the RSEM-STAR-DESeq2 pipeline. Differentially expressed genes (DEGs) were subjected to enrichment analysis with clusterProfiler. Genes selected from candidate enriched pathways were validated by Western blotting. Principal component analysis showed distinct groupings based on cell growth state and DHA concentration. DESeq2 identified 104 and 173 DEGs unique to growing and quiescent cells, respectively, at 20 μM DHA. Pathway analysis revealed significant enrichment for cholesterol biosynthesis-related terms of downregulated DEGs, including HMGCR , SREBF2 , and INSIG1 , in quiescent cells treated with 20 μM DHA, while SREBF1 was downregulated in both states. DHA also reduced HMGCR expression in human monocytes, concomitant with less cellular cholesterol content. Moreover, many genes associated with the Rho GTPase pathway, downstream of HMG CoA reductase (HMGCR), were downregulated by 20 μM DHA only in quiescent cells. These results attest to similarities between the effects of DHA and statins, inhibitors of HMGCR that address CVD via both cholesterol reduction and pleiotropic actions on Rho GTPase. Our study reveals that only quiescent endothelial cells respond positively to DHA. It pioneers a novel perspective that DHA should be targeted for CVD prevention in healthy people versus as a therapeutic for those with endothelial dysfunction. Further studies are required to validate the in vivo responses to DHA in healthy versus dysfunctional endothelium, and the optimal DHA dose to achieve maximal benefits.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.035
GPT teacher head0.308
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), 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
Published2024
Admission routes1
Has abstractyes

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