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Record W2981725735 · doi:10.1093/eurheartj/ehz747.0340

P736Histone marks induced by the methyltransferase SETD7 modulate angiogenic response in diabetes

2019· article· en· W2981725735 on OpenAlexaff
S A Mohammed, Sarah Costantino, Alexander Akhmedov, Samuele Ambrosini, G Karsay, Peter J. Brown, Thomas F. Lüscher, Francesco Paneni

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsStructural Genomics ConsortiumUniversity of Toronto
Fundersnot available
KeywordsMedicineEpigeneticsGene knockdownHistoneAngiogenesisChromatin immunoprecipitationMethyltransferaseGene expressionEnosCancer researchMolecular biologyGeneEndocrinologyMethylationNitric oxide synthaseBiologyNitric oxidePromoterGenetics

Abstract

fetched live from OpenAlex

Abstract Introduction Despite advances in revascularization strategies, type 2 diabetic (T2D) patients with peripheral artery disease (PAD) continue to have a high risk of limb amputation. Hence, strategies that promote vascularization can be considered as a novel therapeutic option in T2D patients with PAD. Epigenetic modifications of histones and DNA have emerged as key modulators of gene expression. Mono-methylation of histone 3 at lysine 4 (H3K4m1) – a specific epigenetic signature induced by the methyltransferase SETD7 – favours a chromatin conformation enabling the transcription of genes involved in inflammation and oxidative stress. Purpose To investigate whether SETD7 modulates angiogenesis in experimental diabetes. Methods Human aortic endothelial cells (HAECs) were cultured in growth factor-free medium and exposed either to normal glucose (NG, 5 mM) or high glucose (HG, 25 mM) for 48 hours. SETD7 protein and H3K4me1 levels were investigated by Western blot and chromatin immunoprecipitation (ChIP). Knockdown of SETD7 was achieved by small interfering RNA (siRNA). Pharmacological blockade of SETD7 was performed by using the highly selective inhibitor (R)-PFI-2, while its inactive enantiomer, (S)-PFI-2, was used as a control. Scratch and tube formation assays were performed to investigate the impact of SETD7 on angiogenic properties of HAECs. RNA sequencing (RNA-seq) and Ingenuity Pathway Analysis (IPA) were employed to unveil putative genes regulated by SETD7 in HG-treated HAECs. SETD7 expression was also investigated in muscular specimens isolated from type 2 diabetic (db/db) mice and non-diabetic mice undergoing hindlimb ischemia for 21 days. Results HG exposure in HAECs led to a time-dependent increase of both SETD7 gene and protein expression, as compared to NG. SETD7 upregulation in HG-treated HAECs was associated with an increase of H3K4me1 levels as well as with impaired endothelial cell migration and tube formation. Of interest, both gene silencing and pharmacological blockade of SETD7 rescued hyperglycemia-induced impairment of angiogenic properties in HAECs. RNA-seq in HG-treated HAECs with and without SETD7 depletion unveiled an array of differentially expressed genes, which were mainly involved in blood vessel growth and angiogenic response, as assessed by IPA analysis. Among dysregulated genes, ChIP assays showed that SETD7-dependent chromatin changes enabled the transcription of Semaphorin 3G (SEMA-3G), a negative regulator of endothelial cell migration. Indeed, gene silencing of SETD7 blunted SEMA-3G expression in HAECs exposed to HG. Consistent with our in vitro observations, SETD7 was upregulated in adductor muscle specimens from db/db mice undergoing hindlimb ischemia as compared to non-diabetic animals. Conclusions Pharmacological blockade of SETD7 by (R)-PFI-2 may represent a potential therapeutic approach to boost post-ischemic vascularization in T2D patients with PAD.

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)0.000
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.000

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 head0.266
Teacher spread0.250 · 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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