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Record W2923582754

Epigenetic Activation of Pro-angiogenic Signaling Pathways in Human Endothelial Progenitors Increases Vasculogenesis

2017· article· en· W2923582754 on OpenAlexaff
Sylvain Fraineau, Carmen G. Palii, Brian McNeill, Morten Ritso, W. Christopher Shelley, Nutan Prasain, Alphonse Chu, Elodie Vion, Kristy Rieck, Sharmin Nilufar, Theodore J. Perkins, Michael A. Rudnicki, David Allan, Mervin C. Yöder, Erik J. Suuronen, Marjorie Brand

Bibliographic record

VenuePMC · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsUniversity of OttawaOttawa Hospital
Fundersnot available
KeywordsCell biologyEpigeneticsVasculogenesisProgenitor cellStem cellWnt signaling pathwayBiologyAngiogenesisEx vivoH3K4me3Cancer researchSignal transductionImmunologyIn vivoGenetics
DOInot available

Abstract

fetched live from OpenAlex

Summary Human endothelial colony-forming cells (ECFCs) represent a promising source of adult stem cells for vascular repair, yet their regenerative capacity is limited. Here, we set out to understand the molecular mechanism restricting the repair function of ECFCs. We found that key pro-angiogenic pathways are repressed in ECFCs due to the presence of bivalent (H3K27me3/H3K4me3) epigenetic marks, which decreases the cells' regenerative potential. Importantly, ex vivo treatment with a combination of epigenetic drugs that resolves bivalent marks toward the transcriptionally active H3K4me3 state leads to the simultaneous activation of multiple pro-angiogenic signaling pathways (VEGFR, CXCR4, WNT, NOTCH, SHH). This in turn results in improved capacity of ECFCs to form capillary-like networks in vitro and in vivo . Furthermore, restoration of perfusion is accelerated upon transplantation of drug-treated ECFCs in a model of hindlimb ischemia. Thus, ex vivo treatment with epigenetic drugs increases the vascular repair properties of ECFCs through transient activation of pro-angiogenic signaling pathways.

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)0.000
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 score0.078
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.028
GPT teacher head0.281
Teacher spread0.253 · 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
Published2017
Admission routes1
Has abstractyes

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