Acceleration of Diabetic Wound Healing with PHD2- and miR-210-Targeting Oligonucleotides
Bibliographic record
Abstract
In diabetes-associated chronic wounds, the normal response to hypoxia is impaired and many cellular processes involved in wound healing are hindered. Central to the hypoxia response is hypoxia-inducible factor-1α (HIF-1α), which activates multiple factors that enhance wound healing by promoting cellular motility and proliferation, new vessel formation, and re-epithelialization. Prolyl hydroxylase domain-containing protein 2 (PHD2) regulates HIF-1α activity by targeting it for degradation under normoxia. HIF-1α also upregulates microRNA miR-210, which in turn regulates proteins involved in cell cycle control, DNA repair, and mitochondrial respiration in ways that are antagonistic to wound repair. We have identified a highly potent short synthetic hairpin RNA (sshRNA) that inhibits expression of PHD2 and an antisense oligonucleotide (antimiR) that inhibits miR-210. Both oligonucleotides were chemically modified for improved biostability and to mitigate potential immunostimulatory effects. Using the sshRNA to silence PHD2 transcripts stabilizes HIF-1α and, in combination with the antimiR targeting miR-210, increases proliferation and migration of keratinocytes in vitro. To assess activity and delivery in an impaired wound healing model in diabetic mice, PHD2-targeting sshRNAs and miR-210 antimiRs both alone and in combination were formulated for local delivery to wounds using layer-by-layer (LbL) technology. LbL nanofabrication was applied to incorporate sshRNA into a thin polymer coating on a Tegaderm mesh. This coating gradually degrades under physiological conditions, releasing sshRNA and antimiR for sustained cellular uptake. Formulated treatments were applied directly to splinted full-thickness excisional wounds in db/db mice. Cellular uptake was confirmed using fluorescent sshRNA. Wounds treated with a single application of PHD2 sshRNA or antimiR-210 closed 4 days faster than untreated wounds, and wounds treated with both oligonucleotides closed on average 4.75 days faster. Markers for neovascularization and cell proliferation (CD31 and Ki67, respectively) were increased in the wound area following treatment, and vascular endothelial growth factor (VEGF) was increased in sshRNA-treated wounds. Our results suggest that silencing of PHD2 and miR-210 either together or separately by localized delivery of sshRNAs and antimiRs is a promising approach for the treatment of chronic wounds, with the potential for rapid clinical translation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".