Discovery of FBP1 as novel therapeutic target and asiatic acid-hydrogen sulfide donors accelerate diabetic wound healing
Bibliographic record
Abstract
Introduction Wound healing impairment is highly prevalent in diabetes and frequently progresses to serious complications, including refractory ulcers and necessitated amputations. RNA sequencing in methylglyoxal (MGO)-injured HaCaT cells implicated fructose-1,6-bisphosphatase 1 (FBP1) in suppressing keratinocyte proliferation and migration, identifying it as a potential therapeutic target. Objectives This study aimed to validate FBP1 as a therapeutic target for diabetic wounds and evaluate asiatic acid (AA) and its novel hydrogen sulfide (H 2 S)-donor derivatives, designed to enhance efficacy, as FBP1-targeted interventions. Methods Target discovery was performed via transcriptomics in MGO-injured HaCaT cells, identifying FBP1 as a key regulator. Virtual screening of compound libraries was combined with experimental screening to discover AA as a potent FBP1 inhibitor. Based on AA’s structure, novel H 2 S-donor derivatives were rationally designed and synthesized to enhance therapeutic properties. A topical AA4 gel was formulated and tested for its therapeutic impact on diabetic wound repair in mouse models. Results AA was identified as a potent FBP1 inhibitor (IC 50 = 2.5 μM). AA4 , a synthesized H 2 S-donor derivative, exhibited dual mechanisms: direct FBP1 enzymatic inhibition and H 2 S-mediated FBP1 downregulation. This synergistically restored proliferation pathways (AKT/mTOR/HIF-1α/uPAR) and reduced apoptosis (Bcl-2/Bax/Caspase-3). Topical AA4 gel markedly enhanced wound closure rates in diabetic mice, primarily through promoting epidermal regeneration and collagen deposition. Conclusion This study validates FBP1 targeting as a feasible strategy to address diabetic wound healing. It establishes AA-H 2 S donor derivatives, particularly AA4 acting via dual FBP1 targeting, as an encouraging precision therapy for diabetic wound healing.
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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) | 0.002 | 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".