Gene Therapy for Traumatic Central Nervous System Injury and Stroke Using an Engineered Zinc Finger Protein that Upregulates VEGF-A
Bibliographic record
Abstract
Recent studies have identified anti-apoptotic functions for vascular endothelial growth factor (VEGF) in the central nervous system (CNS). However, VEGF therapy has been hampered by a tendency to promote vascular permeability, edema, and inflammation. Recently, engineered zinc finger proteins (ZFPs) that upregulate multiple forms of VEGF in their natural biological ratios, have been developed to overcome these negative side effects. We used retinal trauma and ischemia models, and a cortical pial strip ischemia model to determine if VEGF upregulating ZFPs are neuroprotective in the adult CNS. Optic nerve transection and ophthalmic artery ligation lead to the apoptotic degeneration of retinal ganglion cells (RGCs) and are, respectively, two highly reproducible models for CNS trauma or ischemia. Adeno-associated vectors (AAV) vectors encoding VEGF-ZFPs (AAV-VEGF-ZFP) significantly increased RGC survival by ∼twofold at 14 days after optic nerve transection or ophthalmic artery ligation. Furthermore, AAV-VEGF-ZFP enhanced recovery of the pupillary light reflex. RECA-1 immunostaining demonstrated no appreciable differences between retinas treated with AAV-VEGF-ZFP and controls, suggesting that AAV-VEGF-ZFP treatment did not affect retinal vasculature. Following pial strip of the forelimb motor cortex, brains treated with an adenovirus encoding VEGF ZFPs (AdV-ZFP) showed higher neuronal survival, accelerated wound contraction, and reduced lesion volume between 1 and 6 weeks after injury. Behavioral testing using the cylinder test for vertical exploration showed that AdV-VEGF-ZFP treatment enhanced contralateral forelimb function within the first 2 weeks after injury. Our results indicate that VEGF ZFP therapy is neuroprotective following traumatic injury or stroke in the adult mammalian CNS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".