Targeting K<sub>V</sub>channels rescues retinal ganglion cells in vivo directly and by reducing inflammation
Bibliographic record
Abstract
Retinal ganglion cell (RGC) degeneration is an important cause of visual impairment, and results in part from microglia-mediated inflammation. Numerous experimental studies have focused on identifying drug targets to rescue these neurons. We recently showed that KV1.1 and KV1.3 channels are expressed in adult rat RGCs and that siRNA-mediated knockdown of either channel reduces RGC death after optic nerve transection. Earlier we found that KV1.3 channels also contribute to microglial activation and neurotoxicity; raising the possibility that these channels contribute to neurodegeneration through direct roles in RGCs and through inflammatory mechanisms. Here, RGC survival was increased by combined siRNA-mediated knockdown of KV1.1 and KV1.3 in RGCs, but survival was much greater when knockdown of either channel was combined with intraocular injection of a KV1.3 channel blocker (agitoxin-2 or margatoxin). After axotomy, increased expression of several inflammation-related molecules preceded RGC loss and, consistent with a dual mechanism, their expression was differentially affected when channel knockdown in RGCs was combined with KV1.3 blocker injection. KV1.3 blockers reduced activation of retinal microglia and their tight apposition along RGC axon fascicles after axotomy, but did not prevent their migration from the inner plexiform to the damaged ganglion cell layer. Expression of several growth factors increased after axotomy; and again, there were differences following blocker injection compared with RGC-selective channel knockdown. These results provide evidence that KV1.3 channels play important roles in apoptotic degeneration of adult RGCs through cell-autonomous mechanisms mediated by channels in the neurons, and non-autonomous mechanisms mediated by microglia and inflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".