Functional genomic screening identifies dual leucine zipper kinase as a key mediator of retinal ganglion cell death
Bibliographic record
Abstract
Glaucoma, a major cause of blindness worldwide, is a neurodegenerative optic neuropathy in which vision loss is caused by loss of retinal ganglion cells (RGCs). To better define the pathways mediating RGC death and identify targets for the development of neuroprotective drugs, we developed a high-throughput RNA interference screen with primary RGCs and used it to screen the full mouse kinome. The screen identified dual leucine zipper kinase (DLK) as a key neuroprotective target in RGCs. In cultured RGCs, DLK signaling is both necessary and sufficient for cell death. DLK undergoes robust posttranscriptional up-regulation in response to axonal injury in vitro and in vivo. Using a conditional knockout approach, we confirmed that DLK is required for RGC JNK activation and cell death in a rodent model of optic neuropathy. In addition, tozasertib, a small molecule protein kinase inhibitor with activity against DLK, protects RGCs from cell death in rodent glaucoma and traumatic optic neuropathy models. Together, our results establish a previously undescribed drug/drug target combination in glaucoma, identify an early marker of RGC injury, and provide a starting point for the development of more specific neuroprotective DLK inhibitors for the treatment of glaucoma, nonglaucomatous forms of optic neuropathy, and perhaps other CNS neurodegenerations.
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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".