Combination AAV therapy with galectin-1 and SOD1 downregulation demonstrates superior therapeutic effect in a severe ALS mouse model
Bibliographic record
Abstract
Neuroinflammation is a miscreant in accelerating progression of many neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). However, treatments targeting neuroinflammation alone have led to disappointing results in clinical trials. Both neuronal and non-neuronal cell types have been implicated in the pathogenesis of ALS, and multiple studies have shown correction of each cell type has beneficial effects on disease outcome. Previously, we shown that AAV9-mediated superoxide dismutase 1 (SOD1) suppression in motor neurons and astrocytes significantly improves motor function and extends survival in ALS mouse models. Despite neuron and astrocyte correction, ALS mice still succumb to death with microgliosis observed in endpoint tissue. Therefore, we hypothesized that the optimal therapeutic approach will target and simultaneously correct motor neurons, astrocytes, and microglia. Here, we developed a novel approach to indirectly target microglia with galectin-1 (Gal1) and combined this with our previously established AAV9.SOD1.short hairpin RNA treatment. We show Gal1 conditioning of SOD1 G93A microglia decreases inflammatory markers and rescues motor neuron death in vitro . When paired with SOD1 downregulation, we found a synergistic effect of combination treatment in vivo and show a significant extension of survival of SOD1 G93A mice over SOD1 suppression alone. These results highlight the importance of targeting inflammatory microglia as a critical component in future therapeutic development.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".