Ubiquilin-2 drives NF-κB activity and cytosolic TDP-43 aggregation in neuronal cells
Bibliographic record
Abstract
BACKGROUND: Mutations in the gene encoding Ubiquilin-2 (UBQLN2) are linked to amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). UBQLN2 plays a central role in ubiquitin proteasome system (UPS) and UBQLN2 mutants can form cytoplasmic aggregates in vitro and in vivo. RESULTS: Here, we report that overexpression of WT or mutant UBQLN2 species enhanced nuclear factor κB (NF-κB) activation in Neuro2A cells. The inhibition of NF-κB stress-mediated activation with SB203580, a p38 MAPK inhibitor, demonstrated a role for MAPK in NF-κB activation by UBQLN2 species. Live cell imaging and microscopy showed that UBQLN2 aggregates are dynamic structures that promote cytoplasmic accumulation of TAR DNA-binding protein (TDP-43), a major component of ALS inclusion bodies. Furthermore, up-regulation of UBQLN2 species in neurons caused an ER-stress response and increased their vulnerability to death by toxic mediator TNF-α. Withaferin A, a known NF-κB inhibitor, reduced mortality of Neuro2A cells overexpressing UBQLN2 species. CONCLUSIONS: These results suggest that UBQLN2 dysregulation in neurons can drive NF-κB activation and cytosolic TDP-43 aggregation, supporting the concept of pathway convergence in ALS pathogenesis. These Ubiquilin-2 pathogenic pathways might represent suitable therapeutic targets for future ALS treatment.
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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".