FUS/TLS acts as an aggregation-dependent modifier of polyglutamine disease model mice
Bibliographic record
Abstract
Abstract FUS/TLS is an RNA/DNA-binding protein associated with neurodegenerative diseases including amyotrophic lateral sclerosis and frontotemporal lobar degeneration. Previously, we found that a prion-like domain in the N-terminus of FUS/TLS mediates co-aggregation between FUS/TLS and mutant huntingtin, the gene product of Huntington’s disease (HD). Here, we show that heterozygous knockout of FUS/TLS worsened the phenotypes of model mice of (HD, but not spinal and bulbar muscular atrophy (SBMA). This difference was correlated with the degree of pathological association between disease proteins and FUS/TLS. Co-aggregation between FUS/TLS and mutant huntingtin resulted in the depletion of free FUS/TLS protein in HD mice that was detected as a monomer in SDS-PAGE analysis. Recently, we found that FUS/TLS paralogs, TAF15 and EWS, were up-regulated in homozygous FUS/TLS knockout mice. These two proteins were up-regulated in both HD and FUS/TLS heterozygote mice, and were further elevated in HD-TLS+/−double mutant mice, consistent with the functional impairment of FUS/TLS. These results suggest that FUS/TLS sequestration by co-aggregation is a rate-limiting factor of disease phenotypes of HD and that inclusions may have an adverse aspect, rather than being simply benign or protective. In addition, our results highlight inclusions as repositories of potential modifiers of neurodegeneration.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".