TDP-43 aggregation inside micronuclei reveals a potential mechanism for protein inclusion formation in ALS
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a devastating progressive neurodegenerative disease with no known etiology. The formation of pathological protein inclusions, including RNA-binding proteins such as TDP-43 and rho guanine nucleotide exchange factor (RGNEF) are a hallmark of ALS. Despite intensive research, the mechanisms behind protein aggregate formation in ALS remains unclear. We have investigated the role of metabolic stress in protein aggregate formation analyzing how it is relevant to the co-aggregation observed between RGNEF and TDP-43 in motor neurons of ALS patients. Metabolic stress was able to induce formation of micronuclei, small nuclear fragments, in cultured cells. Notably, we observed the formation TDP-43 protein inclusions within micronuclei that co-aggregate with RGNEF and can be released to the cytoplasm. We observed that the leucine-rich domain of RGNEF is critical for its interaction with TDP-43 and localization in micronuclei. Finally, we described that micronuclei-like structures can be found in brain and spinal cord of ALS patients. This work is the first description of protein inclusion formation within micronuclei which also is linked with a neurodegenerative disease. The formation of TDP-43 inclusions within micronuclei induced by metabolic stress is a novel mechanism of protein aggregate formation which may have broad relevance for ALS and other neurodegenerative diseases.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".