Epigenetics in the formation of pathological aggregates in amyotrophic lateral sclerosis
Bibliographic record
Abstract
The progressive degeneration of motor neurons in amyotrophic lateral sclerosis (ALS) is accompanied by the formation of a broad array of cytoplasmic and nuclear neuronal inclusions (protein aggregates) largely containing RNA-binding proteins such as TAR DNA-binding protein 43 (TDP-43) or fused in sarcoma/translocated in liposarcoma (FUS/TLS). This process is driven by a liquid-to-solid phase separation generally from proteins in membrane-less organelles giving rise to pathological biomolecular condensates. The formation of these protein aggregates suggests a fundamental alteration in the mRNA expression or the levels of the proteins involved. Considering the role of the epigenome in gene expression, alterations in DNA methylation, histone modifications, chromatin remodeling, non-coding RNAs, and RNA modifications become highly relevant to understanding how this pathological process takes effect. In this review, we explore the evidence that links epigenetic mechanisms with the formation of protein aggregates in ALS. We propose that a greater understanding of the role of the epigenome and how this inter-relates with the formation of pathological LLPS in ALS will provide an attractive therapeutic target.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".