Sequestering sequestosome 1 via S-acylation in autophagy, Huntington disease, and beyond
Bibliographic record
Abstract
Protein mislocalization and aggregation are hallmark features in neurodegeneration. As proteins mislocalize, proteostasis deficiency and protein aggregation typically follow. Autophagy is a crucial pathway for the removal of protein aggregates to maintain neuronal health, but is impaired in various neurodegenerative diseases, including Huntington disease (HD). We identified S-acylation, a reversible lipid modification of proteins, as an important regulator in protein trafficking and autophagy. SQSTM1 (sequestosome 1/p62) is an essential selective autophagy receptor for the sequestration of ubiquitinated cargoes within autophagosomes and subsequent delivery into lysosomes for degradation. Recently, we reported that S-acylation of SQSTM1 at the di-cysteine motif C289,290 directs SQSTM1 to lysosomes. We further showed that SQSTM1 S-acylation is significantly reduced in brains from both HD patients and mouse HD model, which may result in the cargo sequestration defect within autophagosomes in HD. Treatment with palmostatin B, a deacylation inhibitor, significantly increases SQSTM1 localization to lysosomes. Our work highlights SQSTM1 S-acylation as a novel potential therapeutic strategy in HD. As a crucial autophagy component, our work suggests S-acylation of SQSTM1 may have a broader role in neurodegeneration.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".