Lost in traffic: consequences of altered palmitoylation in neurodegeneration
Bibliographic record
Abstract
One of the first molecular events in neurodegenerative diseases, regardless of etiology, is protein mislocalization. Protein mislocalization in neurons is often linked to proteostasis deficiencies leading to the build-up of misfolded proteins and/or organelles that contributes to cellular toxicity and cell death. By understanding how proteins mislocalize in neurons, we can develop novel therapeutics that target the earliest stages of neurodegeneration. A critical mechanism regulating protein localization and proteostasis in neurons is the protein-lipid modification S-acylation, the reversible addition of fatty acids to cysteine residues. S-acylation is more commonly referred to as S-palmitoylation or simply palmitoylation, which is the addition of the 16-carbon fatty acid palmitate to proteins. Like phosphorylation, palmitoylation is highly dynamic and tightly regulated by writers (i.e., palmitoyl acyltransferases) and erasers (i.e., depalmitoylating enzymes). The hydrophobic fatty acid anchors proteins to membranes; thus, the reversibility allows proteins to be re-directed to and from membranes based on local signaling factors. This is particularly important in the nervous system, where axons (output projections) can be meters long. Any disturbance in protein trafficking can have dire consequences. Indeed, many proteins involved in neurodegenerative diseases are palmitoylated, and many more have been identified in palmitoyl-proteomic studies. It follows that palmitoyl acyl transferase enzymes have also been implicated in numerous diseases. In addition, palmitoylation can work in concert with cellular mechanisms, like autophagy, to affect cell health and protein modifications, such as acetylation, nitrosylation, and ubiquitination, to affect protein function and turnover. Limited studies have further revealed a sexually dimorphic pattern of protein palmitoylation. Therefore, palmitoylation can have wide-reaching consequences in neurodegenerative diseases.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".