Straight A’s: protein S-acylation in the activation and autophagic degradation of NOD-like receptors
Bibliographic record
Abstract
Over the past decade, S-acylation has emerged as a crucial regulator of several innate immune signaling pathways, with new insights continually being gained. S-acylation, a reversible post-translational modification, involves the attachment of fatty acyl chains to cysteine residues, influencing protein localization, function, and stability. In this mini-review, we examine the accumulating evidence of the role of S-acylation in regulating nucleotide oligomerization domain (NOD)-like receptors. NOD-like receptor subfamily P3 (NLRP3), a key player in inflammasome formation, undergoes S-acylation at specific cysteine residues, which are essential for its localization to the trans-Golgi network and other organelles. Various zinc finger Asp-His-His-Cys motif-containing (zDHHC) enzymes mediate this modification, with zDHHC5 being particularly important for activation and the ability of NLRP3 to interact with never in mitosis gene A (NIMA)-related protein kinase 7 (NEK7), promoting inflammasome assembly, caspase-1 activation, and pyroptosis. Alternatively, S-acylation by zDHHC12 targets NLRP3 for chaperone-mediated autophagy, preventing excessive inflammation. NOD2, another NLR, requires S-acylation for membrane localization and effective signaling via the NF-κB and mitogen-activated protein kinase pathways in response to peptidoglycan components. Dysregulation of S-acylation in NOD2 is associated with Crohn's Disease (hypo-acylated) and Blau syndrome/early-onset sarcoidosis (hyper-acylated). Soluble NOD2 lacking S-acylation is ubiquitinated and eliminated by the autophagic pathway. This review highlights the significance of understanding the S-acylation cycle and its regulatory mechanisms in developing potential therapeutic interventions for related inflammatory diseases. We also discuss unresolved questions regarding the S-acylation of NOD2 and NLRP3, as well as the regulation of S-acylation in general.
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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) | 0.000 | 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".