Post-Translational Modifications in NETosis and NETs-Mediated Diseases
Bibliographic record
Abstract
Neutrophils undergo a unique form of cell death that generates neutrophil extracellular traps (NETs) that may help to neutralize invading pathogens and restore homeostasis. However, uncontrolled NET formation (NETosis) can result in numerous diseases that adversely affect health. Recent studies further elucidate the mechanistic details of the different forms of NETosis and their common end structure, as NETs were constantly found to contain DNA, modified histones and cytotoxic enzymes. In fact, emerging evidence reveal that the post translational modifications (PTMs) of histones in neutrophils have a critical role in regulating neutrophil death. Histone citrullination is shown to promote a rapid form of NET formation independent of NADPH oxidase (NOX), which relies on calcium influx. Interestingly, few studies suggest an association between histone citrullination and other types of PTMs to control cell survival and death, such as histone methylation. Even more exciting is the finding that histone acetylation has a biphasic effect upon NETosis, where histone deacetylase (HDAC) inhibitors promote baseline, NOX-dependent and -independent NETosis. However, increasing levels of histone acetylation suppresses NETosis, and to switch neutrophil death to apoptosis. Interestingly, in the presence of NETosis-promoting stimuli, high levels of HDACis limit both NETosis and apoptosis, and promote neutrophil survival. Recent studies also reveal the importance of the PTMs of neutrophils in influencing numerous pathologies. Histone modifications in NETs can act as a double-edged sword, as they are capable of altering multiple types of neutrophil death, and influencing numerous NET-mediated diseases, such as acute lung injury (ALI), thrombosis, sepsis, systemic lupus erythematosus, and cancer progression. A clear understanding of the role of different PTMs in neutrophils would be important for an understanding of the molecular mechanisms of NETosis, and to appropriately treat NETs-mediated 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 | 0.001 |
| 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".