Purine catabolism regulates the production of IL-1beta in macrophages
Bibliographic record
Abstract
Abstract Activation of pro-inflammatory (M1-like) macrophages leads to the production of pro-inflammatory cytokines, such as IL-1b, TNFa and IL-6. Those cytokines are important in the control of infections, but overproduction can lead to chronic inflammation. M1-like macrophages undergo metabolic remodeling, including the upregulation of purine metabolism. Using inhibitors of purine breakdown or purine synthesis, we aimed to identify the role of purine metabolism in M1-like macrophage function. Blocking purine synthesis or purine breakdown did not affect the frequency of CD80+CD86+iNOS+macrophages, nor the accumulation of the M1-like macrophages hallmark metabolites itaconate and succinate, critical for immune function. However, inhibition of purine synthesis significantly reduced IL-1bsecretion. Mature IL-1b release is dependent on both transcriptional activation of the pro-IL1 gene and NLRP3-inflammasome. Blockade of purine synthesis sustained pro-IL1 transcription, an accumulation of pro-IL1b and significantly reduced caspase-1 activity. When purine degradation was inhibited, we observed no change in IL-1b production, but a reduced TNFa and IL-6. In summary, purine metabolism is dispensable for the immunophenotype of M1-like macrophages, but it regulates macrophage cytokine production. Targeting specific nodes in purine metabolism in macrophages provides opportunities for immunomodulation in inflammatory diseases affected by pro-inflammatory macrophage-linked cytokines.
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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 | 0.000 | 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) | 0.001 | 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 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".