S100A8 acts as an autocrine priming signal for heme-induced human <b>Mϕ</b> pro-inflammatory responses in hemolytic inflammation
Bibliographic record
Abstract
Abstract Intravascular hemolysis, in addition to reducing red cell counts, incurs extensive vascular inflammation and oxidative stress. One product of hemolysis, heme, is a potent danger associated molecular pattern (DAMP), activating leukocytes and inducing cytokine expression and processing, among other pro-inflammatory effects. We explored pathways by which heme-induced inflammation may be amplified under sterile conditions. Incubation of human Mϕs, differentiated from CD14+ cells, with heme induced time- and concentration-dependent gene and protein expression of S100A8, a myeloid cell-derived alarmin. Human Mϕ stimulation with recombinant S100A8, in turn, induced robust pro-IL-1β expression that was dependent upon NF-κB activation, gene transcription, and partially dependent upon TLR4-mediated signaling. Moreover, heme itself stimulated significant Mϕ pro-IL-1β gene and protein expression via an S100A8-mediated mechanism and greatly amplified S100A8-driven NLRP3 inflammasome-mediated IL-1β secretion. In vivo, induction of acute intravascular hemolysis in mice induced a rapid elevation of plasma S100A8 that could be abolished by hemopexin, a heme scavenger. Finally, plasma S100A8 levels were found to be significantly elevated in patients with the inherited hemolytic anemia, sickle cell anemia, when compared with levels in healthy individuals. In conclusion, we demonstrate that hemolytic processes are associated with S100A8 generation and that some of the inflammatory effects of heme may be amplified by autocrine S100A8 production. Findings suggest a mechanism by which hemolytic inflammation could be propagated via leukocyte priming by endogenous proteins, even in sterile inflammatory environments such as those that occur in the hemolytic diseases. S100A8 may represent a therapeutic target for reducing inflammation in hemolytic disorders.
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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".