Identification of a Unique Hybrid Macrophage-Polarization State following Recovery from Lipopolysaccharide Tolerance
Bibliographic record
Abstract
LPS tolerance is an essential immune-homeostatic response to repeated exposure to LPS that prevents excessive inflammatory responses. LPS tolerance induces a state of altered responsiveness in macrophages, resulting in repression of proinflammatory gene expression and increased expression of factors that mediate the resolution of inflammation. In this study, we analyzed the transcriptional plasticity of macrophages following LPS tolerance using genome-wide transcriptional profiling. We demonstrate that LPS tolerance is a transient state and that the expression of proinflammatory genes is restored to levels comparable to the acute response to LPS. However, following recovery from LPS tolerance a number of genes remained locked in a tolerizable state, including IL-33, CD86, IL-10, and NFIL3. Furthermore, we identified of a number of genes uniquely induced following recovery from LPS tolerance. Thus, macrophages adopt a unique transcriptional profile following recovery from LPS tolerance and have a distinct expression pattern of regulators of Ag presentation, antiviral responses, and transcription factors. Our data suggest that recovery from LPS tolerance leads to a hybrid macrophage activation state that is proinflammatory and microbicidal in nature but that possesses a regulatory anti-inflammatory profile distinct from that of LPS-tolerant and LPS-activated macrophages.
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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 | 0.001 |
| 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".