Delineation of the Role of Toll-like Receptor Signaling during Peritonitis by a Gradually Growing Pathogenic Escherichia coli
Bibliographic record
Abstract
In a mouse model of Escherichia coli sepsis characterized by a primary peritoneal infection with 10(4) E. coli and a gradually growing bacterial load, we here show that the early cytokine response and antibacterial defense are dominated by TLR4 via a cooperative action of MyD88 and Trif. Although MyD88(-/-) mice succumbed earlier than WT mice in this E. coli peritonitis model, Trif(-/-) mice displayed a small but significant survival advantage. Despite a large early deficit in antimicrobial defense, TLR4(-/-) mice showed an unaltered survival with normal neutrophil attraction to the peritoneal cavity and normal or even elevated late cytokine release. TLR2 compensated for the lack of TLR4 because TLR2(-/-)/TLR4(-/-) mice did show decreased neutrophil attraction and increased mortality compared with WT mice. Nearly normal early peritoneal TNFα production and lack of early counterregulating systemic levels of the chemoattractant KC were associated with normal peritoneal neutrophil attraction in TLR4(-/-) mice. Late stage increased TNF, IL-1β, IFN-β, and typical IFN-γ production in TLR4(-/-) mice prompted us to evaluate expression of the negative feedback regulator SOCS-1. Lack of early hepatic SOCS-1 expression in TLR4(-/-) mice explained the late innate production of IFN-γ by the liver in TLR4(-/-) mice in this low dose E. coli peritonitis model. In contrast, early TLR4-induced IFN-γ production is described as a hallmark in high dose E. coli peritonitis models. The present study displays how the kinetics of pro- and anti-inflammatory mechanisms are regulated by TLRs during peritonitis by a gradually growing E. coli load and how these kinetics may affect outcome.
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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 | 0.001 | 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".