<i>Salmonella enterica</i>Serovar Typhimurium Exploits Toll-Like Receptor Signaling during the Host-Pathogen Interaction
Bibliographic record
Abstract
Salmonella survives and replicates in host cells by using a type III secretion system to evade host immune defenses. The innate immune system plays an important role as a first line of defense against pathogens and is mediated in part by Toll-like receptors (TLRs); however, the infection dynamics of Salmonella enterica serovar Typhimurium within macrophages stimulated with TLR ligands is poorly understood. We studied the infection dynamics of Salmonella in murine macrophages previously exposed to TLR ligands and report that treatment of macrophages with four different TLR agonists resulted in their increased phagocytic capacity toward Salmonella but not fluorescent microspheres. Further analysis revealed that the intracellular replication of Salmonella was enhanced in TLR-stimulated macrophages in a manner requiring a functional type III secretion system and enhanced transcriptional activity of the sseA virulence gene operon. Studies of mice that normally resolve an acute primary infection with Salmonella revealed that pretreatment of animals with CpG DNA had a detrimental effect on disease outcome. CpG-treated mice infected with Salmonella all succumbed to infection and had higher bacterial loads in the spleen than did control animals. These data suggest that Salmonella can exploit macrophages activated via the innate immune system for increased intracellular survival.
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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".