IFNγ receptor down-regulation facilitates <i>Legionella</i> survival in alveolar macrophages
Bibliographic record
Abstract
Legionella pneumophila is an opportunistic human pathogen and causative agent of the acute pneumonia known as Legionnaire's disease. Upon inhalation, the bacteria replicate in alveolar macrophages (AM), within an intracellular vacuole termed the Legionella-containing vacuole. We recently found that, in vivo, IFNγ was required for optimal clearance of intracellular L. pneumophila by monocyte-derived cells (MC), but the cytokine did not appear to influence clearance by AM. Here, we report that during L. pneumophila lung infection, expression of the IFNγ receptor subunit 1 (IFNGR1) is down-regulated in AM and neutrophils, but not MC, offering a possible explanation for why AM are unable to effectively restrict L. pneumophila replication in vivo. To test this, we used mice that constitutively express IFNGR1 in AM and found that prevention of IFNGR1 down-regulation enhanced the ability of AM to restrict L. pneumophila intracellular replication. IFNGR1 down-regulation was independent of the type IV Dot/Icm secretion system of L. pneumophila indicating that bacterial effector proteins were not involved. In contrast to previous work, we found that signaling via type I IFN receptors was not required for IFNGR1 down-regulation in macrophages but rather that MyD88- or Trif- mediated NF-κB activation was required. This work has uncovered an alternative signaling pathway responsible for IFNGR1 down-regulation in macrophages during bacterial infection.
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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 | 0.001 |
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".