STAT1 Regulates IFN-αβ- and IFN-γ-Dependent Control of Infection with <i>Chlamydia pneumoniae</i> by Nonhemopoietic Cells
Bibliographic record
Abstract
STAT1 mediates signaling in response to IFN-alpha, -beta, and -gamma, cytokines required for protective immunity against several viral, bacterial, and eukaryotic pathogens. The protective role of STAT1 in the control of intranasal infection with the obligate intracellular bacterium Chlamydia pneumoniae was analyzed. IFN-gamma-/- or IFN-gamma receptor (R)-/- mice were highly susceptible to infection with C. pneumoniae. We found that STAT1-/- mice were even more susceptible to C. pneumoniae than IFN-gamma-/- or IFN-gammaR-/- mice. Phosphorylation of STAT1 was detected in the lungs of C. pneumoniae-infected wild-type, IFN-gammaR-/-, and IFN-alphabetaR-/- mice, but not in mice lacking both IFN-alphabetaR and IFN-gammaR. In line with this, IFN-alphabetaR-/-/IFN-gammaR-/- mice showed increased susceptibility to infection compared with IFN-gammaR-/- mice. However, C. pneumoniae-infected IFN-alphabetaR-/- or IFN regulatory factor 3-/- mice showed no increased susceptibility and similar IFN-gamma expression compared with wild-type mice. CD4+ or CD8+ cells released IFN-gamma in vivo and conferred protection against C. pneumoniae in a STAT1-independent manner. In contrast, STAT1 mediated a nonredundant protective role of nonhemopoietic cells but not of hemopoietic cells. Nonhemopoietic cells accounted for the expression of STAT1-mediated indoleamine 2, 3-dioxygenase and the p47 GTPase LRG-47, but not inducible NO synthase mRNA. In summary, we demonstrate that STAT1 mediates a cooperative effect of IFN-alphabeta and IFN-gamma on nonhemopoietic cells, resulting in protection against C. pneumoniae.
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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) | 0.002 | 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".