<i>Klebsiella pneumoniae</i> hijacks the Toll-IL-1R protein SARM1 in a type I IFN-dependent manner to antagonize host immunity
Bibliographic record
Abstract
SUMMARY Many bacterial pathogens antagonize host defence responses by translocating effector proteins into cells. It remains an open question how those pathogens not encoding effectors counteract anti-bacterial immunity. Here, we show that Klebsiella pneumoniae hijacks the evolutionary conserved innate immune protein SARM1 to control cell intrinsic immunity. Klebsiella exploits SARM1 to regulate negatively MyD88 and TRIF-governed inflammation, and the activation of the MAP kinases ERK and JNK. SARM1 is required for Klebsiella induction of IL10 by fine-tuning the p38-type I IFN axis. SARM1 inhibits the activation of Klebsiella -induced absent in melanoma 2 inflammasome to limit IL1β production, suppressing further inflammation. Klebsiella exploits type I IFNs to induce SARM1 in a capsule and LPS O-polysaccharide-dependent manner via TLR4-TRAM-TRIF-IRF3-IFNAR1 pathway. Absence of SARM1 reduces the intracellular survival of K. pneumonaie in macrophages whereas sarm1 deficient mice control the infection. Altogether, our results illustrate a hitherto unknown anti-immunology strategy deployed by a human pathogen.
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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) | 0.005 | 0.002 |
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".