Negative Cross-Talk between TLR2/4-Independent AMPKα1 and TLR2/4-Dependent JNK Regulates <i>S. pneumoniae</i>–Induced Mucosal Innate Immune Response
Bibliographic record
Abstract
Abstract Streptococcus pneumoniae is major cause of otitis media (OM) and life-threatening pneumonia. Overproduction of mucin, the major component of mucus, plays a critical role in the pathogenesis of both OM and pneumonia. However, the molecular mechanisms underlying the tight regulation of mucin upregulation in the mucosal epithelium by S. pneumoniae infection remain largely unknown. In this study, we show that S. pneumoniae pneumolysin (PLY) activates AMP-activated protein kinase α1 (AMPKα1), the master regulator of energy homeostasis, which is required for S. pneumoniae–induced mucin MUC5AC upregulation in vitro and in vivo. Moreover, we found that PLY activates AMPKα1 via cholesterol-dependent membrane binding of PLY and subsequent activation of the Ca2+– Ca2+/calmodulin-dependent kinase kinase β (CaMKKβ) and Cdc42–mixed-lineage protein kinase 3 (MLK3) signaling axis in a TLR2/4-independent manner. AMPKα1 positively regulates PLY-induced MUC5AC expression via negative cross-talk with TLR2/4-dependent activation of MAPK JNK, the negative regulator of MUC5AC expression. Moreover, pharmacological inhibition of AMPKα1 suppressed MUC5AC induction in the S. pneumoniae–induced OM mouse model, thereby demonstrating its therapeutic potential in suppressing mucus overproduction in OM. Taken together, our data unveil a novel mechanism by which negative cross-talk between TLR2/4-independent activation of AMPKα1 and TLR2/4-dependent activation of JNK tightly regulates the S. pneumoniae PLY-induced host mucosal innate immune response.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".