TLR2 and RIP2 Pathways Mediate Autophagy of Listeria monocytogenes via Extracellular Signal-regulated Kinase (ERK) Activation
Bibliographic record
Abstract
Listeria monocytogenes is a facultative intracellular pathogen that invades both phagocytic and non-phagocytic cells. Recent studies have shown that L. monocytogenes infection activates the autophagy pathway. However, the innate immune receptors involved and the downstream signaling pathways remain unknown. Here, we show that macrophages deficient in the TLR2 and NOD/RIP2 pathway display defective autophagy induction in response to L. monocytogenes. Inefficient autophagy in Tlr2(-/-) and Nod2(-/-) macrophages led to a defect in bacteria colocalization with the autophagosomal marker GFP-LC3. Consequently, macrophages lacking TLR2 and NOD2 were found to be more susceptible to L. monocytogenes infection, as were the Rip2(-/-) mice. Tlr2(-/-) and Nod2(-/-) cells showed perturbed NF-κB and ERK signaling. However, autophagy against L. monocytogenes was dependent selectively on the ERK pathway. In agreement, wild-type cells treated with a pharmacological inhibitor of ERK or ERK-deficient cells displayed inefficient autophagy activation in response to L. monocytogenes. Accordingly, fewer bacteria were targeted to the autophagosomes and, consequently, higher bacterial growth was observed in cells deficient in the ERK signaling pathway. These findings thus demonstrate that TLR2 and NOD proteins, acting via the downstream ERK pathway, are crucial to autophagy activation and provide a mechanistic link between innate immune receptors and induction of autophagy against cytoplasm-invading microbes, such as L. monocytogenes.
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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 | 0.001 |
| 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".