NOD2 Activation Induces Muscle Cell-Autonomous Innate Immune Responses and Insulin Resistance
Bibliographic record
Abstract
Insulin resistance is associated with chronic low-grade inflammation in vivo, largely mediated by activated innate immune cells. Cytokines and pathogen-derived ligands of surface toll-like receptors can directly cause insulin resistance in muscle cells. However, it is not known if intracellular pathogen sensors can, on their own, provoke insulin resistance. Here, we show that the cytosolic pattern recognition receptors nucleotide-binding oligomerization domain-containing protein (NOD)1 and NOD2 are expressed in immune and metabolic tissues and hypothesize that their activation in muscle cells would result in cell-autonomous responses leading to insulin resistance. Bacterial peptidoglycan motifs that selectively activate NOD2 were directly administered to L6- GLUT4myc myotubes in culture. Within 3 h, insulin resistance arose, characterized by reductions in each insulin-stimulated glucose uptake, GLUT4 translocation, Akt Ser(473) phosphorylation, and insulin receptor substrate 1 tyrosine phosphorylation. Muscle cell-autonomous responses to NOD2 ligand included activation of the stress/inflammation markers c-Jun N-terminal kinase, ERK1/2, p38 MAPK, degradation of inhibitor of κBα, and production of proinflammatory cytokines. These results show that NOD2 alone is capable of acutely inducing insulin resistance within muscle cells, possibly by activating endogenous inflammatory signals and/or through cytokine production, curbing upstream insulin signals. NOD2 is hence a new inflammation target connected to insulin resistance, and this link occurs without the need of additional contributing cell types. This study provides supporting evidence for the integration of innate immune and metabolic responses through the involvement of NOD proteins and suggests the possible participation of cell autonomous immune responses in the development of insulin resistance in skeletal muscle, the major depot for postprandial glucose utilization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".