Tyrosine kinase inhibitors of Ripk2 attenuate bacterial cell wall-mediated lipolysis, inflammation and dysglycemia
Bibliographic record
Abstract
Abstract Inflammation underpins aspects of insulin resistance and dysglycemia. Microbiota-derived cell wall components such as muropeptides or endotoxin can trigger changes in host immunity and metabolism. Specific peptidoglycan motifs promote metabolic tissue inflammation, lipolysis and insulin resistance via Nucleotide-binding oligomerization domain-containing protein 1 (Nod1). Receptor-interacting serine/threonine-protein kinase 2 (Ripk2) mediates Nod1-induced immunity, but the role of Ripk2 in metabolism is ill-defined. We hypothesized that Ripk2 was required for Nod1-mediated inflammation, lipolysis and dysglycemia. This is relevant because certain tyrosine kinase inhibitors (TKIs) inhibit Ripk2 and there is clinical evidence of TKIs lowering inflammation and blood glucose. Here, we showed that only a subset of TKIs known to inhibit Ripk2 attenuated Nod1 ligand-mediated adipocyte lipolysis. TKIs that inhibit Ripk2 decreased cytokine responses induced by Nod1-activating peptidoglycan, but not endotoxin in both metabolic and immune cells. Pre-treatment of adipocytes or macrophages with the TKI gefitinib inhibited Nod1-induced Cxcl1 and Il-6 secretion. Furthermore, treatment of mice with gefitinib prevented Nod1-induced glucose intolerance in vivo. Ripk2 was required for these effects on inflammation and metabolism, since Nod1-mediated cytokine and blood glucose changes were absent in Ripk2−/− mice. Our data show that specific TKIs used in cancer also inhibit Nod1-Ripk2 immunometabolism responses indicative of metabolic disease.
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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) | 0.001 | 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 | 0.000 |
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 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".