Postbiotics for NOD2 require nonhematopoietic RIPK2 to improve blood glucose and metabolic inflammation in mice
Bibliographic record
Abstract
Defining the host receptors and metabolic consequences of bacterial components can help explain how the microbiome influences metabolic diseases. Bacterial peptidoglycans that activate nucleotide-binding oligomerization domain-containing (NOD)1 worsen glucose control, whereas NOD2 activation improves glycemia. Receptor-interacting serine/threonine-protein kinase 2 (RIPK2) is required for innate immunity instigated by NOD1 and NOD2. The role of RIPK2 in the divergent effects of NOD1 versus NOD2 on blood glucose was unknown. We found that whole body deletion of RIPK2 negated all effects of NOD1 or NOD2 activation on blood glucose during an acute, low level endotoxin challenge in mice. It was known that NOD1 in hematopoietic cells participates in insulin resistance and metabolic inflammation in obese mice. It was unknown if RIPK2 in hematopoietic cells is required for the glucose-lowering and anti-inflammatory effects of NOD2 activation. We hypothesized that RIPK2 in nonhematopoietic cells dictated the glycemic effects of NOD2 activation. We found that whole body deletion of RIPK2 prevented the glucose-lowering effects of repeated NOD2 activation that were evident during a glucose tolerance test (GTT) in high-fat diet (HFD)-fed wild-type (WT) mice. NOD2 activation lowered glucose during a GTT and lowered adipose tissue inflammation in mice with RIPK2 deleted in hematopoietic cells. We conclude that RIPK2 in nonhematopoietic cells mediates the glucose lowering and anti-inflammatory effects of NOD2-activating postbiotics. We propose a model where lipopolysaccharides and NOD1 ligands synergize in hematopoietic cells to promote insulin resistance but NOD2 activation in nonhematopoietic cells promotes RIPK2-dependent immune tolerance and lowering of inflammation and insulin resistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".