Postbiotics engage <scp>IRF4</scp> in adipocytes to promote sex‐dependent changes in blood glucose during obesity
Bibliographic record
Abstract
Postbiotics are microbial-derived components or metabolites that can influence host immunity and metabolism. Some postbiotics can improve blood glucose control and lower inflammation during bacterial or nutritional stress. Bacterial cell wall-derived muramyl dipeptide (MDP) is a potent insulin-sensitizing postbiotic that engages NOD2, RIPK2, and requires interferon regulatory factor 4 (IRF4) to lower inflammation and improve blood glucose. However, the sex-dependent effects of this postbiotic and the cell type required for IRF4 to cause inflammatory versus glycemic responses to MDP were unknown. Here, we measured how MDP injection altered glucose tolerance and adipose tissue inflammation during low-level endotoxemia and high fat diet (HFD)-induced obesity in male and female adipocyte-specific IRF4 knockout mice (AdipoIRF4fl/fl) compared to WTfl/fl mice. Adipocyte IRF4 was required for the blood glucose-lowering effects of MDP during endotoxemia and HFD-induced obesity in male mice. However, MDP did not alter blood glucose in female WTfl/fl and AdipoIRF4fl/f mice during endotoxemia. Unexpectedly, female HFD-fed AdipoIRF4fl/f mice had lower blood glucose after MDP treatment compared to WTfl/fl mice. MDP lowered inflammatory gene expression in adipose tissue of HFD-fed WTfl/fl and AdipoIRF4fl/fl mice of both sexes. Therefore, MDP-mediated lowering of adipose inflammation does not require adipocyte IRF4 and was independent of sex. Together, these data show that injection of MDP, an insulin-sensitizing postbiotic, lowers adipose tissue inflammation in male and female mice, but lower adipose inflammation is not always associated with improved blood glucose. The blood glucose-lowering effect of the postbiotic MDP and dependence on adipocyte IRF4 is sex-dependent.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".