Microbiota and Nod-like receptors balance inflammation and metabolism during obesity and diabetes
Bibliographic record
Abstract
Gut microbiota influence host immunity and metabolism during obesity. Bacterial sensors of the innate immune system relay signals from specific bacterial components (i.e., postbiotics) that can have opposing outcomes on host metabolic inflammation. NOD-like receptors (NLRs) such as Nod1 and Nod2 both recruit receptor-interacting protein kinase 2 (RIPK2) but have opposite effects on blood glucose control. Nod1 connects bacterial cell wall-derived signals to metabolic inflammation and insulin resistance, whereas Nod2 can promote immune tolerance, insulin sensitivity, and better blood glucose control during obesity. NLR family pyrin domain containing (NLRP) inflammasomes can also generate divergent metabolic outcomes. NLRP1 protects against obesity and metabolic inflammation potentially because of a bias toward IL-18 regulation, whereas NLRP3 appears to have a bias toward IL-1β-mediated metabolic inflammation and insulin resistance. Targeting specific postbiotics that improve immunometabolism is a key goal. The Nod2 ligand, muramyl dipeptide (MDP) is a short-acting insulin sensitizer during obesity or during inflammatory lipopolysaccharide (LPS) stress. LPS with underacylated lipid-A antagonizes TLR4 and counteracts the metabolic effects of inflammatory LPS. Providing underacylated LPS derived from Rhodobacter sphaeroides improved insulin sensitivity in obese mice. Therefore, certain types of LPS can generate metabolically beneficial metabolic endotoxemia. Engaging protective adaptive immunoglobulin immune responses can also improve blood glucose during obesity. A bacterial vaccine approach using an extract of the entire bacterial community in the upper gut promotes protective adaptive immune response and long-lasting improvements in blood glucose control. A key future goal is to identify and combine postbiotics that cooperate to improve blood glucose control.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".