Gut microbiota-based vaccination engages innate immunity to improve blood glucose control in obese mice
Bibliographic record
Abstract
Obesity and diabetes increase circulating levels of microbial components derived from the gut microbiota. Individual bacterial factors (i.e., postbiotics) can have opposing effects on blood glucose. We tested the net effect of gut bacterial extracts on blood glucose in mice using a microbiota-based vaccination strategy. Male and female mice had improved glucose and insulin tolerance five weeks after a single subcutaneous injection of a specific dose of a bacterial extract obtained from the luminal contents of the upper small intestine (SI), lower SI, or cecum. Injection of mice with intestinal extracts from germ-free mice revealed that bacteria were required for a microbiota-based vaccination to improve blood glucose control. Vaccination of Nod1−/−, Nod2−/−, and Ripk2−/− mice showed that each of these innate immune proteins was required for bacterial extract injection to improve blood glucose control. A microbiota-based vaccination promoted an immunoglobulin-G (IgG) response directed against bacterial extract antigens, where subcutaneous injection of mice with the luminal contents of the lower SI elicited a bacterial extract-specific IgG response that is compartmentalized to the lower SI of vaccinated mice. A microbiota-based vaccination was associated with an altered microbiota composition in the lower SI and colon of mice. Lean mice only required a single injection of small intestinal-derived bacterial extract, but high fat diet (HFD)-fed, obese mice required prime-boost bacterial extract injections for improvements in blood glucose control. Subversion of the gut barrier by vaccination with a microbiota-based extract engages innate immunity to promote long-lasting improvements in blood glucose control in a dose-dependent manner.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".