Microbially produced glucagon-like peptide 1 improves glucose tolerance in mice
Bibliographic record
Abstract
OBJECTIVE: The enteroendocrine hormone glucagon-like peptide 1 (GLP-1) is an attractive anti-diabetic therapy. Here, we generated a recombinant Lactococcus lactis strain genetically modified to produce GLP-1 and investigated its ability to improve glucose tolerance in mice on chow or high-fat diet (HFD). METHODS: We transformed L. lactis FI5876 with either empty vector (pUK200) or murine GLP-1 expression vector to generate LL-UK200 and LL-GLP1, respectively, and determined their potential to induce insulin secretion by incubating primary islets from wild-type (WT) and GLP-1 receptor knockout (GLP1R-KO) mice with culture supernatant of these strains. In addition, we administered these strains to mice on chow or HFD. At the end of the study period, we measured plasma GLP-1 levels, performed intraperitoneal glucose tolerance and insulin tolerance tests, and determined hepatic expression of the gluconeogenic genes G6pc and Pepck. RESULTS: Insulin release from primary islets of WT but not GLP1R-KO mice was higher following incubation with culture supernatant from LL-GLP1 compared with LL-UK200. In mice on chow, supplementation with LL-GLP1 versus LL-UK200 promoted increased vena porta levels of GLP-1 in both WT and GLP1R-KO mice; however, LL-GLP1 promoted improved glucose tolerance in WT but not in GLP1R-KO mice, indicating a requirement for the GLP-1 receptor. In mice on HFD and thus with impaired glucose tolerance, supplementation with LL-GLP1 versus LL-UK200 promoted a pronounced improvement in glucose tolerance together with increased insulin levels. Supplementation with LL-GLP1 versus LL-UK200 did not affect insulin tolerance but resulted in reduced expression of G6pc in both chow and HFD-fed mice. CONCLUSIONS: The L. lactis strain genetically modified to produce GLP-1 is capable of stimulating insulin secretion from islets and improving glucose tolerance in mice.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".