Lipopolysaccharides Impair Insulin Gene Expression in Isolated Islets of Langerhans via Toll-Like Receptor-4 and NF-κB Signalling
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes is characterized by pancreatic β-cell dysfunction and is associated with low-grade inflammation. Recent observations suggest that the signalling cascade activated by lipopolysaccharides (LPS) binding to Toll-Like Receptor 4 (TLR4) exerts deleterious effects on pancreatic β-cell function; however, the molecular mechanisms of these effects are incompletely understood. In this study, we tested the hypothesis that LPS alters insulin gene expression via TLR4 and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) in islets. METHODOLOGY/PRINCIPAL FINDINGS: A 24-h exposure of isolated human, rat and mouse islets of Langerhans to LPS dose-dependently reduced insulin gene expression. This was associated in mouse and rat islets with decreased mRNA expression of pancreas-duodenum homebox-1 (PDX-1) and mammalian homologue of avian MafA/l-Maf (MafA). Accordingly, LPS exposure also decreased glucose-induced insulin secretion. LPS repression of insulin, PDX-1 and MafA expression, as well as its inhibition of insulin secretion, were not observed in islets from TLR4-deficient mice. LPS inhibition of β-cell gene expression in rat islets was prevented by inhibition of the NF-κB pathway, but not the p38 mitogen-activated protein kinase (p38 MAPK) pathway. CONCLUSIONS/SIGNIFICANCE: Our findings demonstrate that LPS inhibit β-cell gene expression in a TLR4-dependent manner and via NF-κB signaling in pancreatic islets, suggesting a novel mechanism by which the gut microbiota might affect pancreatic β-cell function.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".