Ex vivo transcriptional profiling of human pancreatic islets following chronic exposure to monounsaturated fatty acids
Bibliographic record
Abstract
The aim of this study was to assess the effects of chronic free fatty acid (FFA) exposure on gene expression and the functional state of human pancreatic islets. Chronic exposure of islets to oleate (OA) resulted in a significant reduction in glucose-stimulated insulin secretion (GSIS) compared with control (466+/-82 vs 234+/-57 ng/microg DNA, P<0.05). OA treatment also led to reduction in total insulin content of the islets (17 609+/-3816 vs 10 599+/-3876 ng insulin/microg DNA) and to an increase in the rate of reactive oxygen species (ROS) generation. Interestingly, the suppressive effects of OA on biosynthesis and secretion of insulin were accompanied by alteration in the expression of 40 genes, as determined by microarray analysis and subsequent qPCR validation. The majority of genes regulated by OA encoded metabolic enzymes. The expression of enzymes involved in oxidative defense was elevated, indicating a link between ROS generation and antioxidant defense activation. Additionally, pretreatment of human islets with OA led to a significant increase (30%) in the rate of oxidation of this fatty acid and to a significant decrease (75%) in glucose oxidation. Importantly, individual analysis of gene clusters from the islets of all donors revealed the induction of genes involved in inflammation and immunity, which provides further evidence that FFA are risk factors for the development of type 2 diabetes. In summary, our data indicate that chronic exposure of human islets to FFA activates inflammatory and metabolic pathways that lead to oxidative stress, reduced beta-cell insulin content, and inhibition of GSIS.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".