Single-Cell RNA Sequencing Reveals a Role for Reactive Oxygen Species and Peroxiredoxins in Fatty Acid–Induced Rat β-Cell Proliferation
Bibliographic record
Abstract
The functional mass of insulin-secreting pancreatic β-cells expands to maintain glucose homeostasis in the face of nutrient excess, in part via replication of existing β-cells. Type 2 diabetes appears when these compensatory mechanisms fail. Nutrients including glucose and fatty acids are important contributors to the β-cell compensatory response, but their underlying mechanisms of action remain poorly understood. We investigated the transcriptional mechanisms of β-cell proliferation in response to fatty acids. Isolated rat islets were exposed to 16.7 mmol/L glucose with or without 0.5 mmol/L oleate (C18:1) or palmitate (C16:0) for 48 h. The islet transcriptome was assessed by single-cell RNA sequencing. β-Cell proliferation was measured by flow cytometry. Unsupervised clustering of pooled β-cells identified different subclusters, including proliferating β-cells. β-Cell proliferation increased in response to oleate but not palmitate. Both fatty acids enhanced the expression of genes involved in energy metabolism and mitochondrial activity. Comparison of proliferating versus nonproliferating β-cells and pseudotime ordering suggested the involvement of reactive oxygen species (ROS) and peroxiredoxin signaling. Accordingly, N-acetyl cysteine and the peroxiredoxin inhibitor conoidin A both blocked oleate-induced β-cell proliferation. Our study reveals a key role for ROS signaling through peroxiredoxin activation in oleate-induced β-cell proliferation.
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) | 0.000 | 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".