Understanding the function of PGC-1α Isoforms in Ã-cell survival and diabetes
Bibliographic record
Abstract
Peroxisome proliferator-activated receptor gamma (PPARγ) co-activator 1 alpha (PGC-1α) is a transcriptional co-activator responsible for mitochondrial biogenesis and oxidative metabolism. Many isoforms of PGC-1α have been described in the literature, most of which are shown to function similarly to the canonical PGC-1α protein. Recently, however, a novel isoform of PGC-1α was identified, PGC-1α4. It was shown to have a different, yet complementary function to canonical PGC-1α (PGC-1α1) in muscle. It is also expressed in other metabolically active tissues; however, it is unknown whether it has additional distinct tissue-specific functions. Furthermore, PGC-1α plays an important role in controlling metabolism in pancreatic β-cells and expression of the co-activator is decreased in diabetic islets; however, the role of PGC-1α isoforms in diabetes is unknown. Our objective is to determine whether PGC-1α4 has a unique function in β-cells and whether it plays a role in the pathogenesis of diabetes. We show that stimulation with forskolin, exendin-4 and a cytokine cocktail of TNFalpha, IL-1beta and IFNgamma, induced specific PGC-1α isoforms in β-cells. Following over-expression of these isoforms in INS-1 cells, PGC-1α4 prevented the cleavage of caspase-3 in response to cytokines, suggesting that the novel isoform is uniquely anti-apoptotic. To assess whether PGC-1α isoforms play a role in β-cell survival in vivo, mice with a β-cell specific PGC-1α knockout of all isoforms were subjected to low-dose streptozotocin (STZ) treatment to induce β-cell apoptosis. Unexpectedly, knockout mice were protected from STZ induced hyperglycemia. However, there was no difference in percentage of cleaved caspase-3 positive cells in control versus knockout mice, suggesting no difference in apoptosis. Therefore, PGC-1α4 could be a novel factor important for β-cell survival and over-expression of this unique isoform may protect against the pathogenesis of diabetes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".