Limitations in PPARα-dependent mitochondrial programming restrain the differentiation of human stem cell-derived β cells
Bibliographic record
Abstract
Pluripotent stem cell (SC)-derived islets offer hope as a renewable source for β cell replacement for type 1 diabetes (T1D), yet functional and metabolic immaturity may limit their long-term therapeutic potential. Here, we show that limitations in mitochondrial transcriptional programming impede the formation of SC-derived β (SC-β) cells. Utilizing transcriptomic profiling, assessments of chromatin accessibility, mitochondrial phenotyping, and lipidomics analyses, we observe that SC-β cells exhibit reduced oxidative and mitochondrial fatty acid metabolism compared to primary human islets that are related to limitations in key mitochondrial transcriptional networks. Surprisingly, we find that reductions in glucose-stimulated mitochondrial respiration in SC-islets were not associated with alterations in mitochondrial mass, structure, or genome integrity. In contrast, SC-islets show limited expression of targets of PPARα, which regulate mitochondrial programming, yet whose functions in β cell differentiation are unknown. Importantly, treatment with WY14643, a potent PPARα agonist, induces expression of mitochondrial targets, improves insulin secretion, and increases the formation of SC-β cells both in vitro and following transplantation. Thus, PPARα-dependent mitochondrial programming promotes the differentiation of SC-β cells and may be a promising target to improve β cell replacement efforts for T1D.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".