Reprogramming progeria fibroblasts re‐establishes a normal epigenetic landscape
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ideally, disease modeling using patient-derived induced pluripotent stem cells (iPSCs) enables analysis of disease initiation and progression. This requires any pathological features of the patient cells used for reprogramming to be eliminated during iPSC generation. Hutchinson-Gilford progeria syndrome (HGPS) is a segmental premature aging disorder caused by the accumulation of the truncated form of Lamin A known as Progerin within the nuclear lamina. Cellular hallmarks of HGPS include nuclear blebbing, loss of peripheral heterochromatin, defective epigenetic inheritance, altered gene expression, and senescence. To model HGPS using iPSCs, detailed genome-wide and structural analysis of the epigenetic landscape is required to assess the initiation and progression of the disease. We generated a library of iPSC lines from fibroblasts of patients with HGPS and controls, including one family trio. HGPS patient-derived iPSCs are nearly indistinguishable from controls in terms of pluripotency, nuclear membrane integrity, as well as transcriptional and epigenetic profiles, and can differentiate into affected cell lineages recapitulating disease progression, despite the nuclear aberrations, altered gene expression, and epigenetic landscape inherent to the donor fibroblasts. These analyses demonstrate the power of iPSC reprogramming to reset the epigenetic landscape to a revitalized pluripotent state in the face of widespread epigenetic defects, validating their use to model the initiation and progression of disease in affected cell lineages.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it