Epigenomic anomalies in induced pluripotent stem cells from Alzheimer’s disease cases
Bibliographic record
Abstract
SUMMARY Reprogramming of adult somatic cells into induced pluripotent stem cells (iPSCs) resets the aging clock. However, primed iPSCs can retain cell-of-origin epigenomic marks, especially those linked to heterochromatin and lamina-associated regions. Here we show that iPSCs produced from dermal fibroblasts of late-onset sporadic Alzheimer’s disease (AD) cases retain epigenomic anomalies that supersede developmental defects and neurodegeneration. When compared to iPSCs from elderly controls, AD iPSCs show reduced BMI1 expression, lower H3K9me3 levels, and an altered DNA methylome. Gene Ontology analysis of differentially methylated DNA regions (DMRs) reveals terms linked to cell-cell adhesion and synapse, with the cognitive resilience-associated MEF2 family of transcription factors being the most enriched at DMRs. Upon noggin exposure, AD iPSCs show lesser efficient neural induction and forebrain specification, together with increased ZIC2, ZIC5 and WNT-related gene expression. Long-term AD neuronal cultures present a dedifferentiation and loss-of-cell identity phenotype. Despite these epigenomic anomalies, AD iPSCs generate cortical neurons in normal proportion and readily form cerebral organoids developing amyloid and Tau pathology. BMI1 overexpression in AD neurons mitigates amyloid and tau accumulation, heterochromatin fragmentation, and G4 DNA induction. These findings implicate reprogramming resistant epigenomic anomalies or uncharacterized genetic alterations working in trans on the epigenome in AD pathophysiology.
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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 | 0.001 | 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".