Lead Exposure Disrupts Global DNA Methylation in Human Embryonic Stem Cells and Alters Their Neuronal Differentiation
Bibliographic record
Abstract
Exposure to lead (Pb) during childhood can result in learning disabilities and behavioral problems. Although described in animal models, whether Pb exposure also alters neuronal differentiation in the developing brains of exposed children is unknown. Here, we investigated the effects of physiologically relevant concentrations of Pb (from 0.4 to 1.9μM) on the capacity of human embryonic stem cells (hESCs) to progress to a neuronal fate. We found that neither acute nor chronic exposure to Pb prevented hESCs from generating neural progenitor cells (NPCs). NPCs derived from hESCs chronically exposed to 1.9μM Pb throughout the neural differentiation process generated 2.5 times more TUJ1-positive neurons than those derived from control hESCs. Pb exposure of hESCs during the stage of neural rosette formation resulted in a significant decrease in the expression levels of the neural marker genes PAX6 and MSI1. Furthermore, the resulting NPCs differentiated into neurons with shorter neurites and less branching than control neurons, as assessed by Sholl analysis. DNA methylation studies of control, acutely treated hESCs and NPCs derived from chronically exposed hESCs using the Illumina HumanMethylation450 BeadChip demonstrated that Pb exposure induced changes in the methylation status of genes involved in neurogenetic signaling pathways. In summary, our study shows that exposure to Pb subtly alters the neuronal differentiation of exposed hESCs and that these changes could be partly mediated by modifications in the DNA methylation status of genes crucial to brain development.
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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 | 0.000 | 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".