Lead exposure induces changes in 5-hydroxymethylcytosine clusters in CpG islands in human embryonic stem cells and umbilical cord blood
Bibliographic record
Abstract
Prenatal exposure to neurotoxicants such as lead (Pb) may cause stable changes in the DNA methylation (5mC) profile of the fetal genome. However, few studies have examined its effect on the DNA de-methylation pathway, specifically the dynamic changes of the 5-hydroxymethylcytosine (5hmC) profile. Therefore, in this study, we investigate the relationship between Pb exposure and 5mC and 5hmC modifications during early development. To study the changes in the 5hmC profile, we use a novel modification of the Infinium™ HumanMethylation450 assay (Illumina, Inc.), which we named HMeDIP-450K assay, in an in vitro human embryonic stem cell model of Pb exposure. We model Pb exposure-associated 5hmC changes as clusters of correlated, adjacent CpG sites, which are co-responding to Pb. We further extend our study to look at Pb-dependent changes in high density 5hmC regions in umbilical cord blood DNA from 48 mother-infant pairs from the Early Life Exposure in Mexico to Environmental Toxicants (ELEMENT) cohort. For our study, we randomly selected umbilical cord blood from 24 male and 24 female children from the 1st and 4th quartiles of Pb levels. Our data show that Pb-associated changes in the 5hmC and 5mC profiles can be divided into sex-dependent and sex-independent categories. Interestingly, differential 5mC sites are better markers of Pb-associated sex-dependent changes compared to differential 5hmC sites. In this study we identified several 5hmC and 5mC genomic loci, which we believe might have some potential as early biomarkers of prenatal Pb exposure.
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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".