Quantification and Mapping of Alkylation in the Human Genome Reveal Single Nucleotide Resolution Precursors of Mutational Signatures
Bibliographic record
Abstract
Chemical modifications to DNA bases, including DNA adducts arising from reactions with electrophilic chemicals, are well-known to impact cell growth, miscode during replication, and influence disease etiology. However, knowledge of how genomic sequences and structures influence the accumulation of alkylated DNA bases is not broadly characterized with high resolution, nor have these patterns been linked with overall quantities of modified bases in the genome. For benzo(a) pyrene (BaP), a ubiquitous environmental carcinogen, we developed a single-nucleotide resolution damage sequencing method to map in a human lung cell line the main mutagenic adduct arising from BaP. Furthermore, we combined this analysis with quantitative mass spectrometry to evaluate the dose-response profile of adduct formation. By comparing damage abundance with DNase hypersensitive sites, transcription levels, and other genome annotation data, we found that although overall adduct levels rose with increasing chemical exposure concentration, genomic distribution patterns consistently correlated with chromatin state and transcriptional status. Moreover, due to the single nucleotide resolution characteristics of this DNA damage map, we could determine preferred DNA triad sequence contexts for alkylation accumulation, revealing a characteristic DNA damage signature. This new BaP damage signature had a profile highly similar to mutational signatures identified previously in lung cancer genomes from smokers. Thus, these data provide insight on how genomic features shape the accumulation of alkylation products in the genome and predictive strategies for linking single-nucleotide resolution in vitro damage maps with human cancer mutations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".