Characterizing Benzo[a]pyrene-induced lacZ mutation spectrum in transgenic mice using next-generation sequencing
Bibliographic record
Abstract
BACKGROUND: The transgenic rodent mutation reporter assay provides an efficient approach to identify mutagenic agents in vivo. A major advantage of this assay is that mutant reporter transgenes can be sequenced to provide information on the mode of action of a mutagen and to identify clonally expanded mutations. However, conventional DNA sequence analysis is laborious and expensive for long transgenes, such as lacZ (3096 bp), and is not normally implemented in routine screening. METHODS: We developed a high-throughput next-generation sequencing (NGS) approach to simultaneously sequence large numbers of barcoded mutant lacZ transgenes from different animals. We collected 3872 mutants derived from the bone marrow DNA of six Muta™Mouse males exposed to the well-established mutagen benzo[a]pyrene (BaP) and six solvent-exposed controls. Mutants within animal samples were pooled, barcoded, and then sequenced using NGS. RESULTS: We identified 1652 mutant sequences from 1006 independent mutations that underwent clonal expansion. This deep sequencing analysis of mutation spectrum demonstrated that BaP causes primarily guanine transversions (e.g. G:C → T:A), which is highly consistent with previous studies employing Sanger sequencing. Furthermore, we identified novel mutational hotspots in the lacZ transgene that were previously uncharacterized by Sanger sequencing. Deep sequencing also allowed for an unprecedented ability to correct for clonal expansion events, improving the sensitivity of the mutation reporter assay by 50 %. CONCLUSION: These results demonstrate that the high-throughput nature and reduced costs offered by NGS provide a sensitive and fast approach for elucidating and comparing mutagenic mechanisms of various agents among tissues and enabling improved evaluation of genotoxins.
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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 | 0.001 |
| 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".