Induced mutagenesis by the DNA cytosine deaminase APOBEC3H Haplotype I protects against lung cancer
Bibliographic record
Abstract
Summary The DNA cytosine deaminases APOBEC3A, APOBEC3B, and APOBEC3H Haplotype I can induce mutations in cells that lead to cancer evolution. The database of cancer biomarkers in DNA repair genes (DNArCdb) identified a single nucleotide polymorphism (rs139298) of APOBEC3H Haplotype I that is involved in lung cancer 1 . Here, we show that this single nucleotide polymorphism causes the destabilization of APOBEC3H Haplotype I. Computational analysis suggests that the resulting K121E change affects the structure of APOBEC3H leading to active site disruption and destabilization of the RNA-mediated dimer interface. A K117E mutation in a K121E background stabilized the APOBEC3H Haplotype I, enabled biochemical study, and showed that the K121E affected catalytic activity, single-stranded DNA binding, and oligomerization on single-stranded DNA. That the destabilization of a DNA mutator would be associated with lung cancer suggests that too much mutation could result in immune recognition or death of tumor cells, suggesting that multiple APOBEC3s would not be expressed in the same tumor cells. In support of this hypothesis, stably expressed APOBEC3H Haplotype I caused a high amount of double-stranded DNA breaks in a lung cancer cell line that endogenously expressed APOBEC3B. Altogether, the data support the model that high APOBEC3 mutations in tumors are protective.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".