Enhancement of microhomology-mediated genomic rearrangements by transient loss of mouse Bloom syndrome helicase
Bibliographic record
Abstract
Bloom syndrome, an autosomal recessive disorder of the BLM gene, confers predisposition to a broad spectrum of early-onset cancers in multiple tissue types. Loss of genomic integrity is a primary hallmark of such human malignancies, but many studies using disease-affected specimens are limited in that they are retrospective and devoid of an appropriate experimental control. To overcome this, we devised an experimental system to recapitulate the early molecular events in genetically engineered mouse embryonic stem cells, in which cells undergoing loss of heterozygosity (LOH) can be enriched after inducible down-regulation of Blm expression, with or without site-directed DNA double-strand break (DSB) induction. Transient loss of BLM increased the rate of LOH, whose breakpoints were distributed along the chromosome. Combined with site-directed DSB induction, loss of BLM synergistically increased the rate of LOH and concentrated the breakpoints around the targeted chromosomal region. We characterized the LOH events using specifically tailored genomic tools, such as high-resolution array comparative genomic hybridization and high-density single nucleotide polymorphism genotyping, revealing that the combination of BLM suppression and DSB induction enhanced genomic rearrangements, including deletions and insertions, whose breakpoints were clustered in genomic inverted repeats and associated with junctional microhomologies. Our experimental approach successfully uncovered the detailed molecular mechanisms of as-yet-uncharacterized loss of heterozygosities and reveals the significant contribution of microhomology-mediated genomic rearrangements, which could be widely applicable to the early steps of cancer formation in general.
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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) | 0.001 | 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".