Single-molecule PCR analysis of an unstable microsatellite for detecting mutations in sperm of mice exposed to chemical mutagens
Bibliographic record
Abstract
Single-molecule PCR (SM-PCR) analysis of long and repetitive DNA sequences, known as expanded simple tandem repeats (ESTRs), has been the most efficient method for studying germline mutation induction in endogenous sequences to date. However, the long length of these sequences makes mutation detection imprecise and laborious, and they have been characterized only in mice. Here, we explore the use of unstable microsatellite sequences that can be typed with high precision by capillary electrophoresis as alternative loci for detecting germline mutations. We screened 24 microsatellite loci across inbred mouse strains and identified Mm2.2.1 as the most polymorphic microsatellite locus. We then optimized SM-PCR of Mm2.2.1 to detect mutations in sperm. SM-PCR analysis of sperm from untreated B6C3F1 and Muta(™)Mouse samples revealed mutation frequencies that are consistent with rates derived from family pedigree analysis (∼ 5 × 10(-3)). To determine whether this locus can be used to detect chemically induced germline mutations, Muta(™)Mouse males were exposed by oral gavage to a single dose of 100mg/kg of N-ethyl-N-nitrosourea (ENU) or to 100mg/kg of benzo(a)pyrene (BaP) for 28 days alongside vehicle treated controls. Sperm were collected 10 weeks post-ENU exposure to sample sperm exposed as spermatogonial stem cells and 6 weeks post-BaP exposure to sample sperm that were dividing spermatogonia when the exposure was terminated. Both treatments resulted in a significant (approximately 2-fold) increase in mutation frequency in sperm compared to the control animals. The work establishes the utility of this microsatellite for studying mutation induction in the germ cells of mice. Because microsatellites are found in virtually every species, this approach holds promise for other organisms, including humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".