CTG/CAG Repeat Instability Is Modulated by the Levels of Human DNA Ligase I and Its Interaction with Proliferating Cell Nuclear Antigen
Bibliographic record
Abstract
Mechanisms contributing to disease-associated trinucleotide repeat instability are poorly understood. DNA ligation is an essential step common to replication and repair, both potential sources of repeat instability. Using derivatives of DNA ligase I (hLigI)-deficient human cells (46BR.1G1), we assessed the effect of hLigI activity, overexpression, and its interaction with proliferating cell nuclear antigen (PCNA) upon the ability to replicate and repair trinucleotide repeats. Compared with LigI(+/+), replication progression through repeats was poor, and repair tracts were broadened beyond the slipped-repeat for all mutant extracts. Increased repeat instability was linked only to hLigI overexpression and expression of a mutant hLigI incapable of interacting with PCNA. The endogenous mutant version of hLigI with reduced ligation activity did not alter instability. We distinguished the DNA processes through which hLigI contributes to trinucleotide instability. The highest levels of repeat instability were observed under the hLigI overexpression and were linked to reduced slipped-DNAs repair efficiencies. Therefore, the replication-mediated instability can partly be attributed to errors during replication but also to the poor repair of slipped-DNAs formed during this process. However, repair efficiencies were unaffected by expression of a PCNA interaction mutant of hLigI, limiting this instability to the replication process. The addition of purified proteins suggests that disruption of LigI and PCNA interactions influences trinucleotide repeat instability. The variable levels of age- and tissue-specific trinucleotide repeat instability observed in myotonic dystrophy patients and transgenic mice may be influenced by varying steady state levels of DNA ligase I in these tissues and during different developmental windows.
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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 | 0.000 |
| 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".