Chromosomal model for analysis of a long CTG/CAG tract stability in wild-type Escherichia coli and its nucleotide excision repair mutants
Bibliographic record
Abstract
Many human hereditary neurological diseases, including fragile X syndrome, myotonic dystrophy, and Friedreich's ataxia, are associated with expansions of the triplet repeat sequences (TRS) (CGG/CCG, CTG/CAG, and GAA/TTC) within or near specific genes. Mechanisms that mediate mutations of TRS include DNA replication, repair, and gene conversion and (or) recombination. The involvement of the repair systems in TRS instability was investigated in Escherichia coli on plasmid models, and the results showed that the deficiency of some nucleotide excision repair (NER) functions dramatically affects the stability of long CTG inserts. In such models in which there are tens or hundreds of plasmid molecules in each bacterial cell, repetitive sequences may interact between themselves and according to a recombination hypothesis, which may lead to expansions and deletions within such repeated tracts. Since one cannot control interaction between plasmids, it is also sometimes difficult to give precise interpretation of the results. Therefore, using modified lambda phage (lambdaInCh), we have constructed a chromosomal model to study the instability of trinucleotide repeat sequences in E. coli. We have shown that the stability of (CTG/CAG)68 tracts in the bacterial chromosome is influenced by mutations in NER genes in E. coli. The absence of the uvrC or uvrD gene products greatly enhances the instability of the TRS in the chromosome, whereas the lack of the functional UvrA or UvrB proteins causes substantial stabilization of (CTG/CAG) tracts.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".