Repeat Associated Non-ATG Translation Initiation: One DNA, Two Transcripts, Seven Reading Frames, Potentially Nine Toxic Entities!
Bibliographic record
Abstract
Diseases associated with unstable repetitive elements in the DNA, RNA, and amino acids have consistently revealed scientific surprises. Most diseases are caused by expansions of trinucleotide repeats, which ultimately lead to diseases like Huntington's disease, myotonic dystrophy, fragile X syndrome, and a series of spinocerebellar ataxias. These repeat mutations are dynamic, changing through generations and within an individual, and the repeats can be bi-directionally transcribed. Unsuspected modes of pathogenesis involve aberrant loss of protein expression; aberrant over-expression of non-mutant proteins; toxic-gain-of-protein function through expanded polyglutamine tracts that are encoded by expanded CAG tracts; and RNA-toxic-gain-of-function caused by transcripts harboring expanded CUG, CAG, or CGG tracts. A recent advance reveals that RNA transcripts with expanded CAG repeats can be translated in the complete absence of a starting ATG, and this Repeat Associated Non-ATG translation (RAN-translation) occurs across expanded CAG repeats in all reading frames (CAG, AGC, and GCA) to produce homopolymeric proteins of long polyglutamine, polyserine, and polyalanine tracts. Expanded CTG tracts expressing CUG transcripts also show RAN-translation occurring in all three frames (CUG, UGC, and GCU), to produce polyleucine, polycysteine, and polyalanine. These RAN-translation products can be toxic. Thus, one unstable (CAG)•(CTG) DNA can produce two expanded repeat transcripts and homopolymeric proteins with reading frames (the AUG-directed polyGln and six RAN-translation proteins), yielding a total of potentially nine toxic entities. The occurrence of RAN-translation in patient tissues expands our horizons of modes of disease pathogenesis. Moreover, since RAN-translation counters the canonical requirements of translation initiation, many new questions are now posed that must be addressed. This review covers RAN-translation and some of the pertinent questions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".