Codon usage bias affects α-amylase mRNA level by altering RNA stability and cytosine methylation patterns in <i>Escherichia coli</i>
Bibliographic record
Abstract
Codon usage bias exists in almost every organism and is reported to regulate protein translation efficiency and folding. Besides translation, the preliminary role of codon usage bias on gene transcription has also been revealed in some eukaryotes such as Neurospora crassa. In this study, we took as an example the α-amylase-coding gene (amyA) and examined the role of codon usage bias in regulating gene expression in the typical prokaryote Escherichia coli. We confirmed the higher translation efficiency on codon-optimized amyA RNAs and found that the RNA level itself was also affected by codon optimization. The decreased RNA level was caused at least in part by altered mRNA stability at the post-transcriptional level. Codon optimization also altered the number of cytosine methylation sites. Examination on dcm knockouts suggested that cytosine methylation may be a minor mechanism adopted by codon bias to regulate gene RNA levels. More studies are required to verify the global effect of codon usage and to reveal its detailed mechanism on transcription.
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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".