Differential Codon Adaptation between dsDNA and ssDNA Phages in Escherichia coli
Bibliographic record
Abstract
Because phages use their host translation machinery, their codon usage should evolve toward that of highly expressed host genes. We used two indices to measure codon adaptation of phages to their host, rRSCU (the correlation in relative synonymous codon usage [RSCU] between phages and their host) and Codon Adaptation Index (CAI) computed with highly expressed host genes as the reference set (because phage translation depends on host translation machinery). These indices used for this purpose are appropriate only when hosts exhibit little mutation bias, so only phages parasitizing Escherichia coli were included in the analysis. For double-stranded DNA (dsDNA) phages, both r(RSCU) and CAI decrease with increasing number of transfer RNA genes encoded by the phage genome. r(RSCU) is greater for dsDNA phages than for single-stranded DNA (ssDNA) phages, and the low r(RSCU) values are mainly due to poor concordance in RSCU values for Y-ending codons between ssDNA phages and the E. coli host, consistent with the predicted effect of C→T mutation bias in the ssDNA phages. Strong C→T mutation bias would improve codon adaptation in codon families (e.g., Gly) where U-ending codons are favored over C-ending codons ("U-friendly" codon families) by highly expressed host genes but decrease codon adaptation in other codon families where highly expressed host genes favor C-ending codons against U-ending codons ("U-hostile" codon families). It is remarkable that ssDNA phages with increasing C→T mutation bias also increased the usage of codons in the "U-friendly" codon families, thereby achieving CAI values almost as large as those of dsDNA phages. This represents a new type of codon adaptation.
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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 | 0.001 |
| 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 | 0.001 | 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 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".