Thermophilic Bacteria Strictly Obey Szybalski's Transcription Direction Rule and Politely Purine-Load RNAs with Both Adenine and Guanine
Bibliographic record
Abstract
When transcription is to the right of the promoter, the "top," mRNA-synonymous strand of DNA tends to be purine-rich. When transcription is to the left of the promoter, the top, mRNA-template strand tends to be pyrimidine-rich. This transcription-direction rule suggests that there has been an evolutionary selection pressure for the purine-loading of RNAs. The politeness hypothesis states that purine-loading prevents distracting RNA-RNA interactions and excessive formation of double-stranded RNA, which might trigger various intracellular alarms. Because RNA-RNA interactions have a distinct entropy-driven component, the pressure for the evolution of purine-loading might be greater in organisms living at high temperatures. In support of this, we find that Chargaff differences (a measure of purine-loading) are greater in thermophiles than in nonthermophiles and extend to both purine bases. In thermophiles the pressure to purine-load affects codon choice, indicating that some features of their amino acid composition (e.g., high levels of glutamic acid) might reflect purine-loading pressure (i.e., constraints on mRNA) rather than direct constraints on protein structure and function.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".