Rolling Circles as a Means of Encoding Genes in the RNA World
Bibliographic record
Abstract
The rolling circle mechanism found in viroids and some RNA viruses is a likely way that replication could have begun in the RNA World. Here, we consider simulations of populations of protocells, each containing multiple copies of rolling circle RNAs that can replicate non-enzymatically. The mechanism requires the presence of short self-cleaving ribozymes such as hammerheads, which can cleave and re-circularize RNA strands. A rolling circle must encode a hammerhead and the complement of a hammerhead, so that both plus and minus strands can cleave. Thus, the minimal functional length is twice the length of the hammerhead sequence. Selection for speed of replication will tend to reduce circles to this minimum length. However, if sequence errors occur when copying the hammerhead sequence, this prevents cleavage at one point, but still allows cleavage on the next passage around the rolling circle. Thus, there is a natural doubling mechanism that creates strands that are multiple times the length of the minimal sequence. This can provide space for the origin of new genes with beneficial functions. We show that if a beneficial gene appears in this new space, the longer sequence with the beneficial function can be selected, even though it replicates more slowly. This provides a route for the evolution of longer circles encoding multiple genes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".