Bacterial DNA Uptake Sequences Can Accumulate by Molecular Drive Alone
Bibliographic record
Abstract
Uptake signal sequences are DNA motifs that promote DNA uptake by competent bacteria in the family Pasteurellaceae and the genus Neisseria. The genomes of these bacteria contain many copies of their canonical uptake sequence (often >100-fold overrepresentation), so the bias of the uptake machinery causes cells to prefer DNA derived from close relatives over DNA from other sources. However, the molecular and evolutionary forces responsible for the abundance of uptake sequences in these genomes are not well understood, and their presence is not easily explained by any of the current models of the evolution of competence. Here we describe use of a computer simulation model to thoroughly evaluate the simplest explanation for uptake sequences, that they accumulate in genomes by a form of molecular drive generated by biased DNA uptake and evolutionarily neutral (i.e., unselected) recombination. In parallel we used an unbiased search algorithm to characterize genomic uptake sequences and DNA uptake assays to refine the Haemophilus influenzae uptake specificity. These analyses showed that biased uptake and neutral recombination are sufficient to drive uptake sequences to high densities, with the spacings, stabilities, and strong consensus typical of uptake sequences in real genomes. This result greatly simplifies testing of hypotheses about the benefits of DNA uptake, because it explains how genomes could have passively accumulated sequences matching the bias of their uptake machineries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".