Natural Competence and the Evolution of DNA Uptake Specificity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many bacteria are naturally competent, able to actively transport environmental DNA fragments across their cell envelope and into their cytoplasm. Because incoming DNA fragments can recombine with and replace homologous segments of the chromosome, competence provides cells with a potent mechanism of horizontal gene transfer as well as access to the nutrients in extracellular DNA. This review starts with an introductory overview of competence and continues with a detailed consideration of the DNA uptake specificity of competent proteobacteria in the Pasteurellaceae and Neisseriaceae. Species in these distantly related families exhibit strong preferences for genomic DNA from close relatives, a self-specificity arising from the combined effects of biases in the uptake machinery and genomic overrepresentation of the sequences this machinery prefers. Other competent species tested lack obvious uptake bias or uptake sequences, suggesting that strong convergent evolutionary forces have acted on these two families. Recent results show that uptake sequences have multiple "dialects," with clades within each family preferring distinct sequence variants and having corresponding variants enriched in their genomes. Although the genomic consensus uptake sequences are 12 and 29 to 34 bp, uptake assays have found that only central cores of 3 to 4 bp, conserved across dialects, are crucial for uptake. The other bases, which differ between dialects, make weaker individual contributions but have important cooperative interactions. Together, these results make predictions about the mechanism of DNA uptake across the outer membrane, supporting a model for the evolutionary accumulation and stability of uptake sequences and suggesting that uptake biases may be more widespread than currently thought.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it