Tracing the Evolution of Competence in Haemophilus influenzae
Bibliographic record
Abstract
Natural competence is the genetically encoded ability of some bacteria to take up DNA from the environment. Although most of the incoming DNA is degraded, occasionally intact homologous fragments can recombine with the chromosome, displacing one resident strand. This potential to use DNA as a source of both nutrients and genetic novelty has important implications for the ecology and evolution of competent bacteria. However, it is not known how frequently competence changes during evolution, or whether non-competent strains can persist for long periods of time. We have previously studied competence in H. influenzae and found that both the amount of DNA taken up and the amount recombined varies extensively between different strains. In addition, several strains are unable to become competent, suggesting that competence has been lost at least once. To investigate how many times competence has increased or decreased during the divergence of these strains, we inferred the evolutionary relationships of strains using the largest datasets currently available. However, despite the use of three datasets and multiple inference methods, few nodes were resolved with high support, perhaps due to extensive mixing by recombination. Tracing the evolution of competence in those clades that were well supported identified changes in DNA uptake and/or transformation in most strains. The recency of these events suggests that competence has changed frequently during evolution but the poor support of basal relationships precludes the determination of whether non-competent strains can persist for long periods of time. In some strains, changes in transformation have occurred that cannot be due to changes in DNA uptake, suggesting that selection can act on transformation independent of DNA uptake.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".