Transformation of Natural Genetic Variation into Haemophilus Influenzae Genomes
Bibliographic record
Abstract
Many bacteria are able to efficiently bind and take up double-stranded DNA fragments, and the resulting natural transformation shapes bacterial genomes, transmits antibiotic resistance, and allows escape from immune surveillance. The genomes of many competent pathogens show evidence of extensive historical recombination between lineages, but the actual recombination events have not been well characterized. We used DNA from a clinical isolate of Haemophilus influenzae to transform competent cells of a laboratory strain. To identify which of the ~40,000 polymorphic differences had recombined into the genomes of four transformed clones, their genomes and their donor and recipient parents were deep sequenced to high coverage. Each clone was found to contain ~1000 donor polymorphisms in 3-6 contiguous runs (8.1±4.5 kb in length) that collectively comprised ~1-3% of each transformed chromosome. Seven donor-specific insertions and deletions were also acquired as parts of larger donor segments, but the presence of other structural variation flanking 12 of 32 recombination breakpoints suggested that these often disrupt the progress of recombination events. This is the first genome-wide analysis of chromosomes directly transformed with DNA from a divergent genotype, connecting experimental studies of transformation with the high levels of natural genetic variation found in isolates of the same species.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".