Comparison of Francisella tularensis genomes reveals evolutionary events associated with the emergence of human pathogenic strains
Bibliographic record
Abstract
BACKGROUND: Francisella tularensis subspecies tularensis and holarctica are pathogenic to humans, whereas the two other subspecies, novicida and mediasiatica, rarely cause disease. To uncover the factors that allow subspecies tularensis and holarctica to be pathogenic to humans, we compared their genome sequences with the genome sequence of Francisella tularensis subspecies novicida U112, which is nonpathogenic to humans. RESULTS: Comparison of the genomes of human pathogenic Francisella strains with the genome of U112 identifies genes specific to the human pathogenic strains and reveals pseudogenes that previously were unidentified. In addition, this analysis provides a coarse chronology of the evolutionary events that took place during the emergence of the human pathogenic strains. Genomic rearrangements at the level of insertion sequences (IS elements), point mutations, and small indels took place in the human pathogenic strains during and after differentiation from the nonpathogenic strain, resulting in gene inactivation. CONCLUSION: The chronology of events suggests a substantial role for genetic drift in the formation of pseudogenes in Francisella genomes. Mutations that occurred early in the evolution, however, might have been fixed in the population either because of evolutionary bottlenecks or because they were pathoadaptive (beneficial in the context of infection). Because the structure of Francisella genomes is similar to that of the genomes of other emerging or highly pathogenic bacteria, this evolutionary scenario may be shared by pathogens from other species.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".