Evolution of <i>Salmonella</i> Chromosomes and Its Influence on Gene Expression and Chromosomal Conformation
Bibliographic record
Abstract
Abstract Salmonella is one of the most important bacterial pathogens in the world, causing an estimated 120 million infections each year. There is considerable diversity, with ∼2,600 serovars, but it remains unclear how the genomes have evolved as the species and subspecies of Salmonella differentiate. Here, we have reconstructed the ancient orthologous chromosomes of each major Salmonella lineage and traced their evolutionary process. In total, 911 rearrangement events were identified, with 64% of events occurring in a locus-specific way. Using RNA sequencing and multi-strain association analysis, we demonstrate that genetic rearrangements have a significant effect on gene expression across Salmonella lineages. Moreover, we perform chromosome conformation capture (3C) sequencing analysis, which demonstrates large variations for the organization of ter chromosomal interaction domains among Salmonella lineages. In conclusion, our work delineates the evolutionary trajectory of Salmonella chromosomes, and demonstrates the influence of rearrangements on gene expression profiles and chromosomal conformation. Importance This study reconstructed the ancient orthologous chromosomes of the Salmonella genus, species and well-recognized subspecies, by which the trajectory of gene flow caused by genetic rearrangements was delineated chronologically. The rearrangements show apparent ‘hotspot’ distribution property and are enriched with genes important for bacterial fitness. Correlation analysis further disclosed the general influence of the rearrangements on gene expression and the organization and conformation of chromosome interaction domains. The results provide new insights on the evolution of Salmonella chromosomes, especially the genetic rearrangements and their epigenetic consequences.
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 | 0.000 |
| 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 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".