Evolution of Salmonella-Host Cell Interactions through a Dynamic Bacterial Genome
Bibliographic record
Abstract
Salmonella Typhimurium has a broad arsenal of genes that are tightly regulated and coordinated to facilitate adaptation to the various host environments it colonizes. The genome of Salmonella Typhimurium has undergone multiple gene acquisition events and has accrued changes in non-coding DNA that have undergone selection by regulatory evolution. Together, at least 17 horizontally acquired pathogenicity islands (SPIs), prophage-associated genes, and changes in core genome regulation contribute to the virulence program of Salmonella. Here, we review the latest understanding of these elements and their contributions to pathogenesis, emphasizing the regulatory circuitry that controls niche-specific gene expression. In addition to an overview of the importance of SPI-1 and SPI-2 to host invasion and colonization, we describe the recently characterized contributions of other SPIs, including the antibacterial activity of SPI-6 and adhesion and invasion mediated by SPI-4. We further discuss how these fitness traits have been integrated into the regulatory circuitry of the bacterial cell through cis-regulatory evolution and by a careful balance of silencing and counter-silencing by regulatory proteins. Detailed understanding of regulatory evolution within Salmonella is uncovering novel aspects of infection biology that relate to host-pathogen interactions and evasion of host immunity.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".