Silencing by H-NS Potentiated the Evolution of Salmonella
Bibliographic record
Abstract
The bacterial H-NS protein silences expression from sequences with higher AT-content than the host genome and is believed to buffer the fitness consequences associated with foreign gene acquisition. Loss of H-NS results in severe growth defects in Salmonella, but the underlying reasons were unclear. An experimental evolution approach was employed to determine which secondary mutations could compensate for the loss of H-NS in Salmonella. Six independently derived S. Typhimurium hns mutant strains were serially passaged for 300 generations prior to whole genome sequencing. Growth rates of all lineages dramatically improved during the course of the experiment. Each of the hns mutant lineages acquired missense mutations in the gene encoding the H-NS paralog StpA encoding a poorly understood H-NS paralog, while 5 of the mutant lineages acquired deletions in the genes encoding the Salmonella Pathogenicity Island-1 (SPI-1) Type 3 secretion system critical to invoke inflammation. We further demonstrate that SPI-1 misregulation is a primary contributor to the decreased fitness in Salmonella hns mutants. Three of the lineages acquired additional loss of function mutations in the PhoPQ virulence regulatory system. Similarly passaged wild type Salmonella lineages did not acquire these mutations. The stpA missense mutations arose in the oligomerization domain and generated proteins that could compensate for the loss of H-NS to varying degrees. StpA variants most able to functionally substitute for H-NS displayed altered DNA binding and oligomerization properties that resembled those of H-NS. These findings indicate that H-NS was central to the evolution of the Salmonellae by buffering the negative fitness consequences caused by the secretion system that is the defining characteristic of the species.
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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 | 0.000 | 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".