RNA-seq Brings New Insights to the Intra-Macrophage Transcriptome of Salmonella Typhimurium
Bibliographic record
Abstract
Salmonella enterica serovar Typhimurium is arguably the world's best-understood bacterial pathogen. However, crucial details about the genetic programs used by the bacterium to survive and replicate in macrophages have remained obscure because of the challenge of studying gene expression of intracellular pathogens during infection. Here, we report the use of deep sequencing (RNA-seq) to reveal the transcriptional architecture and gene activity of Salmonella during infection of murine macrophages, providing new insights into the strategies used by the pathogen to survive in a bactericidal immune cell. We characterized 3583 transcriptional start sites that are active within macrophages, and highlight 11 of these as candidates for the delivery of heterologous antigens from Salmonella vaccine strains. A majority (88%) of the 280 S. Typhimurium sRNAs were expressed inside macrophages, and SPI13 and SPI2 were the most highly expressed pathogenicity islands. We identified 31 S. Typhimurium genes that were strongly up-regulated inside macrophages but expressed at very low levels during in vitro growth. The SalComMac online resource allows the visualisation of every transcript expressed during bacterial replication within mammalian cells. This primary transcriptome of intra-macrophage S.-Typhimurium describes the transcriptional start sites and the transcripts responsible for virulence traits, and catalogues the sRNAs that may play a role in the regulation of gene expression during infection.
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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 | 0.001 |
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".