A transposon-derived small RNA regulates gene expression in Salmonella Typhimurium
Bibliographic record
Abstract
Bacterial sRNAs play an important role in regulating many cellular processes including metabolism, outer membrane homeostasis and virulence. Although sRNAs were initially found in intergenic regions, there is emerging evidence that protein coding regions of the genome are a rich reservoir of sRNAs. Here we report that the 5΄UTR of IS200 transposase mRNA (tnpA) is processed to produce regulatory RNAs that affect expression of over 70 genes in Salmonella Typhimurium. We provide evidence that the tnpA derived sRNA base-pairs with invF mRNA to repress expression. As InvF is a transcriptional activator of SPI-1 encoded and other effector proteins, tnpA indirectly represses these genes. We show that deletion of IS200 elements in S. Typhimurium increases invasion in vitro and reduces growth rate, while over-expression of tnpA suppresses invasion. Our work indicates that tnpA acts as an sRNA 'sponge' that sets a threshold for activation of Salmonella pathogenicity island (SPI)-1 effector proteins and identifies a new class of 'passenger gene' for bacterial transposons, providing the first example of a bacterial transposon producing a regulatory RNA that controls host gene expression.
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".