Chlortetracycline and florfenicol induce expression of genes associated with pathogenicity in multidrug-resistant Salmonella enterica serovar Typhimurium
Bibliographic record
Abstract
Multidrug-resistant (MDR) Salmonella enterica serovar Typhimurium ( S . Typhimurium) is a serious public health threat as infections caused by these strains are more difficult and expensive to treat. Livestock serve as a reservoir for MDR Salmonella , and the antibiotics chlortetracycline and florfenicol are frequently administrated to food-producing animals to treat and prevent various diseases. Therefore, we evaluated the response of MDR S . Typhimurium after exposure to these two antibiotics. We exposed four MDR S . Typhimurium isolates to sub-inhibitory concentrations of chlortetracycline (16 and 32 µg/ml) or florfenicol (16 µg/ml) for 30 min during early-log phase. Differentially expressed genes following antibiotic treatment were identified using RNA-seq, and genes associated with attachment and those located within the Salmonella pathogenicity islands were significantly up-regulated following exposure to either antibiotic. The effect of antibiotic exposure on cellular invasion and motility was also assessed. Swimming and swarming motility were decreased due to antibiotic exposure. However, we observed chlortetracycline enhanced cellular invasion in two strains and florfenicol enhanced invasion in a third isolate. Chlortetracycline and florfenicol exposure during early-log growth altered the expression of nearly half of the genes in the S . Typhimurium genome, including a large number of genes associated with virulence and pathogenesis; this transcriptional alteration was not due to the SOS response. The results suggest that exposure to either of these two antibiotics may lead to the expression of virulence genes that are typically only transcribed in vivo, as well as only during late-log or stationary phase in vitro.
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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".