Metabolic and genomic adaptations of <i>Salmonella</i> Typhimurium grown on itaconate
Bibliographic record
Abstract
Abstract Salmonella enterica ser. Typhimurium (STm) is an enteric pathogen that causes almost 100 million cases of salmonellosis a year. A hallmark of STm is their ability to survive and replicate in the macrophages that phagocytose them. Inside the Salmonella -containing vesicles (SCV) STm is exposed to nutrient limitation and antimicrobial molecules, including itaconate, a dicarboxylate produced at millimolar concentrations by activated macrophages. STm has an itaconate degradation pathway that allows it to detoxify itaconate and convert it into pyruvate and acetyl-CoA. In the SCV, STm undergoes extensive metabolic reprogramming in response to the nutrient limitation and high stress environment, and strict catabolite control is employed at various stages of infection, depending on carbon source availability. The extent to which itaconate is used as a carbon source by STm is not currently well understood. Here, we explore the ability of STm to grow in media containing itaconate as the sole carbon source, identify the genes of the itaconate response operon necessary for itaconate degradation, and confirm their importance during infection of macrophages. Growth on itaconate was substantially slower than in carbon-rich or acetate media, with multiple subcultures allowing for increased growth rates. Genomic analysis suggests that mutations in the RpoS-stress response and increased expression of the DctA transporter improve growth on itaconate. Using metabolomics, it was revealed that growth on itaconate resulted in substantially reduced levels of gluconeogenesis compared to growth on acetate and increased glutathione disulfide levels, indicating a high level of oxidative stress. Overall, our results point towards the STm itaconate degradation pathway having a more important role in detoxifying itaconate, rather than using it as a carbon source inside the macrophage. Additionally, this study establishes media conditions that would enable the high throughput discovery of potential inhibitors of itaconate degradation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".