The <i>Salmonella</i> LysR family regulator, RipR, activates the SPI-13 encoded itaconate degradation cluster
Bibliographic record
Abstract
Abstract Itaconate is a dicarboxylic acid that inhibits the isocitrate lyase enzyme of the bacterial glyoxylate shunt. Activated macrophages have been shown to produce itaconate, suggesting that these immune cells may employ this metabolite as a weapon against invading bacteria. Here we demonstrate that, in vitro , itaconate can exhibit bactericidal effects under acidic conditions similar to the pH of a macrophage phagosome. In parallel, successful pathogens including Salmonella have acquired a genetic operon encoding itaconate degradation proteins, which are induced heavily in macrophages. We characterize the regulation of this operon by the neighbouring gene, ripR , in specific response to itaconate. Moreover, we develop an itaconate biosensor based on the operon promoter that can detect itaconate in a semi-quantitative manner and, when combined with the ripR gene, is sufficient for itaconate-regulated expression in E. coli . Using this biosensor with fluorescence microscopy, we observe bacteria responding to itaconate in the phagosomes of macrophage and provide additional evidence that interferon-γ stimulates macrophage itaconate synthesis and that J774 mouse macrophages produce substantially more itaconate than the human THP-1 monocyte cell line. In summary, we examine the role of itaconate as an antibacterial metabolite in mouse and human macrophage, characterize the regulation of Salmonella ’s defense against it, and develop it as a convenient itaconate biosensor and inducible promoter system. Importance In response to invading bacteria, immune cells can produce a molecule called itaconate, which can inhibit microbial metabolism. Here we show that itaconate can also directly kill Salmonella when combined with moderate acidity, further supporting itaconate’s role as an antibacterial weapon. We also discover how Salmonella recognizes itaconate and activates a defense to degrade it, and we harness this response to make a biosensor that detects the presence of itaconate. This biosensor is versatile, working in Salmonella enterica or lab strains of Escherichia coli , and can detect itaconate quantitatively in the environment and in immune cells. By understanding how immune cells kill bacteria and how the microbes defend themselves, we can better develop novel antibiotics to inhibit pathogens such as Salmonella .
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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) | 0.002 | 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".