The antimicrobial activity of the macrophage metabolite itaconate is synergistic with acidity
Bibliographic record
Abstract
Abstract The production of itaconate by macrophages was only discovered in 2011. A rapidly increasing number of studies have since revealed essential biological roles for itaconate, ranging from antimicrobial to immunomodulator. Itaconate has been estimated to reach low-millimolar concentrations in activated macrophages, including those within infected lungs and brains, whereas itaconate’s MIC towards several bacterial strains were measured to be in the low-to-mid-millimolar range, casting some doubts on the antibacterial role of itaconate in vivo . Several of these investigations, in particular those measuring MIC values of itaconate or itaconic acid, have however tended to ignore the high acidity of this small diacid (pKas 3.85 and 5.45), thereby potentially biasing the MIC measurements. We report herein that: 1) at high concentration, itaconic acid can significantly reduce the pH of growth media; 2) the antibacterial activity of itaconate increases in a synergistic manner with acidity; 3) this synergistic effect is not simply due to increased permeability of monoanionic itaconate; 4) considering that the MIC of itaconate is many fold lower under acidic conditions for all strains tested, itaconate may serve an antimicrobial role, particularly in acidic vesicles such as the phagolysosome; and 5) differential growth behavior in the presence of disodium itaconate versus itaconic acid may serve to rapidly screen bacterial strains for their ability to metabolize itaconate. Our results further support the hypothesis that inhibitors of itaconate degradation in bacteria may provide a new strategy to treat infections.
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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 | 0.000 |
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".