Effect of pH on the antimicrobial activity of the macrophage metabolite itaconate
Bibliographic record
Abstract
The production of itaconate by macrophages was only discovered in 2011. An increasing number of studies have since revealed essential biological functions for this small molecule, ranging from antimicrobial to immunomodulator. The antibacterial role of itaconate has however been questioned because the estimated concentration of itaconate in macrophages (low-millimolar) is lower than the minimum inhibitory concentration (MIC) of itaconate reported for several bacterial strains (low-to-mid-millimolar). We note that some of these investigations have tended to ignore the high acidity of this small diacid (pKas 3.85 and 5.45), thereby potentially biassing activity measurements. We measured the MIC of itaconate in Escherichia coli (not known to metabolize itaconate) and in Salmonella enterica serovar Typhimurium (known to metabolize itaconate) at varying pH values to probe the effect that pH has on itaconate toxicity. Herein, we demonstrate that the antimicrobial effect of itaconate is dependent upon the pH of the media and that itaconate does have antimicrobial activity at biologically relevant pH and concentrations. Under nutrient-poor conditions, the antimicrobial activity of itaconate in both E. coli and S . Typhimurium increased approximately 200-fold when the pH was dropped by one unit, whereas itaconate was not found to be toxic under nutrient rich conditions. Our results also reveal that the activity of itaconate is synergistic with acidity, yet is not a function of increased permeability with protonation. Similar experiments performed with succinate (a pKa-matched diacid) yielded drastically different results, consistent with a target-based mechanism of action for itaconate. Overall, our work shows the importance of controlling the pH when performing experiments with itaconic acid.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".