Itaconate: an emerging determinant of inflammation in activated macrophages
Bibliographic record
Abstract
Macrophages play a central role in innate immunity as the first line of defense against pathogen infection. Upon exposure to inflammatory stimuli, macrophages rapidly respond and subsequently undergo metabolic reprogramming to substantially produce cellular metabolites such as itaconate. As a derivate of the tricarboxylic acid cycle, itaconate is derived from the decarboxylation of cis-aconitate mediated by immunoresponsive gene 1 in the mitochondrial matrix. It is well known that itaconate has a direct antimicrobial effect by inhibiting isocitrate lyase. Strikingly, two recent studies published in Nature showed that itaconate markedly decreases the production of proinflammatory mediators in lipopolysaccharide-treated macrophages and ameliorates sepsis and psoriasis in animal models, revealing a novel biological action of itaconate beyond its regular roles in antimicrobial defense. The mechanism for this anti-inflammatory effect has been proposed to involve the inhibition of succinate dehydrogenase, blockade of IκBζ translation and activation of Nrf2. These intriguing discoveries provide a new explanation for how macrophages are switched from a pro- to an anti-inflammatory state to limit the damage and facilitate tissue repair under proinflammatory conditions. Thus, the emerging effect of itaconate as a crucial determinant of macrophage inflammation has important implications in further understanding cellular immunometabolism and developing future therapeutics for the treatment of inflammatory diseases. In this review, we focus on the roles of itaconate in controlling the inflammatory response during macrophage activation, providing a rationale for future investigation and therapeutic intervention.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".