Malate initiates a proton-sensing pathway essential for pH regulation of inflammation
Bibliographic record
Abstract
Metabolites can double as a signaling modality that initiates physiological adaptations. Metabolism, a chemical language encoding biological information, has been recognized as a powerful principle directing inflammatory responses. Cytosolic pH is a regulator of inflammatory response in macrophages. Here, we found that L-malate exerts anti-inflammatory effect via BiP-IRF2BP2 signaling, which is a sensor of cytosolic pH in macrophages. First, L-malate, a TCA intermediate upregulated in pro-inflammatory macrophages, was identified as a potent anti-inflammatory metabolite through initial screening. Subsequent screening with DARTS and MS led to the isolation of L-malate-BiP binding. Further screening through protein‒protein interaction microarrays identified a L-malate-restrained coupling of BiP with IRF2BP2, a known anti-inflammatory protein. Interestingly, pH reduction, which promotes carboxyl protonation of L-malate, facilitates L-malate and carboxylate analogues such as succinate to bind BiP, and disrupt BiP-IRF2BP2 interaction in a carboxyl-dependent manner. Both L-malate and acidification inhibit BiP-IRF2BP2 interaction, and protect IRF2BP2 from BiP-driven degradation in macrophages. Furthermore, both in vitro and in vivo, BiP-IRF2BP2 signal is required for effects of both L-malate and pH on inflammatory responses. These findings reveal a previously unrecognized, proton/carboxylate dual sensing pathway wherein pH and L-malate regulate inflammatory responses, indicating the role of certain carboxylate metabolites as adaptors in the proton biosensing by interactions between macromolecules.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".