Malate initiates a proton-sensing pathway essential for pH regulation of inflammation
Why this work is in the frame
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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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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 it