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<p>Macrophages function as immune sentinels that distinguish diverse threats and mount appropriate responses. This stimulus-response specificity (SRS) is partly encoded in the dynamics of the NFκB transcription factor. While most studies examine single ligands, physiological exposures involve complex multi-ligand mixtures. Using a mathematical model that captures heterogeneous single-cell NFκB responses, we generated simulation datasets for mixtures of up to five ligands and validated key predictions with live-cell microscopy. Following iterative refinement of the model, we quantified SRS with Wasserstein distance and machine learning classification and found that NFκB temporal coding can partially convey the presence of specific ligands within mixtures. By generating simulation datasets across all ligand pairs at doses spanning the full responsiveness range, we found several cases of synergy and antagonism between stimuli. Antagonism was for example the result of a limited supply of stimulus-cofactor CD14 or endosomal transport capacity. Synergy depended on ultra-sensitive IKK activation in cells with low receptor expression. While synergy does not enhance SRS, antagonism between TLR9 and TLR3 signaling pathways due to endosomal transport competition may enhance the distinguishability of CpG-pIC from ligand mixtures. These studies therefore identified antagonism mechanisms in signaling pathways as key to maintaining immune specificity under complex conditions.</p>

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Macrophage response specificity to ligand mixtures is improved by signaling pathway antagonism

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Xiaolu Guo,
  • Supriya Sen,
  • Julian Gonzalez,
  • Alexander Hoffmann

摘要

Macrophages function as immune sentinels that distinguish diverse threats and mount appropriate responses. This stimulus-response specificity (SRS) is partly encoded in the dynamics of the NFκB transcription factor. While most studies examine single ligands, physiological exposures involve complex multi-ligand mixtures. Using a mathematical model that captures heterogeneous single-cell NFκB responses, we generated simulation datasets for mixtures of up to five ligands and validated key predictions with live-cell microscopy. Following iterative refinement of the model, we quantified SRS with Wasserstein distance and machine learning classification and found that NFκB temporal coding can partially convey the presence of specific ligands within mixtures. By generating simulation datasets across all ligand pairs at doses spanning the full responsiveness range, we found several cases of synergy and antagonism between stimuli. Antagonism was for example the result of a limited supply of stimulus-cofactor CD14 or endosomal transport capacity. Synergy depended on ultra-sensitive IKK activation in cells with low receptor expression. While synergy does not enhance SRS, antagonism between TLR9 and TLR3 signaling pathways due to endosomal transport competition may enhance the distinguishability of CpG-pIC from ligand mixtures. These studies therefore identified antagonism mechanisms in signaling pathways as key to maintaining immune specificity under complex conditions.