The Journal of Immunology · 2014 · 141 citations · 38 references
Life SciencesImmunologyMolecular BiologyGene ExpressionInflammationTranscriptional RegulationCytokine BiologyInflammatory Cell ResponsesInflammatory Response RegulationInflammatory SignalingInnate Immune Signaling PathwaysMacrophage BiologyCellular Inflammatory ResponsesClassical Macrophage ActivationImmunostimulatory PathwaysInterleukin-1 Family SignalingMacrophage Inflammatory ResponsesMacrophage Transcriptional RegulationMicroarray AnalysisTranscription Factor Families
Activated macrophages are crucial for restriction of microbial infection but may also promote inflammatory pathology in a wide range of both infectious and sterile conditions. The pathways that regulate macrophage activation are therefore of great interest. Recent studies in silico have putatively identified key transcription factors that may control macrophage activation, but experimental validation is lacking. In this study, we generated a macrophage regulatory network from publicly available microarray data, employing steps to enrich for physiologically relevant interactions. Our analysis predicted a novel relationship between the AP-1 family transcription factor Junb and the gene Il1b, encoding the pyrogen IL-1β, which macrophages express upon activation by inflammatory stimuli. Previously, Junb has been characterized primarily as a negative regulator of the cell cycle, whereas AP-1 activity in myeloid inflammatory responses has largely been attributed to c-Jun. We confirmed experimentally that Junb is required for full expression of Il1b, and of additional genes involved in classical inflammation, in macrophages treated with LPS and other immunostimulatory molecules. Furthermore, Junb modulates expression of canonical markers of alternative activation in macrophages treated with IL-4. Our results demonstrate that JUNB is a significant modulator of both classical and alternative macrophage activation. Further, this finding provides experimental validation for our network modeling approach, which will facilitate the future use of gene expression data from open databases to reveal novel, physiologically relevant regulatory relationships.
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