The Journal of ImmunologySystems BiologyPeer-reviewed journal article
Network analysis reveals a distinct axis of macrophage activation in response to conflicting inflammatory cues
Xiaji Liu, Jingyuan ZhangiD, Angela C. ZeigleriD, Anders R. NelsoniD, Merry L. LindseyiD, Jeffrey J. SaucermaniD
Abstract
Macrophages are subject to a wide range of cytokine and pathogen signals in vivo, which contribute to differential activation and modulation of inflammation. Understanding the response to multiple, often conflicting, cues that macrophages experience requires a network perspective. Here, we integrate data from literature curation and mRNA expression profiles to develop a large-scale computational model of the macrophage signaling network. In response to stimulation across all pairs of 9 cytokine inputs, the model predicted activation along the classic M1-M2 polarization axis but also a second axis of macrophage activation that distinguishes unstimulated macrophages from a mixed phenotype induced by conflicting cues. Along this second axis, combinations of conflicting stimuli, interleukin 4 (IL4) with lipopolysaccharide (LPS), interferon-{gamma} (IFN{gamma}), IFN{beta}, or tumor necrosis factor- (TNF), produced mutual inhibition of several signaling pathways, e.g. nuclear factor {kappa}B (NF{kappa}B) and signal transducer and activator of transcription 6 (STAT6), but also mutual activation of the phosphoinositide 3-kinases (PI3K) signaling module. In response to combined IFN{gamma} and IL4, the model predicted genes whose expression was mutually inhibited, e.g. inducible nitric oxide synthase (iNOS) and arginase 1 (Arg1), or mutually enhanced, e.g. IL4 receptor- (IL4R) and suppressor of cytokine signaling 1 (SOCS1), which was validated by independent experimental data. Knockdown simulations further predicted network mechanisms underlying functional crosstalk, such as mutual STAT3/STAT6-mediated enhancement of IL4R expression. In summary, the computational model predicts that network crosstalk mediates a broadened spectrum of macrophage activation in response to mixed pro- and anti-inflammatory cytokine cues, making it useful for modeling in vivo scenarios. Summary sentenceNetwork modeling of macrophage activation predicts responses to combinations of cytokines along both the M1-M2 polarization axis and a second axis associated with a mixed macrophage activation phenotype.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 40
- FWCI
- 2.61
- Field percentile
- top 9%
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