Linking transcriptional changes over time in stimulated dendritic cells to identify gene networks activated during the innate immune response.
The innate immune response is primarily mediated by the Toll-like receptors functioning through the MyD88-dependent and TRIF-dependent pathways. Despite being widely studied, it is not yet completely understood and systems-level analyses have been lacking. In this study, we identified a high-probability network of genes activated during the innate immune response using a novel approach to analyze time-course gene expression profiles of activated immune cells in combination with a large gene regulatory and protein-protein interaction network. We classified the immune response into three consecutive time-dependent stages and identified the most probable paths between genes showing a significant change in expression at each stage. The resultant network contained several novel and known regulators of the innate immune response, many of which did not show any observable change in expression at the sampled time points. The response network shows the dominance of genes from specific functional classes during different stages of the immune response. It also suggests a role for the protein phosphatase 2a catalytic subunit α in the regulation of the immunoproteasome during the late phase of the response. In order to clarify the differences between the MyD88-dependent and TRIF-dependent pathways in the innate immune response, time-course gene expression profiles from MyD88-knockout and TRIF-knockout dendritic cells were analyzed. Their response networks suggest the dominance of the MyD88-dependent pathway in the innate immune response, and an association of the circadian regulators and immunoproteasomal degradation with the TRIF-dependent pathway. The response network presented here provides the most probable associations between genes expressed in the early and the late phases of the innate immune response, while taking into account the intermediate regulators. We propose that the method described here can also be used in the identification of time-dependent gene sub-networks in other biological systems.
The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
Ashwini
A
Patil
Yutaro
Y
Kumagai
Kuo-Ching
KC
Liang
Yutaka
Y
Suzuki
Kenta
K
Nakai
D033942:Adaptor Proteins, Vesicular Transport / Q000032:analysis / Q000235:genetics / Q000378:metabolism; D000818:Animals; D002478:Cells, Cultured; D019295:Computational Biology; D003713:Dendritic Cells / Q000276:immunology*; D015870:Gene Expression / Q000276:immunology*; D055786:Gene Knockout Techniques; D053263:Gene Regulatory Networks / Q000276:immunology*; D007113:Immunity, Innate / Q000276:immunology*; D008070:Lipopolysaccharides / Q000276:immunology; D051379:Mice; D008810:Mice, Inbred C57BL; D053594:Myeloid Differentiation Factor 88 / Q000032:analysis / Q000235:genetics / Q000378:metabolism; D060066:Protein Interaction Maps / Q000276:immunology
D033942:Adaptor Proteins, Vesicular Transport; D008070:Lipopolysaccharides; D053594:Myeloid Differentiation Factor 88; C507780:Myd88 protein, mouse; C476286:Ticam1 protein, mouse