Research Summary: Inflammatory Gene Regulatory Networks in Amnion Cells Following Cytokine Stimulation: Translational Systems Approach to Modeling Human Parturition


A majority of the studies examining the molecular regulation of human labor have
been conducted using single gene approaches. While the technology to produce
multi-dimensional datasets is readily available, the means for facile analysis
of such data are limited. The objective of this study was to develop a systems
approach to infer regulatory mechanisms governing global gene expression in
cytokine-challenged cells in vitro, and to apply these methods
to predict gene regulatory networks (GRNs) in intrauterine tissues during term
parturition. To this end, microarray analysis was applied to human amnion
mesenchymal cells (AMCs) stimulated with interleukin-1β, and differentially
expressed transcripts were subjected to hierarchical clustering, temporal
expression profiling, and motif enrichment analysis, from which a GRN was
constructed. These methods were then applied to fetal membrane specimens
collected in the absence or presence of spontaneous term labor. Analysis of
cytokine-responsive genes in AMCs revealed a sterile immune response signature,
with promoters enriched in response elements for several inflammation-associated
transcription factors. In comparison to the fetal membrane dataset, there were
34 genes commonly upregulated, many of which were part of an acute inflammation
gene expression signature. Binding motifs for nuclear factor-κB were
prominent in the gene interaction and regulatory networks for both datasets;
however, we found little evidence to support the utilization of
pathogen-associated molecular pattern (PAMP) signaling. The tissue specimens
were also enriched for transcripts governed by hypoxia-inducible factor. The
approach presented here provides an uncomplicated means to infer global
relationships among gene clusters involved in cellular responses to
labor-associated signals.


Publisher: Public Library of Science

Date Published: 2-June-2011

Author(s): Li R., Ackerman W., Summerfield T., Yu L., Gulati P., Zhang J., Huang K., Romero R., Kniss D.


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