title: A strategy to study pathway cross-talks of cells under repetitive exposure to stimuli.
abstract: BACKGROUND
  Cells are subject to fluctuating and multiple stimuli in their natural environment. The signaling pathways often crosstalk to each other and give rise to complex nonlinear dynamics. Specifically repetitive exposure of a cell to a same stimulus sometime leads to augmented cellular responses. Examples are amplified proinflammatory responses of innate immune cells pretreated with a sub-threshold then a high dose of endotoxin or cytokine stimulation. This phenomenon, called priming effect in the literature, has important pathological and clinical significances.
  
  
  RESULTS
  In a previous study, we enumerated possible mechanisms for priming using a three-node network model. The analysis uncovered three mechanisms. Based on the results, in this work we developed a straightforward procedure to identify molecular candidates contributing to the priming effect and the corresponding mechanisms. The procedure involves time course measurements, e.g., gene expression levels, or protein activities under low, high, and low + high dose of stimulant, then computational analysis of the dynamics patterns, and identification of functional roles in the context of the regulatory network. We applied the procedure to a set of published microarray data on interferon-γ-mediated priming effect of human macrophages. The analysis identified a number of network motifs possibly contributing to Interferon-γ priming. A further detailed mathematical model analysis further reveals how combination of different mechanisms leads to the priming effect.
  
  
  CONCLUSIONS
  One may perform systematic screening using the proposed procedure combining with high throughput measurements, at both transcriptome and proteome levels. It is applicable to various priming phenomena.
authors.affiliation: Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24060, USA.
authors.forename: Yan
authors.initials: Y
authors.lastname: Fu
authors.forename: Xiaoshan
authors.initials: X
authors.lastname: Jiang
authors.forename: Hang
authors.initials: H
authors.lastname: Zhang
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xing
mesh_terms: D000818:Animals; D002460:Cell Line; D003198:Computer Simulation; D016207:Cytokines / Q000378:metabolism; D004731:Endotoxins / Q000378:metabolism / Q000633:toxicity; D020869:Gene Expression Profiling; D006801:Humans; D007113:Immunity, Innate; D007371:Interferon-gamma / Q000378:metabolism*; D008264:Macrophages / Q000166:cytology / Q000378:metabolism; D046228:Microarray Analysis; D011485:Protein Binding; D020543:Proteome / Q000032:analysis; D015398:Signal Transduction; D049490:Systems Biology; D059467:Transcriptome
chemical_list: D016207:Cytokines; D004731:Endotoxins; D020543:Proteome; D007371:Interferon-gamma