A strategy to study pathway cross-talks of cells under repetitive exposure to stimuli

In each individual cell, there are many signaling pathways that may interact or cross talk with each other. Especially, some can sense the same signal and go through different pathways but eventually converge at some points. Therefore repetitive signal stimulations may result in intricate cell responses, among which the priming effect has been extensively studied in monocytes and macrophages as it plays an unambiguously crucial role in immunological protection against pathogen infection. Priming basically describes the phenomena that host cells can launch a dramatically enhanced response to the second higher dose of stimulus if cells have been previously treated with a lower dose of identical stimulus. It was reported to be associated with many human immune diseases (such as rheumatoid arthritis and hepatitis) that are attracting more and more researches on the priming effect. It is undoubtable that many genes are involved in this complicated biological process. Microarray is one of the standard techniques that are applied to do the transcriptome profiling of cells under repetitive stimuli and reveal gene regulatory networks. Therefore a well-established pipeline to analyze microarray data is of special help to investigate the underlying mechanism of priming effect. In this research, we aimed to design a strategy that can be used to interpret microarray data and to propose gene candidates that potentially participate in priming effect. To confirm our analysis results, we used a detailed mathematical model to further demonstrate the mechanism of a specific case of priming effect in a computational perspective.