Bayesian modeling suggests that IL-12 (p40), IL-13 and MCP-1 drive murine cytokine networks in vivo. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Field, Sarah; Dasgupta, Tathagata; Cummings, Michele; Savage, Richard; Adebayo, Julius; McSara, Hema; Gunawardena, Jeremy; Orsi, Nicolas
• Is Part Of: BMC systems biology. Volume 9:Issue 1(2015)
• Page Start: 1
• Page End: 22
• Publication Date: 2015-12
• Subjects: Bayesian network -- Cytokine -- Hormone -- Lactation -- Mouse; Biological systems -- Periodicals; Biology -- Research -- Periodicals; Cell physiology -- Periodicals; Genes -- Analysis -- Periodicals; 571
• DOI: 10.1186/s12918-015-0226-3
• Languages: English
• ISSNs: 1752-0509

Abstract

Abstract Background Cytokine-hormone network deregulations underpin pathologies ranging from autoimmune disorders to cancer, but our understanding of these networks in physiological/pathophysiological states remains patchy. We employed Bayesian networks to analyze cytokine-hormone interactionsin vivo using murine lactation as a dynamic, physiological model system. Results Circulatory levels of estrogen, progesterone, prolactin and twenty-three cytokines were profiled in post partum mice with/without pups. The resultant networks were very robust and assembled about structural hubs, with evidence that interleukin (IL)-12 (p40), IL-13 and monocyte chemoattractant protein (MCP)-1 were the primary drivers of network behavior. Network structural conservation across physiological scenarios coupled with the successful empirical validation of our approach suggested thatin silico network perturbations can predictin vivo qualitative responses.In silico perturbation of network components also captured biological features of cytokine interactions (antagonism, synergy, redundancy). Conclusion These findings highlight the potential of network-based approaches in identifying novel cytokine pharmacological targets and in predicting the effects of their exogenous manipulation in inflammatory/immune disorders.