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BMC Syst Biol. 2015 Nov 9;9:76. doi: 10.1186/s12918-015-0226-3.

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

Author information

1
Women's Health Research Group, Section of Cancer & Pathology, Leeds Institute of Cancer & Pathology, St James's University Hospital, Leeds, LS9 7TF, UK. sarahhonley@gmail.com.
2
Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, Massachusetts, 02115, USA. Tathagata_Dasgupta@hms.harvard.edu.
3
Women's Health Research Group, Section of Cancer & Pathology, Leeds Institute of Cancer & Pathology, St James's University Hospital, Leeds, LS9 7TF, UK. medmic@leeds.ac.uk.
4
Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, UK. r.s.savage@warwick.ac.uk.
5
Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, Massachusetts, 02115, USA. julius.adebayo@gmail.com.
6
IDeA Labs, Department of Computer Science, TMCB 1124, Brigham Young University, Provo, UT, 84602, USA. julius.adebayo@gmail.com.
7
Women's Health Research Group, Section of Cancer & Pathology, Leeds Institute of Cancer & Pathology, St James's University Hospital, Leeds, LS9 7TF, UK. hema.mcsara@leedsth.nhs.uk.
8
Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, Massachusetts, 02115, USA. jeremy_gunawardena@hms.harvard.edu.
9
Women's Health Research Group, Section of Cancer & Pathology, Leeds Institute of Cancer & Pathology, St James's University Hospital, Leeds, LS9 7TF, UK. n.m.orsi@leeds.ac.uk.

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 interactions in 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 that in silico network perturbations can predict in 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.

PMID:
26553024
PMCID:
PMC4640223
DOI:
10.1186/s12918-015-0226-3
[Indexed for MEDLINE]
Free PMC Article

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