Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation

Langerhans cells (LCs) are able to orchestrate adaptive immune responses in the skin by interpreting the microenvironmental context in which they encounter foreign substances, but the regulatory basis for this has not been established. Utilising systems immunology approaches combining in silico mode...

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Bibliographic Details
Published in:	Scientific reports Vol. 7; no. 1; p. 668
Main Authors:	Polak, Marta E., Ung, Chuin Ying, Masapust, Joanna, Freeman, Tom C., Ardern-Jones, Michael R.
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 06-04-2017 Nature Portfolio
Subjects:	13/106 13/21 13/31 38/39 38/61 38/90 631/250/2502 631/250/2504/133/1593 Cell Movement - genetics Cell Movement - immunology Computational Biology - methods Cytokines - genetics Cytokines - metabolism Gene Expression Profiling Gene Expression Regulation Gene Ontology Gene Regulatory Networks Humanities and Social Sciences Humans Interferon Regulatory Factors - genetics Interferon Regulatory Factors - metabolism Langerhans Cells - immunology Langerhans Cells - metabolism Lymphocyte Activation - immunology Models, Biological multidisciplinary Science Science (multidisciplinary) T-Lymphocytes - immunology T-Lymphocytes - metabolism
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Summary:	Langerhans cells (LCs) are able to orchestrate adaptive immune responses in the skin by interpreting the microenvironmental context in which they encounter foreign substances, but the regulatory basis for this has not been established. Utilising systems immunology approaches combining in silico modelling of a reconstructed gene regulatory network (GRN) with in vitro validation of the predictions, we sought to determine the mechanisms of regulation of immune responses in human primary LCs. The key role of Interferon regulatory factors (IRFs) as controllers of the human Langerhans cell response to epidermal cytokines was revealed by whole transcriptome analysis. Applying Boolean logic we assembled a Petri net-based model of the IRF-GRN which provides molecular pathway predictions for the induction of different transcriptional programmes in LCs. In silico simulations performed after model parameterisation with transcription factor expression values predicted that human LC activation of antigen-specific CD8 T cells would be differentially regulated by epidermal cytokine induction of specific IRF-controlled pathways. This was confirmed by in vitro measurement of IFN-γ production by activated T cells. As a proof of concept, this approach shows that stochastic modelling of a specific immune networks renders transcriptome data valuable for the prediction of functional outcomes of immune responses.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-017-00651-5