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

Abstract 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 si...

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Autores principales:	Marta E. Polak, Chuin Ying Ung, Joanna Masapust, Tom C. Freeman, Michael R. Ardern-Jones
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/02cd986922db48bba0b3dc4129b8371e
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spelling	oai:doaj.org-article:02cd986922db48bba0b3dc4129b8371e2021-12-02T16:08:00ZPetri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation10.1038/s41598-017-00651-52045-2322https://doaj.org/article/02cd986922db48bba0b3dc4129b8371e2017-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00651-5https://doaj.org/toc/2045-2322Abstract 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.Marta E. PolakChuin Ying UngJoanna MasapustTom C. FreemanMichael R. Ardern-JonesNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marta E. Polak Chuin Ying Ung Joanna Masapust Tom C. Freeman Michael R. Ardern-Jones Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation
description	Abstract 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.
format	article
author	Marta E. Polak Chuin Ying Ung Joanna Masapust Tom C. Freeman Michael R. Ardern-Jones
author_facet	Marta E. Polak Chuin Ying Ung Joanna Masapust Tom C. Freeman Michael R. Ardern-Jones
author_sort	Marta E. Polak
title	Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation
title_short	Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation
title_full	Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation
title_fullStr	Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation
title_full_unstemmed	Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation
title_sort	petri net computational modelling of langerhans cell interferon regulatory factor network predicts their role in t cell activation
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/02cd986922db48bba0b3dc4129b8371e
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Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation

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