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A dynamic model of the hypoxia‐inducible factor (HIF) network
Author(s) -
Cheong Alex,
Cavadas Miguel AS,
Nguyen Lan,
Scholz Carsten C,
Fitzpatrick Susan F,
Bruning Ulrike,
Tambuwala Murtaza,
Manresa Mario C,
Kholodenko Boris N,
Taylor Cormac C
Publication year - 2013
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.27.1_supplement.717.12
Subject(s) - in silico , hydroxylation , transcriptional activity , hypoxia (environmental) , gene silencing , hypoxia inducible factors , in vitro , chemistry , computational biology , microbiology and biotechnology , mutant , biology , biochemistry , transcription factor , enzyme , gene , oxygen , organic chemistry
Activation of the hypoxia‐inducible factor (HIF) pathway is a critical step in the transcriptional response to hypoxia. While many of the key proteins involved have been characterised, the dynamics of their interactions in generating this response remain unclear. We have generated a comprehensive mathematical model of the HIF pathway based on core validated components and dynamic experimental data. Our model confirms that the steps leading to optimal HIF transcriptional activity require sequential inhibition of both prolyl‐and asparaginyl‐hydroxylases. We also predict from our model and show experimentally that there is residual activity of the asparaginyl‐hydroxylase FIH at low oxygen tension. Furthermore silencing FIH under conditions where prolyl‐hydroxylases are inhibited results in increased HIF transcriptional activity but paradoxically decreased in stability. Using a core module of the HIF network and mathematical proof supported by experimental data, we propose that asparaginyl hydroxylation confers resistance upon HIF to proteosomal degradation. Thus, through in vitro experimental data and in silico predictions, we provide a comprehensive model of the dynamic regulation of HIF transcriptional activity by hydroxylases and use its predictive and adaptive properties to explain counter‐intuitive biological observations.