A Systematic Analysis of the Human Nrf2 Network

Ponsford, AH ORCID: 0000-0002-7178-7862
(2016) A Systematic Analysis of the Human Nrf2 Network. PhD thesis, University of Liverpool.

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Nrf2 is the main transcriptional controller of cellular oxidative stress responses. This is achieved by driving the transcription of a battery of cytoprotective genes that possess a characteristic Antioxidant Response Element (ARE) sequence in their upstream promoter regions. As such, the Nrf2 signalling pathway plays a vital role in health, aging and disease. Defects in Nrf2 activity can lead to impaired mitochondrial function and reduced protection against free radical damage. Over time these effects contribute to common age-related conditions including cardiovascular disease, neurodegeneration and chronic inflammatory conditions. Despite the well-characterised cytoprotective role of Nrf2, inappropriate over or under activation of Nrf2 signalling can also be detrimental. It is therefore important to develop a better understanding of how Nrf2 signalling networks may be regulated. For that reason, the primary aim of this project was to provide a better systems level contextual understanding of Nrf2 signalling in human cells, by generating an improved high-density experimentally defined human Nrf2-centric protein-protein interaction network. Under normal basal conditions Nrf2 binds to Keap1, a component of the Cul3/Rbx1 ubiquitin ligase complex, resulting in a constant process of Nrf2 ubiquitination and proteasomal degradation. However, under conditions of oxidative stress or pharmacological intervention, Keap1 becomes modified, thus altering the interaction with Nrf2 and allowing newly synthesised Nrf2 to be transported into the nucleus, resulting in the transcription of a diverse range of ARE-driven cytoprotective genes. While this simple process appears to operate in most cells, Nrf2 and Keap1 both have many more known and predicted interaction partners. Additionally, other components of the Nrf2 cascade are known to interact with components of other pathways and signalling networks such as the NF-ĸB complex. Currently it is not clear which of these interactions are competitive, sequential or conditional; or how these proteins may work together in multi-protein complexes. By addressing these questions we will be able to provide better insight into the detailed molecular mechanism of Nrf2 signalling and the potential effects of crosstalk between different signalling cascades in human cells. Protein-protein interaction (PPI) networks can also provide insight into the function of uncharacterised proteins and can guide future hypothesis driven research into protein function and the regulation of complex biological processes. PPI data for Nrf2 remains unclear and incomplete, therefore a predicted Nrf2 PPI network was initially generated from publically available datasets, an ‘in-house’ interactome and from literature. The yeast two-hybrid system was then initially employed to test 135 predicted binary interactions and identify novel Nrf2 interaction partners. A combination of secondary interaction methods and transcriptional activity assays were then used to assess confidence limits for Nrf2 partner interaction profiles. Finally, conditional changes in Nrf2 protein complex profiles were investigated in a series of Affinity Purification coupled with Mass Spectrometry assays. This study identified 98 novel experimentally defined binary Nrf2 interaction partners using the yeast two-hybrid assays, together with 96 novel interactions from the Mass Spectrometry studies. This increased the complexity of the binary Nrf2 PPI network by almost 4-fold, and the total Nrf2 interactome by 3.7- fold. This Nrf2-centric network can be used to guide future hypothesis driven research into the physiological mechanisms and functional relevance of these interactions, providing a more in-depth understanding of the molecular mechanisms of Nrf2 regulation and pathway crosstalk in human cells.

Item Type: Thesis (PhD)
Divisions: Faculty of Health and Life Sciences
Depositing User: Symplectic Admin
Date Deposited: 10 Aug 2016 07:52
Last Modified: 19 Jan 2023 07:35
DOI: 10.17638/03001771
  • Sanderson, CM
URI: https://livrepository.liverpool.ac.uk/id/eprint/3001771