Hijacking Of Human Interactome By Hpv Oncoproteins: A Quantitative And Structural Analysis.
Reference : PhD Gilles TRAVE
Offer publication : April 6, 2016
High-risk human papillomaviruses are responsible for all cervical cancers and are also involved in many other cancers (head and neck, anal, skin...). The HPV oncoproteins E6 and E7 bind and often degrade host proteins that control cell division, adhesion and apoptosis. This promotes viral DNA replication but can also cause tumors. E6 captures many of its targets via "motif hijacking strategies". E6 contains a binding pocket for "LxxLL" motifs (i.e. short conserved sequences with LxxLL consensus), often found in proteins controlling transcription , survival, or cell division.
High-risk mucosal HPV E6 also contains a C-terminal motif that recognizes PDZ protein domains, involved in cell adhesion and polarity. Interestingly, different HPV types (mucosal or cutaneous, high or low risk) target distinct pools of host proteins. This certainly participates to observed differences in pathogenicity and tropism between HPVs.
Our team studies structural and interactomic aspects of E6 motif hijacking. We have solved several key 3D structures of E6 bound to PDZ domains, LxxLL motifs and tumour suppressor p53 (Charbonnier et al. JMB 2011, Zanier et al. Science 2013, Martinez Zapien et al. Nature 2016). We have also developed the "holdup" approach to measure domain-motif affinities at high-throughput (Charbonnier et al. , Nature Methods 2015), and applied it to establish binding profiles of E6 proteins for an array of 220 human PDZ domains. This opens the way to quantitative interactomics, as opposed to former binary interactomics ("bind" or "do not bind"). The holdup assay will be used to measure the affinities of 20 different E6 proteins to our PDZ array. In this way, we will identify and classify the PDZ proteins systematically targeted by HPV, hence potentially involved in orogenital cancers.
The candidate will also measure the affinities of 12 to 20 E6
proteins covering the phylogenetic diversity of HPVs, for arrays of LxxLL motifs derived from human proteins targeted by E6. This will allow us todefine very precisely the fine LxxLL motif recognition specificity of each E6. All E6-PDZ and E6-LxxLL holdup measurements will be cross-validated by SPOT (a binding assay using synthetic peptides spotted on nitrocellulose). We will also validate the strongest or most interesting interactions detected in our screens, by high-accuracy approaches such as ITC and BIAcore. The data will then be used to screen in silico the human proteome to identify and rank potential target proteins of each E6 protein and compare them to published E6 binders. Thus, we will enhance the reliability of E6 interactomic data, gain knowledge about the interfaces between E6 proteins and their targets, and classify these different E6 targets according to their
In parallel, the PhD student will explore approaches to increase the throughput of the holdup assay up to 10,000 affinity measurements per day (versus 1000 currently). The planned improvements will use HPLC and mass-spectrometry as a readout for the measurements, and specific holdup
data treatment programs developed by fellow informaticians.
The PhD student will also collaborate with the team's crystallographers, to obtain the structures of at least five different E6 proteins representative of HPV phylogenetic diversity, complexed to LxxLL motifs from major target proteins of E6 ( p53, MAML1 , IRF3 , mediator complex...).
This will allow us to describe, at the atomic scale, interactions of primary importance for HPV-induced diseases (cancers, warts, condylomas). Using the structures of E6-PDZ and E6-LxxLL complexes (solved either previously or during this project), we will also build homology models of other E6-target complexes.
These structures, experimental or modeled, will guide us to interpret the different affinities measured, and will serve as a basis for modeling and screening of E6 inhibitors for the development of future anti-HPV therapeutic drugs.
- WISHED SKILLS :
Cloning, production, purification and characterisation of recombinant proteins
High-throughput robotized approaches
Excell data handling and treatment
Programming basics (Python)
- EXPERTISES WHICH WILL BE ACQUIRED DURING THE TRAINING :
Structural Biology of cancer
Domain-Motif Interaction networks
Application Deadline : Dec. 31, 2016