Protein ADP-ribosylation is an important yet relatively understudied protein post-translational modification (PTM) that has diverse roles in cellular biology, from DNA damage response to emerging roles in RNA splicing. This PTM is catalyzed by enzymes known as poly-ADP-ribose polymerases (PARPs), which transfer one or more ADP-ribose units from a NAD+ cofactor onto their protein targets. Across the 17 human PARP enzymes currently identified, we lack a detailed understanding of the protein targets they modify due to the previous analytical challenges of measuring ADP-ribosylation. In particular, the proteomic characterisation of ADP-ribosylation has been limited as there are no selective enrichment tools for this PTM. To address this technology gap, we have developed a novel proteomics workflow that incorporates a chemically tagged ADP-ribosylation onto intracellular proteins, and this has enabled the quantitative profiling of ADP-ribosylation in live cells at an unprecedented scale.
It is well known that many cancers rely upon reprogramming of the glycolysis pathway into a Warbug state to enable their growth and survival, which requires high levels of NAD+ and NADH to maintain a high glycolytic flux. Importantly, our recent work has identified specific cancers that are selectively sensitive to NAD+ depletion via chemical inhibition of the NAD+ salvage pathway. This project will combine our transformative platform for mass spectrometry quantitation of ADP-ribosylation with targeted metabolomic flux analysis to study the impact of the levels of NAD+ on the changes in protein ADP-ribosylation that are linked to metabolic reprogramming in cancer. As our preliminary data has elucidated a strong connection between ADP-ribosylation and mRNA splicing, RNA-sequencing will be used to profile the spliceosome for changes in response to synergistic inhibition of PARP enzymes and the NAD+ salvage pathway.
Candidates will be trained in state-of-the-art workflows for mass spectrometry-based metabolomics and proteomics, as well as the statistical tools used for robust interpretation and integration of these large-scale omics datasets.