£279,762.72 - Blood Cancer UK - Co-Investigator
The roll of CXXC1 and SETD1A in multiple myeloma. WIth Prof Anastasios Karadimitris and Dr Mohammad Mahdi Karimi
£63,000 - EPSRC CORE EQUIPMENT AWARD 2022 - Co-Investigator
£673,345 - MRC WORLD CLASS LABS CAPITAL EQUIPMENT AWARD - Lead Applicant
£433,938.96 - BBSRC ALERT21 - Co-Investigator
£507,787.12 - CRUK-EPSRC Early Detection and Diagnosis Award - Co-investigator
Serial Artificial Intelligence/Machine Learning Classifiers for Personalised Risk Stratification and Early Detection of Lung, Bowel and Pancreatic Cancers in Women. With Dr Oleg Blyuss, Dr John Timms, Dr Tatjana Crnogorac-Jurcevic, Prof Alexey Zaikin, Prof Usha Menon
£10,183 - UKDRI Collaborative Proteomics Studies Award
Nuclear Factors Associated with Microglia Activation as Modulators of Disease-associated Cell States. With Dr Alexei Nott
£14,205 - UKDRI Collaborative Proteomics Studies Award
Developing a High Throughput Proteomics Assay for Dementia Research With Dr Brenan Durainayagam, Dr Rui Pino and Dr Abbas Dehghan
£557,000 - MRC-NIHR Methodology Research Programme - Co-investigator
Construction of graph-based network longitudinal algorithms to identify screening and prognostic biomarkers and therapeutic targets (GBNLA).
Research Interests and aims
Protein methylation is a common post translational modification spread across the proteome. It is involved in all stages of genetic regulation, from gene activation via the histones code (epigenetics) to protein activation and regulation. Misregulation of methylation is involved in a number of cancers and is of particular importance in mixed-lineage leukaemia, where MLL1 methyl-transferase becomes fused with other genes involved in regulation and recognition of histones methylation. The consequence of this is widespread mis-regulation of oncogenes via inappropriate methylation. My research is to uncover novel protein targets of DOT1L, a methyltransferase that is strong effector of MLL-fusion protein generated gene activation. DOT1L is a lysine-methyl transferase, that is known to mono, di and tri methylate K79 of histone H3.
Identification of new PTMs can be performed by mass spectrometry, however the discovery of new targets amongst a single peptide is challenging owing to the sparsity of the potential target. Therefore, a strategy to enrich for methyl-lysine containing peptides is essential. Current strategies depend on using antibodies raised against small peptides carrying methylation marks, and despite there being many “pan” methylation antibodies available, there is very little similarity between the methyl-peptides detected using these antibodies for immunoprecipitation enrichment. This suggests that either the antibodies are very specific to only a small subset of methyl-K, or that are very unspecific, leading to false positives - neither situation is desirable. I am developing a chemical labelling strategy to selectively enrich for methylated lysine residues that will capture mono, di and tri-methyl marks in an unbiased manner. The identification and understanding of the global effect of protein methylation has been hampered by the difficulty in enriching for these methyl marks, this new technique has the potential to revolutionise the field.
Chemical technologies for DOT1L Target identification
In order to identify DOT1L targets, I am, in conjunction with Dr Matthew Fuchter in Chemistry, using novel probes that specifically bind to DOT1L and result in the heavy labelling of their substrates. This allows us to identify methylation marks by mass spectrometry from the resulting MS1 profile. The approach will be coupled to methyl-enrichment and or heavy fractionation of cell lysates to identify new methylated peptides and any potential alternative DOT1L substrates.
I have developed a parenclitic network-based approach to disease classification. Unlike other network/graph orientated approaches, this technique does not require prior knowledge of protein/protein interaction or any other metric, extrapolation etc; it derives the connections by comparing differences between two data sets. By using graph-topology analysis, we have used these networks for disease classification in Ovarian Cancer (Whitwell et al, Oncotarget 2018). Whats-more, the underlying basis for how the graphs are derived is clear and therefore, unlike "black-box" machine learning techniques such as neural-networks, further information on the underlying biology can be gleaned. Using this approach, I will investigate protein methylation in a number of diseases.
The Impact of MTAP Deletion on Protein Methylation
MTAP has an important role in maintaining cellular homeostasis by metabolising toxic MTA through the methionine salvage pathway. Loss of MTAP occurs in approximately 15% of all cancers, with a knock-on effect on the methionine salvage pathways, and the intersecting SAM-cycle pathways. The potential reduction of methionine and SAM resulting from this deletion may have wide ranging impacts in the cell, especially in protein-methylation, a wide-spread, well conserved mechanism of protein regulation. By understanding more about the proteomic changes occurring following MTAP deletion, we hope to understand its role in cancer and identify potential therapeutic targets.