Research Interests

The Protein Profiling Group is a project-driven interactive proteomics facility studying molecular mechanisms in toxicology, cancer and neurodegeneration.

The group specialises in ‘label free’ quantitative proteomics, using affinity targeting of important biological proteins and chromatographic separation of both soluble complexes and insoluble membrane complexes involved in cell death, neurodegeneration and cancer-related toxicity (e.g. death receptors and DNA repair complexes).  Quantitative proteomics allows rapid identification and quantitation of proteins after separation of native protein(s)/complexes on SDS-PAGE gels, tryptic digestion and analysis on Waters Synapt G2-Si mass spectrometers using data-independent acquisition (DIA) and ion mobility (HDMSE), LC-MS/MS. This approach yields extensive protein identification and quantitation which can be analysed using Scaffold Proteome, Progenesis QI, ISOQuant and R-based statistical software packages. Using these approaches provides an essential and powerful technology platform for protein profiling of cells, tissue and protein complexes in a variety of toxicological, cancer and neurodegenerative scenarios (Fig.1).  Pathway bioinformatics software such as Ingenuity is also used to assess pathway interactions and interpret label-free quantitative proteomics data.

Fig. 1

Quantitative proteomics to identify new cytotoxicity targets

The protein profiling group interacts with and supports all Toxicology Unit Programmes in varying ways. In some projects, the group provides protein identifications on a sample by sample basis. However, in the majority of projects the group is closely involved with, and active in, planning and designing the most appropriate methodology and subsequent analysis to answer a particular question.  Some projects involve joint PhD studentships with Unit Programme leaders. Other projects may also involve the advancement of new methodologies and are developed proactively with Unit Researchers. This approach provides a continually expanding repertoire of expertise, which can be applied to other ongoing and future projects.

The following examples illustrate some of the approaches and successes of the group.

1. Cell death and signalling complexes

The group is expert in isolating, identifying and characterising large multi-protein caspase-activating complexes which are involved in apoptotic/necrotic cell death and survival pathways. Using gel filtration, density gradient purification, coupled with a variety of affinity labelling tagging strategies we have made significant contributions to identifying and characterising:

i) the apoptosome which is involved in mitochondrial-mediated cell death (Cain et al., 2002; Cain et al., 1999; Twiddy et al., 2006)

ii) the ripoptosome (necroptosis inducing complex) (Feoktistova et al., 2011; Tenev et al., 2011a)

iii) the DISC (death inducing signalling complex) which is responsible for transducing CD95/TRAIL1 and 2 signalling to cell death (Dickens et al., 2012; Hughes et al., 2009).

The characterisation of the DISC showed that quantitative label-free proteomics not only identifies novel proteins in such complexes, but also provides important stoichiometric information on the complex itself. This can be used in structural studies and to determine how these complexes can signal cell death or survival. These studies were carried out in collaboration with Professor MacFarlane’s group and coupled with structural modelling and biochemical studies led to a new model structure (Fig. 2 (Dickens et al., 2012)).

More recent findings using the same approaches have shown how c-FLIP isoforms differentially control cell fate (Hughes et al., 2016).

Fig. 2

Proteomics and Structural Modelling define the TRAIL DISC

2. Protein profiling of leukemic plasma membrane and cell surface receptors

Chronic lymphocytic leukaemia (CLL) and Mantle Cell Lymphoma (MCL) are aggressive B- cell malignancies which are not susceptible to current highly toxic chemotherapy. We have used plasma membrane/lipid raft purification and quantitative proteomics, RT-PCR and immunoblotting to characterise MCL plasma membrane proteins (Boyd et al., 2009). Major changes in the lipid raft signalling domains were identified along with a number of novel proteins, including a cation channel protein HVCN1.  HVCN1 has since been shown to be an abundant protein in normal human peripheral B cells and modulates BCR signalling via reactive oxygen species (Capasso et al., 2010).  New, unanticipated, BCR interacting proteins continue to be identified and our studies with MCL highlighted that quantitative proteomics has the ability to identify such changes. Thus, in HVCN1 deficient B-cells attenuated BCR signalling results in decreased activation of PI3K and AKT and impaired increases in glycolysis and oxidative phosphorylation.

3. Identification of novel proteins involved in DNA repair, protein translation, initiation and metabolic signalling pathways.

A recent project with Dr Malwewic’s group combined affinity purification with label-free proteomics to identify XLS a novel evolutionarily conserved protein associated with DNA-PKcs which is involved in double-stranded DNA break repair (Craxton et al., 2015). Other projects involve studies with groups (Professor Anne Willis and Professor Martin Bushell) who are concentrating on the role of protein translation/initiation in the cytotoxic response (King et al., 2014).  Studies with Dr Mark Stoneley and Professor Anne Willis are also identifying RNA binding proteins involved in controlling the response to toxic injury. Other projects are identifying potential kinase targets and proteins involved in the miR-34c driven response and the Ccr4-NOT complex, which is involved in the control of translational repression (Professor Martin Bushell). Many cellular processes involve the regulation and assembly of large protein complexes and the protein profiling group has developed combinatorial pathways using gel filtration, followed by immuno-affinity purification and label-free mass spectrometry to characterise such complexes (Feoktistova et al., 2011; Tenev et al., 2011b; Twiddy et al., 2004). These studies and the metabolic studies described below are mapping the proteomic changes involved in a variety of cell-signalling pathways.

4. Bioenergetic and mitochondrial proteome studies.

Tumours often rewire their metabolism (Warburg effect) to ensure constant supplies of ATP, reducing equivalents and intermediary metabolites for growth. Oxidative phosphorylation and aerobic glycolysis can cooperate to provide the energy for growth, but emerging evidence shows that modulating ox phos and aerobic glycolysis can also provide a powerful approach to killing cancer cells. Our ongoing studies are aiming to correlate proteomic changes in cellular organelles and proteomes that can affect or be affected by modulating aerobic glycolysis, ox phos, levels of ATP and ROS production, all of which can affect cell death and survival. These studies use a combination of Seahorse Bioscience extra-cellular flux analysis to simultaneously analyse mitochondrial respiration and aerobic glycolysis in intact cells. This characterises the metabolic status of the cell and we are correlating these studies with proteomic analysis of cells and cellular organelles such as mitiochondria.  For example, using this approach we identifed aberrant B-cell metabolism in HVCN1 knock out mice and altered neuronal bioenergetics in cerebral granule neurones isolated from thymidine kinase-2 KO mice (Bartesaghi et al., 2010; Capasso et al., 2010).  We have also investigated with Professor MacFarlanes group how switching metabolism from aerobic glycolysis to oxidative phosphorylation can modify the response of cells to both intrinsic (TRAIL) and extrinsic (ABT-737) cell death stimuli (MacFarlane et al., 2012; Robinson et al., 2012). Metabolic switching is controlled by interacting stress and cell-signalling pathways, particularly the AMPK and mTOR pathways, resulting in changes in protein translation and degradation (Fig. 3), leading to changes in mitochondrial and cellular proteomes.

Fig. 3

Glucose withdrawal inhibits cell death

In this respect we have identified/quantified over 900 core mitochondrial proteins in primary leukaemic cells and cell lines. Using Progenesis QI, and R-statistical analyses we can detect significant changes in the mitochondrial proteomes and associated proteins which can influence cellular metabolism, apoptotic cell death and response to cytotoxins. An important goal in toxicology is to identify potential mitotoxins and one way of testing for mitotoxicity is to condition cells with galactose which makes them totally reliant on ox phos for ATP production and cell survival. This model can be applied to a variety of cells resulting in increased sensitivity to pure mitochondrial toxins, but also rather surprisingly, changing the way in which cells die (Miles, Langlais, Jukes-Jones, MacFarlane and Cain, unpublished data).

Metabolic rewiring also leads to major changes in the mitochondrial proteome which we can detect and quantitate. Thus, there are major changes in the mitochondrial proteomes after 2DG or galactose conditioning and also significant protein differences between primary CLL cells and cell lines (unpublished data and Fig. 4). We can identify these changes using R-based statistics and Progenesis QI software to predict possible changes in proteins involved in metabolism and cell death.  For example, this analysis predicted that there would be changes in mitochondrial ultra-structures. Ongoing experiments are investigating this possibility using confocal microscopy and 3D view SEM which can visualise the mitochondrial ultrastructure within the cell (Fig.5, see also Dr Nobu Morone’s web page for further details).  This combination of proteomics, cell biology and electron microscopy illustrates how we can readily multiplex a variety of high technology platforms, providing a powerful approach to investigating the effects of toxic agents on biological systems.

Fig. 4

Volcano plot showing differences in mitochondrial proteomes of CLL and MEC-1 cells

Fig. 5

3D view of ultrastructure of mitochondria in a Z138 cell

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