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A systems approach to sub-cellular localisation of proteins Kathryn S. Lilley PowerPoint Presentation
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A systems approach to sub-cellular localisation of proteins Kathryn S. Lilley

A systems approach to sub-cellular localisation of proteins Kathryn S. Lilley

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A systems approach to sub-cellular localisation of proteins Kathryn S. Lilley

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  1. SPEAF 2012 Rouen A systems approach to sub-cellular localisation of proteins Kathryn S. Lilley Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, United Kingdom, CB2 1QR

  2. Organelles of the cell Eucaryote cells have many different types of sub-cellular compartments (some specific to a cell type) Many proteins reside in multiple locations Within these locations many form functional units Dynamic changes in these locations (and binding partners) reflect biological processes in which a protein functions Most proteomics protocols start with addition of detergents which destroy delicate sub-cellular structures Changes in sub-cellular dynamics are as important as changes in abundance, post translational status and interacting partners. http://media.web.britannica.com/eb-media

  3. Tagging of a fluorescent fusion proteins Immunofluorescence Mass spectrometry based methods Prediction from sequence? Limited Static LC-MS/MS derived catalogue Quantitative proteomics methods to show enrichment of proteins within different subcellular preparations

  4. Databases often carry contradictory assignments Different approaches can lead to conflicting data ....but can be highly complementary

  5. Organelle “enrichment” rather than organelle “purification” Purification strategies can result in contamination by other organelles and false-positive hits Dynamic proteome: proteins in transit (cargo proteins) may be just passing through! Purification of an organelle gives no information about steady state location of proteins with multiple localisations

  6. Sampling the cell as a whole? or in fact any method that gives differential enrichment Equilibrium density centrifugation …….. PCP and LOPIT de Duve’s Principle (adapted) Based on the principle that during analytical centrifugation, organelle structures will migrate until they reach their buoyant densities Proteins from the same organelle will have identical distribution profiles through the gradient Novel organelle residents can be assigned by matching their profiles to the distribution of known marker proteins Winner of the 1974 Nobel Prize in physiology or medicine for his discovery of the lysosome and the peroxisome.

  7. LOPIT Workflow Density gradient centrifugation Differential Centrifugation Organelle Fractionation Western blot

  8. Reporter ion intensities mimic the peptide distribution profiles TMT127 TMT128 TMT130 TMT131 TMT126 TMT129

  9. Steady State Position Mixed Locations Gattoet al., 2010

  10. LOPIT in a whole organism Drosophila embryos Tan et al (2009) J. Prot Res 8(6):2667-2678

  11. Arabidopsis thaliana root derived callus Nino Nikolovski Paul Dupree Denis Rubstov

  12. Increased coverage by combining experiments membrane + membrane associated 2205 proteins identified 1826 quantified in all replicates after imputation 163 Golgi proteins 320 ER 266 PM Nikolovski 2012 in press

  13. Saccharomyces cerevisiae Comparison with GFP dataset (Huh et al, 2003) revealed good overlap for some organelles, but not for PM or Golgi >1500 proteins Y. Wang and S. Oliver - unpublished

  14. Golgi Lysosome Mitochondrion Chicken DT40 cell line Combine with next slide Plasma membrane Endoplasmic reticulum Tony Jackson Stephanie Hall Matthew Trotter Hall et al 2009

  15. PC2 B C D PC1 E G F Dynamic system – in action IgM clathrin B+C IgM E+F Rab4 Clathrin IgM B-cell receptor and clathrin show average position away from plasma membrane cluster Hall et al 2009.

  16. E14TG2a mouse embryonic stem cell line Dppa5a Erk-2 Alkaline phosphatase E-ras Sox2 β-catenin LIF receptor Fgf4 Mcl-1 Dppa4 Risc Tdgf-1 Oct4 Utf1 Andy Christoforou

  17. HEK293T Human cell line Andy Christoforou

  18. Comparison with Human Protein Atlas Andy Christoforou

  19. Organelle Fractionation Machine learning methods to allow greater data mining Dynamics changes in location Predict multiple locations Western blot Trypsinization and labelling Combine MSnBASE ? ? MS/MS LOPIT pipeline Gatto and Lilley , Bioinformatics 2012

  20. Drosophila embryos Identification and assignment of proteins to organelles with phenoDisco Original Dataset PC2 Proteasome Cytoplasm PC1 PC2 Ribosomal (40S) cluster Protein-organelle prediction with supervised KNN Nucleus Ribosomal (60S) cluster PC2 Peroxisome PC1 Tan et al. J .of Proteome Res. (2009) 8(6):2667-78 PC1

  21. LOPIT on Arabidopsis Identification and assignment of proteins to organelles with phenoDisco PC2 Chloroplast envelope Original Dataset Ribosomal (40S) cluster PC1 PC2 TGN Ribosomal (60S) cluster PC2 Protein-organelle prediction with supervised KNN ABC transporters ER membrane associated PC1 Dunkley et al. PNAS (2006) 103(17):6518-23 PC1

  22. Summary

  23. The ‘LOPITEERS’ Andy Christoforou Laurent Gatto ArnoudGroen Adam Gutteres Claire Mulvey Dan Nightingale Nino Nikolovski Konstanze Schott PavelShliaha Lisa Simpson Matthew Trotter Yuchong Wang Houjiang Zhou Cambridge Collaborators Paul Dupree Stephanie Hall Tony Jackson Alfonso Martinez Arias LudovicVallier Dirk Walther Matthias Mann Peter James Isaac Newton Trust