High-throughput Proteomics

1. High-throughput Proteomics David Birnbaum

2. Introduction • What is Proteomics ? • Proteomics is the analysis of genomic complements of proteins. • Why proteomics ? • Until now we have looked at many methods which deal with RNA & DNA. • However, Proteins ultimately define how the cell behaves. • Therefore, information about RNA & DNA, while a necessary prerequisite to analyzing proteins on large scale can’t tell us much about the function of the proteins they encode for. • Proteomics tries to define the function, quantities and structures of large complements of proteins.

3. What will we see? • We will see two studies, from Erin O’shea’s lab who used the same method to manipulate large complements of proteins. • They checked localization and expression levels of proteins. • A study which tries to build a better model of the cell’s dynamics. • Some proteomics methods

4. Global analysis of protein localization in budding yeastWon-Ki Huh et. al. nature 425 (october 2003) • Goal: to classify for each Protein the cell compartment(s) in which he resides. • This would lead to understand and verify data about protein-protein interactions and protein function

5. Howson et al.Construction, verification and experimental use of two epitope-tagged collections of budding yeast strains Comp Funct Genom 2005; 6: 2–16.

6. Howson et al.Construction, verification and experimental use of two epitope-tagged collections of budding yeast strains Comp Funct Genom 2005; 6: 2–16.

7. Localization determination • Micrographs of each strain lacking ORF name were evaluated independently by two scorers. • Initial classification of each protein to one or more of 12 subcellular localization categories • Refinement by co-localization

8. Example micrograph Nic96 Nuclear perphery Rox3 nucleus Pho86 ER Hof1 Bud neck llv6 mitochondrion Erg6 Lipid particle

9. Co-localization Utp13 was initially localized to the nucleus nucleolus marker Final localization to the nucleolus Cbf2 was initially localized to the nucleus Final localization to the nucleus nucleolus marker

10. Results

11. Results

12. Comparison with older data • The Saccharomyces Genome Database (SGD) contained localization information for 60% of the proteins visualized. • 80% aggrement with SGD • A mass spectrometric analysis of the nuclear pore complex revealed 29 NPC proteins. • 25 of those were visualized, 23 to the nuclear periphery.

13. Localization and interaction • There is large-scale data about protein-protein interaction • However, total coverage is poor and false positive rates remain high. • To interact proteins need to be in close proximity. • Therefore, combining the localization and interaction data might help to verify interaction results.

14. Localization and interaction • Data about interactions was taken from the GRID db. • The statistical method: • For each interaction pair the fraction of total number of interactions occurring between it was calculated • A set of random pairs was chosen and analyzed the same way • Fold enrichment was calculated between the two sets.

15. The interaction matrix

16. Interaction map

17. problems • Possible destruction of localization signals • Possible Interruption of post translational modifications • Fusion proteins might have steric hindrance

18. Summary • Localization data for many previously unlocalized proteins was collected. • Agreement with older data. • Identification of interacting compartments.

19. Web Site • The authors created a web site with all the micrographs and the localization data: • http://yeastgfp.ucsf.edu/

20. Global analysis of proteinexpression in yeastGhaemmaghami S. et. al. nature 425 (October 2003) • Goal: to globally determine protein expression levels in the infamous budding yeast. • Identification of mis-annotated genes

21. The experiment • Creation of fusion library: • The tag is the Tandem Affinity Purification Tag (TAP) • Marker for histidine

22. The experiment Western blot Clb2, Sic1 comparison • Checking that the proteins are fused and are active • Western blot analysis. • Clb2, Sic 1 comparison. • Successful integration for 98% of ORF’s annotated in the Sacchromyces DB. • Successful integration for 93% of essential genes

23. Results • Detected 79 % of essential ORF’s. • 83 % of ORF’s with assigned gene names • Identified Protein product for 1,018 functionally uncharacterized ORF’s • 73 % of all annotated ORF’s • Sporious ORF’s • Genes encoding for product not needed in log phase

24. Eliminating spurious ORF’s • Product not identified both by GFP & TAP. • CEC values below an arbitary cut-off.

25. Codon Enrichment Correlation • Codon usage in genuine coding regions deviates systematically from randomly generated ORF’s • Preference in amino acid composition • Bias in the usage of synonymous codons • The CEC evaluates the pattern of codon usage in potential ORF’s

26. CEC • Calculation of CEC: • The prevalence of each codon in the 3,753 named ORF’s was calculated. • The prevalance of each codon in a random sequence was calculated as well. • The enrichment of each codon :

27. CEC • The enrichment was calculated in the same way for each test ORF • The CEC is the linear correlation coefficient between the test ORF and the positive set • They could have calculated P-values instead.

28. CEC - example YDL121C and YKR047W are two small, uncharacterized ORFs that are listed as ‘hypothetical’ in the yeast genome database CEC -0.33 No product detected Marked as sporious CEC 0.7 Protein observed

29. Remember kellis ? Dedicated to Nir & Sonia

30. Sequencing and comparison of yeast species to identify genes and regulatory elements(*) • חוקר ראשי: Mannollis Kellis • ראש מעבדה: Eric s. Lander (*) Kellis M, et al. (2003) Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature423: 241–254

31. Remember Kellis ? • Kellis identified 496 sporious ORF’s • 489 of those were not observed in this study. • 381 sporious ORF’s that this study identified overlap with Kellis.

32. Results

33. Results • Range of abundance 50-106 • Greater sensetivity than previous methods (MS, 2d gels)

34. Results • Are the small amounts of proteins identified represent functionally significant amounts of Protein? • Proteins not needed are not detected at all • Same distribution profile for essential proteins and for the full set. • Transcription factors are present at low levels

35. Results

36. mRNA - Protein • Observed a significant relationship between mRNA levels1 to Protein levels(rs=0.57) • The Codon Adaptation Index is used to measure the composition of codons in a given ORF in comparison with a predefined set of reference genes. • Higher CAI values usually mean faster translation. • Relationship also between protein abundance and CAI values (rs=0.55) 1.Holstege, F. C. et al. Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95, 717–728 (1998).

37. mRNA-Protein Protein levels vs. Protein abundance mRNA CAI mRNA & CAI CAI values CAI values mRNA concentration

38. Summary • The research demonstrated the possibility to globally determine Protein abundances. • The results show a great variation in expression level between proteins. • The method also helps in the identification of spurious ORF’s

39. Post transcriptional expression regulation in the yeast Saccharomyces cerevisiae on a genomic scaleBeyer et. al.Molecular & Cellular Proteomics 3.11 • Goal: to invesigate the relation of transcription, translation and protein turnover on a genomic scale.

40. Motivation • To make use of the large scale data to create a more complete picture of the cell’s strategy in determining expression profiles of proteins

41. Source Data • mRNA abundance data from 36 experiments • 30 independent measurements for > 6000 ORF’s • The data was normalized to reduce inconsistencies between the data sets. • Resulting dataset characterized with low noise

42. Source Data • Protein abundance data from Previous article and from Greenbaum D et. al. Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 4, 117.1-117.8 • Only 1,669 ORF’s are contained in both studies. • Therefore, protein abundances are much less certain than mRNA abundances. • However, when using the average the correlation between Protein and mRNA levels are most significant. • With new data the correlations will improve.

43. Results • Protein – mRNA abundance • The median value for the entire cell • Protein 2,800 • mRNA 0.7 • Median values are less affected by extreme values • Median is more stable against variations between the studies. • The correlation discovered was rs=0.58.

44. Source Data • Trying to make a better model. • Adding two more values which determine the translation rate • Ribosome occupancy – the fraction of mRNA bound to ribosomes • Ribosome density – how many ribosomes are on one mRNA • Translational rate – Ribosome density * Ribosome occupancy • Translational activity – mRNA levels * Translational rate

45. The advanced model Ribosome occupancy Ribosome density mRNA level

46. results

47. Results • Spearman rank correlation coefficents were taken between mRNA abundance and Protein abundance and between Translational activity and protein abundance • The rs improved only to 0.596

48. Results • Protein to mRNA ratio is indicative of post transcriptional regulation as well as to translational activity • The turnover rate of proteins has a significant affect on Protein-mRNA ratios • The Protein Half-life Descriptor (PHD)

49. PHD • [Pi] – Protein concentration of the i ORF. • [mRNAi] – mRNA concentration of the i ORF • Ktransl,I – Translational Rate (Ribosome density * Ribosome occupancy) • Kp – Genome wide translation constant • Kd,I – Destruction rate of the i ORF.

50. Results