Paulien Smits & Thijs Ettema Department of Paediatrics, NCMD

1. Two stories1) reconstruction the evolution of a complex2) Adding qualitative labels to predicted interactions Paulien Smits & Thijs Ettema Department of Paediatrics, NCMD

2. 12S 31 28S 48 55S 39S 16S Introduction – MRPs • Human mitoribosome • 2 rRNAs, encoded by mtDNA • 79 MRPs, encoded by nDNA • Select candidate MRPs for genetic disease • Conservation • Function • Location Science at a Distance. http://www.brooklyn.cuny.edu/bc/ahp/BioInfo/TT/Tlatr.html, 2006

3. Objectives Detection of MRPs • Orthology relations between MRPs from different species • New human MRPs based on comparison with MRPs in other species • Specific functions of MRPs based on comparison with MRPs in other species • Extra domains in MRPs • Find MRP associated proteins

4. New orthology relations (profile-to-profile)

5. New mammalian MRPs: Rsm22 • Small subunit protein in yeast mitoribosome • Orthologs in eukaryotes and prokaryotes • Homologous to rRNA methylase • S. pombe: fusion protein Rsm22+Cox11 Yeast: Cox11 attached to mitoribosome • Rsm22 is novel mammal MRP with a rRNA methylase function

6. New mammalian MRPs: Mrp10 • Small subunit protein in yeast mitoribosome • Yeast mutant has mitochondrial translation defect • Orthologs in eukaryotes • Distant homology with Cox19 • Mrp10 orthologs in Mammals are novel candidate MRPs

7. Proteome data available Smits et al, NAR 2007

8. Origins of supernumerary subunits • MRPL43, MRPS25 & complex I subunit

9. Origins of supernumerary subunits • MRPL43, MRPS25 & complex I subunit • MRPL39 & threonyl-tRNA synthetase

10. Origins of supernumerary subunits • MRPL43, MRPS25 & complex I subunit • MRPL39 & threonyl-tRNA synthetase • MRPL44, dsRNA-binding proteins

11. Origins of supernumerary subunits • MRPL43, MRPS25 & complex I subunit • MRPL39 & threonyl-tRNA synthetase • MRPL44, dsRNA-binding proteins • Mrp1, Rsm26 & superoxide dismutase

12. Where do the supernumerary subunits come from? Triplication of the S18 protein in the metazoa

13. Where do the supernumerary subunits come from? One new, metazoa specific protein of the Large subunit (L48) has been obtained by duplication of a protein from the small subunit (S10)

14. Where do the supernumerary subunits come from? Addition of « new » paralogous subunits in the large and the small subunit in the metazoa

15. Addition of a new subunit (L45 / MBA1) that is homologous to TIM44 (protein import) and bacterial proteins of unknown function

16. Homology between Mba1/MRPL45 and TIM44 Dolezal P, Likic V, Tachezy J, Lithgow T. Evolution of the molecular machines for protein import into mitochondria. Science 2006;313:314-8

17. MRPL45, Mba1 & Tim44 • Mba1 is physically associated with LSU • Transcription of Mba1 and MRPs is co-regulated • Function of MRPL45 unknown • COG4395 (MRPL45&Tim44) has similar phylogenetic distribution as COG3175 (Cox11) • Alpha-proteobacterial Tim44 is ancestor of MRPL45 and yeast ortholog Mba1, losing the N-terminus and acquiring a function in translation and COX assembly as a constituent of the mitoribosome

18. Extra domains

19. MRP interactors Translation “hypothetical gene”, essential in bacteria, Mitochondrial phenotype in yeast Protein import Acyl carrier proteins Other

20. Conclusions • Established orthology relations between bacterial, fungal and metazoa specific ribosomal proteins • Highly dynamic evolution of a mitochondrial protein complex • 2 Potential novel human MRPs • Homologies show diverse origins of supernumerary MRPs • Some MRPs have extra domains • Identification of novel MRP interactors

21. Acknowledgements Paulien Smits Thijs Ettema Bert van den Heuvel Jan Smeitink

22. Exploration of the omics evidence landscape to distinguish metabolic from physical interactions Vera van Noort Berend Snel Martijn Huynen

23. Interactome Networks “the network” “the cell” the genome http://www.yeastgenome.org/MAP/GENOMICVIEW/GenomicView.shtml Snel Bork Huynen PNAS 2002 Important to know not only that two proteins interact but also how

24. Genomic data sets • Comprehensive complex purification data (Krogan, Gavin) • Shared Synthetic lethality • Co-regulation (ChIP-on-chip) • Co-expression • Conserved co-expression (orthologous, paralogous, four species) • Gene Neighborhood conservation (STRING pink) • Gene CoOccurrence (STRING pink)

25. Complex purifications • Fuse query protein with a hook • Pull down hook from in vivo extracts • Identify proteins that co-purify • Socio-Affinity score

26. Synthetic lethality • One knock-out not lethal, second knock-out not lethal, knock-out both lethal • Points to complementary pathways • Shared synthetic lethality points to same pathway

27. Objective: distinguish physical from metabolic in omics data • We integrate omics data sets for the budding yeast S.cerevisiae because of many high quality data sets as well as classical knowledge about protein functions • We construct two separate reference sets: one for physical interactions and one for metabolic interactions. • Physical interactions (Mips complexes) • Remove cytosolic ribosomes • Remove “possible”, “hypothetical”, “predicted” • Remove “other” • Metabolic interactions (KEGG pathways < 2000) • Remove paralogs • Remove interactions between same EC numbers • Remove interactions that are already physical

28. Metabolic and Physical accuracy Positive metabolic Negative metabolic Positive physical Negative physical • in bin TP meta FP meta TP phys FP phys • A meta = TP meta / (TP meta + FP meta + TP phys + FP phys) • A phys = TP phys / (TP meta + FP meta + TP phys + FP phys) • A total = A meta + A phys

29. Physical and metabolic accuracy No single data set

30. Differential accuracy • Good at predicting metabolic + bad at predicting physical interactions Positive metabolic Negative metabolic Positive physical Negative physical • in bin TP meta FP meta TP phys FP phys • A meta = TP meta / (TP meta + FP meta + TP phys + FP phys) • A phys = TP phys / (TP meta + FP meta + TP phys + FP phys) • A total = A meta + A phys • A diff = A meta – A phys

31. Evidence Landscape 1 Gavin CoExp2Sp Krogan+Gavin Krogan • Absence of physical interactions • Metabolic relations in areas where proteomic approaches report no co-purification while strong indications for co-regulation. Logical in hindsight? • We should not only use integrations based on the top scoring proteins but also use non-scoring proteins. • Need physical protein interaction data sets where the nulls are really true nulls rather than the absence of results

32. Evidence Landscape 2 sTF*CoExp CoExp2Sp Krogan+Gavin Krogan+Gavin GeNe CoExp2Sp GeNe CoOcc

33. Network • PPI C: 0.53, k 4.1 • Met C: 0.031, k 2.0 Threonine biosynthesis • Some pathway links between complexes

34. Conclusion & Discussion • We can in principle distinguish metabolic and physical interactions, if 2 reference sets, if comprehensive • Yet sparse (problem for multi-dimensional) • Novel ways of integration and more types of omics data will allow extraction of more qualitative predictions on the nature of protein interactions

35. Acknowledgements • EMBL • Peer Bork • Lars Juhl Jensen • Christian von Mering • Department of Biology, Utrecht University • Berend Snel