Two storiesTwo stories1) reconstruction the evolution of 1) reconstruction the evolution of

a complexa complex2) Adding qualitative labels to 2) Adding qualitative labels to

predicted interactionspredicted interactions

Paulien Smits & Thijs Ettema

Department of Paediatrics, NCMD

Introduction – MRPs

• Human mitoribosome– 2 rRNAs, encoded by mtDNA

– 79 MRPs, encoded by nDNA

• Select candidate MRPs for genetic disease– Conservation

– Function

– Location

55S

28S

39S

12S

16S

31

48

Science at a Distance. http://www.brooklyn.cuny.edu/bc/ahp/BioInfo/TT/Tlatr.html, 2006

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

New orthology relations (profile-to-profile)

Human MRP Yeast MRP

MRPS25 Mrp49

MRPS33 Rsm27

MRPL9 Mrpl50

MRPL24 Mrpl40

MRPL40 Mrpl28

MRPL45 Mba1

MRPL53 Mrpl44

Human MRP Bacterial MRP

MRPS24 S3

MRPL47 L29

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 mitoribosomeRsm22 is novel mammal MRP with a rRNA

methylase function

New mammalian MRPs: Mrp10

• Small subunit protein in yeast mitoribosome

• Yeast mutant has mitochondrial translation defect

• Orthologs in eukaryotes

• Distant homology with Cox19Mrp10 orthologs in Mammals are novel

candidate MRPs

Proteome data available

Smits et al, NAR 2007

Origins of supernumerary subunits

• MRPL43, MRPS25 & complex I subunit

• MRPL43, MRPS25 & complex I subunit

• MRPL39 & threonyl-tRNA synthetase

Origins of supernumerary subunits

• MRPL43, MRPS25 & complex I subunit

• MRPL39 & threonyl-tRNA synthetase

• MRPL44, dsRNA-binding proteins

Origins of supernumerary subunits

Origins of supernumerary subunits

• MRPL43, MRPS25 & complex I subunit

• MRPL39 & threonyl-tRNA synthetase

• MRPL44, dsRNA-binding proteins

• Mrp1, Rsm26 & superoxide dismutase

Triplication of the S18 protein in the metazoa

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)

Where do the supernumerary subunits come from?

Addition of « new » paralogous subunits in the large and the small subunit in the metazoa

Where do the supernumerary subunits come from?

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

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

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

Extra domains

MRP interactorsScore COG Description0.952 COG0480 Translation elongation factors G1 and G2 (GTPases)0.946 COG0264 Translation elongation factor Ts0.945 COG0290 Translation initiation factor 30.915 COG0193 Peptidyl-tRNA hydrolase0.908 COG0223 Ribosome recycling factor0.905 COG0050 GTPases - translation elongation factor Tu0.839 COG0441 Threonyl-tRNA synthetase0.795 COG0016 Phenylalanyl-tRNA synthetase alpha subunit0.795 COG0130 Pseudouridine synthase0.772 COG0216 Mitochondrial class I peptide chain release factor 0.765 COG0024 Methionine aminopeptidase0.765 COG0858 Ribosome-binding factor A0.747 COG0072 Phenylalanyl-tRNA synthetase beta subunit0.735 COG0101 Pseudouridylate synthase0.728 COG0532 Translation initiation factor 2 (GTPase)0.625 COG2890 Methylase of polypeptide chain release factors0.916 COG0536 Predicted GTPase0.831 COG0486 Predicted GTPase0.584 COG0012 Predicted GTPase, probable translation factor0.57 COG0218 Predicted GTPase0.954 COG0201 Preprotein translocase subunit SecY0.934 COG0706 YidC/Oxa1/COX180.916 COG0457 FOG: TPR repeat0.664 COG0443 Heat shock protein SSC1 and SSE0.62 COG4775 Outer membrane protein/protective antigen OMA870.818 COG0236 Acyl carrier protein0.73 COG0304 3-oxoacyl-(acyl-carrier-protein) synthase0.772 COG0331 (acyl-carrier-protein) S-malonyltransferase0.896 COG0629 Single-stranded DNA-binding protein0.892 COG0575 CDP-diglyceride synthetase0.887 COG0305 Replicative DNA helicase0.87 COG0563 Adenylate kinase and related kinases0.669 COG0263 Glutamate 5-kinase0.609 COG0439 Biotin carboxylase0.589 COG0557 Exoribonuclease R

Translation

Protein import

Acyl carrier proteins

Other

“hypothetical gene”, essential in bacteria, Mitochondrial phenotype in yeast

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

Acknowledgements

Paulien Smits

Thijs Ettema

Bert van den Heuvel

Jan Smeitink

Exploration of the omics evidence landscape to distinguish metabolic

from physical interactions

Exploration of the omics evidence landscape to distinguish metabolic

from physical interactions

Vera van Noort

Berend Snel

Martijn Huynen

Vera van Noort

Berend Snel

Martijn Huynen

Interactome Networks

Important to know not only that two proteins interact but also how

“the cell”“the network”

the genome

Snel Bork Huynen PNAS 2002

http://www.yeastgenome.org/MAP/GENOMICVIEW/GenomicView.shtml

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)

Complex purifications

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

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

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

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

Physical and metabolic accuracy

No single data set

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

Evidence Landscape 1Evidence Landscape 1

• 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

• 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

Krogan

Gav

in

Krogan+Gavin

CoE

xp2S

p

Evidence Landscape 2

Krogan+Gavin

CoE

xp2S

p

Krogan+Gavin

sTF

*CoE

xp

CoOcc

GeN

e

GeNe

CoE

xp2S

p

Network• PPI C: 0.53, k 4.1 • Met C: 0.031, k 2.0

Threonine biosynthesis

• Some pathway links between complexes

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

Acknowledgements

• EMBL– Peer Bork– Lars Juhl Jensen– Christian von Mering

• Department of Biology, Utrecht University– Berend Snel