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
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