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Two stories
1) reconstruction the evolution of
a complex
2) Adding qualitative labels to
predicted 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
12S
31
28S
48
55S
39S
16S
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
MRPS33
MRPL9
MRPL24
MRPL40
MRPL45
MRPL53
Human MRP
Mrp49
Rsm27
Mrpl50
Mrpl40
Mrpl28
Mba1
Mrpl44
Bacterial MRP
MRPS24
MRPL47
S3
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 mitoribosome
Rsm22 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 Cox19
Mrp10 orthologs in Mammals are novel
candidate MRPs
Proteome data
available
Smits et al, NAR 2007
Origins of supernumerary subunits
• MRPL43, MRPS25 & complex I subunit
Origins of supernumerary subunits
• 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
•
•
•
•
MRPL43, MRPS25 & complex I subunit
MRPL39 & threonyl-tRNA synthetase
MRPL44, dsRNA-binding proteins
Mrp1, Rsm26 & superoxide dismutase
Where do the supernumerary subunits come from?
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
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 Nterminus and acquiring a function in translation
and COX assembly as a constituent of the
mitoribosome
Extra domains
MRP interactors
Score
0.952
0.946
0.945
0.915
0.908
0.905
0.839
0.795
0.795
0.772
0.765
0.765
0.747
0.735
0.728
0.625
0.916
0.831
0.584
0.57
0.954
0.934
0.916
0.664
0.62
0.818
0.73
0.772
0.896
0.892
0.887
0.87
0.669
0.609
0.589
COG
COG0480
COG0264
COG0290
COG0193
COG0223
COG0050
COG0441
COG0016
COG0130
COG0216
COG0024
COG0858
COG0072
COG0101
COG0532
COG2890
COG0536
COG0486
COG0012
COG0218
COG0201
COG0706
COG0457
COG0443
COG4775
COG0236
COG0304
COG0331
COG0629
COG0575
COG0305
COG0563
COG0263
COG0439
COG0557
Description
Translation elongation factors G1 and G2 (GTPases)
Translation elongation factor Ts
Translation initiation factor 3
Peptidyl-tRNA hydrolase
Ribosome recycling factor
GTPases - translation elongation factor Tu
Threonyl-tRNA synthetase
Phenylalanyl-tRNA synthetase alpha subunit
Pseudouridine synthase
Mitochondrial class I peptide chain release factor
Methionine aminopeptidase
Ribosome-binding factor A
Phenylalanyl-tRNA synthetase beta subunit
Pseudouridylate synthase
Translation initiation factor 2 (GTPase)
Methylase of polypeptide chain release factors
Predicted GTPase
Predicted GTPase
Predicted GTPase, probable translation factor
Predicted GTPase
Preprotein translocase subunit SecY
YidC/Oxa1/COX18
FOG: TPR repeat
Heat shock protein SSC1 and SSE
Outer membrane protein/protective antigen OMA87
Acyl carrier protein
3-oxoacyl-(acyl-carrier-protein) synthase
(acyl-carrier-protein) S-malonyltransferase
Single-stranded DNA-binding protein
CDP-diglyceride synthetase
Replicative DNA helicase
Adenylate kinase and related kinases
Glutamate 5-kinase
Biotin carboxylase
Exoribonuclease R
Translation
“hypothetical gene”,
essential in bacteria,
Mitochondrial phenotype
in yeast
Protein import
Acyl carrier proteins
Other
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
Vera van Noort
Berend Snel
Martijn Huynen
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
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 knockout not lethal, knockout 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)
•
Metabolic interactions (KEGG pathways < 2000)
– Remove cytosolic ribosomes
– Remove “possible”, “hypothetical”, “predicted”
– Remove “other”
– Remove paralogs
– Remove interactions between same EC numbers
– Remove interactions that are already physical
Metabolic and
Physical accuracy
Negative metabolic
Positive physical
Negative
•
Positive metabolic
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
TP meta
•
in bin
•
•
•
•
A meta
A phys=
A total
A diff =
Negative metabolic
FP meta
Positive physical
TP phys
Negative physical
FP phys
=
TP meta / (TP meta + FP meta + TP phys + FP phys)
TP phys / (TP meta + FP meta + TP phys + FP phys)
=
A meta + A phys
A meta – A phys
Gavin
CoExp2Sp
Evidence Landscape 1
Krogan
Krogan+Gavin
• Absence of physical interactions
• Metabolic relations in areas where proteomic approaches report no copurification 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
CoExp2Sp
sTF*CoExp
Evidence Landscape 2
Krogan+Gavin
GeNe
CoExp2Sp
Krogan+Gavin
CoOcc
GeNe
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