In silico metabolic reconstruction of Fe-S cluster biogenesis in yeast

1
In silico metabolic reconstruction of Fe-S
cluster biogenesis in yeast

• Rui Alves
• Ciencies Mediques Basiques
• Universitat de Lleida

2
Introduction

• Novel pathways are being discovered with genome
  sequencing
• Well known proteins are shown to be involved in
  some of the pathway but information about how the
  pathway structure is formed in unknown.
• Finding these circuits pathways is an important
  problem

3
Objective of the research line

• Develop coherent framework where different
  computational methods and data sets are
  integrated to predict the connectivity of
  biological pathways circuits.
• Today focus on the biology and the
  reconstruction of FeS cluster biogenesis in yeast

4
Fe-S clusters

• Iron-Sulfur Clusters are coordinated ions that
  participate in electron transfer

e-
e-
5
What is known about FeSC biogenesis

• About 15 different mitochondrial proteins are
  known to be involved in yeast
• The assembly process is ill-understood
• All 15 proteins have one thing in common

6
Phenotype of FeSC machinery deletion mutants
FeSC Dependent Protein Activity
Fe Level
WT D
WT D
FeSC dependent protein activity is impaired
Fe Accumulates
7
FeSC biogenesis in a nutshell
Synthesis
(S)
Transfer
(T)
Repair
(R)
8
The proteins and their function

• Understanding the role of seven proteins in S.
  cerevisiae. FeS cluster biogenesis
• Grx5, Arh1-Yah1, Ssq1-Jac1-Mge1, Nfs1

9
Grx5 is involved in FeSC biogenesis in S.
cerevisiae

• Glutaredoxin
• Mediates glutathionylation state of Cys residues
• May mediate protein-protein disulfide bridge
  reduction (Belli et al. 2002, Tamarit et al.
  2003, JBC)
• FeSC coordinate (mostly) with Cys residues
• Is Grx5 regulation of Cys reduction state in any
  specific protein(s) involved in FeSC biogenesis
  sufficient for phenotype?

10
Predicting partners for Grx5 the protocol

• Combine literature analysis, phylogenetic
  analysis of fully sequenced genomes and in silico
  protein docking to predict the most likely
  targets of Grx5

11
iHOP literature network reconstruction

• Literature co-occurence of genes can be taken as
  a signal that they are functionaly related and
  maybe interact physically
• iHOP performes this type of analysis
  automatically

12
Using phylogenetic profiles to predict protein
interactions
Sequence (Grx5)
Grx5 1 C 0.9
B 0.11
Target Genome Homologue in Genome 1? Homologue in Genome 2?
Grx5 B C Y N Y N Y N

Calculate coincidence index
Proteins (Grx5 and C) that are present and absent
in the same set of genomes are likely to be
involved in the same process and therefore
interact
Protein id Grx5
Grx5
B
C
Similarly, if Grx5 is absent in all genomes in
which protein B is present there is a likelihood
that they perform the same function! Grx5 has
highest CIs with the scaffold proteins
i/number of genomeslt1
j/number of genomes
0
0
1
2
13
Low level study of docking interactions in silico
Sequence of known structure
SSQIE SSQEE
Homologue sequence for structure prediction
THREAD
DOCK
OPTIMIZE
Scaffold proteins
Nfs1
(Cys Desulfurase)
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Possible partners of Grx5 in FeSC biogenesis
Bibliography Docking Phylogeny Docking
Isu1
Isa1
Isa2
Nfs1
Grx5
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FeSC biogenesis in a nutshell
Synthesis
(S)
Transfer
(T)
Repair
(R)
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Possible roles of Grx5 in FeSC
biogenesisregulation of glutathionylation
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Possible roles of Grx5 in FeSC
BiogenesisRecovery of dead-end complex
Grx5
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Studying the effect of Grx5 The modeling
Grx5,
Nfs1-SSG Nfs1
glt0 inhibits flux g0 no influence on flux ggt0
activates flux
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Studying the effect of Grx5 the protocol

• Create models for alternative networks
• Normalize equations and scan parameters
• Compare simulations with known systemic behavior
  to validate or invalidate alternatives

20
Model reproduces effect of gene deletion on
protein activity if Grx5 recovers Nfs1 activity
FeSC Dependent Protein Activity
1000s of simulations
0.5
Recovering Nfs1 and Scaffold
Not recovering Nfs1 and Scaffold
Belli et al. MBC 131109
21
Model reproduces effect of gene deletion on
protein activity if Grx5 recovers Nfs1 activity
Fe Levels
1000s of simulations
1
1
WT D
Not recovering Nfs1and Scaffold
Recovering Nfs1 and Scaffold
Belli et al. MBC 131109
22
Grx5 is predicted to dock facing the Nfs1 active
center
Active center Grx5 Cys residue
Alternative Grx5 Binding solutions
Alternative Grx5 Binding solution
Nfs1 dimer
Active center Nfs1 Cys residue
23
Conclusions
Possible Modes of action for Grx5
Reproducing experimental phenotype?
6
9
3
Nfs1-Scaffold

• Grx5 modulates Nfs1 and Scaffold
  activity/Interactions

24
Predictions for Grx5

• Grx5 modulates Cysteine Desulfurase (Nfs1) and
  scaffold activity and maybe the interaction
  between both

25
Grx5 interacts with scaffold in two-hybrid assay
26
Arh1-Yah1 proteins are involved in FeSC
biogenesis in S. cerevisiae

• Arh1-Yah1
• Ferredoxin Reductase Ferredoxin
• These proteins supply/drain electrons from other
  processes
• What is their role in FeSC biogenesis?

27
Possible partners of Arh1-Yah1 in FeSC biogenesis
Docking Phylogeny Bibliography Docking
Phylogeny
Yfh1
Isu1
Yah1
Isu2
Isa1
Arh1
Isa2
Nfu1
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Possible modes of Arh1-Yah1 action
Arh1/Yah1
Synthesis
(S)
Arh1/Yah1
Transfer
(T)
Repair
(R)
Arh1/Yah1
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Model reproduces gene deletion effect on FeSC
dependent activity if Arh1-Yah1 act on ST
FeSC Dependent Protein Activity
1000s of simulations
T RS
R RST RT
S ST
Li et al. JBC 2761503
Lange et al. PNAS 971050
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Model reproduces Fe accumulation upon gene
deletion if Arh1-Yah1 act on ST
Fe Levels
WT D
Li et al. JBC 2761503
Lange et al. PNAS 971050
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Conclusions for Arh1-Yah1
Possible Modes of Arh1-Yah1 action
Reproducing experimental phenotype?
5
7
S ST
2

• Arh1-Yah1 act on ST steps of FeSC biogenesis

32
Ssq1-Jac1-Mge1 proteins are involved in FeSC
biogenesis in S. cerevisiae

• Ssq1-Jac1-Mge1
• Chaperone Co-Chaperone Nucleotide Release
  Factor
• These proteins help fold-stabilize other proteins
• It has been suggested that they help stabilize
  the FeSC in the scaffold for transfer.
• Is this role necessary to justify the D
  phenotypes?

33
Possible Modes of Chaperone Action
Synthesis
(S)
Ssq1/Jac1/Mge1
Transfer
(T)
Repair
(R)
34
Model reproduces gene deletion effect on FeSC
dependent activity if Arh1-Yah1 act on 3 steps
1000s of simulations
FeSC Dependent Protein Activity
Stability
Fold
35
Model reproduces Fe accumulation upon gene
deletion if chaperones act on stability
Fe Levels
1000s of simulat ions
Fold
WT D
Stability
36
Conclusions
Possible Modes of Ssq1-Jac1-Mge1 action
Reproducing experimental phenotype?
1
3
Fold Stability/Fold
2

• Scaffolds need to act only on folding in FeSC
  biogenesis

37
Nfs1 is involved in FeSC biogenesis in S.
cerevisiae

• Nfs1
• Cisteine Desulfurase
• This protein provides sulfur for the biogenesis
• It has been shown in vitro that it is able to
  repair FeSC in situ and bypass the biogenesis
  pathway
• Is this role important?

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Possible modes of Nfs1 action
Nfs1
Synthesis
(S)
Transfer
(T)
Repair
(R)
Nfs1
39
Model reproduces gene deletion effect on FeSC
dependent activity if Nfs1 acts on SR
FeSC Dependent Protein Activity
1000s of simulations
R
S RS
40
Model reproduces Fe accumulation upon gene
deletion if Nfs1 acts on S
Fe Levels
1000s of simulat ions
WT D
41
Conclusions
Possible Modes of Nfs1 action
Reproducing experimental phenotype?
1
3
S SR
2

• Nfs1 needs only to act on synthesis but can also
  act on repair of FeSC

42
Summary

• Arh1-Yah1 acts on Synthesis and transfer of FeSC
• Grx5 modulates Cysteine Desulfurase (Nfs1)
  activity and maybe Scaffold activity
• Ssq1-Jac1-Yah1 act on folding FeSC proteins
• Nfs1 acts on in situ synthesis of clusters
• Yfh1 does not modulate Fe import into the
  mitochondria

Alves Sorribas 2007 BMC Systems Biology
Alves et. al. 2004 Proteins 56354
Vilella et. al. 2004 Comp. Func. Genomics 5328
Alves et. al. 2004 Proteins 57481
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PS The reconstruction method
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Acknowledgments

• FCT
• Spanish Government
• NIH (Mike Savageau)

• Albert Sorribas
• Enric Herrero
• Armindo Salvador

45
Possible partners of Nfs1 in FeSC biogenesis
Docking Docking Phylogeny Bibliography
Docking Phylogeny
Isu1
Isu2
Isa1
Isa2
Nfs1
Nfu1
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Metabolic Reconstruction FeSC biogenesisThe
view from here

• Test the predictions
• Extend the analysis to a variety of bacteria
  (Preliminary results for E. coli and Buchnera)
• Create an interactive database/server to
  implement the methodology and apply it to other
  systems

47
Process of interest
Refining using phylogenetic and omics data
Design new experiments to distinguish between
alternatives
Determining geneproteins involved in process
Refining using automated literature analysis
Protein Structure (PDB/ Models)
Two Hybrid Screens
Co-evolution analysis
Omics data analysis
Some models are validated by comparison with
existing data
No model is validated by existing data
Predict networks
In silico Protein Docking
Predict networks
Predict networks
Predict networks
Analysis of model behavior
Baeysian network/human curation for alternative
network structures
Automated creation of mathematical models
48
Reconstructing Metabolism and Investigating
Design Principles in Molecular Biology II

• Rui Alves
• Ciencies Mediques Basiques
• Universitat de Lleida

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Outline

• Metabolic Network Reconstruction
• Iron-Sulfur Cluster Biogenesis Pathway in S.
  cerevisiae.
• Pathway Evolution
• Amino acid biosynthetic pathways protein
  composition
• Design Principles
• Regulatory Design in Networks
• Two Component Systems
• Mono-functionality vs. Bi-functionality of Sensor
  Proteins

50
Studying an organism
ACTG
Stress
gtDna MAACTG gtDNA Pol MTC
Measure Response
Find signatures for physiological dynamics in
genomic data
51
Stress Regulation

• Some genes are expressed specifically in response
  to a stimulus.
• For such genes, the amino acid composition of the
  proteins can be biased to facilitate their own
  synthesis and the physiological response.
• This creates a signature of the response in the
  protein composition

52
Cognate Bias

• Cognate Bias
• Carbon Sulfur fixing enzymes have a lower
  content of amino acids with sidechains containing
  a lot of carbon or sulfur atoms (Baudouin-Cornu
  et al. Science 293297)
• Amino acid biosynthetic enzymes have a lower
  content of their cognate amino acid (Alves
  Savageau 2005 Mol. Microbiol. 561017-34)

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Regulation of amino acid biosynthetic pathways
gene expression
aa levels
protein levels
aa rich medium
aa poor medium
t
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Effect of relative content of cognate amino acid
on protein synthesis
Low cognate aa content in biosynthetic pathway
aa levels
Protein Synthesis
t
High cognate aa content in biosynthetic pathway
aa levels
Protein Synthesis
t
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The Low Cognate Bias Hypothesis

• Thus, we hypothesize that evolution would lead to
  the selection of amino-acid biosynthetic enzymes
  that have a relatively low content of their
  cognate amino acid. We call this the
  cognate-bias hypothesis.
• Test Cases
• E. coli
• S. typhimurium
• B. subtilis

56
Calculating the bias
Calculate aa composition
aa-biosynthesis proteins
Rank w/respect to proteome
Protein Sequences
P(aa)
Control Groups
Proteome
Growing cells aa composition

57
There is low cognate bias in aa biosynthesis
58
Positive correlation between relative cognate aa
composition and specific activity of proteins
Plt0.003
59
Negative correlation between relative cognate aa
composition and number of proteins in pathway
Plt0.003 (except Bs)
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Higher bias is due to functional requirements
Active Center
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Exceptions to the cognate bias hypothesis

• Functional Reasons
• Active Centers (Phe, Tyr)
• Dimerization Domains (Asp)

62
Low cognate bias is present in organisms with the
full complement of pathways
63
Environmental effects in cognate bias

• The relative abundance of amino acid should
  influence the pressure to keep a low cognate
  amino acid content in biosynthetic pathways
• E. coli (or S. typhimurium) and B. subtilis live
  in different environments
• Amino acid availability is different
• Is there a signal for these differences?

64
Differences in amino acid composition of distinct
habitats
Soil
Gut
Low amounts Arg, Glu, Lys, Trp Tyr
Intermediate amounts Ala, Asp, Thr
High amounts Gly, Ser
Low amounts Ala, Asp, Gly, Ser, Thr
High amounts Arg, Glu, Lys, Trp, Tyr
Savageau 1983 Am. Naturalist. 122732
65
Positive correlation between environmental levels
of aa and cognate bias
Presence of aa in environment decreases pressure
for low cognate bias
Plt0.004
66
Environmental availability influences cognate bias

• Thus the effect of amino acid availability in the
  environment and the consequent regulation and
  physiological dynamics of gene expression does
  leaves a signature in the cognate bias of the
  different pathways.

67
Conclusions

• The low cognate bias hypothesis is supported by
• Analysis of protein composition
• Analysis of protein specific activity
• Analysis of pathway length
• Some factors that overcome selective pressure for
  low cognate bias
• Functional requirements for specific cognate
  residues
• Environmental factors

Alves Savageau 2005 Mol. Microbiol. in press
68
Amino acid composition the view from here

• Extend the work for other bacteria and attempt to
  create organism/environment biosignatures
• Analyze ribosomal proteins amino acid bias, amino
  acid transport proteins bias and catabolic
  protein bias
• Analyze influence of oxydizability on selection
  of surface amino acids in proteins

69
Outline

• Metabolic Network Reconstruction
• Iron-Sulfur Cluster Biogenesis Pathway in S.
  cerevisiae.
• Pathway Evolution
• Amino acid biosynthetic pathways protein
  composition
• Design Principles
• Regulatory Design in Networks
• Two Component Systems
• Mono-functionality vs. Bi-functionality of Sensor
  Proteins

70
Alternative sensor design in Two Component Systems
Monofunctional Sensor Bifunctional Sensor
71
Studying physiological differences of alternative
designs
72
Physiological Predictions

• Bifunctional design lowers Q2 signal
  amplification
• prefered when cross-talk is undesirable
• Monofunctional design elevates Q2 signal
  amplification
• prefered when cross-talk is desirable.

73
Predicting Monofunctionality from structure
1000 sequences from genomic data of dozens of
bacteria
100s predicted structures by modeling
25 monofunctional sensors
Alves Savageau 2003 Mol. Microbiol. 4825
74
Two Component Systems the view from here

• Independent phosphatase activity (directly
  dependent on HK vs. Not directly dependent on HK)
• Analysis of Phosphorelays
• TCS vs. Eukaryotic signal transduction
• Alternative designs of TCS (Nar system)
• Metabolic Reconstruction

75
Summary

• Development and integration of computational
  tools to address quantitative biological problems
• Biological results form the application of these
  methods
• Reconstruction of Metabolism
• Testing evolutionary hypothesis using large scale
  genome data
• Understand the design principles of biological
  networks.

76
Acknowledgments

• PGDBM
• JNICT
• FCT
• Spanish Government
• Portuguese Government
• NIH (Mike Savageau)
• DOD (ONR) (Mike Savageau)

• Mike Savageau
• Mike Sternberg
• Albert Sorribas
• Enric Herrero
• Armindo Salvador

77
Metabolic Reconstruction the view from here (I)
78
(No Transcript)
79
The Putting it together Section
Use Know how to reconstruct TCS network in M.
xanthus
Continue FeSC work
Analyze aa biosyntesis and enzyme networks
evolution
Analyze more TCS designs
80
Grx5 interacts with Scaffold in Two-Hybrid assay
Grx5 Scaffold
Positive Control
Negative Controls
Two-hybrid analysis of the interaction between
Grx5 and Scaffold. Numbers over bars indicate the
beta-galactosidase activity (Miller units) in
cultures of S. cerevisiae cells co-transformed
with plasmids pGBT9 and pACT2 vectors alone, or
derivatives expressing the respective Gal4 fusion
proteins with Grx5, Scaffold, and two proteins
known to interact. Results are the mean of three
independent experiments.
81
ATP binding domain important in functionality of
sensor
Sensor with known structure
SSQIE SSQ-E
Sensor sequence for structure prediction
100s of structure predictions
Alves Savageau 2003 Mol. Microbiol. 4825