Pathway identification and reconstruction by cooperative use of computational biology methods
The case of mitochondrial Iron-Sulfur assembly metabolism in Saccharomyces cerevisiae
Rui Alves & Albert SorribasGrup de Bioestadística i BiomatemàticaDepartament de Ciències Mèdiques BàsiquesInstitut de Recerca Biomèdica de Lleida (IRBLLEIDA)Universitat de Lleida (Espanya)
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Introduction Pathway identification is an important issue.
Bioinformatics provides valuable tools for automatically extracting information that can be used for identifying the structure of metabolic networks. Text mining, phylogenetic profiling, protein-protein interaction,
pathway databases, structural methods. Expert knowledge complements all these tools
In most cases, we obtain static descriptions and alternative network structures that require further investigation.
Mathematical modeling and simulation can be used for critically evaluating the various alternatives obtained from these tools. Best case: Network assessment through the analysis of dynamic
data (requires good experimental data) Worst case: Network assessment through intensive computation
and evaluation of alternatives (simulate alternative scenarios) We shall discuss the integration of the various approaches and
the role of mathematical models by focusing in the Iron-Sulfur Cluster (FESC) biogenesis process.
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An interesting biological problem: Iron-sulfur cluster biogenesis
Iron-sulfur clusters (FeSC) are important prostetic groups.
A number of proteins have been putatively identified as being involved in the process (Grx5, Arh1, Yah1, Nfs1,….).
No agreement exists on the pathway structure and some proteins may play alternative roles. Almost no kinetic data and metabolic data are available on the underlying processes (few
experimental data available).
Goals Identify the most likely network accounting for the
available information Test alternative roles for some of the involved proteins Suggest experiments to test our predictions
2Fe2S Cluster in Ferredoxin
4Fe4S Cluster
Proteins Protein Function Suggested Function In FESC Assembly
Arh1 Ferredoxin Reductase
Provides electrons for FESC assembly/transfer/repair
Yah1 Ferredoxin Provides electrons for FESC assembly/transfer/repair
Yfh1 Frataxin Stores/Provides Fe directly to FESC assembly and Heme synthesis
Grx5 Glutaredoxin Regulates glutathionylation state of protein cysteinyl residues
Nfs1 Cysteine Desulfurase Provides sulfur for FESC assembly in scaffold dimers or in situ FESC assembly/repair
Isa1 Scaffold As dimer scaffolds initial FESC assembly between two monomers and then transfers it to apo-proteins
Isa2 Scaffold As dimer scaffolds initial FESC assembly between two monomers and then transfers it to apo-proteins
Isu1 Scaffold As dimer scaffolds initial FESC assembly between two monomers and then transfers it to apo-proteins
Isu2 Scaffold As dimer scaffolds initial FESC assembly between two monomers and then transfers it to apo-proteins
Nfu1 Scaffold Assembles FESC
Ssq1 Hsp70 Chaperone Assists in proper folding of FESC biosynthetic proteins, namely Yfh1 and Isa-Isu proteins/Assists in maintaining FESC assembled in scaffold dimer for proper transfer
Jac1 Hsp40 Co-chaperone Assists Ssq1 in interacting with Isu/Isa proteins
Mge1 Co-chaperone/ Nucleotide exchange factor
Assists Ssq1
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Challenges in the identification of the network involved in FeSC biogenesis
FeSC are labile, protein-protein interactions (substrate channeling) are expected to play an important role.
No experimental measurements exist on fluxes and/or dynamic patterns. Nevertheless, experimental observation show that
depletion on some of the involved proteins result on Fe accumulation and in a decrease in the activity of FeSC enzymes.
Redox state of proteins and their regulation through glutationylation/deglutationylation may play an important role. However, different alternative steps are possible.
Alves R, Herrero E, Sorribas A. Predictive reconstruction of the mitochondrial iron-sulfur cluster assembly metabolism: I. The role of the protein pair ferredoxin-ferredoxin reductase (Yah1-Arh1). Proteins. 2004 Aug 1;56(2):354-66.
Alves R, Herrero E, Sorribas A. Predictive reconstruction of the mitochondrial iron-sulfur cluster assembly metabolism. II. Role of glutaredoxin Grx5. Proteins. 2004 Nov 15;57(3):481-92.
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Methods for identifying network structure Text mining of existing literatureRelationships between genes identified to play a role in FeSC assembly in S.cerevisiae
Bibliometric tools (iHOP) Identify genes that have been
reported to be involved in FeSC assembly
Suggest a static network accounting for the published results
Procedure Select a gene suggested to
play a role in the process (say Grx5)
Search for new genes that appear as related to that gene.
Select abstracts in which iron-sulfur biogenesis is discussed.
Start a new search using the new genes as a seed.
Stop when no new genes are added.
Network of relationships between genes involved in FESC biogenesis resulting from iHOP analysis
Text mining is able of finding allthe genes that account for proteins
suggested to play a role in FeSC biogenesis
ISA1 ISA2
NFU1 YHF1
ISU2
ISU1
SSQ1
GRX5
MGE1
JAC1
HSPA4 / SSA3
NFS1
ATM1
YAH1
ARH1
POU2F1 / SLC22A1 / OCT1
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Phylogenetic profilingAnalyze possible co-evolution
Presence/absence of some proteins in different genomes can be taken as a clue that these participate in the same process Compute a vector of presence/absence of homologues in
each genome for each yeast protein Compute a co-ocurrence index (CI)
Criteria: Significant phylogenetic co-evolution if the CI is higher than that for 95% of the proteins in the genome
analyzed genomes totalofNumber
otherwise 0
genomein homolgues have)not do(or haveboth protein and protein reference if 1
/
n
ijPR
nCI
iPRij
iPRij
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Phylogenetic profilingResults
Yah1 and Arh1. Grx5, Isa1, Isa2, Yah1 Isu1, Isu2, Nfu1, Jac1
Results strongly suggest participation of some proteins in the same process.
However, we can not infer the order of the reactions or the physical interactions between proteins.
Network of interactions derived from phylogenetic profiling. Edges between two genes are shown if and only if the CI rank is within the top two for both genes. Although the CI for two genes is always the same, in relative terms,
this CI can be on the Top 2 for one of the genes and not for the other.
NFS1
ATM1
JAC1
MGE1
SSQ1
YFH1
YAH1
ARH1
ISA1 ISA2
GRX5
ISU2ISU1
NFU1
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Methods for identifying network structure Protein-protein docking
Check for possible physical interactions among proteins Derive protein structures Compute interactions Identify the most likely
interactions Problems
It is always possible to compute a interaction between two proteins
Lack of high resolution structures Methods
Homology modeling (3DJIGSAW, SWISSMODEL)
Ab initio modeling (ROSETTA) Model optimization (DEEPVIEW,
GROMACS97) Docking (GRAMM)
Complement this analysis with actual protein-protein interaction data (BIND, DIP, GRIP, YRC).
Arh1 structure derived (green) from the crystal structure of the bovine adrenodoxin
reductase homologue (yellow).
Network reconstruction from docking computations
For each protein, the most strong interactions (as computed in silico) are considered.
False positive results are restricted because we considered only proteins that are identified to play some role in FeSC biogenesis (same compartment).
Docking computations confirm some of the previous results and suggest new possibilities We couldn’t confirm the predictions as few
experimental data ara available in two-hybrid experiments
The only cases are Nfs1 and Isu1, Nfs1 and Isu2, Isa1 and Isa2, and Isa1 and Grx5
All these cases agree with in silico data
Putative protein-protein interactions resulting fromin silico docking and available protein-protein interaction
data.
Y AH1
ARH1
GRX5
ISA1
ISA1 ISA2
ISA2
NFS1 NFS1
ISU1
ISU1 ISU2
ISU2
NFU1
NFU1
Y FH1
Y AH1
ARH1
GRX5
ISA1
ISA1 ISA2
ISA2
NFS1 NFS1
ISU1
ISU1 ISU2
ISU2
NFU1
NFU1
Y FH1
5784Jac13
6118Jac1
15330Ssq1
6860Nfs12
9584Nfs1
5895Grx5
7278Nfu12
4965Isu22
2180Isu12
11796Isa22
4724Isa12
4476Nfu1
7592Isu2
4185Isu1
5509Isa2
3752Isa1
6062Yfh13
3065Yfh1
9744Yah1
10552Arh1
Arh1
5784Jac13
6118Jac1
15330Ssq1
6860Nfs12
9584Nfs1
5895Grx5
7278Nfu12
4965Isu22
2180Isu12
11796Isa22
4724Isa12
4476Nfu1
7592Isu2
4185Isu1
5509Isa2
3752Isa1
6062Yfh13
3065Yfh1
9744Yah1
10552Arh1
Arh1
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Methods for identifying network structureHuman curation
Some of the important information concerning FESC assembly could not be automatically incorporated in the models derived from these techniques.
Human curation (expert assessment) was necessary to obtain a network structure incorporating the available information. Introduce putative network relationships based on experimental
data Incorporate putative network relationships based on expert
suggestions Some of the proteins can play various roles.
Structural methods and actual knowledge can not resolve the alternatives
It is necessary to check the model predictions (system’s behavior). Do the models reproduce observed phenotypes?.
R
s sT
T
I
F
D
NIs
A
Alternative models
St St
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Checking alternative network estructuresMathematical models
A GMA model is derived for the general case Include all the alternative roles of the proteins (global
model). Normalize.
Alternative models Alternative networks are obtained by setting to zero some
of the kinetic orders in the global model. Parameter scanning helps evaluating each alternative
Absolute values of the kinetic orders have a restricted range of possible values
Change turnover values to evaluate different time scales Millions of cases can be systematically evaluated to identify
pathway structures that can reproduce observed results independently of the parameter values.
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Mathematical modelsInterpretation of results
Experimental data Increase of mitochondrial Fe in mutants lacking some
of the proteins Decrease in FeSC-dependent enzyme activity in
mutants lacking some of the proteins
Three possible outcomes on the simulated computations A model is able of reproducing actual data
independently of the parameter values A model is able of reproducing actual data only for
some parameter values A model cannot reproduce actual data for any of the
parameter values
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ResultsSimulation of Nfs1 depletion
R
S
Experimental data in mutants•Increase of mitochondrial Fe•Decrease in FeSC-dependent enzyme activity
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Predictions from the model
Arh1Yah1
Yfh1 Nfs1 Ssq1-Jac1
Grx5
S + + + +
R + + +
T + +
I +
F +
D/N/Is/A +
Possible modes of action for each protein tested
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Predictions from the model
Results from simulations
Arh1Yah1
Yfh1 Nfs1 Ssq1-Jac1
Grx5
S + + + +
R + + +
T + +
I +
F +
D/N/Is/A +
Possible modes of action for each protein tested
Arh1 Yah1
Yfh1 Nfs1 Ssq1-Jac1 Grx5
S S S F D
ST T SR St Is
ST FSt N
FR DIs
RSt DN
FRsT IN
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Conclusions This procedure can be applied to many problems Based of our simulations and on the results from structural
analysis, we can devise experiments for checking the predictions Arh1-Yah1 interaction, Glutathionylation/deglutathionylation
of Nfs1 by Grx5, etc.
Requeriments for extending and using this approach An integrated application would be needed to speed-up the merging
of results from different techniques A graphical interface for changing model structures would be very
useful Automatic generation of mathematical models with parameter
scanning would facilitate the analysis of alternative networks GMA and S-system models play an important role at this
point. Expert knowledge (collaboration with experimentalists) is
crucial.