Pathway identification and reconstruction by cooperative use of computational biology methodsThe 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

ATM1YAH1

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 DST T SR St Is

ST FSt NFR DIsRSt 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.