Pathway identification and reconstruction by cooperative use of computational biology methods

1. 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 Sorribas Grup de Bioestadística i Biomatemàtica Departament de Ciències Mèdiques Bàsiques Institut de Recerca Biomèdica de Lleida (IRBLLEIDA) Universitat de Lleida (Espanya)

2. 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 descriptionsand 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.

3. 4Fe4S Cluster 2Fe2S Cluster in Ferredoxin 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

5. 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.

6. Methods for identifying network structureText 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. ISA1 ISA2 POU2F1 / SLC22A1 / OCT1 NFU1 YHF1 JAC1 NFS1 ISU2 SSQ1 HSPA4 / SSA3 ATM1 ISU1 YAH1 GRX5 ARH1 MGE1 Text mining is able of finding allthe genes that account for proteinssuggested to play a role in FeSC biogenesis Network of relationships between genes involved in FESC biogenesis resulting from iHOP analysis

7. 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

8. 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. YFH1 YAH1 ARH1 ATM1 ISA1 ISA2 JAC1 NFU1 GRX5 NFS1 MGE1 ISU1 ISU2 SSQ1 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.

9. Methods for identifying network structureProtein-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).

10. 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). Putative protein-protein interactions resulting fromin silico docking and available protein-protein interaction data. • 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

11. 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?.

12. Alternative models I T F St St T s s D N Is A R

13. 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.

14. 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

15. Experimental data in mutants • Increase of mitochondrial Fe • Decrease in FeSC-dependent enzyme activity ResultsSimulation of Nfs1 depletion R S

16. Predictions from the model • Possible modes of action for each protein tested

17. Predictions from the model • Results from simulations • Possible modes of action for each protein tested

18. 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.