Systems biology of genetic interactions in yeast metabolism

Transcription

1 Systems biology of genetic interactions in yeast metabolism Balázs Papp Evolutionary Systems Biology Group Biological Research Centre of the HAS Szeged, Hungary

2 Genetic interaction (GI) refers to the nonindependence of mutational effects Interaction between two gene deletions: A B WT ab Ab Fitness ab No interaction (ε=0) Positive interaction (ε>0) Negative interaction (ε<0) ε measures deviation from no interaction

3 wild type 1 st mutation 2 nd mutation A X B Y A X A X C Z B Y B Y function C Z C Z function function A X B Y C Z function

4 GIs are important to understand gene function and complex genetic diseases Functional genomics: GIs provide rich functional information Human genetics: disease susceptibility is often affected by interactions between multiple genes

5 Evolutionary biology: genetic interactions influence many population genetic processes and determine the accessible evolutionary trajectories fitness genotype genotype Fitness landscapes of different shapes

6 Empirical data on genetic interactions are accumulating rapidly Example: > 125,000 published genetic interactions in yeast Number of published interactions Year

7 Computational systems biology studies are also abound Example: metabolic network based studies

8 But lack of integration of computational and empirical approaches Open issues: How far can we explain genetic interactions based on known biochemical interactions? How to extract biological knowledge from GI data? We need unbiased high-throughput GI data + modelling

9 Metabolism as a model system One of the best characterized cellular subsystem Availability of large-scale mathematical models to predict the effect of mutations Yeast metabolic network reconstruction with ~900 genes and ~1400 reactions* *Mo et al. (2009) BMC Systems Biol. 3:37

10 Flux balance analysis (FBA) models: mapping from genotype to phenotype FBA: simple structural model without kinetic details Predicts single knockout viability with 80-90% accuracy

11 Constructing an empirical genetic interaction map of metabolism Screens performed in the Boone lab using automated genetic analysis: >200,000 gene pairs tested

12 The data Data on >80% of metabolic network genes 3,572 negative (synergistic) and 1,901 positive (antagonistic) interactions detected Many gene pairs have been screened more than once extracted high-confidence interactions (529 negative, 194 positive) Szappanos et al. (2011) Nat Genet

13 Most interactions connect across functional modules A B C X Y Z

14 Bridging the gap between theory and experiments Experimental map of genetic interactions in yeast metabolism Metabolic network reconstruction Probe the model s performance Generate new hypotheses in an automated way

15 Constructing an in silico genetic interaction map Use a genome-scale metabolic reconstruction of yeast* Mimic experimental growth conditions in the model Compute fitness for all experimentally screened single and double gene knockouts *Mo et al. (2009) BMC Systems Biol. 3:37

16 Can the genome-scale model explain the large-scale properties of GI networks?

17 The number of empirically observed genetic interactions per gene is highly uneven

18 Genes with low single mutant fitness show high number of genetic interactions ( hubs ) Costanzo et al. (2010) Science 327:425

19 The model recapitulates the empirical correlation between single mutant fitness and interaction degree Spearman s rho = -0.66, P < Spearman s rho = -0.89, P < Szappanos et al. (2011) Nat Genet

20 Hypothesis: pleiotropy might explain genetic interaction degree

21 Single mutant fitness is strongly associated with pleiotropy in the model Computed pleiotropy: number of biomass compounds whose production is affected by the gene knockout mutant fitness pleiotropy + interaction degree Spearman s rho = -0.78, P < 10-7

22 Can we predict individual interactions? Based on a set of high-confidence interactions: ~100-fold enrichment of true interactions among predicted ones However, only <3% of negative and <13% of positive interactions are captured by the model (low recall)

23 Strong in vivo genetic interactions are more successfully captured 17% of strong negative and 25% of strong positive interactions are captured

24 Bridging the gap between theory and experiments Experimental map of genetic interactions in yeast metabolism Genome-scale model Probe the model s performance Generate new hypotheses in an automated way

25 Using discrepancies between in silico and in vivo GI to refine the metabolic model

26 Define allowed model modifications Removing reactions Changing reaction reversibility Altering the list of biomass compounds needed for growth List of modifiable biomass compounds Ergosterol Glycogen Trehalose camp Chitin Coenzyme A Flavin adenine dinucleotide Reduced glutathione Protoheme Etc..

27 Find model modifications that minimize mispredictions 2-stage optimization using genetic algorithm

28 Refined models show improved predictions < 10 model modifications can increase recall by ~2-3-fold cross-validation confirms the increase in prediction accuracy Szappanos et al. (2011) Nat Genet

29 Example of a suggested modification Significant improvement by inactivating a two-step pathway involved in NAD biosynthesis

30 Verifying a suggested model modification No bioinformatics evidence for the presence of quinolinate synthase and L-aspartate oxidase in yeast

31 Verifying a suggested model modification Experimental support: bna1, bna2, bna4 and bna5 show severe growth defect in the absence of nicotinate

32 Summary A simple biochemical model without kinetic or regulatory details explains the connectivity of genetic interaction networks It is predictive of individual genetic interactions, but misses the majority of them: details on various layers of regulation should be incorporated Possible to automatically infer biological hypotheses from high-throughput GI data using the model

33 Acknowledgements Papp and Pál labs (Szeged): Balázs Szappanos Károly Kovács Béla Szamecz Ferenc Honti Csaba Pál Toronto: Michael Costanzo Anastasia Baryshnikova Brenda Andrews Charles Boone Minnesota: Chad Myers Cambridge: Steve Oliver Düsseldorf: Gabriel Gelius-Dietrich Martin Lercher University of Szeged: Márk Jelasity