The Impact of Genome Duplication on t he Evolvability of Expression Phenotypes

1. The Impact of Genome Duplication on the Evolvability of Expression Phenotypes Jayson Gutiérrez

2. Outline • On the notion of Genotype-Phenotype Maps (GPMs): why a mechanistic understanding of GPMs is so important? • Why adopting an evolutionary systems biology perspective is important for understanding the function of biological systems? • An evolutionary systems biology framework : In silico evolution of artificial gene regulatory networks (GRNs) • Studying the consequences of genome duplications in the evolutionary optimization of GRNs

3. On the notion of Genotype-Phenotype Maps (GPMs): why a mechanistic understanding of GPMs is so important? Widely used conceptual framework → Instrumental for understanding how genetic variation and changes in the environment can affect the functional behavior of molecular networks (i.e. gene expression patterns, cell signaling and metabolic flows, developmental patters, etc.). • GPM in quantitative genetics: associations between genotypes and phenotypic variation through multivariate statistical analysis (i.e. regression analysis) Statistical association of eQTLs With disease phenotypes Fehrmann et.al, 2011

4. Notions of GPMs • GPM in computational biology: Metagraphs representing the functional associations between genes and phenotypes → constructed on the basis of systematic perturbation (i.e. gene deletions, chemical perturbations) of model organisms. Large-scale interaction maps: association of combinations of genetic pertubations (gene deletions) with diverse cell-level phenotypes Constanzo et.al, 2010

5. Notions of GPMs • GPMs in synthetic biology, evolutionary systems biology and evolutionary developmental biology: Munteanu and Solé, 2008 Jaeger et.al., 2012 Salazar-Ciudad and Marin-Riera, 2013

6. Why adopting an evolutionary systems biology perspective is important for understanding the function of biological systems? • Traditional approach → end products of evolution, e.g. robust biological solutions to a given problem • Structural and functional properties observable in biological systems have been molded by complex evolutionary processes. • Number of extant solutions to a given biological problem most likely does not give a comprehensive account of what is realizable through the process of evolution

7. By modeling and simulating the evolution of complex molecular networks, evolutionary systems biology aims at: • Inferring and reconstructing evolutionary processes: • Ancestral solutions → intermediates → extant solutions • Predicting future evolutionary outcomes • Exploring a huge combinatorial space of possible solutions to a given biological problem → efficient and robust solutions

8. Evolutionary systems biology framework: Modeling the GPM of GRNs

9. Evolutionary systems biology framework: In silico evolution of GRNs Ancestral GRN Genotype (RegulatoryWiring+ ExpressionPhenotype) Evolve through sequence space via an MCMC-like evolutionary algorithm equipped with a fitness function to optimize an ancestral phenotype. Fitness = OP*f(offset)*g(amplitude)*h(frequency) Allowable mutational moves through sequence space simulated according to formal models of DNA evolution : K2P (nucleotide substitution) and GY94 (codon substitution) Intermediate GRN genotypes are recorded along an ascending pathway that leads from a suboptimal solution toward the optimum OptmizedGRN genotype encoding a novel regulatorywiring and phenotype

10. Whole genome duplications (WGD) • WGD → doubling of an entire set of chromosomes → a form of saltational evolution at the genomic level (not necessarily phenotypic level at the outset) • WGDs across the eukaryotic lineages • WGDs: major driving force of plant genome evolution. Polyploidy: pervasive in angiosperm lineages Van de Peer, Maere, Meyer (2009) Nat Rev Genet

11. A wave of WGDs around the K-T boundary • K-T boundary: geologically period of time (66 Mya) → Mass extinction: 75% of species • WGDs may have conferred plants a higher chance to survive the K-T mass extinction • K-T extinction may have promoted long-term evolutionary success of WGDs Fawcett, Maere and Van de Peer (2009) PNAS

12. Hypothesized evolutionary roles of WGDs • Polyploids : • Increased adaptive potential to sudden environmental shifts • Huge potential for diversification and phenotypic innovation: Increased evolvability. Problem: none of these hypotheses have currently been proven conclusively. Circumstantial evidence To get more insight, evolution of biological systems after WGD needs to be modeled mechanistic detail through formal quantitative modeling approaches.

13. Evolutionary : Assessing evolvability before and after WGD 100 Starting GRN Genotypes Ancestral Phenotypes Intermediate frequency A R Ploidy Diploid Tetraploid O 50 replicates 2 goals 5000 MCMC steps Evolutionary runs High frequency Low frequency

14. Why biochemical activities with periodic dynamics? Single-cell oscillators Multicellular oscillators: Vertebrate segmentation Rosef and Regev (2011) Cell Multicellular oscillators: Plant root development Oates et.al. (2012) Development Moreno-Risueno et.al. (2010) Science

15. Starting diploid and tetraploid GRNs with similar phenotypic outputs Protein expression outputs for diploid (red) and tetraploid (blue) GRNs match each other, except for slight numerical integration errors Protein concentration Time steps

16. Evolutionary optimization of GRNs: Fitness trajectories Consistently, tetraploid GRNs perform much better than diploid GRNs at climbing up the fitness landscape Fitness MCMC Step

17. Fitness trajectories vary across replicates GRN  LFP, Ancestral GConfig 48 GRN  HFP, Ancestral GConfig 48 Tetraploid Tetraploid Fitness Diploid Diploid MCMC Step

18. Tracking the dynamics of evolutionary optimization Diploid GRN → LFP Evolutionary optimization of Diploid GRNs is driven by mutational fine tuning of regulatory interactions under a fixed network topology Tetraploid GRN → LFP Evolutionary optimization of Tetraploid GRNs is mainly driven by network rewiring events

19. Visualizing evolutionary pathways: Sequence divergence from ancestral genomes Diploid GRNs  LFP Tetraploid GRNs  LFP Fitness Promoter Seq Divergence AncestorID: 12 AncestorID: 39 DBD Seq Divergence Diploid GRNs  LFP Tetraploid GRNs  LFP AncestorID: 54 AncestorID: 65

20. Genome duplication speeds up the evolutionary optimization process of gene regulatory networks 0.95*Fmax Diploids Tetraploids

21. Relationship between sequence changes and GRN rewiring along ascending pathways Small fitness increments with considerable network rewiring achieved via substantial sequence divergence How many sequence changes to induce a rewiring? DeltaFitness %

22. Wiring transitions along ascending evolutionary pathways LFPC Pathway GConfig39 HFPC Pathway GConfig99 Ancestral wiring

23. Conclusions Duplication of an entire GRN favors a combinatorial explosion of rewiring events along ascending pathways that lead to high fitness → Speeds up evolution towards a newly imposed optimum Future perspectives • Population-based simulations, other phenotypes… • Enhanced evolvability of molecular systems through WGD is only one of the long-standing questions on which quantitative modelling of systems evolution may shed light… Others include e.g. mechanisms leading to hybrid vigor.

24. Thanks Dr. Steven Maere Dr. Krzysztof Wabnik www.psb.ugent.be/esb