Mechanisms of evolution in experiment and theory. Christopher Knight

Transcription

1 Mechanisms of evolution in experiment and theory Christopher Knight

2 Evolution and molecular biology Nothing in Biology makes sense except in the light of evolution (Dobzhansy 1964 in Biology, molecular and organismic ) Explicit discrete genotypes Only some aligned Long-term evolutionary hypotheses Phylogenetic scale low-level phenotype only e.g. amino acid properties Wright 1932 Arbitrary, continuous genetic axes Scale un-defined Finer scale? high-level phenotype inclusive fitness

3 Evolution and molecular biology Global fitness landscape from genotype? evolution of protein physical properties Focus in on an individual mutational step Adaptive evolution Put mutational steps together In vitro In vivo Focus on feedback between model and experiment

4 Molecular weight (kda) Evolution and molecular biology Global fitness landscape from genotype? evolution of protein physical properties Mass, iso-electric point Predictable from raw sequence Consider across genome Predictable from raw sequence pi pi Knight, C.G. et al. (2004) PNAS

5 Predicted proteomes Global fitness landscape from genotype? evolution of protein physical properties Mass, iso-electric point Predictable from raw sequence Can we predict fitness from predicted proteome distribution? Mycobacterium genetalium Halobacterium sp.nrc1

6 Predicted proteomes Can we predict fitness from predicted proteome distribution? Measure fitness as ability to grow in different environments Gram negative Gram positive Do organisms with similar growth have similar proteomes? Not just because closely related g proteobacteria Pseudomonas 3 4 a proteobacteria 2 1 Bacillus subtilis Sulfolobus solfataricus Sulfolobus tokodaii Knight, C.G. et al. (2004) PNAS

7 Contrasts in Biolog profile Predicted proteomes Can we predict fitness from predicted proteome distribution? Measure fitness as ability to grow in different environments Gram negative Gram positive Do organisms with similar growth have similar proteomes? Yes BUT need better methods Small sample Complexity of omics unclear Hadjipantelis, P.Z. et al. (2013). J R Soc Interface g proteobacteria Pseudomonas 3 4 a proteobacteria Bacillus subtilis Contrasts in proteome distribution Knight, C.G. et al. (2004) PNAS

8 Predicted proteomes Can we predict fitness from predicted proteome distribution? Focus in on the individual, adaptive mutational step Yes BUT need better methods Small sample Complexity of omics unclear Hadjipantelis, P.Z. et al. (2013). J R Soc Interface

9 Adaptive evolution Focus in on the individual, adaptive mutational step

10 Adaptive evolution Can we predict molecular change from adaptive landscape? Smooth SM Wrinkly Spreader (LSWS) Rainey and Travisano (1998) Nature 394:69-72

11 Adaptive evolution Can we predict molecular change from adaptive landscape? + P - P P P SM LSWS Bantinaki, E. et al. (2007) Genetics Yes BUT (Prote) omics complex (pleiotropy) CheV Catechol 1,2, dioxygenase SM LSWS Expectation if predictable Reality for most proteins Andrew Spiers Knight, C.G. et al. (2006). Nat Genet,

12 Adaptive evolution Can we predict molecular change from adaptive landscape? P P P P SM LSWS Bantinaki, E. et al. (2007) Yes BUT (Prote) omics complex (pleiotropy) Time-scale matters Phylogenetic scale Experimental scale Andrew Spiers Farrell and Knight, unpublished

13 Adaptive evolution Can we predict molecular change from adaptive landscape? Yes BUT (Prote) omics complex (pleiotropy) Time-scale matters

14 Evolution and molecular biology Can go both one way or the other in vivo BUT omics complex (e.g. pleiotropy) Evolutionary scale matters Can go both ways in vitro Simplified system Works on average Wright 1932 Generalise over Genotype-Phenotype map Think carefully about rate of movement mutation rate Fomalise and make predictions for in vivo experiments?

15 Can go both one way or the other in vivo BUT omics complex (e.g. pleiotropy) Evolutionary scale matters Can go both ways in vitro Simplified system Works on average Wright 1932 Generalise over Genotype-Phenotype map Think carefully about rate of movement mutation rate Fomalise and make predictions for in vivo experiments?

16 Wright 1932 Different mutation rates ideal at different points in the landscape Generalise over Genotype-Phenotype map Think carefully about rate of movement mutation rate Fomalise and make predictions for in vivo experiments?

17 % Mutation Distance from peak fitness Can derive various optimal functions 4 All monotonic and decreasing with fitness 2 Belavkin et al.(2015). In review Theory Simulation Different mutation rates ideal at different distances from peak Mutation rate Generalise over Genotype-Phenotype map Think carefully about rate of movement mutation rate Distance from Optimum Fitness

18 % Mutation Distance from peak fitness 4 2 Belavkin et al.(2015). In review Theory Simulation Different mutation rates at different distances from peak? Mutation rate Experiment: Escherichia coli K12 give different fitnesses (numbers of doublings) via different amounts of sugar Assay mutation rate Distance from Optimum Fitness

19 Mutation rate x 10^ Mutation Rate versus Absolute Fitness Consider a subset of ~80 mutations in rpob gene, give antibiotic resistance (Rif R ) 10-9 Mutations Generation Absolute fitness, Wabs w abs (generations/day) Different mutation rates ideal at different points in the landscape Experiment: Escherichia coli K12 give different fitnesses (numbers of doublings) via different amounts of sugar Assay mutation rate

20 % Mutation Mutation rate x 10^ Mutation Rate versus Absolute Fitness Consider a subset of ~80 mutations in rpob gene, give antibiotic resistance (Rif R ) 10-9 Mutations Generation Absolute fitness, Wabs w abs (generations/day) Different mutation rates at different points in the landscape? Yes, Mutation Rate Plasticity (MRP), consistent with theory BUT How is fitness sensed/ transduced? Just one organism Theory Simulation Distance from Optimum Fitness

21 % Mutation Mutation_rate_per10.9 Mutation rate x 10^ Mutations Generation Theory e+08 1e+09 density_per_ml_culture Selective_agent Nal30 Rif50 Population density (10 8 c.f.u. ml -1 ) Simulation Nal R in gyra gene 10-9 Mutations Generation -1 Mutation Rate versus Absolute Fitness Absolute fitness, Wabs w abs (generations/day) Different mutation rates at different points in the landscape? Yes, Mutation Rate Plasticity (MRP), consistent with theory BUT How is fitness sensed/ transduced? Just one organism Statistical association with final population density Distance from Optimum Fitness

22 Mutation rate per cell (10-9 mutations/generation) Mutation rate x 10^ Density dependence in E. coli suggests Quorum Sensing (QS) Does density-dependent mutation rate plasticity (DD-MRP) require luxs QS gene? Test effect of luxs Grows very similarly to WT Effect of LuxS on mutation rate plasticity wild-type quorum-sensing mutant ΔluxS Xavier K B, and Bassler B L J. Bacteriol. 2005;187: e e e e e+08 Population density, cells/ml D (10 8 c.f.u./ml) 4

23 Density dependence in E. coli suggests Quorum Sensing (QS) Does density-dependent mutation rate plasticity (DD-MRP) require luxs QS gene? Yes, needs luxs AND cell-cell interactions But exactly what is the signal? Test effect of luxs Grows very similarly to WT 10-9 Mutations Generation -1 Co-cultures of wild-type and ΔluxS Measure mutation rate in wild-type Population density of signal producing cells Overall population density (10 8 c.f.u./ml) (10 8 c.f.u./ml) Krašovec Rok et al. Nature Communications, (2014).

24 Complement with synthetic AI-2 LuxS Complement with aspartate Yes Activated methyl cycle activated methyl cycle Halliday et al. Anal Biochem., (2010).

25 Mutation rate x 10^ luxs-dependent MRP is mediated by the activated methyl cycle consistent with AI-3 Effect of Aspartate on LuxS mutation rate plasticity 10-9 Mutations Generation -1 ΔluxS - Asp ΔluxS + Asp Krašovec, R. et al. (2014) Nat Commun, Population density cells/ml(10 8 c.f.u./ml) 1.5e e e e e+

26 Mutation_rate_per10.9 Distance from Optimum Fitness Mutation rate x 10^ % Mutation Theory and simulation predict inverse fitness-mutation rate relationship likely to be advantageous Density-Dependent Mutation Rate Plasticity exists in E. coli strains: Higher density, lower mutation Select N R 0.1 Consistent, but not identical, with theory 1e+08 1e+09 density_per_ml_culture Effect of Aspartate on LuxS mutation rate plasticity Depends upon role of luxs in activated methyl cycle 1.5e e e e e cells/ml Signal unknown (AI-3) Downstream mechanism? Truly adaptive? Just E. coli? Collate published mutation rates Estimated by fluctuation test

27 Mutations Generation Population density (10 8 c.f.u. ml -1 ) Richards, Krašovec and Knight, unpublished Just E. coli? Hypothesis: widespread densitydependent MRP? Collate published mutation rates Estimated by fluctuation test

28 10-9 Mutations Generation Baker s yeast Saccharomyces cerevisiae Population density (10 8 c.f.u. ml -1 ) Richards, Krašovec and Knight, unpublished Just E. coli? Hypothesis: widespread densitydependent MRP? Collate published mutation rates Estimated by fluctuation test

29 10-9 Mutations Generation Baker s yeast Saccharomyces cerevisiae Universal DD-MRP? Population density (10 8 c.f.u. ml -1 ) Krašovec and Knight, unpublished Yes even to eukaryotes Just E. coli? No Hypothesis: widespread densitydependent MRP? Collate published mutation rates Estimated by fluctuation test

30 10-9 Mutations Generation Pseudomonas aeruginosa Gamma proteobacterium (like E. coli) No luxs Universal DD-MRP? Population density (10 8 c.f.u. ml -1 ) Richards, Krašovec and Knight, unpublished Just E. coli? Hypothesis: widespread densitydependent MRP? Collate published mutation rates Estimated by fluctuation test

31 10-9 Mutations Generation Population density (10 8 c.f.u. ml -1 ) Pseudomonas aeruginosa Gamma proteobacterium (like E. coli) No luxs Universal DD-MRP? No varies with luxs Yes even to eukaryotes Just E. coli? No Hypothesis: widespread densitydependent MRP? Collate published mutation rates Estimated by fluctuation test

32 Mutation_rate_per % Mutation Conclusions Moving between levels of molecules and evolutionary landscapes Can move either way BUT caveats about evolutionary scale and genotypephenotype mapping Going both ways: mutation rate plasticity (MRP) Modify landscape theory for molecules Find processes consistent with theory Find molecules associated with process (luxs) Do molecules perform function for which theory created? Wide-spread density-dependent MRP e+08 1e+09 density_per_ml_culture Selective_agent Krašovec, R. et al. (2014) Nat Commun, Nal30 Rif Distance from Optimum Fitness

33 Rok Krašovec Roman Belavkin John Aston Alastair Channon Liz Aston Bharat Rash Mani Kadirvel Sarah Forbes Acknowledgements Paul Rainey Nicole Zitzmann, Holger Hebestreit Sripardi Prabhakar Chris Brock Andrew Spiers Rees Kassen Rich Lenski Karina Xavier Andrew McBain Dan Smith Casey Bergman Mike Brockhurst Craig Maclean Chris Simms