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In silico experimental evolution:
a tool to test evolutionary scenarios
Bérénice Batut, D. P. Parsons, S. Fischer, G. Besl...
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...
Elaboration of hypothetical evolutionary
scenarios with comparative genomics
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Adaptation from [Hindré et al 2012]
In vitro
Weaknesses
Long-term experiments
Difficulty to interpret the events
Difficult...
Experimental evolution to test evolutionary
scenarios coming from comparative genomics
[Hindré et al 2012]
In vitro
In sil...
In silico experimental evolution
Population of
sequences /
Phenotype
Selection processVariation process
Changes in
lifesty...
Aevol: an in silico experimental evolution
platform - www.aevol.fr
evol
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An organism => a structured genome with
a variable number of genes in a variable orde...
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Smoother distribution of growth rates
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Genome shrinkage under relaxed
selection
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Genome shrinkage...
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Smaller and more compact genomes
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Smaller and more compact genomes
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Smaller and more compact genomes
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Comparison with endosymbionts and
Prochlorococcus
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An exact “fossil record” is saved
during each run
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Mutational events on winning lineage
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150,000⇧200,000
Numberoffixedevents
020040060080010001200
Switch
Smallinsertion
Smalldeletion
Duplication
Deletion
Translo...
150,000⇧200,000
Numberoffixedevents
020040060080010001200
Switch
Smallinsertion
Smalldeletion
Duplication
Deletion
Translo...
In silico experimental evolution:
a tool to test evolutionary scenarios
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In silico experimental evolution:
a tool to test evolutionary scenarios
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Recomb-CG 2013 - In silico experimental evolution: a tool to test evolutionary scenarios
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Recomb-CG 2013 - In silico experimental evolution: a tool to test evolutionary scenarios

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11th Recomb - Comparative Genomics, Oct 2013, Villeurbanne, France

Comparative genomics has revealed that some species have exceptional genomes, compared to their closest relatives. For instance, some species have undergone a strong reduction of their genome with a drastic reduction of their genic repertoire. Deciphering the causes of these atypical trajectories can be very difficult because of the many phenomena that are intertwined during their evolution (e.g. changes of population size, environment structure and dynamics, selection strength, mutation rates...). Here we propose a methodology based on synthetic experiments to test the individual effect of these phenomena on a population of simulated organisms. We developed an evolutionary model - aevol - in which evolutionary conditions can be changed one at a time to test their effects on genome size and organization (e.g. coding ratio). To illustrate the proposed approach, we used aevol to test the effects of a strong reduction in the selection strength on a population of (simulated) bacteria. Our results show that this reduction of selection strength leads to a genome reduction of ~35% with a slight loss of coding sequences (~15% of the genes are lost - mainly those for which the contribution to fitness is the lowest). More surprisingly, under a low selection strength, genomes undergo a strong reduction of the noncoding compartment (~55% of the noncoding sequences being lost). These results are consistent with what is observed in reduced Prochlorococcus strains (marine cyanobacteria) when compared to close relatives.

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Recomb-CG 2013 - In silico experimental evolution: a tool to test evolutionary scenarios

  1. 1. In silico experimental evolution: a tool to test evolutionary scenarios Bérénice Batut, D. P. Parsons, S. Fischer, G. Beslon, C. Knibbe Adaptation from [Hindré et al 2012]
  2. 2.              Elaboration of hypothetical evolutionary scenarios with comparative genomics 2  
  3. 3. Elaboration of hypothetical evolutionary scenarios with comparative genomics 3                -100% -50% 0% 50% -100% -50% 0% 50%
  4. 4.              Elaboration of hypothetical evolutionary scenarios with comparative genomics Changes in lifestyle Changes in environmental conditions? Changes in population structure? Changes in mutation rates? * * 4  
  5. 5. Adaptation from [Hindré et al 2012] In vitro Weaknesses Long-term experiments Difficulty to interpret the events Difficulty to reproduce any real environmental conditions and their variations Experimental evolution to test evolutionary scenarios coming from comparative genomics 5  
  6. 6. Experimental evolution to test evolutionary scenarios coming from comparative genomics [Hindré et al 2012] In vitro In silico 6  
  7. 7. In silico experimental evolution Population of sequences / Phenotype Selection processVariation process Changes in lifestyle Changes in mutation processes Evolutionary loop Analysis of evolved “organisms” 7  
  8. 8. Aevol: an in silico experimental evolution platform - www.aevol.fr evol              Selection Replication (mutations, rearrangements) Population 8  
  9. 9.              Selection Replication (mutations, rearrangements) Population              Selection Replication (mutations, rearrangements) Population   Aevol: an in silico experimental evolution platform - www.aevol.fr evol 9  
  10. 10.    An organism => a structured genome with a variable number of genes in a variable order a variable amount of non coding sequences a variable number of operons … Aevol: an in silico experimental evolution platform - www.aevol.fr evol 10  
  11. 11.                Changes in lifestyle Changes in environmental conditions? Changes in population structure? Change in mutation rates? * * Changes in lifestyle and/or in mutation processes * evol Comparative genomics In silico experimental evolution Using aevol to test evolutionary scenarios 11  
  12. 12.                         evolUsing aevol to test evolutionary scenarios 12  
  13. 13.                         13                                                                                                                                                                                                                          
  14. 14.                         14                                                                                                                                                                                                                                
  15. 15. Smoother distribution of growth rates                Control k = 750 Scenario k = 250 15  
  16. 16. Genome shrinkage under relaxed selection         16  
  17. 17.            Genome shrinkage under relaxed selection 17  
  18. 18.                   Genome shrinkage under relaxed selection 18  
  19. 19.                       Genome shrinkage under relaxed selection 19  
  20. 20. Smaller and more compact genomes                               20  
  21. 21. Smaller and more compact genomes                               21  
  22. 22. Smaller and more compact genomes                               22  
  23. 23. Comparison with endosymbionts and Prochlorococcus                -100% -50% 0% 50% -100% -50% 0% 50% -100% -50% 0% 50% 23  
  24. 24. An exact “fossil record” is saved during each run           24  
  25. 25. Mutational events on winning lineage                        25  
  26. 26. 150,000⇧200,000 Numberoffixedevents 020040060080010001200 Switch Smallinsertion Smalldeletion Duplication Deletion Translocation Inversion Mutational events on winning lineage 26   Control   Scenario  
  27. 27. 150,000⇧200,000 Numberoffixedevents 020040060080010001200 Switch Smallinsertion Smalldeletion Duplication Deletion Translocation Inversion Mutational events on winning lineage 27   Control   Scenario   5⋅10−6 per  base   5⋅10−5 per  base  
  28. 28. In silico experimental evolution: a tool to test evolutionary scenarios                  Adaptation from [Wagner 2009]
  29. 29. In silico experimental evolution: a tool to test evolutionary scenarios                  Adaptation from [Wagner 2009] 29   www.aevol.fr                 
  30. 30. 31                                                                                                                         

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