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Analysis of E. coli
ancestral metabolic networks
reveals past adaption
Tin Yau Pang
Heinrich Heine University Duesseldorf
Horizontal gene transfer in bacteria
● Horizontal gene transfer (HGT):
○ gene transfer between bacteria
○ provide new func...
Exaptation in evolution
metabolic core
● Complex phenotype:
○ Has multiple genes clusters
○ Requires multiple HGT
○ Suppre...
Exaptation in evolution
metabolic core
● Complex phenotype:
○ Has multiple genes clusters
○ Requires multiple HGT
○ Suppre...
Exaptation in evolution
metabolic core
● Complex phenotype:
○ Has multiple genes clusters
○ Requires multiple HGT
○ Suppre...
Exaptation in evolution
● Complex phenotype:
○ Has multiple genes clusters
○ Requires multiple HGT
○ Suppressed by use-it-...
Flux balance analysis connects genes to phenotypes
● Genes (in genome) → Reactions → Metabolic network → Stoichiometric ma...
Reconstruction of ancestral genomes
● 53 E. coli strains + 17 outgroups
○ [Monk et al. 2013]
○ 16,265 total genes in E. co...
Reconstruction of transferred segments
● Group the gained genes in a branch into segments:
○ Possible configuration drawn ...
Reconstruction of transferred segments
10
HGT in an evolutionary step
● Each branch of the phylogenetic tree is considered as an evolutionary step
gained new
phenot...
Flux variability analysis (FVA) to define reaction / gene /
segment essentiality
● R package ‘sybilcycleFreeFlux’ to perfo...
Functions of a transferred segment
■ 20% of the segments are
essential to the newly-added
phenotypes
13
Functions of a transferred segment
■ 20% of the segments are
essential to the newly-added
phenotypes
■ 10% of the segments...
Functions of a transferred segment
■ 20% of the segments are
essential to the newly-added
phenotypes
■ 10% of the segments...
Number of segments associated with a phenotype
■ most newly-added
phenotypes are associated with
just one essential segmen...
Grouping transferred genes / rxns according to
branches
17
All reactions
Grouping transferred genes / rxns according to
branches
18
All reactions
step 1
Grouping transferred genes / rxns according to
branches
19
All reactions
step 1
step 2
Grouping transferred genes / rxns according to
branches
20
All reactions
step 1
step 2
step 3
Number of phenotypes supported by a segment
in the future
■5% of the segments become
essential to phenotypes in the
future...
Number of segments associated with a phenotype
■ number of essential
segments corresponding to a
newly-added phenotype
■ n...
Example of interacting segments
Evolution of psicoselysine metabolism in the ancestor of O157:H7-EDL933 and
O157:H7-str.-S...
Conclusion
● Transferred genes are grouped into segments
● FVA help mapping genes to phenotypes
Individual HGTs are linked...
25
Acknowledgements
Collaborator:
● Martin Lercher
Funding source:
● Deutsche
Forschungsgemeinschaft
(German Research
Foun...
26
27
Exaptation in evolution
● Complex phenotype:
○ Has multiple genes clusters
○ Requires multiple HGT
○ Suppressed by use-it-...
Reconstruction of ancestral genome
● 53 E. coli strains + 17 outgroups
○ [Monk et al. 2013]
○ 16,265 total genes in E. col...
Flux variability analysis (FVA) to define reaction / gene /
segment essentiality
● R package ‘sybilcycleFreeFlux’ to perfo...
Interacting segments
● Two segments “interact” with
each other if they are essential to
the same phenotype
● Out of 205 se...
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Analysis of E. coli ancestral metabolic networks reveals past adaption - Tin Yau Pang

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Analysis of E. coli ancestral metabolic networks reveals past adaption - Tin Yau Pang

  1. 1. Analysis of E. coli ancestral metabolic networks reveals past adaption Tin Yau Pang Heinrich Heine University Duesseldorf
  2. 2. Horizontal gene transfer in bacteria ● Horizontal gene transfer (HGT): ○ gene transfer between bacteria ○ provide new functions ○ facilitate bacterial evolution ● Example of HGT: ○ transduction: through bacteriophage ● We want to understand how the HGT had affected the phenotypes 2
  3. 3. Exaptation in evolution metabolic core ● Complex phenotype: ○ Has multiple genes clusters ○ Requires multiple HGT ○ Suppressed by use-it-or-lose-it principle (purifying selection) ○ Exaptation to evolve complex phenotypes ● Exaptation: ○ Also called pre-adaptation ○ Shift of function of a trait (gene) ○ Refers to a gene added earlier and recruited for another phenotype later ● To detect exaptation: ○ Search for transferred genes ○ Group transferred genes into transferred segments ○ Look for phenotypes supported by multiple segments 3
  4. 4. Exaptation in evolution metabolic core ● Complex phenotype: ○ Has multiple genes clusters ○ Requires multiple HGT ○ Suppressed by use-it-or-lose-it principle (purifying selection) ○ Exaptation to evolve complex phenotypes ● Exaptation: ○ Also called pre-adaptation ○ Shift of function of a trait (gene) ○ Refers to a gene added earlier and recruited for another phenotype later ● To detect exaptation: ○ Search for transferred genes ○ Group transferred genes into transferred segments ○ Look for phenotypes supported by multiple segments 4
  5. 5. Exaptation in evolution metabolic core ● Complex phenotype: ○ Has multiple genes clusters ○ Requires multiple HGT ○ Suppressed by use-it-or-lose-it principle (purifying selection) ○ Exaptation to evolve complex phenotypes ● Exaptation: ○ Also called pre-adaptation ○ Shift of function of a trait (gene) ○ Refers to a gene added earlier and recruited for another phenotype later ● To detect exaptation: ○ Search for transferred genes ○ Group transferred genes into transferred segments ○ Look for phenotypes supported by multiple segments 5
  6. 6. Exaptation in evolution ● Complex phenotype: ○ Has multiple genes clusters ○ Requires multiple HGT ○ Suppressed by use-it-or-lose-it principle (purifying selection) ○ Exaptation to evolve complex phenotypes ● Exaptation: ○ Also called pre-adaptation ○ Shift of function of a trait (gene) ○ Refers to a gene added earlier and recruited for another phenotype later ● To detect exaptation: ○ Search for transferred genes ○ Group transferred genes into transferred segments ○ Look for phenotypes supported by multiple segments metabolic core 6
  7. 7. Flux balance analysis connects genes to phenotypes ● Genes (in genome) → Reactions → Metabolic network → Stoichiometric matrix ● Flux balance analysis (FBA) performed on a stoichiometric matrix ○ Nutritional metabolites as sources (e.g. glucose) → nutritional environments → phenotypes ○ Vital metabolites as sinks (e.g. ATP) ○ Linear programming to solve for steady state maximum biomass flux, and also the reaction flux distribution ● Flux variability analysis (FVA) probes the possible flux range for different reactions ○ FVA defines reaction and gene essentiality 7
  8. 8. Reconstruction of ancestral genomes ● 53 E. coli strains + 17 outgroups ○ [Monk et al. 2013] ○ 16,265 total genes in E. coli ○ 1,334 universal genes for phylogenetic tree (RAxML) ■ Each internal node with at least 60% bootstrap support ○ 1,610 total / 743 universal metabolic genes ● Maximal likelihood algorithm (GLOOME) to reconstruct ancestral genomes ○ Different gain-loss rates for different genes ○ Different weights for different branches ● Infer genes in the 30kb segments 8
  9. 9. Reconstruction of transferred segments ● Group the gained genes in a branch into segments: ○ Possible configuration drawn from extant strains ○ Segment length 30kb 9
  10. 10. Reconstruction of transferred segments 10
  11. 11. HGT in an evolutionary step ● Each branch of the phylogenetic tree is considered as an evolutionary step gained new phenotypes lost phenotypes conserved phenotypes biomass flux enhanced biomass flux reduced no-change Classification of phenotypes in an evolutionary step 11 Ancestral strain Descendant strain
  12. 12. Flux variability analysis (FVA) to define reaction / gene / segment essentiality ● R package ‘sybilcycleFreeFlux’ to perform FVA, which removes futile cycles in a pathway ● FVA calculates the possible flux range of reactions (rxn) 12 ● Nonessential rxn: knockout (single rxn) causes no effect on biomass flux ● Essential rxn: knockout causes lower biomass flux ● Essential segment: one that uniquely support an essential rxn nonessential reaction fluxrange essential reaction Gene essentiality defined by FVA flux range
  13. 13. Functions of a transferred segment ■ 20% of the segments are essential to the newly-added phenotypes 13
  14. 14. Functions of a transferred segment ■ 20% of the segments are essential to the newly-added phenotypes ■ 10% of the segments are essential the enhanced- existing phenotypes 14
  15. 15. Functions of a transferred segment ■ 20% of the segments are essential to the newly-added phenotypes ■ 10% of the segments are essential the enhanced- existing phenotypes ■ 70% of the segments are relevant to the any phenotype considered 15
  16. 16. Number of segments associated with a phenotype ■ most newly-added phenotypes are associated with just one essential segment ■ most enhanced-existing phenotypes are associated with just one essential segment 16
  17. 17. Grouping transferred genes / rxns according to branches 17 All reactions
  18. 18. Grouping transferred genes / rxns according to branches 18 All reactions step 1
  19. 19. Grouping transferred genes / rxns according to branches 19 All reactions step 1 step 2
  20. 20. Grouping transferred genes / rxns according to branches 20 All reactions step 1 step 2 step 3
  21. 21. Number of phenotypes supported by a segment in the future ■5% of the segments become essential to phenotypes in the future branches ■20% of the segments support phenotypes in the future branches 21
  22. 22. Number of segments associated with a phenotype ■ number of essential segments corresponding to a newly-added phenotype ■ number of essential segments corresponding to a enhanced-existing phenotype ■ number of essential segments corresponding to a remaining phenotype 22
  23. 23. Example of interacting segments Evolution of psicoselysine metabolism in the ancestor of O157:H7-EDL933 and O157:H7-str.-Sakai 23
  24. 24. Conclusion ● Transferred genes are grouped into segments ● FVA help mapping genes to phenotypes Individual HGTs are linked to the metabolic phenotypes ● As expected from the use-it-or-lose-it principle, just one HGT segment can enable a new phenotype ● Genes added at an earlier time can be recruited to support phenotypes developed at a later time, which infers exaptation ● Gene acquisition does not always led to new phenotype; it can provide alternative enzymes to the existing reactions 24
  25. 25. 25 Acknowledgements Collaborator: ● Martin Lercher Funding source: ● Deutsche Forschungsgemeinschaft (German Research Foundation), SFB 680
  26. 26. 26
  27. 27. 27
  28. 28. Exaptation in evolution ● Complex phenotype: ○ Has multiple genes clusters ○ Requires multiple HGT ○ Suppressed by use-it-or-lose-it principle ○ Exaptation to evolve complex phenotypes ● Exaptation: ○ Also called pre-adaptation ○ Shift of function of a trait (gene) ○ Refers to a gene added earlier and recruited for another phenotype later ● To detect exaptation: ○ Search for transferred genes ○ Group transferred genes into transferred segments ○ Look for phenotypes supported by multiple segments metabolic core 28
  29. 29. Reconstruction of ancestral genome ● 53 E. coli strains + 17 outgroups ○ [Monk et al. 2013] ○ 16,265 total genes in E. coli ○ 1,334 universal genes for phylogenetic tree ○ 1,610 total + 743 universal metabolic genes ● Maximal likelihood to reconstruct ancestral genomes ● Gene turnover per branch: ○ All genes: 8.8% ○ Metabolic genes: 1.8% 29
  30. 30. Flux variability analysis (FVA) to define reaction / gene / segment essentiality ● R package ‘sybilcycleFreeFlux’ to perform FVA, which removes futile cycles in a pathway ● FVA calculates the possible flux range of reactions (rxn) 30 ● Nonessential rxn: knockout (single rxn) causes no effect on biomass flux ● Essential rxn: knockout causes lower biomass flux ● Super-essential rxn: knockout kills the phenotype ● An essential segment uniquely support an essential rxn nonessential reaction fluxrange essential reaction super essential reaction Gene essentiality defined by FVA flux range
  31. 31. Interacting segments ● Two segments “interact” with each other if they are essential to the same phenotype ● Out of 205 segments considered, only 9 segments are found to have interaction ● As a segment can be essential to multiple phenotypes, they correspond to 976 phenotype evolution events 31

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