The document describes a study that analyzed horizontal gene transfer (HGT) in E. coli metabolic networks to understand bacterial evolution and identify instances of exaptation. Key points: - Researchers reconstructed ancestral E. coli genomes and grouped transferred genes into 30kb segments. Flux balance analysis was used to link genes/segments to metabolic phenotypes. - Analysis found that most new phenotypes were associated with a single essential HGT segment, while enhanced existing phenotypes also typically involved a single segment. Segments acquired earlier were sometimes later recruited to support novel phenotypes, indicating exaptation. - Of over 200 analyzed segments, 9 were found to have an "interacting" relationship where both segments were essential to the same metabolic

1. Analysis of E. coli
ancestral metabolic networks
reveals past adaption
Tin Yau Pang
Heinrich Heine University Duesseldorf

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. 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. 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. 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. 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. 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. 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. Reconstruction of transferred segments
● Group the gained genes in a branch into segments:
○ Possible configuration drawn from extant strains
○ Segment length 30kb
9

10. Reconstruction of transferred segments
10

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. 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. Functions of a transferred segment
■ 20% of the segments are
essential to the newly-added
phenotypes
13

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. 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. 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. Grouping transferred genes / rxns according to
branches
17
All reactions

18. Grouping transferred genes / rxns according to
branches
18
All reactions
step 1

19. Grouping transferred genes / rxns according to
branches
19
All reactions
step 1
step 2

20. Grouping transferred genes / rxns according to
branches
20
All reactions
step 1
step 2
step 3

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. 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. Example of interacting segments
Evolution of psicoselysine metabolism in the ancestor of O157:H7-EDL933 and
O157:H7-str.-Sakai
23

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
Acknowledgements
Collaborator:
● Martin Lercher
Funding source:
● Deutsche
Forschungsgemeinschaft
(German Research
Foundation),
SFB 680

26. 26

27. 27

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. 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. 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. 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