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Darwinian Genomics
Csaba Pal
Biological Research Center Szeged, Hungary
Genomics:
Major revolution in the past 10 – 15 years with the rise of high-throughput molecular technologies:
New methods for rapid and relatively cheap measurements of biological molecules on a global scale
Systematic mapping components, interactions and functional states of the cell
Genomics: genome sequencing
and annotation
Transcriptomics: mRNA levels,
mRNA half-lives
Proteomics: protein levels, protein – protein interactions, protein modificationsMetabolomics: metabolite concentrationsPhenomics: creating collections of mutant strains and measuring phenotypes (e.g. cell growth) under various conditions
Darwinian genomics: Testing key issues in evolutionary biology
Examples:
1) Role of chance and necessity
2) Gradual changes or jumps
3) Extent and evolution of robustness against mutations
Darwinian genomics: Testing key issues in evolutionary biology
Examples:
1) Role of chance and necessity
2) Gradual changes or jumps
3) Extent and evolution of robustness against mutations
Yeast (S. cerevisiae) is an ideal model organism
1) Complete genome sequence/detailed biochemical studies
-> network reconstruction
2) Genome-scale computational models
-> systems level properties of cellular networks
3) Large-scale mutant libraries
-> test predictions of the models
4) Complete genome sequences for ~30 closely related species
-> study evolution across species
The knock-out paradox
High-throughput single gene knock-out studies: no phenotype for most genes in the lab
Why keep them during evolution?
1) Keep optimal cellular performance in face of harmful mutations and non-heritable errors
2) Allow cellular growth under wide range of external conditions
compensated by a gene duplicate (genetic redundancy)
compensated by alternative genetic pathways (distributed robustness)
have important functions only under specific environmental conditions
(Seemingly) dispensable genes....
Gene A
Gene B
Gene B
Gene A
Redundancy is only apparent, most genes should have important contribution to survival under special environmental conditions
Hillenmeyer et al. Science 2008
Hillenmeyer et al. Science 2008
Compared growth rates of ~ 5000 single gene knock-out strains under >1000 environments
97% of the mutants show slow growth under at least one condition
compensated by a gene duplicate (genetic redundancy)
compensated by alternative genetic pathways (distributed robustness)
have important functions only under specific environmental conditions
Are these explanations mutually exclusive?
Gene A
Gene B
Gene B
Gene A
Does the capacity to compensate the impact of gene deletions depend on the environment?
A
B
A
B
A
B
A B
Environment I.
a B
A b
a b
A B
a B
A b
a b
A B
a B
A b
a b
Environment II. Environment III.
Observed gene deletion phenotypes ( viable, lethal):
synthetic lethality no interactionno interaction
The extent of compensation may depend on nutrient availability
Computational tool: Flux Balance Analysis (FBA)
1) Network reconstruction In S. cerevisiae ~1400 biochemical reactions, including
transport processes.
2) Application of constraints Specify the nutrients available in the environment
(B,E), the key metabolites or biomass constituents (X, Y, Z) essential for survival,
presence/absence of genes
3) Find a particular enzymatic flux distribution -> rate of biomass production
(fitness)
Amino acidsCarbohydratesRibonucleotidesDeoxyribonucleotidesLipidsPhospholipidsSterolesFatty acids
fitness
What are the advantages of flux balance analysis?
1) Study large number of genes and environments
simultaneously
2) Predictions:
a) Changes in enzyme activity as a response to nutrient
conditions and genetic deletions
b) Impact of gene deletions and gene addition on growth
rates
3) Good agreement between experimental studies and model
predictions (~90%)
Forster et al. 2003 OMICS, Papp et al. Nature 2004
Interactions between mutations in metabolic networks
A special case: Synthetic lethal genetic interactions
A Bnormal growth
lethal (or sick)
a– B
A b–
a– b–
Redundant gene duplicates
Gene A
Gene B
Gene B
Gene A
Alternative cellular pathways
Model predictions and verification of genetic interactions
• Using Flux Balance analysis, we simulated all possible single and double gene deletions (~125 000) in the metabolic network under 53 different nutrient conditions
98 gene pairs are synthetic lethal under at least one condition
• We performed lab experiments to validate them:
ΔAB
n AΔB
n
A/ΔAB/ΔB2n
sporulation
Results:
1) 50% of the predictions were correct (only ~ 0.6% expected by chance!)
2) 85% of the interacting gene pairs show condition-dependent synthetic lethality
1 5 9 13 17 21 25 29 33 37 41 45 49 53
Number of environmental conditions
0
5
10
15
20
25
30
35
Num
ber
of S
L ge
ne p
airs
unconditional
synthetic lethality
Harrison et al. (2007) PNAS 104:2307-2312
An example:
Harrison et al. (2007) PNAS 104:2307-2312
An example:
Conclusions
• The metabolic network model can reliably predict
(synthetic lethal) genetic interactions.
• The presence of genetic interactions (and hence the extent
of compensation) vary extensively across nutrient conditions.
Speculations and potential implications:
• Experimental design. Different environments should be
screened to identify the majority of genetic interactions
• Functional genomics. Redundancy is more apparent than
real. Many seemingly dispensable genes have important
physiological role under specific conditions
• Evolution. Robustness against mutations may not be a
directly selected trait, but rather a by-product of evolution of
novel metabolic pathways towards new environmental
conditions
Shortcomings:
The computational model is far from perfect, and ignores many biological details
Only specific genetic interactions have been studied
No systematic experimental screen
Harrison et al. (2007) PNAS 104:2307-2312
Collaboration with Charles Boone lab
1) Using robotic protocols, they map genetic interactions across the whole yeast genome (~107
combinations )
2) They developed high-throughput protocols to measure fitness at high precision
Why study evolution?
Evolution of antibiotics resistance: 33 Billion $ annual costs in US
Ignoring evolution has serious health consequences
Evolutionary Systems Biology Group
http://www.brc.hu/sysbiol/
Projects:
• Analyses of genetic interactions
• Evolution of antibiotics resistance
Interactions between genes are masked by distant gene
duplicates
Confirmed by creating
corresponding triple
knock-outs:
Overlapping enzymatic
activities between
duplicates conserved
across more than 100
million years of
evolution