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