Transcript Integration of chemical-genetic and genetic interaction data links

Integration of chemical-genetic and
genetic interaction data links bioactive
compounds to cellular target pathways
Parsons et al. 2003. Nature
Biotechnology. 22(1):62-69
Presented by Obi Griffith
Outline
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Background
The problem
Approach
Methods
Results
Conclusions
Criticisms
Topics for
discussion
Background
• Yeast as a model
organism
• Yeast genomics
• Tools of yeast
genomics
Yeast as a model organism
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Studied for 100 years
Convenient lab organism
Stable haploid or diploid
Unicellular but can display group
characteristics
• Highly versatile transformation system
• Homologous recombination efficient
Yeast Genomics
• First eukaryotic genome to be sequenced
• ~6000 annotated genes
• 182 genes with significant similarity to human
disease genes.
• No complete comparison between humans and
yeast yet completed but likely many more
orthologous genes than this (Carroll et al, 2003).
• Many metabolic and signal transduction
pathways are conserved
Tools of Yeast Genomics
• Expression profiling (microarrays, SAGE)
• Overexpression of yeast genes
• Two-hyrid analysis of yeast protein
interactions
• Mass specroscopy analysis of protein
complexes
• protein microarrays
• protein localization
Tools of Yeast Genomics
(cont’d)
• Whole genome
deletion collections
 Phenotypic screens
 Synthetic lethality
screens
 Haploinsufficiency
analysis
 Mutant gene mapping
The problem
• Determining how small organic chemicals
interact with living systems
• Traditionally a very laborious process
 Eg biochemical or affinity purification
strategies
 Depend on ability to modify a test compound
 Affinity not always sufficient to allow
purification
The approach
• A global fitness test that reveals genes
involved in mediating the response of
yeast cells to a test compound
• A way to identify molecular targets without
altering test compound
• Use synthetic lethal tests on a genomic
scale.
• Remember, synthetic lethal = lethal event
arising from ‘synthesis’ of two gene
deletions or disruptions (eg. chemical
inhibition)
Method
• Conduct 2 kinds of synthetic lethal tests:
• deletion collection + chemical
= chemical-genetic profile
• deletion collection + 2nd deletion
= genetic interaction profile
• Where profiles are the same the 2nd
deletion is likely target of chemical
Chemical-genetic profiles
• Screened ~4700 viable yeast deletion mutants
for sensitivity to 12 different chemical
compounds.
 Eg. benomyl, a microtubule depolymerizing agent,
FK506, a calcineurin inhibitor, fluconazole, an
antifungal agent that inhibits Erg11, etc…
• Confirmed interactions by serial-dilution spot
assays to minimize false positives
• Assessed false-negatives by comparing results
for rapamycin screen to previously published
results
Genetic Interaction profiles
• First tested system with Erg11, which
encodes the target of the antifungal drug
fluconazole.
 Crossed the Erg11 mutation into the viable
deletion set.
 Screened double-mutant set for lethal or sick.
 Compared fluconazole chemical-genetic
interactions to Erg11 genetic interactions.
• Performed similar analysis with calcineurin
(CNB1).
Clustering of chemical-genetic and
genetic interaction profiles
• Used 2-d hierarchical clustering of a
combined data set:
 Chemical-genetic profiles for FK506, CsA,
fluconazole, benomyl, hydroxyurea, and
camptothecin
 Genetic profiles for genes encoding for the
target genes or their functionally related
genes (57 total).
• Filtered out multidrug-resistance
Conclusions
• a powerful method of understanding
pathways and targets for bioactive
compounds
• A convincing proof of principle.
• Can identify target pathways for drugs that
don’t interact with one specific target only.
• Adaptable to other organisms including
mammals using methods like RNAi
Criticisms
• Reliance on GO annotations.
• Convincing examples but no overall measure of
agreement between profile clustering and what
we expect.
• false-negatives
• Only detects more sensitive reactions to
compounds.
• What about important interactions that do not
result in synthetic lethality?
• In many cases, their method will identify target
pathway but not actual target
References
• Carroll PM, Dougherty B, Ross-Macdonald P,
Browman K, FitzGerald K. 2003. Model
systems in drug discovery: chemical genetics
meets genomics. Pharmacol Ther. 99(2):183220.
• Parsons AB, Brost RL, Ding H, Li Z, Zhang C,
Sheikh B, Brown GW, Kane PM, Hughes TR,
Boone C. 2003. Integration of chemical-genetic
and genetic interaction data links bioactive
compounds to cellular target pathways. Nat
Biotechnol. 22(1):62-9
• Stockwell. 2003. The biological magic behind
the bullet. Nat Biotechnol. 22(1):37-8
Topics for discussion
• Why don’t the two kinds of profiles match
perfectly?
• Other possible applications of this approach
• How could their method be incorporated or
supplemented with data from other
methodologies (eg. microarray,
haploinsufficiency)
• RNAi knockouts for each mouse gene to extend
approach to mammals
• Others?
The First Eukaryotic Proteome
Chip
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Zhu et al. (2001) demonstrate first
Proteome chip.
6566 protein samples
Representing 5800 unique proteins
(80%)
Spotted in duplicate on nickel coated
microscope slide
GST fusion and probing with anti-GST
Tested with biotinylated Calmodulin
A highly conserved calcium binding
protein involved with many other
proteins
Detected by binding of Cy3-labelled
streptavidin
Found 39 proteins that bind to
calmodulin
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6 previously known
6 missed because not in collection or
not successfully attached to chip
Found putative calmodulin binding
motif shared by 14 of 39 proteins
GO – Gene Ontology
• The goal of the Gene
Ontology TM (GO)
Consortium is to produce
a controlled vocabulary
that can be applied to all
organisms even as
knowledge of gene and
protein roles in cells is
accumulating and
changing. GO provides
three structured networks
of defined terms to
describe gene product
attributes.
Why do the genetic interaction and
chemical-genetic interaction
profiles not match exactly?
• Incomplete inactivation by the chemical
• Multiple gene targets for the gene
• May reflect inherent differences in genetic
versus chemical mechanisms of target inhibition.
• Gene deletion completely removes the target
protein from the system whereas chemical
inhibition leaves a protein-chemical complex in
the system that still may play some role in the
cell or have unexpected effects.