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GENE PROFILES
•Synthetic lethality
•Chemical Genetic Interactions
•Pleiotropy
Zohar Itzhaki
Advanced Seminar in Computational Biology
November 2005
What’s a gene profile ?
• Vector: Discrete information regarding a gene; characterization of
a gene according to defined parameters
• Can be clustered
• Main Goals:
• A better understanding of different mechanisms
• Annotation of unknown genes
Gene A
Gene B
Some reminders and definitions
• Synthetic lethality (genetic interactions)
death of double mutants
• Chemical genetic interaction
Hypersensitivity of a mutant to a sub-lethal compound
• Pleiotropy
one single mutant gene causes multiple mutant phenotypes
The Genetic Profiles in the studies
Genetic interactions
Synthetic lethality
Chemical genetic
Pleiotropy
interactions
Tong AH … Boone C. Global Mapping of The Yeast Genetic Interaction Network
Science 2004
Parsons AB … Boone C. Integration of Chemical-genetic and genetic
Interaction data links bioactive compounds to cellular target pathways
Nat Biotechnol 2004
Dudely AM. Janse DM. Tanay A. Shamir R. Church GM. A global View of
Pleiotropy and Phenotypically Derived Gene Function in Yeast
Molecular Systems Biology 2005
General procedures - A yeast array
• Every spot is a yeast colony (different strains)
• Treatment
• Computational detection, usually for growth changes
genetic interactions (GGI)
synthetic lethality
• Is it really an interaction ? (PPI)
• Two ways of interpretation:
 Parallel pathways (ie: gene duplication)
 Same pathway / complex (ie: membrane receptor + signal transducer)
• Essential gene GGI is checked by temperature sensitive (partial) mutations
Davierwala … Boone C. The synthetic genetic interaction spectrum of
essential gene Nature genetics 2005
Parallel pathways
Trait
Same pathways
GGI – Article & Technology
Tong AH … Boone C. Global Mapping of The Yeast Genetic Interaction Network
Science 2004
SGA (synthetic genetic array) analysis
an array that contains ~4700 haploids, every strain is mutated
in a different gene
A query: a single mutant haploid strain is crossed
A computational analysis for detecting the growth changes
The selected query genes are related to cytoskeleton & DNA repair
Tong & Boone 2004
Synthetic lethality – GGI assay
132 SGA screens (cytoskeleton & DNA repair mutated genes)
Pas
s1
*
Pas
s3
Candidate synthetic lethal pairs
Further evaluation
Results
~4000 interactions
of ~1000 genes
34 interactions/gene
8 int/gene in PPI
Evaluation: ~100,000 GGI
Tong & Boone 2004
X3
GGI and Function (GO annotations)
• Significant correlations between the GGI and their GO annotations:
GGI to GO:
12% of the GGI has the same GO annot’ (pVal=10-296)
27% of the GGI has a same or similar GO annot’ (pVal=10-322)
• Bridging GGI:
1755 out of ~285,500 (n2/2) possible pairs of GO attributes were
“bridged” by a GGI (pVal<0.05)
CONCLUSION: The GGI map represents a global map
of functional relationships between genes
Tong & Boone 2004
GGI and Function – Bridging GGI
Tong & Boone 2004
More validations of the concept
• GGI were significantly more abundant between:
 Genes sharing the same phenotype mutant (pVal=10-316)
 Genes encoding proteins with the same localization (pVal=10-70)
 Genes encoding proteins within the same complex (pVal=10-68)
Tong & Boone 2004
Gene profiles and profile clustering
Gene profiles: Foreach “array” gene create a 132d
binary vector, regarding GGI with the query genes
Create a 132*1000 matrix out of these vectors
Bicluster the matrix + GO annotations for every cluster
Tong & Boone 2004
The GGI Bi-cluster
Tong & Boone 2004
What can we learn from the Bi-Cluster ?
A. Connecting pathways
DNA damage
Checkpoint
Microtubule
Spindle
Checkpoint Dynamic
Sister chromatid cohesion during
chromosome replication
Recombination
DNA replication
Checkpoint
Tong & Boone 2004
What can we learn from the Bi-Cluster ?
B. Predict biological functions
• Profile similarity of csm3 (un-annotated) to tof1 and mrc1
 tof1 and mrc1 products response to stress and interact to the DNA
replication machinery
 “Wet” phenotype checks (related to cell cycle):
• ∆tof1 ∆rad9 = ∆csm3 ∆rad9
∆mrc1 ∆rad9 = ∆csm3 ∆rad9
 PPI assays (Y2H): Csm3 interacts Tof1
Tong & Boone 2004
GGI as predictors for PPI
• Analyze the GGI network and a 15,000 PPI network:
# of common GGI neighbors
between a pair of genes
PPI between the pair
of the product proteins
Within a
complex
Parallel
pathways
a
b
ctf18
ctf8
• ctf18 & ctf8 don’t interact as genes but their proteins products do
Tong & Boone 2004
The GGI network features
• Similarity to the PPI, WWW and other famous networks:
 The degrees of the nodes follow the power law distribution (many
with little, few with lots)
 HUBS - important for fitness: gim3-5 – chaperones for actin
 Small world (small shortest path)
Tong & Boone 2004
GGI networks: summary & conclusion
• represent the functional relationships between the genes
• help understand and connect pathways
• help annotate new genes
• predicting PPI
• potential for understanding human multi gene syndromes (ie: Alzheimer)
Tong & Boone 2004
Chemical Genetic Interactions (CGI)
Parsons AB … Boone C. Integration of Chemical-genetic and genetic
Interaction data links bioactive compounds to cellular target pathways
Nat Biotechnol 2004
• CGI - A hypersensitivity of a mutant to a sub-lethal
compound.
• Integrate GGI and CGI data using profiles in order
to understandi the targets of different compounds
Parsons & Boone 2004
Chemical Genetic Interactions (CGI)
CGI (chemical genetic) array analysis
 an array contains ~4700 diploids, every strain has a mutation
in a different gene
 A query: an inhibitory chemical compound
 A computational analysis for detecting the growth changes
In this assay 12 compounds were checked, among them:
Microtubule depolymerization agent
A protein glycosylation inhibitor
aa biosynthesis inhibitor
Signaling inhibitors
A topoisomerase inhibitor
etc…
Parsons & Boone 2004
The CGI assay
12 screens against different inhibitors
Measure colonies’ sizes: same or 3 degrees of reduction
FP
detection
FN
detection
Sensitive strains were grown separately
with different compound concentrations
Comparison between the rapamycin results and
another rapamycin (not large scale) former study
61
24
Profile creation and bi-clustering (12 drugs X 647 relevant genes)
Parsons & Boone 2004
185
The CGI bi-cluster
Parsons & Boone 2004
Multi drug resistant gene (MDR)
• 65 genes are associated with sensitivity to multiple compounds (>4/10
critical compounds):
 Known MDR genes as: drug pumps, ABC proteins
 Membrane lipid composition genes (erg family)
 New multidrug sensitivity strains (vph2)
• It seems that MDR genes are also conserved in mammals
Parsons & Boone 2004
The MDR network & enrichments
Lipid
metabolism
Vacuoles
H+ pumps
Vesicular
transport
Parsons & Boone 2004
Comparison of GGI and CGI results
Perform a GGI assay (SGA array) for several genes, known as drug targets
pVal=10-56
Parsons & Boone 2004
Comparison of GGI and CGI results
• High significance but not a total overlap:
 Statistics (drug) vs. complete (mutation)
 presence of the complex (drug + protein) may prevent alternative
pathways
 Genes that are inhibited by the CGI and not by the GGI may be
involved in cellular import or export
Red = MDR
a better overlap
without them
Parsons & Boone 2004
Comparison of GGI and CGI results
Perform 57 GGI assays (SGA array):
cytoskeleton, DNA related, secretion and more
Filter out the MDR genes (of the CGI DB)
Create gene profiles and a combined matrix
Bicluster of the matrix (803 X 69)
Parsons & Boone 2004
The GGI-CGI Bi-cluster
The ERG11 and the
fluconazole
Were clustered together
Other examples of
clustering of genes and
the drugs against them
Parsons & Boone 2004
What can we learn from the Bi-Cluster ?
• Good correlation between CGI and GGI
 link compounds to their cellular pathways
 link compounds to their gene targets
• Reveal uncharacterized gene functions (ie: similar patterns of GGI and
CGI reflects a functional similarity).
Parsons & Boone 2004
CGI - summary & conclusion
CGI profile summaries GGI profiles & is easier to perform
• Uncharacterized gene functions
• MDR gene detector
Parsons & Boone 2004
Pleiotropy
• Pleiotropy: a single mutant gene - multiple mutant phenotypes
• Drosophila: sex related genes; fertility & development
• Cats: being white and deaf
• Humans: single mutated gene - disease with several symptoms
(heart & limbs defects)
• Good or bad ??
 Good usage of small genomes – good for low organisms
 Disadvantage for higher organisms: problematic adaptation, gene
evolution, contradiction to the modular nature of the evolution (ie: fused
domains)
Pleiotropy – the article
Dudely AM. Janse DM. Tanay A. Shamir R. Church GM. A global View of
Pleiotropy and Phenotypically Derived Gene Function in Yeast
Molecular Systems Biology 2005
• The challenge: loss of single function or loss of multiple functions ?
(difficult to answer experimentally)
• The technique: check the mutant strains under different conditions
Dudely & Church 2005
Pleiotropy – large scale experiments
• use the former arrays (~4700 diploids, every strain has a mutation in a
different gene):
 Every array is put in a different condition (total of 21)
 A computational analysis - detect the growth changes (full,slow,no)
 Every experiment was performed twice and the results were
compared to former studies.
 The 21 conditions, among them:
Nutrient limiting conditions
Stress conditions (high ethanol, low pH, high salt etc…)
Heavy metals
Inhibitors of cellular functions (cytoskeleton…)
Dudely & Church 2005
767 genes
were detected
Gene profiles and clustering
767 strains
with growth changes
551 strains influenced
By 1-2 conditions
216 strains influenced
By 3-14 conditions
Cluster (sort) into
65 groups
Bi-cluster
overlapping groups
Dudely & Church 2005
GO enrichments- low pleiotropy clusters
• “Proper and logical” clusters:
‘galactose only’ cluster was enriched for “galactose” (e-18)
‘UV only’ cluster was enriched for “response to DNA damage” (e-17)
• Some new insights:
‘caffeine’ cluster was enriched for “cell cycle regulation”
• Other significant clusters of unknown genes (and known condition)
Dudely & Church 2005
GO enrichments- high pleiotropy clusters
• Consistency with Parson’s research (CGI) regarding drugs:
 Vacuole, golgi, intracellular transport
• All of the set of genes:
 Vacoular organization and biogenesis;
Protein transport and degradation
Maintaining pH (H+ transport)
• Other significant enrichments:
 Conditions related to RNA pol (inhibitors mainly) – transcription
 MDR genes
Understand gene functions for known and unknown genes
Dudely & Church 2005
Phenotypic profiles, GGI and complexes
• Use of Gavin complexes
 complex 113 – transcriptional elongation
 complex 137 – histone deacetylase
• Phenotypic profiles (boxes/black arrows): same complex proteins have:
 same phenotype (dep1 pho23) – similar function
 different phenotype (cti6 dep1) – distinct groups for distinct conditions
• GGI - Synthetic lethal (blue arrows)
 backup inside a complex (leo1 rtf1)
 similar functions of components of the complexes (cdc73 sap30)
Dudely & Church 2005
Complexes vs. phenotypic profiles
• The similarity between the genes within a complex (Average the phenotype profiles)
Conclusion: Phenotype profiles represent functional relationships
Dudely & Church 2005
Detecting multi functions genes
Chromatin
modification
snf1 is assigned to
Both clusters
Vesicle
transport
Indeed snf1 has several functions (targeting substrates, response
to stress, regulation of filamentations etc…)
Dudely & Church 2005
Multi-function genes
1. Indeed multi functions
2. Overlapping clusters – biological redundancy
Dudely & Church 2005
Conclusions
• New annotations and perspectives: relationship between
phenotype, pathways and genes
• Another perspective regarding MDR genes
• Distinguish between genes with one vs many functionalities
• A large number of pleiotropic genes (pVal<10-9). Were thought
to be a disadvantage, maybe an advantage ?
Dudely & Church 2005
However…
• “Small scale”: only 132 genes (GGI), 12 compounds (CGI) and 21 conditions.
• Selected genes had similar “narrow” annotations (cytoskeleton, DNA repair)
• Only growth rates were measured, what about other phenotypes ?
• Binary systems: influenced or not. (Even when quantitatively measured)
• Severe thresholds and problematic attitude, in some cases (CGI), only
genes that “passed” an initial stage, were tested using different
compound concentration.
Tong & Boone 2004
Parsons & Boone 2004
Dudely & Church 2005
Summary and conclusions
• Relatively simple methods
 large scale
 unsupervised
• Integration of the DBs + use other data: Get a more complex view on
genes, functions and pathways.
• A less central field, with high potential
CGI
profiles
GGI
profiles
Phenotypic
profiles
A better understanding
Tong & Boone 2004
Parsons & Boone 2004
Dudely & Church 2005
Thank you,
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