Download Class4_Synthetic_Genetics

Synthetic Lethality
X
A
Viable! X
B
Y
X
Z
C
Viable!
Product
Dead!
•Inactivating two interacting pathways
causes lethality (or sickness)
Synthetic Lethality
A
B
Wild-type
aD
X
A
X
aD
B
bD
bD
Viable
Viable
Lethal
• Synthetic Lethality Identifies Functional Relationships
• Large-Scale Synthetic Lethality Analysis Should Generate a
Global Map of Functional Relationships between Genes
and Pathways
• Gene Conservation = Genetic Network Conservation
Similar Patterns of Genetic Interactions Identify Pathways or Complexes
XA
X
X
XA
X
XA
X
A
X
B
Y
B
Y
X
B
Y
B
Y
C
Z
C
Z
C
X
Z
C
Z
Essential
Product
Essential
Product
Essential
Product
Essential
Product
Dead
Dead
Dead
Genetic
Interaction
Network
Scenarios That May Give Rise to Synthetic
Interaction
A
or
B
A
B
A
B
or
regulates
etc. etc.
• Interpretation depends on context
• Each synthetic interaction must be interpreted on a case-by-case
basis (Guarente (1993) TIG, 9:362)
MATa
D
wild-type
X
bni1D
MATa
Mating
xxxD
a/a
Sporulation
MATa Haploid Selection
(MFA1pr-HIS3)
Double Mutant Selection
Synthetic Gene Array (SGA)
Statistics
• 132 query gene mutations were crossed into ~4700 yeast deletion mutants.
• Query genes derived from 3 basic functional groups: (1) actin/polarity/secretion,
(2) microtubule/mitosis, and (3) DNA synthesis/repair.
• Number of interactions per query varied from 1 to 146 with an average of 36.
• (Genes, Genetic Interactions): ~1000 nodes and ~4000 edges.
• 17 to 41% false negative rate
• False positive rate?
• Data quality is good
Making Sense of Genetic
Interaction Network
• Correlation with GO annotations
• Hierarchical clustering groups according to their SGA profile
– Useful for inferring function of unknown genes
• Correlation with protein-protein interactions?
– Only 30/4039 encode physically-interacting proteins
• Statistical properties of genetic interaction network graph
Network of GO Attributes
Clustering
Array
Cell polarity
• Actin patches
• Endocytosis
• Cell wall synthesis
• Cell integrity (PKC)
Query
bni1D : Genome-Wide Synthetic Lethality Screen
Cell Polarity
Cell Wall Maintenance
BEM1
BEM2
BEM4
BUD6
SLA1
CLA4
ELM1
GIN4
NAP1
SWE1
BCK1
SLT2
BNI4
CHS3
SKT5/CHS4
CHS5
CHS7
FAB1
SMI1
Cytokinesis
BNR1
CYK3
SHS1
Cell Structure
ATS1
PAC11
YKE2/GIM1
Cell Polarity
20%
Cytokinesis
6%
Unknown
22%
Mitosis
16%
Cell Structure
6%
Cell Wall
Maintenance
18%
Mitosis
Vesicular Transport
Unknown
ARP1
ASE1
DYN1
DYN2
JNM1
PAC1
NIP100
NUM1
SNC2
VPS28
YPT6
BBC1/YJL020c
NBP2
TUS1
YBL051c
YBL062w
YDR149c
YHR111w
YKR047w
YLR190w
YMR299c
YNL119w
Others
PCL1
ELP2
ELP3
bni1D : Genome-Wide Synthetic Lethality Screen
Cell Polarity
Cell Wall Maintenance
BEM1
BEM2
BEM4
BUD6
SLA1
CLA4
ELM1
GIN4
NAP1
SWE1
BCK1
SLT2
SMI1
CHS3
SKT5/CHS4
CHS5
CHS7
BNI4
SMI1
Cytokinesis
BNR1
CYK3
SHS1
Cell Structure
ATS1
PAC11
YKE2/GIM1
Cell Polarity
20%
Cytokinesis
6%
Unknown
22%
Mitosis
16%
Cell Structure
6%
Cell Wall
Maintenance
18%
Mitosis
Vesicular Transport
Unknown
ASE1
ARP1
DYN1
DYN2
JNM1
PAC1
PAC11
NIP100
NUM1
SNC2
VPS28
YPT6
BBC1/YJL020c
NBP2
TUS1
YBL051c
YBL062w
YDR149c
YHR111w
YKR047w
YLR190w
YMR299c
YNL119w
Others
PCL1
ELP2
ELP3
sgs1D : Genome-Wide Synthetic Lethality Screen
(24 Interactions)
DNA Repair
Meiosis
Others
Unknown
ASF1
HPR5
POL32
RAD27
RAD50
SAE2
SLX1
MMS4/SLX2
MUS81/SLX3
SLX4
WSS1
CSM3
PUB1
RPL24A
SWE1
SIS2
SOD1
YBR094w
Chromatin Structure
ESC2
ESC4
TOP1
DNA Repair
46%
Unknown
4%
DNA Synthesis
RNR1
RRM3
YNL218w
Cell Polarity
4%
Chromatin Structure
13%
Meiosis
4%
DNA Synthesis
13%
8 SGA Screens:
291 Interactions
204 Genes
Cell Polarity
Cell Wall Maintenance
Cell Structure
Mitosis
Chromosome Structure
DNA Synthesis
DNA Repair
Unknown
Others
Extension of SGA: E-MAP
• E-MAP = epistatic miniarray profiles
• Quantitative measurement of phenotype (e.g. growth rate)
– Measure both aggravating and alleviating genetic interactions
• Hypomorphic alleles (not null mutations)
• Focus on subset of genes
• Maya Schuldiner/Jonathan Weissman
Organizing Complexes into Pathways Using Genetic Interactions
Complex A
Complex X
X
Positive=
Complex B
Complex Y
X
X
Complex C
Complex Z
P
= Negative
“RNA World” E-MAP (600 genes)
Positive Genetic Interactions
Negative Genetic Interactions
Positive Genetic Interactions
Negative Genetic Interactions
Proteasome Mutants Suppress Deletions in THP1/SAC3
WT
∆sem1
∆thp1
∆thp1 ∆sem1
rpn11-DAmP
∆thp1 rpn11-DAmP
rpt6 ts
∆thp1 rpt6 ts
Proteasome Mutants Suppress mRNA Export Defects of thp1∆
WT
polyA
RNA
polyA
RNA
Nuclei
Merge
∆thp1
∆thp1∆sem1
Proteasome is Required for Efficient polyA mRNA Export
WT
∆sem1
Organizing Complexes into Pathways Using Genetic Interactions
Complex A
Complex X
X
Complex B
epistatic/
suppressive=
X
Complex C
Complex Y
= synthetic lethality
X
Complex Z
P
What about essential genes??????
Essential vs. Non-essential Genes in Budding Yeast
Non-Essential Genes (~4800)
Essential Genes (~1050)
CREATING MUTANT ALLELES OF ESSENTIAL GENES
1. TET-Promoter Shut-Off Mutants
2. DAmP Alleles
3. Conditional point mutants
1. TET-Promoter SHUT-Off Strains
-Hughes and colleagues created a library of promoter-shutoff strains comprising
nearly two-thirds of all essential genes in yeast (602 genes)
1. TET-Promoter SHUT-Off Strains
-the library was subjected to morphological analysis, size profiling, drug
sensitivity screening and microarray expression profiling
1. TET-Promoter SHUT-Off Strains
Cell Morphology
rRNA Processing
Cell Size
Cdc53
1. TET-Promoter SHUT-Off Strains
Gene Expression Analysis
1. TET-Promoter SHUT-Off Strains
Protein Secretion
Ylr440c
Mitochondrial Regulation
Yol026c
Ribosome Biogenesis
Ymr290c, Ykl014c, Yjr041c
1. Genetic Analsyis using the TET-Promoter SHUT-Off Strains
-30 different mutants X TET-promoter collection
-found many interactions between dissimilar genes
-claimed that there are five times as many
“negative” genetic interactions for essential genes
when compared to non-essential genes
-however, the cause of this may be due to the fact
that the TET strains were very sick (and they were
not quantitatively assessing the growth of the
double mutant by considering the growth of the two
single mutants)
2. DAmP Alleles
(Schuldiner et al., Cell, 2005)
2. DAmP Alleles
3. Point Mutants of Essential Genes
Genetic Profiling of Point Mutants Reveals Insight on Structure-Function
PCNA (Pol30)
-PCNA is important in many aspects of DNA metabolism,
including DNA replication and DNA repair
-PCNA interacts with CAF-1, a three-subunit protein, to
couple DNA replication or DNA repair to nucleosome
deposition
-Two mutants of PCNA (pol30-8 and pol30-79) generated
by Stillman and colleagues
Genetic Profiling of Point Mutants Reveals Insight on Structure-Function
PCNA (Pol30)
-PCNA is important in many aspects of DNA metabolism,
including DNA replication and DNA repair
-PCNA interacts with CAF-1, a three-subunit protein, to
couple DNA replication or DNA repair to nucleosome
deposition
-Two mutants of PCNA (pol30-8 and pol30-79) generated
by Stillman and colleagues
What is “Chemical Genetics?”
Chemical genetics is the use of exogenous ligands to alter the function of a single
gene product within the context of a complex cellular environment.
Find ligands that affect a biological process (forward)
Optimize ligands to study protein function (reverse)
Forward Chemical Genetics
• Goal is target identification
• Screening large sets of small molecules
• Those that cause a specific phenotype of interest are
used to isolate and identify the protein target
Forward Chemical Genetics
Target Identification
N
Plate with cells
Identify protein
Target
(deconvolution)
Add one compound
per well
Select compound that
produces phenotype
of interest
N
Reverse Chemical Genetics
• Goal is target function and validation
• Screen for compounds that bind to a given protein
• Optimize for selectivity
Reverse Chemical Genetics
Target Validation
Find ligand for
protein of interest
Optimize for selectivty
N
Assay for
phenotype
Add ligand
to cells
N
FORWARD Chemical Genetic Studies in Yeast
1. Screening the deletion set for drug sensitivities
2. Comparing mutant profiles to drug profiles
3. Haploinsufficieny analysis
Organizing Complexes into Pathways Using Genetic Interactions
Complex A
Complex X
X
Complex B
Complex Y
= synthetic lethality
X
Complex C
Complex Z
P
X= Drug
Deletion Mutants Sensitive to a Particular Drug Should
be Synthetically Lethal with the Drug Target
Synthetic Lethal Interactions
Synthetic Chemical Interactions
Alive
Alive
Drug
Alive
Alive
Drug
Dead
Dead
1. Screening the deletion set for drug sensitivities
1. Screening the deletion set for drug sensitivities
FORWARD Chemical Genetic Studies in Yeast
1. Screening the deletion set for drug sensitivities
2. Comparing mutant profiles to drug profiles
3. Haploinsufficieny analysis
2. Comparing mutant profiles to drug profiles
1. Clustering of the Drug Profiles:
Camptothecin and Hydroxyurea have a similar mode of action: they both inhibit DNA replication
Parsons et al., 2004, Nature Biotechnology
2. Comparison of drug profiles to mutant profiles:
CAMPTOTHECIN: causes single-stranded DNA nicks and inhibits DNA replication
Also known as : Hycamtin (GlaxoSmithKline) and Camptosar (Pfizer)
-used as an anti-cancer agent
RFA1
RTT105
POL30-79
POL30-879
POL32
DNA Replication
RAD27
RFC5
Factors
POL30
ELG1
RFA2
PRI1
RFC4
CDC9
TSA1
CAMPTOTHECIN (15 g/ml)
CAMPTOTHECIN (30 g/ml)
2. Comparison of drug profiles to mutant profiles:
Benomyl: a drug that targets microtubules and affects chromosome segregation
TUB3
PAC2
CIN1
CIN2
CIN4
BENOMYL
(15 g/ml)
TUB3: alpha-tubulin
PAC2: tubulin chaperone
CIN1, CIN2, CIN4: genes required for microtubule stability
FORWARD Chemical Genetic Studies in Yeast
1. Screening the deletion set for drug sensitivities
2. Comparing mutant profiles to drug profiles
3. Haploinsufficieny analysis
3. Haploinsufficieny Analysis
Haploinsufficiency: Protein A
Reduced
Levels of
Protein A
Protein B
Protein C
P
Drug
Lethality!!!
3. Haploinsufficieny Analysis
TUB1/TUB1 vs. tub1D/TUB1
25 ug/ml benomyl
50 ug/ml benomyl
-used a genome-wide pool of tagged heterozygotes to assess the cellular
effects of 78 compounds in Saccharomyces cerevisiae
Strategy for Global Haploinsufficiency Analysis Using Microarrays
Comprehensive View of Fitness Profiles for 78 Compounds
No Drug-Specific Fitness Changes
Small Number of Highly Significant
Outliers
Widespread Fitness Changes
Identification of Erg7 as the Target for Molsidomine
Molsidomine: potent vasodilator used clinically to treat angina
Erg7: Lanosterol synthase is a highly conserved and essential component of ergosterol biosynthesis
Overexpression of Erg7 results in Resistance to Molsidomine
5-Fluorouracil Targets rRNA Processing
-one of the most widely used chemotherapeutics for the treatment of solid tumors in cancer patients
-thought to affect DNA synthesis as a competitive inhibitor of thymidylate synthetase
5-Fluorouracil
Rrp6, Rrp41, Rrp46, Rrp44: Exosome
Mak21, Ssf1, Nop4, Has1: rRNA Processing
The yeast knockout collection
http://www-sequence.stanford.edu/group/yeast_deletion_project/deletions3.html
Using the knockouts for microarrays

A Robust Toolkit for Functional Profiling of the Yeast Genome


Takes advantage of the MATa/a heterozygous diploid collection


Pan et al. (2004) Mol Cell 16, 487
identifies synthetic lethal interactions via diploid-based synthetic
lethality analysis by microarrays (“dSLAM”)
Uses dSLAM to identify those strains that upon knockout of a
query gene, show growth defects


synthetic lethal (the new double mutant = dead)
synthetic fitness (the new double mutant = slow growth)
Step 1: Creating the haploid convertible
heterozygotes
Important point:
This HIS3 gene is only
expressed in MATa
haploids, not in MATa
haploids or MATa/a
diploids
So in other words, can
select against
MATa/a diploids to
ensure you’re looking at
only haploids later on.
Step 2: Inserting the query mutation
Knockout one copy of
your gene of interest
(“Your Favorite
Gene”) with URA3
Step 3: Make new haploids and select
for strains of interest
Sporulate to get new haploids
Select on –his medium to
ensure only haploids
survive (no diploids)
selects against query
mutation so genotype is
xxxD::KanMX YFG1
selects for query mutation so
genotype is xxxD::KanMX
yfg1::URA3
Reminder about YKO construction
Step 4: Prepare genomic DNA and do PCR
with common TAG sequences
U1
D1
U2
D2
Using common oligos U1 and U2 (or D1 and D2) amplifies the
UPTAG (or DNTAG) sequence unique to each of the KOs
Step 4: Prepare genomic DNA and do PCR
with common TAG sequences
The two different conditions
are labeled with two
different colors**
The labeled DNA is then
incubated with a TAG
microarray
**The PCR reactions create a mixture of TAGs (representing all the strains in
the pool), since each KO has a unique set of identifier tags (UPTAG and
DNTAG) bounded by common oligonucleotides
Evidence this really works – part I
On average, the
intensity is the same
before and after 1 copy
of the CAN1 gene is
knocked out
Strains
x-axis
XXX/xxxD::KanMX
CAN1/CAN1
y-axis
XXX/xxxD::KanMX
CAN1/can1D::MFA1pr-HIS3
Evidence this really works – part II
Red spots illustrate that
fraction of the strains
with KOs in essential
genes, so when haploid,
not present in pool
Strains
x-axis
y-axis
DIPLOIDS
XXX/xxxD::KanMX
CAN1/can1D::MFA1pr-HIS3
HAPLOIDS
XXX or xxxD::KanMX
can1D::MFA1pr-HIS3
Another variation: Drug sensitivity
Another variation: Drug sensitivity
Summary

If you can compare two different
conditions and you have a way to stick
things to slides, some sort of microarray
is possible!
HOW NOT TO LOOK AT INTERACTION DATA!!!!!!!!