X X X A Viable! B Y Viable! Z C Product Dead! Synthetic Lethality

X X X A Viable! B Y Viable! Z C Product Dead! Synthetic Lethality
Inactivating two interacting pathways causes lethality (or sickness)

Synthetic Lethality A aD B bD Wild-type Viable Lethal
X bD Viable Lethal Wild-type 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

X X X X X X A B C Essential Product X Y Z A B C Essential Product X Y
Similar Patterns of Genetic Interactions Identify Pathways or Complexes X A B C Essential Product X Y Z X X A B C Essential Product X Y Z X A B C Essential Product X Y Z A B C Essential Product X Y Z X X Dead Dead Dead Genetic Interaction Network

Scenarios That May Give Rise to Synthetic Interaction
or regulates A B A or B A B etc. etc. Interpretation depends on context Each synthetic interaction must be interpreted on a case-by-case basis (Guarente (1993) TIG, 9:362)

X D a/a wild-type MATa MATa bni1 xxx Mating 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 Cell polarity Actin patches Endocytosis Cell wall synthesis
Cell integrity (PKC) Array Query

bni1D : Genome-Wide Synthetic Lethality Screen
Others PCL1 ELP2 ELP3 Vesicular Transport SNC2 VPS28 YPT6 Unknown BBC1/YJL020c NBP2 TUS1 YBL051c YBL062w YDR149c YHR111w YKR047w YLR190w YMR299c YNL119w Mitosis ARP1 ASE1 DYN1 DYN2 JNM1 PAC1 NIP100 NUM1 Cell Wall Maintenance BCK1 SLT2 BNI4 CHS3 SKT5/CHS4 CHS5 CHS7 FAB1 SMI1 Cell Structure ATS1 PAC11 YKE2/GIM1 Cell Polarity BEM1 BEM2 BEM4 BUD6 SLA1 CLA4 ELM1 GIN4 NAP1 SWE1 Cytokinesis BNR1 CYK3 SHS1 Cell Polarity 20% Cytokinesis 6% Cell Wall Maintenance 18% Cell Structure Mitosis 16% Unknown 22%

bni1D : Genome-Wide Synthetic Lethality Screen
Others PCL1 ELP2 ELP3 Vesicular Transport SNC2 VPS28 YPT6 Unknown BBC1/YJL020c NBP2 TUS1 YBL051c YBL062w YDR149c YHR111w YKR047w YLR190w YMR299c YNL119w Mitosis ASE1 ARP1 DYN1 DYN2 JNM1 PAC1 PAC11 NIP100 NUM1 Cell Wall Maintenance BCK1 SLT2 SMI1 CHS3 SKT5/CHS4 CHS5 CHS7 BNI4 Cell Structure ATS1 YKE2/GIM1 Cell Polarity BEM1 BEM2 BEM4 BUD6 SLA1 CLA4 ELM1 GIN4 NAP1 SWE1 Cytokinesis BNR1 CYK3 SHS1 Cell Polarity 20% Cytokinesis 6% Cell Wall Maintenance 18% Cell Structure Mitosis 16% Unknown 22%

sgs1D : Genome-Wide Synthetic Lethality Screen
(24 Interactions) DNA Repair ASF1 HPR5 POL32 RAD27 RAD50 SAE2 SLX1 MMS4/SLX2 MUS81/SLX3 SLX4 WSS1 Meiosis CSM3 Others PUB1 RPL24A SWE1 SIS2 SOD1 Unknown YBR094w Chromatin Structure ESC2 ESC4 TOP1 DNA Repair 46% DNA Synthesis 13% Meiosis 4% Chromatin Structure Cell Polarity Unknown DNA Synthesis RNR1 RRM3 YNL218w

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

X X X P Organizing Complexes into Pathways Using Genetic Interactions
Complex A Complex X X Complex B Complex Y X Positive= X = Negative Complex C Complex Z P

“RNA World” E-MAP (600 genes)

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 ∆thp1 ∆thp1∆sem1 polyA RNA polyA RNA Nuclei Merge

Proteasome is Required for Efficient polyA mRNA Export
WT ∆sem1

What about essential genes??????
Organizing Complexes into Pathways Using Genetic Interactions Complex A Complex X X Complex B Complex Y = synthetic lethality epistatic/ suppressive= X X Complex C Complex Z P What about essential genes??????

Essential vs. Non-essential Genes in Budding Yeast

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)

-the library was subjected to morphological analysis, size profiling, drug sensitivity screening and microarray expression profiling

Cell Morphology rRNA Processing Cell Size Cdc53

Gene Expression Analysis

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

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
Plate with cells Identify protein Target (deconvolution) Add one compound per well Select compound that produces phenotype of interest

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 Assay for phenotype Add ligand to cells

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

Synthetic Lethal Interactions Synthetic Chemical Interactions
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

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

DNA Replication Factors
2. Comparison of drug profiles to mutant profiles: RFA1 RTT105 POL30-79 POL30-879 POL32 RAD27 RFC5 POL30 ELG1 RFA2 PRI1 RFC4 CDC9 TSA1 CAMPTOTHECIN (15 g/ml) CAMPTOTHECIN (30 g/ml) DNA Replication Factors CAMPTOTHECIN: causes single-stranded DNA nicks and inhibits DNA replication Also known as : Hycamtin (GlaxoSmithKline) and Camptosar (Pfizer) -used as an anti-cancer agent

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

3. Haploinsufficieny Analysis

P Lethality!!! Protein A Drug Haploinsufficiency:
Reduced Levels of Protein A Lethality!!! Protein B Protein C P

3. Haploinsufficieny Analysis

TUB1/TUB1 vs. tub1/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 Rrp6, Rrp41, Rrp46, Rrp44: Exosome 5-Fluorouracil Mak21, Ssf1, Nop4, Has1: rRNA Processing

The yeast knockout collection

Using the knockouts for microarrays
A Robust Toolkit for Functional Profiling of the Yeast Genome Pan et al. (2004) Mol Cell 16, 487 Takes advantage of the MATa/a heterozygous diploid collection 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
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 y-axis XXX/xxxD::KanMX CAN1/CAN1 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

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!!!!!!!!

X X X A Viable! B Y Viable! Z C Product Dead! Synthetic Lethality

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