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

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Presentation on theme: "Synthetic Lethality Inactivating two interacting pathwaysInactivating two interacting pathways causes lethality (or sickness) A B C Product X Y Z X Viable!"— Presentation transcript:

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

2 Synthetic Lethality A B aa B X A bb Viable Lethal aa bb Wild-type Viable X 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

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

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

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6 xxx  bni1  X Mating MAT  MAT a a/   wild-type Sporulation MAT a Haploid Selection (MFA1pr-HIS3) Double Mutant Selection

7 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

8 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

9 Network of GO Attributes

10 Clustering Array Query Cell polarity Actin patches Endocytosis Cell wall synthesis Cell integrity (PKC)

11 Cell Polarity 20% Cytokinesis 6% Cell Wall Maintenance 18% Cell Structure 6% Mitosis 16% Unknown 22% 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 bni1   : Genome-Wide Synthetic Lethality Screen

12 Cell Polarity 20% Cytokinesis 6% Cell Wall Maintenance 18% Cell Structure 6% Mitosis 16% Unknown 22% 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 SMI1 Cell Structure ATS1 PAC11 YKE2/GIM1 Cell Polarity BEM1 BEM2 BEM4 BUD6 SLA1 CLA4 ELM1 GIN4 NAP1 SWE1 Cytokinesis BNR1 CYK3 SHS1 bni1   : Genome-Wide Synthetic Lethality Screen

13 DNA Repair ASF1 HPR5 POL32 RAD27 RAD50 SAE2 SLX1 MMS4/SLX2 MUS81/SLX3 SLX4 WSS1 DNA Synthesis RNR1 RRM3 YNL218w Meiosis CSM3 Unknown YBR094w Others PUB1 RPL24A SWE1 SIS2 SOD1 sgs1  : Genome-Wide Synthetic Lethality Screen (24 Interactions) Chromatin Structure ESC2 ESC4 TOP1 DNA Repair 46% DNA Synthesis 13% Meiosis 4% Chromatin Structure 13% Cell Polarity 4% Unknown 4%

14 Cell Polarity Cell Wall Maintenance Cell Structure Mitosis Chromosome Structure DNA Synthesis DNA Repair Unknown Others 8 SGA Screens: 291 Interactions 204 Genes

15 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

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

17 “RNA World” E-MAP (600 genes)

18 Positive Genetic Interactions Negative Genetic Interactions

19 Positive Genetic Interactions Negative Genetic Interactions

20 Proteasome Mutants Suppress Deletions in THP1/SAC3 WT ∆thp1 ∆thp1 ∆sem1 ∆sem1 rpn11-DAmP ∆thp1 rpn11-DAmP ∆thp1 rpt6 ts rpt6 ts

21 Proteasome Mutants Suppress mRNA Export Defects of thp1∆ polyA RNA Nuclei Merge WT∆thp1 ∆thp1∆sem1 polyA RNA

22 Proteasome is Required for Efficient polyA mRNA Export WT∆sem1

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

24 Essential vs. Non-essential Genes in Budding Yeast Non-Essential Genes (~4800) Essential Genes (~1050)

25 3. Conditional point mutants CREATING MUTANT ALLELES OF ESSENTIAL GENES 1. TET-Promoter Shut-Off Mutants 2. DAmP Alleles

26 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)

27 1. TET-Promoter SHUT-Off Strains -the library was subjected to morphological analysis, size profiling, drug sensitivity screening and microarray expression profiling

28 1. TET-Promoter SHUT-Off Strains Cell Morphology Cell Size Cdc53 rRNA Processing

29 1. TET-Promoter SHUT-Off Strains Gene Expression Analysis

30 1. TET-Promoter SHUT-Off Strains Ribosome Biogenesis Ymr290c, Ykl014c, Yjr041c Mitochondrial Regulation Yol026c Protein Secretion Ylr440c

31 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)

32 2. DAmP Alleles (Schuldiner et al., Cell, 2005)

33 2. DAmP Alleles

34 3. Point Mutants of Essential Genes

35 Genetic Profiling of Point Mutants Reveals Insight on Structure-Function PCNA (Pol30) -PCNA interacts with CAF-1, a three-subunit protein, to couple DNA replication or DNA repair to nucleosome deposition -PCNA is important in many aspects of DNA metabolism, including DNA replication and DNA repair -Two mutants of PCNA (pol30-8 and pol30-79) generated by Stillman and colleagues

36 Genetic Profiling of Point Mutants Reveals Insight on Structure-Function PCNA (Pol30) -PCNA interacts with CAF-1, a three-subunit protein, to couple DNA replication or DNA repair to nucleosome deposition -PCNA is important in many aspects of DNA metabolism, including DNA replication and DNA repair -Two mutants of PCNA (pol30-8 and pol30-79) generated by Stillman and colleagues

37 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)

38 Forward Chemical Genetics Screening large sets of small molecules Goal is target identification Those that cause a specific phenotype of interest are used to isolate and identify the protein target

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

40 Reverse Chemical Genetics Screen for compounds that bind to a given protein Optimize for selectivity Goal is target function and validation

41 Reverse Chemical Genetics Target Validation Find ligand for protein of interest Optimize for selectivty Add ligand to cells Assay for phenotype

42 FORWARD Chemical Genetic Studies in Yeast 1. Screening the deletion set for drug sensitivities 2. Comparing mutant profiles to drug profiles 3. Haploinsufficieny analysis

43 Complex A P X X = synthetic lethality Complex B Complex C Complex X Complex Y Complex Z Organizing Complexes into Pathways Using Genetic Interactions X= Drug

44 Alive Dead Alive Dead Synthetic Lethal Interactions Synthetic Chemical Interactions Deletion Mutants Sensitive to a Particular Drug Should be Synthetically Lethal with the Drug Target Drug

45 1. Screening the deletion set for drug sensitivities

46

47 FORWARD Chemical Genetic Studies in Yeast 1. Screening the deletion set for drug sensitivities 2. Comparing mutant profiles to drug profiles 3. Haploinsufficieny analysis

48 2. Comparing mutant profiles to drug profiles

49 Parsons et al., 2004, Nature Biotechnology 1. Clustering of the Drug Profiles: Camptothecin and Hydroxyurea have a similar mode of action: they both inhibit DNA replication

50 RFA1 RTT105 POL30-79 POL 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:

51 TUB3 PAC2 CIN1 CIN2 CIN4 BENOMYL (15  g/ml) Benomyl: a drug that targets microtubules and affects chromosome segregation CIN1, CIN2, CIN4: genes required for microtubule stability TUB3: alpha-tubulin PAC2: tubulin chaperone 2. Comparison of drug profiles to mutant profiles:

52 FORWARD Chemical Genetic Studies in Yeast 1. Screening the deletion set for drug sensitivities 2. Comparing mutant profiles to drug profiles 3. Haploinsufficieny analysis

53 3. Haploinsufficieny Analysis

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

55 3. Haploinsufficieny Analysis

56 TUB1/TUB1 vs. tub1  /TUB1 25 ug/ml benomyl50 ug/ml benomyl

57 -used a genome-wide pool of tagged heterozygotes to assess the cellular effects of 78 compounds in Saccharomyces cerevisiae

58 Strategy for Global Haploinsufficiency Analysis Using Microarrays

59 Comprehensive View of Fitness Profiles for 78 Compounds No Drug-Specific Fitness Changes Small Number of Highly Significant Outliers Widespread Fitness Changes

60 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 Identification of Erg7 as the Target for Molsidomine

61 5-Fluorouracil Targets rRNA Processing 5-Fluorouracil -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 Mak21, Ssf1, Nop4, Has1: rRNA Processing

62 The yeast knockout collection

63 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/ 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)

64 Step 1: Creating the haploid convertible heterozygotes Important point: This HIS3 gene is only expressed in MATa haploids, not in MAT haploids or MATa/ diploids So in other words, can select against MATa/ diploids to ensure you’re looking at only haploids later on.

65 Step 2: Inserting the query mutation Knockout one copy of your gene of interest (“Your Favorite Gene”) with URA3

66 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 xxx::KanMX YFG1 selects for query mutation so genotype is xxx::KanMX yfg1::URA3

67 Reminder about YKO construction

68 U1D1 U2D2 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

69 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

70 Evidence this really works – part I Strains x-axisy-axis XXX/xxx::KanMX CAN1/CAN1 XXX/xxx::KanMX CAN1/can1::MFA1pr-HIS3 On average, the intensity is the same before and after 1 copy of the CAN1 gene is knocked out

71 Evidence this really works – part II Strains x-axisy-axis DIPLOIDS XXX/xxx::KanMX CAN1/can1::MFA1pr-HIS3 HAPLOIDS XXX or xxx::KanMX can1::MFA1pr-HIS3 Red spots illustrate that fraction of the strains with KOs in essential genes, so when haploid, not present in pool

72 Another variation: Drug sensitivity

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74 Summary  If you can compare two different conditions and you have a way to stick things to slides, some sort of microarray is possible!

75

76 HOW NOT TO LOOK AT INTERACTION DATA!!!!!!!!


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