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GENE PROFILES Synthetic lethality Chemical Genetic Interactions Pleiotropy Zohar Itzhaki Advanced Seminar in Computational Biology November 2005.

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Presentation on theme: "GENE PROFILES Synthetic lethality Chemical Genetic Interactions Pleiotropy Zohar Itzhaki Advanced Seminar in Computational Biology November 2005."— Presentation transcript:

1 GENE PROFILES Synthetic lethality Chemical Genetic Interactions Pleiotropy Zohar Itzhaki Advanced Seminar in Computational Biology November 2005

2 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

3 Some reminders and definitions Pleiotropy one single mutant gene causes multiple mutant phenotypes Chemical genetic interaction Hypersensitivity of a mutant to a sub-lethal compound Synthetic lethality (genetic interactions) death of double mutants

4 The Genetic Profiles in the studies Tong AH … Boone C. Global Mapping of The Yeast Genetic Interaction Network Science 2004 Genetic interactions Synthetic lethality Parsons AB … Boone C. Integration of Chemical-genetic and genetic Interaction data links bioactive compounds to cellular target pathways Nat Biotechnol 2004 Chemical genetic interactions 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 Pleiotropy

5 General procedures - A yeast array Every spot is a yeast colony (different strains) Treatment Computational detection, usually for growth changes

6 genetic interactions (GGI) synthetic lethality Is it really an interaction ? (PPI) Parallel pathways Same pathways Trait 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

7 GGI – Article & Technology Tong & Boone 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 AH … Boone C. Global Mapping of The Yeast Genetic Interaction Network Science 2004

8 Synthetic lethality – GGI assay Tong & Boone SGA screens (cytoskeleton & DNA repair mutated genes) X 3 Further evaluation Pas s 3 Candidate synthetic lethal pairs Pas s 1 * ~4000 interactions of ~1000 genes Results 34 interactions/gene 8 int/gene in PPI Evaluation: ~100,000 GGI

9 GGI and Function (GO annotations) Tong & Boone 2004 Significant correlations between the GGI and their GO annotations:  GGI to GO:  12% of the GGI has the same GO annot’ (pVal= )  27% of the GGI has a same or similar GO annot’ (pVal= ) Bridging GGI:  1755 out of ~285,500 (n 2 /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

10 GGI and Function – Bridging GGI Tong & Boone 2004

11 More validations of the concept Tong & Boone 2004 GGI were significantly more abundant between:  Genes sharing the same phenotype mutant (pVal= )  Genes encoding proteins with the same localization (pVal= )  Genes encoding proteins within the same complex (pVal= )

12 Gene profiles and profile clustering Tong & Boone 2004 Gene profiles: Foreach “array” gene create a 132d binary vector, regarding GGI with the query genes Create a 132*1000 matrix out of these vectorsBicluster the matrix + GO annotations for every cluster

13 The GGI Bi-cluster Tong & Boone 2004

14 What can we learn from the Bi-Cluster ? Tong & Boone 2004 A. Connecting pathways Sister chromatid cohesion during chromosome replication DNA replication Checkpoint Spindle Checkpoint DNA damage Checkpoint Recombination Microtubule Dynamic

15 What can we learn from the Bi-Cluster ? Tong & Boone 2004 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

16 Within a complex GGI as predictors for PPI Tong & Boone 2004 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 ctf18 ctf8 ctf18 & ctf8 don’t interact as genes but their proteins products do ab Parallel pathways

17 The GGI network features Tong & Boone 2004 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)

18 GGI networks: summary & conclusion Tong & Boone 2004 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)

19 Chemical Genetic Interactions (CGI) Parsons & Boone 2004 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

20 Chemical Genetic Interactions (CGI) Parsons & Boone 2004 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…

21 The CGI assay Parsons & Boone screens against different inhibitors Sensitive strains were grown separately with different compound concentrations FP detection Comparison between the rapamycin results and another rapamycin (not large scale) former study FN detection Measure colonies’ sizes: same or 3 degrees of reductionProfile creation and bi-clustering (12 drugs X 647 relevant genes)

22 The CGI bi-cluster Parsons & Boone 2004

23 Multi drug resistant gene (MDR) Parsons & Boone 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

24 Parsons & Boone 2004 The MDR network & enrichments Vacuoles H+ pumps Lipid metabolism Vesicular transport

25 Comparison of GGI and CGI results Parsons & Boone 2004 Perform a GGI assay (SGA array) for several genes, known as drug targets pVal=10 -56

26 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

27 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 matrixBicluster of the matrix (803 X 69)

28 The GGI-CGI Bi-cluster Parsons & Boone 2004 The ERG11 and the fluconazole Were clustered together Other examples of clustering of genes and the drugs against them

29 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).

30 Parsons & Boone 2004 CGI - summary & conclusion Uncharacterized gene functions MDR gene detector CGI profile summaries GGI profiles & is easier to perform

31 Pleiotropy 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: a single mutant gene - multiple mutant phenotypes Drosophila: sex related genes; fertility & development Cats: being white and deaf

32 Pleiotropy – the article Dudely & Church 2005 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 technique: check the mutant strains under different conditions The challenge: loss of single function or loss of multiple functions ? (difficult to answer experimentally)

33 Pleiotropy – large scale experiments Dudely & Church 2005 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. 767 genes were detected  The 21 conditions, among them: Nutrient limiting conditions Stress conditions (high ethanol, low pH, high salt etc…) Heavy metals Inhibitors of cellular functions (cytoskeleton…)

34 551 strains influenced By 1-2 conditions Cluster (sort) into 65 groups 216 strains influenced By 3-14 conditions Bi-cluster overlapping groups Dudely & Church 2005 Gene profiles and clustering 767 strains with growth changes

35 GO enrichments- low pleiotropy clusters Dudely & Church 2005 “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)

36 GO enrichments- high pleiotropy clusters Dudely & Church 2005 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

37 Phenotypic profiles, GGI and complexes Dudely & Church 2005 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)

38 Complexes vs. phenotypic profiles Dudely & Church 2005 The similarity between the genes within a complex (Average the phenotype profiles) Conclusion: Phenotype profiles represent functional relationships

39 Dudely & Church 2005 Detecting multi functions genes snf1 is assigned to Both clusters Indeed snf1 has several functions (targeting substrates, response to stress, regulation of filamentations etc…) Chromatin modification Vesicle transport

40 Dudely & Church 2005 Multi-function genes 1.Indeed multi functions 2.Overlapping clusters – biological redundancy

41 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 ?

42 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. Dudely & Church 2005Parsons & Boone 2004 Tong & Boone 2004

43 Dudely & Church 2005Parsons & Boone 2004 Tong & Boone 2004 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 Phenotypic profiles GGI profiles CGI profiles A better understanding

44 Thank you, And don’t worry, the cookies are caffeine, sorbitol and heavy metal free


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