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Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways Parsons et al. 2003. Nature Biotechnology.

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Presentation on theme: "Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways Parsons et al. 2003. Nature Biotechnology."— Presentation transcript:

1 Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways Parsons et al Nature Biotechnology. 22(1):62-69 Presented by Obi Griffith

2 Outline Background The problem Approach Methods Results Conclusions Criticisms Topics for discussion

3 Background Yeast as a model organism Yeast genomics Tools of yeast genomics

4 Yeast as a model organism Studied for 100 years Convenient lab organism Stable haploid or diploid Unicellular but can display group characteristics Highly versatile transformation system Homologous recombination efficient

5 Yeast Genomics First eukaryotic genome to be sequenced ~6000 annotated genes 182 genes with significant similarity to human disease genes. No complete comparison between humans and yeast yet completed but likely many more orthologous genes than this (Carroll et al, 2003). Many metabolic and signal transduction pathways are conserved

6 Tools of Yeast Genomics Expression profiling (microarrays, SAGE) Overexpression of yeast genes Two-hyrid analysis of yeast protein interactions Mass specroscopy analysis of protein complexes protein microarrays protein localization

7 Tools of Yeast Genomics (cont’d) Whole genome deletion collections  Phenotypic screens  Synthetic lethality screens  Haploinsufficiency analysis  Mutant gene mapping

8 The problem Determining how small organic chemicals interact with living systems Traditionally a very laborious process  Eg biochemical or affinity purification strategies  Depend on ability to modify a test compound  Affinity not always sufficient to allow purification

9 The approach A global fitness test that reveals genes involved in mediating the response of yeast cells to a test compound A way to identify molecular targets without altering test compound Use synthetic lethal tests on a genomic scale. Remember, synthetic lethal = lethal event arising from ‘synthesis’ of two gene deletions or disruptions (eg. chemical inhibition)

10 Method Conduct 2 kinds of synthetic lethal tests: deletion collection + chemical = chemical-genetic profile deletion collection + 2 nd deletion = genetic interaction profile Where profiles are the same the 2 nd deletion is likely target of chemical



13 Chemical-genetic profiles Screened ~4700 viable yeast deletion mutants for sensitivity to 12 different chemical compounds.  Eg. benomyl, a microtubule depolymerizing agent, FK506, a calcineurin inhibitor, fluconazole, an antifungal agent that inhibits Erg11, etc… Confirmed interactions by serial-dilution spot assays to minimize false positives Assessed false-negatives by comparing results for rapamycin screen to previously published results



16 Genetic Interaction profiles First tested system with Erg11, which encodes the target of the antifungal drug fluconazole.  Crossed the Erg11 mutation into the viable deletion set.  Screened double-mutant set for lethal or sick.  Compared fluconazole chemical-genetic interactions to Erg11 genetic interactions. Performed similar analysis with calcineurin (CNB1).



19 Clustering of chemical-genetic and genetic interaction profiles Used 2-d hierarchical clustering of a combined data set:  Chemical-genetic profiles for FK506, CsA, fluconazole, benomyl, hydroxyurea, and camptothecin  Genetic profiles for genes encoding for the target genes or their functionally related genes (57 total). Filtered out multidrug-resistance



22 Conclusions a powerful method of understanding pathways and targets for bioactive compounds A convincing proof of principle. Can identify target pathways for drugs that don’t interact with one specific target only. Adaptable to other organisms including mammals using methods like RNAi

23 Criticisms Reliance on GO annotations. Convincing examples but no overall measure of agreement between profile clustering and what we expect. false-negatives Only detects more sensitive reactions to compounds. What about important interactions that do not result in synthetic lethality? In many cases, their method will identify target pathway but not actual target

24 References Carroll PM, Dougherty B, Ross-Macdonald P, Browman K, FitzGerald K Model systems in drug discovery: chemical genetics meets genomics. Pharmacol Ther. 99(2): Parsons AB, Brost RL, Ding H, Li Z, Zhang C, Sheikh B, Brown GW, Kane PM, Hughes TR, Boone C Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways. Nat Biotechnol. 22(1):62-9 Stockwell The biological magic behind the bullet. Nat Biotechnol. 22(1):37-8

25 Topics for discussion Why don’t the two kinds of profiles match perfectly? Other possible applications of this approach How could their method be incorporated or supplemented with data from other methodologies (eg. microarray, haploinsufficiency) RNAi knockouts for each mouse gene to extend approach to mammals Others?

26 The First Eukaryotic Proteome Chip Zhu et al. (2001) demonstrate first Proteome chip protein samples Representing 5800 unique proteins (80%) Spotted in duplicate on nickel coated microscope slide GST fusion and probing with anti-GST Tested with biotinylated Calmodulin A highly conserved calcium binding protein involved with many other proteins Detected by binding of Cy3-labelled streptavidin Found 39 proteins that bind to calmodulin –6 previously known –6 missed because not in collection or not successfully attached to chip Found putative calmodulin binding motif shared by 14 of 39 proteins

27 GO – Gene Ontology The goal of the Gene Ontology TM (GO) Consortium is to produce a controlled vocabulary that can be applied to all organisms even as knowledge of gene and protein roles in cells is accumulating and changing. GO provides three structured networks of defined terms to describe gene product attributes.

28 Why do the genetic interaction and chemical-genetic interaction profiles not match exactly? Incomplete inactivation by the chemical Multiple gene targets for the gene May reflect inherent differences in genetic versus chemical mechanisms of target inhibition. Gene deletion completely removes the target protein from the system whereas chemical inhibition leaves a protein-chemical complex in the system that still may play some role in the cell or have unexpected effects.

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